System

Abstract

A system is provided.SOLUTION: A system that includes means for obtaining registration information of a user, means for obtaining past task data of the user, means for obtaining a schedule from a calendar of the user, means for calculating a priority of a task based on the obtained information, means for predicting a required time of the task, means for performing time allocation of the task and automatically inserting the task into the calendar, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, and means for interacting with the user and reporting a progress status of the task.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many people, both at work and in their personal lives, struggle to complete tasks at the last minute, resulting in decreased productivity and a decline in the quality of their output. To solve this problem, a system that can properly manage tasks is needed. Conventional calendars and task management tools require users to manually set tasks and determine priorities, making schedule management cumbersome and inefficient. [Means for solving the problem]

[0005] In order to solve these problems, the present invention provides a system including means for acquiring user information, means for acquiring past task data, means for acquiring schedules from a calendar, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into a calendar, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, and means for communicating with the user to report task progress, thereby realizing user task management and efficient scheduling. This enables task automation and optimization, thereby improving the user's daily productivity.

[0006] "Means for obtaining user registration information" refers to a mechanism for registering basic profile information such as the user's name, occupation, and areas of interest in the system and obtaining that information.

[0007] "Means for obtaining user's past task data" refers to a mechanism for collecting historical data such as tasks that users have previously completed on the system, the time required for completing them, and their performance.

[0008] "Means for obtaining events from the user's calendar" refers to a mechanism for obtaining current schedule information from the calendar service used by the user (e.g., Google Calendar, Microsoft Outlook).

[0009] The "means for calculating task priority" is a mechanism for automatically determining the priority of each task based on the importance, deadline, urgency, etc. of the task.

[0010] A "means for predicting the time required for a task" is a system for predicting the time required for a specific task based on past performance data.

[0011] "Means for allocating time for tasks and automatically inserting them into the calendar" is a mechanism that divides each task into multiple parts, allocates appropriate time to each part, and automatically schedules it into the user's calendar.

[0012] "Means for synchronizing with external calendar services" refers to a mechanism for linking with external calendar services such as Google Calendar and Microsoft Outlook to synchronize schedule information.

[0013] The "means for automatically extracting relevant reference materials and data" is a mechanism for automatically collecting relevant reference materials and data from the Internet based on the theme and content of the task.

[0014] "Means of interacting with the user and reporting task progress" refers to a mechanism for a conversational AI assistant to verbally or textually report task progress and the next task to be tackled when the user queries the system. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] In the following, specific program processing will be described in detail in natural language in the mode for carrying out the present invention.

[0037] System Overview

[0038] This system is a calendar service that automatically schedules tasks, taking into account the user's personal data, past task performance, and priority. The system consists of three main components: a server, a terminal, and a user. The server is responsible for data processing and scheduling, while the terminal provides an interface with the user, who then uses the system to manage tasks.

[0039] 1. Collection of User Information

[0040] When a user logs in to the system, the server first obtains the user's registration information (such as name, occupation, and areas of interest). It also collects past task data (such as the content, time required, and completion status of previously completed tasks) from the database. It also connects via API with external calendar services (such as Google Calendar or Microsoft Outlook) to obtain the user's current schedule.

[0041] 2. Priority and performance analysis

[0042] The server calculates the priority of the user's tasks based on the collected information. This is achieved by taking into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a specific task. For example, if a user previously spent five hours on a document creation task, it will expect a similar amount of time in the future.

[0043] 3. Automatic task scheduling

[0044] The server divides the listed tasks into multiple parts (e.g., research, writing, review), allocates appropriate time to each part, and then automatically inserts each part into the calendar so that it does not overlap with existing schedules, allowing users to efficiently manage their schedules.

[0045] 4. Integration with external services

[0046] The server continuously synchronizes with an external calendar service, ensuring that the user's schedule is always up-to-date, even when updates are made to their schedule. Furthermore, the server automatically retrieves relevant reference materials and data from the Internet based on the task theme, and provides them to the user as needed.

[0047] 5. Interacting with a conversational AI assistant

[0048] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant then explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the user's request and automatically updates the calendar.

[0049] Specific examples

[0050] For example, if a user adds a task to their calendar that reads, "Submit materials for the generative AI contest in 10 days," the system will act as follows:

[0051] 1. The server predicts the required time based on the user's past performance in document creation tasks.

[0052] 2. The server divides the document creation task into three parts: research, writing, and review, and allocates a predicted time to each part.

[0053] 3. The server automatically inserts each part into the calendar, adjusting it to the user's existing schedule.

[0054] 4. By linking with external services, relevant reference materials are automatically extracted from the Internet and provided to users.

[0055] 5. If the user wants to check the progress, they can ask the AI ​​assistant through their device, and the AI ​​assistant will explain the current progress and the next task to be tackled.

[0056] In this way, the system optimizes users' task management and efficient scheduling, helping them improve their daily productivity.

[0057] The processing flow will be explained below.

[0058] Specific processing flow of the program

[0059] 1. Collection of User Information

[0060] Step 1:

[0061] A user logs in to a device and enters registration information, including basic profile information such as name, occupation, and areas of interest.

[0062] Step 2:

[0063] The terminal sends the entered user information to the server, which stores this information in a database.

[0064] Step 3:

[0065] The server retrieves the user's past task data from the database and links it to the user's profile.

[0066] Step 4:

[0067] The server connects to an external calendar service (such as Google Calendar or Microsoft Outlook) via API to obtain the user's current schedule.

[0068] 2. Priority and performance analysis

[0069] Step 1:

[0070] The server analyzes the importance and deadlines of tasks based on the acquired schedule information and past task data.

[0071] Step 2:

[0072] The server calculates the priority of each task based on the analysis results, which is determined by comprehensively evaluating the importance, proximity of the deadline, urgency, and other factors.

[0073] Step 3:

[0074] The server uses past performance data to predict the average time required for each task. For example, if a previous document creation task took five hours, it predicts that a similar amount of time will be required this time.

[0075] 3. Automatic task scheduling

[0076] Step 1:

[0077] The server receives new tasks that users have entered into their calendars (e.g., the deadline for submitting materials is in 10 days).

[0078] Step 2:

[0079] The server breaks the task down into specific parts (e.g., research, writing, review) and allocates the required time for each.

[0080] Step 3:

[0081] The server automatically inserts each part into the calendar, taking into account the user's current schedule, and places them so that they do not overlap with existing important events.

[0082] 4. Integration with external services

[0083] Step 1:

[0084] The server constantly synchronizes with external calendar services (Google Calendar and Microsoft Outlook).

[0085] Step 2:

[0086] The server automatically extracts relevant reference materials and data from the Internet based on the task theme and provides them to the user, for example, by collecting articles from papers and databases related to the topic of document creation.

[0087] 5. Interacting with a conversational AI assistant

[0088] Step 1:

[0089] The user makes an inquiry such as "Tell me about the progress of the task" through the terminal.

[0090] Step 2:

[0091] The device sends the query to the server, which retrieves the latest task progress and returns the data to the device.

[0092] Step 3:

[0093] Based on the data it collects, the AI ​​assistant will report to the user verbally or in text about its progress and the next task to tackle.

[0094] Step 4:

[0095] When a user requests a change in schedule (e.g., "Tomorrow's meeting has been canceled, so I would like to prepare the materials earlier"), the server receives the request and generates a new schedule proposal.

[0096] Step 5:

[0097] The server updates the calendar with the new schedule and prompts the user for confirmation.

[0098] Through this processing flow, the system optimizes users' task management and efficient scheduling, helping to improve their daily productivity.

[0099] Example 1

[0100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0101] In today's busy daily lives, users need to efficiently manage tasks and increase productivity. However, manual scheduling and task management is time-consuming and inefficient, and integrating with external calendar services and data sources is complicated. Given this background, it is important to provide a task management system that is easy for users to use, fast, and accurate.

[0102] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0103] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring the user's schedule data, means for calculating task priorities based on the acquired information, means for predicting the time required for each task, means for dividing the task into multiple parts and allocating the time, means for automatically inserting the task into the schedule based on the allocation, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, and means for communicating with the user to report the progress of the task. This allows users to efficiently manage tasks and easily integrate with external calendars, thereby improving productivity.

[0104] "Means for obtaining user registration information" refers to a function that obtains information such as the name, occupation, and areas of interest of users who log in to the system from a database.

[0105] "Means for obtaining the user's past task data" refers to a function that collects from a database the content, time required, completion status, etc. of tasks that the user has completed in the past.

[0106] "Means for obtaining user schedule data" refers to a function for obtaining the user's current and future schedules from external calendar services, etc.

[0107] The "means for calculating task priorities" is a function that calculates priorities based on collected information, taking into account the importance, deadline, urgency, etc. of tasks.

[0108] "Means for predicting task duration" is a function that analyzes a user's past task performance and estimates the time required for a specific task.

[0109] "A means of dividing tasks into multiple parts and allocating time" is a function that divides listed tasks into multiple parts such as research, writing, and review, and allocates appropriate time to each part.

[0110] "Means for automatically inserting tasks into the schedule" is a function that automatically registers each part in the calendar while coordinating with the user's existing schedule.

[0111] "Means for synchronizing with external calendar services" refers to a function that synchronizes users' schedule data by linking with external calendar services such as Google Calendar and Microsoft Outlook.

[0112] "Means for automatically extracting relevant reference materials and data" refers to a function that automatically collects reference materials and data related to the user's task from the Internet and other data sources and provides them to the user.

[0113] "Means of interacting with the user and reporting task progress" refers to a function of an interactive AI assistant that responds to inquiries from the user via the device and reports the progress of the current task and the next task to be tackled.

[0114] The following describes in detail the specific program processing for implementing the present invention. This system is a calendar service that automatically schedules tasks taking into account the user's personal data, past task performance, and priority. The system consists of three main components: a server, a terminal, and a user. The server is responsible for data processing and scheduling, while the terminal provides an interface with the user, who uses the system to manage tasks.

[0115] Collection of User Information

[0116] When a user logs in to the system, the server first obtains the user's registration information (such as name, occupation, and areas of interest). To do this, the server retrieves the necessary information from the database using the user ID as a key. For example, if the user is registered as a "software engineer," that information is retrieved. The server also collects the user's past task data from the database. This data includes tasks that have been completed in the past, the time required, and the completion status. The server also connects via API to external calendar services such as Google Calendar and Microsoft Outlook to obtain the user's current schedule.

[0117] Priority and performance analysis

[0118] The server calculates the priority of the user's tasks based on the collected information. This is done using an algorithm that takes into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a particular task. For example, if a user previously spent five hours on a document creation task, the server will estimate a similar amount of time in the future.

[0119] Automatic task scheduling

[0120] The server divides the listed tasks into multiple parts, such as research, writing, and review, and allocates appropriate time to each part. It then automatically inserts each part into the calendar so that it does not overlap with existing schedules. If a user has free time from 10:00 to 12:00 on Monday and 14:00 to 17:00 on Tuesday, the server will assign research from 10:00 to 12:00 on Monday and writing from 14:00 to 17:00 on Tuesday.

[0121] Integration with external services

[0122] The server continuously synchronizes with external calendar services such as Google Calendar and Microsoft Outlook, ensuring that users' calendars are kept up to date with any changes to their schedules. Additionally, based on the topic of the task, the app automatically retrieves relevant references and data from the internet and provides them to users.

[0123] Interacting with a conversational AI assistant

[0124] When a user inquires about task progress or changes through their device, the device sends that information to the server, which obtains the latest task progress. The AI ​​assistant explains the user's current progress and the next task to tackle. For example, if the user types "What should I do next?" into the device, the server will respond with "Next, please work on reviewing the document." If the user requests a schedule change, the server will generate a new schedule proposal based on that request and automatically update the calendar.

[0125] Specific use cases

[0126] For example, if a user adds the task "Submit materials for a generative AI contest in 10 days" to their calendar, the system works as follows: First, the server predicts the required time based on the user's past performance in document creation tasks. Then, it divides the document creation task into three parts - research, writing, and review - and allocates the predicted time to each part. Next, it automatically inserts each part into the calendar, aligning it with the user's existing schedule. In addition, it extracts relevant reference materials from the internet and provides them to the user. If the user wants to check their progress, they can inquire about it through their device, and the AI ​​assistant will explain their current progress and the next task they should tackle.

[0127] Prompt Sentence Examples

[0128] Here is an example of a prompt that a user might enter into the system:

[0129] "Schedule submission of materials for the Generative AI contest in 10 days."

[0130] In this way, the system optimizes users' task management and efficient scheduling, helping them improve their daily productivity.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1:

[0133] User login and information collection

[0134] Input: User login information (user ID, password)

[0135] Output: User registration information, past task data, schedule data

[0136] When a user logs in to the system, the server first retrieves the user's registration information (such as name, occupation, and areas of interest) from the database. It also collects the user's past task data (such as the content, time required, and completion status of completed tasks) from the database. It then uses the APIs of Google Calendar and Microsoft Outlook to retrieve the user's current schedule data. Specifically, the server queries the database using the user ID as a key to extract the required information.

[0137] Step 2:

[0138] Task Priority Calculation

[0139] Input: Registration information, past task data, schedule data

[0140] Output: A prioritized task list

[0141] The server calculates the priority of the user's tasks based on the collected information. The calculation takes into account factors such as the task's importance, deadline, and urgency. For example, if a document creation task has an importance level of 8, a deadline in one week, and an urgency level of 5, the server calculates an overall score based on these parameters. As a result, a prioritized task list is generated.

[0142] Step 3:

[0143] Estimated travel time

[0144] Input: Past task data, new task list

[0145] Output: A list of tasks with estimated durations

[0146] The server analyzes past task performance and predicts the time required for new tasks. For example, if a user previously spent five hours on a document creation task, the server predicts that the current task will take a similar amount of time based on that performance data. This generates a task list with predicted completion times.

[0147] Step 4:

[0148] Task division and time allocation

[0149] Input: Task list with estimated duration, user schedule data

[0150] Output: Time-allocated task list

[0151] The server divides the listed tasks into multiple parts, such as research, writing, and review, and allocates appropriate time to each part. For example, it divides document creation into research (2 hours), writing (3 hours), and review (1 hour). Then, it automatically inserts each part into the calendar while adjusting it with the user's schedule data.

[0152] Step 5:

[0153] Automatic insertion into the calendar

[0154] Input: Time-phased task list, user schedule data

[0155] Output: Updated calendar

[0156] The server automatically inserts each part into the calendar so that it doesn't overlap with the user's existing schedule. For example, if there is free time between 10:00 and 12:00 on Monday, the server will schedule the survey for that time. The result is an updated calendar.

[0157] Step 6:

[0158] Integration with external services

[0159] Input: User's task list, related keywords

[0160] Output: Related references and resources

[0161] The server continuously synchronizes with external calendar services such as Google Calendar and Microsoft Outlook. It also automatically retrieves reference materials and data related to the user's tasks from the Internet and provides them to the user. For example, it uses the Google Search API to collect materials related to "document creation" from the Internet.

[0162] Step 7:

[0163] Task progress reporting and interaction

[0164] Input: User's query, latest task list

[0165] Output: Progress report, next task suggestions

[0166] When a user inquires about the progress or changes of a task through their device, the device sends that information to the server, which retrieves the latest task progress. The server then explains the current progress and the next task to the user through the AI ​​assistant. For example, if a user types "What should I do next?", the server will respond with "Next, please work on reviewing the document creation."

[0167] (Application example 1)

[0168] 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."

[0169] In modern factories, improving the efficiency of production plans and optimizing employee work schedules are important issues. However, manual scheduling and individual task management require time and effort, and there are limits to how much can be done. Furthermore, updating schedules in real time while linking with external calendar services is difficult, which can easily lead to information discrepancies at the production site. For this reason, there is a demand for a system that automatically generates schedules based on factory operation data and employee past work performance, and updates them in real time.

[0170] 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.

[0171] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring schedules from the user's calendar, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into the calendar, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, means for communicating with the user to report task progress, means for automatically generating a work schedule based on factory operation data and employee task performance, and means for updating employee work schedules in real time to streamline factory production planning. This enables the automatic generation of efficient work schedules that are updated in real time based on the factory's operation status.

[0172] "Means for obtaining user registration information" refers to a system or method for obtaining basic information such as a user's name, occupation, and role from a database.

[0173] "Means for obtaining a user's past task data" refers to a system or method for obtaining detailed information (content, required time, completion status, etc.) of tasks that a user has performed in the past from a database.

[0174] A "means for retrieving events from a user's calendar" is a system or method for retrieving current events and schedules from a calendar service used by a user.

[0175] A "means for calculating task priority" is a system or method for numerically evaluating the priority of a task based on various factors such as the importance, deadline, and urgency of the task.

[0176] A "means for predicting the time required for a task" is a system or method for predicting the time required to complete a similar task based on performance data of past tasks.

[0177] "Means for allocating task time and automatically inserting it into a calendar" refers to a system or method for automatically adding the optimal schedule to a user's calendar based on the calculated task duration and priority.

[0178] "Means for synchronizing with an external calendar service" means a system or method for synchronizing data with an external calendar service, such as Google Calendar or Microsoft Outlook.

[0179] "Means for automatically extracting relevant reference materials and data" refers to a system or method for automatically retrieving and providing relevant materials and data from the Internet or databases based on the user's task content or theme.

[0180] "Means for communicating with the user and reporting task progress" refers to a system or method by which an AI assistant communicates with the user to report the progress of the current task and the next task to be tackled.

[0181] "Means for automatically generating work schedules based on factory operation data and employee task performance" refers to a system or method that automatically generates an optimal work schedule based on the operation status of factory equipment and employees' past work history.

[0182] "Means for updating employee work schedules in real time to streamline factory production plans" refers to a system or method for updating and optimizing employee work schedules in real time in response to changes in and progress in the factory's production plans.

[0183] The mode for carrying out the present invention will be described in detail below: The system required for carrying out the invention consists of three main components: a server, a terminal, and a user.

[0184] System Overview

[0185] This system is a management service that automatically generates work schedules based on factory operation data and employee task performance, and updates employee work schedules in real time to streamline production planning. The system processes data and performs scheduling on the server, and provides a user interface on the terminal. The server also connects to an external calendar service and interacts with users via an AI assistant.

[0186] 1. Collection of User Information

[0187] When a user logs in to the system, the server first retrieves the user's registration information (such as name, occupation, and role) from the database. It also collects past task data (such as the content, time required, and completion status of past tasks) from the database. It also retrieves the user's current schedule through API integration with external calendar services (such as Google Calendar and Microsoft Outlook).

[0188] 2. Priority and performance analysis

[0189] The server calculates the priority of each user's task based on the collected information. This is achieved by taking into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a specific task. For example, if an employee previously spent three hours on machine maintenance, it will expect a similar amount of time in the future.

[0190] 3. Automatic task scheduling

[0191] The server divides the listed tasks into multiple parts (e.g., research, work, confirmation), allocates appropriate time to each part, and then automatically inserts each part into the calendar so that it does not overlap with existing schedules, allowing employees to efficiently manage their work schedules.

[0192] 4. Integration with external services

[0193] The server continuously synchronizes with an external calendar service, ensuring that employee schedules are always up-to-date when they are updated. Furthermore, the system automatically retrieves relevant reference materials and data from the Internet based on the task topic, and provides them to users as needed.

[0194] 5. Interacting with a conversational AI assistant

[0195] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant then explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the user's request and automatically updates the calendar.

[0196] Specific examples

[0197] For example, if a user adds a task to their calendar that reads, "Submit materials for the generative AI contest in 10 days," the system will act as follows:

[0198] 1. The server predicts the required time based on the user's past performance in document creation tasks.

[0199] 2. The server divides the document creation task into three parts: research, writing, and review, and allocates a predicted time to each part.

[0200] 3. The server automatically inserts each part into the calendar, adjusting it to the user's existing schedule.

[0201] 4. By linking with external services, relevant reference materials are automatically extracted from the Internet and provided to users.

[0202] 5. If the user wants to check the progress, they can ask the AI ​​assistant through their device, and the AI ​​assistant will explain the current progress and the next task to be tackled.

[0203] Prompt Sentence Examples

[0204] Here is an example of a real prompt:

[0205] Below is an example of task scheduling. If a user adds a task "Submit materials for the Generative AI Contest in 10 days," please generate the optimal schedule based on the following information:

[0206] Task name: Generative AI Contest Materials

[0207] Submission deadline: 10 days later

[0208] Task details:

[0209] Investigation: 2 hours

[0210] Writing: 3 hours

[0211] Review: 2 hours

[0212] Past task history:

[0213] Task A (2 hours, 3 hours, 2 hours)

[0214] Task B (1 hour, 1.5 hours, 1.2 hours)

[0215] Current schedule:

[0216] November 20th 09:00 - 11:00: Available

[0217] November 21st 13:00 - 16:00: Available

[0218] November 22nd 10:00 - 12:00: Available

[0219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0220] Step 1:

[0221] Collection of user registration information

[0222] When a user logs in to the system, the server first retrieves the user's registration information from the database. Specifically, it retrieves basic information such as the user's name, occupation, and role. This allows the user's basic profile to be collected as input data. The output is a dataset containing the user's basic information.

[0223] Step 2:

[0224] Retrieving past task data

[0225] The server retrieves data on tasks the user has performed in the past from a database, including task content, required time, and completion status. This provides the user's past task history as input data. The output is detailed data on each individual task.

[0226] Step 3:

[0227] Get a user's calendar events

[0228] The server connects to an external calendar service via API to obtain the user's current schedule. Specifically, it obtains the user's schedule data from Google Calendar or Microsoft Outlook via API. This obtains the user's current schedule as input data. The output is the user's current schedule information.

[0229] Step 4:

[0230] Calculate task priorities

[0231] The server calculates task priorities based on the collected user basic information, past task data, and current schedule data. This takes into account factors such as the task's importance, deadline, and urgency. Specifically, the priority is numerically evaluated by multiplying the importance and urgency coefficients. The input data is processed, and the task priority is obtained as the output.

[0232] Step 5:

[0233] Task duration prediction

[0234] The server analyzes past task data and predicts the time required for a specific task. It uses statistical methods based on past performance data to predict the average time required for similar tasks. It processes the input historical data and outputs the predicted time required.

[0235] Step 6:

[0236] Time allocation and auto-scheduling of tasks

[0237] The server allocates each task to an appropriate time slot based on the priority and estimated time required, and automatically inserts it into the calendar. Specifically, it detects the user's free time slots and assigns tasks to those slots. Scheduling is performed based on the input data, and a new schedule is generated as the output.

[0238] Step 7:

[0239] Synchronization with external calendar services

[0240] The server synchronizes the new schedule with the external calendar service, sending the schedule data using an API to update the user's calendar. The newly generated schedule is taken as input and reflected in the external calendar service as output.

[0241] Step 8:

[0242] Automatic extraction of references and data

[0243] The server automatically extracts relevant reference materials and data from the Internet and databases based on the task theme. For example, it searches for papers and materials related to generative AI contests and provides them to users. It obtains relevant keywords from the input and provides resources as output.

[0244] Step 9:

[0245] Interacting with a conversational AI assistant

[0246] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the request and automatically updates the calendar. The server receives user inquiries and change requests as input and provides the latest schedule and progress as output.

[0247] 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.

[0248] The following describes in detail in natural language the processing of a specific program for carrying out the present invention, particularly the operation of a system that combines an emotion engine that recognizes the user's emotions.

[0249] System Overview

[0250] This system is an innovative calendar service that automatically schedules tasks based on the user's personal data, past task performance, and priorities. In addition, it incorporates an emotion engine that recognizes the user's emotions and adjusts task management according to their emotional state.

[0251] 1. Collection of User Information

[0252] A user logs in to their device and enters their registration information, including basic profile information such as name, occupation, and areas of interest. The device then sends the information to the server, which stores it in a database. The server also retrieves the user's past task data and schedules from their existing calendar.

[0253] 2. Priority and performance analysis

[0254] The server analyzes the importance and deadline of each task based on the collected user information and past task performance. This determines the priority of the task and calculates the specific estimated time. It receives new tasks entered by the user in the calendar, divides them into multiple parts, and allocates the time accordingly.

[0255] 3. Automatic task scheduling

[0256] The server automatically schedules tasks into the user's calendar based on the calculated priority and estimated time, achieving optimal scheduling without conflicting with existing schedules.

[0257] 4. Integration with external services

[0258] The server synchronizes with external calendar services (e.g., Google Calendar, Microsoft Outlook) to keep schedule information up to date, and automatically retrieves reference materials and data related to specific tasks from the Internet and provides them to users.

[0259] 5. Introduction of Emotion Engine and Dialogue Interface

[0260] The device is equipped with an emotion engine that monitors emotions from the user's input and actions, analyzing the user's facial expressions, voice tone, input patterns, etc. to recognize emotions in real time.

[0261] 6. Adjusting task management based on emotions

[0262] The server performs the following actions based on the emotion data received from the emotion engine:

[0263] 1. Adjust task priorities: If a user is stressed, they can postpone less important tasks and prioritize relaxing tasks.

[0264] 2. Reminder Modification: If the user is relaxed, send reminders at different times.

[0265] 3. Break Suggestion: If the user is recognized as tired, it will suggest an appropriate break.

[0266] Specific examples

[0267] As a concrete example, let's explain the process when a user adds a task "Submit materials for the generative AI contest in 10 days":

[0268] 1. The server obtains data on the user's past document creation tasks and predicts the required time.

[0269] 2. The server divides the document creation into three parts: research, writing, and review, and estimates the time required for each.

[0270] 3. The server automatically inserts each part into the calendar, adjusting it to the user's current schedule.

[0271] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or setting a reminder if they are relaxed.

[0272] 5. When the user queries the device to check the progress, the AI ​​assistant retrieves the necessary information from the server and reports it to the user.

[0273] 6. The server synchronizes with an external calendar service, automatically collects relevant reference materials from the Internet, and provides them to the user.

[0274] With these features, the system not only provides users with task management and efficient scheduling, but also realizes flexible task management that takes into account users' emotions, thereby improving users' daily productivity and satisfaction.

[0275] The processing flow will be explained below.

[0276] Specific processing flow of the program (system combining emotion engine)

[0277] 1. Collection of User Information

[0278] Step 1:

[0279] A user logs in to a device and enters registration information, including basic profile information such as name, occupation, and areas of interest.

[0280] Step 2:

[0281] The terminal sends the entered user information to the server, which stores this information in a database.

[0282] Step 3:

[0283] The server retrieves the user's past task data from the database and links it to the user's profile.

[0284] Step 4:

[0285] The server connects to an external calendar service (such as Google Calendar or Microsoft Outlook) via API to obtain the user's current schedule.

[0286] 2. Priority and performance analysis

[0287] Step 1:

[0288] The server analyzes the importance and deadlines of tasks based on the acquired schedule information and past task data.

[0289] Step 2:

[0290] The server calculates the priority of each task based on the analysis results, which is determined by comprehensively evaluating the importance, proximity of the deadline, urgency, and other factors.

[0291] Step 3:

[0292] The server uses past performance data to predict the average time required for each task. For example, if a previous document creation task took five hours, it predicts that a similar amount of time will be required this time.

[0293] 3. Automatic task scheduling

[0294] Step 1:

[0295] The server receives new tasks that users have entered into their calendars (e.g., the deadline for submitting materials is in 10 days).

[0296] Step 2:

[0297] The server breaks the task down into specific parts (e.g., research, writing, review) and allocates the required time for each.

[0298] Step 3:

[0299] The server automatically inserts each part into the calendar, taking into account the user's current schedule, and places them so that they do not overlap with existing important events.

[0300] 4. Integration with external services

[0301] Step 1:

[0302] The server constantly synchronizes with external calendar services (Google Calendar and Microsoft Outlook).

[0303] Step 2:

[0304] The server automatically extracts relevant reference materials and data from the Internet based on the task theme and provides them to the user, for example, collecting articles from papers and databases related to the topic of document creation.

[0305] 5. Introduction of Emotion Engine and Dialogue Interface

[0306] Step 1:

[0307] The device is equipped with an emotion engine that monitors emotions from the user's input and actions, analyzing the user's facial expressions, voice tone, input patterns, etc.

[0308] Step 2:

[0309] The device sends the analysis results to a server, which then uses the received emotional data to understand the user's emotional state in real time.

[0310] 6. Adjusting task management based on emotions

[0311] Step 1:

[0312] The server adjusts task priorities based on data from the emotion engine: for example, if the user is feeling stressed, it will postpone less important tasks and prioritize relaxing tasks.

[0313] Step 2:

[0314] The server changes the timing of reminders depending on the user's emotional state: for example, if the user is relaxed, it sends a reminder earlier than usual.

[0315] Step 3:

[0316] The server detects the user's fatigue level and suggests appropriate break times, and if the user works for a long time, it sends regular break reminders.

[0317] Specific examples

[0318] Example 1:

[0319] If a user adds a task saying "Submit materials for the Generative AI Contest in 10 days":

[0320] 1. The server obtains data on the user's past document creation tasks and predicts the required time.

[0321] 2. The server divides the document creation into three parts: research, writing, and review, and estimates the time required for each.

[0322] 3. The server automatically inserts each part into the calendar, adjusting it to the user's current schedule.

[0323] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or adjusting reminders if they are relaxed.

[0324] 5. When the user queries the device to check the progress, the AI ​​assistant retrieves the necessary information from the server and reports it to the user.

[0325] 6. The server synchronizes with an external calendar service, automatically collects relevant reference materials from the Internet, and provides them to the user.

[0326] This not only supports users' task management and efficient scheduling, but also enables flexible task management that takes users' emotions into consideration.

[0327] Example 2

[0328] 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."

[0329] In today's busy society, users are required to efficiently manage numerous tasks. However, manual task scheduling is cumbersome and time-consuming, and it is easy for schedule overlaps and important tasks to be overlooked. Furthermore, current systems have difficulty flexibly adjusting tasks based on the user's emotional state. In particular, when users are stressed or tired, they need to take breaks at appropriate times and set reminders, but current systems have difficulty automatically performing these tasks.

[0330] 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 acquiring basic information of the user, a means for acquiring past task data of the user, and a means for acquiring schedule information of the user. This improves the efficiency of task management for the user and enables flexible task adjustment according to the emotional state.

[0331] "Basic user information" refers to basic information about a user, such as name, occupation, and areas of interest.

[0332] "User's past task data" refers to information about tasks that the user has performed in the past, including the content of the task, the time required, and the degree of completion.

[0333] "User's schedule information" refers to the user's current and future plans and appointments.

[0334] "Calculating task priorities" refers to evaluating and ranking the importance and deadlines of each task based on the collected information.

[0335] "Predicting task duration" refers to using historical data and algorithms to estimate the amount of time each task will require.

[0336] "Divide into subtasks and allocate time" refers to dividing a task into smaller units of work and allocating appropriate time to each part.

[0337] "External schedule management services" refers to external schedule management systems such as Google Calendar and Microsoft Outlook.

[0338] "Automatically collecting relevant references and data from the internet" refers to using web scraping or APIs to extract information from the internet that is relevant to a specific task.

[0339] "Analyzing user emotions" refers to analyzing data such as facial expressions, voice tone, and input patterns to understand the user's emotional state in real time.

[0340] "Adjusting task management based on the user's emotions" refers to dynamically changing task priorities, time allocation, reminder settings, break suggestions, etc. according to the user's emotional state.

[0341] The following describes in detail in natural language the processing of a specific program for carrying out the present invention, particularly the operation of a system that combines an emotion engine that recognizes the user's emotions.

[0342] System Overview

[0343] This system is an innovative calendar service that automatically schedules tasks based on the user's personal data, past task performance, and priorities. In addition, it incorporates an emotion engine that recognizes the user's emotions and adjusts task management according to their emotional state.

[0344] Collection of User Information

[0345] A user logs in to a device and enters basic profile information such as name, occupation, and areas of interest. The device sends this information to the server. The server stores the received user information in a database (e.g., MySQL or PostgreSQL) and retrieves the user's past task data and existing schedule information using an API.

[0346] Priority and performance analysis

[0347] The server processes the collected user information and past task results using analytical tools such as Apache Spark and TensorFlow. This evaluates the importance and deadlines of tasks and determines their priorities. When a user enters a new task, it divides it into multiple subtasks (e.g., research, writing, review) and calculates the time required for each.

[0348] Automatic task scheduling

[0349] The server automatically schedules tasks based on their calculated priority and estimated time, using an algorithm to place them in the optimal time slots so that they do not conflict with existing schedules.

[0350] Integration with external services

[0351] The server uses OAuth to connect to external schedule management services (e.g., Google Calendar, Microsoft Outlook) and synchronizes information bidirectionally. It also has the ability to automatically retrieve reference materials and data related to specific tasks from the Internet via web scraping or APIs.

[0352] Introducing an emotion engine and a dialogue interface

[0353] The device monitors the user's emotions using an emotion engine, which uses image analysis libraries such as OpenCV to analyze the user's facial expressions, voice tone, and input patterns to recognize emotions in real time.

[0354] Emotion-based adjustment of task management

[0355] The server adjusts task management based on the data received from the emotion engine: if the user is stressed, it postpones less important tasks and prioritizes relaxing tasks, if the user is relaxed, it sends reminders at different times, and if the user is perceived as tired, it suggests appropriate breaks.

[0356] Specific examples

[0357] Let's explain the process when a user adds a task "Submit materials for the generative AI contest in 10 days":

[0358] 1. The server obtains data on the user's past document creation tasks.

[0359] 2. The server divides the document creation into three subtasks: research, writing, and review, and estimates the time required for each.

[0360] 3. The server coordinates with the user's current schedule and automatically inserts each subtask into the schedule.

[0361] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or setting a reminder if they are relaxed.

[0362] 5. The user queries the terminal to check progress.

[0363] 6. The server synchronizes with an external schedule management service and collects relevant reference materials from the Internet.

[0364] Prompt Sentence Examples

[0365] "Create a schedule for creating materials for the next 10 days for the Generative AI Contest. Use past data on creating materials to predict the time required and suggest appropriate breaks."

[0366] This system improves the efficiency of task management and enables flexible scheduling that takes emotions into account, thereby improving user productivity and satisfaction.

[0367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0368] Step 1: Collect user information

[0369] The user enters their ID and password on the login screen and is authenticated.

[0370] Input: ID, password

[0371] Output: Authenticated user session

[0372] The user enters basic information (name, occupation, interests).

[0373] Input: Basic information (name, occupation, areas of interest)

[0374] Output: User basic information data

[0375] The terminal transmits the input information to the server.

[0376] Input: User basic information

[0377] Output: Basic information data transferred to the server

[0378] The server stores the received basic information in a database.

[0379] Input: Basic information data

[0380] Output: User information stored in the database

[0381] The server uses an API to retrieve the user's past task data and existing schedule information.

[0382] Input: API request

[0383] Output: Past task data, existing schedule information

[0384] Step 2: Analyze priorities and performance

[0385] The server processes the collected data using analysis tools (e.g., Apache Spark, TensorFlow).

[0386] Input: User basic information, past task data, existing schedule information

[0387] Output: Analysis results (task importance, deadline)

[0388] The server calculates the priority of the task.

[0389] Input: Analysis results (task importance, deadline)

[0390] Output: Priority list

[0391] The server divides a newly input task into multiple subtasks and predicts the required time.

[0392] Input: New task data

[0393] Output: Subtask list and estimated duration

[0394] Step 3: Automatically schedule tasks

[0395] The server schedules tasks based on priority and estimated duration.

[0396] Inputs: Subtask list, estimated duration, priority list, existing schedule

[0397] Output: Updated schedule data

[0398] The server places tasks at different times to prevent schedule overlaps.

[0399] Input: Updated schedule data

[0400] Output: Optimized schedule

[0401] Step 4: Integrate with external services

[0402] The server synchronizes with external scheduling services (e.g., Google Calendar, Microsoft Outlook).

[0403] Input: OAuth token, schedule data

[0404] Output: Schedules synced to external services

[0405] The server uses web scraping or APIs to gather material from the internet relevant to a specific task.

[0406] Input: Task identification information

[0407] Output: Collected reference data

[0408] Step 5: Introducing the emotion engine and dialogue interface

[0409] The device uses an emotion engine (e.g., OpenCV) to monitor the user's emotions.

[0410] Input: User's facial expression data, voice data, input pattern data

[0411] Output: Emotion recognition result

[0412] The device analyzes emotions in real time and sends the data to a server.

[0413] Input: Emotion recognition results

[0414] Output: Emotion data sent to the server

[0415] Step 6: Adjust your task management based on emotions

[0416] The server adjusts task management based on the emotion data.

[0417] Input: Emotion data, task data, schedule data

[0418] Output: Adjusted task schedule

[0419] If the user is feeling stressed, the server will postpone less important tasks and prioritize relaxing tasks.

[0420] Input: Emotion data, task priority data

[0421] Output: Adjusted priority list

[0422] The server will suggest appropriate breaks if the user is tired.

[0423] Input: Emotion data

[0424] Output: Break suggestion notification

[0425] The server will send reminders at different times if you are relaxed.

[0426] Input: Emotion data, reminder setting data

[0427] Output: Reminders sent

[0428] (Application example 2)

[0429] 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."

[0430] While conventional task management systems can efficiently manage users' schedules, they have the problem of making users feel stressed because they schedule without taking into account their emotional state.In addition, they lack a function to suggest breaks at appropriate times or adjust task priorities based on emotions, making it difficult to fully improve user productivity and satisfaction.

[0431] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0432] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring the user's schedule information, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into the schedule, means for synchronizing with an external schedule service, means for automatically extracting related reference materials and data, means for communicating with the user and reporting task progress, means for recognizing the user's emotional state, means for adjusting task priorities and reminders based on the emotional state, and means for suggesting breaks the user needs. This enables flexible task management that takes the user's emotional state into consideration.

[0433] "Means for obtaining user registration information" refers to the method for sending the user's basic profile information (such as name, occupation, areas of interest, etc.) to the server and storing it in the database.

[0434] The "means for acquiring the user's past task data" is a method for acquiring the tasks that the user has performed in the past and their results from the database.

[0435] "Means for obtaining user schedule information" refers to a method for obtaining the user's current and future schedules from a calendar or schedule application.

[0436] The "means for calculating task priorities based on acquired information" is a method for determining priorities by analyzing the importance and deadlines of tasks based on collected user information and past task performance.

[0437] A "means for predicting the time required for a task" is a method for predicting the time required for each task based on data such as the user's past task performance.

[0438] The "means for allocating task time and automatically inserting tasks into a schedule" is a method for appropriately allocating tasks into a user's schedule based on the calculated priorities and estimated times.

[0439] "Means for synchronizing with external scheduling services" refers to methods for synchronizing data with external calendar or scheduling services (e.g., Google Calendar or Microsoft Outlook).

[0440] "Means for automatically extracting relevant reference materials and data" refers to a method for automatically collecting information and data related to a specific task from the Internet, etc.

[0441] The "means for communicating with the user and reporting the progress of the task" refers to a method for obtaining the necessary information from the server and reporting it when the user inquires about the progress.

[0442] "Means for recognizing the user's emotional state" refers to a method for recognizing the user's emotions in real time by analyzing facial expressions, voice tone, input patterns, etc.

[0443] "Means for adjusting task priorities and reminders based on emotional state" refers to a method for reevaluating the importance of tasks and changing the timing of reminders depending on the user's emotional state.

[0444] The "means for suggesting a break the user needs" is a method for recognizing the user's emotional state and suggesting an appropriate break if the user feels tired or stressed.

[0445] A specific embodiment of the present invention will be described below. This system is a smart operation management system for autonomous vehicles. In particular, it has the function of recognizing the driver's emotional state and making operation plans and break suggestions based on that state.

[0446] System Overview

[0447] The system optimizes the driving schedule by taking into account the user's (driver's) registration information, past task performance, and emotional state. It uses the following main hardware and software components:

[0448] Camera: Used to capture the driver's facial expressions and analyze their emotions.

[0449] Microphone: Used to analyze the tone of the driver's voice and identify emotions.

[0450] EmotionEngine: Software that recognizes the driver's emotional state in real time based on data obtained from cameras and microphones.

[0451] ScheduleOptimizer: An algorithm that optimizes operation plans and adjusts schedules based on emotional states.

[0452] CalendarSync: Software for synchronizing data with external scheduling services (e.g., Google Calendar or Microsoft Outlook).

[0453] System Operation

[0454] 1. Collection of User Information

[0455] When a driver logs in to the terminal, the driver's registration information (name, route, etc.) is sent to the server, which then stores this information in a database.

[0456] The server acquires past operation performance data and calculates the next operation schedule based on that data.

[0457] 2. Operation scheduling

[0458] The server uses the collected information to optimize the operation schedule, including optimizing the operation route, pick-up time, and drop-off time.

[0459] 3. Emotion recognition

[0460] The device (self-driving vehicle) uses a camera and microphone to monitor the driver's emotional state in real time.

[0461] The EmotionEngine recognizes the driver's emotions (stress, relaxation, etc.) and sends the results to the server.

[0462] 4. Emotion-based operation adjustment

[0463] The server adjusts the trip plan depending on the driver's emotional state, for example, suggesting appropriate breaks if the driver is feeling stressed.

[0464] If the driver is relaxed, they will be notified that they have time until the next pickup time.

[0465] 5. Data synchronization

[0466] The server synchronizes with an external schedule service and always maintains the latest operating schedule.

[0467] Specific scenarios

[0468] Below is a specific scenario that shows how this system works in practice.

[0469] While the driver is traveling on Route A, the server predicts the next pickup time based on the driver's past driving data. If EmotionEngine detects the driver's stress state, the server notifies the driver, suggesting that they take a 10-minute break.

[0470] After the driver takes a break, the server recalculates the next optimal route and synchronizes it with an external scheduling service.

[0471] Prompt Sentence Examples

[0472] Here is an example of a prompt to input to a generative AI model:

[0473] Please provide some ideas on optimizing the operation schedule based on driver emotion recognition. How to adjust the schedule when the driver is stressed, and what to do when the driver is relaxed?

[0474] This enables the system to realize flexible and efficient driving management that takes into account the driver's emotional state.

[0475] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0476] Step 1:

[0477] The user's device receives the driver's login information as input. Based on this, the device obtains the driver's registration information and sends it to the server. The server stores the registration information in a database.

[0478] Step 2:

[0479] The server retrieves past task performance data from the database, then retrieves the user's current schedule information from an external schedule service (e.g., Google Calendar), analyzes how much time the user has spent on the task in the past, and predicts the task's priority and required time.

[0480] Step 3:

[0481] The server optimizes the schedule based on the predicted task duration and priority. During this process, it allocates time for tasks and automatically inserts them into the schedule. Specifically, it arranges tasks so that time is used most efficiently.

[0482] Step 4:

[0483] The device uses a camera and microphone to receive the driver's emotional state as input. The EmotionEngine analyzes facial expressions and vocal tone to recognize the driver's emotional state (relaxed, stressed, etc.). This data is sent to the server in real time.

[0484] Step 5:

[0485] The server reevaluates the existing schedule based on the received emotional state data. If the driver is stressed, it postpones low-priority tasks and suggests a break. If the driver is relaxed, it calculates the time lag until the next task pick-up time. This information is then communicated to the driver.

[0486] Step 6:

[0487] The server synchronizes with an external scheduling service, ensuring that schedule data is always up to date and that trip plans are adjusted as needed. It also automatically extracts relevant reference materials and data and provides them to drivers. This procedure ensures that data remains consistent and up to date.

[0488] Step 7:

[0489] When a user queries the device to check the progress, the server retrieves the progress data and reports it to the user, allowing the user to understand the operation status and the next task required in real time.

[0490] Specific actions

[0491] Through these processing steps, the driver can efficiently manage the operation of the autonomous vehicle and flexibly adjust according to their emotional state. The generative AI model will then assist the driver appropriately based on the following prompts:

[0492] Please provide some ideas on optimizing the operation schedule based on driver emotion recognition. How to adjust the schedule when the driver is stressed, and what to do when the driver is relaxed?

[0493] This prompt allows the system to plan an optimal trip that takes into account the driver's emotional state.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] [Second embodiment]

[0498] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0499] 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.

[0500] 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).

[0501] 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.

[0502] 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.

[0503] 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).

[0504] 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.

[0505] 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.

[0506] 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.

[0507] 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.

[0508] 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.

[0509] 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."

[0510] In the following, specific program processing will be described in detail in natural language in the mode for carrying out the present invention.

[0511] System Overview

[0512] This system is a calendar service that automatically schedules tasks, taking into account the user's personal data, past task performance, and priority. The system consists of three main components: a server, a terminal, and a user. The server is responsible for data processing and scheduling, while the terminal provides an interface with the user, who then uses the system to manage tasks.

[0513] 1. Collection of User Information

[0514] When a user logs in to the system, the server first obtains the user's registration information (such as name, occupation, and areas of interest). It also collects past task data (such as the content, time required, and completion status of previously completed tasks) from the database. It also connects via API with external calendar services (such as Google Calendar or Microsoft Outlook) to obtain the user's current schedule.

[0515] 2. Priority and performance analysis

[0516] The server calculates the priority of the user's tasks based on the collected information. This is achieved by taking into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a specific task. For example, if a user previously spent five hours on a document creation task, it will expect a similar amount of time in the future.

[0517] 3. Automatic task scheduling

[0518] The server divides the listed tasks into multiple parts (e.g., research, writing, review), allocates appropriate time to each part, and then automatically inserts each part into the calendar so that it does not overlap with existing schedules, allowing users to efficiently manage their schedules.

[0519] 4. Integration with external services

[0520] The server continuously synchronizes with an external calendar service, ensuring that the user's schedule is always up-to-date, even when updates are made to their schedule. Furthermore, the server automatically retrieves relevant reference materials and data from the Internet based on the task theme, and provides them to the user as needed.

[0521] 5. Interacting with a conversational AI assistant

[0522] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant then explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the user's request and automatically updates the calendar.

[0523] Specific examples

[0524] For example, if a user adds a task to their calendar that reads, "Submit materials for the generative AI contest in 10 days," the system will act as follows:

[0525] 1. The server predicts the required time based on the user's past performance in document creation tasks.

[0526] 2. The server divides the document creation task into three parts: research, writing, and review, and allocates a predicted time to each part.

[0527] 3. The server automatically inserts each part into the calendar, adjusting it to the user's existing schedule.

[0528] 4. By linking with external services, relevant reference materials are automatically extracted from the Internet and provided to users.

[0529] 5. If the user wants to check the progress, they can ask the AI ​​assistant through their device, and the AI ​​assistant will explain the current progress and the next task to be tackled.

[0530] In this way, the system optimizes users' task management and efficient scheduling, helping them improve their daily productivity.

[0531] The processing flow will be explained below.

[0532] Specific processing flow of the program

[0533] 1. Collection of User Information

[0534] Step 1:

[0535] A user logs in to a device and enters registration information, including basic profile information such as name, occupation, and areas of interest.

[0536] Step 2:

[0537] The terminal sends the entered user information to the server, which stores this information in a database.

[0538] Step 3:

[0539] The server retrieves the user's past task data from the database and links it to the user's profile.

[0540] Step 4:

[0541] The server connects to an external calendar service (such as Google Calendar or Microsoft Outlook) via API to obtain the user's current schedule.

[0542] 2. Priority and performance analysis

[0543] Step 1:

[0544] The server analyzes the importance and deadlines of tasks based on the acquired schedule information and past task data.

[0545] Step 2:

[0546] The server calculates the priority of each task based on the analysis results, which is determined by comprehensively evaluating the importance, proximity of the deadline, urgency, and other factors.

[0547] Step 3:

[0548] The server uses past performance data to predict the average time required for each task. For example, if a previous document creation task took five hours, it predicts that a similar amount of time will be required this time.

[0549] 3. Automatic task scheduling

[0550] Step 1:

[0551] The server receives new tasks that users have entered into their calendars (e.g., the deadline for submitting materials is in 10 days).

[0552] Step 2:

[0553] The server breaks the task down into specific parts (e.g., research, writing, review) and allocates the required time for each.

[0554] Step 3:

[0555] The server automatically inserts each part into the calendar, taking into account the user's current schedule, and places them so that they do not overlap with existing important events.

[0556] 4. Integration with external services

[0557] Step 1:

[0558] The server constantly synchronizes with external calendar services (Google Calendar and Microsoft Outlook).

[0559] Step 2:

[0560] The server automatically extracts relevant reference materials and data from the Internet based on the task theme and provides them to the user, for example, by collecting articles from papers and databases related to the topic of document creation.

[0561] 5. Interacting with a conversational AI assistant

[0562] Step 1:

[0563] The user makes an inquiry such as "Tell me about the progress of the task" through the terminal.

[0564] Step 2:

[0565] The device sends the query to the server, which retrieves the latest task progress and returns the data to the device.

[0566] Step 3:

[0567] Based on the data it collects, the AI ​​assistant will report to the user verbally or in text about its progress and the next task to tackle.

[0568] Step 4:

[0569] When a user requests a change in schedule (e.g., "Tomorrow's meeting has been canceled, so I would like to prepare the materials earlier"), the server receives the request and generates a new schedule proposal.

[0570] Step 5:

[0571] The server updates the calendar with the new schedule and prompts the user for confirmation.

[0572] Through this processing flow, the system optimizes users' task management and efficient scheduling, helping to improve their daily productivity.

[0573] Example 1

[0574] 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."

[0575] In today's busy daily lives, users need to efficiently manage tasks and increase productivity. However, manual scheduling and task management is time-consuming and inefficient, and integrating with external calendar services and data sources is complicated. Given this background, it is important to provide a task management system that is easy for users to use, fast, and accurate.

[0576] 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.

[0577] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring the user's schedule data, means for calculating task priorities based on the acquired information, means for predicting the time required for each task, means for dividing the task into multiple parts and allocating the time, means for automatically inserting the task into the schedule based on the allocation, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, and means for communicating with the user to report the progress of the task. This allows users to efficiently manage tasks and easily integrate with external calendars, thereby improving productivity.

[0578] "Means for obtaining user registration information" refers to a function that obtains information such as the name, occupation, and areas of interest of users who log in to the system from a database.

[0579] "Means for obtaining the user's past task data" refers to a function that collects from a database the content, time required, completion status, etc. of tasks that the user has completed in the past.

[0580] "Means for obtaining user schedule data" refers to a function for obtaining the user's current and future schedules from external calendar services, etc.

[0581] The "means for calculating task priorities" is a function that calculates priorities based on collected information, taking into account the importance, deadline, urgency, etc. of tasks.

[0582] "Means for predicting task duration" is a function that analyzes a user's past task performance and estimates the time required for a specific task.

[0583] "A means of dividing tasks into multiple parts and allocating time" is a function that divides listed tasks into multiple parts such as research, writing, and review, and allocates appropriate time to each part.

[0584] "Means for automatically inserting tasks into the schedule" is a function that automatically registers each part in the calendar while coordinating with the user's existing schedule.

[0585] "Means for synchronizing with external calendar services" refers to a function that synchronizes users' schedule data by linking with external calendar services such as Google Calendar and Microsoft Outlook.

[0586] "Means for automatically extracting relevant reference materials and data" refers to a function that automatically collects reference materials and data related to the user's task from the Internet and other data sources and provides them to the user.

[0587] "Means of interacting with the user and reporting task progress" refers to a function of an interactive AI assistant that responds to inquiries from the user via the device and reports the progress of the current task and the next task to be tackled.

[0588] The following describes in detail the specific program processing for implementing the present invention. This system is a calendar service that automatically schedules tasks taking into account the user's personal data, past task performance, and priority. The system consists of three main components: a server, a terminal, and a user. The server is responsible for data processing and scheduling, while the terminal provides an interface with the user, who uses the system to manage tasks.

[0589] Collection of User Information

[0590] When a user logs in to the system, the server first obtains the user's registration information (such as name, occupation, and areas of interest). To do this, the server retrieves the necessary information from the database using the user ID as a key. For example, if the user is registered as a "software engineer," that information is retrieved. The server also collects the user's past task data from the database. This data includes tasks that have been completed in the past, the time required, and the completion status. The server also connects via API to external calendar services such as Google Calendar and Microsoft Outlook to obtain the user's current schedule.

[0591] Priority and performance analysis

[0592] The server calculates the priority of the user's tasks based on the collected information. This is done using an algorithm that takes into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a particular task. For example, if a user previously spent five hours on a document creation task, the server will estimate a similar amount of time in the future.

[0593] Automatic task scheduling

[0594] The server divides the listed tasks into multiple parts, such as research, writing, and review, and allocates appropriate time to each part. It then automatically inserts each part into the calendar so that it does not overlap with existing schedules. If a user has free time from 10:00 to 12:00 on Monday and 14:00 to 17:00 on Tuesday, the server will assign research from 10:00 to 12:00 on Monday and writing from 14:00 to 17:00 on Tuesday.

[0595] Integration with external services

[0596] The server continuously synchronizes with external calendar services such as Google Calendar and Microsoft Outlook, ensuring that users' calendars are kept up to date with any changes to their schedules. Additionally, based on the topic of the task, the app automatically retrieves relevant references and data from the internet and provides them to users.

[0597] Interacting with a conversational AI assistant

[0598] When a user inquires about task progress or changes through their device, the device sends that information to the server, which obtains the latest task progress. The AI ​​assistant explains the user's current progress and the next task to tackle. For example, if the user types "What should I do next?" into the device, the server will respond with "Next, please work on reviewing the document." If the user requests a schedule change, the server will generate a new schedule proposal based on that request and automatically update the calendar.

[0599] Specific use cases

[0600] For example, if a user adds the task "Submit materials for a generative AI contest in 10 days" to their calendar, the system works as follows: First, the server predicts the required time based on the user's past performance in document creation tasks. Then, it divides the document creation task into three parts - research, writing, and review - and allocates the predicted time to each part. Next, it automatically inserts each part into the calendar, aligning it with the user's existing schedule. In addition, it extracts relevant reference materials from the internet and provides them to the user. If the user wants to check their progress, they can inquire about it through their device, and the AI ​​assistant will explain their current progress and the next task they should tackle.

[0601] Prompt Sentence Examples

[0602] Here is an example of a prompt that a user might enter into the system:

[0603] "Schedule submission of materials for the Generative AI contest in 10 days."

[0604] In this way, the system optimizes users' task management and efficient scheduling, helping them improve their daily productivity.

[0605] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0606] Step 1:

[0607] User login and information collection

[0608] Input: User login information (user ID, password)

[0609] Output: User registration information, past task data, schedule data

[0610] When a user logs in to the system, the server first retrieves the user's registration information (such as name, occupation, and areas of interest) from the database. It also collects the user's past task data (such as the content, time required, and completion status of completed tasks) from the database. It then uses the APIs of Google Calendar and Microsoft Outlook to retrieve the user's current schedule data. Specifically, the server queries the database using the user ID as a key to extract the required information.

[0611] Step 2:

[0612] Task Priority Calculation

[0613] Input: Registration information, past task data, schedule data

[0614] Output: A prioritized task list

[0615] The server calculates the priority of the user's tasks based on the collected information. The calculation takes into account factors such as the task's importance, deadline, and urgency. For example, if a document creation task has an importance level of 8, a deadline in one week, and an urgency level of 5, the server calculates an overall score based on these parameters. As a result, a prioritized task list is generated.

[0616] Step 3:

[0617] Estimated travel time

[0618] Input: Past task data, new task list

[0619] Output: A list of tasks with estimated durations

[0620] The server analyzes past task performance and predicts the time required for new tasks. For example, if a user previously spent five hours on a document creation task, the server predicts that the current task will take a similar amount of time based on that performance data. This generates a task list with predicted completion times.

[0621] Step 4:

[0622] Task division and time allocation

[0623] Input: Task list with estimated duration, user schedule data

[0624] Output: Time-allocated task list

[0625] The server divides the listed tasks into multiple parts, such as research, writing, and review, and allocates appropriate time to each part. For example, it divides document creation into research (2 hours), writing (3 hours), and review (1 hour). Then, it automatically inserts each part into the calendar while adjusting it with the user's schedule data.

[0626] Step 5:

[0627] Automatic insertion into the calendar

[0628] Input: Time-phased task list, user schedule data

[0629] Output: Updated calendar

[0630] The server automatically inserts each part into the calendar so that it doesn't overlap with the user's existing schedule. For example, if there is free time between 10:00 and 12:00 on Monday, the server will schedule the survey for that time. The result is an updated calendar.

[0631] Step 6:

[0632] Integration with external services

[0633] Input: User's task list, related keywords

[0634] Output: Related references and resources

[0635] The server continuously synchronizes with external calendar services such as Google Calendar and Microsoft Outlook. It also automatically retrieves reference materials and data related to the user's tasks from the Internet and provides them to the user. For example, it uses the Google Search API to collect materials related to "document creation" from the Internet.

[0636] Step 7:

[0637] Task progress reporting and interaction

[0638] Input: User's query, latest task list

[0639] Output: Progress report, next task suggestions

[0640] When a user inquires about the progress or changes of a task through their device, the device sends that information to the server, which retrieves the latest task progress. The server then explains the current progress and the next task to the user through the AI ​​assistant. For example, if a user types "What should I do next?", the server will respond with "Next, please work on reviewing the document creation."

[0641] (Application example 1)

[0642] 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."

[0643] In modern factories, improving the efficiency of production plans and optimizing employee work schedules are important issues. However, manual scheduling and individual task management require time and effort, and there are limits to how much can be done. Furthermore, updating schedules in real time while linking with external calendar services is difficult, which can easily lead to information discrepancies at the production site. For this reason, there is a demand for a system that automatically generates schedules based on factory operation data and employee past work performance, and updates them in real time.

[0644] 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.

[0645] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring schedules from the user's calendar, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into the calendar, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, means for communicating with the user to report task progress, means for automatically generating a work schedule based on factory operation data and employee task performance, and means for updating employee work schedules in real time to streamline factory production planning. This enables the automatic generation of efficient work schedules that are updated in real time based on the factory's operation status.

[0646] "Means for obtaining user registration information" refers to a system or method for obtaining basic information such as a user's name, occupation, and role from a database.

[0647] "Means for obtaining a user's past task data" refers to a system or method for obtaining detailed information (content, required time, completion status, etc.) of tasks that a user has performed in the past from a database.

[0648] A "means for retrieving events from a user's calendar" is a system or method for retrieving current events and schedules from a calendar service used by a user.

[0649] A "means for calculating task priority" is a system or method for numerically evaluating the priority of a task based on various factors such as the importance, deadline, and urgency of the task.

[0650] A "means for predicting the time required for a task" is a system or method for predicting the time required to complete a similar task based on performance data of past tasks.

[0651] "Means for allocating task time and automatically inserting it into a calendar" refers to a system or method for automatically adding the optimal schedule to a user's calendar based on the calculated task duration and priority.

[0652] "Means for synchronizing with an external calendar service" means a system or method for synchronizing data with an external calendar service, such as Google Calendar or Microsoft Outlook.

[0653] "Means for automatically extracting relevant reference materials and data" refers to a system or method for automatically retrieving and providing relevant materials and data from the Internet or databases based on the user's task content or theme.

[0654] "Means for communicating with the user and reporting task progress" refers to a system or method by which an AI assistant communicates with the user to report the progress of the current task and the next task to be tackled.

[0655] "Means for automatically generating work schedules based on factory operation data and employee task performance" refers to a system or method that automatically generates an optimal work schedule based on the operation status of factory equipment and employees' past work history.

[0656] "Means for updating employee work schedules in real time to streamline factory production plans" refers to a system or method for updating and optimizing employee work schedules in real time in response to changes in and progress in the factory's production plans.

[0657] The mode for carrying out the present invention will be described in detail below: The system required for carrying out the invention consists of three main components: a server, a terminal, and a user.

[0658] System Overview

[0659] This system is a management service that automatically generates work schedules based on factory operation data and employee task performance, and updates employee work schedules in real time to streamline production planning. The system processes data and performs scheduling on the server, and provides a user interface on the terminal. The server also connects to an external calendar service and interacts with users via an AI assistant.

[0660] 1. Collection of User Information

[0661] When a user logs in to the system, the server first retrieves the user's registration information (such as name, occupation, and role) from the database. It also collects past task data (such as the content, time required, and completion status of past tasks) from the database. It also retrieves the user's current schedule through API integration with external calendar services (such as Google Calendar and Microsoft Outlook).

[0662] 2. Priority and performance analysis

[0663] The server calculates the priority of each user's task based on the collected information. This is achieved by taking into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a specific task. For example, if an employee previously spent three hours on machine maintenance, it will expect a similar amount of time in the future.

[0664] 3. Automatic task scheduling

[0665] The server divides the listed tasks into multiple parts (e.g., research, work, confirmation), allocates appropriate time to each part, and then automatically inserts each part into the calendar so that it does not overlap with existing schedules, allowing employees to efficiently manage their work schedules.

[0666] 4. Integration with external services

[0667] The server continuously synchronizes with an external calendar service, ensuring that employee schedules are always up-to-date when they are updated. Furthermore, the system automatically retrieves relevant reference materials and data from the Internet based on the task topic, and provides them to users as needed.

[0668] 5. Interacting with a conversational AI assistant

[0669] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant then explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the user's request and automatically updates the calendar.

[0670] Specific examples

[0671] For example, if a user adds a task to their calendar that reads, "Submit materials for the generative AI contest in 10 days," the system will act as follows:

[0672] 1. The server predicts the required time based on the user's past performance in document creation tasks.

[0673] 2. The server divides the document creation task into three parts: research, writing, and review, and allocates a predicted time to each part.

[0674] 3. The server automatically inserts each part into the calendar, adjusting it to the user's existing schedule.

[0675] 4. By linking with external services, relevant reference materials are automatically extracted from the Internet and provided to users.

[0676] 5. If the user wants to check the progress, they can ask the AI ​​assistant through their device, and the AI ​​assistant will explain the current progress and the next task to be tackled.

[0677] Prompt Sentence Examples

[0678] Here is an example of a real prompt:

[0679] Below is an example of task scheduling. If a user adds a task "Submit materials for the Generative AI Contest in 10 days," please generate the optimal schedule based on the following information:

[0680] Task name: Generative AI Contest Materials

[0681] Submission deadline: 10 days later

[0682] Task details:

[0683] Investigation: 2 hours

[0684] Writing: 3 hours

[0685] Review: 2 hours

[0686] Past task history:

[0687] Task A (2 hours, 3 hours, 2 hours)

[0688] Task B (1 hour, 1.5 hours, 1.2 hours)

[0689] Current schedule:

[0690] November 20th 09:00 - 11:00: Available

[0691] November 21st 13:00 - 16:00: Available

[0692] November 22nd 10:00 - 12:00: Available

[0693] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0694] Step 1:

[0695] Collection of user registration information

[0696] When a user logs in to the system, the server first retrieves the user's registration information from the database. Specifically, it retrieves basic information such as the user's name, occupation, and role. This allows the user's basic profile to be collected as input data. The output is a dataset containing the user's basic information.

[0697] Step 2:

[0698] Retrieving past task data

[0699] The server retrieves data on tasks the user has performed in the past from a database, including task content, required time, and completion status. This provides the user's past task history as input data. The output is detailed data on each individual task.

[0700] Step 3:

[0701] Get a user's calendar events

[0702] The server connects to an external calendar service via API to obtain the user's current schedule. Specifically, it obtains the user's schedule data from Google Calendar or Microsoft Outlook via API. This obtains the user's current schedule as input data. The output is the user's current schedule information.

[0703] Step 4:

[0704] Calculate task priorities

[0705] The server calculates task priorities based on the collected user basic information, past task data, and current schedule data. This takes into account factors such as the task's importance, deadline, and urgency. Specifically, the priority is numerically evaluated by multiplying the importance and urgency coefficients. The input data is processed, and the task priority is obtained as the output.

[0706] Step 5:

[0707] Task duration prediction

[0708] The server analyzes past task data and predicts the time required for a specific task. It uses statistical methods based on past performance data to predict the average time required for similar tasks. It processes the input historical data and outputs the predicted time required.

[0709] Step 6:

[0710] Time allocation and auto-scheduling of tasks

[0711] The server allocates each task to an appropriate time slot based on the priority and estimated time required, and automatically inserts it into the calendar. Specifically, it detects the user's free time slots and assigns tasks to those slots. Scheduling is performed based on the input data, and a new schedule is generated as the output.

[0712] Step 7:

[0713] Synchronization with external calendar services

[0714] The server synchronizes the new schedule with the external calendar service, sending the schedule data using an API to update the user's calendar. The newly generated schedule is taken as input and reflected in the external calendar service as output.

[0715] Step 8:

[0716] Automatic extraction of references and data

[0717] The server automatically extracts relevant reference materials and data from the Internet and databases based on the task theme. For example, it searches for papers and materials related to generative AI contests and provides them to users. It obtains relevant keywords from the input and provides resources as output.

[0718] Step 9:

[0719] Interacting with a conversational AI assistant

[0720] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the request and automatically updates the calendar. The server receives user inquiries and change requests as input and provides the latest schedule and progress as output.

[0721] 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.

[0722] The following describes in detail in natural language the processing of a specific program for carrying out the present invention, particularly the operation of a system that combines an emotion engine that recognizes the user's emotions.

[0723] System Overview

[0724] This system is an innovative calendar service that automatically schedules tasks based on the user's personal data, past task performance, and priorities. In addition, it incorporates an emotion engine that recognizes the user's emotions and adjusts task management according to their emotional state.

[0725] 1. Collection of User Information

[0726] A user logs in to their device and enters their registration information, including basic profile information such as name, occupation, and areas of interest. The device then sends the information to the server, which stores it in a database. The server also retrieves the user's past task data and schedules from their existing calendar.

[0727] 2. Priority and performance analysis

[0728] The server analyzes the importance and deadline of each task based on the collected user information and past task performance. This determines the priority of the task and calculates the specific estimated time. It receives new tasks entered by the user in the calendar, divides them into multiple parts, and allocates the time accordingly.

[0729] 3. Automatic task scheduling

[0730] The server automatically schedules tasks into the user's calendar based on the calculated priority and estimated time, achieving optimal scheduling without conflicting with existing schedules.

[0731] 4. Integration with external services

[0732] The server synchronizes with external calendar services (e.g., Google Calendar, Microsoft Outlook) to keep schedule information up to date, and automatically retrieves reference materials and data related to specific tasks from the Internet and provides them to users.

[0733] 5. Introduction of Emotion Engine and Dialogue Interface

[0734] The device is equipped with an emotion engine that monitors emotions from the user's input and actions, analyzing the user's facial expressions, voice tone, input patterns, etc. to recognize emotions in real time.

[0735] 6. Adjusting task management based on emotions

[0736] The server performs the following actions based on the emotion data received from the emotion engine:

[0737] 1. Adjust task priorities: If a user is stressed, they can postpone less important tasks and prioritize relaxing tasks.

[0738] 2. Reminder Modification: If the user is relaxed, send reminders at different times.

[0739] 3. Break Suggestion: If the user is recognized as tired, it will suggest an appropriate break.

[0740] Specific examples

[0741] As a concrete example, let's explain the process when a user adds a task "Submit materials for the generative AI contest in 10 days":

[0742] 1. The server obtains data on the user's past document creation tasks and predicts the required time.

[0743] 2. The server divides the document creation into three parts: research, writing, and review, and estimates the time required for each.

[0744] 3. The server automatically inserts each part into the calendar, adjusting it to the user's current schedule.

[0745] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or setting a reminder if they are relaxed.

[0746] 5. When the user queries the device to check the progress, the AI ​​assistant retrieves the necessary information from the server and reports it to the user.

[0747] 6. The server synchronizes with an external calendar service, automatically collects relevant reference materials from the Internet, and provides them to the user.

[0748] With these features, the system not only provides users with task management and efficient scheduling, but also realizes flexible task management that takes into account users' emotions, thereby improving users' daily productivity and satisfaction.

[0749] The processing flow will be explained below.

[0750] Specific processing flow of the program (system combining emotion engine)

[0751] 1. Collection of User Information

[0752] Step 1:

[0753] A user logs in to a device and enters registration information, including basic profile information such as name, occupation, and areas of interest.

[0754] Step 2:

[0755] The terminal sends the entered user information to the server, which stores this information in a database.

[0756] Step 3:

[0757] The server retrieves the user's past task data from the database and links it to the user's profile.

[0758] Step 4:

[0759] The server connects to an external calendar service (such as Google Calendar or Microsoft Outlook) via API to obtain the user's current schedule.

[0760] 2. Priority and performance analysis

[0761] Step 1:

[0762] The server analyzes the importance and deadlines of tasks based on the acquired schedule information and past task data.

[0763] Step 2:

[0764] The server calculates the priority of each task based on the analysis results, which is determined by comprehensively evaluating the importance, proximity of the deadline, urgency, and other factors.

[0765] Step 3:

[0766] The server uses past performance data to predict the average time required for each task. For example, if a previous document creation task took five hours, it predicts that a similar amount of time will be required this time.

[0767] 3. Automatic task scheduling

[0768] Step 1:

[0769] The server receives new tasks that users have entered into their calendars (e.g., the deadline for submitting materials is in 10 days).

[0770] Step 2:

[0771] The server breaks the task down into specific parts (e.g., research, writing, review) and allocates the required time for each.

[0772] Step 3:

[0773] The server automatically inserts each part into the calendar, taking into account the user's current schedule, and places them so that they do not overlap with existing important events.

[0774] 4. Integration with external services

[0775] Step 1:

[0776] The server constantly synchronizes with external calendar services (Google Calendar and Microsoft Outlook).

[0777] Step 2:

[0778] The server automatically extracts relevant reference materials and data from the Internet based on the task theme and provides them to the user, for example, collecting articles from papers and databases related to the topic of document creation.

[0779] 5. Introduction of Emotion Engine and Dialogue Interface

[0780] Step 1:

[0781] The device is equipped with an emotion engine that monitors emotions from the user's input and actions, analyzing the user's facial expressions, voice tone, input patterns, etc.

[0782] Step 2:

[0783] The device sends the analysis results to a server, which then uses the received emotional data to understand the user's emotional state in real time.

[0784] 6. Adjusting task management based on emotions

[0785] Step 1:

[0786] The server adjusts task priorities based on data from the emotion engine: for example, if the user is feeling stressed, it will postpone less important tasks and prioritize relaxing tasks.

[0787] Step 2:

[0788] The server changes the timing of reminders depending on the user's emotional state: for example, if the user is relaxed, it sends a reminder earlier than usual.

[0789] Step 3:

[0790] The server detects the user's fatigue level and suggests appropriate break times, and if the user works for a long time, it sends regular break reminders.

[0791] Specific examples

[0792] Example 1:

[0793] If a user adds a task saying "Submit materials for the Generative AI Contest in 10 days":

[0794] 1. The server obtains data on the user's past document creation tasks and predicts the required time.

[0795] 2. The server divides the document creation into three parts: research, writing, and review, and estimates the time required for each.

[0796] 3. The server automatically inserts each part into the calendar, adjusting it to the user's current schedule.

[0797] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or adjusting reminders if they are relaxed.

[0798] 5. When the user queries the device to check the progress, the AI ​​assistant retrieves the necessary information from the server and reports it to the user.

[0799] 6. The server synchronizes with an external calendar service, automatically collects relevant reference materials from the Internet, and provides them to the user.

[0800] This not only supports users' task management and efficient scheduling, but also enables flexible task management that takes users' emotions into consideration.

[0801] Example 2

[0802] 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."

[0803] In today's busy society, users are required to efficiently manage numerous tasks. However, manual task scheduling is cumbersome and time-consuming, and it is easy for schedule overlaps and important tasks to be overlooked. Furthermore, current systems have difficulty flexibly adjusting tasks based on the user's emotional state. In particular, when users are stressed or tired, they need to take breaks at appropriate times and set reminders, but current systems have difficulty automatically performing these tasks.

[0804] 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 acquiring basic information of the user, a means for acquiring past task data of the user, and a means for acquiring schedule information of the user. This improves the efficiency of task management for the user and enables flexible task adjustment according to the emotional state.

[0805] "Basic user information" refers to basic information about a user, such as name, occupation, and areas of interest.

[0806] "User's past task data" refers to information about tasks that the user has performed in the past, including the content of the task, the time required, and the degree of completion.

[0807] "User's schedule information" refers to the user's current and future plans and appointments.

[0808] "Calculating task priorities" refers to evaluating and ranking the importance and deadlines of each task based on the collected information.

[0809] "Predicting task duration" refers to using historical data and algorithms to estimate the amount of time each task will require.

[0810] "Divide into subtasks and allocate time" refers to dividing a task into smaller units of work and allocating appropriate time to each part.

[0811] "External schedule management services" refers to external schedule management systems such as Google Calendar and Microsoft Outlook.

[0812] "Automatically collecting relevant references and data from the internet" refers to using web scraping or APIs to extract information from the internet that is relevant to a specific task.

[0813] "Analyzing user emotions" refers to analyzing data such as facial expressions, voice tone, and input patterns to understand the user's emotional state in real time.

[0814] "Adjusting task management based on the user's emotions" refers to dynamically changing task priorities, time allocation, reminder settings, break suggestions, etc. according to the user's emotional state.

[0815] The following describes in detail in natural language the processing of a specific program for carrying out the present invention, particularly the operation of a system that combines an emotion engine that recognizes the user's emotions.

[0816] System Overview

[0817] This system is an innovative calendar service that automatically schedules tasks based on the user's personal data, past task performance, and priorities. In addition, it incorporates an emotion engine that recognizes the user's emotions and adjusts task management according to their emotional state.

[0818] Collection of User Information

[0819] A user logs in to a device and enters basic profile information such as name, occupation, and areas of interest. The device sends this information to the server. The server stores the received user information in a database (e.g., MySQL or PostgreSQL) and retrieves the user's past task data and existing schedule information using an API.

[0820] Priority and performance analysis

[0821] The server processes the collected user information and past task results using analytical tools such as Apache Spark and TensorFlow. This evaluates the importance and deadlines of tasks and determines their priorities. When a user enters a new task, it divides it into multiple subtasks (e.g., research, writing, review) and calculates the time required for each.

[0822] Automatic task scheduling

[0823] The server automatically schedules tasks based on their calculated priority and estimated time, using an algorithm to place them in the optimal time slots so that they do not conflict with existing schedules.

[0824] Integration with external services

[0825] The server uses OAuth to connect to external schedule management services (e.g., Google Calendar, Microsoft Outlook) and synchronizes information bidirectionally. It also has the ability to automatically retrieve reference materials and data related to specific tasks from the Internet via web scraping or APIs.

[0826] Introducing an emotion engine and a dialogue interface

[0827] The device monitors the user's emotions using an emotion engine, which uses image analysis libraries such as OpenCV to analyze the user's facial expressions, voice tone, and input patterns to recognize emotions in real time.

[0828] Emotion-based adjustment of task management

[0829] The server adjusts task management based on the data received from the emotion engine: if the user is stressed, it postpones less important tasks and prioritizes relaxing tasks, if the user is relaxed, it sends reminders at different times, and if the user is perceived as tired, it suggests appropriate breaks.

[0830] Specific examples

[0831] Let's explain the process when a user adds a task "Submit materials for the generative AI contest in 10 days":

[0832] 1. The server obtains data on the user's past document creation tasks.

[0833] 2. The server divides the document creation into three subtasks: research, writing, and review, and estimates the time required for each.

[0834] 3. The server coordinates with the user's current schedule and automatically inserts each subtask into the schedule.

[0835] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or setting a reminder if they are relaxed.

[0836] 5. The user queries the terminal to check progress.

[0837] 6. The server synchronizes with an external schedule management service and collects relevant reference materials from the Internet.

[0838] Prompt Sentence Examples

[0839] "Create a schedule for creating materials for the next 10 days for the Generative AI Contest. Use past data on creating materials to predict the time required and suggest appropriate breaks."

[0840] This system improves the efficiency of task management and enables flexible scheduling that takes emotions into account, thereby improving user productivity and satisfaction.

[0841] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0842] Step 1: Collect user information

[0843] The user enters their ID and password on the login screen and is authenticated.

[0844] Input: ID, password

[0845] Output: Authenticated user session

[0846] The user enters basic information (name, occupation, interests).

[0847] Input: Basic information (name, occupation, areas of interest)

[0848] Output: User basic information data

[0849] The terminal transmits the input information to the server.

[0850] Input: User basic information

[0851] Output: Basic information data transferred to the server

[0852] The server stores the received basic information in a database.

[0853] Input: Basic information data

[0854] Output: User information stored in the database

[0855] The server uses an API to retrieve the user's past task data and existing schedule information.

[0856] Input: API request

[0857] Output: Past task data, existing schedule information

[0858] Step 2: Analyze priorities and performance

[0859] The server processes the collected data using analysis tools (e.g., Apache Spark, TensorFlow).

[0860] Input: User basic information, past task data, existing schedule information

[0861] Output: Analysis results (task importance, deadline)

[0862] The server calculates the priority of the task.

[0863] Input: Analysis results (task importance, deadline)

[0864] Output: Priority list

[0865] The server divides a newly input task into multiple subtasks and predicts the required time.

[0866] Input: New task data

[0867] Output: Subtask list and estimated duration

[0868] Step 3: Automatically schedule tasks

[0869] The server schedules tasks based on priority and estimated duration.

[0870] Inputs: Subtask list, estimated duration, priority list, existing schedule

[0871] Output: Updated schedule data

[0872] The server places tasks at different times to prevent schedule overlaps.

[0873] Input: Updated schedule data

[0874] Output: Optimized schedule

[0875] Step 4: Integrate with external services

[0876] The server synchronizes with external scheduling services (e.g., Google Calendar, Microsoft Outlook).

[0877] Input: OAuth token, schedule data

[0878] Output: Schedules synced to external services

[0879] The server uses web scraping or APIs to gather material from the internet relevant to a specific task.

[0880] Input: Task identification information

[0881] Output: Collected reference data

[0882] Step 5: Introducing the emotion engine and dialogue interface

[0883] The device uses an emotion engine (e.g., OpenCV) to monitor the user's emotions.

[0884] Input: User's facial expression data, voice data, input pattern data

[0885] Output: Emotion recognition result

[0886] The device analyzes emotions in real time and sends the data to a server.

[0887] Input: Emotion recognition results

[0888] Output: Emotion data sent to the server

[0889] Step 6: Adjust your task management based on emotions

[0890] The server adjusts task management based on the emotion data.

[0891] Input: Emotion data, task data, schedule data

[0892] Output: Adjusted task schedule

[0893] If the user is feeling stressed, the server will postpone less important tasks and prioritize relaxing tasks.

[0894] Input: Emotion data, task priority data

[0895] Output: Adjusted priority list

[0896] The server will suggest appropriate breaks if the user is tired.

[0897] Input: Emotion data

[0898] Output: Break suggestion notification

[0899] The server will send reminders at different times if you are relaxed.

[0900] Input: Emotion data, reminder setting data

[0901] Output: Reminders sent

[0902] (Application example 2)

[0903] 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."

[0904] While conventional task management systems can efficiently manage users' schedules, they have the problem of making users feel stressed because they schedule without taking into account their emotional state.In addition, they lack a function to suggest breaks at appropriate times or adjust task priorities based on emotions, making it difficult to fully improve user productivity and satisfaction.

[0905] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0906] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring the user's schedule information, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into the schedule, means for synchronizing with an external schedule service, means for automatically extracting related reference materials and data, means for communicating with the user and reporting task progress, means for recognizing the user's emotional state, means for adjusting task priorities and reminders based on the emotional state, and means for suggesting breaks the user needs. This enables flexible task management that takes the user's emotional state into consideration.

[0907] "Means for obtaining user registration information" refers to the method for sending the user's basic profile information (such as name, occupation, areas of interest, etc.) to the server and storing it in the database.

[0908] The "means for acquiring the user's past task data" is a method for acquiring the tasks that the user has performed in the past and their results from the database.

[0909] "Means for obtaining user schedule information" refers to a method for obtaining the user's current and future schedules from a calendar or schedule application.

[0910] The "means for calculating task priorities based on acquired information" is a method for determining priorities by analyzing the importance and deadlines of tasks based on collected user information and past task performance.

[0911] A "means for predicting the time required for a task" is a method for predicting the time required for each task based on data such as the user's past task performance.

[0912] The "means for allocating task time and automatically inserting tasks into a schedule" is a method for appropriately allocating tasks into a user's schedule based on the calculated priorities and estimated times.

[0913] "Means for synchronizing with external scheduling services" refers to methods for synchronizing data with external calendar or scheduling services (e.g., Google Calendar or Microsoft Outlook).

[0914] "Means for automatically extracting relevant reference materials and data" refers to a method for automatically collecting information and data related to a specific task from the Internet, etc.

[0915] The "means for communicating with the user and reporting the progress of the task" refers to a method for obtaining the necessary information from the server and reporting it when the user inquires about the progress.

[0916] "Means for recognizing the user's emotional state" refers to a method for recognizing the user's emotions in real time by analyzing facial expressions, voice tone, input patterns, etc.

[0917] "Means for adjusting task priorities and reminders based on emotional state" refers to a method for reevaluating the importance of tasks and changing the timing of reminders depending on the user's emotional state.

[0918] The "means for suggesting a break the user needs" is a method for recognizing the user's emotional state and suggesting an appropriate break if the user feels tired or stressed.

[0919] A specific embodiment of the present invention will be described below. This system is a smart operation management system for autonomous vehicles. In particular, it has the function of recognizing the driver's emotional state and making operation plans and break suggestions based on that state.

[0920] System Overview

[0921] The system optimizes the driving schedule by taking into account the user's (driver's) registration information, past task performance, and emotional state. It uses the following main hardware and software components:

[0922] Camera: Used to capture the driver's facial expressions and analyze their emotions.

[0923] Microphone: Used to analyze the tone of the driver's voice and identify emotions.

[0924] EmotionEngine: Software that recognizes the driver's emotional state in real time based on data obtained from cameras and microphones.

[0925] ScheduleOptimizer: An algorithm that optimizes operation plans and adjusts schedules based on emotional states.

[0926] CalendarSync: Software for synchronizing data with external scheduling services (e.g., Google Calendar or Microsoft Outlook).

[0927] System Operation

[0928] 1. Collection of User Information

[0929] When a driver logs in to the terminal, the driver's registration information (name, route, etc.) is sent to the server, which then stores this information in a database.

[0930] The server acquires past operation performance data and calculates the next operation schedule based on that data.

[0931] 2. Operation scheduling

[0932] The server uses the collected information to optimize the operation schedule, including optimizing the operation route, pick-up time, and drop-off time.

[0933] 3. Emotion recognition

[0934] The device (self-driving vehicle) uses a camera and microphone to monitor the driver's emotional state in real time.

[0935] The EmotionEngine recognizes the driver's emotions (stress, relaxation, etc.) and sends the results to the server.

[0936] 4. Emotion-based operation adjustment

[0937] The server adjusts the trip plan depending on the driver's emotional state, for example, suggesting appropriate breaks if the driver is feeling stressed.

[0938] If the driver is relaxed, they will be notified that they have time until the next pickup time.

[0939] 5. Data synchronization

[0940] The server synchronizes with an external schedule service and always maintains the latest operating schedule.

[0941] Specific scenarios

[0942] Below is a specific scenario that shows how this system works in practice.

[0943] While the driver is traveling on Route A, the server predicts the next pickup time based on the driver's past driving data. If EmotionEngine detects the driver's stress state, the server notifies the driver, suggesting that they take a 10-minute break.

[0944] After the driver takes a break, the server recalculates the next optimal route and synchronizes it with an external scheduling service.

[0945] Prompt Sentence Examples

[0946] Here is an example of a prompt to input to a generative AI model:

[0947] Please provide some ideas on optimizing the operation schedule based on driver emotion recognition. How to adjust the schedule when the driver is stressed, and what to do when the driver is relaxed?

[0948] This enables the system to realize flexible and efficient driving management that takes into account the driver's emotional state.

[0949] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0950] Step 1:

[0951] The user's device receives the driver's login information as input. Based on this, the device obtains the driver's registration information and sends it to the server. The server stores the registration information in a database.

[0952] Step 2:

[0953] The server retrieves past task performance data from the database, then retrieves the user's current schedule information from an external schedule service (e.g., Google Calendar), analyzes how much time the user has spent on the task in the past, and predicts the task's priority and required time.

[0954] Step 3:

[0955] The server optimizes the schedule based on the predicted task duration and priority. During this process, it allocates time for tasks and automatically inserts them into the schedule. Specifically, it arranges tasks so that time is used most efficiently.

[0956] Step 4:

[0957] The device uses a camera and microphone to receive the driver's emotional state as input. The EmotionEngine analyzes facial expressions and vocal tone to recognize the driver's emotional state (relaxed, stressed, etc.). This data is sent to the server in real time.

[0958] Step 5:

[0959] The server reevaluates the existing schedule based on the received emotional state data. If the driver is stressed, it postpones low-priority tasks and suggests a break. If the driver is relaxed, it calculates the time lag until the next task pick-up time. This information is then communicated to the driver.

[0960] Step 6:

[0961] The server synchronizes with an external scheduling service, ensuring that schedule data is always up to date and that trip plans are adjusted as needed. It also automatically extracts relevant reference materials and data and provides them to drivers. This procedure ensures that data remains consistent and up to date.

[0962] Step 7:

[0963] When a user queries the device to check the progress, the server retrieves the progress data and reports it to the user, allowing the user to understand the operation status and the next task required in real time.

[0964] Specific actions

[0965] Through these processing steps, the driver can efficiently manage the operation of the autonomous vehicle and flexibly adjust according to their emotional state. The generative AI model will then assist the driver appropriately based on the following prompts:

[0966] Please provide some ideas on optimizing the operation schedule based on driver emotion recognition. How to adjust the schedule when the driver is stressed, and what to do when the driver is relaxed?

[0967] This prompt allows the system to plan an optimal trip that takes into account the driver's emotional state.

[0968] 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.

[0969] 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.

[0970] 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.

[0971] [Third embodiment]

[0972] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0973] 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.

[0974] 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).

[0975] 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.

[0976] 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.

[0977] 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).

[0978] 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.

[0979] 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.

[0980] 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.

[0981] 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.

[0982] 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.

[0983] 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."

[0984] In the following, specific program processing will be described in detail in natural language in the mode for carrying out the present invention.

[0985] System Overview

[0986] This system is a calendar service that automatically schedules tasks, taking into account the user's personal data, past task performance, and priority. The system consists of three main components: a server, a terminal, and a user. The server is responsible for data processing and scheduling, while the terminal provides an interface with the user, who then uses the system to manage tasks.

[0987] 1. Collection of User Information

[0988] When a user logs in to the system, the server first obtains the user's registration information (such as name, occupation, and areas of interest). It also collects past task data (such as the content, time required, and completion status of previously completed tasks) from the database. It also connects via API with external calendar services (such as Google Calendar or Microsoft Outlook) to obtain the user's current schedule.

[0989] 2. Priority and performance analysis

[0990] The server calculates the priority of the user's tasks based on the collected information. This is achieved by taking into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a specific task. For example, if a user previously spent five hours on a document creation task, it will expect a similar amount of time in the future.

[0991] 3. Automatic task scheduling

[0992] The server divides the listed tasks into multiple parts (e.g., research, writing, review), allocates appropriate time to each part, and then automatically inserts each part into the calendar so that it does not overlap with existing schedules, allowing users to efficiently manage their schedules.

[0993] 4. Integration with external services

[0994] The server continuously synchronizes with an external calendar service, ensuring that the user's schedule is always up-to-date, even when updates are made to their schedule. Furthermore, the server automatically retrieves relevant reference materials and data from the Internet based on the task theme, and provides them to the user as needed.

[0995] 5. Interacting with a conversational AI assistant

[0996] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant then explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the user's request and automatically updates the calendar.

[0997] Specific examples

[0998] For example, if a user adds a task to their calendar that reads, "Submit materials for the generative AI contest in 10 days," the system will act as follows:

[0999] 1. The server predicts the required time based on the user's past performance in document creation tasks.

[1000] 2. The server divides the document creation task into three parts: research, writing, and review, and allocates a predicted time to each part.

[1001] 3. The server automatically inserts each part into the calendar, adjusting it to the user's existing schedule.

[1002] 4. By linking with external services, relevant reference materials are automatically extracted from the Internet and provided to users.

[1003] 5. If the user wants to check the progress, they can ask the AI ​​assistant through their device, and the AI ​​assistant will explain the current progress and the next task to be tackled.

[1004] In this way, the system optimizes users' task management and efficient scheduling, helping them improve their daily productivity.

[1005] The processing flow will be explained below.

[1006] Specific processing flow of the program

[1007] 1. Collection of User Information

[1008] Step 1:

[1009] A user logs in to a device and enters registration information, including basic profile information such as name, occupation, and areas of interest.

[1010] Step 2:

[1011] The terminal sends the entered user information to the server, which stores this information in a database.

[1012] Step 3:

[1013] The server retrieves the user's past task data from the database and links it to the user's profile.

[1014] Step 4:

[1015] The server connects to an external calendar service (such as Google Calendar or Microsoft Outlook) via API to obtain the user's current schedule.

[1016] 2. Priority and performance analysis

[1017] Step 1:

[1018] The server analyzes the importance and deadlines of tasks based on the acquired schedule information and past task data.

[1019] Step 2:

[1020] The server calculates the priority of each task based on the analysis results, which is determined by comprehensively evaluating the importance, proximity of the deadline, urgency, and other factors.

[1021] Step 3:

[1022] The server uses past performance data to predict the average time required for each task. For example, if a previous document creation task took five hours, it predicts that a similar amount of time will be required this time.

[1023] 3. Automatic task scheduling

[1024] Step 1:

[1025] The server receives new tasks that users have entered into their calendars (e.g., the deadline for submitting materials is in 10 days).

[1026] Step 2:

[1027] The server breaks the task down into specific parts (e.g., research, writing, review) and allocates the required time for each.

[1028] Step 3:

[1029] The server automatically inserts each part into the calendar, taking into account the user's current schedule, and places them so that they do not overlap with existing important events.

[1030] 4. Integration with external services

[1031] Step 1:

[1032] The server constantly synchronizes with external calendar services (Google Calendar and Microsoft Outlook).

[1033] Step 2:

[1034] The server automatically extracts relevant reference materials and data from the Internet based on the task theme and provides them to the user, for example, by collecting articles from papers and databases related to the topic of document creation.

[1035] 5. Interacting with a conversational AI assistant

[1036] Step 1:

[1037] The user makes an inquiry such as "Tell me about the progress of the task" through the terminal.

[1038] Step 2:

[1039] The device sends the query to the server, which retrieves the latest task progress and returns the data to the device.

[1040] Step 3:

[1041] Based on the data it collects, the AI ​​assistant will report to the user verbally or in text about its progress and the next task to tackle.

[1042] Step 4:

[1043] When a user requests a change in schedule (e.g., "Tomorrow's meeting has been canceled, so I would like to prepare the materials earlier"), the server receives the request and generates a new schedule proposal.

[1044] Step 5:

[1045] The server updates the calendar with the new schedule and prompts the user for confirmation.

[1046] Through this processing flow, the system optimizes users' task management and efficient scheduling, helping to improve their daily productivity.

[1047] Example 1

[1048] 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."

[1049] In today's busy daily lives, users need to efficiently manage tasks and increase productivity. However, manual scheduling and task management is time-consuming and inefficient, and integrating with external calendar services and data sources is complicated. Given this background, it is important to provide a task management system that is easy for users to use, fast, and accurate.

[1050] 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.

[1051] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring the user's schedule data, means for calculating task priorities based on the acquired information, means for predicting the time required for each task, means for dividing the task into multiple parts and allocating the time, means for automatically inserting the task into the schedule based on the allocation, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, and means for communicating with the user to report the progress of the task. This allows users to efficiently manage tasks and easily integrate with external calendars, thereby improving productivity.

[1052] "Means for obtaining user registration information" refers to a function that obtains information such as the name, occupation, and areas of interest of users who log in to the system from a database.

[1053] "Means for obtaining the user's past task data" refers to a function that collects from a database the content, time required, completion status, etc. of tasks that the user has completed in the past.

[1054] "Means for obtaining user schedule data" refers to a function for obtaining the user's current and future schedules from external calendar services, etc.

[1055] The "means for calculating task priorities" is a function that calculates priorities based on collected information, taking into account the importance, deadline, urgency, etc. of tasks.

[1056] "Means for predicting task duration" is a function that analyzes a user's past task performance and estimates the time required for a specific task.

[1057] "A means of dividing tasks into multiple parts and allocating time" is a function that divides listed tasks into multiple parts such as research, writing, and review, and allocates appropriate time to each part.

[1058] "Means for automatically inserting tasks into the schedule" is a function that automatically registers each part in the calendar while coordinating with the user's existing schedule.

[1059] "Means for synchronizing with external calendar services" refers to a function that synchronizes users' schedule data by linking with external calendar services such as Google Calendar and Microsoft Outlook.

[1060] "Means for automatically extracting relevant reference materials and data" refers to a function that automatically collects reference materials and data related to the user's task from the Internet and other data sources and provides them to the user.

[1061] "Means of interacting with the user and reporting task progress" refers to a function of an interactive AI assistant that responds to inquiries from the user via the device and reports the progress of the current task and the next task to be tackled.

[1062] The following describes in detail the specific program processing for implementing the present invention. This system is a calendar service that automatically schedules tasks taking into account the user's personal data, past task performance, and priority. The system consists of three main components: a server, a terminal, and a user. The server is responsible for data processing and scheduling, while the terminal provides an interface with the user, who uses the system to manage tasks.

[1063] Collection of User Information

[1064] When a user logs in to the system, the server first obtains the user's registration information (such as name, occupation, and areas of interest). To do this, the server retrieves the necessary information from the database using the user ID as a key. For example, if the user is registered as a "software engineer," that information is retrieved. The server also collects the user's past task data from the database. This data includes tasks that have been completed in the past, the time required, and the completion status. The server also connects via API to external calendar services such as Google Calendar and Microsoft Outlook to obtain the user's current schedule.

[1065] Priority and performance analysis

[1066] The server calculates the priority of the user's tasks based on the collected information. This is done using an algorithm that takes into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a particular task. For example, if a user previously spent five hours on a document creation task, the server will estimate a similar amount of time in the future.

[1067] Automatic task scheduling

[1068] The server divides the listed tasks into multiple parts, such as research, writing, and review, and allocates appropriate time to each part. It then automatically inserts each part into the calendar so that it does not overlap with existing schedules. If a user has free time from 10:00 to 12:00 on Monday and 14:00 to 17:00 on Tuesday, the server will assign research from 10:00 to 12:00 on Monday and writing from 14:00 to 17:00 on Tuesday.

[1069] Integration with external services

[1070] The server continuously synchronizes with external calendar services such as Google Calendar and Microsoft Outlook, ensuring that users' calendars are kept up to date with any changes to their schedules. Additionally, based on the topic of the task, the app automatically retrieves relevant references and data from the internet and provides them to users.

[1071] Interacting with a conversational AI assistant

[1072] When a user inquires about task progress or changes through their device, the device sends that information to the server, which obtains the latest task progress. The AI ​​assistant explains the user's current progress and the next task to tackle. For example, if the user types "What should I do next?" into the device, the server will respond with "Next, please work on reviewing the document." If the user requests a schedule change, the server will generate a new schedule proposal based on that request and automatically update the calendar.

[1073] Specific use cases

[1074] For example, if a user adds the task "Submit materials for a generative AI contest in 10 days" to their calendar, the system works as follows: First, the server predicts the required time based on the user's past performance in document creation tasks. Then, it divides the document creation task into three parts - research, writing, and review - and allocates the predicted time to each part. Next, it automatically inserts each part into the calendar, aligning it with the user's existing schedule. In addition, it extracts relevant reference materials from the internet and provides them to the user. If the user wants to check their progress, they can inquire about it through their device, and the AI ​​assistant will explain their current progress and the next task they should tackle.

[1075] Prompt Sentence Examples

[1076] Here is an example of a prompt that a user might enter into the system:

[1077] "Schedule submission of materials for the Generative AI contest in 10 days."

[1078] In this way, the system optimizes users' task management and efficient scheduling, helping them improve their daily productivity.

[1079] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1080] Step 1:

[1081] User login and information collection

[1082] Input: User login information (user ID, password)

[1083] Output: User registration information, past task data, schedule data

[1084] When a user logs in to the system, the server first retrieves the user's registration information (such as name, occupation, and areas of interest) from the database. It also collects the user's past task data (such as the content, time required, and completion status of completed tasks) from the database. It then uses the APIs of Google Calendar and Microsoft Outlook to retrieve the user's current schedule data. Specifically, the server queries the database using the user ID as a key to extract the required information.

[1085] Step 2:

[1086] Task Priority Calculation

[1087] Input: Registration information, past task data, schedule data

[1088] Output: A prioritized task list

[1089] The server calculates the priority of the user's tasks based on the collected information. The calculation takes into account factors such as the task's importance, deadline, and urgency. For example, if a document creation task has an importance level of 8, a deadline in one week, and an urgency level of 5, the server calculates an overall score based on these parameters. As a result, a prioritized task list is generated.

[1090] Step 3:

[1091] Estimated travel time

[1092] Input: Past task data, new task list

[1093] Output: A list of tasks with estimated durations

[1094] The server analyzes past task performance and predicts the time required for new tasks. For example, if a user previously spent five hours on a document creation task, the server predicts that the current task will take a similar amount of time based on that performance data. This generates a task list with predicted completion times.

[1095] Step 4:

[1096] Task division and time allocation

[1097] Input: Task list with estimated duration, user schedule data

[1098] Output: Time-allocated task list

[1099] The server divides the listed tasks into multiple parts, such as research, writing, and review, and allocates appropriate time to each part. For example, it divides document creation into research (2 hours), writing (3 hours), and review (1 hour). Then, it automatically inserts each part into the calendar while adjusting it with the user's schedule data.

[1100] Step 5:

[1101] Automatic insertion into the calendar

[1102] Input: Time-phased task list, user schedule data

[1103] Output: Updated calendar

[1104] The server automatically inserts each part into the calendar so that it doesn't overlap with the user's existing schedule. For example, if there is free time between 10:00 and 12:00 on Monday, the server will schedule the survey for that time. The result is an updated calendar.

[1105] Step 6:

[1106] Integration with external services

[1107] Input: User's task list, related keywords

[1108] Output: Related references and resources

[1109] The server continuously synchronizes with external calendar services such as Google Calendar and Microsoft Outlook. It also automatically retrieves reference materials and data related to the user's tasks from the Internet and provides them to the user. For example, it uses the Google Search API to collect materials related to "document creation" from the Internet.

[1110] Step 7:

[1111] Task progress reporting and interaction

[1112] Input: User's query, latest task list

[1113] Output: Progress report, next task suggestions

[1114] When a user inquires about the progress or changes of a task through their device, the device sends that information to the server, which retrieves the latest task progress. The server then explains the current progress and the next task to the user through the AI ​​assistant. For example, if a user types "What should I do next?", the server will respond with "Next, please work on reviewing the document creation."

[1115] (Application example 1)

[1116] 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."

[1117] In modern factories, improving the efficiency of production plans and optimizing employee work schedules are important issues. However, manual scheduling and individual task management require time and effort, and there are limits to how much can be done. Furthermore, updating schedules in real time while linking with external calendar services is difficult, which can easily lead to information discrepancies at the production site. For this reason, there is a demand for a system that automatically generates schedules based on factory operation data and employee past work performance, and updates them in real time.

[1118] 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.

[1119] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring schedules from the user's calendar, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into the calendar, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, means for communicating with the user to report task progress, means for automatically generating a work schedule based on factory operation data and employee task performance, and means for updating employee work schedules in real time to streamline factory production planning. This enables the automatic generation of efficient work schedules that are updated in real time based on the factory's operation status.

[1120] "Means for obtaining user registration information" refers to a system or method for obtaining basic information such as a user's name, occupation, and role from a database.

[1121] "Means for obtaining a user's past task data" refers to a system or method for obtaining detailed information (content, required time, completion status, etc.) of tasks that a user has performed in the past from a database.

[1122] A "means for retrieving events from a user's calendar" is a system or method for retrieving current events and schedules from a calendar service used by a user.

[1123] A "means for calculating task priority" is a system or method for numerically evaluating the priority of a task based on various factors such as the importance, deadline, and urgency of the task.

[1124] A "means for predicting the time required for a task" is a system or method for predicting the time required to complete a similar task based on performance data of past tasks.

[1125] "Means for allocating task time and automatically inserting it into a calendar" refers to a system or method for automatically adding the optimal schedule to a user's calendar based on the calculated task duration and priority.

[1126] "Means for synchronizing with an external calendar service" means a system or method for synchronizing data with an external calendar service, such as Google Calendar or Microsoft Outlook.

[1127] "Means for automatically extracting relevant reference materials and data" refers to a system or method for automatically retrieving and providing relevant materials and data from the Internet or databases based on the user's task content or theme.

[1128] "Means for communicating with the user and reporting task progress" refers to a system or method by which an AI assistant communicates with the user to report the progress of the current task and the next task to be tackled.

[1129] "Means for automatically generating work schedules based on factory operation data and employee task performance" refers to a system or method that automatically generates an optimal work schedule based on the operation status of factory equipment and employees' past work history.

[1130] "Means for updating employee work schedules in real time to streamline factory production plans" refers to a system or method for updating and optimizing employee work schedules in real time in response to changes in and progress in the factory's production plans.

[1131] The mode for carrying out the present invention will be described in detail below: The system required for carrying out the invention consists of three main components: a server, a terminal, and a user.

[1132] System Overview

[1133] This system is a management service that automatically generates work schedules based on factory operation data and employee task performance, and updates employee work schedules in real time to streamline production planning. The system processes data and performs scheduling on the server, and provides a user interface on the terminal. The server also connects to an external calendar service and interacts with users via an AI assistant.

[1134] 1. Collection of User Information

[1135] When a user logs in to the system, the server first retrieves the user's registration information (such as name, occupation, and role) from the database. It also collects past task data (such as the content, time required, and completion status of past tasks) from the database. It also retrieves the user's current schedule through API integration with external calendar services (such as Google Calendar and Microsoft Outlook).

[1136] 2. Priority and performance analysis

[1137] The server calculates the priority of each user's task based on the collected information. This is achieved by taking into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a specific task. For example, if an employee previously spent three hours on machine maintenance, it will expect a similar amount of time in the future.

[1138] 3. Automatic task scheduling

[1139] The server divides the listed tasks into multiple parts (e.g., research, work, confirmation), allocates appropriate time to each part, and then automatically inserts each part into the calendar so that it does not overlap with existing schedules, allowing employees to efficiently manage their work schedules.

[1140] 4. Integration with external services

[1141] The server continuously synchronizes with an external calendar service, ensuring that employee schedules are always up-to-date when they are updated. Furthermore, the system automatically retrieves relevant reference materials and data from the Internet based on the task topic, and provides them to users as needed.

[1142] 5. Interacting with a conversational AI assistant

[1143] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant then explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the user's request and automatically updates the calendar.

[1144] Specific examples

[1145] For example, if a user adds a task to their calendar that reads, "Submit materials for the generative AI contest in 10 days," the system will act as follows:

[1146] 1. The server predicts the required time based on the user's past performance in document creation tasks.

[1147] 2. The server divides the document creation task into three parts: research, writing, and review, and allocates a predicted time to each part.

[1148] 3. The server automatically inserts each part into the calendar, adjusting it to the user's existing schedule.

[1149] 4. By linking with external services, relevant reference materials are automatically extracted from the Internet and provided to users.

[1150] 5. If the user wants to check the progress, they can ask the AI ​​assistant through their device, and the AI ​​assistant will explain the current progress and the next task to be tackled.

[1151] Prompt Sentence Examples

[1152] Here is an example of a real prompt:

[1153] Below is an example of task scheduling. If a user adds a task "Submit materials for the Generative AI Contest in 10 days," please generate the optimal schedule based on the following information:

[1154] Task name: Generative AI Contest Materials

[1155] Submission deadline: 10 days later

[1156] Task details:

[1157] Investigation: 2 hours

[1158] Writing: 3 hours

[1159] Review: 2 hours

[1160] Past task history:

[1161] Task A (2 hours, 3 hours, 2 hours)

[1162] Task B (1 hour, 1.5 hours, 1.2 hours)

[1163] Current schedule:

[1164] November 20th 09:00 - 11:00: Available

[1165] November 21st 13:00 - 16:00: Available

[1166] November 22nd 10:00 - 12:00: Available

[1167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1168] Step 1:

[1169] Collection of user registration information

[1170] When a user logs in to the system, the server first retrieves the user's registration information from the database. Specifically, it retrieves basic information such as the user's name, occupation, and role. This allows the user's basic profile to be collected as input data. The output is a dataset containing the user's basic information.

[1171] Step 2:

[1172] Retrieving past task data

[1173] The server retrieves data on tasks the user has performed in the past from a database, including task content, required time, and completion status. This provides the user's past task history as input data. The output is detailed data on each individual task.

[1174] Step 3:

[1175] Get a user's calendar events

[1176] The server connects to an external calendar service via API to obtain the user's current schedule. Specifically, it obtains the user's schedule data from Google Calendar or Microsoft Outlook via API. This obtains the user's current schedule as input data. The output is the user's current schedule information.

[1177] Step 4:

[1178] Calculate task priorities

[1179] The server calculates task priorities based on the collected user basic information, past task data, and current schedule data. This takes into account factors such as the task's importance, deadline, and urgency. Specifically, the priority is numerically evaluated by multiplying the importance and urgency coefficients. The input data is processed, and the task priority is obtained as the output.

[1180] Step 5:

[1181] Task duration prediction

[1182] The server analyzes past task data and predicts the time required for a specific task. It uses statistical methods based on past performance data to predict the average time required for similar tasks. It processes the input historical data and outputs the predicted time required.

[1183] Step 6:

[1184] Time allocation and auto-scheduling of tasks

[1185] The server allocates each task to an appropriate time slot based on the priority and estimated time required, and automatically inserts it into the calendar. Specifically, it detects the user's free time slots and assigns tasks to those slots. Scheduling is performed based on the input data, and a new schedule is generated as the output.

[1186] Step 7:

[1187] Synchronization with external calendar services

[1188] The server synchronizes the new schedule with the external calendar service, sending the schedule data using an API to update the user's calendar. The newly generated schedule is taken as input and reflected in the external calendar service as output.

[1189] Step 8:

[1190] Automatic extraction of references and data

[1191] The server automatically extracts relevant reference materials and data from the Internet and databases based on the task theme. For example, it searches for papers and materials related to generative AI contests and provides them to users. It obtains relevant keywords from the input and provides resources as output.

[1192] Step 9:

[1193] Interacting with a conversational AI assistant

[1194] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the request and automatically updates the calendar. The server receives user inquiries and change requests as input and provides the latest schedule and progress as output.

[1195] 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.

[1196] The following describes in detail in natural language the processing of a specific program for carrying out the present invention, particularly the operation of a system that combines an emotion engine that recognizes the user's emotions.

[1197] System Overview

[1198] This system is an innovative calendar service that automatically schedules tasks based on the user's personal data, past task performance, and priorities. In addition, it incorporates an emotion engine that recognizes the user's emotions and adjusts task management according to their emotional state.

[1199] 1. Collection of User Information

[1200] A user logs in to their device and enters their registration information, including basic profile information such as name, occupation, and areas of interest. The device then sends the information to the server, which stores it in a database. The server also retrieves the user's past task data and schedules from their existing calendar.

[1201] 2. Priority and performance analysis

[1202] The server analyzes the importance and deadline of each task based on the collected user information and past task performance. This determines the priority of the task and calculates the specific estimated time. It receives new tasks entered by the user in the calendar, divides them into multiple parts, and allocates the time accordingly.

[1203] 3. Automatic task scheduling

[1204] The server automatically schedules tasks into the user's calendar based on the calculated priority and estimated time, achieving optimal scheduling without conflicting with existing schedules.

[1205] 4. Integration with external services

[1206] The server synchronizes with external calendar services (e.g., Google Calendar, Microsoft Outlook) to keep schedule information up to date, and automatically retrieves reference materials and data related to specific tasks from the Internet and provides them to users.

[1207] 5. Introduction of Emotion Engine and Dialogue Interface

[1208] The device is equipped with an emotion engine that monitors emotions from the user's input and actions, analyzing the user's facial expressions, voice tone, input patterns, etc. to recognize emotions in real time.

[1209] 6. Adjusting task management based on emotions

[1210] The server performs the following actions based on the emotion data received from the emotion engine:

[1211] 1. Adjust task priorities: If a user is stressed, they can postpone less important tasks and prioritize relaxing tasks.

[1212] 2. Reminder Modification: If the user is relaxed, send reminders at different times.

[1213] 3. Break Suggestion: If the user is recognized as tired, it will suggest an appropriate break.

[1214] Specific examples

[1215] As a concrete example, let's explain the process when a user adds a task "Submit materials for the generative AI contest in 10 days":

[1216] 1. The server obtains data on the user's past document creation tasks and predicts the required time.

[1217] 2. The server divides the document creation into three parts: research, writing, and review, and estimates the time required for each.

[1218] 3. The server automatically inserts each part into the calendar, adjusting it to the user's current schedule.

[1219] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or setting a reminder if they are relaxed.

[1220] 5. When the user queries the device to check the progress, the AI ​​assistant retrieves the necessary information from the server and reports it to the user.

[1221] 6. The server synchronizes with an external calendar service, automatically collects relevant reference materials from the Internet, and provides them to the user.

[1222] With these features, the system not only provides users with task management and efficient scheduling, but also realizes flexible task management that takes into account users' emotions, thereby improving users' daily productivity and satisfaction.

[1223] The processing flow will be explained below.

[1224] Specific processing flow of the program (system combining emotion engine)

[1225] 1. Collection of User Information

[1226] Step 1:

[1227] A user logs in to a device and enters registration information, including basic profile information such as name, occupation, and areas of interest.

[1228] Step 2:

[1229] The terminal sends the entered user information to the server, which stores this information in a database.

[1230] Step 3:

[1231] The server retrieves the user's past task data from the database and links it to the user's profile.

[1232] Step 4:

[1233] The server connects to an external calendar service (such as Google Calendar or Microsoft Outlook) via API to obtain the user's current schedule.

[1234] 2. Priority and performance analysis

[1235] Step 1:

[1236] The server analyzes the importance and deadlines of tasks based on the acquired schedule information and past task data.

[1237] Step 2:

[1238] The server calculates the priority of each task based on the analysis results, which is determined by comprehensively evaluating the importance, proximity of the deadline, urgency, and other factors.

[1239] Step 3:

[1240] The server uses past performance data to predict the average time required for each task. For example, if a previous document creation task took five hours, it predicts that a similar amount of time will be required this time.

[1241] 3. Automatic task scheduling

[1242] Step 1:

[1243] The server receives new tasks that users have entered into their calendars (e.g., the deadline for submitting materials is in 10 days).

[1244] Step 2:

[1245] The server breaks the task down into specific parts (e.g., research, writing, review) and allocates the required time for each.

[1246] Step 3:

[1247] The server automatically inserts each part into the calendar, taking into account the user's current schedule, and places them so that they do not overlap with existing important events.

[1248] 4. Integration with external services

[1249] Step 1:

[1250] The server constantly synchronizes with external calendar services (Google Calendar and Microsoft Outlook).

[1251] Step 2:

[1252] The server automatically extracts relevant reference materials and data from the Internet based on the task theme and provides them to the user, for example, collecting articles from papers and databases related to the topic of document creation.

[1253] 5. Introduction of Emotion Engine and Dialogue Interface

[1254] Step 1:

[1255] The device is equipped with an emotion engine that monitors emotions from the user's input and actions, analyzing the user's facial expressions, voice tone, input patterns, etc.

[1256] Step 2:

[1257] The device sends the analysis results to a server, which then uses the received emotional data to understand the user's emotional state in real time.

[1258] 6. Adjusting task management based on emotions

[1259] Step 1:

[1260] The server adjusts task priorities based on data from the emotion engine: for example, if the user is feeling stressed, it will postpone less important tasks and prioritize relaxing tasks.

[1261] Step 2:

[1262] The server changes the timing of reminders depending on the user's emotional state: for example, if the user is relaxed, it sends a reminder earlier than usual.

[1263] Step 3:

[1264] The server detects the user's fatigue level and suggests appropriate break times, and if the user works for a long time, it sends regular break reminders.

[1265] Specific examples

[1266] Example 1:

[1267] If a user adds a task saying "Submit materials for the Generative AI Contest in 10 days":

[1268] 1. The server obtains data on the user's past document creation tasks and predicts the required time.

[1269] 2. The server divides the document creation into three parts: research, writing, and review, and estimates the time required for each.

[1270] 3. The server automatically inserts each part into the calendar, adjusting it to the user's current schedule.

[1271] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or adjusting reminders if they are relaxed.

[1272] 5. When the user queries the device to check the progress, the AI ​​assistant retrieves the necessary information from the server and reports it to the user.

[1273] 6. The server synchronizes with an external calendar service, automatically collects relevant reference materials from the Internet, and provides them to the user.

[1274] This not only supports users' task management and efficient scheduling, but also enables flexible task management that takes users' emotions into consideration.

[1275] Example 2

[1276] 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."

[1277] In today's busy society, users are required to efficiently manage numerous tasks. However, manual task scheduling is cumbersome and time-consuming, and it is easy for schedule overlaps and important tasks to be overlooked. Furthermore, current systems have difficulty flexibly adjusting tasks based on the user's emotional state. In particular, when users are stressed or tired, they need to take breaks at appropriate times and set reminders, but current systems have difficulty automatically performing these tasks.

[1278] 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 acquiring basic information of the user, a means for acquiring past task data of the user, and a means for acquiring schedule information of the user. This improves the efficiency of task management for the user and enables flexible task adjustment according to the emotional state.

[1279] "Basic user information" refers to basic information about a user, such as name, occupation, and areas of interest.

[1280] "User's past task data" refers to information about tasks that the user has performed in the past, including the content of the task, the time required, and the degree of completion.

[1281] "User's schedule information" refers to the user's current and future plans and appointments.

[1282] "Calculating task priorities" refers to evaluating and ranking the importance and deadlines of each task based on the collected information.

[1283] "Predicting task duration" refers to using historical data and algorithms to estimate the amount of time each task will require.

[1284] "Divide into subtasks and allocate time" refers to dividing a task into smaller units of work and allocating appropriate time to each part.

[1285] "External schedule management services" refers to external schedule management systems such as Google Calendar and Microsoft Outlook.

[1286] "Automatically collecting relevant references and data from the internet" refers to using web scraping or APIs to extract information from the internet that is relevant to a specific task.

[1287] "Analyzing user emotions" refers to analyzing data such as facial expressions, voice tone, and input patterns to understand the user's emotional state in real time.

[1288] "Adjusting task management based on the user's emotions" refers to dynamically changing task priorities, time allocation, reminder settings, break suggestions, etc. according to the user's emotional state.

[1289] The following describes in detail in natural language the processing of a specific program for carrying out the present invention, particularly the operation of a system that combines an emotion engine that recognizes the user's emotions.

[1290] System Overview

[1291] This system is an innovative calendar service that automatically schedules tasks based on the user's personal data, past task performance, and priorities. In addition, it incorporates an emotion engine that recognizes the user's emotions and adjusts task management according to their emotional state.

[1292] Collection of User Information

[1293] A user logs in to a device and enters basic profile information such as name, occupation, and areas of interest. The device sends this information to the server. The server stores the received user information in a database (e.g., MySQL or PostgreSQL) and retrieves the user's past task data and existing schedule information using an API.

[1294] Priority and performance analysis

[1295] The server processes the collected user information and past task results using analytical tools such as Apache Spark and TensorFlow. This evaluates the importance and deadlines of tasks and determines their priorities. When a user enters a new task, it divides it into multiple subtasks (e.g., research, writing, review) and calculates the time required for each.

[1296] Automatic task scheduling

[1297] The server automatically schedules tasks based on their calculated priority and estimated time, using an algorithm to place them in the optimal time slots so that they do not conflict with existing schedules.

[1298] Integration with external services

[1299] The server uses OAuth to connect to external schedule management services (e.g., Google Calendar, Microsoft Outlook) and synchronizes information bidirectionally. It also has the ability to automatically retrieve reference materials and data related to specific tasks from the Internet via web scraping or APIs.

[1300] Introducing an emotion engine and a dialogue interface

[1301] The device monitors the user's emotions using an emotion engine, which uses image analysis libraries such as OpenCV to analyze the user's facial expressions, voice tone, and input patterns to recognize emotions in real time.

[1302] Emotion-based adjustment of task management

[1303] The server adjusts task management based on the data received from the emotion engine: if the user is stressed, it postpones less important tasks and prioritizes relaxing tasks, if the user is relaxed, it sends reminders at different times, and if the user is perceived as tired, it suggests appropriate breaks.

[1304] Specific examples

[1305] Let's explain the process when a user adds a task "Submit materials for the generative AI contest in 10 days":

[1306] 1. The server obtains data on the user's past document creation tasks.

[1307] 2. The server divides the document creation into three subtasks: research, writing, and review, and estimates the time required for each.

[1308] 3. The server coordinates with the user's current schedule and automatically inserts each subtask into the schedule.

[1309] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or setting a reminder if they are relaxed.

[1310] 5. The user queries the terminal to check progress.

[1311] 6. The server synchronizes with an external schedule management service and collects relevant reference materials from the Internet.

[1312] Prompt Sentence Examples

[1313] "Create a schedule for creating materials for the next 10 days for the Generative AI Contest. Use past data on creating materials to predict the time required and suggest appropriate breaks."

[1314] This system improves the efficiency of task management and enables flexible scheduling that takes emotions into account, thereby improving user productivity and satisfaction.

[1315] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1316] Step 1: Collect user information

[1317] The user enters their ID and password on the login screen and is authenticated.

[1318] Input: ID, password

[1319] Output: Authenticated user session

[1320] The user enters basic information (name, occupation, interests).

[1321] Input: Basic information (name, occupation, areas of interest)

[1322] Output: User basic information data

[1323] The terminal transmits the input information to the server.

[1324] Input: User basic information

[1325] Output: Basic information data transferred to the server

[1326] The server stores the received basic information in a database.

[1327] Input: Basic information data

[1328] Output: User information stored in the database

[1329] The server uses an API to retrieve the user's past task data and existing schedule information.

[1330] Input: API request

[1331] Output: Past task data, existing schedule information

[1332] Step 2: Analyze priorities and performance

[1333] The server processes the collected data using analysis tools (e.g., Apache Spark, TensorFlow).

[1334] Input: User basic information, past task data, existing schedule information

[1335] Output: Analysis results (task importance, deadline)

[1336] The server calculates the priority of the task.

[1337] Input: Analysis results (task importance, deadline)

[1338] Output: Priority list

[1339] The server divides a newly input task into multiple subtasks and predicts the required time.

[1340] Input: New task data

[1341] Output: Subtask list and estimated duration

[1342] Step 3: Automatically schedule tasks

[1343] The server schedules tasks based on priority and estimated duration.

[1344] Inputs: Subtask list, estimated duration, priority list, existing schedule

[1345] Output: Updated schedule data

[1346] The server places tasks at different times to prevent schedule overlaps.

[1347] Input: Updated schedule data

[1348] Output: Optimized schedule

[1349] Step 4: Integrate with external services

[1350] The server synchronizes with external scheduling services (e.g., Google Calendar, Microsoft Outlook).

[1351] Input: OAuth token, schedule data

[1352] Output: Schedules synced to external services

[1353] The server uses web scraping or APIs to gather material from the internet relevant to a specific task.

[1354] Input: Task identification information

[1355] Output: Collected reference data

[1356] Step 5: Introducing the emotion engine and dialogue interface

[1357] The device uses an emotion engine (e.g., OpenCV) to monitor the user's emotions.

[1358] Input: User's facial expression data, voice data, input pattern data

[1359] Output: Emotion recognition result

[1360] The device analyzes emotions in real time and sends the data to a server.

[1361] Input: Emotion recognition results

[1362] Output: Emotion data sent to the server

[1363] Step 6: Adjust your task management based on emotions

[1364] The server adjusts task management based on the emotion data.

[1365] Input: Emotion data, task data, schedule data

[1366] Output: Adjusted task schedule

[1367] If the user is feeling stressed, the server will postpone less important tasks and prioritize relaxing tasks.

[1368] Input: Emotion data, task priority data

[1369] Output: Adjusted priority list

[1370] The server will suggest appropriate breaks if the user is tired.

[1371] Input: Emotion data

[1372] Output: Break suggestion notification

[1373] The server will send reminders at different times if you are relaxed.

[1374] Input: Emotion data, reminder setting data

[1375] Output: Reminders sent

[1376] (Application example 2)

[1377] 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."

[1378] While conventional task management systems can efficiently manage users' schedules, they have the problem of making users feel stressed because they schedule without taking into account their emotional state.In addition, they lack a function to suggest breaks at appropriate times or adjust task priorities based on emotions, making it difficult to fully improve user productivity and satisfaction.

[1379] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1380] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring the user's schedule information, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into the schedule, means for synchronizing with an external schedule service, means for automatically extracting related reference materials and data, means for communicating with the user and reporting task progress, means for recognizing the user's emotional state, means for adjusting task priorities and reminders based on the emotional state, and means for suggesting breaks the user needs. This enables flexible task management that takes the user's emotional state into consideration.

[1381] "Means for obtaining user registration information" refers to the method for sending the user's basic profile information (such as name, occupation, areas of interest, etc.) to the server and storing it in the database.

[1382] The "means for acquiring the user's past task data" is a method for acquiring the tasks that the user has performed in the past and their results from the database.

[1383] "Means for obtaining user schedule information" refers to a method for obtaining the user's current and future schedules from a calendar or schedule application.

[1384] The "means for calculating task priorities based on acquired information" is a method for determining priorities by analyzing the importance and deadlines of tasks based on collected user information and past task performance.

[1385] A "means for predicting the time required for a task" is a method for predicting the time required for each task based on data such as the user's past task performance.

[1386] The "means for allocating task time and automatically inserting tasks into a schedule" is a method for appropriately allocating tasks into a user's schedule based on the calculated priorities and estimated times.

[1387] "Means for synchronizing with external scheduling services" refers to methods for synchronizing data with external calendar or scheduling services (e.g., Google Calendar or Microsoft Outlook).

[1388] "Means for automatically extracting relevant reference materials and data" refers to a method for automatically collecting information and data related to a specific task from the Internet, etc.

[1389] The "means for communicating with the user and reporting the progress of the task" refers to a method for obtaining the necessary information from the server and reporting it when the user inquires about the progress.

[1390] "Means for recognizing the user's emotional state" refers to a method for recognizing the user's emotions in real time by analyzing facial expressions, voice tone, input patterns, etc.

[1391] "Means for adjusting task priorities and reminders based on emotional state" refers to a method for reevaluating the importance of tasks and changing the timing of reminders depending on the user's emotional state.

[1392] The "means for suggesting a break the user needs" is a method for recognizing the user's emotional state and suggesting an appropriate break if the user feels tired or stressed.

[1393] A specific embodiment of the present invention will be described below. This system is a smart operation management system for autonomous vehicles. In particular, it has the function of recognizing the driver's emotional state and making operation plans and break suggestions based on that state.

[1394] System Overview

[1395] The system optimizes the driving schedule by taking into account the user's (driver's) registration information, past task performance, and emotional state. It uses the following main hardware and software components:

[1396] Camera: Used to capture the driver's facial expressions and analyze their emotions.

[1397] Microphone: Used to analyze the tone of the driver's voice and identify emotions.

[1398] EmotionEngine: Software that recognizes the driver's emotional state in real time based on data obtained from cameras and microphones.

[1399] ScheduleOptimizer: An algorithm that optimizes operation plans and adjusts schedules based on emotional states.

[1400] CalendarSync: Software for synchronizing data with external scheduling services (e.g., Google Calendar or Microsoft Outlook).

[1401] System Operation

[1402] 1. Collection of User Information

[1403] When a driver logs in to the terminal, the driver's registration information (name, route, etc.) is sent to the server, which then stores this information in a database.

[1404] The server acquires past operation performance data and calculates the next operation schedule based on that data.

[1405] 2. Operation scheduling

[1406] The server uses the collected information to optimize the operation schedule, including optimizing the operation route, pick-up time, and drop-off time.

[1407] 3. Emotion recognition

[1408] The device (self-driving vehicle) uses a camera and microphone to monitor the driver's emotional state in real time.

[1409] The EmotionEngine recognizes the driver's emotions (stress, relaxation, etc.) and sends the results to the server.

[1410] 4. Emotion-based operation adjustment

[1411] The server adjusts the trip plan depending on the driver's emotional state, for example, suggesting appropriate breaks if the driver is feeling stressed.

[1412] If the driver is relaxed, they will be notified that they have time until the next pickup time.

[1413] 5. Data synchronization

[1414] The server synchronizes with an external schedule service and always maintains the latest operating schedule.

[1415] Specific scenarios

[1416] Below is a specific scenario that shows how this system works in practice.

[1417] While the driver is traveling on Route A, the server predicts the next pickup time based on the driver's past driving data. If EmotionEngine detects the driver's stress state, the server notifies the driver, suggesting that they take a 10-minute break.

[1418] After the driver takes a break, the server recalculates the next optimal route and synchronizes it with an external scheduling service.

[1419] Prompt Sentence Examples

[1420] Here is an example of a prompt to input to a generative AI model:

[1421] Please provide some ideas on optimizing the operation schedule based on driver emotion recognition. How to adjust the schedule when the driver is stressed, and what to do when the driver is relaxed?

[1422] This enables the system to realize flexible and efficient driving management that takes into account the driver's emotional state.

[1423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1424] Step 1:

[1425] The user's device receives the driver's login information as input. Based on this, the device obtains the driver's registration information and sends it to the server. The server stores the registration information in a database.

[1426] Step 2:

[1427] The server retrieves past task performance data from the database, then retrieves the user's current schedule information from an external schedule service (e.g., Google Calendar), analyzes how much time the user has spent on the task in the past, and predicts the task's priority and required time.

[1428] Step 3:

[1429] The server optimizes the schedule based on the predicted task duration and priority. During this process, it allocates time for tasks and automatically inserts them into the schedule. Specifically, it arranges tasks so that time is used most efficiently.

[1430] Step 4:

[1431] The device uses a camera and microphone to receive the driver's emotional state as input. The EmotionEngine analyzes facial expressions and vocal tone to recognize the driver's emotional state (relaxed, stressed, etc.). This data is sent to the server in real time.

[1432] Step 5:

[1433] The server reevaluates the existing schedule based on the received emotional state data. If the driver is stressed, it postpones low-priority tasks and suggests a break. If the driver is relaxed, it calculates the time lag until the next task pick-up time. This information is then communicated to the driver.

[1434] Step 6:

[1435] The server synchronizes with an external scheduling service, ensuring that schedule data is always up to date and that trip plans are adjusted as needed. It also automatically extracts relevant reference materials and data and provides them to drivers. This procedure ensures that data remains consistent and up to date.

[1436] Step 7:

[1437] When a user queries the device to check the progress, the server retrieves the progress data and reports it to the user, allowing the user to understand the operation status and the next task required in real time.

[1438] Specific actions

[1439] Through these processing steps, the driver can efficiently manage the operation of the autonomous vehicle and flexibly adjust according to their emotional state. The generative AI model will then assist the driver appropriately based on the following prompts:

[1440] Please provide some ideas on optimizing the operation schedule based on driver emotion recognition. How to adjust the schedule when the driver is stressed, and what to do when the driver is relaxed?

[1441] This prompt allows the system to plan an optimal trip that takes into account the driver's emotional state.

[1442] 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.

[1443] 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.

[1444] 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.

[1445] [Fourth embodiment]

[1446] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1447] 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.

[1448] 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).

[1449] 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.

[1450] 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.

[1451] 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).

[1452] 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.

[1453] 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.

[1454] 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.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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."

[1459] In the following, specific program processing will be described in detail in natural language in the mode for carrying out the present invention.

[1460] System Overview

[1461] This system is a calendar service that automatically schedules tasks, taking into account the user's personal data, past task performance, and priority. The system consists of three main components: a server, a terminal, and a user. The server is responsible for data processing and scheduling, while the terminal provides an interface with the user, who then uses the system to manage tasks.

[1462] 1. Collection of User Information

[1463] When a user logs in to the system, the server first obtains the user's registration information (such as name, occupation, and areas of interest). It also collects past task data (such as the content, time required, and completion status of previously completed tasks) from the database. It also connects via API with external calendar services (such as Google Calendar or Microsoft Outlook) to obtain the user's current schedule.

[1464] 2. Priority and performance analysis

[1465] The server calculates the priority of the user's tasks based on the collected information. This is achieved by taking into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a specific task. For example, if a user previously spent five hours on a document creation task, it will expect a similar amount of time in the future.

[1466] 3. Automatic task scheduling

[1467] The server divides the listed tasks into multiple parts (e.g., research, writing, review), allocates appropriate time to each part, and then automatically inserts each part into the calendar so that it does not overlap with existing schedules, allowing users to efficiently manage their schedules.

[1468] 4. Integration with external services

[1469] The server continuously synchronizes with an external calendar service, ensuring that the user's schedule is always up-to-date, even when updates are made to their schedule. Furthermore, the server automatically retrieves relevant reference materials and data from the Internet based on the task theme, and provides them to the user as needed.

[1470] 5. Interacting with a conversational AI assistant

[1471] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant then explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the user's request and automatically updates the calendar.

[1472] Specific examples

[1473] For example, if a user adds a task to their calendar that reads, "Submit materials for the generative AI contest in 10 days," the system will act as follows:

[1474] 1. The server predicts the required time based on the user's past performance in document creation tasks.

[1475] 2. The server divides the document creation task into three parts: research, writing, and review, and allocates a predicted time to each part.

[1476] 3. The server automatically inserts each part into the calendar, adjusting it to the user's existing schedule.

[1477] 4. By linking with external services, relevant reference materials are automatically extracted from the Internet and provided to users.

[1478] 5. If the user wants to check the progress, they can ask the AI ​​assistant through their device, and the AI ​​assistant will explain the current progress and the next task to be tackled.

[1479] In this way, the system optimizes users' task management and efficient scheduling, helping them improve their daily productivity.

[1480] The processing flow will be explained below.

[1481] Specific processing flow of the program

[1482] 1. Collection of User Information

[1483] Step 1:

[1484] A user logs in to a device and enters registration information, including basic profile information such as name, occupation, and areas of interest.

[1485] Step 2:

[1486] The terminal sends the entered user information to the server, which stores this information in a database.

[1487] Step 3:

[1488] The server retrieves the user's past task data from the database and links it to the user's profile.

[1489] Step 4:

[1490] The server connects to an external calendar service (such as Google Calendar or Microsoft Outlook) via API to obtain the user's current schedule.

[1491] 2. Priority and performance analysis

[1492] Step 1:

[1493] The server analyzes the importance and deadlines of tasks based on the acquired schedule information and past task data.

[1494] Step 2:

[1495] The server calculates the priority of each task based on the analysis results, which is determined by comprehensively evaluating the importance, proximity of the deadline, urgency, and other factors.

[1496] Step 3:

[1497] The server uses past performance data to predict the average time required for each task. For example, if a previous document creation task took five hours, it predicts that a similar amount of time will be required this time.

[1498] 3. Automatic task scheduling

[1499] Step 1:

[1500] The server receives new tasks that users have entered into their calendars (e.g., the deadline for submitting materials is in 10 days).

[1501] Step 2:

[1502] The server breaks the task down into specific parts (e.g., research, writing, review) and allocates the required time for each.

[1503] Step 3:

[1504] The server automatically inserts each part into the calendar, taking into account the user's current schedule, and places them so that they do not overlap with existing important events.

[1505] 4. Integration with external services

[1506] Step 1:

[1507] The server constantly synchronizes with external calendar services (Google Calendar and Microsoft Outlook).

[1508] Step 2:

[1509] The server automatically extracts relevant reference materials and data from the Internet based on the task theme and provides them to the user, for example, by collecting articles from papers and databases related to the topic of document creation.

[1510] 5. Interacting with a conversational AI assistant

[1511] Step 1:

[1512] The user makes an inquiry such as "Tell me about the progress of the task" through the terminal.

[1513] Step 2:

[1514] The device sends the query to the server, which retrieves the latest task progress and returns the data to the device.

[1515] Step 3:

[1516] Based on the data it collects, the AI ​​assistant will report to the user verbally or in text about its progress and the next task to tackle.

[1517] Step 4:

[1518] When a user requests a change in schedule (e.g., "Tomorrow's meeting has been canceled, so I would like to prepare the materials earlier"), the server receives the request and generates a new schedule proposal.

[1519] Step 5:

[1520] The server updates the calendar with the new schedule and prompts the user for confirmation.

[1521] Through this processing flow, the system optimizes users' task management and efficient scheduling, helping to improve their daily productivity.

[1522] Example 1

[1523] 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."

[1524] In today's busy daily lives, users need to efficiently manage tasks and increase productivity. However, manual scheduling and task management is time-consuming and inefficient, and integrating with external calendar services and data sources is complicated. Given this background, it is important to provide a task management system that is easy for users to use, fast, and accurate.

[1525] 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.

[1526] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring the user's schedule data, means for calculating task priorities based on the acquired information, means for predicting the time required for each task, means for dividing the task into multiple parts and allocating the time, means for automatically inserting the task into the schedule based on the allocation, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, and means for communicating with the user to report the progress of the task. This allows users to efficiently manage tasks and easily integrate with external calendars, thereby improving productivity.

[1527] "Means for obtaining user registration information" refers to a function that obtains information such as the name, occupation, and areas of interest of users who log in to the system from a database.

[1528] "Means for obtaining the user's past task data" refers to a function that collects from a database the content, time required, completion status, etc. of tasks that the user has completed in the past.

[1529] "Means for obtaining user schedule data" refers to a function for obtaining the user's current and future schedules from external calendar services, etc.

[1530] The "means for calculating task priorities" is a function that calculates priorities based on collected information, taking into account the importance, deadline, urgency, etc. of tasks.

[1531] "Means for predicting task duration" is a function that analyzes a user's past task performance and estimates the time required for a specific task.

[1532] "A means of dividing tasks into multiple parts and allocating time" is a function that divides listed tasks into multiple parts such as research, writing, and review, and allocates appropriate time to each part.

[1533] "Means for automatically inserting tasks into the schedule" is a function that automatically registers each part in the calendar while coordinating with the user's existing schedule.

[1534] "Means for synchronizing with external calendar services" refers to a function that synchronizes users' schedule data by linking with external calendar services such as Google Calendar and Microsoft Outlook.

[1535] "Means for automatically extracting relevant reference materials and data" refers to a function that automatically collects reference materials and data related to the user's task from the Internet and other data sources and provides them to the user.

[1536] "Means of interacting with the user and reporting task progress" refers to a function of an interactive AI assistant that responds to inquiries from the user via the device and reports the progress of the current task and the next task to be tackled.

[1537] The following describes in detail the specific program processing for implementing the present invention. This system is a calendar service that automatically schedules tasks taking into account the user's personal data, past task performance, and priority. The system consists of three main components: a server, a terminal, and a user. The server is responsible for data processing and scheduling, while the terminal provides an interface with the user, who uses the system to manage tasks.

[1538] Collection of User Information

[1539] When a user logs in to the system, the server first obtains the user's registration information (such as name, occupation, and areas of interest). To do this, the server retrieves the necessary information from the database using the user ID as a key. For example, if the user is registered as a "software engineer," that information is retrieved. The server also collects the user's past task data from the database. This data includes tasks that have been completed in the past, the time required, and the completion status. The server also connects via API to external calendar services such as Google Calendar and Microsoft Outlook to obtain the user's current schedule.

[1540] Priority and performance analysis

[1541] The server calculates the priority of the user's tasks based on the collected information. This is done using an algorithm that takes into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a particular task. For example, if a user previously spent five hours on a document creation task, the server will estimate a similar amount of time in the future.

[1542] Automatic task scheduling

[1543] The server divides the listed tasks into multiple parts, such as research, writing, and review, and allocates appropriate time to each part. It then automatically inserts each part into the calendar so that it does not overlap with existing schedules. If a user has free time from 10:00 to 12:00 on Monday and 14:00 to 17:00 on Tuesday, the server will assign research from 10:00 to 12:00 on Monday and writing from 14:00 to 17:00 on Tuesday.

[1544] Integration with external services

[1545] The server continuously synchronizes with external calendar services such as Google Calendar and Microsoft Outlook, ensuring that users' calendars are kept up to date with any changes to their schedules. Additionally, based on the topic of the task, the app automatically retrieves relevant references and data from the internet and provides them to users.

[1546] Interacting with a conversational AI assistant

[1547] When a user inquires about task progress or changes through their device, the device sends that information to the server, which obtains the latest task progress. The AI ​​assistant explains the user's current progress and the next task to tackle. For example, if the user types "What should I do next?" into the device, the server will respond with "Next, please work on reviewing the document." If the user requests a schedule change, the server will generate a new schedule proposal based on that request and automatically update the calendar.

[1548] Specific use cases

[1549] For example, if a user adds the task "Submit materials for a generative AI contest in 10 days" to their calendar, the system works as follows: First, the server predicts the required time based on the user's past performance in document creation tasks. Then, it divides the document creation task into three parts - research, writing, and review - and allocates the predicted time to each part. Next, it automatically inserts each part into the calendar, aligning it with the user's existing schedule. In addition, it extracts relevant reference materials from the internet and provides them to the user. If the user wants to check their progress, they can inquire about it through their device, and the AI ​​assistant will explain their current progress and the next task they should tackle.

[1550] Prompt Sentence Examples

[1551] Here is an example of a prompt that a user might enter into the system:

[1552] "Schedule submission of materials for the Generative AI contest in 10 days."

[1553] In this way, the system optimizes users' task management and efficient scheduling, helping them improve their daily productivity.

[1554] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1555] Step 1:

[1556] User login and information collection

[1557] Input: User login information (user ID, password)

[1558] Output: User registration information, past task data, schedule data

[1559] When a user logs in to the system, the server first retrieves the user's registration information (such as name, occupation, and areas of interest) from the database. It also collects the user's past task data (such as the content, time required, and completion status of completed tasks) from the database. It then uses the APIs of Google Calendar and Microsoft Outlook to retrieve the user's current schedule data. Specifically, the server queries the database using the user ID as a key to extract the required information.

[1560] Step 2:

[1561] Task Priority Calculation

[1562] Input: Registration information, past task data, schedule data

[1563] Output: A prioritized task list

[1564] The server calculates the priority of the user's tasks based on the collected information. The calculation takes into account factors such as the task's importance, deadline, and urgency. For example, if a document creation task has an importance level of 8, a deadline in one week, and an urgency level of 5, the server calculates an overall score based on these parameters. As a result, a prioritized task list is generated.

[1565] Step 3:

[1566] Estimated travel time

[1567] Input: Past task data, new task list

[1568] Output: A list of tasks with estimated durations

[1569] The server analyzes past task performance and predicts the time required for new tasks. For example, if a user previously spent five hours on a document creation task, the server predicts that the current task will take a similar amount of time based on that performance data. This generates a task list with predicted completion times.

[1570] Step 4:

[1571] Task division and time allocation

[1572] Input: Task list with estimated duration, user schedule data

[1573] Output: Time-allocated task list

[1574] The server divides the listed tasks into multiple parts, such as research, writing, and review, and allocates appropriate time to each part. For example, it divides document creation into research (2 hours), writing (3 hours), and review (1 hour). Then, it automatically inserts each part into the calendar while adjusting it with the user's schedule data.

[1575] Step 5:

[1576] Automatic insertion into the calendar

[1577] Input: Time-phased task list, user schedule data

[1578] Output: Updated calendar

[1579] The server automatically inserts each part into the calendar so that it doesn't overlap with the user's existing schedule. For example, if there is free time between 10:00 and 12:00 on Monday, the server will schedule the survey for that time. The result is an updated calendar.

[1580] Step 6:

[1581] Integration with external services

[1582] Input: User's task list, related keywords

[1583] Output: Related references and resources

[1584] The server continuously synchronizes with external calendar services such as Google Calendar and Microsoft Outlook. It also automatically retrieves reference materials and data related to the user's tasks from the Internet and provides them to the user. For example, it uses the Google Search API to collect materials related to "document creation" from the Internet.

[1585] Step 7:

[1586] Task progress reporting and interaction

[1587] Input: User's query, latest task list

[1588] Output: Progress report, next task suggestions

[1589] When a user inquires about the progress or changes of a task through their device, the device sends that information to the server, which retrieves the latest task progress. The server then explains the current progress and the next task to the user through the AI ​​assistant. For example, if a user types "What should I do next?", the server will respond with "Next, please work on reviewing the document creation."

[1590] (Application example 1)

[1591] 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."

[1592] In modern factories, improving the efficiency of production plans and optimizing employee work schedules are important issues. However, manual scheduling and individual task management require time and effort, and there are limits to how much can be done. Furthermore, updating schedules in real time while linking with external calendar services is difficult, which can easily lead to information discrepancies at the production site. For this reason, there is a demand for a system that automatically generates schedules based on factory operation data and employee past work performance, and updates them in real time.

[1593] 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.

[1594] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring schedules from the user's calendar, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into the calendar, means for synchronizing with an external calendar service, means for automatically extracting related reference materials and data, means for communicating with the user to report task progress, means for automatically generating a work schedule based on factory operation data and employee task performance, and means for updating employee work schedules in real time to streamline factory production planning. This enables the automatic generation of efficient work schedules that are updated in real time based on the factory's operation status.

[1595] "Means for obtaining user registration information" refers to a system or method for obtaining basic information such as a user's name, occupation, and role from a database.

[1596] "Means for obtaining a user's past task data" refers to a system or method for obtaining detailed information (content, required time, completion status, etc.) of tasks that a user has performed in the past from a database.

[1597] A "means for retrieving events from a user's calendar" is a system or method for retrieving current events and schedules from a calendar service used by a user.

[1598] A "means for calculating task priority" is a system or method for numerically evaluating the priority of a task based on various factors such as the importance, deadline, and urgency of the task.

[1599] A "means for predicting the time required for a task" is a system or method for predicting the time required to complete a similar task based on performance data of past tasks.

[1600] "Means for allocating task time and automatically inserting it into a calendar" refers to a system or method for automatically adding the optimal schedule to a user's calendar based on the calculated task duration and priority.

[1601] "Means for synchronizing with an external calendar service" means a system or method for synchronizing data with an external calendar service, such as Google Calendar or Microsoft Outlook.

[1602] "Means for automatically extracting relevant reference materials and data" refers to a system or method for automatically retrieving and providing relevant materials and data from the Internet or databases based on the user's task content or theme.

[1603] "Means for communicating with the user and reporting task progress" refers to a system or method by which an AI assistant communicates with the user to report the progress of the current task and the next task to be tackled.

[1604] "Means for automatically generating work schedules based on factory operation data and employee task performance" refers to a system or method that automatically generates an optimal work schedule based on the operation status of factory equipment and employees' past work history.

[1605] "Means for updating employee work schedules in real time to streamline factory production plans" refers to a system or method for updating and optimizing employee work schedules in real time in response to changes in and progress in the factory's production plans.

[1606] The mode for carrying out the present invention will be described in detail below: The system required for carrying out the invention consists of three main components: a server, a terminal, and a user.

[1607] System Overview

[1608] This system is a management service that automatically generates work schedules based on factory operation data and employee task performance, and updates employee work schedules in real time to streamline production planning. The system processes data and performs scheduling on the server, and provides a user interface on the terminal. The server also connects to an external calendar service and interacts with users via an AI assistant.

[1609] 1. Collection of User Information

[1610] When a user logs in to the system, the server first retrieves the user's registration information (such as name, occupation, and role) from the database. It also collects past task data (such as the content, time required, and completion status of past tasks) from the database. It also retrieves the user's current schedule through API integration with external calendar services (such as Google Calendar and Microsoft Outlook).

[1611] 2. Priority and performance analysis

[1612] The server calculates the priority of each user's task based on the collected information. This is achieved by taking into account the task's importance, deadline, urgency, etc. At the same time, it analyzes past task performance and predicts the time required for a specific task. For example, if an employee previously spent three hours on machine maintenance, it will expect a similar amount of time in the future.

[1613] 3. Automatic task scheduling

[1614] The server divides the listed tasks into multiple parts (e.g., research, work, confirmation), allocates appropriate time to each part, and then automatically inserts each part into the calendar so that it does not overlap with existing schedules, allowing employees to efficiently manage their work schedules.

[1615] 4. Integration with external services

[1616] The server continuously synchronizes with an external calendar service, ensuring that employee schedules are always up-to-date when they are updated. Furthermore, the system automatically retrieves relevant reference materials and data from the Internet based on the task topic, and provides them to users as needed.

[1617] 5. Interacting with a conversational AI assistant

[1618] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant then explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the user's request and automatically updates the calendar.

[1619] Specific examples

[1620] For example, if a user adds a task to their calendar that reads, "Submit materials for the generative AI contest in 10 days," the system will act as follows:

[1621] 1. The server predicts the required time based on the user's past performance in document creation tasks.

[1622] 2. The server divides the document creation task into three parts: research, writing, and review, and allocates a predicted time to each part.

[1623] 3. The server automatically inserts each part into the calendar, adjusting it to the user's existing schedule.

[1624] 4. By linking with external services, relevant reference materials are automatically extracted from the Internet and provided to users.

[1625] 5. If the user wants to check the progress, they can ask the AI ​​assistant through their device, and the AI ​​assistant will explain the current progress and the next task to be tackled.

[1626] Prompt Sentence Examples

[1627] Here is an example of a real prompt:

[1628] Below is an example of task scheduling. If a user adds a task "Submit materials for the Generative AI Contest in 10 days," please generate the optimal schedule based on the following information:

[1629] Task name: Generative AI Contest Materials

[1630] Submission deadline: 10 days later

[1631] Task details:

[1632] Investigation: 2 hours

[1633] Writing: 3 hours

[1634] Review: 2 hours

[1635] Past task history:

[1636] Task A (2 hours, 3 hours, 2 hours)

[1637] Task B (1 hour, 1.5 hours, 1.2 hours)

[1638] Current schedule:

[1639] November 20th 09:00 - 11:00: Available

[1640] November 21st 13:00 - 16:00: Available

[1641] November 22nd 10:00 - 12:00: Available

[1642] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1643] Step 1:

[1644] Collection of user registration information

[1645] When a user logs in to the system, the server first retrieves the user's registration information from the database. Specifically, it retrieves basic information such as the user's name, occupation, and role. This allows the user's basic profile to be collected as input data. The output is a dataset containing the user's basic information.

[1646] Step 2:

[1647] Retrieving past task data

[1648] The server retrieves data on tasks the user has performed in the past from a database, including task content, required time, and completion status. This provides the user's past task history as input data. The output is detailed data on each individual task.

[1649] Step 3:

[1650] Get a user's calendar events

[1651] The server connects to an external calendar service via API to obtain the user's current schedule. Specifically, it obtains the user's schedule data from Google Calendar or Microsoft Outlook via API. This obtains the user's current schedule as input data. The output is the user's current schedule information.

[1652] Step 4:

[1653] Calculate task priorities

[1654] The server calculates task priorities based on the collected user basic information, past task data, and current schedule data. This takes into account factors such as the task's importance, deadline, and urgency. Specifically, the priority is numerically evaluated by multiplying the importance and urgency coefficients. The input data is processed, and the task priority is obtained as the output.

[1655] Step 5:

[1656] Task duration prediction

[1657] The server analyzes past task data and predicts the time required for a specific task. It uses statistical methods based on past performance data to predict the average time required for similar tasks. It processes the input historical data and outputs the predicted time required.

[1658] Step 6:

[1659] Time allocation and auto-scheduling of tasks

[1660] The server allocates each task to an appropriate time slot based on the priority and estimated time required, and automatically inserts it into the calendar. Specifically, it detects the user's free time slots and assigns tasks to those slots. Scheduling is performed based on the input data, and a new schedule is generated as the output.

[1661] Step 7:

[1662] Synchronization with external calendar services

[1663] The server synchronizes the new schedule with the external calendar service, sending the schedule data using an API to update the user's calendar. The newly generated schedule is taken as input and reflected in the external calendar service as output.

[1664] Step 8:

[1665] Automatic extraction of references and data

[1666] The server automatically extracts relevant reference materials and data from the Internet and databases based on the task theme. For example, it searches for papers and materials related to generative AI contests and provides them to users. It obtains relevant keywords from the input and provides resources as output.

[1667] Step 9:

[1668] Interacting with a conversational AI assistant

[1669] When a user inquires about task progress or changes through their device, the device sends that information to the server and obtains the latest task progress. The AI ​​assistant explains the user's current progress and the next task to tackle. If the user requests a schedule change, the server generates a new schedule proposal based on the request and automatically updates the calendar. The server receives user inquiries and change requests as input and provides the latest schedule and progress as output.

[1670] 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.

[1671] The following describes in detail in natural language the processing of a specific program for carrying out the present invention, particularly the operation of a system that combines an emotion engine that recognizes the user's emotions.

[1672] System Overview

[1673] This system is an innovative calendar service that automatically schedules tasks based on the user's personal data, past task performance, and priorities. In addition, it incorporates an emotion engine that recognizes the user's emotions and adjusts task management according to their emotional state.

[1674] 1. Collection of User Information

[1675] A user logs in to their device and enters their registration information, including basic profile information such as name, occupation, and areas of interest. The device then sends the information to the server, which stores it in a database. The server also retrieves the user's past task data and schedules from their existing calendar.

[1676] 2. Priority and performance analysis

[1677] The server analyzes the importance and deadline of each task based on the collected user information and past task performance. This determines the priority of the task and calculates the specific estimated time. It receives new tasks entered by the user in the calendar, divides them into multiple parts, and allocates the time accordingly.

[1678] 3. Automatic task scheduling

[1679] The server automatically schedules tasks into the user's calendar based on the calculated priority and estimated time, achieving optimal scheduling without conflicting with existing schedules.

[1680] 4. Integration with external services

[1681] The server synchronizes with external calendar services (e.g., Google Calendar, Microsoft Outlook) to keep schedule information up to date, and automatically retrieves reference materials and data related to specific tasks from the Internet and provides them to users.

[1682] 5. Introduction of Emotion Engine and Dialogue Interface

[1683] The device is equipped with an emotion engine that monitors emotions from the user's input and actions, analyzing the user's facial expressions, voice tone, input patterns, etc. to recognize emotions in real time.

[1684] 6. Adjusting task management based on emotions

[1685] The server performs the following actions based on the emotion data received from the emotion engine:

[1686] 1. Adjust task priorities: If a user is stressed, they can postpone less important tasks and prioritize relaxing tasks.

[1687] 2. Reminder Modification: If the user is relaxed, send reminders at different times.

[1688] 3. Break Suggestion: If the user is recognized as tired, it will suggest an appropriate break.

[1689] Specific examples

[1690] As a concrete example, let's explain the process when a user adds a task "Submit materials for the generative AI contest in 10 days":

[1691] 1. The server obtains data on the user's past document creation tasks and predicts the required time.

[1692] 2. The server divides the document creation into three parts: research, writing, and review, and estimates the time required for each.

[1693] 3. The server automatically inserts each part into the calendar, adjusting it to the user's current schedule.

[1694] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or setting a reminder if they are relaxed.

[1695] 5. When the user queries the device to check the progress, the AI ​​assistant retrieves the necessary information from the server and reports it to the user.

[1696] 6. The server synchronizes with an external calendar service, automatically collects relevant reference materials from the Internet, and provides them to the user.

[1697] With these features, the system not only provides users with task management and efficient scheduling, but also realizes flexible task management that takes into account users' emotions, thereby improving users' daily productivity and satisfaction.

[1698] The processing flow will be explained below.

[1699] Specific processing flow of the program (system combining emotion engine)

[1700] 1. Collection of User Information

[1701] Step 1:

[1702] A user logs in to a device and enters registration information, including basic profile information such as name, occupation, and areas of interest.

[1703] Step 2:

[1704] The terminal sends the entered user information to the server, which stores this information in a database.

[1705] Step 3:

[1706] The server retrieves the user's past task data from the database and links it to the user's profile.

[1707] Step 4:

[1708] The server connects to an external calendar service (such as Google Calendar or Microsoft Outlook) via API to obtain the user's current schedule.

[1709] 2. Priority and performance analysis

[1710] Step 1:

[1711] The server analyzes the importance and deadlines of tasks based on the acquired schedule information and past task data.

[1712] Step 2:

[1713] The server calculates the priority of each task based on the analysis results, which is determined by comprehensively evaluating the importance, proximity of the deadline, urgency, and other factors.

[1714] Step 3:

[1715] The server uses past performance data to predict the average time required for each task. For example, if a previous document creation task took five hours, it predicts that a similar amount of time will be required this time.

[1716] 3. Automatic task scheduling

[1717] Step 1:

[1718] The server receives new tasks that users have entered into their calendars (e.g., the deadline for submitting materials is in 10 days).

[1719] Step 2:

[1720] The server breaks the task down into specific parts (e.g., research, writing, review) and allocates the required time for each.

[1721] Step 3:

[1722] The server automatically inserts each part into the calendar, taking into account the user's current schedule, and places them so that they do not overlap with existing important events.

[1723] 4. Integration with external services

[1724] Step 1:

[1725] The server constantly synchronizes with external calendar services (Google Calendar and Microsoft Outlook).

[1726] Step 2:

[1727] The server automatically extracts relevant reference materials and data from the Internet based on the task theme and provides them to the user, for example, collecting articles from papers and databases related to the topic of document creation.

[1728] 5. Introduction of Emotion Engine and Dialogue Interface

[1729] Step 1:

[1730] The device is equipped with an emotion engine that monitors emotions from the user's input and actions, analyzing the user's facial expressions, voice tone, input patterns, etc.

[1731] Step 2:

[1732] The device sends the analysis results to a server, which then uses the received emotional data to understand the user's emotional state in real time.

[1733] 6. Adjusting task management based on emotions

[1734] Step 1:

[1735] The server adjusts task priorities based on data from the emotion engine: for example, if the user is feeling stressed, it will postpone less important tasks and prioritize relaxing tasks.

[1736] Step 2:

[1737] The server changes the timing of reminders depending on the user's emotional state: for example, if the user is relaxed, it sends a reminder earlier than usual.

[1738] Step 3:

[1739] The server detects the user's fatigue level and suggests appropriate break times, and if the user works for a long time, it sends regular break reminders.

[1740] Specific examples

[1741] Example 1:

[1742] If a user adds a task saying "Submit materials for the Generative AI Contest in 10 days":

[1743] 1. The server obtains data on the user's past document creation tasks and predicts the required time.

[1744] 2. The server divides the document creation into three parts: research, writing, and review, and estimates the time required for each.

[1745] 3. The server automatically inserts each part into the calendar, adjusting it to the user's current schedule.

[1746] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or adjusting reminders if they are relaxed.

[1747] 5. When the user queries the device to check the progress, the AI ​​assistant retrieves the necessary information from the server and reports it to the user.

[1748] 6. The server synchronizes with an external calendar service, automatically collects relevant reference materials from the Internet, and provides them to the user.

[1749] This not only supports users' task management and efficient scheduling, but also enables flexible task management that takes users' emotions into consideration.

[1750] Example 2

[1751] 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."

[1752] In today's busy society, users are required to efficiently manage numerous tasks. However, manual task scheduling is cumbersome and time-consuming, and it is easy for schedule overlaps and important tasks to be overlooked. Furthermore, current systems have difficulty flexibly adjusting tasks based on the user's emotional state. In particular, when users are stressed or tired, they need to take breaks at appropriate times and set reminders, but current systems have difficulty automatically performing these tasks.

[1753] 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 acquiring basic information of the user, a means for acquiring past task data of the user, and a means for acquiring schedule information of the user. This improves the efficiency of task management for the user and enables flexible task adjustment according to the emotional state.

[1754] "Basic user information" refers to basic information about a user, such as name, occupation, and areas of interest.

[1755] "User's past task data" refers to information about tasks that the user has performed in the past, including the content of the task, the time required, and the degree of completion.

[1756] "User's schedule information" refers to the user's current and future plans and appointments.

[1757] "Calculating task priorities" refers to evaluating and ranking the importance and deadlines of each task based on the collected information.

[1758] "Predicting task duration" refers to using historical data and algorithms to estimate the amount of time each task will require.

[1759] "Divide into subtasks and allocate time" refers to dividing a task into smaller units of work and allocating appropriate time to each part.

[1760] "External schedule management services" refers to external schedule management systems such as Google Calendar and Microsoft Outlook.

[1761] "Automatically collecting relevant references and data from the internet" refers to using web scraping or APIs to extract information from the internet that is relevant to a specific task.

[1762] "Analyzing user emotions" refers to analyzing data such as facial expressions, voice tone, and input patterns to understand the user's emotional state in real time.

[1763] "Adjusting task management based on the user's emotions" refers to dynamically changing task priorities, time allocation, reminder settings, break suggestions, etc. according to the user's emotional state.

[1764] The following describes in detail in natural language the processing of a specific program for carrying out the present invention, particularly the operation of a system that combines an emotion engine that recognizes the user's emotions.

[1765] System Overview

[1766] This system is an innovative calendar service that automatically schedules tasks based on the user's personal data, past task performance, and priorities. In addition, it incorporates an emotion engine that recognizes the user's emotions and adjusts task management according to their emotional state.

[1767] Collection of User Information

[1768] A user logs in to a device and enters basic profile information such as name, occupation, and areas of interest. The device sends this information to the server. The server stores the received user information in a database (e.g., MySQL or PostgreSQL) and retrieves the user's past task data and existing schedule information using an API.

[1769] Priority and performance analysis

[1770] The server processes the collected user information and past task results using analytical tools such as Apache Spark and TensorFlow. This evaluates the importance and deadlines of tasks and determines their priorities. When a user enters a new task, it divides it into multiple subtasks (e.g., research, writing, review) and calculates the time required for each.

[1771] Automatic task scheduling

[1772] The server automatically schedules tasks based on their calculated priority and estimated time, using an algorithm to place them in the optimal time slots so that they do not conflict with existing schedules.

[1773] Integration with external services

[1774] The server uses OAuth to connect to external schedule management services (e.g., Google Calendar, Microsoft Outlook) and synchronizes information bidirectionally. It also has the ability to automatically retrieve reference materials and data related to specific tasks from the Internet via web scraping or APIs.

[1775] Introducing an emotion engine and a dialogue interface

[1776] The device monitors the user's emotions using an emotion engine, which uses image analysis libraries such as OpenCV to analyze the user's facial expressions, voice tone, and input patterns to recognize emotions in real time.

[1777] Emotion-based adjustment of task management

[1778] The server adjusts task management based on the data received from the emotion engine: if the user is stressed, it postpones less important tasks and prioritizes relaxing tasks, if the user is relaxed, it sends reminders at different times, and if the user is perceived as tired, it suggests appropriate breaks.

[1779] Specific examples

[1780] Let's explain the process when a user adds a task "Submit materials for the generative AI contest in 10 days":

[1781] 1. The server obtains data on the user's past document creation tasks.

[1782] 2. The server divides the document creation into three subtasks: research, writing, and review, and estimates the time required for each.

[1783] 3. The server coordinates with the user's current schedule and automatically inserts each subtask into the schedule.

[1784] 4. The emotion engine monitors the user's emotional state in real time, suggesting a break if they are stressed, or setting a reminder if they are relaxed.

[1785] 5. The user queries the terminal to check progress.

[1786] 6. The server synchronizes with an external schedule management service and collects relevant reference materials from the Internet.

[1787] Prompt Sentence Examples

[1788] "Create a schedule for creating materials for the next 10 days for the Generative AI Contest. Use past data on creating materials to predict the time required and suggest appropriate breaks."

[1789] This system improves the efficiency of task management and enables flexible scheduling that takes emotions into account, thereby improving user productivity and satisfaction.

[1790] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1791] Step 1: Collect user information

[1792] The user enters their ID and password on the login screen and is authenticated.

[1793] Input: ID, password

[1794] Output: Authenticated user session

[1795] The user enters basic information (name, occupation, interests).

[1796] Input: Basic information (name, occupation, areas of interest)

[1797] Output: User basic information data

[1798] The terminal transmits the input information to the server.

[1799] Input: User basic information

[1800] Output: Basic information data transferred to the server

[1801] The server stores the received basic information in a database.

[1802] Input: Basic information data

[1803] Output: User information stored in the database

[1804] The server uses an API to retrieve the user's past task data and existing schedule information.

[1805] Input: API request

[1806] Output: Past task data, existing schedule information

[1807] Step 2: Analyze priorities and performance

[1808] The server processes the collected data using analysis tools (e.g., Apache Spark, TensorFlow).

[1809] Input: User basic information, past task data, existing schedule information

[1810] Output: Analysis results (task importance, deadline)

[1811] The server calculates the priority of the task.

[1812] Input: Analysis results (task importance, deadline)

[1813] Output: Priority list

[1814] The server divides a newly input task into multiple subtasks and predicts the required time.

[1815] Input: New task data

[1816] Output: Subtask list and estimated duration

[1817] Step 3: Automatically schedule tasks

[1818] The server schedules tasks based on priority and estimated duration.

[1819] Inputs: Subtask list, estimated duration, priority list, existing schedule

[1820] Output: Updated schedule data

[1821] The server places tasks at different times to prevent schedule overlaps.

[1822] Input: Updated schedule data

[1823] Output: Optimized schedule

[1824] Step 4: Integrate with external services

[1825] The server synchronizes with external scheduling services (e.g., Google Calendar, Microsoft Outlook).

[1826] Input: OAuth token, schedule data

[1827] Output: Schedules synced to external services

[1828] The server uses web scraping or APIs to gather material from the internet relevant to a specific task.

[1829] Input: Task identification information

[1830] Output: Collected reference data

[1831] Step 5: Introducing the emotion engine and dialogue interface

[1832] The device uses an emotion engine (e.g., OpenCV) to monitor the user's emotions.

[1833] Input: User's facial expression data, voice data, input pattern data

[1834] Output: Emotion recognition result

[1835] The device analyzes emotions in real time and sends the data to a server.

[1836] Input: Emotion recognition results

[1837] Output: Emotion data sent to the server

[1838] Step 6: Adjust your task management based on emotions

[1839] The server adjusts task management based on the emotion data.

[1840] Input: Emotion data, task data, schedule data

[1841] Output: Adjusted task schedule

[1842] If the user is feeling stressed, the server will postpone less important tasks and prioritize relaxing tasks.

[1843] Input: Emotion data, task priority data

[1844] Output: Adjusted priority list

[1845] The server will suggest appropriate breaks if the user is tired.

[1846] Input: Emotion data

[1847] Output: Break suggestion notification

[1848] The server will send reminders at different times if you are relaxed.

[1849] Input: Emotion data, reminder setting data

[1850] Output: Reminders sent

[1851] (Application example 2)

[1852] 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."

[1853] While conventional task management systems can efficiently manage users' schedules, they have the problem of making users feel stressed because they schedule without taking into account their emotional state.In addition, they lack a function to suggest breaks at appropriate times or adjust task priorities based on emotions, making it difficult to fully improve user productivity and satisfaction.

[1854] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1855] In this invention, the server includes means for acquiring user registration information, means for acquiring the user's past task data, means for acquiring the user's schedule information, means for calculating task priorities based on the acquired information, means for predicting task durations, means for allocating task time and automatically inserting the time into the schedule, means for synchronizing with an external schedule service, means for automatically extracting related reference materials and data, means for communicating with the user and reporting task progress, means for recognizing the user's emotional state, means for adjusting task priorities and reminders based on the emotional state, and means for suggesting breaks the user needs. This enables flexible task management that takes the user's emotional state into consideration.

[1856] "Means for obtaining user registration information" refers to the method for sending the user's basic profile information (such as name, occupation, areas of interest, etc.) to the server and storing it in the database.

[1857] The "means for acquiring the user's past task data" is a method for acquiring the tasks that the user has performed in the past and their results from the database.

[1858] "Means for obtaining user schedule information" refers to a method for obtaining the user's current and future schedules from a calendar or schedule application.

[1859] The "means for calculating task priorities based on acquired information" is a method for determining priorities by analyzing the importance and deadlines of tasks based on collected user information and past task performance.

[1860] A "means for predicting the time required for a task" is a method for predicting the time required for each task based on data such as the user's past task performance.

[1861] The "means for allocating task time and automatically inserting tasks into a schedule" is a method for appropriately allocating tasks into a user's schedule based on the calculated priorities and estimated times.

[1862] "Means for synchronizing with external scheduling services" refers to methods for synchronizing data with external calendar or scheduling services (e.g., Google Calendar or Microsoft Outlook).

[1863] "Means for automatically extracting relevant reference materials and data" refers to a method for automatically collecting information and data related to a specific task from the Internet, etc.

[1864] The "means for communicating with the user and reporting the progress of the task" refers to a method for obtaining the necessary information from the server and reporting it when the user inquires about the progress.

[1865] "Means for recognizing the user's emotional state" refers to a method for recognizing the user's emotions in real time by analyzing facial expressions, voice tone, input patterns, etc.

[1866] "Means for adjusting task priorities and reminders based on emotional state" refers to a method for reevaluating the importance of tasks and changing the timing of reminders depending on the user's emotional state.

[1867] The "means for suggesting a break the user needs" is a method for recognizing the user's emotional state and suggesting an appropriate break if the user feels tired or stressed.

[1868] A specific embodiment of the present invention will be described below. This system is a smart operation management system for autonomous vehicles. In particular, it has the function of recognizing the driver's emotional state and making operation plans and break suggestions based on that state.

[1869] System Overview

[1870] The system optimizes the driving schedule by taking into account the user's (driver's) registration information, past task performance, and emotional state. It uses the following main hardware and software components:

[1871] Camera: Used to capture the driver's facial expressions and analyze their emotions.

[1872] Microphone: Used to analyze the tone of the driver's voice and identify emotions.

[1873] EmotionEngine: Software that recognizes the driver's emotional state in real time based on data obtained from cameras and microphones.

[1874] ScheduleOptimizer: An algorithm that optimizes operation plans and adjusts schedules based on emotional states.

[1875] CalendarSync: Software for synchronizing data with external scheduling services (e.g., Google Calendar or Microsoft Outlook).

[1876] System Operation

[1877] 1. Collection of User Information

[1878] When a driver logs in to the terminal, the driver's registration information (name, route, etc.) is sent to the server, which then stores this information in a database.

[1879] The server acquires past operation performance data and calculates the next operation schedule based on that data.

[1880] 2. Operation scheduling

[1881] The server uses the collected information to optimize the operation schedule, including optimizing the operation route, pick-up time, and drop-off time.

[1882] 3. Emotion recognition

[1883] The device (self-driving vehicle) uses a camera and microphone to monitor the driver's emotional state in real time.

[1884] The EmotionEngine recognizes the driver's emotions (stress, relaxation, etc.) and sends the results to the server.

[1885] 4. Emotion-based operation adjustment

[1886] The server adjusts the trip plan depending on the driver's emotional state, for example, suggesting appropriate breaks if the driver is feeling stressed.

[1887] If the driver is relaxed, they will be notified that they have time until the next pickup time.

[1888] 5. Data synchronization

[1889] The server synchronizes with an external schedule service and always maintains the latest operating schedule.

[1890] Specific scenarios

[1891] Below is a specific scenario that shows how this system works in practice.

[1892] While the driver is traveling on Route A, the server predicts the next pickup time based on the driver's past driving data. If EmotionEngine detects the driver's stress state, the server notifies the driver, suggesting that they take a 10-minute break.

[1893] After the driver takes a break, the server recalculates the next optimal route and synchronizes it with an external scheduling service.

[1894] Prompt Sentence Examples

[1895] Here is an example of a prompt to input to a generative AI model:

[1896] Please provide some ideas on optimizing the operation schedule based on driver emotion recognition. How to adjust the schedule when the driver is stressed, and what to do when the driver is relaxed?

[1897] This enables the system to realize flexible and efficient driving management that takes into account the driver's emotional state.

[1898] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1899] Step 1:

[1900] The user's device receives the driver's login information as input. Based on this, the device obtains the driver's registration information and sends it to the server. The server stores the registration information in a database.

[1901] Step 2:

[1902] The server retrieves past task performance data from the database, then retrieves the user's current schedule information from an external schedule service (e.g., Google Calendar), analyzes how much time the user has spent on the task in the past, and predicts the task's priority and required time.

[1903] Step 3:

[1904] The server optimizes the schedule based on the predicted task duration and priority. During this process, it allocates time for tasks and automatically inserts them into the schedule. Specifically, it arranges tasks so that time is used most efficiently.

[1905] Step 4:

[1906] The device uses a camera and microphone to receive the driver's emotional state as input. The EmotionEngine analyzes facial expressions and vocal tone to recognize the driver's emotional state (relaxed, stressed, etc.). This data is sent to the server in real time.

[1907] Step 5:

[1908] The server reevaluates the existing schedule based on the received emotional state data. If the driver is stressed, it postpones low-priority tasks and suggests a break. If the driver is relaxed, it calculates the time lag until the next task pick-up time. This information is then communicated to the driver.

[1909] Step 6:

[1910] The server synchronizes with an external scheduling service, ensuring that schedule data is always up to date and that trip plans are adjusted as needed. It also automatically extracts relevant reference materials and data and provides them to drivers. This procedure ensures that data remains consistent and up to date.

[1911] Step 7:

[1912] When a user queries the device to check the progress, the server retrieves the progress data and reports it to the user, allowing the user to understand the operation status and the next task required in real time.

[1913] Specific actions

[1914] Through these processing steps, the driver can efficiently manage the operation of the autonomous vehicle and flexibly adjust according to their emotional state. The generative AI model will then assist the driver appropriately based on the following prompts:

[1915] Please provide some ideas on optimizing the operation schedule based on driver emotion recognition. How to adjust the schedule when the driver is stressed, and what to do when the driver is relaxed?

[1916] This prompt allows the system to plan an optimal trip that takes into account the driver's emotional state.

[1917] 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.

[1918] 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.

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] 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).

[1924] 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.

[1925] 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."

[1926] 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.

[1927] 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).

[1928] 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.

[1929] 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.

[1930] 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.

[1931] 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.

[1932] 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.

[1933] 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.

[1934] 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 proce...

Claims

1. a means for obtaining user registration information; A means of obtaining the user's past task data; A means of retrieving events from the user's calendar; means for calculating task priorities based on the obtained information; a means of predicting task duration; A way to allocate time for tasks and automatically insert them into the calendar, A means of syncing with external calendar services, A means of automatically extracting relevant references and data; A system that includes a means of interacting with a user and reporting task progress.

2. 2. The system according to claim 1, further comprising means for acquiring a user's schedule from an external calendar service and optimally allocating tasks based on the schedule.

3. 2. The system according to claim 1, further comprising means for predicting the time required for each task based on past task performance, and for scheduling tasks based on the predicted data.

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