system

Abstract

Provide a system. A means for accepting task information input by a user; A means of using AI models to select relevant images and icons based on input task information; and A way to set reminders that combine task information with selected images or icons and send notifications at specified times. A system including:

Description

[Technical Field]

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

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

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

[0004] In today's society, it is difficult to efficiently manage and remember daily tasks amid information overload. As a result, young professionals in particular are prone to delaying or forgetting important tasks, leading to mental stress and reduced productivity. Therefore, to overcome these problems, a visually assisted task management system that can effectively remind people of important tasks and schedules is needed. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a means for accepting task information input by a user, a means for using an AI model to select highly relevant images or icons based on the input task information, and a means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time. Furthermore, the present invention provides a system in which the selected images or icons are optimized based on the user's past behavioral data and individually customized to adapt to the user's behavioral patterns and preferences, thereby realizing more efficient task management and reducing mental stress.

[0006] A "user" is an entity that uses this system to manage tasks and set reminders.

[0007] "Task information" refers to data such as the task content, title, details, and deadline date that a user enters into the system.

[0008] "AI model" refers to an algorithm or program that uses artificial intelligence technology to select highly relevant images or icons based on input information.

[0009] "Highly relevant images and icons" are visual content that are highly associated with the input task information and are selected to help users intuitively understand the task.

[0010] A "reminder" refers to notification information that is set to notify a user of important tasks at an appropriate time.

[0011] "Past behavioral data" refers to historical data such as the tasks a user has performed using the system and the images and icons they have selected.

[0012] "Behavioral patterns" refer to a series of actions or habitual operating tendencies that a user exhibits when using a system.

[0013] "Preferences" refer to personal preferences and visual selection criteria that a user exhibits while using the system. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention relates to a task management system that visually reminds you of important tasks and schedules. The program processing of this system will be explained in natural language below, along with specific examples.

[0036] Overall system overview

[0037] This system uses an AI model to select relevant images and icons based on task information entered by the user, creates reminders, and notifies the user at the specified time.The system consists of three entities: a server, a device, and the user.

[0038] Program processing

[0039] User Login

[0040] The user opens the login screen using the device and enters their ID and password.

[0041] The terminal transmits the entered authentication information to the server.

[0042] The server checks the user's authentication information against a database and allows login if authentication is successful.

[0043] Task Input

[0044] The user opens the task entry screen of a task management app and enters the title, details, and deadline of a new task.

[0045] The terminal transmits the input task information to the server.

[0046] Image selection

[0047] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[0048] The server retrieves the user's past tasks and the image data they used from a database, and uses an AI model to identify the images and icons that are most relevant to the task.

[0049] Reminder Settings

[0050] The server generates a reminder that combines the selected image or icon with task information and sets it to notify you at the specified reminder time.

[0051] The server stores the reminder information in a database.

[0052] Reminder notifications

[0053] When the set reminder time approaches, the server sends a reminder notification to the device.

[0054] The device receives the notification and displays a popup with task information along with an image or icon to the user.

[0055] Specific examples

[0056] For example, when a user named Tanaka inputs a task called "Create materials for Friday's meeting," the following processing is performed.

[0057] 1. User Login

[0058] Tanaka launches the app on his smartphone and logs in by entering his ID and password.

[0059] The terminal transmits authentication information to the server, and the server performs authentication.

[0060] 2. Task Input

[0061] Tanaka enters the task details as "Prepare meeting materials" and sets the deadline to Friday.

[0062] The terminal sends this information to the server.

[0063] 3. Image Selection

[0064] The server analyzes the task information for "creating meeting materials" and uses an AI model to search for relevant images and icons.

[0065] Based on Tanaka's past usage history, an image of a businessman working on a computer is selected.

[0066] 4. Reminder Settings

[0067] The server generates a reminder that combines the image and task information and sets it to notify you at 9:00 AM on Friday.

[0068] Save reminder information in a database.

[0069] 5. Reminder notifications

[0070] At 9:00 AM on Friday, the server will send a reminder to Tanaka's smartphone.

[0071] The terminal displays to Tanaka an image of a businessman working on a computer and a notification that "meeting materials are being prepared."

[0072] This allows Mr. Tanaka to visually check his tasks quickly and intuitively, enabling effective task management. In this way, we provide a system that supports users in completing tasks, increases daily productivity, and reduces stress.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user launches the application on the device and enters their ID and password on the login screen.

[0076] The terminal transmits the entered login information to the server.

[0077] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[0078] Step 2:

[0079] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[0080] The terminal receives the input task information and sends it to the server as a request.

[0081] Step 3:

[0082] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[0083] The server retrieves the user's past task data from a database and references the history of related images and icons.

[0084] The server uses AI models to select the most relevant images and icons for the task.

[0085] Step 4:

[0086] The server combines the selected image with the task information and stores it in a database as a reminder.

[0087] The server schedules reminder dates and times based on task due dates and user preferences.

[0088] Step 5:

[0089] When the reminder date and time approaches, the server sends a reminder notification to the terminal.

[0090] Your device will receive the notification and display a pop-up notification on your screen.

[0091] Examples:

[0092] Step 1:

[0093] Tanaka launches the app on his smartphone and enters his login information.

[0094] The device sends Tanaka's authentication information to the server.

[0095] The server checks the information against the database and returns a message to the terminal indicating that authentication was successful.

[0096] Step 2:

[0097] Tanaka enters "Create meeting materials for Friday" and sets the deadline to Friday.

[0098] The terminal sends this information to the server.

[0099] Step 3:

[0100] The server analyzes the task of "creating meeting materials" and understands its content.

[0101] The server refers to the task history that Tanaka has used in the past and searches for images related to "meetings."

[0102] The AI ​​model selects images of businessmen working on computers.

[0103] Step 4:

[0104] The server stores the task and the selected image as a reminder.

[0105] The server sets the reminder date and time to 9:00 AM on Friday.

[0106] Step 5:

[0107] At 9:00 AM on Friday, the server sends a reminder to the device.

[0108] The device displays an image of a businessman working on a computer on Tanaka's smartphone along with the message "Preparing materials for Friday's meeting."

[0109] Tanaka checks the notification and confirms the details as necessary.

[0110] Example 1

[0111] 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."

[0112] Conventional task management systems make it difficult for users to properly manage task information and receive visual reminders. Furthermore, they lack the ability to optimize and customize reminders based on past behavioral data and individual user preferences. This has resulted in insufficient improvements in user productivity and stress reduction.

[0113] 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.

[0114] In this invention, the server includes means for accepting task information entered by a user, means for using an AI model to select highly relevant images or icons based on the entered task information, means for generating a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, means for saving the reminder information in a database and setting a notification schedule, and means for verifying authentication information entered by the user and generating a session. This enables automatic and visual reminders based on the task information entered by the user. Furthermore, optimization and customization based on past behavioral data and individual preferences are performed, enabling effective task management and improving user productivity and reducing stress.

[0115] A "user" is a person or entity that uses the system.

[0116] "Task information" is data regarding the title, details, deadline, etc. of a task entered by the user.

[0117] An "AI model" is an algorithm or program that uses artificial intelligence to perform a specific task.

[0118] "Highly relevant images and icons" are images and icons that are judged to be visually appropriate for the task content based on the input task information.

[0119] A "reminder" is a message containing task information and visual elements (images or icons) that is notified to the user at a specified time.

[0120] A "database" is a data storage system for storing task information, images, icons, and associated metadata.

[0121] "Authentication Information" means information used to authorize access to a system, such as a user ID and password.

[0122] A "session" is temporary connection information for maintaining a state in which a user is logged in to a system.

[0123] "Natural Language Processing (NLP)" is an artificial intelligence technology for analyzing human language and understanding its content.

[0124] The "notification schedule" is schedule information for setting the date and time when a reminder is to be notified to the user.

[0125] "Reminder notification" is an action that sends a reminder to the user at a specified time.

[0126] "Visual reminder" refers to visually reminding a user of a task by notifying the user of task information using images or icons.

[0127] "Productivity" means enabling users to manage tasks more efficiently and get more done.

[0128] "Stress reduction" means reducing the user's mental burden through effective task management.

[0129] This invention is a system that uses an AI model to visually remind users of task information and assist them in managing their tasks. This system includes three components: a server, a terminal, and a user. Below, we explain the program processing of this system in natural language and provide detailed descriptions with specific examples of its operation.

[0130] Overall system overview

[0131] Based on the task information entered by the user, this system uses an AI model to select relevant images or icons and notify them as reminders at the specified time. The entire system consists of a server, a terminal, and the user.

[0132] Hardware and Software Use

[0133] This system uses the following hardware and software:

[0134] Server: Database Management System (DBMS), Natural Language Processing (NLP) engine, generative AI model.

[0135] Device: Smartphone or personal computer, task management application.

[0136] Database: A data storage system.

[0137] Specific examples of programs

[0138] For example, the process when the user inputs a task "Create materials for the Friday meeting" is shown below.

[0139] 1. Log in

[0140] The user enters their ID and password on the terminal and accesses the system from the login screen.

[0141] The terminal sends encrypted authentication information to the server, which then compares it with a database to perform authentication.

[0142] If the authentication is successful, the server creates a session and sends a login success message to the terminal.

[0143] 2. Task Input

[0144] The user opens the task input screen of the task management app, enters the details as "Create meeting materials," and sets the deadline to Friday.

[0145] This information is sent from the terminal to the server, which stores it in a database.

[0146] 3. Image Selection

[0147] The server analyzes the task information using natural language processing (NLP) and understands its content.

[0148] The server retrieves the user's past task history from a database and then uses an AI model to identify the images and icons most relevant to "creating meeting materials."

[0149] 4. Reminder Settings

[0150] The server combines task information with relevant images and icons to generate reminders.

[0151] The server saves this reminder in a database and sets the notification time to "Friday at 9 AM."

[0152] 5. Reminder notifications

[0153] When the reminder time approaches, the server sends a reminder notification to the device.

[0154] Your device will receive a reminder notification and display a pop-up with task information along with an image or icon.

[0155] Prompt Sentence Examples

[0156] Below are some examples of prompts to input to an AI model.

[0157] "When a user inputs a task such as creating materials for a meeting on Friday, the system should select a relevant image or icon and provide a reminder at the specified time."

[0158] The system allows users to visually and efficiently manage tasks, improving daily productivity and reducing stress. Technical features of the invention include automatic reminder generation based on task information and optimization of images and icons based on the user's past behavior data.

[0159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0160] Step 1: User Login

[0161] 1. The user opens the login screen using the terminal and enters their ID and password. This is the input data.

[0162] 2. The device encrypts the entered authentication information and sends it to the server.

[0163] 3. The server checks the received authentication information against a database. This process involves a data calculation that compares it with the corresponding record in the database.

[0164] 4. If authentication is successful, the server creates a session and returns a login success message to the terminal. This becomes the output data.

[0165] 5. The user will receive a confirmation of successful login and will be able to access the system.

[0166] Specific working example:

[0167] Tanaka opens the login screen on his smartphone, enters his ID and password, and taps the "Login" button. The device encrypts the authentication information and sends it to the server, which then verifies it in a database.

[0168] Step 2: Task Input

[0169] 1. The user opens the task entry screen of the task management app.

[0170] 2. The user enters task information such as the task title, details, deadline, etc. This is the input data.

[0171] 3. The terminal sends the entered task information to the server.

[0172] 4. The server saves the received task information in the database. During this process, data is written to the database.

[0173] Specific working example:

[0174] Tanaka opens the task input screen, enters the details as "Prepare meeting materials," and sets the deadline to "Friday."

[0175] The smartphone sends this information to a server, which stores it in a database.

[0176] Step 3: Image Selection

[0177] 1. The server analyzes the task information received from the database using natural language processing (NLP). The input data is the received task information.

[0178] 2. The server retrieves the user's past task information and corresponding image data from the database. The data processing performed in this process involves filtering and extracting past data.

[0179] 3. The server uses the generative AI model to identify the image or icon that is most relevant to the task. The AI ​​model performs data calculations and outputs the optimal image or icon.

[0180] Specific working example:

[0181] The server analyzes the task of "creating meeting materials" using natural language processing and understands its content.

[0182] The server retrieves Tanaka's past task history from the database, and the AI ​​model determines that the most suitable image is "an image of a businessman working on a computer."

[0183] Step 4: Set reminders

[0184] 1. The server generates a reminder by combining the selected image or icon with the input task information. The input data is the image, icon, and task information.

[0185] 2. The server saves the reminder information to the database and sets the notification schedule. This process involves writing to the database and setting the notification timing.

[0186] 3. Once the reminder and notification time settings are complete, the server saves this information as output data.

[0187] Specific working example:

[0188] The server combines the task information with an image of a businessman working on a computer to generate a reminder to be displayed to Tanaka.

[0189] The server saves this reminder in a database and sets the notification time to "Friday at 9 AM."

[0190] Step 5: Reminders

[0191] 1. When the reminder time approaches, the server sends a reminder notification to the device. The input data is the notification schedule information.

[0192] 2. The device receives the notification and displays the task information along with the selected image or icon in a pop-up. The notification data from the server becomes the output data displayed on the device.

[0193] Specific working example:

[0194] At 9:00 AM on Friday, the server will send a reminder to Tanaka's smartphone.

[0195] Tanaka's smartphone will pop up a notification for him, displaying an image of a businessman working on a computer and a notification that a meeting document is being prepared.

[0196] This clarifies the specific operations performed at each processing step and the data input and output based on those operations, allowing users to visually confirm tasks quickly and intuitively.

[0197] (Application example 1)

[0198] 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."

[0199] Conventional task management systems have limited visual reminder functions for tasks, and lack effective means to improve work efficiency, especially in work environments such as factories. There is also a need for a system that ensures workers can obtain the necessary information at the right time. Therefore, there is a need for a new system that improves task management for factory workers and supports efficient work through visual reminders.

[0200] 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.

[0201] In this invention, the server includes a means for accepting task information input by a user, a means for using an AI model to select highly relevant images or icons based on the input task information, a means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, and a means for sending a notification to a device (e.g., smart glasses) that displays the visual reminder, thereby significantly improving the work efficiency of factory workers and preventing them from missing tasks.

[0202] A "user" is a person who uses the system to enter and manage task information.

[0203] "Task information" is information about the work content, details, and deadline entered by the user.

[0204] The "means" are the functional elements that make up this system, and are modules and processes for performing specific processing.

[0205] An "AI model" is an algorithm that uses machine learning to analyze input data and generate output that is highly relevant to a specific task.

[0206] "Images and icons" are visual elements selected by the system to visually represent a task.

[0207] A "reminder" is a mechanism that sends a notification to a user at a specified time to remind them of a task.

[0208] A "notification" is a message sent from the system to a user device that acts as a reminder.

[0209] "Device" means the hardware device used to display notifications, including smart glasses and smartphones.

[0210] A "visual reminder" is a visual notification of task information to a user using images or icons.

[0211] A "factory worker" is a person who performs work in a factory and receives work instructions and reminders using a task management system.

[0212] This invention relates to a task management system for factory workers to improve their work efficiency. Based on task information entered by users, the system uses an AI model to select relevant images and icons, and sends visual reminders to devices such as smart glasses.

[0213] System Program

[0214] The system includes the following program processes:

[0215] 1. User login:

[0216] The user opens the login screen using the smart glasses and enters their ID and password. The device sends the entered authentication information to the server, which then verifies the user's authentication information against a database.

[0217] 2. Task Input:

[0218] The user enters the title, details, and deadline of a new task through the task management app, and the device sends the entered task information to the server.

[0219] 3. Image Selection:

[0220] The server analyzes the task information and uses an AI model (e.g., powered by Tensorflow®) to identify the most relevant images or icons for the task, using natural language processing (NLP) and taking into account the user's previous task data.

[0221] 4. Reminder Settings:

[0222] The server generates a visual reminder by combining the selected image or icon with the task information, and sets it to notify the user at the specified time. The server saves the reminder information in a database.

[0223] 5. Reminders:

[0224] When the set reminder time approaches, the server sends a reminder notification to the smart glasses. The device (smart glasses) receives the notification and displays task information along with an image or icon to the user.

[0225] Hardware and software used

[0226] Hardware: Smart glasses, server, user terminal

[0227] Software: TensorFlow (AI model), Natural Language Processing (NLP), Database Management System

[0228] Specific examples

[0229] For example, if a factory worker enters a task called "Assembly parts on Monday," it will be processed as follows:

[0230] 1. The user logs in to the smart glasses.

[0231] 2. In your task management app, enter the details as "Parts assembly work" and set the due date to Monday.

[0232] 3. The server analyzes the task information and uses an AI model to select images relevant to the factory work (for example, images of the worker at work).

[0233] 4. The server combines the selected image with the task information and sets it to notify you at 8:00 AM on Monday.

[0234] 5. At 8:00 AM on Monday, a reminder notification will appear on the smart glasses. The screen will show the message "Assembly work" and an image of the work being done.

[0235] Prompt Sentence Examples

[0236] "Please select an image of the parts assembly work."

[0237] The system allows factory workers to efficiently manage tasks through visual reminders, ensuring that important work is not overlooked.

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

[0239] Step 1:

[0240] The user opens the login screen using smart glasses and enters their ID and password. The entered authentication information is sent to the server by the device. The server checks the database and allows the user to log in if authentication is successful. Here, the input is the user's ID and password, and the output is the success or failure of the login.

[0241] Step 2:

[0242] A user inputs the title, details, and deadline of a new task through a task management app. The device sends this task information to a server. Here, the input is the task title, details, and deadline, and the output is the task information stored on the server.

[0243] Step 3:

[0244] The server analyzes the received task information. First, it uses natural language processing (NLP) to understand the task content and extract task keywords. Then, the server uses a generative AI model to identify relevant images and icons. The input here is the task information, and the output is images and icons related to the keywords.

[0245] Step 4:

[0246] The server combines the selected image or icon with the task information to generate a reminder. This reminder is set to be notified at the specified reminder time. The server saves the reminder information in a database. The input is the task information and the selected image or icon, and the output is the generated reminder.

[0247] Step 5:

[0248] When the reminder time approaches, the server sends a reminder notification to the device (smart glasses). The device receives the notification and displays the task information along with the specified image or icon to the user. The input here is the reminder information, and the output is the notification displayed to the user.

[0249] The description should include specific actions such as checking the server's database (step 1), using NLP analysis and a generative AI model (step 3), saving the reminder information to the database (step 4), and sending a notification to the device (step 5).

[0250] 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.

[0251] This invention relates to a system that processes task information entered by a user using an AI model, selects relevant images and icons, and generates reminders. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system adjusts the timing and content of reminders, providing a more refined task management and user experience.

[0252] Overall system overview

[0253] This system uses an AI model to select relevant images and icons based on task information entered by the user, and adjusts reminders by analyzing the user's emotions using an emotion engine.The system consists of three entities: a server, a device, and the user.

[0254] Program processing

[0255] User Login

[0256] The user launches the application on the device and enters their ID and password on the login screen.

[0257] The terminal transmits the entered login information to the server.

[0258] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[0259] Task Input

[0260] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[0261] The terminal receives the input task information and sends it to the server as a request.

[0262] Image selection

[0263] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[0264] The server retrieves the user's past task data from a database and references the history of related images and icons.

[0265] The server uses AI models to select the most relevant images and icons for the task.

[0266] emotion recognition

[0267] The device uses the user's camera and microphone to capture facial expressions and voice.

[0268] The device sends the captured data to the emotion engine for analysis.

[0269] The server receives the results of the emotion engine and determines the user's current emotional state.

[0270] Reminder Settings

[0271] The server adjusts the timing and content of reminders based on the user's emotional state.

[0272] For example, if the user is feeling stressed, add a relaxing image or an encouraging message.

[0273] The server generates a reminder that combines the selected image and task information, and sets it to notify the user at the specified reminder time.

[0274] The server stores the reminder information in a database.

[0275] Reminder notifications

[0276] When the set reminder time approaches, the server sends a reminder notification to the device.

[0277] The device receives the notification and displays a popup with task information along with an image or icon to the user.

[0278] Specific examples

[0279] For example, when a user named Tanaka inputs a task called "Create materials for Friday's meeting," the following processing is performed.

[0280] 1. User Login

[0281] Tanaka launches the app on his smartphone and logs in by entering his ID and password.

[0282] The terminal transmits authentication information to the server, and the server performs authentication.

[0283] 2. Task Input

[0284] Tanaka enters the details for "Prepare meeting materials" and sets the deadline to Friday.

[0285] The terminal sends this information to the server.

[0286] 3. Image Selection

[0287] The server analyzes the task information for "creating meeting materials" and uses an AI model to search for relevant images and icons.

[0288] Based on Tanaka's past usage history, an image of a businessman working on a computer is selected.

[0289] 4. Emotion recognition

[0290] The device captures Tanaka's facial expressions and voice and analyzes them through an emotion engine.

[0291] The server determines from the results of the emotion engine that Tanaka is feeling stressed.

[0292] 5. Reminder Settings

[0293] The server adds relaxing images and encouraging messages to reduce stress and generates reminders.

[0294] Set the reminder date and time for Friday at 9:00 AM and save the information.

[0295] 6. Reminder notifications

[0296] At 9:00 AM on Friday, the server sends a reminder to the device.

[0297] The device displays stress-relieving images and a notification to "create meeting materials" on Tanaka's smartphone.

[0298] Tanaka checks the notification and views the details as necessary.

[0299] This allows Tanaka to not only visually grasp tasks, but also receive support tailored to his emotional state. In this way, we provide a system that more effectively supports users' task management, increases daily productivity, and reduces stress.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] The user launches the application on the device and enters their ID and password on the login screen.

[0303] The terminal transmits the entered login information to the server.

[0304] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[0305] Step 2:

[0306] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[0307] The terminal receives the input task information and sends it to the server as a request.

[0308] Step 3:

[0309] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[0310] The server retrieves the user's past task data from a database and references the history of related images and icons.

[0311] The server uses AI models to select the most relevant images and icons for the task.

[0312] Step 4:

[0313] The device uses the user's camera and microphone to capture facial expressions and voice.

[0314] The device sends the captured data to the emotion engine for analysis.

[0315] The server receives the results of the emotion engine and determines the user's current emotional state.

[0316] Step 5:

[0317] The server adjusts the timing and content of reminders based on the user's emotional state.

[0318] For example, if the user is feeling stressed, add a relaxing image or an encouraging message.

[0319] The server generates a reminder that combines the selected image and task information, and sets it to notify the user at the specified reminder time.

[0320] The server stores the reminder information in a database.

[0321] Step 6:

[0322] When the set reminder time approaches, the server sends a reminder notification to the terminal.

[0323] The device receives the notification and displays a popup with the task information along with the selected image or icon to the user.

[0324] Examples:

[0325] Step 1:

[0326] Tanaka launches the app on his smartphone and enters his login information.

[0327] The device sends Tanaka's authentication information to the server.

[0328] The server checks the information against the database and returns a message to the terminal indicating that authentication was successful.

[0329] Step 2:

[0330] Tanaka enters "Create meeting materials for Friday" and sets the deadline to Friday.

[0331] The terminal sends this information to the server.

[0332] Step 3:

[0333] The server analyzes the task of "creating meeting materials" and understands its content.

[0334] The server refers to the task history that Tanaka has used in the past and searches for images related to "meetings."

[0335] The AI ​​model selects images of businessmen working on computers.

[0336] Step 4:

[0337] The device captures Tanaka's facial expressions and voice and analyzes them through an emotion engine.

[0338] The server determines from the results of the emotion engine that Tanaka is feeling stressed.

[0339] Step 5:

[0340] The server adds relaxing images and encouraging messages to reduce stress and generates reminders.

[0341] Set the reminder date and time for Friday at 9:00 AM and save the information.

[0342] Step 6:

[0343] At 9:00 AM on Friday, the server sends a reminder to the device.

[0344] The device displays stress-relieving images and a notification to "create meeting materials" on Tanaka's smartphone.

[0345] Tanaka checks the notification and views the details as necessary.

[0346] Example 2

[0347] 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."

[0348] Conventional task management systems have the ability to generate visual materials and notifications based on task information entered by the user, but it is difficult to generate accurate reminders that take into account the user's emotional state and past behavioral data. Furthermore, they are unable to appropriately adjust the content and timing of reminders according to the user's emotional state, which rarely improves the user experience. Therefore, there is a need for more effective and customized task management that takes into account the user's emotions and past behavioral data.

[0349] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for accepting task information input by a user, a means for selecting highly relevant visual data based on the input task information using an AI model, a means for capturing sensory data of the terminal and analyzing the user's emotional state, and a means for adjusting a reminder based on the selected visual data and the emotion analysis results and sending a notification at a specified time. This enables more personalized reminder setting and notification based on the user's emotional state and past behavioral data.

[0350] "User" refers to an individual who utilizes the system to enter task information and receive reminders and notifications.

[0351] "Task information" refers to detailed information about work or plans that a user enters into the system, including the title, content, deadline, etc.

[0352] "Means of acceptance" refers to the functions and mechanisms for incorporating task information entered by the user into the system and processing it.

[0353] "AI model" refers to an algorithm or machine learning model that uses artificial intelligence technology to analyze task information and select highly relevant data based on that information.

[0354] "Visual data" refers to visual content such as images and icons related to a task.

[0355] A "terminal" is a device used by a user to operate the system, and includes smartphones, tablets, personal computers, etc.

[0356] "Sensory data" refers to data such as the user's facial expressions and voice that are captured using the device's camera, microphone, etc.

[0357] "Means for analyzing" refers to a program or function for analyzing the user's emotional state using sensory data.

[0358] "Reminder" refers to a mechanism that notifies users of tasks along with visual data, and is a message or alert to remind them of a task.

[0359] "Means for sending notifications" refers to a function or program that sends a reminder to a user's device at a specified time.

[0360] "Behavioral data" refers to information recorded based on a user's past task history and activities.

[0361] This invention is a system that uses an AI model to select relevant visual data based on task information entered by the user, analyzes sensory data from the device to determine the user's emotional state, and generates and adjusts reminders to notify them at the specified time.

[0362] The entire system consists of three entities: the server, the terminal, and the user.

[0363] First, the user uses the application to input task information, including the task title, details, due date, etc. This input data is temporarily stored on the device and encrypted before being sent to the server.

[0364] Once the server receives the task information, it uses a natural language processing (NLP) engine to analyze the task content, using software such as Python's NLTK library, and then selects visual data (images and icons) related to the task based on the analysis results and an AI model (e.g., using TensorFlow).

[0365] Furthermore, the device uses a camera and microphone to capture the user's sensory data (facial expressions and voice). The captured data is temporarily stored locally and sent to an emotion engine (e.g., Microsoft® Azure® Emotion API). The emotion engine analyzes the data and determines the user's emotional state.

[0366] The server receives the results from the emotion engine and adjusts the content and timing of reminders accordingly: for example, if the user is feeling stressed, a relaxing image or an encouraging message will be added to the reminder.

[0367] Finally, the server stores the adjusted reminder information in a database and notifies the device of the reminder at the specified time. The device then displays the received reminder in a pop-up window to the user, presenting task information along with visual data.

[0368] A specific example is given below.

[0369] For example, if a user inputs a task called "Create materials for Friday's meeting," the following process will be performed.

[0370] The user launches the app on their smartphone and enters task information.

[0371] The terminal sends this information to the server.

[0372] The server analyzes the task information with an NLP engine and selects appropriate images using an AI model.

[0373] The device captures the user's emotional data using a camera or microphone and sends it to the emotion engine.

[0374] The server receives the results of the emotion engine and adds relaxing images and encouraging messages to the reminder.

[0375] The server transmits the adjusted reminder data to the terminal at the specified time, and the terminal displays a pop-up notification.

[0376] Example prompt sentence:

[0377] "Describe a process where an AI recognizes that a user is finding a task stressful and then sets and sends an encouraging reminder along with a relaxing image."

[0378] In this way, the system of the present invention effectively assists users in managing their tasks and provides personalized support based on their emotional state.

[0379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0380] Step 1: User Login

[0381] The user launches the application installed on the device and enters their ID and password on the login screen.

[0382] Input: User ID and password

[0383] What it does: Enter your user ID and password into the form.

[0384] The terminal temporarily stores the login information received from the user in memory, encrypts it, and then sends it to the server.

[0385] Input: Encrypted user ID and password

[0386] Output: Authentication request to the server

[0387] What it does: Encrypts your login information and sends it to the server.

[0388] The server receives the user's authentication information and checks it against the user data in its database.

[0389] Input: Encrypted user ID and password

[0390] Output: Authentication success or failure result

[0391] What it does: Retrieves and collates user data from a database.

[0392] The server generates an authentication result and sends it to the terminal.

[0393] Input: Authentication result

[0394] Output: Sending authentication result

[0395] Operation: The authentication result is sent to the terminal.

[0396] The terminal displays a success or failure message to the user based on the received authentication result.

[0397] Input: Authentication result

[0398] Output: Login success or failure message

[0399] Behavior: Displays the authentication result to the user.

[0400] Step 2: Task Input

[0401] After logging in, the user opens the task entry screen of the app and enters the task title, details, and deadline date.

[0402] Input: Task title, details, due date

[0403] Action: Enter task information into a form.

[0404] The terminal acquires the input task information, organizes it into a data object, and sends it to the server.

[0405] Input: Task information

[0406] Output: Request to send task data to the server

[0407] Action: Sends task information to the server.

[0408] The server stores the received task information in a database.

[0409] Input: Task information

[0410] Output: Save task information to database

[0411] What it does: Saves task information to a database.

[0412] Step 3: Image Selection

[0413] The server uses a natural language processing (NLP) engine to analyze the stored task information.

[0414] Input: Task information

[0415] Output: Parsed task information

[0416] How it works: The NLP engine analyzes task information.

[0417] After analysis, the server uses an AI model (e.g., TensorFlow) to select visual data relevant to the task.

[0418] Input: Parsed task information

[0419] Output: Selected visual data

[0420] How it works: Curates visual data using AI models.

[0421] The server associates the selected visual data with the task information.

[0422] Input: Selected visual data

[0423] Output: Associate with task information

[0424] Action: Link visual data to task information.

[0425] Step 4: Emotion Recognition

[0426] The device captures the user's facial expressions and voice using a camera and microphone.

[0427] Input: User's facial expressions and voice

[0428] Output: Captured sensory data

[0429] How it works: Captures data using the camera and microphone.

[0430] The device temporarily stores the captured data and sends it to the emotion engine for analysis.

[0431] Input: Captured sensory data

[0432] Output: Send to emotion engine

[0433] Action: Sends sensory data to the emotion engine.

[0434] The server receives the analysis results from the emotion engine and determines the user's emotional state.

[0435] Input: Parsed sensory data

[0436] Output: User's emotional state

[0437] Action: Determines emotional state.

[0438] Step 5: Set reminders

[0439] The server adjusts the content and timing of reminders based on the user's emotional state.

[0440] Input: User's emotional state, task information, visual data

[0441] Output: Adjusted reminders

[0442] How it works: Adjust reminders based on your emotional state.

[0443] The server stores the adjusted reminder information in a database.

[0444] Input: Adjusted reminders

[0445] Output: Reminder saved to database

[0446] Behavior: Saves reminder information.

[0447] Step 6: Reminders

[0448] When the reminder time approaches, the server retrieves the saved reminder information and sends it to the device.

[0449] Input: Reminder information

[0450] Output: Reminder notification to device

[0451] Action: Sends a reminder to your device.

[0452] The device displays the received reminder information in a pop-up display to the user.

[0453] Input: Received reminder information

[0454] Output: Popup notification

[0455] Behavior: Generates a popup notification and displays it to the user.

[0456] This allows users to not only visually understand their tasks, but also receive reminders that reflect their emotional state.

[0457] (Application example 2)

[0458] 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."

[0459] Conventional task management systems can generate reminders based on task information entered by users, but they cannot take the user's emotional state into account, and may send reminders at inappropriate times or with inappropriate content. As a result, users may feel stressed or the reminders may not be effective enough. Furthermore, there is a need for task management that combines efficiency with psychological support, especially in workplaces such as factory robot operators.

[0460] 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 accepting task information input by a user, means for using an AI model to select highly relevant images or icons based on the input task information, means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, and means including an emotion engine that analyzes the user's emotional state and adjusting the content and timing of the reminder based on the analysis results. This makes it possible to notify reminders with appropriate timing and content that take the user's emotional state into consideration, allowing factory robot operators and others to efficiently manage tasks and perform their work while receiving psychological support.

[0461] "Task information entered by the user" refers to information that the user enters into the system, such as the task title, details, deadline date, etc.

[0462] An "AI model" is a model that uses artificial intelligence technology to analyze and classify data.

[0463] "Highly relevant images and icons" are visual elements that are determined to be most appropriate for the input task information.

[0464] A "reminder" is notification information that notifies the user of a task that the user has set and prompts the user to check it again.

[0465] An "emotion engine" is a system that analyzes a user's facial expressions and voice data to determine the user's emotional state.

[0466] A "server" is a computer system that receives and processes requests from clients.

[0467] "User's past behavior data" refers to data such as task information that the user has entered so far and associated access logs.

[0468] "Behavior patterns" refer to recurring patterns of user behavior and operations, and are the basic data that the system learns from.

[0469] "Preferences" are the characteristics or patterns that a user particularly prefers, and are data used to customize reminders.

[0470] "Notifications" are messages or alerts that the system sends to inform the user.

[0471] This system uses an AI model to select relevant images and icons based on task information entered by the user, and an emotion engine to analyze the user's emotions and adjust reminders. This system is primarily composed of three entities: a server, a terminal, and the user.

[0472] System Overview

[0473] This system accepts task information entered by the user and generates and notifies reminders based on that information. It uses a generative AI model to select relevant images and icons based on the task information, and further combines this with an emotion engine that recognizes the user's emotional state to adjust the timing and content of reminder notifications. This enables appropriate reminder notifications based on the user's emotional state.

[0474] Hardware and software used

[0475] Hardware:

[0476] Smart glasses: A device worn by the robot operator that is used to display reminders and various notifications.

[0477] Camera and microphone: Used to capture the user's facial expressions and voice data for the emotion engine.

[0478] software:

[0479] Server: Analyzes task information, selects images, recognizes emotions, and generates reminders.

[0480] NLP engine: Analyzes task information and extracts keywords.

[0481] Emotion recognition engine: Analyzes emotions from facial expressions and voice.

[0482] Image Selection Model: Select images and icons relevant to the task.

[0483] Notification system: Schedule reminders and send notifications.

[0484] What the program does

[0485] 1. User login: The user logs in from the device (smart glasses) and is authenticated by the server.

[0486] 2. Task input: The user inputs task information using voice commands or gestures and sends the information to the server.

[0487] 3. Image Selection: The server uses an AI model to select relevant images and icons based on the input task information.

[0488] 4. Emotion recognition: The smart glasses' camera and microphone are used to capture the user's emotions, which are then analyzed by the emotion engine.

[0489] 5. Reminder generation and notification settings: Adjust the timing and content of reminders based on the analysis results of the emotion engine, and schedule reminders in the notification system.

[0490] Specific examples

[0491] For example, if a factory robot operator inputs the task "Inspect conveyor belts and sensors at 2 p.m.", the following process will occur:

[0492] 1. The robot operator puts on the smart glasses and logs into the system with their ID and password.

[0493] 2. The operator uses voice commands to enter task information such as "Inspect the conveyor belt and sensors at 2 p.m." and set a deadline.

[0494] 3. The server analyzes the task information, extracts keywords using an NLP engine, and selects relevant images based on those keywords.

[0495] 4. The user's facial expressions and voice are captured through the camera and microphone, and the emotion engine analyzes their stress level.

[0496] 5. Based on the emotion engine analysis, add a relaxing image or an encouraging message to the reminder and set it to notify you at 2 PM.

[0497] 6. At 2 p.m., the smart glasses will display a reminder along with encouraging messages such as "Do your best today!", helping the operator work efficiently.

[0498] Prompt Sentence Examples

[0499] This system selects relevant images and icons based on task information entered by the worker user through smart glasses, and generates reminders according to the worker's emotional state. Example: If the task "Inspect the conveyor belt and sensors at 2:00 PM on December 2, 2023" is entered, the system will select images highly relevant to the task and set a reminder with relaxing images and encouraging messages based on the worker's emotional analysis. A notification will be displayed on the smart glasses at the reminder time.

[0500] This allows factory robot operators to efficiently manage tasks and receive psychological support when needed.

[0501] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0502] Step 1:

[0503] The user puts on the device (smart glasses), launches the application, and accesses the login screen. The user enters their ID and password and presses the login button to request authentication. The device sends the entered authentication information to the server, which compares it with the database and returns the authentication result to the device. If authentication is successful, the user can proceed to the task input screen. The input is the ID and password, and the output is the authentication result.

[0504] Step 2:

[0505] The user inputs task information using voice commands or gestures. Specifically, the user speaks the task title, such as "Inspect the conveyor belt and sensors at 2 PM," into the microphone of the smart glasses and specifies the deadline. The device converts this voice into text and sends it to the server as task information. The input is voice data, and the output is text data of the task information.

[0506] Step 3:

[0507] The server analyzes the received task information and uses a generative AI model to extract highly relevant keywords from the input task information. Based on these keywords, the server refers to past behavioral data and related information to select the most appropriate image or icon. The input is the text data of the task information, and the output is the selected image or icon.

[0508] Step 4:

[0509] The device's camera and microphone are used to capture the user's emotional state from their face and voice. The device then sends this captured data to the emotion engine for emotion analysis. The server receives the analysis results from the emotion engine and determines the user's emotional state. The input is the captured data, and the output is the analysis results of the emotional state.

[0510] Step 5:

[0511] The server adjusts the content and timing of the reminder based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the server will add a relaxing image or an encouraging message. The server combines this information to generate a reminder and sets it to send a notification at a specified date and time. The input is task information, selected image, and emotional state, and the output is reminder information.

[0512] Step 6:

[0513] When the specified reminder time approaches, the server sends the reminder information to the device. The device displays the reminder content on the smart glasses display and notifies the user. The user checks the notification and performs the task as necessary. The input is the reminder information, and the output is the notification to the user.

[0514] 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.

[0515] 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.

[0516] 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.

[0517] [Second embodiment]

[0518] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0519] 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.

[0520] 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).

[0521] 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.

[0522] 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.

[0523] 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).

[0524] 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.

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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."

[0530] This invention relates to a task management system that visually reminds you of important tasks and schedules. The program processing of this system will be explained in natural language below, along with specific examples.

[0531] Overall system overview

[0532] This system uses an AI model to select relevant images and icons based on task information entered by the user, creates reminders, and notifies the user at the specified time.The system consists of three entities: a server, a device, and the user.

[0533] Program processing

[0534] User Login

[0535] The user opens the login screen using the device and enters their ID and password.

[0536] The terminal transmits the entered authentication information to the server.

[0537] The server checks the user's authentication information against a database and allows login if authentication is successful.

[0538] Task Input

[0539] The user opens the task entry screen of a task management app and enters the title, details, and deadline of a new task.

[0540] The terminal transmits the input task information to the server.

[0541] Image selection

[0542] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[0543] The server retrieves the user's past tasks and the image data they used from a database, and uses an AI model to identify the images and icons that are most relevant to the task.

[0544] Reminder Settings

[0545] The server generates a reminder that combines the selected image or icon with task information and sets it to notify you at the specified reminder time.

[0546] The server stores the reminder information in a database.

[0547] Reminder notifications

[0548] When the set reminder time approaches, the server sends a reminder notification to the device.

[0549] The device receives the notification and displays a popup with task information along with an image or icon to the user.

[0550] Specific examples

[0551] For example, when a user named Tanaka inputs a task called "Create materials for Friday's meeting," the following processing is performed.

[0552] 1. User Login

[0553] Tanaka launches the app on his smartphone and logs in by entering his ID and password.

[0554] The terminal transmits authentication information to the server, and the server performs authentication.

[0555] 2. Task Input

[0556] Tanaka enters the task details as "Prepare meeting materials" and sets the deadline to Friday.

[0557] The terminal sends this information to the server.

[0558] 3. Image Selection

[0559] The server analyzes the task information for "creating meeting materials" and uses an AI model to search for relevant images and icons.

[0560] Based on Tanaka's past usage history, an image of a businessman working on a computer is selected.

[0561] 4. Reminder Settings

[0562] The server generates a reminder that combines the image and task information and sets it to notify you at 9:00 AM on Friday.

[0563] Save reminder information in a database.

[0564] 5. Reminder notifications

[0565] At 9:00 AM on Friday, the server will send a reminder to Tanaka's smartphone.

[0566] The terminal displays to Tanaka an image of a businessman working on a computer and a notification that "meeting materials are being prepared."

[0567] This allows Mr. Tanaka to visually check his tasks quickly and intuitively, enabling effective task management. In this way, we provide a system that supports users in completing tasks, increases daily productivity, and reduces stress.

[0568] The processing flow will be explained below.

[0569] Step 1:

[0570] The user launches the application on the device and enters their ID and password on the login screen.

[0571] The terminal transmits the entered login information to the server.

[0572] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[0573] Step 2:

[0574] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[0575] The terminal receives the input task information and sends it to the server as a request.

[0576] Step 3:

[0577] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[0578] The server retrieves the user's past task data from a database and references the history of related images and icons.

[0579] The server uses AI models to select the most relevant images and icons for the task.

[0580] Step 4:

[0581] The server combines the selected image with the task information and stores it in a database as a reminder.

[0582] The server schedules reminder dates and times based on task due dates and user preferences.

[0583] Step 5:

[0584] When the reminder date and time approaches, the server sends a reminder notification to the terminal.

[0585] Your device will receive the notification and display a pop-up notification on your screen.

[0586] Examples:

[0587] Step 1:

[0588] Tanaka launches the app on his smartphone and enters his login information.

[0589] The device sends Tanaka's authentication information to the server.

[0590] The server checks the information against the database and returns a message to the terminal indicating that authentication was successful.

[0591] Step 2:

[0592] Tanaka enters "Create meeting materials for Friday" and sets the deadline to Friday.

[0593] The terminal sends this information to the server.

[0594] Step 3:

[0595] The server analyzes the task of "creating meeting materials" and understands its content.

[0596] The server refers to the task history that Tanaka has used in the past and searches for images related to "meetings."

[0597] The AI ​​model selects images of businessmen working on computers.

[0598] Step 4:

[0599] The server stores the task and the selected image as a reminder.

[0600] The server sets the reminder date and time to 9:00 AM on Friday.

[0601] Step 5:

[0602] At 9:00 AM on Friday, the server sends a reminder to the device.

[0603] The device displays an image of a businessman working on a computer on Tanaka's smartphone along with the message "Preparing materials for Friday's meeting."

[0604] Tanaka checks the notification and confirms the details as necessary.

[0605] Example 1

[0606] 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."

[0607] Conventional task management systems make it difficult for users to properly manage task information and receive visual reminders. Furthermore, they lack the ability to optimize and customize reminders based on past behavioral data and individual user preferences. This has resulted in insufficient improvements in user productivity and stress reduction.

[0608] 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.

[0609] In this invention, the server includes means for accepting task information entered by a user, means for using an AI model to select highly relevant images or icons based on the entered task information, means for generating a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, means for saving the reminder information in a database and setting a notification schedule, and means for verifying authentication information entered by the user and generating a session. This enables automatic and visual reminders based on the task information entered by the user. Furthermore, optimization and customization based on past behavioral data and individual preferences are performed, enabling effective task management and improving user productivity and reducing stress.

[0610] A "user" is a person or entity that uses the system.

[0611] "Task information" is data regarding the title, details, deadline, etc. of a task entered by the user.

[0612] An "AI model" is an algorithm or program that uses artificial intelligence to perform a specific task.

[0613] "Highly relevant images and icons" are images and icons that are judged to be visually appropriate for the task content based on the input task information.

[0614] A "reminder" is a message containing task information and visual elements (images or icons) that is notified to the user at a specified time.

[0615] A "database" is a data storage system for storing task information, images, icons, and associated metadata.

[0616] "Authentication Information" means information used to authorize access to a system, such as a user ID and password.

[0617] A "session" is temporary connection information for maintaining a state in which a user is logged in to a system.

[0618] "Natural Language Processing (NLP)" is an artificial intelligence technology for analyzing human language and understanding its content.

[0619] The "notification schedule" is schedule information for setting the date and time when a reminder is to be notified to the user.

[0620] "Reminder notification" is an action that sends a reminder to the user at a specified time.

[0621] "Visual reminder" refers to visually reminding a user of a task by notifying the user of task information using images or icons.

[0622] "Productivity" means enabling users to manage tasks more efficiently and get more done.

[0623] "Stress reduction" means reducing the user's mental burden through effective task management.

[0624] This invention is a system that uses an AI model to visually remind users of task information and assist them in managing their tasks. This system includes three components: a server, a terminal, and a user. Below, we explain the program processing of this system in natural language and provide detailed descriptions with specific examples of its operation.

[0625] Overall system overview

[0626] Based on the task information entered by the user, this system uses an AI model to select relevant images or icons and notify them as reminders at the specified time. The entire system consists of a server, a terminal, and the user.

[0627] Hardware and Software Use

[0628] This system uses the following hardware and software:

[0629] Server: Database Management System (DBMS), Natural Language Processing (NLP) engine, generative AI model.

[0630] Device: Smartphone or personal computer, task management application.

[0631] Database: A data storage system.

[0632] Specific examples of programs

[0633] For example, the process when the user inputs a task "Create materials for the Friday meeting" is shown below.

[0634] 1. Log in

[0635] The user enters their ID and password on the terminal and accesses the system from the login screen.

[0636] The terminal sends encrypted authentication information to the server, which then compares it with a database to perform authentication.

[0637] If the authentication is successful, the server creates a session and sends a login success message to the terminal.

[0638] 2. Task Input

[0639] The user opens the task input screen of the task management app, enters the details as "Create meeting materials," and sets the deadline to Friday.

[0640] This information is sent from the terminal to the server, which stores it in a database.

[0641] 3. Image Selection

[0642] The server analyzes the task information using natural language processing (NLP) and understands its content.

[0643] The server retrieves the user's past task history from a database and then uses an AI model to identify the images and icons most relevant to "creating meeting materials."

[0644] 4. Reminder Settings

[0645] The server combines task information with relevant images and icons to generate reminders.

[0646] The server saves this reminder in a database and sets the notification time to "Friday at 9 AM."

[0647] 5. Reminder notifications

[0648] When the reminder time approaches, the server sends a reminder notification to the device.

[0649] Your device will receive a reminder notification and display a pop-up with task information along with an image or icon.

[0650] Prompt Sentence Examples

[0651] Below are some examples of prompts to input to an AI model.

[0652] "When a user inputs a task such as creating materials for a meeting on Friday, the system should select a relevant image or icon and provide a reminder at the specified time."

[0653] The system allows users to visually and efficiently manage tasks, improving daily productivity and reducing stress. Technical features of the invention include automatic reminder generation based on task information and optimization of images and icons based on the user's past behavior data.

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

[0655] Step 1: User Login

[0656] 1. The user opens the login screen using the terminal and enters their ID and password. This is the input data.

[0657] 2. The device encrypts the entered authentication information and sends it to the server.

[0658] 3. The server checks the received authentication information against a database. This process involves a data calculation that compares it with the corresponding record in the database.

[0659] 4. If authentication is successful, the server creates a session and returns a login success message to the terminal. This becomes the output data.

[0660] 5. The user will receive a confirmation of successful login and will be able to access the system.

[0661] Specific working example:

[0662] Tanaka opens the login screen on his smartphone, enters his ID and password, and taps the "Login" button. The device encrypts the authentication information and sends it to the server, which then verifies it in a database.

[0663] Step 2: Task Input

[0664] 1. The user opens the task entry screen of the task management app.

[0665] 2. The user enters task information such as the task title, details, deadline, etc. This is the input data.

[0666] 3. The terminal sends the entered task information to the server.

[0667] 4. The server saves the received task information in the database. During this process, data is written to the database.

[0668] Specific working example:

[0669] Tanaka opens the task input screen, enters the details as "Prepare meeting materials," and sets the deadline to "Friday."

[0670] The smartphone sends this information to a server, which stores it in a database.

[0671] Step 3: Image Selection

[0672] 1. The server analyzes the task information received from the database using natural language processing (NLP). The input data is the received task information.

[0673] 2. The server retrieves the user's past task information and corresponding image data from the database. The data processing performed in this process involves filtering and extracting past data.

[0674] 3. The server uses the generative AI model to identify the image or icon that is most relevant to the task. The AI ​​model performs data calculations and outputs the optimal image or icon.

[0675] Specific working example:

[0676] The server analyzes the task of "creating meeting materials" using natural language processing and understands its content.

[0677] The server retrieves Tanaka's past task history from the database, and the AI ​​model determines that the most suitable image is "an image of a businessman working on a computer."

[0678] Step 4: Set reminders

[0679] 1. The server generates a reminder by combining the selected image or icon with the input task information. The input data is the image, icon, and task information.

[0680] 2. The server saves the reminder information to the database and sets the notification schedule. This process involves writing to the database and setting the notification timing.

[0681] 3. Once the reminder and notification time settings are complete, the server saves this information as output data.

[0682] Specific working example:

[0683] The server combines the task information with an image of a businessman working on a computer to generate a reminder to be displayed to Tanaka.

[0684] The server saves this reminder in a database and sets the notification time to "Friday at 9 AM."

[0685] Step 5: Reminders

[0686] 1. When the reminder time approaches, the server sends a reminder notification to the device. The input data is the notification schedule information.

[0687] 2. The device receives the notification and displays the task information along with the selected image or icon in a pop-up. The notification data from the server becomes the output data displayed on the device.

[0688] Specific working example:

[0689] At 9:00 AM on Friday, the server will send a reminder to Tanaka's smartphone.

[0690] Tanaka's smartphone will pop up a notification for him, displaying an image of a businessman working on a computer and a notification that a meeting document is being prepared.

[0691] This clarifies the specific operations performed at each processing step and the data input and output based on those operations, allowing users to visually confirm tasks quickly and intuitively.

[0692] (Application example 1)

[0693] 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."

[0694] Conventional task management systems have limited visual reminder functions for tasks, and lack effective means to improve work efficiency, especially in work environments such as factories. There is also a need for a system that ensures workers can obtain the necessary information at the right time. Therefore, there is a need for a new system that improves task management for factory workers and supports efficient work through visual reminders.

[0695] 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.

[0696] In this invention, the server includes a means for accepting task information input by a user, a means for using an AI model to select highly relevant images or icons based on the input task information, a means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, and a means for sending a notification to a device (e.g., smart glasses) that displays the visual reminder, thereby significantly improving the work efficiency of factory workers and preventing them from missing tasks.

[0697] A "user" is a person who uses the system to enter and manage task information.

[0698] "Task information" is information about the work content, details, and deadline entered by the user.

[0699] The "means" are the functional elements that make up this system, and are modules and processes for performing specific processing.

[0700] An "AI model" is an algorithm that uses machine learning to analyze input data and generate output that is highly relevant to a specific task.

[0701] "Images and icons" are visual elements selected by the system to visually represent a task.

[0702] A "reminder" is a mechanism that sends a notification to a user at a specified time to remind them of a task.

[0703] A "notification" is a message sent from the system to a user device that acts as a reminder.

[0704] "Device" means the hardware device used to display notifications, including smart glasses and smartphones.

[0705] A "visual reminder" is a visual notification of task information to a user using images or icons.

[0706] A "factory worker" is a person who performs work in a factory and receives work instructions and reminders using a task management system.

[0707] This invention relates to a task management system for factory workers to improve their work efficiency. Based on task information entered by users, the system uses an AI model to select relevant images and icons, and sends visual reminders to devices such as smart glasses.

[0708] System Program

[0709] The system includes the following program processes:

[0710] 1. User login:

[0711] The user opens the login screen using the smart glasses and enters their ID and password. The device sends the entered authentication information to the server, which then verifies the user's authentication information against a database.

[0712] 2. Task Input:

[0713] The user enters the title, details, and deadline of a new task through the task management app, and the device sends the entered task information to the server.

[0714] 3. Image Selection:

[0715] The server analyzes the task information and uses an AI model (e.g., powered by TensorFlow) to identify the most relevant images or icons for the task, using natural language processing (NLP) and taking into account the user's previous task data.

[0716] 4. Reminder Settings:

[0717] The server generates a visual reminder by combining the selected image or icon with the task information, and sets it to notify the user at the specified time. The server saves the reminder information in a database.

[0718] 5. Reminders:

[0719] When the set reminder time approaches, the server sends a reminder notification to the smart glasses. The device (smart glasses) receives the notification and displays task information along with an image or icon to the user.

[0720] Hardware and software used

[0721] Hardware: Smart glasses, server, user terminal

[0722] Software: TensorFlow (AI model), Natural Language Processing (NLP), Database Management System

[0723] Specific examples

[0724] For example, if a factory worker enters a task called "Assembly parts on Monday," it will be processed as follows:

[0725] 1. The user logs in to the smart glasses.

[0726] 2. In your task management app, enter the details as "Parts assembly work" and set the due date to Monday.

[0727] 3. The server analyzes the task information and uses an AI model to select images relevant to the factory work (for example, images of the worker at work).

[0728] 4. The server combines the selected image with the task information and sets it to notify you at 8:00 AM on Monday.

[0729] 5. At 8:00 AM on Monday, a reminder notification will appear on the smart glasses. The screen will show the message "Assembly work" and an image of the work being done.

[0730] Prompt Sentence Examples

[0731] "Please select an image of the parts assembly work."

[0732] The system allows factory workers to efficiently manage tasks through visual reminders, ensuring that important work is not overlooked.

[0733] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0734] Step 1:

[0735] The user opens the login screen using smart glasses and enters their ID and password. The entered authentication information is sent to the server by the device. The server checks the database and allows the user to log in if authentication is successful. Here, the input is the user's ID and password, and the output is the success or failure of the login.

[0736] Step 2:

[0737] A user inputs the title, details, and deadline of a new task through a task management app. The device sends this task information to a server. Here, the input is the task title, details, and deadline, and the output is the task information stored on the server.

[0738] Step 3:

[0739] The server analyzes the received task information. First, it uses natural language processing (NLP) to understand the task content and extract task keywords. Then, the server uses a generative AI model to identify relevant images and icons. The input here is the task information, and the output is images and icons related to the keywords.

[0740] Step 4:

[0741] The server combines the selected image or icon with the task information to generate a reminder. This reminder is set to be notified at the specified reminder time. The server saves the reminder information in a database. The input is the task information and the selected image or icon, and the output is the generated reminder.

[0742] Step 5:

[0743] When the reminder time approaches, the server sends a reminder notification to the device (smart glasses). The device receives the notification and displays the task information along with the specified image or icon to the user. The input here is the reminder information, and the output is the notification displayed to the user.

[0744] The description should include specific actions such as checking the server's database (step 1), using NLP analysis and a generative AI model (step 3), saving the reminder information to the database (step 4), and sending a notification to the device (step 5).

[0745] 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.

[0746] This invention relates to a system that processes task information entered by a user using an AI model, selects relevant images and icons, and generates reminders. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system adjusts the timing and content of reminders, providing a more refined task management and user experience.

[0747] Overall system overview

[0748] This system uses an AI model to select relevant images and icons based on task information entered by the user, and adjusts reminders by analyzing the user's emotions using an emotion engine.The system consists of three entities: a server, a device, and the user.

[0749] Program processing

[0750] User Login

[0751] The user launches the application on the device and enters their ID and password on the login screen.

[0752] The terminal transmits the entered login information to the server.

[0753] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[0754] Task Input

[0755] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[0756] The terminal receives the input task information and sends it to the server as a request.

[0757] Image selection

[0758] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[0759] The server retrieves the user's past task data from a database and references the history of related images and icons.

[0760] The server uses AI models to select the most relevant images and icons for the task.

[0761] emotion recognition

[0762] The device uses the user's camera and microphone to capture facial expressions and voice.

[0763] The device sends the captured data to the emotion engine for analysis.

[0764] The server receives the results of the emotion engine and determines the user's current emotional state.

[0765] Reminder Settings

[0766] The server adjusts the timing and content of reminders based on the user's emotional state.

[0767] For example, if the user is feeling stressed, add a relaxing image or an encouraging message.

[0768] The server generates a reminder that combines the selected image and task information, and sets it to notify the user at the specified reminder time.

[0769] The server stores the reminder information in a database.

[0770] Reminder notifications

[0771] When the set reminder time approaches, the server sends a reminder notification to the device.

[0772] The device receives the notification and displays a popup with task information along with an image or icon to the user.

[0773] Specific examples

[0774] For example, when a user named Tanaka inputs a task called "Create materials for Friday's meeting," the following processing is performed.

[0775] 1. User Login

[0776] Tanaka launches the app on his smartphone and logs in by entering his ID and password.

[0777] The terminal transmits authentication information to the server, and the server performs authentication.

[0778] 2. Task Input

[0779] Tanaka enters the details for "Prepare meeting materials" and sets the deadline to Friday.

[0780] The terminal sends this information to the server.

[0781] 3. Image Selection

[0782] The server analyzes the task information for "creating meeting materials" and uses an AI model to search for relevant images and icons.

[0783] Based on Tanaka's past usage history, an image of a businessman working on a computer is selected.

[0784] 4. Emotion recognition

[0785] The device captures Tanaka's facial expressions and voice and analyzes them through an emotion engine.

[0786] The server determines from the results of the emotion engine that Tanaka is feeling stressed.

[0787] 5. Reminder Settings

[0788] The server adds relaxing images and encouraging messages to reduce stress and generates reminders.

[0789] Set the reminder date and time for Friday at 9:00 AM and save the information.

[0790] 6. Reminder notifications

[0791] At 9:00 AM on Friday, the server sends a reminder to the device.

[0792] The device displays stress-relieving images and a notification to "create meeting materials" on Tanaka's smartphone.

[0793] Tanaka checks the notification and views the details as necessary.

[0794] This allows Tanaka to not only visually grasp tasks, but also receive support tailored to his emotional state. In this way, we provide a system that more effectively supports users' task management, increases daily productivity, and reduces stress.

[0795] The processing flow will be explained below.

[0796] Step 1:

[0797] The user launches the application on the device and enters their ID and password on the login screen.

[0798] The terminal transmits the entered login information to the server.

[0799] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[0800] Step 2:

[0801] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[0802] The terminal receives the input task information and sends it to the server as a request.

[0803] Step 3:

[0804] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[0805] The server retrieves the user's past task data from a database and references the history of related images and icons.

[0806] The server uses AI models to select the most relevant images and icons for the task.

[0807] Step 4:

[0808] The device uses the user's camera and microphone to capture facial expressions and voice.

[0809] The device sends the captured data to the emotion engine for analysis.

[0810] The server receives the results of the emotion engine and determines the user's current emotional state.

[0811] Step 5:

[0812] The server adjusts the timing and content of reminders based on the user's emotional state.

[0813] For example, if the user is feeling stressed, add a relaxing image or an encouraging message.

[0814] The server generates a reminder that combines the selected image and task information, and sets it to notify the user at the specified reminder time.

[0815] The server stores the reminder information in a database.

[0816] Step 6:

[0817] When the set reminder time approaches, the server sends a reminder notification to the terminal.

[0818] The device receives the notification and displays a popup with the task information along with the selected image or icon to the user.

[0819] Examples:

[0820] Step 1:

[0821] Tanaka launches the app on his smartphone and enters his login information.

[0822] The device sends Tanaka's authentication information to the server.

[0823] The server checks the information against the database and returns a message to the terminal indicating that authentication was successful.

[0824] Step 2:

[0825] Tanaka enters "Create meeting materials for Friday" and sets the deadline to Friday.

[0826] The terminal sends this information to the server.

[0827] Step 3:

[0828] The server analyzes the task of "creating meeting materials" and understands its content.

[0829] The server refers to the task history that Tanaka has used in the past and searches for images related to "meetings."

[0830] The AI ​​model selects images of businessmen working on computers.

[0831] Step 4:

[0832] The device captures Tanaka's facial expressions and voice and analyzes them through an emotion engine.

[0833] The server determines from the results of the emotion engine that Tanaka is feeling stressed.

[0834] Step 5:

[0835] The server adds relaxing images and encouraging messages to reduce stress and generates reminders.

[0836] Set the reminder date and time for Friday at 9:00 AM and save the information.

[0837] Step 6:

[0838] At 9:00 AM on Friday, the server sends a reminder to the device.

[0839] The device displays stress-relieving images and a notification to "create meeting materials" on Tanaka's smartphone.

[0840] Tanaka checks the notification and views the details as necessary.

[0841] Example 2

[0842] 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."

[0843] Conventional task management systems have the ability to generate visual materials and notifications based on task information entered by the user, but it is difficult to generate accurate reminders that take into account the user's emotional state and past behavioral data. Furthermore, they are unable to appropriately adjust the content and timing of reminders according to the user's emotional state, which rarely improves the user experience. Therefore, there is a need for more effective and customized task management that takes into account the user's emotions and past behavioral data.

[0844] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for accepting task information input by a user, a means for selecting highly relevant visual data based on the input task information using an AI model, a means for capturing sensory data of the terminal and analyzing the user's emotional state, and a means for adjusting a reminder based on the selected visual data and the emotion analysis results and sending a notification at a specified time. This enables more personalized reminder setting and notification based on the user's emotional state and past behavioral data.

[0845] "User" refers to an individual who utilizes the system to enter task information and receive reminders and notifications.

[0846] "Task information" refers to detailed information about work or plans that a user enters into the system, including the title, content, deadline, etc.

[0847] "Means of acceptance" refers to the functions and mechanisms for incorporating task information entered by the user into the system and processing it.

[0848] "AI model" refers to an algorithm or machine learning model that uses artificial intelligence technology to analyze task information and select highly relevant data based on that information.

[0849] "Visual data" refers to visual content such as images and icons related to a task.

[0850] A "terminal" is a device used by a user to operate the system, and includes smartphones, tablets, personal computers, etc.

[0851] "Sensory data" refers to data such as the user's facial expressions and voice that are captured using the device's camera, microphone, etc.

[0852] "Means for analyzing" refers to a program or function for analyzing the user's emotional state using sensory data.

[0853] "Reminder" refers to a mechanism that notifies users of tasks along with visual data, and is a message or alert to remind them of a task.

[0854] "Means for sending notifications" refers to a function or program that sends a reminder to a user's device at a specified time.

[0855] "Behavioral data" refers to information recorded based on a user's past task history and activities.

[0856] This invention is a system that uses an AI model to select relevant visual data based on task information entered by the user, analyzes sensory data from the device to determine the user's emotional state, and generates and adjusts reminders to notify them at the specified time.

[0857] The entire system consists of three entities: the server, the terminal, and the user.

[0858] First, the user uses the application to input task information, including the task title, details, due date, etc. This input data is temporarily stored on the device and encrypted before being sent to the server.

[0859] Once the server receives the task information, it uses a natural language processing (NLP) engine to analyze the task content, using software such as Python's NLTK library, and then selects visual data (images and icons) related to the task based on the analysis results and an AI model (e.g., using TensorFlow).

[0860] Additionally, the device uses a camera and microphone to capture the user's sensory data (facial expressions and voice). The captured data is temporarily stored locally and sent to an emotion engine (e.g., Microsoft Azure's Emotion API). The emotion engine analyzes the data and determines the user's emotional state.

[0861] The server receives the results from the emotion engine and adjusts the content and timing of reminders accordingly: for example, if the user is feeling stressed, a relaxing image or an encouraging message will be added to the reminder.

[0862] Finally, the server stores the adjusted reminder information in a database and notifies the device of the reminder at the specified time. The device then displays the received reminder in a pop-up window to the user, presenting task information along with visual data.

[0863] A specific example is given below.

[0864] For example, if a user inputs a task called "Create materials for Friday's meeting," the following process will be performed.

[0865] The user launches the app on their smartphone and enters task information.

[0866] The terminal sends this information to the server.

[0867] The server analyzes the task information with an NLP engine and selects appropriate images using an AI model.

[0868] The device captures the user's emotional data using a camera or microphone and sends it to the emotion engine.

[0869] The server receives the results of the emotion engine and adds relaxing images and encouraging messages to the reminder.

[0870] The server transmits the adjusted reminder data to the terminal at the specified time, and the terminal displays a pop-up notification.

[0871] Example prompt sentence:

[0872] "Describe a process where an AI recognizes that a user is finding a task stressful and then sets and sends an encouraging reminder along with a relaxing image."

[0873] In this way, the system of the present invention effectively assists users in managing their tasks and provides personalized support based on their emotional state.

[0874] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0875] Step 1: User Login

[0876] The user launches the application installed on the device and enters their ID and password on the login screen.

[0877] Input: User ID and password

[0878] What it does: Enter your user ID and password into the form.

[0879] The terminal temporarily stores the login information received from the user in memory, encrypts it, and then sends it to the server.

[0880] Input: Encrypted user ID and password

[0881] Output: Authentication request to the server

[0882] What it does: Encrypts your login information and sends it to the server.

[0883] The server receives the user's authentication information and checks it against the user data in its database.

[0884] Input: Encrypted user ID and password

[0885] Output: Authentication success or failure result

[0886] What it does: Retrieves and collates user data from a database.

[0887] The server generates an authentication result and sends it to the terminal.

[0888] Input: Authentication result

[0889] Output: Sending authentication result

[0890] Operation: The authentication result is sent to the terminal.

[0891] The terminal displays a success or failure message to the user based on the received authentication result.

[0892] Input: Authentication result

[0893] Output: Login success or failure message

[0894] Behavior: Displays the authentication result to the user.

[0895] Step 2: Task Input

[0896] After logging in, the user opens the task entry screen of the app and enters the task title, details, and deadline date.

[0897] Input: Task title, details, due date

[0898] Action: Enter task information into a form.

[0899] The terminal acquires the input task information, organizes it into a data object, and sends it to the server.

[0900] Input: Task information

[0901] Output: Request to send task data to the server

[0902] Action: Sends task information to the server.

[0903] The server stores the received task information in a database.

[0904] Input: Task information

[0905] Output: Save task information to database

[0906] What it does: Saves task information to a database.

[0907] Step 3: Image Selection

[0908] The server uses a natural language processing (NLP) engine to analyze the stored task information.

[0909] Input: Task information

[0910] Output: Parsed task information

[0911] How it works: The NLP engine analyzes task information.

[0912] After analysis, the server uses an AI model (e.g., TensorFlow) to select visual data relevant to the task.

[0913] Input: Parsed task information

[0914] Output: Selected visual data

[0915] How it works: Curates visual data using AI models.

[0916] The server associates the selected visual data with the task information.

[0917] Input: Selected visual data

[0918] Output: Associate with task information

[0919] Action: Link visual data to task information.

[0920] Step 4: Emotion Recognition

[0921] The device captures the user's facial expressions and voice using a camera and microphone.

[0922] Input: User's facial expressions and voice

[0923] Output: Captured sensory data

[0924] How it works: Captures data using the camera and microphone.

[0925] The device temporarily stores the captured data and sends it to the emotion engine for analysis.

[0926] Input: Captured sensory data

[0927] Output: Send to emotion engine

[0928] Action: Sends sensory data to the emotion engine.

[0929] The server receives the analysis results from the emotion engine and determines the user's emotional state.

[0930] Input: Parsed sensory data

[0931] Output: User's emotional state

[0932] Action: Determines emotional state.

[0933] Step 5: Set reminders

[0934] The server adjusts the content and timing of reminders based on the user's emotional state.

[0935] Input: User's emotional state, task information, visual data

[0936] Output: Adjusted reminders

[0937] How it works: Adjust reminders based on your emotional state.

[0938] The server stores the adjusted reminder information in a database.

[0939] Input: Adjusted reminders

[0940] Output: Reminder saved to database

[0941] Behavior: Saves reminder information.

[0942] Step 6: Reminders

[0943] When the reminder time approaches, the server retrieves the saved reminder information and sends it to the device.

[0944] Input: Reminder information

[0945] Output: Reminder notification to device

[0946] Action: Sends a reminder to your device.

[0947] The device displays the received reminder information in a pop-up display to the user.

[0948] Input: Received reminder information

[0949] Output: Popup notification

[0950] Behavior: Generates a popup notification and displays it to the user.

[0951] This allows users to not only visually understand their tasks, but also receive reminders that reflect their emotional state.

[0952] (Application example 2)

[0953] 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."

[0954] Conventional task management systems can generate reminders based on task information entered by users, but they cannot take the user's emotional state into account, and may send reminders at inappropriate times or with inappropriate content. As a result, users may feel stressed or the reminders may not be effective enough. Furthermore, there is a need for task management that combines efficiency with psychological support, especially in workplaces such as factory robot operators.

[0955] 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 accepting task information input by a user, means for using an AI model to select highly relevant images or icons based on the input task information, means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, and means including an emotion engine that analyzes the user's emotional state and adjusting the content and timing of the reminder based on the analysis results. This makes it possible to notify reminders with appropriate timing and content that take the user's emotional state into consideration, allowing factory robot operators and others to efficiently manage tasks and perform their work while receiving psychological support.

[0956] "Task information entered by the user" refers to information that the user enters into the system, such as the task title, details, deadline date, etc.

[0957] An "AI model" is a model that uses artificial intelligence technology to analyze and classify data.

[0958] "Highly relevant images and icons" are visual elements that are determined to be most appropriate for the input task information.

[0959] A "reminder" is notification information that notifies the user of a task that the user has set and prompts the user to check it again.

[0960] An "emotion engine" is a system that analyzes a user's facial expressions and voice data to determine the user's emotional state.

[0961] A "server" is a computer system that receives and processes requests from clients.

[0962] "User's past behavior data" refers to data such as task information that the user has entered so far and associated access logs.

[0963] "Behavior patterns" refer to recurring patterns of user behavior and operations, and are the basic data that the system learns from.

[0964] "Preferences" are the characteristics or patterns that a user particularly prefers, and are data used to customize reminders.

[0965] "Notifications" are messages or alerts that the system sends to inform the user.

[0966] This system uses an AI model to select relevant images and icons based on task information entered by the user, and an emotion engine to analyze the user's emotions and adjust reminders. This system is primarily composed of three entities: a server, a terminal, and the user.

[0967] System Overview

[0968] This system accepts task information entered by the user and generates and notifies reminders based on that information. It uses a generative AI model to select relevant images and icons based on the task information, and further combines this with an emotion engine that recognizes the user's emotional state to adjust the timing and content of reminder notifications. This enables appropriate reminder notifications based on the user's emotional state.

[0969] Hardware and software used

[0970] Hardware:

[0971] Smart glasses: A device worn by the robot operator that is used to display reminders and various notifications.

[0972] Camera and microphone: Used to capture the user's facial expressions and voice data for the emotion engine.

[0973] software:

[0974] Server: Analyzes task information, selects images, recognizes emotions, and generates reminders.

[0975] NLP engine: Analyzes task information and extracts keywords.

[0976] Emotion recognition engine: Analyzes emotions from facial expressions and voice.

[0977] Image Selection Model: Select images and icons relevant to the task.

[0978] Notification system: Schedule reminders and send notifications.

[0979] What the program does

[0980] 1. User login: The user logs in from the device (smart glasses) and is authenticated by the server.

[0981] 2. Task input: The user inputs task information using voice commands or gestures and sends the information to the server.

[0982] 3. Image Selection: The server uses an AI model to select relevant images and icons based on the input task information.

[0983] 4. Emotion recognition: The smart glasses' camera and microphone are used to capture the user's emotions, which are then analyzed by the emotion engine.

[0984] 5. Reminder generation and notification settings: Adjust the timing and content of reminders based on the analysis results of the emotion engine, and schedule reminders in the notification system.

[0985] Specific examples

[0986] For example, if a factory robot operator inputs the task "Inspect conveyor belts and sensors at 2 p.m.", the following process will occur:

[0987] 1. The robot operator puts on the smart glasses and logs into the system with their ID and password.

[0988] 2. The operator uses voice commands to enter task information such as "Inspect the conveyor belt and sensors at 2 p.m." and set a deadline.

[0989] 3. The server analyzes the task information, extracts keywords using an NLP engine, and selects relevant images based on those keywords.

[0990] 4. The user's facial expressions and voice are captured through the camera and microphone, and the emotion engine analyzes their stress level.

[0991] 5. Based on the emotion engine analysis, add a relaxing image or an encouraging message to the reminder and set it to notify you at 2 PM.

[0992] 6. At 2 p.m., the smart glasses will display a reminder along with encouraging messages such as "Do your best today!", helping the operator work efficiently.

[0993] Prompt Sentence Examples

[0994] This system selects relevant images and icons based on task information entered by the worker user through smart glasses, and generates reminders according to the worker's emotional state. Example: If the task "Inspect the conveyor belt and sensors at 2:00 PM on December 2, 2023" is entered, the system will select images highly relevant to the task and set a reminder with relaxing images and encouraging messages based on the worker's emotional analysis. A notification will be displayed on the smart glasses at the reminder time.

[0995] This allows factory robot operators to efficiently manage tasks and receive psychological support when needed.

[0996] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0997] Step 1:

[0998] The user puts on the device (smart glasses), launches the application, and accesses the login screen. The user enters their ID and password and presses the login button to request authentication. The device sends the entered authentication information to the server, which compares it with the database and returns the authentication result to the device. If authentication is successful, the user can proceed to the task input screen. The input is the ID and password, and the output is the authentication result.

[0999] Step 2:

[1000] The user inputs task information using voice commands or gestures. Specifically, the user speaks the task title, such as "Inspect the conveyor belt and sensors at 2 PM," into the microphone of the smart glasses and specifies the deadline. The device converts this voice into text and sends it to the server as task information. The input is voice data, and the output is text data of the task information.

[1001] Step 3:

[1002] The server analyzes the received task information and uses a generative AI model to extract highly relevant keywords from the input task information. Based on these keywords, the server refers to past behavioral data and related information to select the most appropriate image or icon. The input is the text data of the task information, and the output is the selected image or icon.

[1003] Step 4:

[1004] The device's camera and microphone are used to capture the user's emotional state from their face and voice. The device then sends this captured data to the emotion engine for emotion analysis. The server receives the analysis results from the emotion engine and determines the user's emotional state. The input is the captured data, and the output is the analysis results of the emotional state.

[1005] Step 5:

[1006] The server adjusts the content and timing of the reminder based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the server will add a relaxing image or an encouraging message. The server combines this information to generate a reminder and sets it to send a notification at a specified date and time. The input is task information, selected image, and emotional state, and the output is reminder information.

[1007] Step 6:

[1008] When the specified reminder time approaches, the server sends the reminder information to the device. The device displays the reminder content on the smart glasses display and notifies the user. The user checks the notification and performs the task as necessary. The input is the reminder information, and the output is the notification to the user.

[1009] 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.

[1010] 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.

[1011] 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.

[1012] [Third embodiment]

[1013] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1014] 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.

[1015] 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).

[1016] 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.

[1017] 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.

[1018] 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).

[1019] 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.

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] 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."

[1025] This invention relates to a task management system that visually reminds you of important tasks and schedules. The program processing of this system will be explained in natural language below, along with specific examples.

[1026] Overall system overview

[1027] This system uses an AI model to select relevant images and icons based on task information entered by the user, creates reminders, and notifies the user at the specified time.The system consists of three entities: a server, a device, and the user.

[1028] Program processing

[1029] User Login

[1030] The user opens the login screen using the device and enters their ID and password.

[1031] The terminal transmits the entered authentication information to the server.

[1032] The server checks the user's authentication information against a database and allows login if authentication is successful.

[1033] Task Input

[1034] The user opens the task entry screen of a task management app and enters the title, details, and deadline of a new task.

[1035] The terminal transmits the input task information to the server.

[1036] Image selection

[1037] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[1038] The server retrieves the user's past tasks and the image data they used from a database, and uses an AI model to identify the images and icons that are most relevant to the task.

[1039] Reminder Settings

[1040] The server generates a reminder that combines the selected image or icon with task information and sets it to notify you at the specified reminder time.

[1041] The server stores the reminder information in a database.

[1042] Reminder notifications

[1043] When the set reminder time approaches, the server sends a reminder notification to the device.

[1044] The device receives the notification and displays a popup with task information along with an image or icon to the user.

[1045] Specific examples

[1046] For example, when a user named Tanaka inputs a task called "Create materials for Friday's meeting," the following processing is performed.

[1047] 1. User Login

[1048] Tanaka launches the app on his smartphone and logs in by entering his ID and password.

[1049] The terminal transmits authentication information to the server, and the server performs authentication.

[1050] 2. Task Input

[1051] Tanaka enters the task details as "Prepare meeting materials" and sets the deadline to Friday.

[1052] The terminal sends this information to the server.

[1053] 3. Image Selection

[1054] The server analyzes the task information for "creating meeting materials" and uses an AI model to search for relevant images and icons.

[1055] Based on Tanaka's past usage history, an image of a businessman working on a computer is selected.

[1056] 4. Reminder Settings

[1057] The server generates a reminder that combines the image and task information and sets it to notify you at 9:00 AM on Friday.

[1058] Save reminder information in a database.

[1059] 5. Reminder notifications

[1060] At 9:00 AM on Friday, the server will send a reminder to Tanaka's smartphone.

[1061] The terminal displays to Tanaka an image of a businessman working on a computer and a notification that "meeting materials are being prepared."

[1062] This allows Mr. Tanaka to visually check his tasks quickly and intuitively, enabling effective task management. In this way, we provide a system that supports users in completing tasks, increases daily productivity, and reduces stress.

[1063] The processing flow will be explained below.

[1064] Step 1:

[1065] The user launches the application on the device and enters their ID and password on the login screen.

[1066] The terminal transmits the entered login information to the server.

[1067] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[1068] Step 2:

[1069] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[1070] The terminal receives the input task information and sends it to the server as a request.

[1071] Step 3:

[1072] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[1073] The server retrieves the user's past task data from a database and references the history of related images and icons.

[1074] The server uses AI models to select the most relevant images and icons for the task.

[1075] Step 4:

[1076] The server combines the selected image with the task information and stores it in a database as a reminder.

[1077] The server schedules reminder dates and times based on task due dates and user preferences.

[1078] Step 5:

[1079] When the reminder date and time approaches, the server sends a reminder notification to the terminal.

[1080] Your device will receive the notification and display a pop-up notification on your screen.

[1081] Examples:

[1082] Step 1:

[1083] Tanaka launches the app on his smartphone and enters his login information.

[1084] The device sends Tanaka's authentication information to the server.

[1085] The server checks the information against the database and returns a message to the terminal indicating that authentication was successful.

[1086] Step 2:

[1087] Tanaka enters "Create meeting materials for Friday" and sets the deadline to Friday.

[1088] The terminal sends this information to the server.

[1089] Step 3:

[1090] The server analyzes the task of "creating meeting materials" and understands its content.

[1091] The server refers to the task history that Tanaka has used in the past and searches for images related to "meetings."

[1092] The AI ​​model selects images of businessmen working on computers.

[1093] Step 4:

[1094] The server stores the task and the selected image as a reminder.

[1095] The server sets the reminder date and time to 9:00 AM on Friday.

[1096] Step 5:

[1097] At 9:00 AM on Friday, the server sends a reminder to the device.

[1098] The device displays an image of a businessman working on a computer on Tanaka's smartphone along with the message "Preparing materials for Friday's meeting."

[1099] Tanaka checks the notification and confirms the details as necessary.

[1100] Example 1

[1101] 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."

[1102] Conventional task management systems make it difficult for users to properly manage task information and receive visual reminders. Furthermore, they lack the ability to optimize and customize reminders based on past behavioral data and individual user preferences. This has resulted in insufficient improvements in user productivity and stress reduction.

[1103] 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.

[1104] In this invention, the server includes means for accepting task information entered by a user, means for using an AI model to select highly relevant images or icons based on the entered task information, means for generating a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, means for saving the reminder information in a database and setting a notification schedule, and means for verifying authentication information entered by the user and generating a session. This enables automatic and visual reminders based on the task information entered by the user. Furthermore, optimization and customization based on past behavioral data and individual preferences are performed, enabling effective task management and improving user productivity and reducing stress.

[1105] A "user" is a person or entity that uses the system.

[1106] "Task information" is data regarding the title, details, deadline, etc. of a task entered by the user.

[1107] An "AI model" is an algorithm or program that uses artificial intelligence to perform a specific task.

[1108] "Highly relevant images and icons" are images and icons that are judged to be visually appropriate for the task content based on the input task information.

[1109] A "reminder" is a message containing task information and visual elements (images or icons) that is notified to the user at a specified time.

[1110] A "database" is a data storage system for storing task information, images, icons, and associated metadata.

[1111] "Authentication Information" means information used to authorize access to a system, such as a user ID and password.

[1112] A "session" is temporary connection information for maintaining a state in which a user is logged in to a system.

[1113] "Natural Language Processing (NLP)" is an artificial intelligence technology for analyzing human language and understanding its content.

[1114] The "notification schedule" is schedule information for setting the date and time when a reminder is to be notified to the user.

[1115] "Reminder notification" is an action that sends a reminder to the user at a specified time.

[1116] "Visual reminder" refers to visually reminding a user of a task by notifying the user of task information using images or icons.

[1117] "Productivity" means enabling users to manage tasks more efficiently and get more done.

[1118] "Stress reduction" means reducing the user's mental burden through effective task management.

[1119] This invention is a system that uses an AI model to visually remind users of task information and assist them in managing their tasks. This system includes three components: a server, a terminal, and a user. Below, we explain the program processing of this system in natural language and provide detailed descriptions with specific examples of its operation.

[1120] Overall system overview

[1121] Based on the task information entered by the user, this system uses an AI model to select relevant images or icons and notify them as reminders at the specified time. The entire system consists of a server, a terminal, and the user.

[1122] Hardware and Software Use

[1123] This system uses the following hardware and software:

[1124] Server: Database Management System (DBMS), Natural Language Processing (NLP) engine, generative AI model.

[1125] Device: Smartphone or personal computer, task management application.

[1126] Database: A data storage system.

[1127] Specific examples of programs

[1128] For example, the process when the user inputs a task "Create materials for the Friday meeting" is shown below.

[1129] 1. Log in

[1130] The user enters their ID and password on the terminal and accesses the system from the login screen.

[1131] The terminal sends encrypted authentication information to the server, which then compares it with a database to perform authentication.

[1132] If the authentication is successful, the server creates a session and sends a login success message to the terminal.

[1133] 2. Task Input

[1134] The user opens the task input screen of the task management app, enters the details as "Create meeting materials," and sets the deadline to Friday.

[1135] This information is sent from the terminal to the server, which stores it in a database.

[1136] 3. Image Selection

[1137] The server analyzes the task information using natural language processing (NLP) and understands its content.

[1138] The server retrieves the user's past task history from a database and then uses an AI model to identify the images and icons most relevant to "creating meeting materials."

[1139] 4. Reminder Settings

[1140] The server combines task information with relevant images and icons to generate reminders.

[1141] The server saves this reminder in a database and sets the notification time to "Friday at 9 AM."

[1142] 5. Reminder notifications

[1143] When the reminder time approaches, the server sends a reminder notification to the device.

[1144] Your device will receive a reminder notification and display a pop-up with task information along with an image or icon.

[1145] Prompt Sentence Examples

[1146] Below are some examples of prompts to input to an AI model.

[1147] "When a user inputs a task such as creating materials for a meeting on Friday, the system should select a relevant image or icon and provide a reminder at the specified time."

[1148] The system allows users to visually and efficiently manage tasks, improving daily productivity and reducing stress. Technical features of the invention include automatic reminder generation based on task information and optimization of images and icons based on the user's past behavior data.

[1149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1150] Step 1: User Login

[1151] 1. The user opens the login screen using the terminal and enters their ID and password. This is the input data.

[1152] 2. The device encrypts the entered authentication information and sends it to the server.

[1153] 3. The server checks the received authentication information against a database. This process involves a data calculation that compares it with the corresponding record in the database.

[1154] 4. If authentication is successful, the server creates a session and returns a login success message to the terminal. This becomes the output data.

[1155] 5. The user will receive a confirmation of successful login and will be able to access the system.

[1156] Specific working example:

[1157] Tanaka opens the login screen on his smartphone, enters his ID and password, and taps the "Login" button. The device encrypts the authentication information and sends it to the server, which then verifies it in a database.

[1158] Step 2: Task Input

[1159] 1. The user opens the task entry screen of the task management app.

[1160] 2. The user enters task information such as the task title, details, deadline, etc. This is the input data.

[1161] 3. The terminal sends the entered task information to the server.

[1162] 4. The server saves the received task information in the database. During this process, data is written to the database.

[1163] Specific working example:

[1164] Tanaka opens the task input screen, enters the details as "Prepare meeting materials," and sets the deadline to "Friday."

[1165] The smartphone sends this information to a server, which stores it in a database.

[1166] Step 3: Image Selection

[1167] 1. The server analyzes the task information received from the database using natural language processing (NLP). The input data is the received task information.

[1168] 2. The server retrieves the user's past task information and corresponding image data from the database. The data processing performed in this process involves filtering and extracting past data.

[1169] 3. The server uses the generative AI model to identify the image or icon that is most relevant to the task. The AI ​​model performs data calculations and outputs the optimal image or icon.

[1170] Specific working example:

[1171] The server analyzes the task of "creating meeting materials" using natural language processing and understands its content.

[1172] The server retrieves Tanaka's past task history from the database, and the AI ​​model determines that the most suitable image is "an image of a businessman working on a computer."

[1173] Step 4: Set reminders

[1174] 1. The server generates a reminder by combining the selected image or icon with the input task information. The input data is the image, icon, and task information.

[1175] 2. The server saves the reminder information to the database and sets the notification schedule. This process involves writing to the database and setting the notification timing.

[1176] 3. Once the reminder and notification time settings are complete, the server saves this information as output data.

[1177] Specific working example:

[1178] The server combines the task information with an image of a businessman working on a computer to generate a reminder to be displayed to Tanaka.

[1179] The server saves this reminder in a database and sets the notification time to "Friday at 9 AM."

[1180] Step 5: Reminders

[1181] 1. When the reminder time approaches, the server sends a reminder notification to the device. The input data is the notification schedule information.

[1182] 2. The device receives the notification and displays the task information along with the selected image or icon in a pop-up. The notification data from the server becomes the output data displayed on the device.

[1183] Specific working example:

[1184] At 9:00 AM on Friday, the server will send a reminder to Tanaka's smartphone.

[1185] Tanaka's smartphone will pop up a notification for him, displaying an image of a businessman working on a computer and a notification that a meeting document is being prepared.

[1186] This clarifies the specific operations performed at each processing step and the data input and output based on those operations, allowing users to visually confirm tasks quickly and intuitively.

[1187] (Application example 1)

[1188] 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."

[1189] Conventional task management systems have limited visual reminder functions for tasks, and lack effective means to improve work efficiency, especially in work environments such as factories. There is also a need for a system that ensures workers can obtain the necessary information at the right time. Therefore, there is a need for a new system that improves task management for factory workers and supports efficient work through visual reminders.

[1190] 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.

[1191] In this invention, the server includes a means for accepting task information input by a user, a means for using an AI model to select highly relevant images or icons based on the input task information, a means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, and a means for sending a notification to a device (e.g., smart glasses) that displays the visual reminder, thereby significantly improving the work efficiency of factory workers and preventing them from missing tasks.

[1192] A "user" is a person who uses the system to enter and manage task information.

[1193] "Task information" is information about the work content, details, and deadline entered by the user.

[1194] The "means" are the functional elements that make up this system, and are modules and processes for performing specific processing.

[1195] An "AI model" is an algorithm that uses machine learning to analyze input data and generate output that is highly relevant to a specific task.

[1196] "Images and icons" are visual elements selected by the system to visually represent a task.

[1197] A "reminder" is a mechanism that sends a notification to a user at a specified time to remind them of a task.

[1198] A "notification" is a message sent from the system to a user device that acts as a reminder.

[1199] "Device" means the hardware device used to display notifications, including smart glasses and smartphones.

[1200] A "visual reminder" is a visual notification of task information to a user using images or icons.

[1201] A "factory worker" is a person who performs work in a factory and receives work instructions and reminders using a task management system.

[1202] This invention relates to a task management system for factory workers to improve their work efficiency. Based on task information entered by users, the system uses an AI model to select relevant images and icons, and sends visual reminders to devices such as smart glasses.

[1203] System Program

[1204] The system includes the following program processes:

[1205] 1. User login:

[1206] The user opens the login screen using the smart glasses and enters their ID and password. The device sends the entered authentication information to the server, which then verifies the user's authentication information against a database.

[1207] 2. Task Input:

[1208] The user enters the title, details, and deadline of a new task through the task management app, and the device sends the entered task information to the server.

[1209] 3. Image Selection:

[1210] The server analyzes the task information and uses an AI model (e.g., powered by TensorFlow) to identify the most relevant images or icons for the task, using natural language processing (NLP) and taking into account the user's previous task data.

[1211] 4. Reminder Settings:

[1212] The server generates a visual reminder by combining the selected image or icon with the task information, and sets it to notify the user at the specified time. The server saves the reminder information in a database.

[1213] 5. Reminders:

[1214] When the set reminder time approaches, the server sends a reminder notification to the smart glasses. The device (smart glasses) receives the notification and displays task information along with an image or icon to the user.

[1215] Hardware and software used

[1216] Hardware: Smart glasses, server, user terminal

[1217] Software: TensorFlow (AI model), Natural Language Processing (NLP), Database Management System

[1218] Specific examples

[1219] For example, if a factory worker enters a task called "Assembly parts on Monday," it will be processed as follows:

[1220] 1. The user logs in to the smart glasses.

[1221] 2. In your task management app, enter the details as "Parts assembly work" and set the due date to Monday.

[1222] 3. The server analyzes the task information and uses an AI model to select images relevant to the factory work (for example, images of the worker at work).

[1223] 4. The server combines the selected image with the task information and sets it to notify you at 8:00 AM on Monday.

[1224] 5. At 8:00 AM on Monday, a reminder notification will appear on the smart glasses. The screen will show the message "Assembly work" and an image of the work being done.

[1225] Prompt Sentence Examples

[1226] "Please select an image of the parts assembly work."

[1227] The system allows factory workers to efficiently manage tasks through visual reminders, ensuring that important work is not overlooked.

[1228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1229] Step 1:

[1230] The user opens the login screen using smart glasses and enters their ID and password. The entered authentication information is sent to the server by the device. The server checks the database and allows the user to log in if authentication is successful. Here, the input is the user's ID and password, and the output is the success or failure of the login.

[1231] Step 2:

[1232] A user inputs the title, details, and deadline of a new task through a task management app. The device sends this task information to a server. Here, the input is the task title, details, and deadline, and the output is the task information stored on the server.

[1233] Step 3:

[1234] The server analyzes the received task information. First, it uses natural language processing (NLP) to understand the task content and extract task keywords. Then, the server uses a generative AI model to identify relevant images and icons. The input here is the task information, and the output is images and icons related to the keywords.

[1235] Step 4:

[1236] The server combines the selected image or icon with the task information to generate a reminder. This reminder is set to be notified at the specified reminder time. The server saves the reminder information in a database. The input is the task information and the selected image or icon, and the output is the generated reminder.

[1237] Step 5:

[1238] When the reminder time approaches, the server sends a reminder notification to the device (smart glasses). The device receives the notification and displays the task information along with the specified image or icon to the user. The input here is the reminder information, and the output is the notification displayed to the user.

[1239] The description should include specific actions such as checking the server's database (step 1), using NLP analysis and a generative AI model (step 3), saving the reminder information to the database (step 4), and sending a notification to the device (step 5).

[1240] 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.

[1241] This invention relates to a system that processes task information entered by a user using an AI model, selects relevant images and icons, and generates reminders. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system adjusts the timing and content of reminders, providing a more refined task management and user experience.

[1242] Overall system overview

[1243] This system uses an AI model to select relevant images and icons based on task information entered by the user, and adjusts reminders by analyzing the user's emotions using an emotion engine.The system consists of three entities: a server, a device, and the user.

[1244] Program processing

[1245] User Login

[1246] The user launches the application on the device and enters their ID and password on the login screen.

[1247] The terminal transmits the entered login information to the server.

[1248] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[1249] Task Input

[1250] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[1251] The terminal receives the input task information and sends it to the server as a request.

[1252] Image selection

[1253] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[1254] The server retrieves the user's past task data from a database and references the history of related images and icons.

[1255] The server uses AI models to select the most relevant images and icons for the task.

[1256] emotion recognition

[1257] The device uses the user's camera and microphone to capture facial expressions and voice.

[1258] The device sends the captured data to the emotion engine for analysis.

[1259] The server receives the results of the emotion engine and determines the user's current emotional state.

[1260] Reminder Settings

[1261] The server adjusts the timing and content of reminders based on the user's emotional state.

[1262] For example, if the user is feeling stressed, add a relaxing image or an encouraging message.

[1263] The server generates a reminder that combines the selected image and task information, and sets it to notify the user at the specified reminder time.

[1264] The server stores the reminder information in a database.

[1265] Reminder notifications

[1266] When the set reminder time approaches, the server sends a reminder notification to the device.

[1267] The device receives the notification and displays a popup with task information along with an image or icon to the user.

[1268] Specific examples

[1269] For example, when a user named Tanaka inputs a task called "Create materials for Friday's meeting," the following processing is performed.

[1270] 1. User Login

[1271] Tanaka launches the app on his smartphone and logs in by entering his ID and password.

[1272] The terminal transmits authentication information to the server, and the server performs authentication.

[1273] 2. Task Input

[1274] Tanaka enters the details for "Prepare meeting materials" and sets the deadline to Friday.

[1275] The terminal sends this information to the server.

[1276] 3. Image Selection

[1277] The server analyzes the task information for "creating meeting materials" and uses an AI model to search for relevant images and icons.

[1278] Based on Tanaka's past usage history, an image of a businessman working on a computer is selected.

[1279] 4. Emotion recognition

[1280] The device captures Tanaka's facial expressions and voice and analyzes them through an emotion engine.

[1281] The server determines from the results of the emotion engine that Tanaka is feeling stressed.

[1282] 5. Reminder Settings

[1283] The server adds relaxing images and encouraging messages to reduce stress and generates reminders.

[1284] Set the reminder date and time for Friday at 9:00 AM and save the information.

[1285] 6. Reminder notifications

[1286] At 9:00 AM on Friday, the server sends a reminder to the device.

[1287] The device displays stress-relieving images and a notification to "create meeting materials" on Tanaka's smartphone.

[1288] Tanaka checks the notification and views the details as necessary.

[1289] This allows Tanaka to not only visually grasp tasks, but also receive support tailored to his emotional state. In this way, we provide a system that more effectively supports users' task management, increases daily productivity, and reduces stress.

[1290] The processing flow will be explained below.

[1291] Step 1:

[1292] The user launches the application on the device and enters their ID and password on the login screen.

[1293] The terminal transmits the entered login information to the server.

[1294] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[1295] Step 2:

[1296] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[1297] The terminal receives the input task information and sends it to the server as a request.

[1298] Step 3:

[1299] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[1300] The server retrieves the user's past task data from a database and references the history of related images and icons.

[1301] The server uses AI models to select the most relevant images and icons for the task.

[1302] Step 4:

[1303] The device uses the user's camera and microphone to capture facial expressions and voice.

[1304] The device sends the captured data to the emotion engine for analysis.

[1305] The server receives the results of the emotion engine and determines the user's current emotional state.

[1306] Step 5:

[1307] The server adjusts the timing and content of reminders based on the user's emotional state.

[1308] For example, if the user is feeling stressed, add a relaxing image or an encouraging message.

[1309] The server generates a reminder that combines the selected image and task information, and sets it to notify the user at the specified reminder time.

[1310] The server stores the reminder information in a database.

[1311] Step 6:

[1312] When the set reminder time approaches, the server sends a reminder notification to the terminal.

[1313] The device receives the notification and displays a popup with the task information along with the selected image or icon to the user.

[1314] Examples:

[1315] Step 1:

[1316] Tanaka launches the app on his smartphone and enters his login information.

[1317] The device sends Tanaka's authentication information to the server.

[1318] The server checks the information against the database and returns a message to the terminal indicating that authentication was successful.

[1319] Step 2:

[1320] Tanaka enters "Create meeting materials for Friday" and sets the deadline to Friday.

[1321] The terminal sends this information to the server.

[1322] Step 3:

[1323] The server analyzes the task of "creating meeting materials" and understands its content.

[1324] The server refers to the task history that Tanaka has used in the past and searches for images related to "meetings."

[1325] The AI ​​model selects images of businessmen working on computers.

[1326] Step 4:

[1327] The device captures Tanaka's facial expressions and voice and analyzes them through an emotion engine.

[1328] The server determines from the results of the emotion engine that Tanaka is feeling stressed.

[1329] Step 5:

[1330] The server adds relaxing images and encouraging messages to reduce stress and generates reminders.

[1331] Set the reminder date and time for Friday at 9:00 AM and save the information.

[1332] Step 6:

[1333] At 9:00 AM on Friday, the server sends a reminder to the device.

[1334] The device displays stress-relieving images and a notification to "create meeting materials" on Tanaka's smartphone.

[1335] Tanaka checks the notification and views the details as necessary.

[1336] Example 2

[1337] 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."

[1338] Conventional task management systems have the ability to generate visual materials and notifications based on task information entered by the user, but it is difficult to generate accurate reminders that take into account the user's emotional state and past behavioral data. Furthermore, they are unable to appropriately adjust the content and timing of reminders according to the user's emotional state, which rarely improves the user experience. Therefore, there is a need for more effective and customized task management that takes into account the user's emotions and past behavioral data.

[1339] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for accepting task information input by a user, a means for selecting highly relevant visual data based on the input task information using an AI model, a means for capturing sensory data of the terminal and analyzing the user's emotional state, and a means for adjusting a reminder based on the selected visual data and the emotion analysis results and sending a notification at a specified time. This enables more personalized reminder setting and notification based on the user's emotional state and past behavioral data.

[1340] "User" refers to an individual who utilizes the system to enter task information and receive reminders and notifications.

[1341] "Task information" refers to detailed information about work or plans that a user enters into the system, including the title, content, deadline, etc.

[1342] "Means of acceptance" refers to the functions and mechanisms for incorporating task information entered by the user into the system and processing it.

[1343] "AI model" refers to an algorithm or machine learning model that uses artificial intelligence technology to analyze task information and select highly relevant data based on that information.

[1344] "Visual data" refers to visual content such as images and icons related to a task.

[1345] A "terminal" is a device used by a user to operate the system, and includes smartphones, tablets, personal computers, etc.

[1346] "Sensory data" refers to data such as the user's facial expressions and voice that are captured using the device's camera, microphone, etc.

[1347] "Means for analyzing" refers to a program or function for analyzing the user's emotional state using sensory data.

[1348] "Reminder" refers to a mechanism that notifies users of tasks along with visual data, and is a message or alert to remind them of a task.

[1349] "Means for sending notifications" refers to a function or program that sends a reminder to a user's device at a specified time.

[1350] "Behavioral data" refers to information recorded based on a user's past task history and activities.

[1351] This invention is a system that uses an AI model to select relevant visual data based on task information entered by the user, analyzes sensory data from the device to determine the user's emotional state, and generates and adjusts reminders to notify them at the specified time.

[1352] The entire system consists of three entities: the server, the terminal, and the user.

[1353] First, the user uses the application to input task information, including the task title, details, due date, etc. This input data is temporarily stored on the device and encrypted before being sent to the server.

[1354] Once the server receives the task information, it uses a natural language processing (NLP) engine to analyze the task content, using software such as Python's NLTK library, and then selects visual data (images and icons) related to the task based on the analysis results and an AI model (e.g., using TensorFlow).

[1355] Additionally, the device uses a camera and microphone to capture the user's sensory data (facial expressions and voice). The captured data is temporarily stored locally and sent to an emotion engine (e.g., Microsoft Azure's Emotion API). The emotion engine analyzes the data and determines the user's emotional state.

[1356] The server receives the results from the emotion engine and adjusts the content and timing of reminders accordingly: for example, if the user is feeling stressed, a relaxing image or an encouraging message will be added to the reminder.

[1357] Finally, the server stores the adjusted reminder information in a database and notifies the device of the reminder at the specified time. The device then displays the received reminder in a pop-up window to the user, presenting task information along with visual data.

[1358] A specific example is given below.

[1359] For example, if a user inputs a task called "Create materials for Friday's meeting," the following process will be performed.

[1360] The user launches the app on their smartphone and enters task information.

[1361] The terminal sends this information to the server.

[1362] The server analyzes the task information with an NLP engine and selects appropriate images using an AI model.

[1363] The device captures the user's emotional data using a camera or microphone and sends it to the emotion engine.

[1364] The server receives the results of the emotion engine and adds relaxing images and encouraging messages to the reminder.

[1365] The server transmits the adjusted reminder data to the terminal at the specified time, and the terminal displays a pop-up notification.

[1366] Example prompt sentence:

[1367] "Describe a process where an AI recognizes that a user is finding a task stressful and then sets and sends an encouraging reminder along with a relaxing image."

[1368] In this way, the system of the present invention effectively assists users in managing their tasks and provides personalized support based on their emotional state.

[1369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1370] Step 1: User Login

[1371] The user launches the application installed on the device and enters their ID and password on the login screen.

[1372] Input: User ID and password

[1373] What it does: Enter your user ID and password into the form.

[1374] The terminal temporarily stores the login information received from the user in memory, encrypts it, and then sends it to the server.

[1375] Input: Encrypted user ID and password

[1376] Output: Authentication request to the server

[1377] What it does: Encrypts your login information and sends it to the server.

[1378] The server receives the user's authentication information and checks it against the user data in its database.

[1379] Input: Encrypted user ID and password

[1380] Output: Authentication success or failure result

[1381] What it does: Retrieves and collates user data from a database.

[1382] The server generates an authentication result and sends it to the terminal.

[1383] Input: Authentication result

[1384] Output: Sending authentication result

[1385] Operation: The authentication result is sent to the terminal.

[1386] The terminal displays a success or failure message to the user based on the received authentication result.

[1387] Input: Authentication result

[1388] Output: Login success or failure message

[1389] Behavior: Displays the authentication result to the user.

[1390] Step 2: Task Input

[1391] After logging in, the user opens the task entry screen of the app and enters the task title, details, and deadline date.

[1392] Input: Task title, details, due date

[1393] Action: Enter task information into a form.

[1394] The terminal acquires the input task information, organizes it into a data object, and sends it to the server.

[1395] Input: Task information

[1396] Output: Request to send task data to the server

[1397] Action: Sends task information to the server.

[1398] The server stores the received task information in a database.

[1399] Input: Task information

[1400] Output: Save task information to database

[1401] What it does: Saves task information to a database.

[1402] Step 3: Image Selection

[1403] The server uses a natural language processing (NLP) engine to analyze the stored task information.

[1404] Input: Task information

[1405] Output: Parsed task information

[1406] How it works: The NLP engine analyzes task information.

[1407] After analysis, the server uses an AI model (e.g., TensorFlow) to select visual data relevant to the task.

[1408] Input: Parsed task information

[1409] Output: Selected visual data

[1410] How it works: Curates visual data using AI models.

[1411] The server associates the selected visual data with the task information.

[1412] Input: Selected visual data

[1413] Output: Associate with task information

[1414] Action: Link visual data to task information.

[1415] Step 4: Emotion Recognition

[1416] The device captures the user's facial expressions and voice using a camera and microphone.

[1417] Input: User's facial expressions and voice

[1418] Output: Captured sensory data

[1419] How it works: Captures data using the camera and microphone.

[1420] The device temporarily stores the captured data and sends it to the emotion engine for analysis.

[1421] Input: Captured sensory data

[1422] Output: Send to emotion engine

[1423] Action: Sends sensory data to the emotion engine.

[1424] The server receives the analysis results from the emotion engine and determines the user's emotional state.

[1425] Input: Parsed sensory data

[1426] Output: User's emotional state

[1427] Action: Determines emotional state.

[1428] Step 5: Set reminders

[1429] The server adjusts the content and timing of reminders based on the user's emotional state.

[1430] Input: User's emotional state, task information, visual data

[1431] Output: Adjusted reminders

[1432] How it works: Adjust reminders based on your emotional state.

[1433] The server stores the adjusted reminder information in a database.

[1434] Input: Adjusted reminders

[1435] Output: Reminder saved to database

[1436] Behavior: Saves reminder information.

[1437] Step 6: Reminders

[1438] When the reminder time approaches, the server retrieves the saved reminder information and sends it to the device.

[1439] Input: Reminder information

[1440] Output: Reminder notification to device

[1441] Action: Sends a reminder to your device.

[1442] The device displays the received reminder information in a pop-up display to the user.

[1443] Input: Received reminder information

[1444] Output: Popup notification

[1445] Behavior: Generates a popup notification and displays it to the user.

[1446] This allows users to not only visually understand their tasks, but also receive reminders that reflect their emotional state.

[1447] (Application example 2)

[1448] 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."

[1449] Conventional task management systems can generate reminders based on task information entered by users, but they cannot take the user's emotional state into account, and may send reminders at inappropriate times or with inappropriate content. As a result, users may feel stressed or the reminders may not be effective enough. Furthermore, there is a need for task management that combines efficiency with psychological support, especially in workplaces such as factory robot operators.

[1450] 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 accepting task information input by a user, means for using an AI model to select highly relevant images or icons based on the input task information, means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, and means including an emotion engine that analyzes the user's emotional state and adjusting the content and timing of the reminder based on the analysis results. This makes it possible to notify reminders with appropriate timing and content that take the user's emotional state into consideration, allowing factory robot operators and others to efficiently manage tasks and perform their work while receiving psychological support.

[1451] "Task information entered by the user" refers to information that the user enters into the system, such as the task title, details, deadline date, etc.

[1452] An "AI model" is a model that uses artificial intelligence technology to analyze and classify data.

[1453] "Highly relevant images and icons" are visual elements that are determined to be most appropriate for the input task information.

[1454] A "reminder" is notification information that notifies the user of a task that the user has set and prompts the user to check it again.

[1455] An "emotion engine" is a system that analyzes a user's facial expressions and voice data to determine the user's emotional state.

[1456] A "server" is a computer system that receives and processes requests from clients.

[1457] "User's past behavior data" refers to data such as task information that the user has entered so far and associated access logs.

[1458] "Behavior patterns" refer to recurring patterns of user behavior and operations, and are the basic data that the system learns from.

[1459] "Preferences" are the characteristics or patterns that a user particularly prefers, and are data used to customize reminders.

[1460] "Notifications" are messages or alerts that the system sends to inform the user.

[1461] This system uses an AI model to select relevant images and icons based on task information entered by the user, and an emotion engine to analyze the user's emotions and adjust reminders. This system is primarily composed of three entities: a server, a terminal, and the user.

[1462] System Overview

[1463] This system accepts task information entered by the user and generates and notifies reminders based on that information. It uses a generative AI model to select relevant images and icons based on the task information, and further combines this with an emotion engine that recognizes the user's emotional state to adjust the timing and content of reminder notifications. This enables appropriate reminder notifications based on the user's emotional state.

[1464] Hardware and software used

[1465] Hardware:

[1466] Smart glasses: A device worn by the robot operator that is used to display reminders and various notifications.

[1467] Camera and microphone: Used to capture the user's facial expressions and voice data for the emotion engine.

[1468] software:

[1469] Server: Analyzes task information, selects images, recognizes emotions, and generates reminders.

[1470] NLP engine: Analyzes task information and extracts keywords.

[1471] Emotion recognition engine: Analyzes emotions from facial expressions and voice.

[1472] Image Selection Model: Select images and icons relevant to the task.

[1473] Notification system: Schedule reminders and send notifications.

[1474] What the program does

[1475] 1. User login: The user logs in from the device (smart glasses) and is authenticated by the server.

[1476] 2. Task input: The user inputs task information using voice commands or gestures and sends the information to the server.

[1477] 3. Image Selection: The server uses an AI model to select relevant images and icons based on the input task information.

[1478] 4. Emotion recognition: The smart glasses' camera and microphone are used to capture the user's emotions, which are then analyzed by the emotion engine.

[1479] 5. Reminder generation and notification settings: Adjust the timing and content of reminders based on the analysis results of the emotion engine, and schedule reminders in the notification system.

[1480] Specific examples

[1481] For example, if a factory robot operator inputs the task "Inspect conveyor belts and sensors at 2 p.m.", the following process will occur:

[1482] 1. The robot operator puts on the smart glasses and logs into the system with their ID and password.

[1483] 2. The operator uses voice commands to enter task information such as "Inspect the conveyor belt and sensors at 2 p.m." and set a deadline.

[1484] 3. The server analyzes the task information, extracts keywords using an NLP engine, and selects relevant images based on those keywords.

[1485] 4. The user's facial expressions and voice are captured through the camera and microphone, and the emotion engine analyzes their stress level.

[1486] 5. Based on the emotion engine analysis, add a relaxing image or an encouraging message to the reminder and set it to notify you at 2 PM.

[1487] 6. At 2 p.m., the smart glasses will display a reminder along with encouraging messages such as "Do your best today!", helping the operator work efficiently.

[1488] Prompt Sentence Examples

[1489] This system selects relevant images and icons based on task information entered by the worker user through smart glasses, and generates reminders according to the worker's emotional state. Example: If the task "Inspect the conveyor belt and sensors at 2:00 PM on December 2, 2023" is entered, the system will select images highly relevant to the task and set a reminder with relaxing images and encouraging messages based on the worker's emotional analysis. A notification will be displayed on the smart glasses at the reminder time.

[1490] This allows factory robot operators to efficiently manage tasks and receive psychological support when needed.

[1491] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1492] Step 1:

[1493] The user puts on the device (smart glasses), launches the application, and accesses the login screen. The user enters their ID and password and presses the login button to request authentication. The device sends the entered authentication information to the server, which compares it with the database and returns the authentication result to the device. If authentication is successful, the user can proceed to the task input screen. The input is the ID and password, and the output is the authentication result.

[1494] Step 2:

[1495] The user inputs task information using voice commands or gestures. Specifically, the user speaks the task title, such as "Inspect the conveyor belt and sensors at 2 PM," into the microphone of the smart glasses and specifies the deadline. The device converts this voice into text and sends it to the server as task information. The input is voice data, and the output is text data of the task information.

[1496] Step 3:

[1497] The server analyzes the received task information and uses a generative AI model to extract highly relevant keywords from the input task information. Based on these keywords, the server refers to past behavioral data and related information to select the most appropriate image or icon. The input is the text data of the task information, and the output is the selected image or icon.

[1498] Step 4:

[1499] The device's camera and microphone are used to capture the user's emotional state from their face and voice. The device then sends this captured data to the emotion engine for emotion analysis. The server receives the analysis results from the emotion engine and determines the user's emotional state. The input is the captured data, and the output is the analysis results of the emotional state.

[1500] Step 5:

[1501] The server adjusts the content and timing of the reminder based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the server will add a relaxing image or an encouraging message. The server combines this information to generate a reminder and sets it to send a notification at a specified date and time. The input is task information, selected image, and emotional state, and the output is reminder information.

[1502] Step 6:

[1503] When the specified reminder time approaches, the server sends the reminder information to the device. The device displays the reminder content on the smart glasses display and notifies the user. The user checks the notification and performs the task as necessary. The input is the reminder information, and the output is the notification to the user.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] [Fourth embodiment]

[1508] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1509] 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.

[1510] 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).

[1511] 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.

[1512] 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.

[1513] 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).

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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."

[1521] This invention relates to a task management system that visually reminds you of important tasks and schedules. The program processing of this system will be explained in natural language below, along with specific examples.

[1522] Overall system overview

[1523] This system uses an AI model to select relevant images and icons based on task information entered by the user, creates reminders, and notifies the user at the specified time.The system consists of three entities: a server, a device, and the user.

[1524] Program processing

[1525] User Login

[1526] The user opens the login screen using the device and enters their ID and password.

[1527] The terminal transmits the entered authentication information to the server.

[1528] The server checks the user's authentication information against a database and allows login if authentication is successful.

[1529] Task Input

[1530] The user opens the task entry screen of a task management app and enters the title, details, and deadline of a new task.

[1531] The terminal transmits the input task information to the server.

[1532] Image selection

[1533] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[1534] The server retrieves the user's past tasks and the image data they used from a database, and uses an AI model to identify the images and icons that are most relevant to the task.

[1535] Reminder Settings

[1536] The server generates a reminder that combines the selected image or icon with task information and sets it to notify you at the specified reminder time.

[1537] The server stores the reminder information in a database.

[1538] Reminder notifications

[1539] When the set reminder time approaches, the server sends a reminder notification to the device.

[1540] The device receives the notification and displays a popup with task information along with an image or icon to the user.

[1541] Specific examples

[1542] For example, when a user named Tanaka inputs a task called "Create materials for Friday's meeting," the following processing is performed.

[1543] 1. User Login

[1544] Tanaka launches the app on his smartphone and logs in by entering his ID and password.

[1545] The terminal transmits authentication information to the server, and the server performs authentication.

[1546] 2. Task Input

[1547] Tanaka enters the task details as "Prepare meeting materials" and sets the deadline to Friday.

[1548] The terminal sends this information to the server.

[1549] 3. Image Selection

[1550] The server analyzes the task information for "creating meeting materials" and uses an AI model to search for relevant images and icons.

[1551] Based on Tanaka's past usage history, an image of a businessman working on a computer is selected.

[1552] 4. Reminder Settings

[1553] The server generates a reminder that combines the image and task information and sets it to notify you at 9:00 AM on Friday.

[1554] Save reminder information in a database.

[1555] 5. Reminder notifications

[1556] At 9:00 AM on Friday, the server will send a reminder to Tanaka's smartphone.

[1557] The terminal displays to Tanaka an image of a businessman working on a computer and a notification that "meeting materials are being prepared."

[1558] This allows Mr. Tanaka to visually check his tasks quickly and intuitively, enabling effective task management. In this way, we provide a system that supports users in completing tasks, increases daily productivity, and reduces stress.

[1559] The processing flow will be explained below.

[1560] Step 1:

[1561] The user launches the application on the device and enters their ID and password on the login screen.

[1562] The terminal transmits the entered login information to the server.

[1563] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[1564] Step 2:

[1565] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[1566] The terminal receives the input task information and sends it to the server as a request.

[1567] Step 3:

[1568] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[1569] The server retrieves the user's past task data from a database and references the history of related images and icons.

[1570] The server uses AI models to select the most relevant images and icons for the task.

[1571] Step 4:

[1572] The server combines the selected image with the task information and stores it in a database as a reminder.

[1573] The server schedules reminder dates and times based on task due dates and user preferences.

[1574] Step 5:

[1575] When the reminder date and time approaches, the server sends a reminder notification to the terminal.

[1576] Your device will receive the notification and display a pop-up notification on your screen.

[1577] Examples:

[1578] Step 1:

[1579] Tanaka launches the app on his smartphone and enters his login information.

[1580] The device sends Tanaka's authentication information to the server.

[1581] The server checks the information against the database and returns a message to the terminal indicating that authentication was successful.

[1582] Step 2:

[1583] Tanaka enters "Create meeting materials for Friday" and sets the deadline to Friday.

[1584] The terminal sends this information to the server.

[1585] Step 3:

[1586] The server analyzes the task of "creating meeting materials" and understands its content.

[1587] The server refers to the task history that Tanaka has used in the past and searches for images related to "meetings."

[1588] The AI ​​model selects images of businessmen working on computers.

[1589] Step 4:

[1590] The server stores the task and the selected image as a reminder.

[1591] The server sets the reminder date and time to 9:00 AM on Friday.

[1592] Step 5:

[1593] At 9:00 AM on Friday, the server sends a reminder to the device.

[1594] The device displays an image of a businessman working on a computer on Tanaka's smartphone along with the message "Preparing materials for Friday's meeting."

[1595] Tanaka checks the notification and confirms the details as necessary.

[1596] Example 1

[1597] 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."

[1598] Conventional task management systems make it difficult for users to properly manage task information and receive visual reminders. Furthermore, they lack the ability to optimize and customize reminders based on past behavioral data and individual user preferences. This has resulted in insufficient improvements in user productivity and stress reduction.

[1599] 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.

[1600] In this invention, the server includes means for accepting task information entered by a user, means for using an AI model to select highly relevant images or icons based on the entered task information, means for generating a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, means for saving the reminder information in a database and setting a notification schedule, and means for verifying authentication information entered by the user and generating a session. This enables automatic and visual reminders based on the task information entered by the user. Furthermore, optimization and customization based on past behavioral data and individual preferences are performed, enabling effective task management and improving user productivity and reducing stress.

[1601] A "user" is a person or entity that uses the system.

[1602] "Task information" is data regarding the title, details, deadline, etc. of a task entered by the user.

[1603] An "AI model" is an algorithm or program that uses artificial intelligence to perform a specific task.

[1604] "Highly relevant images and icons" are images and icons that are judged to be visually appropriate for the task content based on the input task information.

[1605] A "reminder" is a message containing task information and visual elements (images or icons) that is notified to the user at a specified time.

[1606] A "database" is a data storage system for storing task information, images, icons, and associated metadata.

[1607] "Authentication Information" means information used to authorize access to a system, such as a user ID and password.

[1608] A "session" is temporary connection information for maintaining a state in which a user is logged in to a system.

[1609] "Natural Language Processing (NLP)" is an artificial intelligence technology for analyzing human language and understanding its content.

[1610] The "notification schedule" is schedule information for setting the date and time when a reminder is to be notified to the user.

[1611] "Reminder notification" is an action that sends a reminder to the user at a specified time.

[1612] "Visual reminder" refers to visually reminding a user of a task by notifying the user of task information using images or icons.

[1613] "Productivity" means enabling users to manage tasks more efficiently and get more done.

[1614] "Stress reduction" means reducing the user's mental burden through effective task management.

[1615] This invention is a system that uses an AI model to visually remind users of task information and assist them in managing their tasks. This system includes three components: a server, a terminal, and a user. Below, we explain the program processing of this system in natural language and provide detailed descriptions with specific examples of its operation.

[1616] Overall system overview

[1617] Based on the task information entered by the user, this system uses an AI model to select relevant images or icons and notify them as reminders at the specified time. The entire system consists of a server, a terminal, and the user.

[1618] Hardware and Software Use

[1619] This system uses the following hardware and software:

[1620] Server: Database Management System (DBMS), Natural Language Processing (NLP) engine, generative AI model.

[1621] Device: Smartphone or personal computer, task management application.

[1622] Database: A data storage system.

[1623] Specific examples of programs

[1624] For example, the process when the user inputs a task "Create materials for the Friday meeting" is shown below.

[1625] 1. Log in

[1626] The user enters their ID and password on the terminal and accesses the system from the login screen.

[1627] The terminal sends encrypted authentication information to the server, which then compares it with a database to perform authentication.

[1628] If the authentication is successful, the server creates a session and sends a login success message to the terminal.

[1629] 2. Task Input

[1630] The user opens the task input screen of the task management app, enters the details as "Create meeting materials," and sets the deadline to Friday.

[1631] This information is sent from the terminal to the server, which stores it in a database.

[1632] 3. Image Selection

[1633] The server analyzes the task information using natural language processing (NLP) and understands its content.

[1634] The server retrieves the user's past task history from a database and then uses an AI model to identify the images and icons most relevant to "creating meeting materials."

[1635] 4. Reminder Settings

[1636] The server combines task information with relevant images and icons to generate reminders.

[1637] The server saves this reminder in a database and sets the notification time to "Friday at 9 AM."

[1638] 5. Reminder notifications

[1639] When the reminder time approaches, the server sends a reminder notification to the device.

[1640] Your device will receive a reminder notification and display a pop-up with task information along with an image or icon.

[1641] Prompt Sentence Examples

[1642] Below are some examples of prompts to input to an AI model.

[1643] "When a user inputs a task such as creating materials for a meeting on Friday, the system should select a relevant image or icon and provide a reminder at the specified time."

[1644] The system allows users to visually and efficiently manage tasks, improving daily productivity and reducing stress. Technical features of the invention include automatic reminder generation based on task information and optimization of images and icons based on the user's past behavior data.

[1645] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1646] Step 1: User Login

[1647] 1. The user opens the login screen using the terminal and enters their ID and password. This is the input data.

[1648] 2. The device encrypts the entered authentication information and sends it to the server.

[1649] 3. The server checks the received authentication information against a database. This process involves a data calculation that compares it with the corresponding record in the database.

[1650] 4. If authentication is successful, the server creates a session and returns a login success message to the terminal. This becomes the output data.

[1651] 5. The user will receive a confirmation of successful login and will be able to access the system.

[1652] Specific working example:

[1653] Tanaka opens the login screen on his smartphone, enters his ID and password, and taps the "Login" button. The device encrypts the authentication information and sends it to the server, which then verifies it in a database.

[1654] Step 2: Task Input

[1655] 1. The user opens the task entry screen of the task management app.

[1656] 2. The user enters task information such as the task title, details, deadline, etc. This is the input data.

[1657] 3. The terminal sends the entered task information to the server.

[1658] 4. The server saves the received task information in the database. During this process, data is written to the database.

[1659] Specific working example:

[1660] Tanaka opens the task input screen, enters the details as "Prepare meeting materials," and sets the deadline to "Friday."

[1661] The smartphone sends this information to a server, which stores it in a database.

[1662] Step 3: Image Selection

[1663] 1. The server analyzes the task information received from the database using natural language processing (NLP). The input data is the received task information.

[1664] 2. The server retrieves the user's past task information and corresponding image data from the database. The data processing performed in this process involves filtering and extracting past data.

[1665] 3. The server uses the generative AI model to identify the image or icon that is most relevant to the task. The AI ​​model performs data calculations and outputs the optimal image or icon.

[1666] Specific working example:

[1667] The server analyzes the task of "creating meeting materials" using natural language processing and understands its content.

[1668] The server retrieves Tanaka's past task history from the database, and the AI ​​model determines that the most suitable image is "an image of a businessman working on a computer."

[1669] Step 4: Set reminders

[1670] 1. The server generates a reminder by combining the selected image or icon with the input task information. The input data is the image, icon, and task information.

[1671] 2. The server saves the reminder information to the database and sets the notification schedule. This process involves writing to the database and setting the notification timing.

[1672] 3. Once the reminder and notification time settings are complete, the server saves this information as output data.

[1673] Specific working example:

[1674] The server combines the task information with an image of a businessman working on a computer to generate a reminder to be displayed to Tanaka.

[1675] The server saves this reminder in a database and sets the notification time to "Friday at 9 AM."

[1676] Step 5: Reminders

[1677] 1. When the reminder time approaches, the server sends a reminder notification to the device. The input data is the notification schedule information.

[1678] 2. The device receives the notification and displays the task information along with the selected image or icon in a pop-up. The notification data from the server becomes the output data displayed on the device.

[1679] Specific working example:

[1680] At 9:00 AM on Friday, the server will send a reminder to Tanaka's smartphone.

[1681] Tanaka's smartphone will pop up a notification for him, displaying an image of a businessman working on a computer and a notification that a meeting document is being prepared.

[1682] This clarifies the specific operations performed at each processing step and the data input and output based on those operations, allowing users to visually confirm tasks quickly and intuitively.

[1683] (Application example 1)

[1684] 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."

[1685] Conventional task management systems have limited visual reminder functions for tasks, and lack effective means to improve work efficiency, especially in work environments such as factories. There is also a need for a system that ensures workers can obtain the necessary information at the right time. Therefore, there is a need for a new system that improves task management for factory workers and supports efficient work through visual reminders.

[1686] 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.

[1687] In this invention, the server includes a means for accepting task information input by a user, a means for using an AI model to select highly relevant images or icons based on the input task information, a means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, and a means for sending a notification to a device (e.g., smart glasses) that displays the visual reminder, thereby significantly improving the work efficiency of factory workers and preventing them from missing tasks.

[1688] A "user" is a person who uses the system to enter and manage task information.

[1689] "Task information" is information about the work content, details, and deadline entered by the user.

[1690] The "means" are the functional elements that make up this system, and are modules and processes for performing specific processing.

[1691] An "AI model" is an algorithm that uses machine learning to analyze input data and generate output that is highly relevant to a specific task.

[1692] "Images and icons" are visual elements selected by the system to visually represent a task.

[1693] A "reminder" is a mechanism that sends a notification to a user at a specified time to remind them of a task.

[1694] A "notification" is a message sent from the system to a user device that acts as a reminder.

[1695] "Device" means the hardware device used to display notifications, including smart glasses and smartphones.

[1696] A "visual reminder" is a visual notification of task information to a user using images or icons.

[1697] A "factory worker" is a person who performs work in a factory and receives work instructions and reminders using a task management system.

[1698] This invention relates to a task management system for factory workers to improve their work efficiency. Based on task information entered by users, the system uses an AI model to select relevant images and icons, and sends visual reminders to devices such as smart glasses.

[1699] System Program

[1700] The system includes the following program processes:

[1701] 1. User login:

[1702] The user opens the login screen using the smart glasses and enters their ID and password. The device sends the entered authentication information to the server, which then verifies the user's authentication information against a database.

[1703] 2. Task Input:

[1704] The user enters the title, details, and deadline of a new task through the task management app, and the device sends the entered task information to the server.

[1705] 3. Image Selection:

[1706] The server analyzes the task information and uses an AI model (e.g., powered by TensorFlow) to identify the most relevant images or icons for the task, using natural language processing (NLP) and taking into account the user's previous task data.

[1707] 4. Reminder Settings:

[1708] The server generates a visual reminder by combining the selected image or icon with the task information, and sets it to notify the user at the specified time. The server saves the reminder information in a database.

[1709] 5. Reminders:

[1710] When the set reminder time approaches, the server sends a reminder notification to the smart glasses. The device (smart glasses) receives the notification and displays task information along with an image or icon to the user.

[1711] Hardware and software used

[1712] Hardware: Smart glasses, server, user terminal

[1713] Software: TensorFlow (AI model), Natural Language Processing (NLP), Database Management System

[1714] Specific examples

[1715] For example, if a factory worker enters a task called "Assembly parts on Monday," it will be processed as follows:

[1716] 1. The user logs in to the smart glasses.

[1717] 2. In your task management app, enter the details as "Parts assembly work" and set the due date to Monday.

[1718] 3. The server analyzes the task information and uses an AI model to select images relevant to the factory work (for example, images of the worker at work).

[1719] 4. The server combines the selected image with the task information and sets it to notify you at 8:00 AM on Monday.

[1720] 5. At 8:00 AM on Monday, a reminder notification will appear on the smart glasses. The screen will show the message "Assembly work" and an image of the work being done.

[1721] Prompt Sentence Examples

[1722] "Please select an image of the parts assembly work."

[1723] The system allows factory workers to efficiently manage tasks through visual reminders, ensuring that important work is not overlooked.

[1724] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1725] Step 1:

[1726] The user opens the login screen using smart glasses and enters their ID and password. The entered authentication information is sent to the server by the device. The server checks the database and allows the user to log in if authentication is successful. Here, the input is the user's ID and password, and the output is the success or failure of the login.

[1727] Step 2:

[1728] A user inputs the title, details, and deadline of a new task through a task management app. The device sends this task information to a server. Here, the input is the task title, details, and deadline, and the output is the task information stored on the server.

[1729] Step 3:

[1730] The server analyzes the received task information. First, it uses natural language processing (NLP) to understand the task content and extract task keywords. Then, the server uses a generative AI model to identify relevant images and icons. The input here is the task information, and the output is images and icons related to the keywords.

[1731] Step 4:

[1732] The server combines the selected image or icon with the task information to generate a reminder. This reminder is set to be notified at the specified reminder time. The server saves the reminder information in a database. The input is the task information and the selected image or icon, and the output is the generated reminder.

[1733] Step 5:

[1734] When the reminder time approaches, the server sends a reminder notification to the device (smart glasses). The device receives the notification and displays the task information along with the specified image or icon to the user. The input here is the reminder information, and the output is the notification displayed to the user.

[1735] The description should include specific actions such as checking the server's database (step 1), using NLP analysis and a generative AI model (step 3), saving the reminder information to the database (step 4), and sending a notification to the device (step 5).

[1736] 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.

[1737] This invention relates to a system that processes task information entered by a user using an AI model, selects relevant images and icons, and generates reminders. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the system adjusts the timing and content of reminders, providing a more refined task management and user experience.

[1738] Overall system overview

[1739] This system uses an AI model to select relevant images and icons based on task information entered by the user, and adjusts reminders by analyzing the user's emotions using an emotion engine.The system consists of three entities: a server, a device, and the user.

[1740] Program processing

[1741] User Login

[1742] The user launches the application on the device and enters their ID and password on the login screen.

[1743] The terminal transmits the entered login information to the server.

[1744] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[1745] Task Input

[1746] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[1747] The terminal receives the input task information and sends it to the server as a request.

[1748] Image selection

[1749] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[1750] The server retrieves the user's past task data from a database and references the history of related images and icons.

[1751] The server uses AI models to select the most relevant images and icons for the task.

[1752] emotion recognition

[1753] The device uses the user's camera and microphone to capture facial expressions and voice.

[1754] The device sends the captured data to the emotion engine for analysis.

[1755] The server receives the results of the emotion engine and determines the user's current emotional state.

[1756] Reminder Settings

[1757] The server adjusts the timing and content of reminders based on the user's emotional state.

[1758] For example, if the user is feeling stressed, add a relaxing image or an encouraging message.

[1759] The server generates a reminder that combines the selected image and task information, and sets it to notify the user at the specified reminder time.

[1760] The server stores the reminder information in a database.

[1761] Reminder notifications

[1762] When the set reminder time approaches, the server sends a reminder notification to the device.

[1763] The device receives the notification and displays a popup with task information along with an image or icon to the user.

[1764] Specific examples

[1765] For example, when a user named Tanaka inputs a task called "Create materials for Friday's meeting," the following processing is performed.

[1766] 1. User Login

[1767] Tanaka launches the app on his smartphone and logs in by entering his ID and password.

[1768] The terminal transmits authentication information to the server, and the server performs authentication.

[1769] 2. Task Input

[1770] Tanaka enters the details for "Prepare meeting materials" and sets the deadline to Friday.

[1771] The terminal sends this information to the server.

[1772] 3. Image Selection

[1773] The server analyzes the task information for "creating meeting materials" and uses an AI model to search for relevant images and icons.

[1774] Based on Tanaka's past usage history, an image of a businessman working on a computer is selected.

[1775] 4. Emotion recognition

[1776] The device captures Tanaka's facial expressions and voice and analyzes them through an emotion engine.

[1777] The server determines from the results of the emotion engine that Tanaka is feeling stressed.

[1778] 5. Reminder Settings

[1779] The server adds relaxing images and encouraging messages to reduce stress and generates reminders.

[1780] Set the reminder date and time for Friday at 9:00 AM and save the information.

[1781] 6. Reminder notifications

[1782] At 9:00 AM on Friday, the server sends a reminder to the device.

[1783] The device displays stress-relieving images and a notification to "create meeting materials" on Tanaka's smartphone.

[1784] Tanaka checks the notification and views the details as necessary.

[1785] This allows Tanaka to not only visually grasp tasks, but also receive support tailored to his emotional state. In this way, we provide a system that more effectively supports users' task management, increases daily productivity, and reduces stress.

[1786] The processing flow will be explained below.

[1787] Step 1:

[1788] The user launches the application on the device and enters their ID and password on the login screen.

[1789] The terminal transmits the entered login information to the server.

[1790] The server checks the user's authentication information against the database and returns the authentication result to the terminal.

[1791] Step 2:

[1792] The user opens the app's task entry screen and enters the title, details, and due date of a new task.

[1793] The terminal receives the input task information and sends it to the server as a request.

[1794] Step 3:

[1795] The server analyzes the received task information and performs natural language processing (NLP) to understand the content of the task.

[1796] The server retrieves the user's past task data from a database and references the history of related images and icons.

[1797] The server uses AI models to select the most relevant images and icons for the task.

[1798] Step 4:

[1799] The device uses the user's camera and microphone to capture facial expressions and voice.

[1800] The device sends the captured data to the emotion engine for analysis.

[1801] The server receives the results of the emotion engine and determines the user's current emotional state.

[1802] Step 5:

[1803] The server adjusts the timing and content of reminders based on the user's emotional state.

[1804] For example, if the user is feeling stressed, add a relaxing image or an encouraging message.

[1805] The server generates a reminder that combines the selected image and task information, and sets it to notify the user at the specified reminder time.

[1806] The server stores the reminder information in a database.

[1807] Step 6:

[1808] When the set reminder time approaches, the server sends a reminder notification to the terminal.

[1809] The device receives the notification and displays a popup with the task information along with the selected image or icon to the user.

[1810] Examples:

[1811] Step 1:

[1812] Tanaka launches the app on his smartphone and enters his login information.

[1813] The device sends Tanaka's authentication information to the server.

[1814] The server checks the information against the database and returns a message to the terminal indicating that authentication was successful.

[1815] Step 2:

[1816] Tanaka enters "Create meeting materials for Friday" and sets the deadline to Friday.

[1817] The terminal sends this information to the server.

[1818] Step 3:

[1819] The server analyzes the task of "creating meeting materials" and understands its content.

[1820] The server refers to the task history that Tanaka has used in the past and searches for images related to "meetings."

[1821] The AI ​​model selects images of businessmen working on computers.

[1822] Step 4:

[1823] The device captures Tanaka's facial expressions and voice and analyzes them through an emotion engine.

[1824] The server determines from the results of the emotion engine that Tanaka is feeling stressed.

[1825] Step 5:

[1826] The server adds relaxing images and encouraging messages to reduce stress and generates reminders.

[1827] Set the reminder date and time for Friday at 9:00 AM and save the information.

[1828] Step 6:

[1829] At 9:00 AM on Friday, the server sends a reminder to the device.

[1830] The device displays stress-relieving images and a notification to "create meeting materials" on Tanaka's smartphone.

[1831] Tanaka checks the notification and views the details as necessary.

[1832] Example 2

[1833] 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."

[1834] Conventional task management systems have the ability to generate visual materials and notifications based on task information entered by the user, but it is difficult to generate accurate reminders that take into account the user's emotional state and past behavioral data. Furthermore, they are unable to appropriately adjust the content and timing of reminders according to the user's emotional state, which rarely improves the user experience. Therefore, there is a need for more effective and customized task management that takes into account the user's emotions and past behavioral data.

[1835] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for accepting task information input by a user, a means for selecting highly relevant visual data based on the input task information using an AI model, a means for capturing sensory data of the terminal and analyzing the user's emotional state, and a means for adjusting a reminder based on the selected visual data and the emotion analysis results and sending a notification at a specified time. This enables more personalized reminder setting and notification based on the user's emotional state and past behavioral data.

[1836] "User" refers to an individual who utilizes the system to enter task information and receive reminders and notifications.

[1837] "Task information" refers to detailed information about work or plans that a user enters into the system, including the title, content, deadline, etc.

[1838] "Means of acceptance" refers to the functions and mechanisms for incorporating task information entered by the user into the system and processing it.

[1839] "AI model" refers to an algorithm or machine learning model that uses artificial intelligence technology to analyze task information and select highly relevant data based on that information.

[1840] "Visual data" refers to visual content such as images and icons related to a task.

[1841] A "terminal" is a device used by a user to operate the system, and includes smartphones, tablets, personal computers, etc.

[1842] "Sensory data" refers to data such as the user's facial expressions and voice that are captured using the device's camera, microphone, etc.

[1843] "Means for analyzing" refers to a program or function for analyzing the user's emotional state using sensory data.

[1844] "Reminder" refers to a mechanism that notifies users of tasks along with visual data, and is a message or alert to remind them of a task.

[1845] "Means for sending notifications" refers to a function or program that sends a reminder to a user's device at a specified time.

[1846] "Behavioral data" refers to information recorded based on a user's past task history and activities.

[1847] This invention is a system that uses an AI model to select relevant visual data based on task information entered by the user, analyzes sensory data from the device to determine the user's emotional state, and generates and adjusts reminders to notify them at the specified time.

[1848] The entire system consists of three entities: the server, the terminal, and the user.

[1849] First, the user uses the application to input task information, including the task title, details, due date, etc. This input data is temporarily stored on the device and encrypted before being sent to the server.

[1850] Once the server receives the task information, it uses a natural language processing (NLP) engine to analyze the task content, using software such as Python's NLTK library, and then selects visual data (images and icons) related to the task based on the analysis results and an AI model (e.g., using TensorFlow).

[1851] Additionally, the device uses a camera and microphone to capture the user's sensory data (facial expressions and voice). The captured data is temporarily stored locally and sent to an emotion engine (e.g., Microsoft Azure's Emotion API). The emotion engine analyzes the data and determines the user's emotional state.

[1852] The server receives the results from the emotion engine and adjusts the content and timing of reminders accordingly: for example, if the user is feeling stressed, a relaxing image or an encouraging message will be added to the reminder.

[1853] Finally, the server stores the adjusted reminder information in a database and notifies the device of the reminder at the specified time. The device then displays the received reminder in a pop-up window to the user, presenting task information along with visual data.

[1854] A specific example is given below.

[1855] For example, if a user inputs a task called "Create materials for Friday's meeting," the following process will be performed.

[1856] The user launches the app on their smartphone and enters task information.

[1857] The terminal sends this information to the server.

[1858] The server analyzes the task information with an NLP engine and selects appropriate images using an AI model.

[1859] The device captures the user's emotional data using a camera or microphone and sends it to the emotion engine.

[1860] The server receives the results of the emotion engine and adds relaxing images and encouraging messages to the reminder.

[1861] The server transmits the adjusted reminder data to the terminal at the specified time, and the terminal displays a pop-up notification.

[1862] Example prompt sentence:

[1863] "Describe a process where an AI recognizes that a user is finding a task stressful and then sets and sends an encouraging reminder along with a relaxing image."

[1864] In this way, the system of the present invention effectively assists users in managing their tasks and provides personalized support based on their emotional state.

[1865] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1866] Step 1: User Login

[1867] The user launches the application installed on the device and enters their ID and password on the login screen.

[1868] Input: User ID and password

[1869] What it does: Enter your user ID and password into the form.

[1870] The terminal temporarily stores the login information received from the user in memory, encrypts it, and then sends it to the server.

[1871] Input: Encrypted user ID and password

[1872] Output: Authentication request to the server

[1873] What it does: Encrypts your login information and sends it to the server.

[1874] The server receives the user's authentication information and checks it against the user data in its database.

[1875] Input: Encrypted user ID and password

[1876] Output: Authentication success or failure result

[1877] What it does: Retrieves and collates user data from a database.

[1878] The server generates an authentication result and sends it to the terminal.

[1879] Input: Authentication result

[1880] Output: Sending authentication result

[1881] Operation: The authentication result is sent to the terminal.

[1882] The terminal displays a success or failure message to the user based on the received authentication result.

[1883] Input: Authentication result

[1884] Output: Login success or failure message

[1885] Behavior: Displays the authentication result to the user.

[1886] Step 2: Task Input

[1887] After logging in, the user opens the task entry screen of the app and enters the task title, details, and deadline date.

[1888] Input: Task title, details, due date

[1889] Action: Enter task information into a form.

[1890] The terminal acquires the input task information, organizes it into a data object, and sends it to the server.

[1891] Input: Task information

[1892] Output: Request to send task data to the server

[1893] Action: Sends task information to the server.

[1894] The server stores the received task information in a database.

[1895] Input: Task information

[1896] Output: Save task information to database

[1897] What it does: Saves task information to a database.

[1898] Step 3: Image Selection

[1899] The server uses a natural language processing (NLP) engine to analyze the stored task information.

[1900] Input: Task information

[1901] Output: Parsed task information

[1902] How it works: The NLP engine analyzes task information.

[1903] After analysis, the server uses an AI model (e.g., TensorFlow) to select visual data relevant to the task.

[1904] Input: Parsed task information

[1905] Output: Selected visual data

[1906] How it works: Curates visual data using AI models.

[1907] The server associates the selected visual data with the task information.

[1908] Input: Selected visual data

[1909] Output: Associate with task information

[1910] Action: Link visual data to task information.

[1911] Step 4: Emotion Recognition

[1912] The device captures the user's facial expressions and voice using a camera and microphone.

[1913] Input: User's facial expressions and voice

[1914] Output: Captured sensory data

[1915] How it works: Captures data using the camera and microphone.

[1916] The device temporarily stores the captured data and sends it to the emotion engine for analysis.

[1917] Input: Captured sensory data

[1918] Output: Send to emotion engine

[1919] Action: Sends sensory data to the emotion engine.

[1920] The server receives the analysis results from the emotion engine and determines the user's emotional state.

[1921] Input: Parsed sensory data

[1922] Output: User's emotional state

[1923] Action: Determines emotional state.

[1924] Step 5: Set reminders

[1925] The server adjusts the content and timing of reminders based on the user's emotional state.

[1926] Input: User's emotional state, task information, visual data

[1927] Output: Adjusted reminders

[1928] How it works: Adjust reminders based on your emotional state.

[1929] The server stores the adjusted reminder information in a database.

[1930] Input: Adjusted reminders

[1931] Output: Reminder saved to database

[1932] Behavior: Saves reminder information.

[1933] Step 6: Reminders

[1934] When the reminder time approaches, the server retrieves the saved reminder information and sends it to the device.

[1935] Input: Reminder information

[1936] Output: Reminder notification to device

[1937] Action: Sends a reminder to your device.

[1938] The device displays the received reminder information in a pop-up display to the user.

[1939] Input: Received reminder information

[1940] Output: Popup notification

[1941] Behavior: Generates a popup notification and displays it to the user.

[1942] This allows users to not only visually understand their tasks, but also receive reminders that reflect their emotional state.

[1943] (Application example 2)

[1944] 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."

[1945] Conventional task management systems can generate reminders based on task information entered by users, but they cannot take the user's emotional state into account, and may send reminders at inappropriate times or with inappropriate content. As a result, users may feel stressed or the reminders may not be effective enough. Furthermore, there is a need for task management that combines efficiency with psychological support, especially in workplaces such as factory robot operators.

[1946] 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 accepting task information input by a user, means for using an AI model to select highly relevant images or icons based on the input task information, means for setting a reminder that combines the selected images or icons with the task information and sending a notification at a specified time, and means including an emotion engine that analyzes the user's emotional state and adjusting the content and timing of the reminder based on the analysis results. This makes it possible to notify reminders with appropriate timing and content that take the user's emotional state into consideration, allowing factory robot operators and others to efficiently manage tasks and perform their work while receiving psychological support.

[1947] "Task information entered by the user" refers to information that the user enters into the system, such as the task title, details, deadline date, etc.

[1948] An "AI model" is a model that uses artificial intelligence technology to analyze and classify data.

[1949] "Highly relevant images and icons" are visual elements that are determined to be most appropriate for the input task information.

[1950] A "reminder" is notification information that notifies the user of a task that the user has set and prompts the user to check it again.

[1951] An "emotion engine" is a system that analyzes a user's facial expressions and voice data to determine the user's emotional state.

[1952] A "server" is a computer system that receives and processes requests from clients.

[1953] "User's past behavior data" refers to data such as task information that the user has entered so far and associated access logs.

[1954] "Behavior patterns" refer to recurring patterns of user behavior and operations, and are the basic data that the system learns from.

[1955] "Preferences" are the characteristics or patterns that a user particularly prefers, and are data used to customize reminders.

[1956] "Notifications" are messages or alerts that the system sends to inform the user.

[1957] This system uses an AI model to select relevant images and icons based on task information entered by the user, and an emotion engine to analyze the user's emotions and adjust reminders. This system is primarily composed of three entities: a server, a terminal, and the user.

[1958] System Overview

[1959] This system accepts task information entered by the user and generates and notifies reminders based on that information. It uses a generative AI model to select relevant images and icons based on the task information, and further combines this with an emotion engine that recognizes the user's emotional state to adjust the timing and content of reminder notifications. This enables appropriate reminder notifications based on the user's emotional state.

[1960] Hardware and software used

[1961] Hardware:

[1962] Smart glasses: A device worn by the robot operator that is used to display reminders and various notifications.

[1963] Camera and microphone: Used to capture the user's facial expressions and voice data for the emotion engine.

[1964] software:

[1965] Server: Analyzes task information, selects images, recognizes emotions, and generates reminders.

[1966] NLP engine: Analyzes task information and extracts keywords.

[1967] Emotion recognition engine: Analyzes emotions from facial expressions and voice.

[1968] Image Selection Model: Select images and icons relevant to the task.

[1969] Notification system: Schedule reminders and send notifications.

[1970] What the program does

[1971] 1. User login: The user logs in from the device (smart glasses) and is authenticated by the server.

[1972] 2. Task input: The user inputs task information using voice commands or gestures and sends the information to the server.

[1973] 3. Image Selection: The server uses an AI model to select relevant images and icons based on the input task information.

[1974] 4. Emotion recognition: The smart glasses' camera and microphone are used to capture the user's emotions, which are then analyzed by the emotion engine.

[1975] 5. Reminder generation and notification settings: Adjust the timing and content of reminders based on the analysis results of the emotion engine, and schedule reminders in the notification system.

[1976] Specific examples

[1977] For example, if a factory robot operator inputs the task "Inspect conveyor belts and sensors at 2 p.m.", the following process will occur:

[1978] 1. The robot operator puts on the smart glasses and logs into the system with their ID and password.

[1979] 2. The operator uses voice commands to enter task information such as "Inspect the conveyor belt and sensors at 2 p.m." and set a deadline.

[1980] 3. The server analyzes the task information, extracts keywords using an NLP engine, and selects relevant images based on those keywords.

[1981] 4. The user's facial expressions and voice are captured through the camera and microphone, and the emotion engine analyzes their stress level.

[1982] 5. Based on the emotion engine analysis, add a relaxing image or an encouraging message to the reminder and set it to notify you at 2 PM.

[1983] 6. At 2 p.m., the smart glasses will display a reminder along with encouraging messages such as "Do your best today!", helping the operator work efficiently.

[1984] Prompt Sentence Examples

[1985] This system selects relevant images and icons based on task information entered by the worker user through smart glasses, and generates reminders according to the worker's emotional state. Example: If the task "Inspect the conveyor belt and sensors at 2:00 PM on December 2, 2023" is entered, the system will select images highly relevant to the task and set a reminder with relaxing images and encouraging messages based on the worker's emotional analysis. A notification will be displayed on the smart glasses at the reminder time.

[1986] This allows factory robot operators to efficiently manage tasks and receive psychological support when needed.

[1987] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1988] Step 1:

[1989] The user puts on the device (smart glasses), launches the application, and accesses the login screen. The user enters their ID and password and presses the login button to request authentication. The device sends the entered authentication information to the server, which compares it with the database and returns the authentication result to the device. If authentication is successful, the user can proceed to the task input screen. The input is the ID and password, and the output is the authentication result.

[1990] Step 2:

[1991] The user inputs task information using voice commands or gestures. Specifically, the user speaks the task title, such as "Inspect the conveyor belt and sensors at 2 PM," into the microphone of the smart glasses and specifies the deadline. The device converts this voice into text and sends it to the server as task information. The input is voice data, and the output is text data of the task information.

[1992] Step 3:

[1993] The server analyzes the received task information and uses a generative AI model to extract highly relevant keywords from the input task information. Based on these keywords, the server refers to past behavioral data and related information to select the most appropriate image or icon. The input is the text data of the task information, and the output is the selected image or icon.

[1994] Step 4:

[1995] The device's camera and microphone are used to capture the user's emotional state from their face and voice. The device then sends this captured data to the emotion engine for emotion analysis. The server receives the analysis results from the emotion engine and determines the user's emotional state. The input is the captured data, and the output is the analysis results of the emotional state.

[1996] Step 5:

[1997] The server adjusts the content and timing of the reminder based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the server will add a relaxing image or an encouraging message. The server combines this information to generate a reminder and sets it to send a notification at a specified date and time. The input is task information, selected image, and emotional state, and the output is reminder information.

[1998] Step 6:

[1999] When the specified reminder time approaches, the server sends the reminder information to the device. The device displays the reminder content on the smart glasses display and notifies the user. The user checks the notification and performs the task as necessary. The input is the reminder information, and the output is the notification to the user.

[2000] 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.

[2001] 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.

[2002] 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.

[2003] 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.

[2004] FIG. 9 is a diagram illustrating 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 actions 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.

[2005] 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.

[2006] 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).

[2007] 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.

[2008] 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."

[2009] 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.

[2010] 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).

[2011] 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.

[2012] 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.

[2013] 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.

[2014] 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.

[2015] 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.

[2016] 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.

[2017] 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.

[2018] 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.

[2019] 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.

[2020] 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.

[2021] The following is further disclosed regarding the above embodiment.

[2022] (Claim 1)

[2023] means for accepting task information input by a user;

[2024] A means of using AI models to select relevant images and icons based on input task information; and

[2025] A way to set reminders that combine task information with selected images or icons and send notifications at specified times.

[2026] A system including:

[2027] (Claim 2)

[2028] 10. The system of claim 1, wherein the selected images or icons are optimized based on the user's past behavioral data.

[2029] (Claim 3)

[2030] 10. The system of claim 1, wherein reminder settings and notifications are adapted to a user's behavioral patterns and preferences and are individually customized.

[2031] "Example 1"

[2032] (Claim 1)

[2033] means for accepting task information input by a user;

[2034] A means of using AI models to select relevant images and icons based on input task information; and

[2035] A way to generate reminders that combine selected images or icons with task information and send notifications at specified times.

[2036] A means of storing reminder information in a database and setting notification schedules;

[2037] a means for verifying authentication information entered by a user and generating a session;

[2038] A system including:

[2039] (Claim 2)

[2040] 10. The system of claim 1, wherein the selected images or icons are optimized based on the user's past behavioral data.

[2041] (Claim 3)

[2042] 10. The system of claim 1, wherein reminder settings and notifications are adapted to a user's behavioral patterns and preferences and are individually customized.

[2043] "Application Example 1"

[2044] (Claim 1)

[2045] means for accepting task information input by a user;

[2046] A means of using AI models to select relevant images and icons based on input task information; and

[2047] A way to set reminders that combine task information with selected images or icons and send notifications at specified times.

[2048] means for sending a notification to a device (e.g., smart glasses) for displaying a visual reminder;

[2049] A system including:

[2050] (Claim 2)

[2051] 10. The system of claim 1, wherein the selected images or icons are optimized based on the user's past behavioral data.

[2052] (Claim 3)

[2053] 10. The system of claim 1, wherein reminder settings and notifications are adapted to a user's behavioral patterns and preferences and are individually customized.

[2054] "Example 2: Combining Emotion Engines"

[2055] (Claim 1)

[2056] means for accepting task information input by a user;

[2057] a means for using an AI model to select relevant visual data based on input task information;

[2058] means for capturing sensory data from the device and analyzing the user's emotional state;

[2059] a means for adjusting reminders based on selected visual data and sentiment analysis results and sending notifications at designated times;

[2060] A system including:

[2061] (Claim 2)

[2062] 10. The system of claim 1, wherein the selected visual data is optimized based on past behavioral data of the user.

[2063] (Claim 3)

[2064] 10. The system of claim 1, wherein reminder settings and notifications are adapted to a user's emotional state and behavioral patterns and are individually customized.

[2065] "Application example 2 when combining emotion engines"

[2066] (Claim 1)

[2067] means for accepting task information input by a user;

[2068] A means of using AI models to select relevant images and icons based on input task information; and

[2069] A way to set reminders that combine task information with selected images or icons and send notifications at specified times.

[2070] a means for adjusting the content and timing of the reminder based on the result of the analysis, the means including an emotion engine for analyzing the emotional state of the user;

[2071] A system including:

[2072] (Claim 2)

[2073] 2. The system of claim 1, wherein the selected images and icons are optimized based on the user's past behavioral data and keywords related to the task.

[2074] (Claim 3)

[2075] 10. The system of claim 1, wherein reminder settings and notifications are adapted and individually customized to a user's behavioral patterns, preferences, and emotional state. [Explanation of symbols]

[2076] 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 accepting task information input by a user; A means of using AI models to select relevant images and icons based on input task information; and A way to set reminders that combine task information with selected images or icons and send notifications at specified times. A system including:

2. 10. The system of claim 1, wherein the selected images or icons are optimized based on the user's past behavior data.

3. 10. The system of claim 1, wherein reminder settings and notifications are adapted to a user's behavioral patterns and preferences and are individually customized.

Images