system

Abstract

The system according to this embodiment aims to efficiently perform automatic task generation, scheduling, and progress tracking in project management. [Solution] The system according to the embodiment comprises a reception unit, a task generation unit, a scheduling unit, a data collection unit, a next task generation unit, a progress tracking unit, and a performance analysis unit. The reception unit registers cases. The task generation unit automatically generates subdivided tasks. The scheduling unit optimally schedules the generated tasks based on the user's abilities, lifestyle, and personal data. The data collection unit automatically handles data collection, research, and analysis necessary for the generated tasks. The next task generation unit automatically generates the next task. The progress tracking unit tracks the progress of tasks in real time, and the generation AI analyzes and manages it. The performance analysis unit analyzes the user's productivity based on the tracked progress data and provides advice for skill improvement.

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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, automatic generation, scheduling, and progress tracking of tasks in project management are not sufficiently performed, and there is room for improvement. <00,00027> The system according to the embodiment aims to efficiently perform automatic generation, scheduling, and progress tracking of tasks in project management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a task generation unit, a scheduling unit, a data collection unit, a next task generation unit, a progress tracking unit, and a performance analysis unit. The reception unit registers cases. The task generation unit automatically generates subdivided tasks based on the cases registered by the reception unit. The scheduling unit optimally schedules the tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. The data collection unit automatically handles the data collection, research, and analysis necessary for the tasks generated by the task generation unit. The next task generation unit automatically generates the next task based on the data collected by the data collection unit. The progress tracking unit tracks the progress of the tasks generated by the task generation unit in real time, and the generation AI analyzes and manages it. The performance analysis unit analyzes the user's productivity based on the progress data tracked by the progress tracking unit and provides advice for skill improvement. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform automatic task generation, scheduling, and progress tracking in project management. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged 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 expressing three or more matters connected 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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) The project management system according to an embodiment of the present invention is a system that dramatically boosts the start of tasks and improves productivity. This system automatically generates subdivided tasks when a project is registered, and can also automatically generate tasks from voice recordings, photos, and screenshots. The generated tasks are optimally scheduled based on the user's abilities, lifestyle, and personal data. Furthermore, it automatically handles tasks such as data collection, research, and analysis required for the automatically generated tasks, and automatically handles subsequent tasks that arise in addition to the output. By tracking task progress in real time and having the generating AI analyze and manage it, users can understand their own productivity and conduct analysis and skill development to further improve productivity. For example, when a user registers a project, subdivided tasks are automatically generated. At the start of the project, the necessary tasks are listed, and each task is broken down into specific steps. This clarifies what the user needs to do, allowing them to work efficiently. The generated tasks are optimally scheduled based on the user's abilities, lifestyle, and personal data. For example, if the user is a morning person, important tasks are scheduled for the morning; if they are a night owl, they are scheduled for the evening. In this way, scheduling tailored to each individual user allows for efficient work. Tasks such as data collection, research, and analysis required for automatically generated tasks are handled automatically. For example, for tasks requiring research, relevant information is automatically collected and analysis results are provided. This allows users to obtain the necessary information without any effort. Subsequent tasks arising from the output are also handled automatically. For example, tasks such as review and revision work required after report creation are automatically generated. This allows users to work smoothly without being confused about what to do next. Task progress is tracked in real time, and the generating AI analyzes and manages it. For example, task completion status and progress rate are displayed in real time, allowing users to understand their own productivity. The generating AI analyzes the progress data and provides users with advice on areas for improvement and skill development.This allows users to improve their work efficiency. Thus, the present invention provides a project management system that dramatically boosts the start of tasks and improves productivity. In this way, the project management system can improve user productivity.

[0029] The project management system according to this embodiment comprises a reception unit, a task generation unit, a scheduling unit, a data collection unit, a next task generation unit, a progress tracking unit, and a performance analysis unit. The reception unit registers cases. The reception unit provides, for example, an interface for users to input cases. The reception unit can support multiple input methods, such as voice input, photo input, and screenshot input. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The reception unit can also extract information from photos and screenshots using image recognition technology. The task generation unit automatically generates subdivided tasks based on the cases registered by the reception unit. For example, the task generation unit analyzes the content of a case and lists the necessary tasks. The task generation unit can understand the content of a case using natural language processing technology and generate appropriate tasks. For example, the task generation unit automatically generates the tasks necessary at the start of a project and breaks each task down into specific steps. The scheduling unit optimally schedules the tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. The scheduling unit adjusts the order and timing of task execution, for example, by considering the user's past work history and daily routine. The scheduling unit can learn the user's behavior patterns using machine learning algorithms and propose an optimal schedule. The data collection unit automatically handles the data collection, research, and analysis necessary for tasks generated by the task generation unit. For example, the data collection unit collects information from the internet and analyzes the relevant data. The data collection unit can automatically collect necessary information using web scraping technology. The next task generation unit automatically generates the next task based on the data collected by the data collection unit. For example, the next task generation unit analyzes the collected data and lists the tasks that should be done next. The next task generation unit can automatically generate the next task using generation AI. The progress tracking unit tracks the progress of tasks generated by the task generation unit in real time, and the generation AI analyzes and manages it. For example, the progress tracking unit displays the task completion status and progress rate in real time.The progress tracking unit can analyze progress data using generative AI and provide users with advice on areas for improvement and skill development. The performance analysis unit analyzes user productivity based on the progress data tracked by the progress tracking unit and provides advice on skill development. For example, the performance analysis unit evaluates the user's work efficiency and the quality of deliverables and proposes areas for improvement. The performance analysis unit can analyze user productivity in detail using generative AI and provide specific advice. As a result, the project management system according to this embodiment can improve user productivity.

[0030] The reception desk registers cases. The reception desk provides an interface for users to input cases. The reception desk can support multiple input methods, such as voice input, photo input, and screenshot input. For example, the reception desk can use voice recognition technology to convert the user's voice input into text data. The reception desk can also extract information from photos and screenshots using image recognition technology. Specifically, voice recognition technology analyzes what the user says in real time and saves it as text data. This allows users to register cases without using their hands. Image recognition technology uses OCR (optical character recognition) to extract text information contained in photos and screenshots. For example, if a user takes a photo of a handwritten memo and uploads the photo to the reception desk, the OCR technology converts the contents of the memo into text data. Furthermore, the reception desk also provides options for users to specify the category and priority of a case when registering it. This makes case management more efficient. For example, when a user starts a new project, specifying the project category as "development" and the priority as "high" allows for smoother subsequent task generation and scheduling. Furthermore, the reception desk also has a function to refer to the history of projects that users have previously registered, allowing users to easily reuse or refer to past projects. This enables the reception desk to allow users to register projects quickly and accurately, significantly improving the efficiency of project management.

[0031] The task generation unit automatically generates subdivided tasks based on cases registered by the reception unit. For example, the task generation unit analyzes the content of a case and lists the necessary tasks. The task generation unit can understand the content of a case using natural language processing technology and generate appropriate tasks. Specifically, natural language processing technology analyzes the case description and extracts keywords and important phrases. This allows the task generation unit to understand the purpose of the case and the necessary steps, and generate tasks accordingly. For example, if a user registers a case to "create a new website design," the task generation unit will automatically generate specific tasks such as "requirements definition," "wireframe creation," and "design review." The task generation unit can also manage the generated tasks in a hierarchical structure. This allows the user to visually grasp the progress of the entire project. Furthermore, the task generation unit can learn from the user's past project data and suggest the most suitable tasks for similar cases. This allows the user to efficiently proceed with new projects while referring to past success stories. The task generation unit also has a function to automatically assign the necessary resources and personnel to the generated tasks. This allows users to save time on task assignment and ensure smooth project progress.

[0032] The scheduling unit optimally schedules tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. For example, the scheduling unit adjusts the order and timing of task execution, taking into account the user's past work history and daily rhythm. The scheduling unit can learn the user's behavior patterns using machine learning algorithms and propose an optimal schedule. Specifically, the machine learning algorithm analyzes the user's past work data to identify when work is performed most efficiently. This allows the scheduling unit to assign important tasks to the time when the user is most focused. For example, if the user is most productive in the morning, the scheduling unit will concentrate important tasks in the morning and assign lighter tasks in the afternoon. The scheduling unit also optimizes the order of task execution, taking into account the user's daily rhythm and personal data. For example, if the user has a habit of exercising at a certain time every day, the scheduling unit will schedule tasks to avoid that time. Furthermore, the scheduling unit can collect user feedback and continuously improve the accuracy of the schedule. This allows users to work on a schedule that suits their daily rhythm and maximize their productivity.

[0033] The data collection unit automatically handles the data collection, research, and analysis required for tasks generated by the task generation unit. For example, the data collection unit collects information from the internet and analyzes the relevant data. The data collection unit can automatically collect necessary information using web scraping technology. Specifically, web scraping technology extracts necessary information from specified websites and stores it in a database. This saves users the trouble of manually collecting information. For example, if a user sets "market research" as a task, the data collection unit collects market data and competitor information from relevant websites and provides the analysis results. The data collection unit can also obtain information from external data sources using APIs. This allows for the acquisition of the latest data in real time, which can be used to help with task progress. Furthermore, the data collection unit automatically organizes the collected data and makes it easily accessible to users. For example, by classifying the collected data by category and displaying it visually, users can quickly find the information they need. In this way, the data collection unit can efficiently provide the information necessary to perform tasks and improve the user's work efficiency.

[0034] The next task generation unit automatically generates the next task based on the data collected by the data collection unit. For example, the next task generation unit analyzes the collected data and lists the tasks that should be done next. The next task generation unit can automatically generate the next task using generation AI. Specifically, the generation AI analyzes the collected data and understands the project's progress and the necessary steps. As a result, the next task generation unit automatically lists the tasks that should be done next and proposes them to the user. For example, after the user completes "market research," the next task generation unit automatically generates the next tasks such as "analysis of research results," "report creation," and "presentation preparation." The next task generation unit can also use generation AI to consider task priorities and dependencies and propose the optimal order of tasks. This allows the user to clearly understand the tasks that should be done next and to efficiently advance the project. Furthermore, the next task generation unit can collect user feedback and continuously improve the accuracy of the generated tasks. As a result, the next task generation unit can always provide the optimal tasks based on the latest information, maximizing the user's work efficiency.

[0035] The progress tracking unit tracks the progress of tasks generated by the task generation unit in real time, and the generation AI analyzes and manages this data. For example, the progress tracking unit displays the completion status and progress rate of tasks in real time. Using the generation AI, the progress tracking unit can analyze progress data and provide users with advice on areas for improvement and skill development. Specifically, the generation AI analyzes the progress of tasks and issues alerts to the user if delays occur or efficiency decreases. This allows users to respond quickly when problems arise. The progress tracking unit also provides a dashboard to visually display the progress of tasks. This allows users to grasp the progress of the entire project at a glance. Furthermore, the progress tracking unit learns the user's work patterns and progress and provides advice on efficient work methods and skill development. For example, if a user is spending too much time on a particular task, the progress tracking unit analyzes the cause and suggests a more efficient work method. In this way, the progress tracking unit can improve the user's work efficiency and contribute to the success of the project.

[0036] The Performance Analysis Department analyzes user productivity based on progress data tracked by the Progress Tracking Department and provides advice for skill improvement. For example, the Performance Analysis Department evaluates users' work efficiency and the quality of their deliverables and proposes areas for improvement. The Performance Analysis Department can use generative AI to analyze user productivity in detail and provide specific advice. Specifically, the generative AI analyzes the user's work data to identify which tasks are being performed efficiently and which tasks have room for improvement. This allows the Performance Analysis Department to provide users with specific areas for improvement and advice for skill development. For example, if a user is spending too much time on a particular task, the Performance Analysis Department will analyze the cause and propose a more efficient way of working. The Performance Analysis Department can also develop long-term skill development plans based on the user's work history. This allows users to continuously improve their skills. Furthermore, the Performance Analysis Department can collect user feedback and continuously improve the accuracy of its analysis results. This allows the Performance Analysis Department to always provide optimal advice based on the latest information, maximizing user productivity.

[0037] The voice input unit accepts voice input. The voice input unit provides, for example, an interface for users to register cases by voice. The voice input unit converts the user's voice input into text data using speech recognition technology. For example, the voice input unit can convert what the user says into text in real time and register it as a case. The voice input unit can also support multiple languages. For example, the voice input unit can recognize voice input in multiple languages ​​such as English, Japanese, and French and convert it into text data. This allows users to easily register cases by voice input. Some or all of the above processing in the voice input unit may be performed using AI or not. For example, the voice input unit can input voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0038] The photo input unit accepts photo input. The photo input unit provides, for example, an interface for users to register photos as projects. The photo input unit extracts information from photos using image recognition technology. For example, the photo input unit can analyze photos taken by users and register them as projects. The photo input unit can also support multiple image formats. For example, the photo input unit can recognize photos in multiple image formats, such as JPEG and PNG, and extract information. This allows users to register visual information as projects through photo input. Some or all of the above processing in the photo input unit may be performed using AI or not. For example, the photo input unit can input image data into a generation AI and have the generation AI perform information extraction from the image data.

[0039] The screenshot input unit accepts screenshot input. The screenshot input unit provides, for example, an interface for users to register screenshots as cases. The screenshot input unit extracts information from screenshots using image recognition technology. For example, the screenshot input unit can analyze screenshots taken by users and register them as cases. The screenshot input unit can also support multiple image formats. For example, the screenshot input unit can recognize screenshots in multiple image formats, such as JPEG and PNG, and extract information. This allows users to easily register digital information as cases by inputting screenshots. Some or all of the above processing in the screenshot input unit may be performed using AI or not. For example, the screenshot input unit can input screenshot data into a generating AI and have the generating AI perform information extraction from the screenshot data.

[0040] The next task generation unit can automatically generate the next task that arises in addition to the output. For example, the next task generation unit automatically generates the next task to be performed, such as review or correction work required after report creation. The next task generation unit can automatically generate the next task using a generation AI. For example, the next task generation unit analyzes the content of the output and lists the next tasks to be performed. This ensures continuity of work by automatically generating the next task based on the output. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output data into a generation AI and have the generation AI perform the generation of the next task.

[0041] The progress tracking unit tracks the progress of tasks in real time, and the generating AI can analyze and manage it. For example, the progress tracking unit displays the completion status and progress rate of tasks in real time. The progress tracking unit can analyze the progress data using the generating AI and provide the user with advice on areas for improvement and skill development. For example, the progress tracking unit can visually display the progress of tasks as graphs or charts, allowing the user to understand their own productivity. This allows for real-time tracking of progress, and analysis and management by the generating AI, thereby improving the user's productivity. Some or all of the above-described processes in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input progress data into the generating AI and have the generating AI perform the analysis of the progress status.

[0042] The Performance Analysis Department can understand user productivity and provide analysis and skill-building advice for further productivity improvement. For example, the Performance Analysis Department can evaluate user work efficiency and the quality of deliverables, and propose areas for improvement. The Performance Analysis Department can use generative AI to analyze user productivity in detail and provide specific advice. For example, the Performance Analysis Department can analyze a user's work history and propose efficient work methods and training plans for skill improvement. This allows for further productivity improvements by analyzing user productivity and providing skill-building advice. Some or all of the above processes in the Performance Analysis Department may be performed using AI or not. For example, the Performance Analysis Department can input user work data into a generative AI and have the generative AI perform productivity analysis.

[0043] The reception department can analyze past case submission history and select the optimal submission method. For example, the reception department can automatically display cases that the user has frequently registered in the past as candidates. The reception department can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception department can predict and suggest cases to be used during specific time periods based on the user's past case submission history. This improves user convenience by selecting the optimal submission method based on past history. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input past case data into a generating AI and have the generating AI select the optimal submission method.

[0044] The reception desk can filter incoming cases based on the user's current projects and areas of interest. For example, the reception desk can prioritize displaying cases related to projects the user is currently working on. It can also suggest highly relevant cases based on the user's areas of interest. Furthermore, the reception desk can analyze the user's past project history and suggest the most suitable cases. This allows for priority reception of highly relevant cases by filtering them based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0045] The reception desk can prioritize accepting cases that are highly relevant to the user, taking into account the user's geographical location. For example, the reception desk can prioritize displaying cases that are close to the user's current location. It can also suggest highly relevant cases based on the user's geographical location. Furthermore, the reception desk can analyze the user's travel history and suggest the most suitable cases. This allows for the priority acceptance of highly relevant cases by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's location data into a generating AI and have the generating AI perform case filtering.

[0046] The reception department can analyze a user's social media activity when receiving a request and accept relevant requests. For example, the reception department can analyze a user's social media activity and suggest relevant requests. It can also suggest requests based on topics the user has shown interest in on social media. Furthermore, the reception department can suggest requests by referring to the activities of the user's followers and friends on social media. This allows for the priority acceptance of highly relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's social media data into a generating AI and have the generating AI generate requests.

[0047] The task generation unit can adjust the level of detail of tasks based on the importance of the case when generating tasks. For example, the task generation unit can generate detailed tasks for high-importance cases. It can also generate simplified tasks for low-importance cases. Furthermore, the task generation unit can adjust the priority of tasks according to their importance. This allows for efficient task management by adjusting the level of detail of tasks based on the importance of the case. Some or all of the above processing in the task generation unit may be performed using AI or not. For example, the task generation unit can input case importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the tasks.

[0048] The task generation unit can apply different task generation algorithms depending on the category of the project when generating tasks. For example, for technical projects, the task generation unit can apply an algorithm that generates technical tasks. It can also apply an algorithm that generates creative tasks for creative projects. Furthermore, it can apply an algorithm that generates management tasks for management projects. This allows for efficient task management by applying task generation algorithms according to the project category. Some or all of the above-described processes in the task generation unit may be performed using AI, or they may not. For example, the task generation unit can input project category data into a generation AI and have the generation AI execute the application of task generation algorithms.

[0049] The task generation unit can determine task priorities based on the submission deadlines of each project when generating tasks. For example, the task generation unit can prioritize generating tasks for projects with approaching deadlines. It can also postpone generating tasks for projects with distant deadlines. Furthermore, the task generation unit can adjust task priorities according to the submission deadlines. This enables efficient task management by determining task priorities based on the submission deadlines of each project. Some or all of the above-described processes in the task generation unit may be performed using AI or not. For example, the task generation unit can input project submission deadline data into a generation AI and have the generation AI determine task priorities.

[0050] The task generation unit can adjust the order of tasks based on the relevance of the cases when generating tasks. For example, the task generation unit can prioritize the generation of highly relevant tasks. It can also postpone the generation of less relevant tasks. Furthermore, the task generation unit can adjust the order of tasks according to the relevance of the cases. This allows for efficient task management by adjusting the order of tasks based on the relevance of the cases. Some or all of the above processing in the task generation unit may be performed using AI or not. For example, the task generation unit can input case relevance data into a generation AI and have the generation AI perform the task order adjustment.

[0051] The scheduling unit can create an optimal schedule by referring to the user's past schedule history during scheduling. For example, the scheduling unit can create a new schedule by referring to the user's past successful schedules. The scheduling unit can also suggest an optimal schedule based on the user's past schedule history. Furthermore, the scheduling unit can analyze the user's past schedule history and create an efficient schedule. In this way, an optimal schedule can be created by referring to past schedule history. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the user's schedule data into a generation AI and have the generation AI create an optimal schedule.

[0052] The scheduling unit can customize the schedule based on the user's current lifestyle. For example, if the user is a morning person, the scheduling unit can schedule important tasks in the morning. Similarly, if the user is a night owl, the scheduling unit can schedule important tasks in the evening. Furthermore, the scheduling unit can create an optimal schedule tailored to the user's lifestyle. This allows for efficient schedule management by customizing the schedule based on the user's lifestyle. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user lifestyle data into a generating AI and have the generating AI perform the schedule customization.

[0053] The scheduling unit can create an optimal schedule by considering the user's geographical location information during scheduling. For example, the scheduling unit prioritizes scheduling tasks that are close to the user's current location. The scheduling unit can also suggest highly relevant tasks based on the user's geographical location information. Furthermore, the scheduling unit can analyze the user's travel history to create an optimal schedule. This allows for the creation of an optimal schedule by considering the user's geographical location information. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the user's location data into a generation AI and have the generation AI create the schedule.

[0054] The scheduling unit can analyze the user's social media activity and adjust the schedule during scheduling. For example, the scheduling unit can analyze the user's social media activity and schedule relevant tasks. The scheduling unit can also schedule tasks based on topics the user has shown interest in on social media. Furthermore, the scheduling unit can schedule tasks by referring to the activities of the user's followers and friends on social media. This allows for the scheduling of highly relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the user's social media data into a generating AI and have the generating AI perform the schedule adjustments.

[0055] The data collection unit can select the optimal data collection method by referring to past data collection history when collecting data. For example, the data collection unit can select a new data collection method by referring to data collection methods that have been successful for the user in the past. The data collection unit can also suggest the optimal data collection method based on the user's past data collection history. Furthermore, the data collection unit can analyze the user's past data collection history and select an efficient data collection method. In this way, the optimal data collection method can be selected by referring to past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's data collection history into a generating AI and have the generating AI perform the selection of the optimal data collection method.

[0056] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current ongoing projects. It can also collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can analyze the user's past project history and collect the most relevant data. This allows for the priority collection of highly relevant data by filtering data based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's project data into a generating AI and have the generating AI perform data filtering.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also suggest highly relevant data based on the user's geographical location information. Furthermore, the data collection unit can analyze the user's movement history and collect the most suitable data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI perform the data collection.

[0058] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media activities and collect relevant data. The data collection unit can also collect data based on topics that the user has shown interest in on social media. Furthermore, the data collection unit can collect data by referring to the activities of the user's followers and friends on social media. This allows for the priority collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the data collection.

[0059] The next task generation unit can adjust the level of detail of the next task based on the importance of the output when generating the next task. For example, the next task generation unit can generate a detailed next task for high-importance outputs. It can also generate a simplified next task for low-importance outputs. Furthermore, the next task generation unit can adjust the priority of the next task according to its importance. This allows for efficient task management by adjusting the level of detail of the next task based on the importance of the output. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the next task.

[0060] The next task generation unit can apply different task generation algorithms depending on the output category when generating the next task. For example, for technical outputs, the next task generation unit can apply an algorithm to generate a technical next task. It can also apply an algorithm to generate a creative next task for creative outputs. Furthermore, it can apply an algorithm to generate management tasks for management outputs. This allows for efficient task management by applying task generation algorithms according to the output category. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output category data into a generation AI and have the generation AI execute the application of the task generation algorithm.

[0061] The next task generation unit can determine the priority of the next task based on the submission date of the output when generating the next task. For example, the next task generation unit will prioritize generating the next task for outputs with an approaching submission deadline. It can also postpone generating the next task for outputs with a distant submission deadline. Furthermore, the next task generation unit can adjust the priority of the next task according to the submission date. This enables efficient task management by determining the priority of the next task based on the submission date of the output. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output submission date data into a generation AI and have the generation AI perform the determination of the next task priority.

[0062] The next task generation unit can adjust the order of subsequent tasks based on the relevance of their outputs when generating them. For example, the next task generation unit can prioritize generating the next tasks that are highly relevant. It can also postpone the generation of the next tasks that are less relevant. Furthermore, the next task generation unit can adjust the order of the next tasks according to the relevance of their outputs. This allows for efficient task management by adjusting the order of the next tasks based on the relevance of their outputs. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output relevance data into a generation AI and have the generation AI perform the adjustment of the order of the next tasks.

[0063] The progress tracking unit can select the optimal progress tracking method by referring to past progress data during progress tracking. For example, the progress tracking unit can select a new progress tracking method by referring to progress tracking methods that have been successful for the user in the past. The progress tracking unit can also propose the optimal progress tracking method based on the user's past progress data. Furthermore, the progress tracking unit can analyze the user's past progress data and select an efficient progress tracking method. In this way, the optimal progress tracking method can be selected by referring to past progress data. Some or all of the above processing in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input the user's progress data into a generating AI and have the generating AI perform the selection of the optimal progress tracking method.

[0064] The progress tracking unit can filter progress data based on the user's current projects and areas of interest during progress tracking. For example, the progress tracking unit can prioritize tracking progress data related to projects the user is currently working on. The progress tracking unit can also track highly relevant progress data based on the user's areas of interest. Furthermore, the progress tracking unit can analyze the user's past project history and track the most relevant progress data. This allows for the priority tracking of highly relevant progress data by filtering progress data based on the user's areas of interest. Some or all of the above processing in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input the user's project data into a generating AI and have the generating AI perform the filtering of progress data.

[0065] The progress tracking unit can prioritize displaying highly relevant progress data while considering the user's geographical location information during progress tracking. For example, the progress tracking unit can prioritize displaying progress data related to the user's current location. Furthermore, the progress tracking unit can suggest highly relevant progress data based on the user's geographical location information. In addition, the progress tracking unit can analyze the user's movement history and display the most appropriate progress data. This allows for the priority display of highly relevant progress data by considering the user's geographical location information. Some or all of the above processing in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input the user's location data into a generating AI and have the generating AI display the progress data.

[0066] The progress tracking unit can analyze the user's social media activity and display relevant progress data during progress tracking. For example, the progress tracking unit can analyze the user's social media activity and display relevant progress data. The progress tracking unit can also display progress data based on topics the user has shown interest in on social media. Furthermore, the progress tracking unit can display progress data by referring to the activity of the user's followers and friends on social media. This allows for the priority display of highly relevant progress data by analyzing the user's social media activity. Some or all of the above processing in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input the user's social media data into a generating AI and have the generating AI perform the display of progress data.

[0067] The performance analysis unit can select the optimal analysis method by referring to past performance data during performance analysis. For example, the performance analysis unit can select a new analysis method by referring to performance analysis methods that have been successful for the user in the past. The performance analysis unit can also propose the optimal analysis method based on the user's past performance data. Furthermore, the performance analysis unit can analyze the user's past performance data and select an efficient analysis method. In this way, the optimal analysis method can be selected by referring to past performance data. Some or all of the above processes in the performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input the user's performance data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0068] The performance analysis unit can filter performance data based on the user's current projects and areas of interest during performance analysis. For example, the performance analysis unit can prioritize the analysis of performance data related to the user's current ongoing projects. It can also analyze highly relevant performance data based on the user's areas of interest. Furthermore, the performance analysis unit can analyze the user's past project history to identify the most relevant performance data. This allows for the prioritization of highly relevant data by filtering performance data based on the user's areas of interest. Some or all of the above processes in the performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input the user's project data into a generating AI and have the generating AI perform the filtering of performance data.

[0069] The performance analysis unit can prioritize displaying highly relevant performance data by considering the user's geographical location during performance analysis. For example, the performance analysis unit can prioritize displaying performance data related to the user's current location. Furthermore, the performance analysis unit can suggest highly relevant performance data based on the user's geographical location. In addition, the performance analysis unit can analyze the user's movement history and display the most relevant performance data. This allows for the priority display of highly relevant performance data by considering the user's geographical location. Some or all of the above processing in the performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input the user's location data into a generating AI and have the generating AI display the performance data.

[0070] The performance analysis unit can analyze a user's social media activity and display relevant performance data during performance analysis. For example, the performance analysis unit can analyze the content of a user's social media activity and display relevant performance data. It can also display performance data based on topics the user has shown interest in on social media. Furthermore, the performance analysis unit can display performance data by referring to the activity of the user's followers and friends on social media. This allows for the priority display of highly relevant performance data by analyzing the user's social media activity. Some or all of the above processing in the performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input the user's social media data into a generating AI and have the generating AI perform the display of performance data.

[0071] The voice input unit can select the optimal voice input method by referring to past voice input data during voice input. For example, the voice input unit can select a new voice input method by referring to a voice input method that the user has successfully used in the past. The voice input unit can also suggest the optimal voice input method from the user's past voice input data. Furthermore, the voice input unit can analyze the user's past voice input data and select an efficient voice input method. In this way, the optimal voice input method can be selected by referring to past voice input data. Some or all of the above processing in the voice input unit may be performed using AI or not. For example, the voice input unit can input the user's voice input data into a generating AI and have the generating AI perform the selection of the optimal voice input method.

[0072] The voice input unit can prioritize processing highly relevant voice input data by considering the user's geographical location information during voice input. For example, the voice input unit can prioritize processing voice input data related to the user's current location. Furthermore, the voice input unit can suggest highly relevant voice input data based on the user's geographical location information. In addition, the voice input unit can analyze the user's movement history and process the most appropriate voice input data. This allows for the priority processing of highly relevant voice input data by considering the user's geographical location information. Some or all of the above processing in the voice input unit may be performed using AI, or without AI. For example, the voice input unit can input the user's location data into a generating AI and have the generating AI process the voice input data.

[0073] The photo input unit can select the optimal photo input method by referring to past photo input data when a photo is being input. For example, the photo input unit can select a new photo input method by referring to a photo input method that the user has successfully used in the past. The photo input unit can also suggest the optimal photo input method from the user's past photo input data. Furthermore, the photo input unit can analyze the user's past photo input data and select an efficient photo input method. In this way, the optimal photo input method can be selected by referring to past photo input data. Some or all of the above processing in the photo input unit may be performed using AI or not. For example, the photo input unit can input the user's photo input data into a generating AI and have the generating AI perform the selection of the optimal photo input method.

[0074] The photo input unit can prioritize processing highly relevant photo input data by considering the user's geographical location information when a photo is input. For example, the photo input unit can prioritize processing photo input data related to the user's current location. Furthermore, the photo input unit can suggest highly relevant photo input data based on the user's geographical location information. In addition, the photo input unit can analyze the user's movement history and process the most suitable photo input data. This allows for the priority processing of highly relevant photo input data by considering the user's geographical location information. Some or all of the above processing in the photo input unit may be performed using AI, or without AI. For example, the photo input unit can input the user's location data into a generating AI and have the generating AI process the photo input data.

[0075] The screenshot input unit can select the optimal screenshot input method by referring to past screenshot input data when a screenshot is taken. For example, the screenshot input unit can select a new screenshot input method by referring to screenshot input methods that the user has successfully used in the past. The screenshot input unit can also suggest the optimal screenshot input method from the user's past screenshot input data. Furthermore, the screenshot input unit can analyze the user's past screenshot input data and select an efficient screenshot input method. In this way, the optimal screenshot input method can be selected by referring to past screenshot input data. Some or all of the above processing in the screenshot input unit may be performed using AI or not. For example, the screenshot input unit can input the user's screenshot input data into a generating AI and have the generating AI perform the selection of the optimal screenshot input method.

[0076] The screenshot input unit can prioritize processing highly relevant screenshot input data by considering the user's geographical location information when a screenshot is taken. For example, the screenshot input unit can prioritize processing screenshot input data related to the user's current location. Furthermore, the screenshot input unit can suggest highly relevant screenshot input data based on the user's geographical location information. In addition, the screenshot input unit can analyze the user's movement history and process the most appropriate screenshot input data. This allows for the priority processing of highly relevant screenshot input data by considering the user's geographical location information. Some or all of the above processing in the screenshot input unit may be performed using AI, or without AI. For example, the screenshot input unit can input the user's location data into a generating AI and have the generating AI process the screenshot input data.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] Project management systems can also include features to analyze a user's past task history and select the optimal task generation method. For example, they can generate new tasks by referencing successful task generation methods used by the user in the past. They can also suggest the optimal task generation method based on the user's past task history. Furthermore, they can analyze the user's past task history and select an efficient task generation method. This allows for the selection of the optimal task generation method by referring to past task history.

[0079] Project management systems can also include features to filter tasks based on the user's current projects and areas of interest. For example, they can prioritize generating tasks related to projects the user is currently working on. They can also generate highly relevant tasks based on the user's areas of interest. Furthermore, they can analyze the user's past project history to generate optimal tasks. This allows for the prioritization of highly relevant tasks by filtering them based on the user's areas of interest.

[0080] Project management systems can also incorporate features that prioritize the generation of highly relevant tasks by considering the user's geographical location. For example, they can prioritize tasks related to the user's current location. They can also suggest highly relevant tasks based on the user's geographical location. Furthermore, they can analyze the user's travel history to generate optimal tasks. This allows for the priority generation of highly relevant tasks by considering the user's geographical location.

[0081] Project management systems can also incorporate features to analyze users' social media activity and generate relevant tasks. For example, they can analyze a user's social media activity and generate relevant tasks. They can also generate tasks based on topics the user has shown interest in on social media. Furthermore, they can generate tasks by referencing the activity of the user's followers and friends on social media. This allows for the priority generation of highly relevant tasks by analyzing the user's social media activity.

[0082] Project management systems can also include features to create optimal schedules by referencing the user's past schedule history. For example, they can create new schedules by referencing successful schedules from the past. They can also suggest optimal schedules based on the user's past schedule history. Furthermore, they can analyze the user's past schedule history to create efficient schedules. This allows for the creation of optimal schedules by referencing past schedule history.

[0083] Project management systems can also include features that customize schedules based on the user's current lifestyle. For example, if a user is a morning person, important tasks can be scheduled for the morning. Conversely, if a user is a night owl, important tasks can be scheduled for the evening. Furthermore, it's possible to create an optimal schedule tailored to the user's daily rhythm. This allows for efficient schedule management by customizing schedules based on the user's lifestyle.

[0084] Project management systems can also include features to select the optimal data collection method by referring to the user's past data collection history. For example, they can select a new data collection method by referencing the user's past successful data collection methods. They can also suggest the optimal data collection method based on the user's past data collection history. Furthermore, they can analyze the user's past data collection history to select an efficient data collection method. This allows for the selection of the optimal data collection method by referring to past data collection history.

[0085] The following briefly describes the processing flow for example form 1.

[0086] Step 1: The reception desk registers the case. The reception desk provides, for example, an interface for users to input the case. The reception desk can support multiple input methods, such as voice input, photo input, and screenshot input. For example, the reception desk can use voice recognition technology to convert the user's voice input into text data. The reception desk can also use image recognition technology to extract information from photos and screenshots. Step 2: The task generation unit automatically generates subdivided tasks based on the cases registered by the reception unit. For example, the task generation unit analyzes the content of the case and lists the necessary tasks. The task generation unit can understand the content of the case using natural language processing technology and generate appropriate tasks. For example, the task generation unit automatically generates the tasks required at the start of a project and breaks each task down into specific steps. Step 3: The scheduling unit optimally schedules the tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. For example, the scheduling unit adjusts the order and timing of task execution, taking into account the user's past work history and daily rhythm. The scheduling unit can learn the user's behavior patterns using machine learning algorithms and propose an optimal schedule. Step 4: The data collection unit automatically handles the data collection, investigation, and analysis required for the tasks generated by the task generation unit. For example, the data collection unit collects information from the internet and analyzes the relevant data. The data collection unit can automatically collect the necessary information using web scraping technology. Step 5: The next task generation unit automatically generates the next task based on the data collected by the data collection unit. For example, the next task generation unit analyzes the collected data and lists the tasks that should be done next. The next task generation unit can automatically generate the next task using a generation AI. Step 6: The progress tracking unit tracks the progress of tasks generated by the task generation unit in real time, and the generation AI analyzes and manages the data. For example, the progress tracking unit displays the task completion status and progress rate in real time. The progress tracking unit can analyze the progress data using the generation AI and provide the user with advice on areas for improvement and skill development. Step 7: The Performance Analysis Department analyzes user productivity based on progress data tracked by the Progress Tracking Department and provides advice for skill improvement. For example, the Performance Analysis Department evaluates user work efficiency and the quality of deliverables and proposes areas for improvement. The Performance Analysis Department can use generative AI to analyze user productivity in detail and provide specific advice.

[0087] (Example of form 2) The project management system according to an embodiment of the present invention is a system that dramatically boosts the start of tasks and improves productivity. This system automatically generates subdivided tasks when a project is registered, and can also automatically generate tasks from voice recordings, photos, and screenshots. The generated tasks are optimally scheduled based on the user's abilities, lifestyle, and personal data. Furthermore, it automatically handles tasks such as data collection, research, and analysis required for the automatically generated tasks, and automatically handles subsequent tasks that arise in addition to the output. By tracking task progress in real time and having the generating AI analyze and manage it, users can understand their own productivity and conduct analysis and skill development to further improve productivity. For example, when a user registers a project, subdivided tasks are automatically generated. At the start of the project, the necessary tasks are listed, and each task is broken down into specific steps. This clarifies what the user needs to do, allowing them to work efficiently. The generated tasks are optimally scheduled based on the user's abilities, lifestyle, and personal data. For example, if the user is a morning person, important tasks are scheduled for the morning; if they are a night owl, they are scheduled for the evening. In this way, scheduling tailored to each individual user allows for efficient work. Tasks such as data collection, research, and analysis required for automatically generated tasks are handled automatically. For example, for tasks requiring research, relevant information is automatically collected and analysis results are provided. This allows users to obtain the necessary information without any effort. Subsequent tasks arising from the output are also handled automatically. For example, tasks such as review and revision work required after report creation are automatically generated. This allows users to work smoothly without being confused about what to do next. Task progress is tracked in real time, and the generating AI analyzes and manages it. For example, task completion status and progress rate are displayed in real time, allowing users to understand their own productivity. The generating AI analyzes the progress data and provides users with advice on areas for improvement and skill development.This allows users to improve their work efficiency. Thus, the present invention provides a project management system that dramatically boosts the start of tasks and improves productivity. In this way, the project management system can improve user productivity.

[0088] The project management system according to this embodiment comprises a reception unit, a task generation unit, a scheduling unit, a data collection unit, a next task generation unit, a progress tracking unit, and a performance analysis unit. The reception unit registers cases. The reception unit provides, for example, an interface for users to input cases. The reception unit can support multiple input methods, such as voice input, photo input, and screenshot input. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The reception unit can also extract information from photos and screenshots using image recognition technology. The task generation unit automatically generates subdivided tasks based on the cases registered by the reception unit. For example, the task generation unit analyzes the content of a case and lists the necessary tasks. The task generation unit can understand the content of a case using natural language processing technology and generate appropriate tasks. For example, the task generation unit automatically generates the tasks necessary at the start of a project and breaks each task down into specific steps. The scheduling unit optimally schedules the tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. The scheduling unit adjusts the order and timing of task execution, for example, by considering the user's past work history and daily routine. The scheduling unit can learn the user's behavior patterns using machine learning algorithms and propose an optimal schedule. The data collection unit automatically handles the data collection, research, and analysis necessary for tasks generated by the task generation unit. For example, the data collection unit collects information from the internet and analyzes the relevant data. The data collection unit can automatically collect necessary information using web scraping technology. The next task generation unit automatically generates the next task based on the data collected by the data collection unit. For example, the next task generation unit analyzes the collected data and lists the tasks that should be done next. The next task generation unit can automatically generate the next task using generation AI. The progress tracking unit tracks the progress of tasks generated by the task generation unit in real time, and the generation AI analyzes and manages it. For example, the progress tracking unit displays the task completion status and progress rate in real time.The progress tracking unit can analyze progress data using generative AI and provide users with advice on areas for improvement and skill development. The performance analysis unit analyzes user productivity based on the progress data tracked by the progress tracking unit and provides advice on skill development. For example, the performance analysis unit evaluates the user's work efficiency and the quality of deliverables and proposes areas for improvement. The performance analysis unit can analyze user productivity in detail using generative AI and provide specific advice. As a result, the project management system according to this embodiment can improve user productivity.

[0089] The reception desk registers cases. The reception desk provides an interface for users to input cases. The reception desk can support multiple input methods, such as voice input, photo input, and screenshot input. For example, the reception desk can use voice recognition technology to convert the user's voice input into text data. The reception desk can also extract information from photos and screenshots using image recognition technology. Specifically, voice recognition technology analyzes what the user says in real time and saves it as text data. This allows users to register cases without using their hands. Image recognition technology uses OCR (optical character recognition) to extract text information contained in photos and screenshots. For example, if a user takes a photo of a handwritten memo and uploads the photo to the reception desk, the OCR technology converts the contents of the memo into text data. Furthermore, the reception desk also provides options for users to specify the category and priority of a case when registering it. This makes case management more efficient. For example, when a user starts a new project, specifying the project category as "development" and the priority as "high" allows for smoother subsequent task generation and scheduling. Furthermore, the reception desk also has a function to refer to the history of projects that users have previously registered, allowing users to easily reuse or refer to past projects. This enables the reception desk to allow users to register projects quickly and accurately, significantly improving the efficiency of project management.

[0090] The task generation unit automatically generates subdivided tasks based on cases registered by the reception unit. For example, the task generation unit analyzes the content of a case and lists the necessary tasks. The task generation unit can understand the content of a case using natural language processing technology and generate appropriate tasks. Specifically, natural language processing technology analyzes the case description and extracts keywords and important phrases. This allows the task generation unit to understand the purpose of the case and the necessary steps, and generate tasks accordingly. For example, if a user registers a case to "create a new website design," the task generation unit will automatically generate specific tasks such as "requirements definition," "wireframe creation," and "design review." The task generation unit can also manage the generated tasks in a hierarchical structure. This allows the user to visually grasp the progress of the entire project. Furthermore, the task generation unit can learn from the user's past project data and suggest the most suitable tasks for similar cases. This allows the user to efficiently proceed with new projects while referring to past success stories. The task generation unit also has a function to automatically assign the necessary resources and personnel to the generated tasks. This allows users to save time on task assignment and ensure smooth project progress.

[0091] The scheduling unit optimally schedules tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. For example, the scheduling unit adjusts the order and timing of task execution, taking into account the user's past work history and daily rhythm. The scheduling unit can learn the user's behavior patterns using machine learning algorithms and propose an optimal schedule. Specifically, the machine learning algorithm analyzes the user's past work data to identify when work is performed most efficiently. This allows the scheduling unit to assign important tasks to the time when the user is most focused. For example, if the user is most productive in the morning, the scheduling unit will concentrate important tasks in the morning and assign lighter tasks in the afternoon. The scheduling unit also optimizes the order of task execution, taking into account the user's daily rhythm and personal data. For example, if the user has a habit of exercising at a certain time every day, the scheduling unit will schedule tasks to avoid that time. Furthermore, the scheduling unit can collect user feedback and continuously improve the accuracy of the schedule. This allows users to work on a schedule that suits their daily rhythm and maximize their productivity.

[0092] The data collection unit automatically handles the data collection, research, and analysis required for tasks generated by the task generation unit. For example, the data collection unit collects information from the internet and analyzes the relevant data. The data collection unit can automatically collect necessary information using web scraping technology. Specifically, web scraping technology extracts necessary information from specified websites and stores it in a database. This saves users the trouble of manually collecting information. For example, if a user sets "market research" as a task, the data collection unit collects market data and competitor information from relevant websites and provides the analysis results. The data collection unit can also obtain information from external data sources using APIs. This allows for the acquisition of the latest data in real time, which can be used to help with task progress. Furthermore, the data collection unit automatically organizes the collected data and makes it easily accessible to users. For example, by classifying the collected data by category and displaying it visually, users can quickly find the information they need. In this way, the data collection unit can efficiently provide the information necessary to perform tasks and improve the user's work efficiency.

[0093] The next task generation unit automatically generates the next task based on the data collected by the data collection unit. For example, the next task generation unit analyzes the collected data and lists the tasks that should be done next. The next task generation unit can automatically generate the next task using generation AI. Specifically, the generation AI analyzes the collected data and understands the project's progress and the necessary steps. As a result, the next task generation unit automatically lists the tasks that should be done next and proposes them to the user. For example, after the user completes "market research," the next task generation unit automatically generates the next tasks such as "analysis of research results," "report creation," and "presentation preparation." The next task generation unit can also use generation AI to consider task priorities and dependencies and propose the optimal order of tasks. This allows the user to clearly understand the tasks that should be done next and to efficiently advance the project. Furthermore, the next task generation unit can collect user feedback and continuously improve the accuracy of the generated tasks. As a result, the next task generation unit can always provide the optimal tasks based on the latest information, maximizing the user's work efficiency.

[0094] The progress tracking unit tracks the progress of tasks generated by the task generation unit in real time, and the generation AI analyzes and manages this data. For example, the progress tracking unit displays the completion status and progress rate of tasks in real time. Using the generation AI, the progress tracking unit can analyze progress data and provide users with advice on areas for improvement and skill development. Specifically, the generation AI analyzes the progress of tasks and issues alerts to the user if delays occur or efficiency decreases. This allows users to respond quickly when problems arise. The progress tracking unit also provides a dashboard to visually display the progress of tasks. This allows users to grasp the progress of the entire project at a glance. Furthermore, the progress tracking unit learns the user's work patterns and progress and provides advice on efficient work methods and skill development. For example, if a user is spending too much time on a particular task, the progress tracking unit analyzes the cause and suggests a more efficient work method. In this way, the progress tracking unit can improve the user's work efficiency and contribute to the success of the project.

[0095] The Performance Analysis Department analyzes user productivity based on progress data tracked by the Progress Tracking Department and provides advice for skill improvement. For example, the Performance Analysis Department evaluates users' work efficiency and the quality of their deliverables and proposes areas for improvement. The Performance Analysis Department can use generative AI to analyze user productivity in detail and provide specific advice. Specifically, the generative AI analyzes the user's work data to identify which tasks are being performed efficiently and which tasks have room for improvement. This allows the Performance Analysis Department to provide users with specific areas for improvement and advice for skill development. For example, if a user is spending too much time on a particular task, the Performance Analysis Department will analyze the cause and propose a more efficient way of working. The Performance Analysis Department can also develop long-term skill development plans based on the user's work history. This allows users to continuously improve their skills. Furthermore, the Performance Analysis Department can collect user feedback and continuously improve the accuracy of its analysis results. This allows the Performance Analysis Department to always provide optimal advice based on the latest information, maximizing user productivity.

[0096] The voice input unit accepts voice input. The voice input unit provides, for example, an interface for users to register cases by voice. The voice input unit converts the user's voice input into text data using speech recognition technology. For example, the voice input unit can convert what the user says into text in real time and register it as a case. The voice input unit can also support multiple languages. For example, the voice input unit can recognize voice input in multiple languages ​​such as English, Japanese, and French and convert it into text data. This allows users to easily register cases by voice input. Some or all of the above processing in the voice input unit may be performed using AI or not. For example, the voice input unit can input voice data into a generating AI and have the generating AI perform the conversion from voice data to text data.

[0097] The photo input unit accepts photo input. The photo input unit provides, for example, an interface for users to register photos as projects. The photo input unit extracts information from photos using image recognition technology. For example, the photo input unit can analyze photos taken by users and register them as projects. The photo input unit can also support multiple image formats. For example, the photo input unit can recognize photos in multiple image formats, such as JPEG and PNG, and extract information. This allows users to register visual information as projects through photo input. Some or all of the above processing in the photo input unit may be performed using AI or not. For example, the photo input unit can input image data into a generation AI and have the generation AI perform information extraction from the image data.

[0098] The screenshot input unit accepts screenshot input. The screenshot input unit provides, for example, an interface for users to register screenshots as cases. The screenshot input unit extracts information from screenshots using image recognition technology. For example, the screenshot input unit can analyze screenshots taken by users and register them as cases. The screenshot input unit can also support multiple image formats. For example, the screenshot input unit can recognize screenshots in multiple image formats, such as JPEG and PNG, and extract information. This allows users to easily register digital information as cases by inputting screenshots. Some or all of the above processing in the screenshot input unit may be performed using AI or not. For example, the screenshot input unit can input screenshot data into a generating AI and have the generating AI perform information extraction from the screenshot data.

[0099] The next task generation unit can automatically generate the next task that arises in addition to the output. For example, the next task generation unit automatically generates the next task to be performed, such as review or correction work required after report creation. The next task generation unit can automatically generate the next task using a generation AI. For example, the next task generation unit analyzes the content of the output and lists the next tasks to be performed. This ensures continuity of work by automatically generating the next task based on the output. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output data into a generation AI and have the generation AI perform the generation of the next task.

[0100] The progress tracking unit tracks the progress of tasks in real time, and the generating AI can analyze and manage it. For example, the progress tracking unit displays the completion status and progress rate of tasks in real time. The progress tracking unit can analyze the progress data using the generating AI and provide the user with advice on areas for improvement and skill development. For example, the progress tracking unit can visually display the progress of tasks as graphs or charts, allowing the user to understand their own productivity. This allows for real-time tracking of progress, and analysis and management by the generating AI, thereby improving the user's productivity. Some or all of the above-described processes in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input progress data into the generating AI and have the generating AI perform the analysis of the progress status.

[0101] The Performance Analysis Department can understand user productivity and provide analysis and skill-building advice for further productivity improvement. For example, the Performance Analysis Department can evaluate user work efficiency and the quality of deliverables, and propose areas for improvement. The Performance Analysis Department can use generative AI to analyze user productivity in detail and provide specific advice. For example, the Performance Analysis Department can analyze a user's work history and propose efficient work methods and training plans for skill improvement. This allows for further productivity improvements by analyzing user productivity and providing skill-building advice. Some or all of the above processes in the Performance Analysis Department may be performed using AI or not. For example, the Performance Analysis Department can input user work data into a generative AI and have the generative AI perform productivity analysis.

[0102] The reception desk can estimate the user's emotions and adjust the process of receiving a case based on those emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick case registration. By adjusting the case reception process according to the user's emotions, it is possible to reduce user stress and achieve efficient case registration. 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 reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The reception department can analyze past case submission history and select the optimal submission method. For example, the reception department can automatically display cases that the user has frequently registered in the past as candidates. The reception department can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception department can predict and suggest cases to be used during specific time periods based on the user's past case submission history. This improves user convenience by selecting the optimal submission method based on past history. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input past case data into a generating AI and have the generating AI select the optimal submission method.

[0104] The reception desk can filter incoming cases based on the user's current projects and areas of interest. For example, the reception desk can prioritize displaying cases related to projects the user is currently working on. It can also suggest highly relevant cases based on the user's areas of interest. Furthermore, the reception desk can analyze the user's past project history and suggest the most suitable cases. This allows for priority reception of highly relevant cases by filtering them based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0105] The reception desk can estimate the user's emotions and determine the priority of cases to be received based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone less important cases. Conversely, if the user is relaxed, the reception desk may prioritize more important cases. Furthermore, if the user is in a hurry, the reception desk may prioritize more urgent cases. This enables efficient case management by prioritizing cases 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 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 desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The reception desk can prioritize accepting cases that are highly relevant to the user, taking into account the user's geographical location. For example, the reception desk can prioritize displaying cases that are close to the user's current location. It can also suggest highly relevant cases based on the user's geographical location. Furthermore, the reception desk can analyze the user's travel history and suggest the most suitable cases. This allows for the priority acceptance of highly relevant cases by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's location data into a generating AI and have the generating AI perform case filtering.

[0107] The reception department can analyze a user's social media activity when receiving a request and accept relevant requests. For example, the reception department can analyze a user's social media activity and suggest relevant requests. It can also suggest requests based on topics the user has shown interest in on social media. Furthermore, the reception department can suggest requests by referring to the activities of the user's followers and friends on social media. This allows for the priority acceptance of highly relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's social media data into a generating AI and have the generating AI generate requests.

[0108] The task generation unit can estimate the user's emotions and adjust the task generation method based on the estimated emotions. For example, if the user is relaxed, the task generation unit can generate detailed tasks. If the user is in a hurry, the task generation unit can also generate simplified tasks. Furthermore, if the user is stressed, the task generation unit can postpone lower-priority tasks. This allows for efficient task management by adjusting the task 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 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 task generation unit may be performed using AI or not. For example, the task generation unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0109] The task generation unit can adjust the level of detail of tasks based on the importance of the case when generating tasks. For example, the task generation unit can generate detailed tasks for high-importance cases. It can also generate simplified tasks for low-importance cases. Furthermore, the task generation unit can adjust the priority of tasks according to their importance. This allows for efficient task management by adjusting the level of detail of tasks based on the importance of the case. Some or all of the above processing in the task generation unit may be performed using AI or not. For example, the task generation unit can input case importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the tasks.

[0110] The task generation unit can apply different task generation algorithms depending on the category of the project when generating tasks. For example, for technical projects, the task generation unit can apply an algorithm that generates technical tasks. It can also apply an algorithm that generates creative tasks for creative projects. Furthermore, it can apply an algorithm that generates management tasks for management projects. This allows for efficient task management by applying task generation algorithms according to the project category. Some or all of the above-described processes in the task generation unit may be performed using AI, or they may not. For example, the task generation unit can input project category data into a generation AI and have the generation AI execute the application of task generation algorithms.

[0111] The task generation unit can estimate the user's emotions and determine the priority of tasks to generate based on the estimated emotions. For example, if the user is relaxed, the task generation unit will prioritize generating high-importance tasks. It can also prioritize generating high-urgency tasks if the user is in a hurry. Furthermore, if the user is stressed, the task generation unit can postpone lower-priority tasks. This allows for 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 a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the task generation unit may be performed using AI or not. For example, the task generation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0112] The task generation unit can determine task priorities based on the submission deadlines of each project when generating tasks. For example, the task generation unit can prioritize generating tasks for projects with approaching deadlines. It can also postpone generating tasks for projects with distant deadlines. Furthermore, the task generation unit can adjust task priorities according to the submission deadlines. This enables efficient task management by determining task priorities based on the submission deadlines of each project. Some or all of the above-described processes in the task generation unit may be performed using AI or not. For example, the task generation unit can input project submission deadline data into a generation AI and have the generation AI determine task priorities.

[0113] The task generation unit can adjust the order of tasks based on the relevance of the cases when generating tasks. For example, the task generation unit can prioritize the generation of highly relevant tasks. It can also postpone the generation of less relevant tasks. Furthermore, the task generation unit can adjust the order of tasks according to the relevance of the cases. This allows for efficient task management by adjusting the order of tasks based on the relevance of the cases. Some or all of the above processing in the task generation unit may be performed using AI or not. For example, the task generation unit can input case relevance data into a generation AI and have the generation AI perform the task order adjustment.

[0114] The scheduling unit can estimate the user's emotions and adjust the scheduling method based on the estimated emotions. For example, if the user is relaxed, the scheduling unit can create a flexible schedule. Conversely, if the user is in a hurry, the scheduling unit can create a tight schedule. Furthermore, if the user is stressed, the scheduling unit can postpone less important tasks. This allows for efficient schedule management by adjusting the scheduling 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 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 scheduling unit may be performed using AI or not. For example, the scheduling unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0115] The scheduling unit can create an optimal schedule by referring to the user's past schedule history during scheduling. For example, the scheduling unit can create a new schedule by referring to the user's past successful schedules. The scheduling unit can also suggest an optimal schedule based on the user's past schedule history. Furthermore, the scheduling unit can analyze the user's past schedule history and create an efficient schedule. In this way, an optimal schedule can be created by referring to past schedule history. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the user's schedule data into a generation AI and have the generation AI create an optimal schedule.

[0116] The scheduling unit can customize the schedule based on the user's current lifestyle. For example, if the user is a morning person, the scheduling unit can schedule important tasks in the morning. Similarly, if the user is a night owl, the scheduling unit can schedule important tasks in the evening. Furthermore, the scheduling unit can create an optimal schedule tailored to the user's lifestyle. This allows for efficient schedule management by customizing the schedule based on the user's lifestyle. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user lifestyle data into a generating AI and have the generating AI perform the schedule customization.

[0117] The scheduling unit can estimate the user's emotions and determine schedule priorities based on those emotions. For example, if the user is relaxed, the scheduling unit will prioritize scheduling high-importance tasks. If the user is in a hurry, the scheduling unit can also prioritize scheduling high-urgency tasks. Furthermore, if the user is stressed, the scheduling unit can postpone lower-priority tasks. This allows for efficient schedule 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0118] The scheduling unit can create an optimal schedule by considering the user's geographical location information during scheduling. For example, the scheduling unit prioritizes scheduling tasks that are close to the user's current location. The scheduling unit can also suggest highly relevant tasks based on the user's geographical location information. Furthermore, the scheduling unit can analyze the user's travel history to create an optimal schedule. This allows for the creation of an optimal schedule by considering the user's geographical location information. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the user's location data into a generation AI and have the generation AI create the schedule.

[0119] The scheduling unit can analyze the user's social media activity and adjust the schedule during scheduling. For example, the scheduling unit can analyze the user's social media activity and schedule relevant tasks. The scheduling unit can also schedule tasks based on topics the user has shown interest in on social media. Furthermore, the scheduling unit can schedule tasks by referring to the activities of the user's followers and friends on social media. This allows for the scheduling of highly relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can input the user's social media data into a generating AI and have the generating AI perform the schedule adjustments.

[0120] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect detailed data. If the user is in a hurry, the data collection unit can also collect only the minimum necessary data. Furthermore, if the user is stressed, the data collection unit can postpone collecting less important data. This allows for efficient data collection by adjusting the data collection 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 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0121] The data collection unit can select the optimal data collection method by referring to past data collection history when collecting data. For example, the data collection unit can select a new data collection method by referring to data collection methods that have been successful for the user in the past. The data collection unit can also suggest the optimal data collection method based on the user's past data collection history. Furthermore, the data collection unit can analyze the user's past data collection history and select an efficient data collection method. In this way, the optimal data collection method can be selected by referring to past data collection history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's data collection history into a generating AI and have the generating AI perform the selection of the optimal data collection method.

[0122] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current ongoing projects. It can also collect highly relevant data based on the user's areas of interest. Furthermore, the data collection unit can analyze the user's past project history and collect the most relevant data. This allows for the priority collection of highly relevant data by filtering data based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's project data into a generating AI and have the generating AI perform data filtering.

[0123] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting high-importance data. If the user is in a hurry, the data collection unit can also prioritize collecting high-urgency data. Furthermore, if the user is stressed, the data collection unit can postpone collecting lower-priority data. This allows for efficient data collection by prioritizing data 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0124] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also suggest highly relevant data based on the user's geographical location information. Furthermore, the data collection unit can analyze the user's movement history and collect the most suitable data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI perform the data collection.

[0125] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media activities and collect relevant data. The data collection unit can also collect data based on topics that the user has shown interest in on social media. Furthermore, the data collection unit can collect data by referring to the activities of the user's followers and friends on social media. This allows for the priority collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the data collection.

[0126] The next task generation unit can estimate the user's emotions and adjust the method of generating the next task based on the estimated emotions. For example, if the user is relaxed, the next task generation unit can generate a detailed next task. If the user is in a hurry, the next task generation unit can also generate a simplified next task. Furthermore, if the user is stressed, the next task generation unit can postpone lower-priority next tasks. This allows for efficient task management by adjusting the method of generating the next task according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 next task generation unit may be performed using AI or not. For example, the next task generation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0127] The next task generation unit can adjust the level of detail of the next task based on the importance of the output when generating the next task. For example, the next task generation unit can generate a detailed next task for high-importance outputs. It can also generate a simplified next task for low-importance outputs. Furthermore, the next task generation unit can adjust the priority of the next task according to its importance. This allows for efficient task management by adjusting the level of detail of the next task based on the importance of the output. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the next task.

[0128] The next task generation unit can apply different task generation algorithms depending on the output category when generating the next task. For example, for technical outputs, the next task generation unit can apply an algorithm to generate a technical next task. It can also apply an algorithm to generate a creative next task for creative outputs. Furthermore, it can apply an algorithm to generate management tasks for management outputs. This allows for efficient task management by applying task generation algorithms according to the output category. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output category data into a generation AI and have the generation AI execute the application of the task generation algorithm.

[0129] The next task generation unit can estimate the user's emotions and determine the priority of the next task to be generated based on the estimated user emotions. For example, if the user is relaxed, the next task generation unit will prioritize generating high-importance next tasks. It can also prioritize generating high-urgency next tasks if the user is in a hurry. Furthermore, if the user is stressed, the next task generation unit can postpone lower-priority next tasks. This allows for efficient task management by determining the priority of next tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0130] The next task generation unit can determine the priority of the next task based on the submission date of the output when generating the next task. For example, the next task generation unit will prioritize generating the next task for outputs with an approaching submission deadline. It can also postpone generating the next task for outputs with a distant submission deadline. Furthermore, the next task generation unit can adjust the priority of the next task according to the submission date. This enables efficient task management by determining the priority of the next task based on the submission date of the output. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output submission date data into a generation AI and have the generation AI perform the determination of the next task priority.

[0131] The next task generation unit can adjust the order of subsequent tasks based on the relevance of their outputs when generating them. For example, the next task generation unit can prioritize generating the next tasks that are highly relevant. It can also postpone the generation of the next tasks that are less relevant. Furthermore, the next task generation unit can adjust the order of the next tasks according to the relevance of their outputs. This allows for efficient task management by adjusting the order of the next tasks based on the relevance of their outputs. Some or all of the above processing in the next task generation unit may be performed using AI or not. For example, the next task generation unit can input output relevance data into a generation AI and have the generation AI perform the adjustment of the order of the next tasks.

[0132] The progress tracking unit can estimate the user's emotions and adjust the progress tracking method based on the estimated emotions. For example, if the user is relaxed, the progress tracking unit can perform detailed progress tracking. If the user is in a hurry, the progress tracking unit can also perform simplified progress tracking. Furthermore, if the user is stressed, the progress tracking unit can postpone less important progress. This allows for efficient progress management by adjusting the progress tracking 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 progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0133] The progress tracking unit can select the optimal progress tracking method by referring to past progress data during progress tracking. For example, the progress tracking unit can select a new progress tracking method by referring to progress tracking methods that have been successful for the user in the past. The progress tracking unit can also propose the optimal progress tracking method based on the user's past progress data. Furthermore, the progress tracking unit can analyze the user's past progress data and select an efficient progress tracking method. In this way, the optimal progress tracking method can be selected by referring to past progress data. Some or all of the above processing in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input the user's progress data into a generating AI and have the generating AI perform the selection of the optimal progress tracking method.

[0134] The progress tracking unit can filter progress data based on the user's current projects and areas of interest during progress tracking. For example, the progress tracking unit can prioritize tracking progress data related to projects the user is currently working on. The progress tracking unit can also track highly relevant progress data based on the user's areas of interest. Furthermore, the progress tracking unit can analyze the user's past project history and track the most relevant progress data. This allows for the priority tracking of highly relevant progress data by filtering progress data based on the user's areas of interest. Some or all of the above processing in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input the user's project data into a generating AI and have the generating AI perform the filtering of progress data.

[0135] The progress tracking unit can estimate the user's emotions and adjust how progress data is displayed based on the estimated emotions. For example, if the user is relaxed, the progress tracking unit can display detailed progress data. If the user is in a hurry, the progress tracking unit can also display simplified progress data. Furthermore, if the user is stressed, the progress tracking unit can postpone less important progress data. This allows for efficient progress management by adjusting how progress data is displayed 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 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 progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0136] The progress tracking unit can prioritize displaying highly relevant progress data while considering the user's geographical location information during progress tracking. For example, the progress tracking unit can prioritize displaying progress data related to the user's current location. Furthermore, the progress tracking unit can suggest highly relevant progress data based on the user's geographical location information. In addition, the progress tracking unit can analyze the user's movement history and display the most appropriate progress data. This allows for the priority display of highly relevant progress data by considering the user's geographical location information. Some or all of the above processing in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input the user's location data into a generating AI and have the generating AI display the progress data.

[0137] The progress tracking unit can analyze the user's social media activity and display relevant progress data during progress tracking. For example, the progress tracking unit can analyze the user's social media activity and display relevant progress data. The progress tracking unit can also display progress data based on topics the user has shown interest in on social media. Furthermore, the progress tracking unit can display progress data by referring to the activity of the user's followers and friends on social media. This allows for the priority display of highly relevant progress data by analyzing the user's social media activity. Some or all of the above processing in the progress tracking unit may be performed using AI or not. For example, the progress tracking unit can input the user's social media data into a generating AI and have the generating AI perform the display of progress data.

[0138] The performance analysis unit can estimate the user's emotions and adjust the performance analysis method based on the estimated emotions. For example, if the user is relaxed, the performance analysis unit can perform a detailed performance analysis. If the user is in a hurry, the performance analysis unit can also perform a simplified performance analysis. Furthermore, if the user is stressed, the performance analysis unit can postpone less important performance analyses. This allows for efficient performance analysis by adjusting the performance analysis 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 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 performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0139] The performance analysis unit can select the optimal analysis method by referring to past performance data during performance analysis. For example, the performance analysis unit can select a new analysis method by referring to performance analysis methods that have been successful for the user in the past. The performance analysis unit can also propose the optimal analysis method based on the user's past performance data. Furthermore, the performance analysis unit can analyze the user's past performance data and select an efficient analysis method. In this way, the optimal analysis method can be selected by referring to past performance data. Some or all of the above processes in the performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input the user's performance data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0140] The performance analysis unit can filter performance data based on the user's current projects and areas of interest during performance analysis. For example, the performance analysis unit can prioritize the analysis of performance data related to the user's current ongoing projects. It can also analyze highly relevant performance data based on the user's areas of interest. Furthermore, the performance analysis unit can analyze the user's past project history to identify the most relevant performance data. This allows for the prioritization of highly relevant data by filtering performance data based on the user's areas of interest. Some or all of the above processes in the performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input the user's project data into a generating AI and have the generating AI perform the filtering of performance data.

[0141] The performance analysis unit can estimate the user's emotions and adjust how performance data is displayed based on the estimated emotions. For example, if the user is relaxed, the performance analysis unit can display detailed performance data. If the user is in a hurry, the performance analysis unit can also display simplified performance data. Furthermore, if the user is stressed, the performance analysis unit can postpone displaying less important performance data. This allows for efficient performance management by adjusting how performance data is displayed 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 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 performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0142] The performance analysis unit can prioritize displaying highly relevant performance data by considering the user's geographical location during performance analysis. For example, the performance analysis unit can prioritize displaying performance data related to the user's current location. Furthermore, the performance analysis unit can suggest highly relevant performance data based on the user's geographical location. In addition, the performance analysis unit can analyze the user's movement history and display the most relevant performance data. This allows for the priority display of highly relevant performance data by considering the user's geographical location. Some or all of the above processing in the performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input the user's location data into a generating AI and have the generating AI display the performance data.

[0143] The performance analysis unit can analyze a user's social media activity and display relevant performance data during performance analysis. For example, the performance analysis unit can analyze the content of a user's social media activity and display relevant performance data. It can also display performance data based on topics the user has shown interest in on social media. Furthermore, the performance analysis unit can display performance data by referring to the activity of the user's followers and friends on social media. This allows for the priority display of highly relevant performance data by analyzing the user's social media activity. Some or all of the above processing in the performance analysis unit may be performed using AI or not. For example, the performance analysis unit can input the user's social media data into a generating AI and have the generating AI perform the display of performance data.

[0144] The voice input unit can estimate the user's emotions and adjust the voice input method based on the estimated emotions. For example, if the user is relaxed, the voice input unit can provide detailed voice input options. If the user is in a hurry, it can also provide simplified voice input options. Furthermore, if the user is stressed, it can provide simple voice input options. This allows for efficient voice input by adjusting the voice input 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 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 voice input unit may be performed using AI or not. For example, the voice input unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0145] The voice input unit can select the optimal voice input method by referring to past voice input data during voice input. For example, the voice input unit can select a new voice input method by referring to a voice input method that the user has successfully used in the past. The voice input unit can also suggest the optimal voice input method from the user's past voice input data. Furthermore, the voice input unit can analyze the user's past voice input data and select an efficient voice input method. In this way, the optimal voice input method can be selected by referring to past voice input data. Some or all of the above processing in the voice input unit may be performed using AI or not. For example, the voice input unit can input the user's voice input data into a generating AI and have the generating AI perform the selection of the optimal voice input method.

[0146] The voice input unit can estimate the user's emotions and prioritize voice input data based on the estimated emotions. For example, if the user is relaxed, the voice input unit will prioritize processing high-importance voice input data. It can also prioritize processing high-urgency voice input data if the user is in a hurry. Furthermore, if the user is stressed, the voice input unit can postpone processing lower-priority voice input data. This allows for efficient voice input by prioritizing voice input data 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 voice input unit may be performed using AI or not. For example, the voice input unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0147] The voice input unit can prioritize processing highly relevant voice input data by considering the user's geographical location information during voice input. For example, the voice input unit can prioritize processing voice input data related to the user's current location. Furthermore, the voice input unit can suggest highly relevant voice input data based on the user's geographical location information. In addition, the voice input unit can analyze the user's movement history and process the most appropriate voice input data. This allows for the priority processing of highly relevant voice input data by considering the user's geographical location information. Some or all of the above processing in the voice input unit may be performed using AI, or without AI. For example, the voice input unit can input the user's location data into a generating AI and have the generating AI process the voice input data.

[0148] The photo input unit can estimate the user's emotions and adjust the photo input method based on the estimated emotions. For example, if the user is relaxed, the photo input unit can provide detailed photo input options. If the user is in a hurry, it can also provide simplified photo input options. Furthermore, if the user is stressed, it can provide simple photo input options. This allows for efficient photo input by adjusting the photo input 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 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 photo input unit may be performed using AI or not. For example, the photo input unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0149] The photo input unit can select the optimal photo input method by referring to past photo input data when a photo is being input. For example, the photo input unit can select a new photo input method by referring to a photo input method that the user has successfully used in the past. The photo input unit can also suggest the optimal photo input method from the user's past photo input data. Furthermore, the photo input unit can analyze the user's past photo input data and select an efficient photo input method. In this way, the optimal photo input method can be selected by referring to past photo input data. Some or all of the above processing in the photo input unit may be performed using AI or not. For example, the photo input unit can input the user's photo input data into a generating AI and have the generating AI perform the selection of the optimal photo input method.

[0150] The photo input unit can estimate the user's emotions and prioritize photo input data based on the estimated emotions. For example, if the user is relaxed, the photo input unit will prioritize processing high-importance photo input data. It can also prioritize processing high-urgency photo input data if the user is in a hurry. Furthermore, if the user is stressed, the photo input unit can postpone processing lower-priority photo input data. This allows for efficient photo input by prioritizing photo input data 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 photo input unit may be performed using AI or not. For example, the photo input unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0151] The photo input unit can prioritize processing highly relevant photo input data by considering the user's geographical location information when a photo is input. For example, the photo input unit can prioritize processing photo input data related to the user's current location. Furthermore, the photo input unit can suggest highly relevant photo input data based on the user's geographical location information. In addition, the photo input unit can analyze the user's movement history and process the most suitable photo input data. This allows for the priority processing of highly relevant photo input data by considering the user's geographical location information. Some or all of the above processing in the photo input unit may be performed using AI, or without AI. For example, the photo input unit can input the user's location data into a generating AI and have the generating AI process the photo input data.

[0152] The screenshot input unit can estimate the user's emotions and adjust the screenshot input method based on the estimated emotions. For example, if the user is relaxed, the screenshot input unit can provide detailed screenshot input options. If the user is in a hurry, it can also provide simplified screenshot input options. Furthermore, if the user is stressed, it can provide a simple screenshot input option. This allows for efficient screenshot input by adjusting the screenshot input 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 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 screenshot input unit may be performed using AI or not. For example, the screenshot input unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0153] The screenshot input unit can select the optimal screenshot input method by referring to past screenshot input data when a screenshot is taken. For example, the screenshot input unit can select a new screenshot input method by referring to screenshot input methods that the user has successfully used in the past. The screenshot input unit can also suggest the optimal screenshot input method from the user's past screenshot input data. Furthermore, the screenshot input unit can analyze the user's past screenshot input data and select an efficient screenshot input method. In this way, the optimal screenshot input method can be selected by referring to past screenshot input data. Some or all of the above processing in the screenshot input unit may be performed using AI or not. For example, the screenshot input unit can input the user's screenshot input data into a generating AI and have the generating AI perform the selection of the optimal screenshot input method.

[0154] The screenshot input unit can estimate the user's emotions and prioritize screenshot input data based on the estimated emotions. For example, if the user is relaxed, the screenshot input unit will prioritize processing high-importance screenshot input data. It can also prioritize processing high-urgency screenshot input data if the user is in a hurry. Furthermore, if the user is stressed, the screenshot input unit can postpone processing low-priority screenshot input data. This allows for efficient screenshot input by prioritizing screenshot input data 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 screenshot input unit may be performed using AI or not. For example, the screenshot input unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0155] The screenshot input unit can prioritize processing highly relevant screenshot input data by considering the user's geographical location information when a screenshot is taken. For example, the screenshot input unit can prioritize processing screenshot input data related to the user's current location. Furthermore, the screenshot input unit can suggest highly relevant screenshot input data based on the user's geographical location information. In addition, the screenshot input unit can analyze the user's movement history and process the most appropriate screenshot input data. This allows for the priority processing of highly relevant screenshot input data by considering the user's geographical location information. Some or all of the above processing in the screenshot input unit may be performed using AI, or without AI. For example, the screenshot input unit can input the user's location data into a generating AI and have the generating AI process the screenshot input data.

[0156] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0157] Project management systems can also be equipped with the ability to estimate user emotions and dynamically adjust task priorities based on those emotions. For example, if a user is stressed, the system can postpone less important tasks and prioritize tasks that promote relaxation. Conversely, if a user is relaxed, it can prioritize high-priority tasks. Furthermore, if a user is in a hurry, it can prioritize urgent tasks. This allows for efficient task management by dynamically adjusting task priorities according to user emotions.

[0158] Project management systems can also include features to analyze a user's past task history and select the optimal task generation method. For example, they can generate new tasks by referencing successful task generation methods used by the user in the past. They can also suggest the optimal task generation method based on the user's past task history. Furthermore, they can analyze the user's past task history and select an efficient task generation method. This allows for the selection of the optimal task generation method by referring to past task history.

[0159] Project management systems can also include features to filter tasks based on the user's current projects and areas of interest. For example, they can prioritize generating tasks related to projects the user is currently working on. They can also generate highly relevant tasks based on the user's areas of interest. Furthermore, they can analyze the user's past project history to generate optimal tasks. This allows for the prioritization of highly relevant tasks by filtering them based on the user's areas of interest.

[0160] Project management systems can also incorporate features that prioritize the generation of highly relevant tasks by considering the user's geographical location. For example, they can prioritize tasks related to the user's current location. They can also suggest highly relevant tasks based on the user's geographical location. Furthermore, they can analyze the user's travel history to generate optimal tasks. This allows for the priority generation of highly relevant tasks by considering the user's geographical location.

[0161] Project management systems can also incorporate features to analyze users' social media activity and generate relevant tasks. For example, they can analyze a user's social media activity and generate relevant tasks. They can also generate tasks based on topics the user has shown interest in on social media. Furthermore, they can generate tasks by referencing the activity of the user's followers and friends on social media. This allows for the priority generation of highly relevant tasks by analyzing the user's social media activity.

[0162] Project management systems can also incorporate features to estimate user emotions and adjust scheduling based on those emotions. For example, if a user is relaxed, a more flexible schedule can be created. Conversely, if a user is in a hurry, a tighter schedule can be created. Furthermore, if a user is stressed, less important tasks can be postponed. This allows for efficient schedule management by adjusting scheduling methods according to user emotions.

[0163] Project management systems can also include features to create optimal schedules by referencing the user's past schedule history. For example, they can create new schedules by referencing successful schedules from the past. They can also suggest optimal schedules based on the user's past schedule history. Furthermore, they can analyze the user's past schedule history to create efficient schedules. This allows for the creation of optimal schedules by referencing past schedule history.

[0164] Project management systems can also include features that customize schedules based on the user's current lifestyle. For example, if a user is a morning person, important tasks can be scheduled for the morning. Conversely, if a user is a night owl, important tasks can be scheduled for the evening. Furthermore, it's possible to create an optimal schedule tailored to the user's daily rhythm. This allows for efficient schedule management by customizing schedules based on the user's lifestyle.

[0165] Project management systems can also be equipped with the ability to estimate user emotions and adjust data collection methods based on those emotions. For example, if a user is relaxed, detailed data can be collected. If a user is in a hurry, only the minimum necessary data can be collected. Furthermore, if a user is stressed, less important data can be postponed. This allows for efficient data collection by adjusting data collection methods according to user emotions.

[0166] Project management systems can also include features to select the optimal data collection method by referring to the user's past data collection history. For example, they can select a new data collection method by referencing the user's past successful data collection methods. They can also suggest the optimal data collection method based on the user's past data collection history. Furthermore, they can analyze the user's past data collection history to select an efficient data collection method. This allows for the selection of the optimal data collection method by referring to past data collection history.

[0167] The following briefly describes the processing flow for example form 2.

[0168] Step 1: The reception desk registers the case. The reception desk provides, for example, an interface for users to input the case. The reception desk can support multiple input methods, such as voice input, photo input, and screenshot input. For example, the reception desk can use voice recognition technology to convert the user's voice input into text data. The reception desk can also use image recognition technology to extract information from photos and screenshots. Step 2: The task generation unit automatically generates subdivided tasks based on the cases registered by the reception unit. For example, the task generation unit analyzes the content of the case and lists the necessary tasks. The task generation unit can understand the content of the case using natural language processing technology and generate appropriate tasks. For example, the task generation unit automatically generates the tasks required at the start of a project and breaks each task down into specific steps. Step 3: The scheduling unit optimally schedules the tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. For example, the scheduling unit adjusts the order and timing of task execution, taking into account the user's past work history and daily rhythm. The scheduling unit can learn the user's behavior patterns using machine learning algorithms and propose an optimal schedule. Step 4: The data collection unit automatically handles the data collection, investigation, and analysis required for the tasks generated by the task generation unit. For example, the data collection unit collects information from the internet and analyzes the relevant data. The data collection unit can automatically collect the necessary information using web scraping technology. Step 5: The next task generation unit automatically generates the next task based on the data collected by the data collection unit. For example, the next task generation unit analyzes the collected data and lists the tasks that should be done next. The next task generation unit can automatically generate the next task using a generation AI. Step 6: The progress tracking unit tracks the progress of tasks generated by the task generation unit in real time, and the generation AI analyzes and manages the data. For example, the progress tracking unit displays the task completion status and progress rate in real time. The progress tracking unit can analyze the progress data using the generation AI and provide the user with advice on areas for improvement and skill development. Step 7: The Performance Analysis Department analyzes user productivity based on progress data tracked by the Progress Tracking Department and provides advice for skill improvement. For example, the Performance Analysis Department evaluates user work efficiency and the quality of deliverables and proposes areas for improvement. The Performance Analysis Department can use generative AI to analyze user productivity in detail and provide specific advice.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, task generation unit, scheduling unit, data collection unit, next task generation unit, progress tracking unit, performance analysis unit, voice input unit, photo input unit, and screenshot input unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input a case. The task generation unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the content of the case and lists the necessary tasks. The scheduling unit is implemented by the control unit 46A of the smart device 14 and optimally schedules tasks based on the user's abilities and lifestyle. The data collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects information from the internet and analyzes the relevant data. The next task generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates the next task based on the collected data. The progress tracking unit is implemented, for example, by the control unit 46A of the smart device 14, and tracks the progress of tasks in real time. The performance analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes user productivity based on progress data. The voice input unit is implemented, for example, by the control unit 46A of the smart device 14, and converts voice input into text data. The photo input unit is implemented, for example, by the control unit 46A of the smart device 14, and extracts information from photos. The screenshot input unit is implemented, for example, by the control unit 46A of the smart device 14, and extracts information from screenshots. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0173] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the reception unit, task generation unit, scheduling unit, data collection unit, next task generation unit, progress tracking unit, performance analysis unit, voice input unit, photo input unit, and screenshot input 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 provides an interface for the user to input a case. The task generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the case and lists the necessary tasks. The scheduling unit is implemented by the control unit 46A of the smart glasses 214 and optimally schedules tasks based on the user's abilities and lifestyle. The data collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information from the internet and analyzes the relevant data. The next task generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates the next task based on the collected data. The progress tracking unit is implemented, for example, by the control unit 46A of the smart glasses 214, and tracks the progress of tasks in real time. The performance analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the user's productivity based on the progress data. The voice input unit is implemented, for example, by the control unit 46A of the smart glasses 214, and converts voice input into text data. The photo input unit is implemented, for example, by the control unit 46A of the smart glasses 214, and extracts information from photos. The screenshot input unit is implemented, for example, by the control unit 46A of the smart glasses 214, and extracts information from screenshots. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0189] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] Each of the multiple elements described above, including the reception unit, task generation unit, scheduling unit, data collection unit, next task generation unit, progress tracking unit, performance analysis unit, voice input unit, photo input unit, and screenshot input 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 provides an interface for the user to input a case. The task generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the case and lists the necessary tasks. The scheduling unit is implemented by, for example, the control unit 46A of the headset terminal 314 and optimally schedules tasks based on the user's abilities and lifestyle. The data collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information from the internet and analyzes the relevant data. The next task generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates the next task based on the collected data. The progress tracking unit is implemented, for example, by the control unit 46A of the headset terminal 314, and tracks the progress of tasks in real time. The performance analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's productivity based on the progress data. The voice input unit is implemented, for example, by the control unit 46A of the headset terminal 314, and converts voice input into text data. The photo input unit is implemented, for example, by the control unit 46A of the headset terminal 314, and extracts information from photos. The screenshot input unit is implemented, for example, by the control unit 46A of the headset terminal 314, and extracts information from screenshots. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0205] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] Each of the multiple elements described above, including the reception unit, task generation unit, scheduling unit, data collection unit, next task generation unit, progress tracking unit, performance analysis unit, voice input unit, photo input unit, and screenshot input 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 provides an interface for the user to input a case. The task generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the case and lists the necessary tasks. The scheduling unit is implemented by, for example, the control unit 46A of the robot 414 and optimally schedules tasks based on the user's abilities and lifestyle. The data collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information from the internet and analyzes the relevant data. The next task generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates the next task based on the collected data. The progress tracking unit is implemented, for example, by the control unit 46A of the robot 414, and tracks the progress of the task in real time. The performance analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's productivity based on the progress data. The voice input unit is implemented, for example, by the control unit 46A of the robot 414, and converts voice input into text data. The photo input unit is implemented, for example, by the control unit 46A of the robot 414, and extracts information from photos. The screenshot input unit is implemented, for example, by the control unit 46A of the robot 414, and extracts information from screenshots. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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."

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] (Note 1) The reception department where cases are registered, A task generation unit that automatically generates subdivided tasks based on cases registered by the aforementioned reception unit, A scheduling unit optimally schedules the tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. The data collection unit automatically handles the data collection, investigation, and analysis necessary for the tasks generated by the task generation unit, A next task generation unit automatically generates the next task based on the data collected by the data collection unit, The progress tracking unit tracks the progress of tasks generated by the task generation unit in real time, and the generation AI analyzes and manages this progress. The system includes a performance analysis unit that analyzes user productivity based on progress data tracked by the aforementioned progress tracking unit and provides advice for skill improvement. A system characterized by the following features. (Note 2) It is equipped with a voice input unit that accepts voice input. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a photo input section that accepts photo input. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a screenshot input section that accepts screenshot input. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned next task generation unit, Automatically generate the next additional task that arises from the output. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned progress tracking unit, Task progress is tracked in real time, and generated AI analyzes and manages it. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned performance analysis unit, We understand user productivity and provide analysis and skill development advice to further improve productivity. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the process of accepting requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze past application history to select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a project is submitted, it is filtered based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of cases to be accepted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When accepting a project, the system prioritizes accepting projects that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When a project is submitted, the system analyzes the user's social media activity and accepts relevant projects. The system described in Appendix 1, characterized by the features described herein. (Note 14) The task generation unit, It estimates the user's emotions and adjusts how tasks are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The task generation unit, When creating a task, adjust the level of detail based on the importance of the case. The system described in Appendix 1, characterized by the features described herein. (Note 16) The task generation unit, When generating tasks, different task generation algorithms are applied depending on the category of the project. The system described in Appendix 1, characterized by the features described herein. (Note 17) The task generation unit, It estimates the user's emotions and determines the priority of tasks to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The task generation unit, When creating tasks, prioritize them based on the submission deadline for each project. The system described in Appendix 1, characterized by the features described herein. (Note 19) The task generation unit, When creating tasks, the order of tasks is adjusted based on the relevance of the projects. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned scheduling unit, It estimates the user's emotions and adjusts the scheduling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned scheduling unit, During scheduling, the system references the user's past schedule history to create the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned scheduling unit, When scheduling, customize the schedule based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The scheduling unit estimates the user's emotion and determines the priority of the schedule based on the estimated user emotion. The system according to Appendix 1, characterized in that. (Appendix 24) The scheduling unit creates an optimal schedule considering the user's geographical location information during scheduling. The system according to Appendix 1, characterized in that. (Appendix 25) The scheduling unit analyzes the user's social media activities and adjusts the schedule during scheduling. The system according to Appendix 1, characterized in that. (Appendix 26) The data collection unit estimates the user's emotion and adjusts the data collection method based on the estimated user emotion. The system according to Appendix 1, characterized in that. (Appendix 27) The data collection unit selects an optimal data collection method by referring to the past data collection history during data collection. The system according to Appendix 1, characterized in that. (Appendix 28) The data collection unit filters data based on the user's current project or area of interest during data collection. The system according to Appendix 1, characterized in that. (Appendix 29) The data collection unit estimates the user's emotion and determines the priority of the data to be collected based on the estimated user emotion. The system according to Appendix 1, characterized in that. (Appendix 30) The data collection unit prioritizes the collection of highly relevant data considering the user's geographical location information during data collection. The system according to Appendix 1, characterized in that... (Appendix 31) The data collection unit Analyzes the user's social media activities and collects relevant data during data collection The system according to Appendix 1, characterized in that... (Appendix 32) The next task generation unit Estimates the user's sentiment and adjusts the method for generating the next task based on the estimated user sentiment The system according to Appendix 1, characterized in that... (Appendix 33) The next task generation unit Adjusts the detail level of the next task based on the importance of the output during next task generation The system according to Appendix 1, characterized in that... (Appendix 34) The next task generation unit Applies different task generation algorithms according to the category of the output during next task generation The system according to Appendix 1, characterized in that... (Appendix 35) The next task generation unit Estimates the user's sentiment and determines the priority of the next task to be generated based on the estimated user sentiment The system according to Appendix 1, characterized in that... (Appendix 36) The next task generation unit Determines the priority of the next task based on the submission time of the output during next task generation The system according to Appendix 1, characterized in that... (Appendix 37) The next task generation unit Adjusts the order of the next task based on the relevance of the output during next task generation The system according to Appendix 1, characterized in that... (Appendix 38) The progress tracking unit We estimate the user's emotions and adjust the progress tracking method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned progress tracking unit, When tracking progress, refer to past progress data to select the most suitable progress tracking method. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned progress tracking unit, When tracking progress, filter progress data based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned progress tracking unit, It estimates the user's emotions and adjusts how progress data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned progress tracking unit, When tracking progress, the system prioritizes displaying highly relevant progress data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned progress tracking unit, When tracking progress, the system analyzes the user's social media activity and displays relevant progress data. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned performance analysis unit, We estimate user sentiment and adjust performance analysis methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned performance analysis unit, When performing performance analysis, historical performance data is referenced to select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 46) The performance analysis unit filters performance data based on the user's current project and area of interest during performance analysis The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 47) The performance analysis unit estimates the user's emotion and adjusts the display method of performance data based on the estimated user emotion The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 48) The performance analysis unit prioritizes and displays highly relevant performance data considering the user's geographical location information during performance analysis The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 49) The performance analysis unit analyzes the user's social media activities and displays relevant performance data during performance analysis The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 50) The voice input unit estimates the user's emotion and adjusts the voice input method based on the estimated user emotion The system according to Supplementary Note 2, characterized by the above. (Supplementary Note 51) The voice input unit selects the optimal voice input method by referring to past voice input data during voice input The system according to Supplementary Note 2, characterized by the above. (Supplementary Note 52) The voice input unit estimates the user's emotion and determines the priority of voice input data based on the estimated user emotion The system according to Supplementary Note 2, characterized by the above. (Supplementary Note 53) The voice input unit During voice input, the system prioritizes processing of highly relevant voice input data, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 54) The aforementioned photo input unit is It estimates the user's emotions and adjusts the photo input method based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 55) The aforementioned photo input unit is When entering photos, the system refers to past photo entry data to select the optimal photo entry method. The system described in Appendix 3, characterized by the features described herein. (Note 56) The aforementioned photo input unit is The system estimates the user's emotions and prioritizes photo input data based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 57) The aforementioned photo input unit is When users input photos, the system prioritizes processing highly relevant photo data, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 58) The aforementioned screenshot input unit is It estimates the user's emotions and adjusts the screenshot input method based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 59) The aforementioned screenshot input unit is When taking a screenshot, the system will refer to past screenshot input data to select the optimal screenshot input method. The system described in Appendix 4, characterized by the features described herein. (Note 60) The aforementioned screenshot input unit is The system estimates the user's emotions and prioritizes screenshot input data based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 61) The aforementioned screenshot input unit is When a screenshot is taken, the system prioritizes processing screenshots that are highly relevant to the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0241] 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. The reception department where cases are registered, A task generation unit that automatically generates subdivided tasks based on cases registered by the aforementioned reception unit, A scheduling unit optimally schedules the tasks generated by the task generation unit based on the user's abilities, lifestyle, and personal data. The data collection unit automatically handles the data collection, investigation, and analysis necessary for the tasks generated by the task generation unit, A next task generation unit automatically generates the next task based on the data collected by the data collection unit, A progress tracking unit tracks the progress of tasks generated by the task generation unit in real time, and the generation AI analyzes and manages the progress. The system includes a performance analysis unit that analyzes user productivity based on progress data tracked by the aforementioned progress tracking unit and provides advice for skill improvement. A system characterized by the following features.

2. It is equipped with a voice input unit that accepts voice input. The system according to feature 1.

3. It is equipped with a photo input section that accepts photo input. The system according to feature 1.

4. It is equipped with a screenshot input section that accepts screenshot input. The system according to feature 1.

5. The aforementioned next task generation unit, Automatically generate the next additional task that arises from the output. The system according to feature 1.

6. The aforementioned progress tracking unit, Task progress is tracked in real time, and generated AI analyzes and manages it. The system according to feature 1.

7. The aforementioned performance analysis unit, We understand user productivity and provide analysis and skill development advice to further improve productivity. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and adjusts the process of accepting requests based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze past application history to select the most suitable application method. The system according to feature 1.

10. The aforementioned reception unit is When a project is submitted, it is filtered based on the user's current projects and areas of interest. The system according to feature 1.

Images