system

Abstract

The system according to this embodiment aims to automate routine tasks and improve the efficiency of task management. [Solution] The system according to the embodiment comprises a collection unit, an agency unit, an assignment unit, and a scheduling unit. The collection unit collects business information related to the user's work. The agency unit performs tasks on behalf of the user, such as routinely entering data and creating routine reports, based on the business information. The assignment unit assigns priority to emails received by the user based on the business information. The scheduling unit manages the user's tasks and streamlines the schedule based on the business information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prior art, the automation of routine operations and the improvement of task management efficiency have not been fully achieved, and there is room for improvement.

[0005] The system according to the embodiment aims to realize the automation of routine operations and the improvement of task management efficiency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an agency unit, an assignment unit, and a scheduling unit. The collection unit collects business information related to the user's work. The agency unit performs tasks on behalf of the user, such as routinely entering data and creating routine reports, based on the business information. The assignment unit assigns priority to emails received by the user based on the business information. The scheduling unit manages the user's tasks and streamlines the schedule based on the business information. [Effects of the Invention]

[0007] The system according to this embodiment can automate routine tasks and improve the efficiency of task 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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The AI ​​assistant for automating office work according to an embodiment of the present invention is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if the user needs to enter data for expense reimbursement or inventory management, the AI ​​assistant will do this automatically. This allows the user to concentrate on other important tasks. The AI ​​assistant also analyzes the content of emails using natural language processing and determines their importance. This allows the user to prioritize checking important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on the estimated emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. Furthermore, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by the work being performed by a substitute department. For example, if the work being performed by a substitute department, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails that have been assigned priorities by the system. For example, if an email about an important meeting arrives, that meeting can be added to the schedule. This allows the AI ​​assistant, which supports the automation of office tasks, to efficiently automate the user's work and optimize task management and scheduling.

[0029] The AI ​​assistant for automating office work according to this embodiment comprises a collection unit, an agency unit, an assignment unit, and a scheduling unit. The collection unit collects business information related to the user's work. Business information includes, but is not limited to, work progress information, task details, and project status. The collection unit can use, for example, sensors or databases to automatically collect the user's business information. The collection unit can also collect business information based on user input. For example, the collection unit collects detailed task information entered by the user and stores it in a database. The agency unit performs tasks on behalf of the user, such as routine data entry or creating routine reports, based on the business information. For example, the agency unit can automatically enter data for expense reimbursement or inventory management. The agency unit can also automatically generate routine reports. For example, the agency unit can automatically create monthly reports or sales reports and provide them to the user. The assignment unit assigns priorities to emails received by the user based on the business information. The assignment unit can, for example, analyze the content of emails using natural language processing to determine their importance. Furthermore, the prioritization unit can assign priorities based on the sender's job title and urgency of emails. For example, it might prioritize emails from a supervisor and postpone promotional emails. The scheduling unit manages the user's tasks and optimizes the schedule based on work information. For example, the scheduling unit can manage task schedules using a visual scheduling tool. It can also estimate the user's emotions and optimize the schedule based on those emotions. For example, if the scheduling unit is stressed, it can adjust the schedule to increase break times. This enables the AI ​​assistant for office work automation to efficiently automate the user's tasks and optimize task management and schedules.

[0030] The data collection unit collects business information related to the user's work. This business information includes, but is not limited to, work progress information, task details, and project status. The data collection unit can use sensors and databases to automatically collect user business information. Specifically, applications installed on the user's PC or smartphone can collect user operation history and input data in real time. Data can also be obtained from business management systems and project management tools used by the user. This allows the data collection unit to collect business information efficiently, saving the user the trouble of manual input. Furthermore, the data collection unit can also collect business information based on user input. For example, when a user enters detailed task information, that information can be saved in a database for later analysis and use. The data collection unit can also collect what the user says as text data using voice input and natural language processing technology. This allows users to provide business information by voice without relying on keyboard input. By combining these diverse means, the data collection unit can comprehensively collect user business information and improve the accuracy and efficiency of the entire system.

[0031] The proxy department performs tasks on behalf of users, such as routinely entering data or creating standardized reports based on business information. For example, the proxy department can automate data entry for expense reimbursement and inventory management. Specifically, in the case of expense reimbursement, when a user takes a photo of a receipt and uploads it to the system, the proxy department analyzes the image, extracts the necessary data, and enters it into the expense reimbursement system. In inventory management, it can automatically record product inbound and outbound information and update inventory status in real time. Furthermore, the proxy department can also automatically generate standardized reports. For example, the proxy department can automatically create and provide monthly reports and sales reports to users. This includes a process of generating reports according to a specific format based on data collected by the collection department. By utilizing AI-powered natural language generation technology, the content of reports can be automatically converted into text, eliminating the need for manual editing by users. As a result, the proxy department can reduce the workload of users and perform tasks efficiently.

[0032] The prioritization unit assigns priorities to emails delivered to the user based on business information. For example, the unit can analyze the content of emails using natural language processing to determine their importance. Specifically, it analyzes the email body and subject line, and scores the importance of emails by evaluating the frequency of keyword and phrase occurrences and context. The unit can also assign priorities based on the sender's job title and urgency. For example, emails from superiors or emails requiring urgent attention are given high priority, while promotional emails and general notification emails are given low priority. Furthermore, the unit can learn from the user's past email processing history and understand what types of emails the user tends to prioritize, enabling more accurate prioritization. In this way, the unit helps users process emails efficiently without missing important emails.

[0033] The scheduling unit manages user tasks and optimizes schedules based on business information. For example, the scheduling unit can manage task schedules using a visual scheduling tool. Specifically, it provides a calendar-style interface, allowing users to easily add tasks to their schedules via drag-and-drop. The scheduling unit can also estimate user emotions and optimize schedules based on these estimations. For instance, it analyzes the user's facial expressions and voice tone to detect signs of stress or fatigue. Based on this, if the user is feeling stressed, the scheduling unit can adjust the schedule to increase break times. Furthermore, the scheduling unit can suggest the optimal task order, considering task priorities and dependencies. This allows users to perform tasks efficiently and improve their work productivity. The scheduling unit can also collect user feedback and continuously improve its schedule optimization algorithms. This enables the scheduling unit to efficiently manage user tasks and provide optimal schedules.

[0034] The agency unit can perform data entry for expense reimbursement and inventory management. For example, the agency unit can automate data entry for expense reimbursement. For example, the agency unit can automatically enter expenses such as transportation, accommodation, and meals. The agency unit can also automate data entry for inventory management. For example, the agency unit can automatically manage the receipt and dispatch of goods and monitor inventory levels. Furthermore, the agency unit can also automate data entry for order management. For example, the agency unit can automatically place orders when inventory reaches a certain level. By automating data entry for expense reimbursement and inventory management, the burden on users can be reduced. Some or all of the above processes in the agency unit may be performed using AI, for example, or without AI. For example, the agency unit can input expense reimbursement data into a generating AI and have the generating AI perform the expense reimbursement data entry.

[0035] The assignment unit can analyze the content of emails using natural language processing and determine their importance. For example, the assignment unit can analyze the content of emails using natural language processing techniques. For example, the assignment unit can decompose the content of emails using morphological analysis and extract important keywords. The assignment unit can also analyze the sentence structure of emails using grammatical analysis and determine their importance. Furthermore, the assignment unit can understand the meaning of the content of emails using semantic analysis and determine their importance. For example, the assignment unit can analyze the frequency and context of keywords contained in the content of emails and determine their importance. This allows the assignment unit to automatically determine the importance of emails and prioritize them, enabling users to check important emails first. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input the content of emails into a generating AI and have the generating AI perform the determination of the importance of the emails.

[0036] The scheduling unit can manage task schedules using graphical scheduling tools. For example, the scheduling unit can manage task schedules using a Gantt chart. For example, the scheduling unit can display project tasks in a Gantt chart and visually manage their progress. The scheduling unit can also manage task schedules using a calendar display. For example, the scheduling unit can display daily work tasks in a calendar and manage their schedules. Furthermore, the scheduling unit can also manage task schedules using a task board. For example, the scheduling unit can display tasks on a board and manage their progress. This allows users to intuitively manage task schedules using visual scheduling tools. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task schedules into a generating AI and have the generating AI manage the schedules.

[0037] The scheduling unit can optimize the schedule based on the remaining tasks of the user, which are determined by the tasks performed by the proxy unit. For example, if the proxy unit handles expense reimbursement, the scheduling unit can allocate that time to other tasks. Similarly, if the proxy unit handles inventory management, the scheduling unit can allocate that time to other tasks. Furthermore, if the proxy unit creates a standardized report, the scheduling unit can allocate that time to other tasks.

[0038] The scheduling unit can set the user's schedule according to the content of emails to which priority has been assigned by the assignment unit. For example, if the scheduling unit receives an email about an important meeting, it can add that meeting to the schedule. The scheduling unit can also prioritize adding an urgent task to the schedule if it receives an email about that task. Furthermore, if the scheduling unit receives an email about the progress of a project, it can adjust the schedule based on that progress. This allows the user to prioritize important tasks by determining the schedule based on the content of emails. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input the data of emails to which priority has been assigned by the assignment unit into a generating AI and have the generating AI set the schedule.

[0039] The data collection unit can analyze the user's past work history and select an efficient data collection method. For example, the data collection unit can prioritize collecting data from data sources that the user has frequently used in the past. The data collection unit can also optimize the types of data to collect at specific time periods based on the user's past work history. Furthermore, the data collection unit can analyze the user's past work history and propose the most efficient data collection method. This enables efficient data collection by selecting the optimal information collection method through analysis of past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past work history data into a generating AI and have the generating AI select an efficient data collection method.

[0040] The data collection unit can filter business information based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to the project the user is currently working on. The data collection unit can also filter and collect highly relevant information based on the user's areas of interest. Furthermore, the data collection unit can collect necessary information according to the progress of the user's current projects. This allows for the efficient collection of highly relevant information by filtering information based on the current project and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when collecting business information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of information related to that region. The data collection unit can also collect information on nearby events and meetings based on the user's geographical location. Furthermore, if the user is on a business trip, the data collection unit can prioritize the collection of information related to the destination. This allows for the efficient collection of information useful for the user's work by collecting highly relevant information based on geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information when collecting business information. For example, the data collection unit can collect information related to topics mentioned by the user on social media. It can also collect information shared by the user's social media followers and friends. Furthermore, the data collection unit can analyze the user's social media activity history and collect highly relevant information. This allows for the efficient collection of highly relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0043] The proxy unit can set the level of detail of processing based on the importance of the task when entering data or generating reports. For example, for tasks with high importance, the proxy unit can perform detailed data entry and report generation. Conversely, for tasks with low importance, the proxy unit can perform simplified data entry and report generation. Furthermore, the proxy unit can automatically select and enter the necessary data items according to the importance of the task. This allows for efficient data entry and report generation by adjusting the level of detail of processing based on the importance of the task. Some or all of the above processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input task importance data into a generating AI and have the generating AI set the level of detail of processing.

[0044] The proxy unit can use different algorithms depending on the business category when entering data or generating reports. For example, in the case of expense reimbursement, the proxy unit can apply different algorithms to each expense item when entering data. Similarly, in the case of inventory management, the proxy unit can apply different algorithms to each type and quantity of inventory when entering data. Furthermore, when generating reports, the proxy unit can generate reports using different templates depending on the business category. This enables efficient data entry and report generation by applying different algorithms depending on the business category. Some or all of the above processes in the proxy unit may be performed using AI, for example, or not. For example, the proxy unit can input business category data into a generation AI and have the generation AI execute the application of different algorithms.

[0045] The proxy department can prioritize processing based on the submission deadline of tasks when entering data or generating reports. For example, the proxy department can prioritize tasks with approaching deadlines. It can also postpone tasks with later deadlines. Furthermore, the proxy department can automatically adjust processing priorities according to the submission deadline. This makes it easier to meet deadlines by determining processing priorities based on the submission deadline of tasks. Some or all of the above processing in the proxy department may be performed using AI, for example, or not using AI. For example, the proxy department can input task submission deadline data into a generating AI and have the generating AI set the priorities.

[0046] The proxy unit can set the processing order based on the relevance of tasks when entering data or generating reports. For example, the proxy unit can prioritize processing tasks that are highly relevant. It can also postpone tasks that are less relevant. Furthermore, the proxy unit can automatically adjust the processing order according to the relevance of tasks. This allows for efficient task execution by adjusting the processing order based on the relevance of tasks. Some or all of the above processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input task relevance data into a generating AI and have the generating AI set the processing order.

[0047] The prioritization unit can set priorities based on the importance of emails when prioritizing them. For example, the prioritization unit can display high-importance emails first. It can also postpone low-importance emails. Furthermore, the prioritization unit can automatically adjust priorities according to the importance of emails. This allows important emails to be reviewed first by adjusting priorities based on their importance. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input email importance data into a generating AI and have the generating AI perform the priority setting.

[0048] The prioritization unit can use different algorithms depending on the email category when prioritizing emails. For example, the prioritization unit can display business-related emails preferentially. It can also postpone promotional emails. Furthermore, the prioritization unit can automatically adjust priorities according to the email category. This enables efficient email management by applying different algorithms depending on the email category. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or not using AI. For example, the prioritization unit can input email category data into a generating AI and have the generating AI execute the application of different algorithms.

[0049] The prioritization unit can determine the priority of emails based on the attribute information of the email sender. For example, the prioritization unit can display emails from a supervisor first. It can also postpone emails from colleagues. Furthermore, the prioritization unit can automatically adjust the priority based on the attribute information of the email sender. This allows important emails to be reviewed first by determining the priority based on the attribute information of the email sender. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or not using AI. For example, the prioritization unit can input email sender attribute information data into a generating AI and have the generating AI perform the priority determination.

[0050] The prioritization unit can adjust the priority of emails based on the relevant literature for each email. For example, the prioritization unit can refer to literature related to the content of an email and determine its importance. The prioritization unit can also automatically adjust the priority based on the relevant literature for each email. Furthermore, the prioritization unit can display literature related to the content of each email, allowing the user to check the priority. This allows users to prioritize important emails by referring to the relevant literature for each email. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or not using AI. For example, the prioritization unit can input email relevant literature data into a generating AI and have the generating AI perform the priority adjustment.

[0051] The scheduling unit can optimize the current schedule based on past schedule data during schedule management. For example, the scheduling unit can propose an optimal schedule based on the user's past schedule data. It can also propose the most suitable tasks for a specific time period based on past schedule data. Furthermore, the scheduling unit can analyze past schedule data and create an efficient schedule. This allows it to propose an optimal schedule by referring to past schedule data. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input past schedule data into a generating AI and have the generating AI perform the optimization of the current schedule.

[0052] The scheduling unit can use different scheduling methods for each task category when managing schedules. For example, the scheduling unit can use a Gantt chart to manage schedules for project tasks. It can also use a calendar to manage schedules for daily tasks. Furthermore, it can use reminders to manage schedules for urgent tasks. This allows for efficient schedule management by applying different scheduling methods according to the task category. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task category data into a generating AI and have the generating AI execute the application of different scheduling methods.

[0053] The scheduling unit can evaluate schedule changes based on task submission dates during schedule management. For example, the scheduling unit can prioritize scheduling tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, the scheduling unit can automatically analyze schedule changes based on submission dates and propose an optimal schedule. This allows for the proposal of an optimal schedule by analyzing schedule changes based on task submission dates. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task submission date data into a generating AI and have the generating AI perform the evaluation of schedule changes.

[0054] The scheduling unit can evaluate schedules based on relevant market data for tasks during schedule management. For example, the scheduling unit can propose an optimal schedule based on market trends. The scheduling unit can also refer to relevant market data for tasks to determine schedule priorities. Furthermore, the scheduling unit can optimize task schedules based on market data. This allows the scheduling unit to propose an optimal schedule by referring to relevant market data for tasks. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input relevant market data for tasks into a generating AI and have the generating AI perform schedule evaluation.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0057] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0058] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0059] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0060] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0061] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0062] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0063] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0064] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0065] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects business information related to the user's work. This business information includes, for example, work progress information, task details, and project status. The data collection unit can use sensors and databases to automatically collect user business information. It can also collect business information based on user input. For example, the data collection unit collects detailed task information entered by the user and stores it in the database. Step 2: The proxy unit performs tasks on behalf of the user, such as routinely entering data or creating routine reports based on business information. For example, it can automate data entry for expense reimbursements and inventory management. It can also automatically create and provide monthly reports and sales reports to the user. Step 3: The prioritization unit assigns priority to emails delivered to the user based on business information. For example, it can analyze the content of emails using natural language processing to determine their importance. It can also assign priority based on the sender's job title and urgency. For example, it can prioritize emails from superiors and delay promotional emails. Step 4: The scheduling unit manages user tasks and optimizes schedules based on business information. For example, it can manage task schedules using a visual scheduling tool. It can also estimate the user's emotions and optimize the schedule based on those emotions. For example, if a user is feeling stressed, the schedule can be adjusted to increase break times.

[0068] (Example of form 2) The AI ​​assistant for automating office work according to an embodiment of the present invention is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if the user needs to enter data for expense reimbursement or inventory management, the AI ​​assistant will do this automatically. This allows the user to concentrate on other important tasks. The AI ​​assistant also analyzes the content of emails using natural language processing and determines their importance. This allows the user to prioritize checking important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on the estimated emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. Furthermore, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by the work being performed by a substitute department. For example, if the work being performed by a substitute department, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails that have been assigned priorities by the system. For example, if an email about an important meeting arrives, that meeting can be added to the schedule. This allows the AI ​​assistant, which supports the automation of office tasks, to efficiently automate the user's work and optimize task management and scheduling.

[0069] The AI ​​assistant for automating office work according to this embodiment comprises a collection unit, an agency unit, an assignment unit, and a scheduling unit. The collection unit collects business information related to the user's work. Business information includes, but is not limited to, work progress information, task details, and project status. The collection unit can use, for example, sensors or databases to automatically collect the user's business information. The collection unit can also collect business information based on user input. For example, the collection unit collects detailed task information entered by the user and stores it in a database. The agency unit performs tasks on behalf of the user, such as routine data entry or creating routine reports, based on the business information. For example, the agency unit can automatically enter data for expense reimbursement or inventory management. The agency unit can also automatically generate routine reports. For example, the agency unit can automatically create monthly reports or sales reports and provide them to the user. The assignment unit assigns priorities to emails received by the user based on the business information. The assignment unit can, for example, analyze the content of emails using natural language processing to determine their importance. Furthermore, the prioritization unit can assign priorities based on the sender's job title and urgency of emails. For example, it might prioritize emails from a supervisor and postpone promotional emails. The scheduling unit manages the user's tasks and optimizes the schedule based on work information. For example, the scheduling unit can manage task schedules using a visual scheduling tool. It can also estimate the user's emotions and optimize the schedule based on those emotions. For example, if the scheduling unit is stressed, it can adjust the schedule to increase break times. This enables the AI ​​assistant for office work automation to efficiently automate the user's tasks and optimize task management and schedules.

[0070] The data collection unit collects business information related to the user's work. This business information includes, but is not limited to, work progress information, task details, and project status. The data collection unit can use sensors and databases to automatically collect user business information. Specifically, applications installed on the user's PC or smartphone can collect user operation history and input data in real time. Data can also be obtained from business management systems and project management tools used by the user. This allows the data collection unit to collect business information efficiently, saving the user the trouble of manual input. Furthermore, the data collection unit can also collect business information based on user input. For example, when a user enters detailed task information, that information can be saved in a database for later analysis and use. The data collection unit can also collect what the user says as text data using voice input and natural language processing technology. This allows users to provide business information by voice without relying on keyboard input. By combining these diverse means, the data collection unit can comprehensively collect user business information and improve the accuracy and efficiency of the entire system.

[0071] The proxy department performs tasks on behalf of users, such as routinely entering data or creating standardized reports based on business information. For example, the proxy department can automate data entry for expense reimbursement and inventory management. Specifically, in the case of expense reimbursement, when a user takes a photo of a receipt and uploads it to the system, the proxy department analyzes the image, extracts the necessary data, and enters it into the expense reimbursement system. In inventory management, it can automatically record product inbound and outbound information and update inventory status in real time. Furthermore, the proxy department can also automatically generate standardized reports. For example, the proxy department can automatically create and provide monthly reports and sales reports to users. This includes a process of generating reports according to a specific format based on data collected by the collection department. By utilizing AI-powered natural language generation technology, the content of reports can be automatically converted into text, eliminating the need for manual editing by users. As a result, the proxy department can reduce the workload of users and perform tasks efficiently.

[0072] The prioritization unit assigns priorities to emails delivered to the user based on business information. For example, the unit can analyze the content of emails using natural language processing to determine their importance. Specifically, it analyzes the email body and subject line, and scores the importance of emails by evaluating the frequency of keyword and phrase occurrences and context. The unit can also assign priorities based on the sender's job title and urgency. For example, emails from superiors or emails requiring urgent attention are given high priority, while promotional emails and general notification emails are given low priority. Furthermore, the unit can learn from the user's past email processing history and understand what types of emails the user tends to prioritize, enabling more accurate prioritization. In this way, the unit helps users process emails efficiently without missing important emails.

[0073] The scheduling unit manages user tasks and optimizes schedules based on business information. For example, the scheduling unit can manage task schedules using a visual scheduling tool. Specifically, it provides a calendar-style interface, allowing users to easily add tasks to their schedules via drag-and-drop. The scheduling unit can also estimate user emotions and optimize schedules based on these estimations. For instance, it analyzes the user's facial expressions and voice tone to detect signs of stress or fatigue. Based on this, if the user is feeling stressed, the scheduling unit can adjust the schedule to increase break times. Furthermore, the scheduling unit can suggest the optimal task order, considering task priorities and dependencies. This allows users to perform tasks efficiently and improve their work productivity. The scheduling unit can also collect user feedback and continuously improve its schedule optimization algorithms. This enables the scheduling unit to efficiently manage user tasks and provide optimal schedules.

[0074] The agency unit can perform data entry for expense reimbursement and inventory management. For example, the agency unit can automate data entry for expense reimbursement. For example, the agency unit can automatically enter expenses such as transportation, accommodation, and meals. The agency unit can also automate data entry for inventory management. For example, the agency unit can automatically manage the receipt and dispatch of goods and monitor inventory levels. Furthermore, the agency unit can also automate data entry for order management. For example, the agency unit can automatically place orders when inventory reaches a certain level. By automating data entry for expense reimbursement and inventory management, the burden on users can be reduced. Some or all of the above processes in the agency unit may be performed using AI, for example, or without AI. For example, the agency unit can input expense reimbursement data into a generating AI and have the generating AI perform the expense reimbursement data entry.

[0075] The assignment unit can analyze the content of emails using natural language processing and determine their importance. For example, the assignment unit can analyze the content of emails using natural language processing techniques. For example, the assignment unit can decompose the content of emails using morphological analysis and extract important keywords. The assignment unit can also analyze the sentence structure of emails using grammatical analysis and determine their importance. Furthermore, the assignment unit can understand the meaning of the content of emails using semantic analysis and determine their importance. For example, the assignment unit can analyze the frequency and context of keywords contained in the content of emails and determine their importance. This allows the assignment unit to automatically determine the importance of emails and prioritize them, enabling users to check important emails first. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input the content of emails into a generating AI and have the generating AI perform the determination of the importance of the emails.

[0076] The scheduling unit can manage task schedules using graphical scheduling tools. For example, the scheduling unit can manage task schedules using a Gantt chart. For example, the scheduling unit can display project tasks in a Gantt chart and visually manage their progress. The scheduling unit can also manage task schedules using a calendar display. For example, the scheduling unit can display daily work tasks in a calendar and manage their schedules. Furthermore, the scheduling unit can also manage task schedules using a task board. For example, the scheduling unit can display tasks on a board and manage their progress. This allows users to intuitively manage task schedules using visual scheduling tools. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task schedules into a generating AI and have the generating AI manage the schedules.

[0077] The scheduling unit can estimate the user's emotions and optimize the schedule based on the estimated emotions. For example, the scheduling unit can estimate the user's emotions using facial recognition technology. For example, the scheduling unit can analyze the user's facial expressions captured by a camera and estimate their emotions. The scheduling unit can also estimate the user's emotions using voice analysis technology. For example, the scheduling unit can record the user's voice and analyze the tone and speed of their voice to estimate their emotions. Furthermore, the scheduling unit can also estimate the user's emotions using text analysis technology. For example, the scheduling unit can analyze text entered by the user and estimate their emotions. By optimizing the schedule according to the user's emotions, it becomes possible to reduce user stress and enable efficient work execution. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the scheduling unit may be performed using AI, for example, or without using AI. For example, the scheduling unit can input user emotion data into a generating AI, allowing the AI ​​to optimize the schedule based on those emotions.

[0078] The scheduling unit can optimize the schedule based on the remaining tasks of the user, which are determined by the tasks performed by the proxy unit. For example, if the proxy unit handles expense reimbursement, the scheduling unit can allocate that time to other tasks. Similarly, if the proxy unit handles inventory management, the scheduling unit can allocate that time to other tasks. Furthermore, if the proxy unit creates a standardized report, the scheduling unit can allocate that time to other tasks.

[0079] The scheduling unit can set the user's schedule according to the content of emails to which priority has been assigned by the assignment unit. For example, if the scheduling unit receives an email about an important meeting, it can add that meeting to the schedule. The scheduling unit can also prioritize adding an urgent task to the schedule if it receives an email about that task. Furthermore, if the scheduling unit receives an email about the progress of a project, it can adjust the schedule based on that progress. This allows the user to prioritize important tasks by determining the schedule based on the content of emails. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or not using AI. For example, the scheduling unit can input the data of emails to which priority has been assigned by the assignment unit into a generating AI and have the generating AI set the schedule.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection of data and collect it when the user is relaxed. Conversely, if the user is focused, the data collection unit can collect data quickly to maintain the user's concentration. Furthermore, if the user is tired, the data collection unit can temporarily stop the collection of data and resume it after the user has taken a break. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions, thereby reducing user stress. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the data collection timing based on the emotions.

[0081] The data collection unit can analyze the user's past work history and select an efficient data collection method. For example, the data collection unit can prioritize collecting data from data sources that the user has frequently used in the past. The data collection unit can also optimize the types of data to collect at specific time periods based on the user's past work history. Furthermore, the data collection unit can analyze the user's past work history and propose the most efficient data collection method. This enables efficient data collection by selecting the optimal information collection method through analysis of past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past work history data into a generating AI and have the generating AI select an efficient data collection method.

[0082] The data collection unit can filter business information based on the user's current projects and areas of interest. For example, the data collection unit can prioritize collecting information related to the project the user is currently working on. The data collection unit can also filter and collect highly relevant information based on the user's areas of interest. Furthermore, the data collection unit can collect necessary information according to the progress of the user's current projects. This allows for the efficient collection of highly relevant information by filtering it based on the current project and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's project data and areas of interest data into a generating AI and have the generating AI perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of business information to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit can postpone collecting less important information and prioritize collecting more important information. Similarly, if the user is relaxed, the data collection unit can prioritize collecting detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that can be quickly gathered. This allows for the priority collection of important information by prioritizing information 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 include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information based on emotions.

[0084] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when collecting business information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of information related to that region. The data collection unit can also collect information on nearby events and meetings based on the user's geographical location. Furthermore, if the user is on a business trip, the data collection unit can prioritize the collection of information related to the destination. This allows for the efficient collection of information useful for the user's work by collecting highly relevant information based on geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0085] The data collection unit can analyze the user's social media activity and collect relevant information when collecting business information. For example, the data collection unit can collect information related to topics mentioned by the user on social media. It can also collect information shared by the user's social media followers and friends. Furthermore, the data collection unit can analyze the user's social media activity history and collect highly relevant information. This allows for the efficient collection of highly relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0086] The proxy unit can estimate the user's emotions and set the method of data entry and report generation based on the estimated emotions. For example, if the user is stressed, the proxy unit can provide a simple data entry interface and minimize the input steps. If the user is relaxed, the proxy unit can also provide detailed data entry options and suggest customizable input methods. Furthermore, if the user is in a hurry, the proxy unit can prioritize voice input for faster data entry. This reduces the user's burden and enables efficient work execution by adjusting the method of data entry and report generation according to the user's emotions. Emotion estimation is performed using, for example, an emotion engine or generative AI.

[0087] The proxy unit can set the level of detail of processing based on the importance of the task when entering data or generating reports. For example, for tasks with high importance, the proxy unit can perform detailed data entry and report generation. Conversely, for tasks with low importance, the proxy unit can perform simplified data entry and report generation. Furthermore, the proxy unit can automatically select and enter the necessary data items according to the importance of the task. This allows for efficient data entry and report generation by adjusting the level of detail of processing based on the importance of the task. Some or all of the above processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input task importance data into a generating AI and have the generating AI set the level of detail of processing.

[0088] The proxy unit can use different algorithms depending on the business category when entering data or generating reports. For example, in the case of expense reimbursement, the proxy unit can apply different algorithms to each expense item when entering data. Similarly, in the case of inventory management, the proxy unit can apply different algorithms to each type and quantity of inventory when entering data. Furthermore, when generating reports, the proxy unit can generate reports using different templates depending on the business category. This enables efficient data entry and report generation by applying different algorithms depending on the business category. Some or all of the above processes in the proxy unit may be performed using AI, for example, or not. For example, the proxy unit can input business category data into a generation AI and have the generation AI execute the application of different algorithms.

[0089] The proxy unit can estimate the user's emotions and determine the priority of data entry and report generation based on the estimated emotions. For example, if the user is stressed, the proxy unit can postpone less important data entry and report generation. Conversely, if the user is relaxed, the proxy unit can prioritize detailed data entry and report generation. Furthermore, if the user is in a hurry, the proxy unit can prioritize data entry and report generation that can be completed quickly. This allows for the prioritization of important tasks by determining the priority of data entry and report generation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proxy unit may be performed using AI or not. For example, the proxy unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0090] The proxy department can prioritize processing based on the submission deadline of tasks when entering data or generating reports. For example, the proxy department can prioritize tasks with approaching deadlines. It can also postpone tasks with later deadlines. Furthermore, the proxy department can automatically adjust processing priorities according to the submission deadline. This makes it easier to meet deadlines by determining processing priorities based on the submission deadline of tasks. Some or all of the above processing in the proxy department may be performed using AI, for example, or not using AI. For example, the proxy department can input task submission deadline data into a generating AI and have the generating AI set the priorities.

[0091] The proxy unit can set the processing order based on the relevance of tasks when entering data or generating reports. For example, the proxy unit can prioritize processing tasks that are highly relevant. It can also postpone tasks that are less relevant. Furthermore, the proxy unit can automatically adjust the processing order according to the relevance of tasks. This allows for efficient task execution by adjusting the processing order based on the relevance of tasks. Some or all of the above processing in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input task relevance data into a generating AI and have the generating AI set the processing order.

[0092] The prioritization unit can estimate the user's emotions and set criteria for prioritizing emails based on the estimated emotions. For example, if the user is stressed, the prioritization unit can postpone less important emails and prioritize displaying more important ones. It can also display detailed email content if the user is relaxed. Furthermore, if the user is in a hurry, the prioritization unit can prioritize displaying emails that can be quickly reviewed. This allows important emails to be prioritized by adjusting the email prioritization criteria 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 prioritization unit may be performed using AI or not. For example, the prioritization unit can input user emotion data into a generative AI and have the generative AI set emotion-based prioritization criteria.

[0093] The prioritization unit can set priorities based on the importance of emails when prioritizing them. For example, the prioritization unit can display high-importance emails first. It can also postpone low-importance emails. Furthermore, the prioritization unit can automatically adjust priorities according to the importance of emails. This allows important emails to be reviewed first by adjusting priorities based on their importance. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input email importance data into a generating AI and have the generating AI perform the priority setting.

[0094] The prioritization unit can use different algorithms depending on the email category when prioritizing emails. For example, the prioritization unit can display business-related emails preferentially. It can also postpone promotional emails. Furthermore, the prioritization unit can automatically adjust priorities according to the email category. This enables efficient email management by applying different algorithms depending on the email category. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or not using AI. For example, the prioritization unit can input email category data into a generating AI and have the generating AI execute the application of different algorithms.

[0095] The assigning unit can estimate the user's emotions and determine the priority of emails based on the estimated user emotions. For example, if the user is feeling stressed, the assigning unit will prioritize important spec-content

[0096] The prioritization unit can determine the priority of emails based on the attribute information of the email sender. For example, the prioritization unit can display emails from a supervisor first. It can also postpone emails from colleagues. Furthermore, the prioritization unit can automatically adjust the priority based on the attribute information of the email sender. This allows important emails to be reviewed first by determining the priority based on the attribute information of the email sender. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or not using AI. For example, the prioritization unit can input email sender attribute information data into a generating AI and have the generating AI perform the priority determination.

[0097] The prioritization unit can adjust the priority of emails based on the relevant literature for each email. For example, the prioritization unit can refer to literature related to the content of an email and determine its importance. The prioritization unit can also automatically adjust the priority based on the relevant literature for each email. Furthermore, the prioritization unit can display literature related to the content of each email, allowing the user to check the priority. This allows users to prioritize important emails by referring to the relevant literature for each email. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or not using AI. For example, the prioritization unit can input email relevant literature data into a generating AI and have the generating AI perform the priority adjustment.

[0098] The scheduling unit can estimate the user's emotions and adjust the schedule display method based on the estimated emotions. For example, if the user is stressed, the scheduling unit can provide a simple and highly visible schedule display. If the user is relaxed, the scheduling unit can provide a detailed schedule display. Furthermore, if the user is in a hurry, the scheduling unit can provide a concise schedule display. This allows for highly visible schedule management by adjusting the schedule display 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. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI, or not using AI. For example, the scheduling unit can input user emotion data into the generative AI and have the generative AI perform the emotion-based adjustment of the display method.

[0099] The scheduling unit can optimize the current schedule based on past schedule data during schedule management. For example, the scheduling unit can propose an optimal schedule based on the user's past schedule data. It can also propose the most suitable tasks for a specific time period based on past schedule data. Furthermore, the scheduling unit can analyze past schedule data and create an efficient schedule. This allows it to propose an optimal schedule by referring to past schedule data. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input past schedule data into a generating AI and have the generating AI perform the optimization of the current schedule.

[0100] The scheduling unit can use different scheduling methods for each task category when managing schedules. For example, the scheduling unit can use a Gantt chart to manage schedules for project tasks. It can also use a calendar to manage schedules for daily tasks. Furthermore, it can use reminders to manage schedules for urgent tasks. This allows for efficient schedule management by applying different scheduling methods according to the task category. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task category data into a generating AI and have the generating AI execute the application of different scheduling methods.

[0101] The scheduling unit can estimate the user's emotions and determine schedule priorities based on those emotions. For example, if the user is stressed, the scheduling unit can postpone less important tasks and prioritize more important tasks. If the user is relaxed, the scheduling unit can also prioritize detailed tasks. Furthermore, if the user is in a hurry, the scheduling unit can prioritize tasks that can be completed quickly. This allows important tasks to be prioritized by determining schedule priorities 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 include, 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 emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0102] The scheduling unit can evaluate schedule changes based on task submission dates during schedule management. For example, the scheduling unit can prioritize scheduling tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, the scheduling unit can automatically analyze schedule changes based on submission dates and propose an optimal schedule. This allows for the proposal of an optimal schedule by analyzing schedule changes based on task submission dates. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task submission date data into a generating AI and have the generating AI perform the evaluation of schedule changes.

[0103] The scheduling unit can evaluate schedules based on relevant market data for tasks during schedule management. For example, the scheduling unit can propose an optimal schedule based on market trends. The scheduling unit can also refer to relevant market data for tasks to determine schedule priorities. Furthermore, the scheduling unit can optimize task schedules based on market data. This allows the scheduling unit to propose an optimal schedule by referring to relevant market data for tasks. Some or all of the above processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input relevant market data for tasks into a generating AI and have the generating AI perform schedule evaluation.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0106] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0107] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0108] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0109] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0110] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0111] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0112] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0113] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0114] The AI ​​assistant for automating office tasks is a system that collects business information related to the user's work and automatically performs routine data entry and report generation. Based on the user's business information, this system prioritizes emails and optimizes tasks and schedules. For example, if a user needs to enter data for expense reports or inventory management, the AI ​​assistant will do this automatically. This allows the user to focus on other important tasks. The AI ​​assistant also uses natural language processing to analyze email content and determine its importance, allowing the user to prioritize important emails. Furthermore, the AI ​​assistant manages task schedules using visual scheduling tools. For example, it uses calendars and Gantt charts to visually display the user's tasks and optimize the schedule. The AI ​​assistant also estimates the user's emotions and optimizes the schedule based on those emotions. For example, if the user is feeling stressed, the schedule can be adjusted to increase break times. In addition, the AI ​​assistant optimizes the schedule based on the user's remaining tasks determined by tasks performed by a substitute department. For example, if an substitute department handles expense reports, that time can be allocated to other tasks. Furthermore, the system determines the user's schedule based on the content of emails to which priority has been assigned. For example, if an email about an important meeting arrives, the user can add that meeting to their schedule.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The data collection unit collects business information related to the user's work. This business information includes, for example, work progress information, task details, and project status. The data collection unit can use sensors and databases to automatically collect user business information. It can also collect business information based on user input. For example, the data collection unit collects detailed task information entered by the user and stores it in the database. Step 2: The proxy unit performs tasks on behalf of the user, such as routinely entering data or creating routine reports based on business information. For example, it can automate data entry for expense reimbursements and inventory management. It can also automatically create and provide monthly reports and sales reports to the user. Step 3: The prioritization unit assigns priority to emails delivered to the user based on business information. For example, it can analyze the content of emails using natural language processing to determine their importance. It can also assign priority based on the sender's job title and urgency. For example, it can prioritize emails from superiors and delay promotional emails. Step 4: The scheduling unit manages user tasks and optimizes schedules based on business information. For example, it can manage task schedules using a visual scheduling tool. It can also estimate the user's emotions and optimize the schedule based on those emotions. For example, if a user is feeling stressed, the schedule can be adjusted to increase break times.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] For example, the collection unit can collect user business information using the camera 42 and microphone 38B of the smart device 14. The collection unit can also collect business information using the specific processing unit 290 of the data processing device 12. The proxy unit can automatically input data for expense reimbursement and inventory management using, for example, the control unit 46A of the smart device 14. The proxy unit can also automatically generate standardized reports using the specific processing unit 290 of the data processing device 12. The assignment unit can analyze the content of emails and assign priorities using, for example, the control unit 46A of the smart device 14. The assignment unit can also assign priorities based on the sender's job title and urgency using the specific processing unit 290 of the data processing device 12. The scheduling unit can manage and optimize task schedules using, for example, the control unit 46A of the smart device 14. The scheduling unit can also estimate the user's emotions using the specific processing unit 290 of the data processing device 12 and optimize the schedule. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] For example, the collection unit can collect user business information using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also collect business information using the specific processing unit 290 of the data processing device 12. The proxy unit can automatically input data for expense reimbursement and inventory management using the control unit 46A of the smart glasses 214. The proxy unit can also automatically generate standardized reports using the specific processing unit 290 of the data processing device 12. The assignment unit can analyze the content of emails and assign priorities using the control unit 46A of the smart glasses 214. The assignment unit can also assign priorities based on the sender's job title and urgency using the specific processing unit 290 of the data processing device 12. The scheduling unit can manage and optimize task schedules using the control unit 46A of the smart glasses 214. The scheduling unit can also estimate the user's emotions using the specific processing unit 290 of the data processing device 12 and optimize the schedule. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] For example, the collection unit can collect user business information using the camera 42 and microphone 238 of the headset terminal 314. The collection unit can also collect business information using the specific processing unit 290 of the data processing device 12. The proxy unit can automatically input expense reimbursement and inventory management data using the control unit 46A of the headset terminal 314. The proxy unit can also automatically generate standardized reports using the specific processing unit 290 of the data processing device 12. The assignment unit can analyze the content of emails and assign priorities using the control unit 46A of the headset terminal 314. The assignment unit can also assign priorities based on the sender's job title and urgency using the specific processing unit 290 of the data processing device 12. The scheduling unit can manage and optimize task schedules using the control unit 46A of the headset terminal 314. The scheduling unit can also estimate the user's emotions using the specific processing unit 290 of the data processing device 12 and optimize the schedule. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0164] In 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.

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

[0166] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0168] The data processing system 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.

[0169] For example, the collection unit can collect user business information using the camera 42 and microphone 238 of the robot 414. The collection unit can also collect business information using the specific processing unit 290 of the data processing device 12. The proxy unit can automatically input expense reimbursement and inventory management data using, for example, the control unit 46A of the robot 414. The proxy unit can also automatically generate standardized reports using the specific processing unit 290 of the data processing device 12. The assignment unit can analyze the content of emails and assign priorities using, for example, the control unit 46A of the robot 414. The assignment unit can also assign priorities based on the sender's job title and urgency using the specific processing unit 290 of the data processing device 12. The scheduling unit can manage and optimize task schedules using, for example, the control unit 46A of the robot 414. The scheduling unit can also estimate the user's emotions using the specific processing unit 290 of the data processing device 12 and optimize the schedule. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A collection unit that collects business information related to the user's work, Based on the aforementioned business information, a proxy unit performs tasks on behalf of the user, such as routinely entering data and creating routine reports. A prioritization unit assigns priority to emails delivered to the user based on the aforementioned business information, The system includes a scheduling unit that manages the user's tasks and streamlines the schedule based on the aforementioned business information. A system characterized by the following features. (Note 2) The aforementioned agency unit is Perform data entry for expense reimbursement and inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned attachment unit is, We use natural language processing to analyze the content of emails and determine their importance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned scheduling unit, Manage task schedules using a graphical scheduling tool. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned scheduling unit, It estimates the user's emotions and optimizes the schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned scheduling unit, Based on the remaining tasks of the user determined by the work performed by the aforementioned agency unit, the schedule is made more efficient. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned scheduling unit, The user's schedule is set according to the content of the emails to which priority has been assigned by the aforementioned assignment unit. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of business information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past work history and select the most efficient data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting business information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and determines the priority of business information to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting business information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting business information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned agency unit is It estimates the user's emotions and sets the data entry and report generation methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned agency unit is When entering data or generating reports, set the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned agency unit is When entering data or generating reports, different algorithms are used depending on the category of the task. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned agency unit is It estimates user sentiment and determines the priority of data entry and report generation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned agency unit is When entering data or generating reports, prioritize processing based on the deadline for submitting tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned agency unit is When entering data or generating reports, the order of processing is set based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned attachment unit is, Estimate the user's emotions and set criteria for prioritizing emails based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned attachment unit is, When prioritizing emails, set the priority based on the importance of the email. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned attachment unit is, When prioritizing emails, use different algorithms depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned attachment unit is, It estimates the user's emotions and prioritizes emails based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned attachment unit is, When prioritizing emails, the priority is determined based on the sender's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned attachment unit is, When prioritizing emails, adjust priorities based on the relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned scheduling unit, It estimates the user's emotions and adjusts how the schedule is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned scheduling unit, When managing schedules, optimize the current schedule based on past schedule data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned scheduling unit, When managing your schedule, use different scheduling methods for each task category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned scheduling unit, It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned scheduling unit, When managing a schedule, evaluate schedule changes based on the timing of task submissions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned scheduling unit, When managing schedules, evaluate schedules based on relevant market data for each task. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects business information related to the user's work, Based on the aforementioned business information, an agency unit performs tasks on behalf of the user, such as routinely entering data and creating routine reports. A prioritization unit assigns priority to emails delivered to the user based on the aforementioned business information, The system includes a scheduling unit that manages the user's tasks and streamlines the schedule based on the aforementioned business information. A system characterized by the following features.

2. The aforementioned agency unit is Perform data entry for expense reimbursement and inventory management. The system according to feature 1.

3. The aforementioned attachment unit is, We use natural language processing to analyze the content of emails and determine their importance. The system according to feature 1.

4. The aforementioned scheduling unit, Manage task schedules using a graphical scheduling tool. The system according to feature 1.

5. The aforementioned scheduling unit, The system estimates the user's emotions and optimizes the schedule based on the estimated user's emotions. The system according to feature 1.

6. The aforementioned scheduling unit, Based on the remaining tasks of the user determined by the work performed by the aforementioned agency unit, the schedule is made more efficient. The system according to feature 1.

7. The aforementioned scheduling unit, The user's schedule is set according to the content of the emails to which priority has been assigned by the aforementioned assignment unit. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting business information based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past work history and select an efficient data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting business information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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