system

Abstract

The system according to this embodiment aims to automate schedule management and improvement management, and to support junior employees. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a presentation unit, a consultation unit, and a confirmation unit. The reception unit receives task input. The generation unit automatically generates a schedule based on the task information received by the reception unit. The presentation unit presents improvements based on the schedule generated by the generation unit. The consultation unit provides a 1-on-1 function based on the improvements presented by the presentation unit. The confirmation unit confirms the schedule based on the 1-on-1 function provided by the consultation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that schedule management and management of improvement points are troublesome, and especially the support for young employees is not sufficient.

[0005] The system according to the embodiment aims to automate schedule management and management of improvement points and support young employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a presentation unit, a consultation unit, and a confirmation unit. The reception unit receives task input. The generation unit automatically generates a schedule based on the task information received by the reception unit. The presentation unit presents suggestions for improvement based on the schedule generated by the generation unit. The consultation unit provides a 1-on-1 function based on the suggestions for improvement presented by the presentation unit. The confirmation unit confirms the schedule based on the 1-on-1 function provided by the consultation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate schedule management and improvement management, and provide support for junior employees. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when three or more matters are connected and expressed by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An agent system according to an embodiment of the present invention is a system that automatically manages the schedules and areas for improvement of junior employees and provides support. This agent system automatically calculates and creates a feasible schedule based on the user's task input. For example, if a user inputs "Planning and preparing a service using AI services" for 3 hours and "Summarizing issues for Company A's project" for 4 hours, the system automatically creates a schedule based on this input. Furthermore, the agent system also provides support for overcoming areas for improvement. Every morning, a dedicated page is displayed, showing the day's schedule and "points to pay attention to today." For example, advice such as "Try to reply as quickly as possible" might be displayed, and the user can check their progress as a daily report at the end of the workday. The agent system also includes a 1-on-1 function, allowing users to consult with senior colleagues at any time, even when they are busy or the topic is difficult to discuss directly. Users input the progress of tasks and areas for improvement, and the agent system manages the schedule and areas for improvement based on this information. This system allows junior employees to easily manage their schedules and receive support for their professional growth. Furthermore, supervisors and other team members can also check the schedules of junior employees and gain a detailed understanding of task progress, enabling more efficient task execution for the department and the company as a whole. This allows the agent system to automatically manage junior employees' schedules and identify areas for improvement, providing support to the team.

[0029] The agent system according to this embodiment comprises a reception unit, a generation unit, a presentation unit, a consultation unit, and a confirmation unit. The reception unit accepts user input of tasks. For example, the reception unit accepts user input of task details, deadlines, required outputs, etc. The reception unit also has a free-form input field where users can freely enter comments. The generation unit automatically generates a schedule based on the task information received by the reception unit. For example, the generation unit automatically calculates a feasible schedule based on the task details and deadlines entered by the user. The generation unit can generate schedules using AI. The presentation unit presents areas for improvement based on the schedule generated by the generation unit. For example, the presentation unit presents points to be aware of and areas for improvement when the user executes the schedule. The presentation unit can present areas for improvement using AI. The consultation unit provides a 1-on-1 function based on the areas for improvement presented by the presentation unit. For example, the consultation unit provides a 1-on-1 function when the user has something they find difficult to discuss with a senior colleague or when they are busy. The consultation unit can provide a 1-on-1 function using AI. The verification unit checks the schedule based on the 1on1 function provided by the consultation unit. The verification unit checks, for example, the progress and areas for improvement of tasks entered by the user. The verification unit can use AI to check the schedule. As a result, the agent system according to this embodiment can automatically manage the schedules and areas for improvement of junior employees and provide support.

[0030] The reception unit accepts task input from users. Specifically, it provides an interface for users to input task details, due dates, required outputs, and other information. For example, users can access a form to enter task details, such as the task name, due date, priority, and required resources. Furthermore, the reception unit provides a free-form field where users can add additional comments or special notes about the task. This free-form field is useful for recording background information or special requirements for the task. The reception unit saves the information entered by users in real time, making it available for subsequent processing. For example, when a user enters a task, the reception unit saves that information to a database, making it accessible to the generation and presentation units. The reception unit also has a function to check the consistency of the information entered by users, ensuring that there is no missing or incorrect information. In this way, the reception unit supports users in entering accurate and complete task information. In addition, the reception unit can also provide input assistance and auto-completion functions to improve the user experience when entering tasks. For example, when a user enters a task name, it can display and allow selection of previously entered task name suggestions, reducing the effort required for input. This allows the reception desk to provide an environment where users can smoothly input tasks, thereby improving the overall efficiency of the system.

[0031] The generation unit automatically generates schedules based on task information received by the reception unit. Specifically, it automatically calculates feasible schedules based on the task content and due dates entered by the user. The generation unit can generate schedules using AI. The AI ​​considers task priorities, dependencies, resource utilization, etc., to generate the optimal schedule. For example, the AI ​​adjusts the start and end dates of tasks and optimizes resource allocation to ensure task deadlines are met. The AI ​​also analyzes past schedule data and task history to learn and improve the accuracy of schedules. As a result, the generation unit can provide efficient and feasible schedules based on the task information entered by the user. Furthermore, the generation unit has a function to detect potential problems and constraints that may occur during the schedule generation process and notify the user. For example, if a particular task cannot be completed on schedule due to insufficient resources or dependency issues, the generation unit will inform the user and suggest alternatives. This allows the generation unit to help users understand scheduling problems in advance and take appropriate measures. In addition, the generation unit can collect user feedback and use it to improve the schedule generation algorithm. This allows the generation unit to always provide highly accurate schedules based on the latest information and user needs, thereby improving the overall system performance.

[0032] The suggestion unit presents areas for improvement based on the schedule generated by the generation unit. Specifically, it presents points and areas for improvement that the user should pay attention to when executing the schedule. The suggestion unit can use AI to suggest areas for improvement. The AI ​​analyzes the generated schedule, considering task priorities, dependencies, resource utilization, etc., to suggest the most suitable areas for improvement for the user. For example, if a particular task depends on other tasks, the AI ​​will clarify those dependencies and suggest optimizing the order of tasks. It will also analyze resource utilization and suggest areas for improvement to prevent overuse or shortage of resources. Furthermore, the AI ​​can learn from past schedule data and task history to identify problems the user has faced in the past and successful strategies, and suggest areas for improvement that reflect this. This allows the suggestion unit to provide specific advice to help the user execute the schedule efficiently and improve the task completion rate. In addition, the suggestion unit can provide a visual interface and interactive guides to help the user easily understand and implement the suggested areas for improvement. For example, areas for improvement can be visually displayed in graphs and charts so that the user can understand them at a glance. Interactive guides can also lead the user through the steps to implement the improvements step by step. This allows the presentation unit to provide support to help users effectively implement schedule improvements and increase their task completion rate.

[0033] The Consultation Department provides 1on1 functionality based on the improvement points suggested by the Presentation Department. Specifically, it provides 1on1 functionality when users find it difficult to consult with their seniors or when they are busy. The Consultation Department can provide 1on1 functionality using AI. The AI ​​analyzes the user's input and past consultation history to provide optimal advice and support. For example, if a user is having difficulty with a particular task, the AI ​​will provide specific advice based on past consultations and solutions related to that task. The AI ​​can also provide 1on1 functionality at the appropriate time, taking into account the user's schedule and task progress. This ensures that users receive appropriate support when needed. Furthermore, the Consultation Department can also provide input assistance and auto-completion functions to improve the usability when users input consultation content. For example, it can reduce the effort of input by displaying and allowing users to select from past consultation content when they input consultation content. This allows the Consultation Department to provide an environment where users can input consultation content smoothly and improve the overall efficiency of the system. In addition, the Consultation Department can collect user feedback and use it to improve the 1on1 functionality. This allows the support department to consistently provide highly accurate support based on the latest information and user needs, thereby improving the overall system performance.

[0034] The verification unit checks the schedule based on the 1on1 function provided by the consultation unit. Specifically, it checks the progress and areas for improvement of tasks entered by the user. The verification unit can use AI to check the schedule. The AI ​​analyzes the progress and areas for improvement of tasks entered by the user and evaluates the degree of schedule achievement and any problems. For example, the AI ​​monitors the progress of tasks in real time to check whether they are proceeding as planned. It also evaluates whether the areas for improvement are being properly implemented and provides additional advice as needed. In this way, the verification unit supports users in properly managing their schedules and completing tasks efficiently. Furthermore, the verification unit can also provide a visual interface and interactive guides so that users can easily understand the results of the schedule check. For example, it can visually display the degree of schedule achievement and problems in graphs and charts so that users can understand them at a glance. It can also guide users step-by-step through interactive guides to take the next steps based on the schedule check results. In this way, the verification unit can support users in effectively utilizing the results of the schedule check and improving their task completion rate. Furthermore, the verification unit can collect user feedback and use it to improve the schedule check function. This allows the verification unit to always provide highly accurate schedule confirmations based on the latest information and user needs, thereby improving the overall system performance.

[0035] The understanding unit can understand the content of the free-form text field. For example, the understanding unit analyzes the content entered by the user in the free-form text field to understand the task input. The understanding unit can understand the content of the free-form text field using AI. For example, the understanding unit uses natural language processing technology to analyze the text entered in the free-form text field to understand the task content. For example, the understanding unit uses keyword extraction technology to extract important information entered in the free-form text field to understand the task content. For example, the understanding unit uses contextual analysis technology to understand the meaning of the content entered in the free-form text field. As a result, understanding the content of the free-form text field makes task input more flexible.

[0036] The checking unit can provide a function to check daily reports. For example, the checking unit can analyze the daily reports entered by the user and check the progress of tasks. The checking unit can use AI to check daily reports. For example, the checking unit can use natural language processing technology to analyze the text entered in the daily reports and check the progress of tasks. For example, the checking unit can use keyword extraction technology to extract important information entered in the daily reports and check the progress of tasks. For example, the checking unit can use contextual analysis technology to understand the meaning of the content entered in the daily reports and check the progress of tasks. In this way, by providing a daily report checking function, the progress of tasks can be checked.

[0037] The reception unit can analyze the user's past task input history and select the optimal input method. For example, the reception unit can automatically display tasks that the user has frequently entered in the past as suggestions. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest tasks to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be provided by analyzing past input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0038] The reception unit can filter tasks based on the user's current projects and areas of interest when they are entered. For example, the reception unit can prioritize displaying tasks related to projects the user is currently working on. For example, the reception unit can automatically suggest relevant tasks based on the user's areas of interest. For example, the reception unit can filter and display appropriate tasks considering the user's project progress. This allows users to prioritize entering highly relevant tasks by filtering tasks based on projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering of relevant tasks.

[0039] The reception unit can prioritize the input of highly relevant tasks by considering the user's geographical location when a task is entered. For example, if the user is in a specific location, the reception unit will prioritize displaying tasks related to that location. For example, the reception unit will prioritize the input of tasks that can be performed in locations close to the user's current location. For example, the reception unit will suggest the most suitable tasks based on the user's geographical location. This allows for the priority input of highly relevant tasks by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI suggest relevant tasks.

[0040] The reception unit can analyze the user's social media activity and input relevant tasks when a task is entered. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant tasks. For example, the reception unit can input the most suitable task based on the user's social media activity history. For example, the reception unit can input relevant tasks considering the user's interests on social media. This allows for the efficient input of relevant tasks by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI suggest relevant tasks.

[0041] The generation unit can adjust the level of detail in the schedule based on the importance of the tasks when generating the schedule. For example, the generation unit can set a detailed schedule for high-importance tasks, or a simplified schedule for low-importance tasks. The generation unit can dynamically adjust the level of detail in the schedule according to the importance of the tasks. This enables efficient schedule management by adjusting the level of detail in the schedule based on the importance of the tasks. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input task importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the schedule.

[0042] The generation unit can apply different generation algorithms depending on the task category when generating schedules. For example, the generation unit applies a project management algorithm to project management tasks. For example, it applies a routine task algorithm to routine tasks. For example, it applies a creative task algorithm to creative tasks. This enables efficient schedule management by applying different generation algorithms depending on the task category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task category data into a generation AI and have the generation AI select the generation algorithm to apply.

[0043] The generation unit can determine the priority of tasks based on their submission dates when generating a schedule. For example, the generation unit prioritizes tasks with approaching deadlines in the schedule. For example, it postpones tasks with distant deadlines. For example, the generation unit dynamically adjusts the schedule priority according to the submission dates. This enables efficient schedule management by determining the schedule priority based on the task submission dates. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task submission date data into a generation AI and have the generation AI perform the task of determining the schedule priority.

[0044] The generation unit can adjust the order of schedules based on the relevance of tasks when generating schedules. For example, the generation unit may include highly related tasks consecutively in the schedule. For example, it may include less relevant tasks in a more distributed manner. For example, the generation unit may dynamically adjust the order of schedules according to the relevance of tasks. This enables efficient schedule management by adjusting the order of schedules based on the relevance of tasks. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task relevance data into a generation AI and have the generation AI perform the adjustment of the schedule order.

[0045] The presentation unit can adjust the level of detail of improvement suggestions based on the importance of the task when suggesting improvements. For example, the presentation unit will suggest detailed improvements for high-importance tasks. For example, the presentation unit will suggest simplified improvements for low-importance tasks. The presentation unit can dynamically adjust the level of detail of improvement suggestions according to the importance of the task. This allows for efficient suggestion of improvements by adjusting the level of detail of improvement suggestions based on the importance of the task. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of improvement suggestions.

[0046] The suggestion unit can apply different suggestion algorithms depending on the task category when suggesting improvements. For example, the suggestion unit applies a suggestion algorithm specifically for project management tasks. For example, it applies a suggestion algorithm specifically for daily work tasks. For example, it applies a suggestion algorithm specifically for creative tasks. By applying different suggestion algorithms depending on the task category, efficient suggestions for improvements can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input task category data into a generating AI and have the generating AI select the suggestion algorithm to apply.

[0047] The suggestion unit can prioritize improvement points based on the task submission deadline when suggesting improvements. For example, the suggestion unit will prioritize suggesting improvements for tasks with approaching deadlines. For example, the suggestion unit will postpone suggesting improvements for tasks with distant deadlines. For example, the suggestion unit can dynamically adjust the priority of improvement points according to the submission timing. This enables efficient suggestion of improvement points by determining the priority of improvements based on the task submission timing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input task submission timing data into a generating AI and have the generating AI determine the priority of improvement points.

[0048] The presentation unit can adjust the order of improvement points based on the relevance of the tasks when presenting improvement points. For example, the presentation unit may present improvement points for highly relevant tasks consecutively. For example, the presentation unit may present improvement points for less relevant tasks in a more dispersed manner. For example, the presentation unit may dynamically adjust the order of improvement points according to the relevance of the tasks. This makes it possible to present improvement points efficiently by adjusting the order of improvement points based on the relevance of the tasks. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit may input task relevance data into a generating AI and have the generating AI perform the adjustment of the order of improvement points.

[0049] The consultation department can provide optimal advice by referring to the user's past consultation history when offering the 1on1 function. For example, the consultation department can provide optimal advice based on the content of the user's past consultations. For example, the consultation department can suggest solutions to similar problems from the user's past consultation history. For example, the consultation department can analyze the user's past consultation history and provide the most effective advice. In this way, optimal advice can be provided by referring to past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's past consultation history data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0050] The consultation department can customize advice based on the user's current project status when providing the 1on1 function. For example, the consultation department provides advice related to the user's current ongoing project. For example, the consultation department customizes appropriate advice considering the user's project progress. For example, the consultation department provides optimal advice based on the user's project status. This enables efficient support by customizing advice based on the current project status. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's project data into a generating AI and have the generating AI perform the advice customization.

[0051] The consultation department can provide optimal advice by considering the user's geographical location when offering the 1-on-1 function. For example, if the user is in a specific location, the consultation department will provide advice related to that location. For example, the consultation department will provide advice related to tasks to be performed in a location close to the user's current location. For example, the consultation department will provide optimal advice based on the user's geographical location. In this way, optimal advice can be provided by considering geographical location. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's geographical location data into a generating AI and have the generating AI perform the provision of advice.

[0052] The consultation department can analyze a user's social media activity and provide advice when providing the 1on1 function. For example, the consultation department can analyze the content of a user's social media posts and provide relevant advice. For example, the consultation department can provide optimal advice based on a user's social media activity history. For example, the consultation department can provide relevant advice considering a user's interests on social media. In this way, optimal advice can be provided by analyzing social media activity. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's social media data into a generating AI and have the generating AI perform the provision of advice.

[0053] The confirmation unit can select the optimal confirmation method by referring to the user's past schedule history when confirming a schedule. For example, the confirmation unit may preferentially suggest schedule confirmation methods that the user has used in the past. For example, the confirmation unit may select the optimal confirmation method from the user's past schedule history. For example, the confirmation unit may analyze the user's past schedule history and provide the most effective confirmation method. In this way, the optimal confirmation method can be provided by referring to the past schedule history. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI. For example, the confirmation unit may input the user's past schedule history data into a generating AI and have the generating AI perform the selection of the optimal confirmation method.

[0054] The verification unit can customize the verification method based on the user's current project status when checking the schedule. For example, the verification unit performs schedule checks related to projects the user is currently working on. For example, the verification unit customizes the appropriate verification method considering the user's project progress. For example, the verification unit provides the optimal verification method based on the user's project status. This enables efficient schedule checking by customizing the verification method based on the current project status. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's project data into a generating AI and have the generating AI perform the customization of the verification method.

[0055] The verification unit can select the optimal verification method when checking the schedule, taking into account the user's geographical location information. For example, if the user is in a specific location, the verification unit will perform schedule checks related to that location. For example, the verification unit will perform schedule checks related to tasks to be performed in locations close to the user's current location. For example, the verification unit will provide the optimal verification method based on the user's geographical location information. In this way, the optimal verification method can be provided by taking geographical location information into consideration. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the verification method.

[0056] The verification unit can analyze the user's social media activity and provide verification methods when checking the schedule. For example, the verification unit analyzes the content of the user's social media posts and performs relevant schedule checks. For example, the verification unit provides the optimal verification method based on the user's social media activity history. For example, the verification unit considers the user's interests on social media and performs relevant schedule checks. In this way, by analyzing social media activity, the optimal verification method can be provided. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into a generating AI and have the generating AI perform the provision of verification methods.

[0057] The understanding unit can select the optimal understanding method by referring to the user's past input history when understanding the content of a free-form input field. For example, the understanding unit selects the optimal understanding method based on content previously entered by the user. For example, the understanding unit understands similar content from the user's past input history. For example, the understanding unit analyzes the user's past input history and provides the most effective understanding method. In this way, the optimal understanding method can be provided by referring to past input history. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's past input history data into a generating AI and have the generating AI perform the selection of the optimal understanding method.

[0058] The understanding unit can select the optimal understanding method when understanding the content of a free-form text field, taking into account the user's geographical location information. For example, if the user is in a specific location, the understanding unit will understand content related to that location. For example, the understanding unit will understand content related to tasks performed in locations close to the user's current location. For example, the understanding unit will provide the optimal understanding method based on the user's geographical location information. In this way, the optimal understanding method can be provided by taking geographical location information into consideration. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without using AI. For example, the understanding unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of an understanding method.

[0059] The checking unit can select the optimal checking method by referring to the user's past daily report history when checking daily reports. For example, the checking unit may prioritize suggesting daily report checking methods previously used by the user. For example, the checking unit may select the optimal checking method from the user's past daily report history. For example, the checking unit may analyze the user's past daily report history and provide the most effective checking method. In this way, the optimal checking method can be provided by referring to past daily report history. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit may input the user's past daily report history data into a generating AI and have the generating AI perform the selection of the optimal checking method.

[0060] The checking unit can customize the checking method based on the user's current project status when checking daily reports. For example, the checking unit checks daily reports related to the user's currently ongoing projects. For example, the checking unit customizes appropriate checking methods considering the user's project progress. For example, the checking unit provides optimal checking methods based on the user's project status. This enables efficient daily report checking by customizing the checking method based on the current project status. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the user's project data into a generating AI and have the generating AI perform the customization of the checking method.

[0061] The checking unit can select the optimal checking method when checking daily reports, taking into account the user's geographical location information. For example, if the user is in a specific location, the checking unit will perform daily report checks related to that location. For example, the checking unit will perform daily report checks related to tasks performed in locations close to the user's current location. For example, the checking unit will provide the optimal checking method based on the user's geographical location information. In this way, the optimal checking method can be provided by taking geographical location information into consideration. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the checking method.

[0062] The checking unit can analyze the user's social media activity and provide checking methods when checking daily reports. For example, the checking unit can analyze the content of the user's social media posts and perform relevant daily report checks. For example, the checking unit can provide the optimal checking method based on the user's social media activity history. For example, the checking unit can perform relevant daily report checks considering the user's interests on social media. In this way, by analyzing social media activity, the optimal checking method can be provided. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the user's social media data into a generating AI and have the generating AI perform the provision of checking methods.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception desk can analyze a user's past task input history and select the optimal input method. For example, the reception desk can automatically display tasks that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest tasks to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the system can provide the optimal input method.

[0065] The presentation unit can adjust the level of detail of improvement suggestions based on the importance of the task. For example, it will present detailed improvement suggestions for high-importance tasks, and simplified improvement suggestions for low-importance tasks. The presentation unit can dynamically adjust the level of detail of improvement suggestions according to the importance of the task. This allows for efficient suggestion of improvement suggestions by adjusting the level of detail based on the importance of the task.

[0066] The confirmation unit can select the optimal confirmation method by referring to the user's past schedule history when confirming a schedule. For example, the confirmation unit may prioritize suggesting schedule confirmation methods that the user has used in the past. For example, the confirmation unit may select the optimal confirmation method from the user's past schedule history. For example, the confirmation unit may analyze the user's past schedule history and provide the most effective confirmation method. In this way, the optimal confirmation method can be provided by referring to the past schedule history.

[0067] The checking unit can select the optimal checking method by referring to the user's past daily report history when checking daily reports. For example, the checking unit may prioritize suggesting daily report checking methods that the user has used in the past. For example, the checking unit may select the optimal checking method from the user's past daily report history. For example, the checking unit may analyze the user's past daily report history and provide the most effective checking method. In this way, the optimal checking method can be provided by referring to past daily report history.

[0068] The input system can prioritize tasks based on the user's geographical location when they enter a task. For example, if a user is in a specific location, the input system will prioritize displaying tasks related to that location. For example, the input system will prioritize tasks that can be performed in locations close to the user's current location. For example, the input system will suggest the most suitable tasks based on the user's geographical location. In this way, by considering geographical location, the system can prioritize the input of highly relevant tasks.

[0069] The generation unit can apply different generation algorithms depending on the task category when generating schedules. For example, the generation unit applies a project management algorithm to project management tasks. For example, the generation unit applies a routine work algorithm to routine work tasks. For example, the generation unit applies a creative algorithm to creative tasks. This allows for efficient schedule management by applying different generation algorithms depending on the task category.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception area accepts task input from users. For example, it accepts input from users regarding the task content, deadline, required output, etc. The reception area also has a free-form field where users can freely add comments. Step 2: The generation unit automatically generates a schedule based on the task information received by the reception unit. For example, it automatically calculates a feasible schedule based on the task content and deadline entered by the user. The generation unit can use AI to generate the schedule. Step 3: The presentation unit presents areas for improvement based on the schedule generated by the generation unit. For example, it may present points to be aware of or areas for improvement when the user executes the schedule. The presentation unit can use AI to present areas for improvement. Step 4: The consultation department provides 1-on-1 functionality based on the improvement points presented by the suggestion department. For example, it provides 1-on-1 functionality when users find it difficult to consult with their seniors or when they are busy. The consultation department can use AI to provide 1-on-1 functionality. Step 5: The confirmation unit checks the schedule based on the 1-on-1 function provided by the consultation unit. For example, it checks the progress and areas for improvement of tasks entered by the user. The confirmation unit can use AI to check the schedule.

[0072] (Example of form 2) An agent system according to an embodiment of the present invention is a system that automatically manages the schedules and areas for improvement of junior employees and provides support. This agent system automatically calculates and creates a feasible schedule based on the user's task input. For example, if a user inputs "Planning and preparing a service using AI services" for 3 hours and "Summarizing issues for Company A's project" for 4 hours, the system automatically creates a schedule based on this input. Furthermore, the agent system also provides support for overcoming areas for improvement. Every morning, a dedicated page is displayed, showing the day's schedule and "points to pay attention to today." For example, advice such as "Try to reply as quickly as possible" might be displayed, and the user can check their progress as a daily report at the end of the workday. The agent system also includes a 1-on-1 function, allowing users to consult with senior colleagues at any time, even when they are busy or the topic is difficult to discuss directly. Users input the progress of tasks and areas for improvement, and the agent system manages the schedule and areas for improvement based on this information. This system allows junior employees to easily manage their schedules and receive support for their professional growth. Furthermore, supervisors and other team members can also check the schedules of junior employees and gain a detailed understanding of task progress, enabling more efficient task execution for the department and the company as a whole. This allows the agent system to automatically manage junior employees' schedules and identify areas for improvement, providing support to the team.

[0073] The agent system according to this embodiment comprises a reception unit, a generation unit, a presentation unit, a consultation unit, and a confirmation unit. The reception unit accepts user input of tasks. For example, the reception unit accepts user input of task details, deadlines, required outputs, etc. The reception unit also has a free-form input field where users can freely enter comments. The generation unit automatically generates a schedule based on the task information received by the reception unit. For example, the generation unit automatically calculates a feasible schedule based on the task details and deadlines entered by the user. The generation unit can generate schedules using AI. The presentation unit presents areas for improvement based on the schedule generated by the generation unit. For example, the presentation unit presents points to be aware of and areas for improvement when the user executes the schedule. The presentation unit can present areas for improvement using AI. The consultation unit provides a 1-on-1 function based on the areas for improvement presented by the presentation unit. For example, the consultation unit provides a 1-on-1 function when the user has something they find difficult to discuss with a senior colleague or when they are busy. The consultation unit can provide a 1-on-1 function using AI. The verification unit checks the schedule based on the 1on1 function provided by the consultation unit. The verification unit checks, for example, the progress and areas for improvement of tasks entered by the user. The verification unit can use AI to check the schedule. As a result, the agent system according to this embodiment can automatically manage the schedules and areas for improvement of junior employees and provide support.

[0074] The reception unit accepts task input from users. Specifically, it provides an interface for users to input task details, due dates, required outputs, and other information. For example, users can access a form to enter task details, such as the task name, due date, priority, and required resources. Furthermore, the reception unit provides a free-form field where users can add additional comments or special notes about the task. This free-form field is useful for recording background information or special requirements for the task. The reception unit saves the information entered by users in real time, making it available for subsequent processing. For example, when a user enters a task, the reception unit saves that information to a database, making it accessible to the generation and presentation units. The reception unit also has a function to check the consistency of the information entered by users, ensuring that there is no missing or incorrect information. In this way, the reception unit supports users in entering accurate and complete task information. In addition, the reception unit can also provide input assistance and auto-completion functions to improve the user experience when entering tasks. For example, when a user enters a task name, it can display and allow selection of previously entered task name suggestions, reducing the effort required for input. This allows the reception desk to provide an environment where users can smoothly input tasks, thereby improving the overall efficiency of the system.

[0075] The generation unit automatically generates schedules based on task information received by the reception unit. Specifically, it automatically calculates feasible schedules based on the task content and due dates entered by the user. The generation unit can generate schedules using AI. The AI ​​considers task priorities, dependencies, resource utilization, etc., to generate the optimal schedule. For example, the AI ​​adjusts the start and end dates of tasks and optimizes resource allocation to ensure task deadlines are met. The AI ​​also analyzes past schedule data and task history to learn and improve the accuracy of schedules. As a result, the generation unit can provide efficient and feasible schedules based on the task information entered by the user. Furthermore, the generation unit has a function to detect potential problems and constraints that may occur during the schedule generation process and notify the user. For example, if a particular task cannot be completed on schedule due to insufficient resources or dependency issues, the generation unit will inform the user and suggest alternatives. This allows the generation unit to help users understand scheduling problems in advance and take appropriate measures. In addition, the generation unit can collect user feedback and use it to improve the schedule generation algorithm. This allows the generation unit to always provide highly accurate schedules based on the latest information and user needs, thereby improving the overall system performance.

[0076] The suggestion unit presents areas for improvement based on the schedule generated by the generation unit. Specifically, it presents points and areas for improvement that the user should pay attention to when executing the schedule. The suggestion unit can use AI to suggest areas for improvement. The AI ​​analyzes the generated schedule, considering task priorities, dependencies, resource utilization, etc., to suggest the most suitable areas for improvement for the user. For example, if a particular task depends on other tasks, the AI ​​will clarify those dependencies and suggest optimizing the order of tasks. It will also analyze resource utilization and suggest areas for improvement to prevent overuse or shortage of resources. Furthermore, the AI ​​can learn from past schedule data and task history to identify problems the user has faced in the past and successful strategies, and suggest areas for improvement that reflect this. This allows the suggestion unit to provide specific advice to help the user execute the schedule efficiently and improve the task completion rate. In addition, the suggestion unit can provide a visual interface and interactive guides to help the user easily understand and implement the suggested areas for improvement. For example, areas for improvement can be visually displayed in graphs and charts so that the user can understand them at a glance. Interactive guides can also lead the user through the steps to implement the improvements step by step. This allows the presentation unit to provide support to help users effectively implement schedule improvements and increase their task completion rate.

[0077] The Consultation Department provides 1on1 functionality based on the improvement points suggested by the Presentation Department. Specifically, it provides 1on1 functionality when users find it difficult to consult with their seniors or when they are busy. The Consultation Department can provide 1on1 functionality using AI. The AI ​​analyzes the user's input and past consultation history to provide optimal advice and support. For example, if a user is having difficulty with a particular task, the AI ​​will provide specific advice based on past consultations and solutions related to that task. The AI ​​can also provide 1on1 functionality at the appropriate time, taking into account the user's schedule and task progress. This ensures that users receive appropriate support when needed. Furthermore, the Consultation Department can also provide input assistance and auto-completion functions to improve the usability when users input consultation content. For example, it can reduce the effort of input by displaying and allowing users to select from past consultation content when they input consultation content. This allows the Consultation Department to provide an environment where users can input consultation content smoothly and improve the overall efficiency of the system. In addition, the Consultation Department can collect user feedback and use it to improve the 1on1 functionality. This allows the support department to consistently provide highly accurate support based on the latest information and user needs, thereby improving the overall system performance.

[0078] The verification unit checks the schedule based on the 1on1 function provided by the consultation unit. Specifically, it checks the progress and areas for improvement of tasks entered by the user. The verification unit can use AI to check the schedule. The AI ​​analyzes the progress and areas for improvement of tasks entered by the user and evaluates the degree of schedule achievement and any problems. For example, the AI ​​monitors the progress of tasks in real time to check whether they are proceeding as planned. It also evaluates whether the areas for improvement are being properly implemented and provides additional advice as needed. In this way, the verification unit supports users in properly managing their schedules and completing tasks efficiently. Furthermore, the verification unit can also provide a visual interface and interactive guides so that users can easily understand the results of the schedule check. For example, it can visually display the degree of schedule achievement and problems in graphs and charts so that users can understand them at a glance. It can also guide users step-by-step through interactive guides to take the next steps based on the schedule check results. In this way, the verification unit can support users in effectively utilizing the results of the schedule check and improving their task completion rate. Furthermore, the verification unit can collect user feedback and use it to improve the schedule check function. This allows the verification unit to always provide highly accurate schedule confirmations based on the latest information and user needs, thereby improving the overall system performance.

[0079] The understanding unit can understand the content of the free-form text field. For example, the understanding unit analyzes the content entered by the user in the free-form text field to understand the task input. The understanding unit can understand the content of the free-form text field using AI. For example, the understanding unit uses natural language processing technology to analyze the text entered in the free-form text field to understand the task content. For example, the understanding unit uses keyword extraction technology to extract important information entered in the free-form text field to understand the task content. For example, the understanding unit uses contextual analysis technology to understand the meaning of the content entered in the free-form text field. As a result, understanding the content of the free-form text field makes task input more flexible.

[0080] The checking unit can provide a function to check daily reports. For example, the checking unit can analyze the daily reports entered by the user and check the progress of tasks. The checking unit can use AI to check daily reports. For example, the checking unit can use natural language processing technology to analyze the text entered in the daily reports and check the progress of tasks. For example, the checking unit can use keyword extraction technology to extract important information entered in the daily reports and check the progress of tasks. For example, the checking unit can use contextual analysis technology to understand the meaning of the content entered in the daily reports and check the progress of tasks. In this way, by providing a daily report checking function, the progress of tasks can be checked.

[0081] The reception unit can estimate the user's emotions and adjust the timing of task input based on the estimated emotions. For example, if the user is stressed, the reception unit can simplify task input to allow for quicker completion. If the user is relaxed, for example, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception unit can prioritize voice input to allow for quick task completion. This improves input efficiency by adjusting the timing of task input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0082] The reception unit can analyze the user's past task input history and select the optimal input method. For example, the reception unit can automatically display tasks that the user has frequently entered in the past as suggestions. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can predict and suggest tasks to be used during a specific time period based on the user's past input history. In this way, the optimal input method can be provided by analyzing past input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0083] The reception unit can filter tasks based on the user's current projects and areas of interest when they are entered. For example, the reception unit can prioritize displaying tasks related to projects the user is currently working on. For example, the reception unit can automatically suggest relevant tasks based on the user's areas of interest. For example, the reception unit can filter and display appropriate tasks considering the user's project progress. This allows users to prioritize entering highly relevant tasks by filtering tasks based on projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering of relevant tasks.

[0084] The reception unit can estimate the user's emotions and determine the priority of tasks to be entered based on the estimated emotions. For example, if the user is stressed, the reception unit will postpone less important tasks and prioritize more important ones. For example, if the user is relaxed, the reception unit will prioritize long-term tasks. For example, if the user is in a hurry, the reception unit will prioritize urgent tasks. This enables efficient task management by prioritizing tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0085] The reception unit can prioritize the input of highly relevant tasks by considering the user's geographical location when a task is entered. For example, if the user is in a specific location, the reception unit will prioritize displaying tasks related to that location. For example, the reception unit will prioritize the input of tasks that can be performed in locations close to the user's current location. For example, the reception unit will suggest the most suitable tasks based on the user's geographical location. This allows for the priority input of highly relevant tasks by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI suggest relevant tasks.

[0086] The reception unit can analyze the user's social media activity and input relevant tasks when a task is entered. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant tasks. For example, the reception unit can input the most suitable task based on the user's social media activity history. For example, the reception unit can input relevant tasks considering the user's interests on social media. This allows for the efficient input of relevant tasks by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI suggest relevant tasks.

[0087] The generation unit can estimate the user's emotions and adjust the schedule generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a schedule with ample time. For example, if the user is in a hurry, the generation unit will generate an efficient schedule. For example, if the user is stressed, the generation unit will generate a schedule with plenty of rest time. This allows for efficient schedule management by adjusting the schedule generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the user's emotion estimation.

[0088] The generation unit can adjust the level of detail in the schedule based on the importance of the tasks when generating the schedule. For example, the generation unit can set a detailed schedule for high-importance tasks, or a simplified schedule for low-importance tasks. The generation unit can dynamically adjust the level of detail in the schedule according to the importance of the tasks. This enables efficient schedule management by adjusting the level of detail in the schedule based on the importance of the tasks. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input task importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the schedule.

[0089] The generation unit can apply different generation algorithms depending on the task category when generating schedules. For example, the generation unit applies a project management algorithm to project management tasks. For example, it applies a routine task algorithm to routine tasks. For example, it applies a creative task algorithm to creative tasks. This enables efficient schedule management by applying different generation algorithms depending on the task category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task category data into a generation AI and have the generation AI select the generation algorithm to apply.

[0090] The generation unit can estimate the user's emotions and adjust the length of the schedule based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a longer schedule. For example, if the user is in a hurry, the generation unit will generate a shorter schedule. For example, if the user is stressed, the generation unit will generate a schedule with more break time. This allows for efficient schedule management by adjusting the length of the schedule according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0091] The generation unit can determine the priority of tasks based on their submission dates when generating a schedule. For example, the generation unit prioritizes tasks with approaching deadlines in the schedule. For example, it postpones tasks with distant deadlines. For example, the generation unit dynamically adjusts the schedule priority according to the submission dates. This enables efficient schedule management by determining the schedule priority based on the task submission dates. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task submission date data into a generation AI and have the generation AI perform the task of determining the schedule priority.

[0092] The generation unit can adjust the order of schedules based on the relevance of tasks when generating schedules. For example, the generation unit may include highly related tasks consecutively in the schedule. For example, it may include less relevant tasks in a more distributed manner. For example, the generation unit may dynamically adjust the order of schedules according to the relevance of tasks. This enables efficient schedule management by adjusting the order of schedules based on the relevance of tasks. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input task relevance data into a generation AI and have the generation AI perform the adjustment of the schedule order.

[0093] The presentation unit can estimate the user's emotions and adjust the method of presenting improvement suggestions based on the estimated emotions. For example, if the user is relaxed, the presentation unit will present detailed improvement suggestions. If the user is in a hurry, the presentation unit will present concise improvement suggestions. If the user is stressed, the presentation unit will present improvement suggestions that include positive feedback. This allows for efficient presentation of improvement suggestions by adjusting the method of presenting them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0094] The presentation unit can adjust the level of detail of improvement suggestions based on the importance of the task when suggesting improvements. For example, the presentation unit will suggest detailed improvements for high-importance tasks. For example, the presentation unit will suggest simplified improvements for low-importance tasks. The presentation unit can dynamically adjust the level of detail of improvement suggestions according to the importance of the task. This allows for efficient suggestion of improvements by adjusting the level of detail of improvement suggestions based on the importance of the task. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of improvement suggestions.

[0095] The suggestion unit can apply different suggestion algorithms depending on the task category when suggesting improvements. For example, the suggestion unit applies a suggestion algorithm specifically for project management tasks. For example, it applies a suggestion algorithm specifically for daily work tasks. For example, it applies a suggestion algorithm specifically for creative tasks. By applying different suggestion algorithms depending on the task category, efficient suggestions for improvements can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input task category data into a generating AI and have the generating AI select the suggestion algorithm to apply.

[0096] The presentation unit can estimate the user's emotions and adjust the length of the suggested improvements based on the estimated emotions. For example, if the user is relaxed, the presentation unit will present detailed suggestions for improvement. If the user is in a hurry, the presentation unit will present concise suggestions for improvement. If the user is stressed, the presentation unit will present suggestions that include positive feedback. This allows for efficient suggestion of improvements by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the presentation unit may be performed using AI or not. For example, the presentation unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0097] The suggestion unit can prioritize improvement points based on the task submission deadline when suggesting improvements. For example, the suggestion unit will prioritize suggesting improvements for tasks with approaching deadlines. For example, the suggestion unit will postpone suggesting improvements for tasks with distant deadlines. For example, the suggestion unit can dynamically adjust the priority of improvement points according to the submission timing. This enables efficient suggestion of improvement points by determining the priority of improvements based on the task submission timing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input task submission timing data into a generating AI and have the generating AI determine the priority of improvement points.

[0098] The presentation unit can adjust the order of improvement points based on the relevance of the tasks when presenting improvement points. For example, the presentation unit may present improvement points for highly relevant tasks consecutively. For example, the presentation unit may present improvement points for less relevant tasks in a more dispersed manner. For example, the presentation unit may dynamically adjust the order of improvement points according to the relevance of the tasks. This makes it possible to present improvement points efficiently by adjusting the order of improvement points based on the relevance of the tasks. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without using AI. For example, the presentation unit may input task relevance data into a generating AI and have the generating AI perform the adjustment of the order of improvement points.

[0099] The consultation unit can estimate the user's emotions and adjust how the 1on1 function is delivered based on the estimated emotions. For example, if the user is relaxed, the consultation unit will provide detailed advice. If the user is in a hurry, the consultation unit will provide concise advice. If the user is stressed, the consultation unit will provide advice that includes positive feedback. This allows for efficient support by adjusting how the 1on1 function is delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI or not using AI. For example, the consultation unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0100] The consultation department can provide optimal advice by referring to the user's past consultation history when offering the 1on1 function. For example, the consultation department can provide optimal advice based on the content of the user's past consultations. For example, the consultation department can suggest solutions to similar problems from the user's past consultation history. For example, the consultation department can analyze the user's past consultation history and provide the most effective advice. In this way, optimal advice can be provided by referring to past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's past consultation history data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0101] The consultation department can customize advice based on the user's current project status when providing the 1on1 function. For example, the consultation department provides advice related to the user's current ongoing project. For example, the consultation department customizes appropriate advice considering the user's project progress. For example, the consultation department provides optimal advice based on the user's project status. This enables efficient support by customizing advice based on the current project status. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's project data into a generating AI and have the generating AI perform the advice customization.

[0102] The consultation unit can estimate the user's emotions and prioritize 1-on-1 features based on the estimated emotions. For example, if the user is stressed, the consultation unit will prioritize providing 1-on-1 features. If the user is relaxed, the consultation unit will prioritize other tasks. If the user is in a hurry, the consultation unit will quickly provide 1-on-1 features. This enables efficient support by prioritizing 1-on-1 features according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI or not. For example, the consultation unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0103] The consultation department can provide optimal advice by considering the user's geographical location when offering the 1-on-1 function. For example, if the user is in a specific location, the consultation department will provide advice related to that location. For example, the consultation department will provide advice related to tasks to be performed in a location close to the user's current location. For example, the consultation department will provide optimal advice based on the user's geographical location. In this way, optimal advice can be provided by considering geographical location. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's geographical location data into a generating AI and have the generating AI perform the provision of advice.

[0104] The consultation department can analyze a user's social media activity and provide advice when providing the 1on1 function. For example, the consultation department can analyze the content of a user's social media posts and provide relevant advice. For example, the consultation department can provide optimal advice based on a user's social media activity history. For example, the consultation department can provide relevant advice considering a user's interests on social media. In this way, optimal advice can be provided by analyzing social media activity. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's social media data into a generating AI and have the generating AI perform the provision of advice.

[0105] The confirmation unit can estimate the user's emotions and adjust the schedule confirmation method based on the estimated user emotions. For example, if the user is relaxed, the confirmation unit will perform a detailed schedule confirmation. For example, if the user is in a hurry, the confirmation unit will perform a concise schedule confirmation. For example, if the user is stressed, the confirmation unit will perform a schedule confirmation that includes positive feedback. This allows for efficient schedule confirmation by adjusting the schedule confirmation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without AI. For example, the confirmation unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0106] The confirmation unit can select the optimal confirmation method by referring to the user's past schedule history when confirming a schedule. For example, the confirmation unit may preferentially suggest schedule confirmation methods that the user has used in the past. For example, the confirmation unit may select the optimal confirmation method from the user's past schedule history. For example, the confirmation unit may analyze the user's past schedule history and provide the most effective confirmation method. In this way, the optimal confirmation method can be provided by referring to the past schedule history. Some or all of the above processing in the confirmation unit may be performed using AI, for example, or without using AI. For example, the confirmation unit may input the user's past schedule history data into a generating AI and have the generating AI perform the selection of the optimal confirmation method.

[0107] The verification unit can customize the verification method based on the user's current project status when checking the schedule. For example, the verification unit performs schedule checks related to projects the user is currently working on. For example, the verification unit customizes the appropriate verification method considering the user's project progress. For example, the verification unit provides the optimal verification method based on the user's project status. This enables efficient schedule checking by customizing the verification method based on the current project status. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's project data into a generating AI and have the generating AI perform the customization of the verification method.

[0108] The confirmation unit can estimate the user's emotions and determine the priority of schedule checks based on the estimated emotions. For example, if the user is stressed, the confirmation unit will prioritize schedule checks. If the user is relaxed, the confirmation unit will prioritize other tasks. If the user is in a hurry, the confirmation unit will perform schedule checks quickly. This enables efficient schedule checks by determining the priority of schedule checks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI or not using AI. For example, the confirmation unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0109] The verification unit can select the optimal verification method when checking the schedule, taking into account the user's geographical location information. For example, if the user is in a specific location, the verification unit will perform schedule checks related to that location. For example, the verification unit will perform schedule checks related to tasks to be performed in locations close to the user's current location. For example, the verification unit will provide the optimal verification method based on the user's geographical location information. In this way, the optimal verification method can be provided by taking geographical location information into consideration. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the verification method.

[0110] The verification unit can analyze the user's social media activity and provide verification methods when checking the schedule. For example, the verification unit analyzes the content of the user's social media posts and performs relevant schedule checks. For example, the verification unit provides the optimal verification method based on the user's social media activity history. For example, the verification unit considers the user's interests on social media and performs relevant schedule checks. In this way, by analyzing social media activity, the optimal verification method can be provided. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into a generating AI and have the generating AI perform the provision of verification methods.

[0111] The understanding unit can estimate the user's emotions and adjust how it understands the content of the free-response field based on the estimated emotions. For example, if the user is relaxed, the understanding unit will understand detailed content. For example, if the user is in a hurry, the understanding unit will understand concise content. For example, if the user is stressed, the understanding unit will understand content that includes positive feedback. This allows for efficient content understanding by adjusting how it understands the content of the free-response field according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI or not using AI. For example, the understanding unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0112] The understanding unit can select the optimal understanding method by referring to the user's past input history when understanding the content of a free-form input field. For example, the understanding unit selects the optimal understanding method based on content previously entered by the user. For example, the understanding unit understands similar content from the user's past input history. For example, the understanding unit analyzes the user's past input history and provides the most effective understanding method. In this way, the optimal understanding method can be provided by referring to past input history. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input the user's past input history data into a generating AI and have the generating AI perform the selection of the optimal understanding method.

[0113] The understanding unit can estimate the user's emotions and determine the priority for understanding the content of the free-response field based on the estimated emotions. For example, if the user is stressed, the understanding unit will prioritize understanding the content of the free-response field. For example, if the user is relaxed, the understanding unit will prioritize other tasks. For example, if the user is in a hurry, the understanding unit will quickly understand the content of the free-response field. This enables efficient content understanding by determining the priority for understanding the content of the free-response field according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI, or not using AI. For example, the understanding unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0114] The understanding unit can select the optimal understanding method when understanding the content of a free-form text field, taking into account the user's geographical location information. For example, if the user is in a specific location, the understanding unit will understand content related to that location. For example, the understanding unit will understand content related to tasks performed in locations close to the user's current location. For example, the understanding unit will provide the optimal understanding method based on the user's geographical location information. In this way, the optimal understanding method can be provided by taking geographical location information into consideration. Some or all of the above processing in the understanding unit may be performed using AI, for example, or without using AI. For example, the understanding unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of an understanding method.

[0115] The checking unit can estimate the user's emotions and adjust the daily report checking method based on the estimated user emotions. For example, if the user is relaxed, the checking unit will perform a detailed daily report check. For example, if the user is in a hurry, the checking unit will perform a concise daily report check. For example, if the user is stressed, the checking unit will perform a daily report check that includes positive feedback. This allows for efficient daily report checking by adjusting the checking method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0116] The checking unit can select the optimal checking method by referring to the user's past daily report history when checking daily reports. For example, the checking unit may prioritize suggesting daily report checking methods previously used by the user. For example, the checking unit may select the optimal checking method from the user's past daily report history. For example, the checking unit may analyze the user's past daily report history and provide the most effective checking method. In this way, the optimal checking method can be provided by referring to past daily report history. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit may input the user's past daily report history data into a generating AI and have the generating AI perform the selection of the optimal checking method.

[0117] The checking unit can customize the checking method based on the user's current project status when checking daily reports. For example, the checking unit checks daily reports related to the user's currently ongoing projects. For example, the checking unit customizes appropriate checking methods considering the user's project progress. For example, the checking unit provides optimal checking methods based on the user's project status. This enables efficient daily report checking by customizing the checking method based on the current project status. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the user's project data into a generating AI and have the generating AI perform the customization of the checking method.

[0118] The checking unit can estimate the user's emotions and determine the priority of checking the daily report based on the estimated emotions. For example, if the user is stressed, the checking unit will prioritize checking the daily report. For example, if the user is relaxed, the checking unit will prioritize other tasks. For example, if the user is in a hurry, the checking unit will quickly check the daily report. This enables efficient daily report checking by determining the priority of checking the daily report according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checking unit may be performed using AI, or not using AI. For example, the checking unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0119] The checking unit can select the optimal checking method when checking daily reports, taking into account the user's geographical location information. For example, if the user is in a specific location, the checking unit will perform daily report checks related to that location. For example, the checking unit will perform daily report checks related to tasks performed in locations close to the user's current location. For example, the checking unit will provide the optimal checking method based on the user's geographical location information. In this way, the optimal checking method can be provided by taking geographical location information into consideration. Some or all of the above processing in the checking unit may be performed using AI, for example, or without using AI. For example, the checking unit can input the user's geographical location information data into a generating AI and have the generating AI perform the selection of the checking method.

[0120] The checking unit can analyze the user's social media activity and provide checking methods when checking daily reports. For example, the checking unit can analyze the content of the user's social media posts and perform relevant daily report checks. For example, the checking unit can provide the optimal checking method based on the user's social media activity history. For example, the checking unit can perform relevant daily report checks considering the user's interests on social media. In this way, by analyzing social media activity, the optimal checking method can be provided. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the user's social media data into a generating AI and have the generating AI perform the provision of checking methods.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The reception desk can analyze a user's past task input history and select the optimal input method. For example, the reception desk can automatically display tasks that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). For example, the reception desk can predict and suggest tasks to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the system can provide the optimal input method.

[0123] The generation unit can estimate the user's emotions and adjust the schedule generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a schedule with ample leeway. If the user is in a hurry, for example, the generation unit will generate an efficient schedule. If the user is stressed, for example, the generation unit will generate a schedule with plenty of rest time. In this way, by adjusting the schedule generation method according to the user's emotions, efficient schedule management becomes possible.

[0124] The presentation unit can adjust the level of detail of improvement suggestions based on the importance of the task. For example, it will present detailed improvement suggestions for high-importance tasks, and simplified improvement suggestions for low-importance tasks. The presentation unit can dynamically adjust the level of detail of improvement suggestions according to the importance of the task. This allows for efficient suggestion of improvement suggestions by adjusting the level of detail based on the importance of the task.

[0125] The consultation department can estimate the user's emotions and adjust how the 1-on-1 function is delivered based on those estimates. For example, if the user is relaxed, the consultation department will provide detailed advice. If the user is in a hurry, for example, the consultation department will provide concise advice. If the user is stressed, for example, the consultation department will provide advice that includes positive feedback. This allows for more efficient support by adjusting how the 1-on-1 function is delivered according to the user's emotions.

[0126] The confirmation unit can select the optimal confirmation method by referring to the user's past schedule history when confirming a schedule. For example, the confirmation unit may prioritize suggesting schedule confirmation methods that the user has used in the past. For example, the confirmation unit may select the optimal confirmation method from the user's past schedule history. For example, the confirmation unit may analyze the user's past schedule history and provide the most effective confirmation method. In this way, the optimal confirmation method can be provided by referring to the past schedule history.

[0127] The understanding unit can estimate the user's emotions and adjust how it interprets the content of the free-response field based on those emotions. For example, if the user is relaxed, the understanding unit will interpret detailed content. If the user is in a hurry, for example, the understanding unit will interpret concise content. If the user is stressed, for example, the understanding unit will interpret content that includes positive feedback. This allows for efficient content interpretation by adjusting how the content of the free-response field is interpreted according to the user's emotions.

[0128] The checking unit can select the optimal checking method by referring to the user's past daily report history when checking daily reports. For example, the checking unit may prioritize suggesting daily report checking methods that the user has used in the past. For example, the checking unit may select the optimal checking method from the user's past daily report history. For example, the checking unit may analyze the user's past daily report history and provide the most effective checking method. In this way, the optimal checking method can be provided by referring to past daily report history.

[0129] The input system can prioritize tasks based on the user's geographical location when they enter a task. For example, if a user is in a specific location, the input system will prioritize displaying tasks related to that location. For example, the input system will prioritize tasks that can be performed in locations close to the user's current location. For example, the input system will suggest the most suitable tasks based on the user's geographical location. In this way, by considering geographical location, the system can prioritize the input of highly relevant tasks.

[0130] The generation unit can apply different generation algorithms depending on the task category when generating schedules. For example, the generation unit applies a project management algorithm to project management tasks. For example, the generation unit applies a routine work algorithm to routine work tasks. For example, the generation unit applies a creative algorithm to creative tasks. This allows for efficient schedule management by applying different generation algorithms depending on the task category.

[0131] The presentation unit can estimate the user's emotions and adjust how it presents suggestions for improvement based on those emotions. For example, if the user is relaxed, the presentation unit will present detailed suggestions for improvement. If the user is in a hurry, for example, the presentation unit will present concise suggestions for improvement. If the user is stressed, for example, the presentation unit will present suggestions that include positive feedback. This allows for efficient suggestion of improvements by adjusting the presentation method according to the user's emotions.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The reception area accepts task input from users. For example, it accepts input from users regarding the task content, deadline, required output, etc. The reception area also has a free-form field where users can freely add comments. Step 2: The generation unit automatically generates a schedule based on the task information received by the reception unit. For example, it automatically calculates a feasible schedule based on the task content and deadline entered by the user. The generation unit can use AI to generate the schedule. Step 3: The presentation unit presents areas for improvement based on the schedule generated by the generation unit. For example, it may present points to be aware of or areas for improvement when the user executes the schedule. The presentation unit can use AI to present areas for improvement. Step 4: The consultation department provides 1-on-1 functionality based on the improvement points presented by the suggestion department. For example, it provides 1-on-1 functionality when users find it difficult to consult with their seniors or when they are busy. The consultation department can use AI to provide 1-on-1 functionality. Step 5: The confirmation unit checks the schedule based on the 1-on-1 function provided by the consultation unit. For example, it checks the progress and areas for improvement of tasks entered by the user. The confirmation unit can use AI to check the schedule.

[0134] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0135] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0136] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the reception unit, generation unit, presentation unit, consultation unit, confirmation unit, understanding unit, and checking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user input of a task. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates a schedule based on the task information received by the reception unit. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents points for improvement based on the schedule generated by the generation unit. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a 1-on-1 function based on the points for improvement presented by the presentation unit. The confirmation unit is implemented by the control unit 46A of the smart device 14 and confirms the schedule based on the 1-on-1 function provided by the consultation unit. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the contents of the free-form input field. The checking unit is implemented, for example, by the control unit 46A of the smart device 14, and provides a function for checking the daily report. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the reception unit, generation unit, presentation unit, consultation unit, confirmation unit, understanding unit, and checking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input for tasks. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates a schedule based on the task information received by the reception unit. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents improvements based on the schedule generated by the generation unit. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a 1-on-1 function based on the improvements presented by the presentation unit. The confirmation unit is implemented by the control unit 46A of the smart glasses 214 and confirms the schedule based on the 1-on-1 function provided by the consultation unit. The understanding unit is implemented by the specific processing unit 290 of the data processing unit 12 and understands the contents of the free-form input field. The checking unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides a function for checking the daily report. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, generation unit, presentation unit, consultation unit, confirmation unit, understanding unit, and checking unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user input of a task. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates a schedule based on the task information received by the reception unit. The presentation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and presents points for improvement based on the schedule generated by the generation unit. The consultation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a 1-on-1 function based on the points for improvement presented by the presentation unit. The confirmation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and confirms the schedule based on the 1-on-1 function provided by the consultation unit. The understanding unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and understands the contents of the free-form input field. The checking function is implemented, for example, by the control unit 46A of the headset terminal 314, and provides a function for checking the daily report. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] As shown in Figure 7, the 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.

[0172] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0176] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0177] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0178] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0179] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] Each of the multiple elements described above, including the reception unit, generation unit, presentation unit, consultation unit, confirmation unit, understanding unit, and checking unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input for tasks. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates a schedule based on the task information received by the reception unit. The presentation unit is implemented by, for example, the control unit 46A of the robot 414 and presents points for improvement based on the schedule generated by the generation unit. The consultation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a 1-on-1 function based on the points for improvement presented by the presentation unit. The confirmation unit is implemented by, for example, the control unit 46A of the robot 414 and confirms the schedule based on the 1-on-1 function provided by the consultation unit. The understanding unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and understands the contents of the free-form input field. The checking unit is implemented, for example, by the control unit 46A of the robot 414, and provides a function for checking the daily report. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0187] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0189] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0190] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0191] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0195] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0196] 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.

[0197] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0198] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0199] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0200] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0202] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0203] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0204] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0205] (Note 1) A reception desk that accepts task inputs, A generation unit that automatically generates a schedule based on the task information received by the reception unit, A presentation unit that presents improvement points based on the schedule generated by the generation unit, A consultation unit that provides a 1on1 function based on the improvement points presented by the aforementioned presentation unit, The system includes a confirmation unit that checks the schedule based on the 1on1 function provided by the consultation unit. A system characterized by the following features. (Note 2) It is equipped with an understanding unit that can comprehend the content of the free-response field. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a checking unit that provides a function to check daily reports. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of task input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze the user's past task input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering tasks, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering tasks, the system prioritizes tasks that are highly relevant to the user, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering tasks, the system analyzes the user's social media activity and enters relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts how the schedule is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating a schedule, adjust the level of detail based on the importance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating a schedule, different generation algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the length of the schedule based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a schedule, prioritize tasks based on their submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a schedule, the order of tasks is adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is, It estimates the user's emotions and adjusts how it presents improvements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is, When suggesting improvements, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is, When suggesting areas for improvement, different suggestion algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, It estimates the user's emotions and adjusts the length of the improvements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When suggesting improvements, prioritize them based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, When suggesting improvements, adjust the order of the improvements based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned consultation department, We estimate the user's emotions and adjust how the 1on1 feature is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned consultation department, When providing the 1-on-1 function, we refer to the user's past consultation history to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned consultation department, When providing the 1on1 feature, the advice will be customized based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned consultation department, It estimates the user's emotions and prioritizes 1on1 features based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned consultation department, When providing the 1-on-1 feature, we will provide optimal advice while taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned consultation department, When providing the 1on1 feature, we analyze the user's social media activity and provide advice. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned verification unit is The system estimates the user's emotions and adjusts the schedule confirmation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned verification unit is When checking the schedule, the system will refer to the user's past schedule history to select the most suitable checking method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned verification unit is When checking the schedule, customize the checking method based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned verification unit is It estimates the user's emotions and determines the priority of schedule checks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned verification unit is When checking the schedule, the system selects the most suitable checking method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned verification unit is When checking schedules, we analyze the user's social media activity and provide a means of confirmation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned understanding unit is, We estimate the user's emotions and adjust how we understand the content of free-response fields based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned understanding unit is, When understanding the content of a free-form text field, the system refers to the user's past input history to select the most appropriate method of understanding. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned understanding unit is, The system estimates the user's emotions and prioritizes understanding the content of free-response fields based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned understanding unit is, When interpreting the content of free-form text fields, the system selects the most appropriate interpretation method, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned checking unit is The system estimates the user's emotions and adjusts the daily report checking method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned checking unit is When checking daily reports, the system selects the optimal checking method by referring to the user's past daily report history. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned checking unit is When checking daily reports, customize the checking method based on the user's current project status. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned checking unit is The system estimates the user's emotions and determines the priority of checking daily reports based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned checking unit is When checking daily reports, the optimal checking method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned checking unit is During daily report checks, we provide a means to analyze and check users' social media activity. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts task inputs, A generation unit that automatically generates a schedule based on the task information received by the reception unit, A presentation unit that presents improvement points based on the schedule generated by the generation unit, A consultation unit that provides a 1on1 function based on the improvement points presented by the aforementioned presentation unit, The system includes a confirmation unit that checks the schedule based on the 1on1 function provided by the consultation unit. A system characterized by the following features.

2. It includes an understanding unit that compresens the content of the free-response field. The system according to feature 1.

3. It includes a checking unit that provides a function to check daily reports. The system according to feature 1.

4. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of task input based on the estimated user emotions. The system according to feature 1.

5. The aforementioned reception unit is Analyze the user's past task input history and select the optimal input method. The system according to feature 1.

6. The aforementioned reception unit is When entering tasks, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input tasks based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is When entering tasks, the system prioritizes tasks that are highly relevant to the user, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When entering tasks, the system analyzes the user's social media activity and enters relevant tasks. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts how the schedule is generated based on those estimated emotions. The system according to feature 1.

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