system

Abstract

The system according to this embodiment aims to automate and streamline business processes and facilitate progress management and coordination. [Solution] The system according to the embodiment comprises a reception unit, an execution unit, a coordination unit, and a monitoring unit. The reception unit receives instructions from the user. The execution unit performs tasks based on the instructions received by the reception unit. The coordination unit shares information about the tasks performed by the execution unit with other agents. The monitoring unit monitors the progress of the tasks based on the information shared by the coordination unit and makes adjustments as necessary.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the automation and efficiency of operations are not sufficiently achieved, and a lot of time and labor are required for the progress management and adjustment of operations.

[0005] The system according to the embodiment aims to automate and improve the efficiency of operations and facilitate the progress management and adjustment of operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an execution unit, a coordination unit, and a monitoring unit. The reception unit receives instructions from the user. The execution unit performs tasks based on the instructions received by the reception unit. The coordination unit shares information about the tasks performed by the execution unit with other agents. The monitoring unit monitors the progress of the tasks based on the information shared by the coordination unit and makes adjustments as necessary. [Effects of the Invention]

[0007] The system according to this embodiment can automate and streamline business processes, and facilitate progress management and coordination. [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 multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 BizAgent Hub system, according to an embodiment of the present invention, is an AI agent platform that supports business owners and entrepreneurs. This BizAgent Hub system allows users to select AI agents specializing in specific tasks, such as a "secretary" or an "accounting department," and assign them tasks. These agents work together to automate and streamline tasks such as information management, scheduling, and accounting. This allows users to save time and effort, providing an environment where they can focus on strategic business activities. For example, the BizAgent Hub system allows users to access and select AI agents specializing in the tasks they need. These agents might include a "secretary" for secretarial duties or an "accounting department" for accounting tasks. Users then assign specific tasks to these agents. The selected AI agents then perform the tasks based on the user's instructions. For example, the "secretary" handles scheduling and email management, while the "accounting department" handles accounting and invoice issuance. These agents collaborate, sharing information and working efficiently. Furthermore, the AI ​​agents monitor the progress of tasks in real time and make adjustments as needed. For example, if schedule changes or additional tasks arise, the AI ​​agent automatically responds and notifies the user. This mechanism enables users to automate and streamline their work, saving time and effort. It also provides users with an environment where they can focus on strategic business activities. For instance, managers of startups and small businesses no longer need to handle multiple tasks themselves, allowing them to concentrate on management and their core business. In this way, the BizAgent Hub system is an innovative platform that supports business owners and entrepreneurs, enabling an efficient and seamless future of work. Through this, the BizAgent Hub system can automate and streamline users' work.

[0029] The BizAgent Hub system according to this embodiment comprises a reception unit, an execution unit, a coordination unit, and a monitoring unit. The reception unit receives instructions from the user. Instructions from the user include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit converts the user's voice instructions into text using, for example, speech recognition technology. The reception unit can also directly receive text instructions. Furthermore, the reception unit can interpret the user's gesture instructions using gesture recognition technology. For example, the reception unit converts the user's voice instructions into text with high accuracy using speech recognition technology. Text instructions can be accepted as text entered by the user. Gesture recognition technology analyzes the user's hand movements and facial expressions and interprets them as instructions. The execution unit performs tasks based on the instructions received by the reception unit. Task execution includes, but is not limited to, scheduling, email management, accounting processing, and invoice issuance. For example, the execution unit coordinates with a calendar application to automatically adjust schedules. Furthermore, the execution unit can integrate with email clients to manage emails, automating the classification and replying to incoming emails. In addition, the execution unit can integrate with accounting software to automate the input of transaction data and the creation of ledgers. For example, the execution unit can integrate with a calendar application to automatically adjust user schedules; integrate with an email client to automatically classify incoming emails and reply as needed; and integrate with accounting software to automatically input transaction data and create ledgers. The integration unit shares information about tasks performed by the execution unit with other agents. This information sharing includes, but is not limited to, task progress, task results, and task notes. For example, the integration unit can share task progress with other agents to improve efficiency. It can also share task results with other agents to help improve operations. Furthermore, the integration unit can share task notes with other agents to achieve centralized information management.For example, the Collaboration Department shares the progress of tasks with other agents in real time. It also shares the results of tasks with other agents to help improve operations. It shares notes related to tasks with other agents to achieve centralized information management. The Monitoring Department monitors the progress of tasks based on the information shared by the Collaboration Department and makes adjustments as needed. Monitoring progress includes, but is not limited to, the progress of tasks, the completion status of tasks, and the status of delays. For example, the Monitoring Department monitors the progress of tasks in real time and issues alerts if progress is behind schedule. The Monitoring Department can also monitor the completion status of tasks and report on completed tasks. Furthermore, the Monitoring Department can monitor for delays and take countermeasures if delays occur. For example, the Monitoring Department monitors the progress of tasks in real time and issues alerts if progress is behind schedule. It monitors the completion status of tasks and reports on completed tasks. It monitors for delays and takes countermeasures if delays occur. As a result, the BizAgent Hub system according to this embodiment can efficiently receive user instructions, perform tasks, share information, and monitor and adjust progress.

[0030] The reception desk receives instructions from users. These instructions may include, but are not limited to, voice, text, and gesture instructions. For example, the reception desk converts voice instructions to text using speech recognition technology. This speech recognition technology uses a deep learning-based speech model to analyze user utterances with high accuracy. Voice instructions are further refined by noise cancellation technology to remove ambient noise and obtain clear audio data. Furthermore, speech recognition technology understands the context of user utterances and converts them into appropriate text using natural language processing technology. Text instructions can be accepted directly from the user. Text input can be done using a keyboard or touchscreen, enhancing user convenience. Gesture recognition technology analyzes user hand movements and facial expressions and interprets them as instructions. Gesture recognition uses cameras and sensors to capture user movements in real time and machine learning algorithms to recognize gestures. For example, hand movements and facial expressions can be analyzed and specific actions interpreted as instructions. This allows the reception desk to support diverse input methods and efficiently receive user instructions. Furthermore, the reception unit can record the user's instruction history and refer to past instructions to more accurately understand the user's intentions. This allows the reception unit to receive user instructions quickly and accurately, improving the overall efficiency of the system.

[0031] The execution unit carries out tasks based on instructions received by the reception unit. Tasks include, but are not limited to, scheduling, email management, accounting, and invoice issuance. To schedule, the execution unit integrates with a calendar application to automatically adjust appointments. The calendar application centrally manages the user's schedule and suggests optimal times to avoid overlaps and conflicts. Based on user instructions, the execution unit can adjust meeting schedules and send notifications to participants. Furthermore, to manage emails, the execution unit can integrate with an email client to automate the categorization and replying to incoming emails. The email client categorizes incoming emails and prioritizes them according to importance. Based on user instructions, the execution unit automatically replies to specific emails, streamlining email processing. Additionally, to perform accounting tasks, the execution unit can integrate with accounting software to automate transaction data entry and ledger creation. The accounting software improves the efficiency of accounting operations by automatically entering transaction data and creating ledgers. The execution unit can issue invoices and manage payments based on user instructions. This allows the execution unit to efficiently perform a variety of tasks based on user instructions, improving the overall productivity of the system. Furthermore, the execution unit can monitor the progress of tasks in real time and make adjustments as needed. This enables the execution unit to achieve both increased efficiency and improved quality.

[0032] The Collaboration Department shares information about tasks performed by the Execution Department with other agents. This information sharing includes, but is not limited to, task progress, task results, and task notes. The Collaboration Department shares task progress with other agents to improve efficiency. For example, by sharing task progress in real time, the Collaboration Department enables each agent to perform tasks based on the latest information. Task results can also be shared with other agents to help improve operations. For example, the Collaboration Department analyzes task results and shares areas for improvement with other agents to enhance the quality of operations. Furthermore, the Collaboration Department can share task notes with other agents to achieve centralized information management. For example, the Collaboration Department can save task notes to cloud storage, making them accessible to other agents. This enables the Collaboration Department to share and centrally manage information, improving efficiency and quality. Additionally, the Collaboration Department can implement security measures when sharing information. For example, encryption technology can be used to ensure data confidentiality and prevent unauthorized access. Furthermore, the liaison department can record the history of information sharing and track and audit the information. This allows the liaison department to share and centrally manage information safely and efficiently.

[0033] The monitoring department monitors the progress of tasks based on information shared by the collaboration department and makes adjustments as needed. Monitoring progress includes, but is not limited to, the progress, completion status, and delay status of tasks. The monitoring department monitors the progress of tasks in real time and issues alerts if progress is behind schedule. For example, the monitoring department visualizes the progress of tasks using graphs and charts so that the progress can be understood at a glance. If progress is behind schedule, it issues an alert to notify relevant parties and encourages a quick response. The monitoring department can also monitor the completion status of tasks and report on completed tasks. For example, the monitoring department can display the completion status of tasks in a list format and automatically generate reports for completed tasks. Furthermore, the monitoring department can monitor the delay status of tasks and take countermeasures if delays occur. For example, the monitoring department can analyze the cause of delays and take countermeasures to minimize delays. In this way, the monitoring department can improve the efficiency and quality of tasks by monitoring the progress of tasks in real time and making adjustments as needed. Furthermore, the monitoring department can regularly report on the progress of the work and provide relevant parties with the latest information. This allows the monitoring department to accurately grasp the progress of the work and take prompt and appropriate action.

[0034] The reception department can instruct the agent selected by the user to perform specific tasks. For example, the reception department can instruct the agent selected by the user to perform specific tasks such as data entry, report creation, and customer service. For example, the reception department can instruct the agent selected by the user to perform data entry. The reception department can also instruct the agent selected by the user to create reports. The reception department can also instruct the agent selected by the user to handle customer service. This allows for increased efficiency in operations by instructing the agent selected by the user to perform specific tasks. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input specific task instructions for the agent selected by the user into a generating AI and have the generating AI execute the task instructions.

[0035] The execution unit can perform scheduling and email management. For example, to schedule, the execution unit can integrate with a calendar application to automatically adjust appointments. For example, the execution unit can integrate with a calendar application to automatically adjust the user's appointments. The execution unit can also integrate with an email client to automate the classification and replying to received emails. For example, the execution unit can integrate with an email client to automatically classify received emails and reply as needed. Furthermore, the execution unit can use a generation AI to perform scheduling and email management. For example, to schedule, the execution unit can input the user's appointments into the generation AI and have the generation AI perform the scheduling adjustments. Similarly, to manage emails, the execution unit can input received emails into the generation AI and have the generation AI classify and reply to the emails. This allows for increased efficiency in work by automating scheduling and email management.

[0036] The execution unit can perform accounting processing and invoice issuance. For example, to perform accounting processing, the execution unit can integrate with accounting software to automate the input of transaction data and the creation of ledgers. For example, the execution unit can integrate with accounting software to automatically input transaction data and create ledgers. The execution unit can also integrate with an invoice issuance system to automate the creation and transmission of invoices. For example, the execution unit can integrate with an invoice issuance system to automatically create and send invoices. Furthermore, the execution unit can use a generation AI to perform accounting processing and invoice issuance. For example, for accounting processing, the execution unit can input transaction data into the generation AI and have the generation AI create ledgers. The execution unit can also have the generation AI create invoices for invoice issuance. This allows for increased efficiency in accounting processing and invoice issuance.

[0037] The collaboration department enables agents to share information and efficiently carry out their work. For example, the collaboration department can share the progress of work with other agents to improve work efficiency. For example, the collaboration department can share the progress of work with other agents in real time. The collaboration department can also share the results of work with other agents to help improve work. For example, the collaboration department can share the results of work with other agents to help improve work. The collaboration department can also share notes related to work with other agents to achieve centralized information management. For example, the collaboration department can share notes related to work with other agents to achieve centralized information management. This allows agents to share information and improve work efficiency. Some or all of the above processes in the collaboration department may be performed using AI, or not. For example, the collaboration department can input the progress of work into a generating AI and have the generating AI perform information sharing.

[0038] The monitoring unit can monitor the progress of tasks in real time and make adjustments as needed. For example, the monitoring unit can monitor the progress of tasks in real time and issue alerts if the progress is behind schedule. The monitoring unit can also monitor the completion status of tasks and report on completed tasks. The monitoring unit can also monitor the delay status of tasks and take countermeasures if delays occur. This allows for increased efficiency in tasks by monitoring progress in real time and making adjustments as needed. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the progress status of tasks into a generating AI and have the generating AI monitor the progress status.

[0039] The reception desk can analyze the user's past instruction history and propose the optimal instruction method. For example, the reception desk can automatically propose instructions that the user has frequently given in the past. The reception desk can also prioritize suggesting instruction methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and propose instructions to be given during specific time periods based on the user's past instruction history. This allows the reception desk to propose the optimal instruction method by analyzing the user's past instruction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past instruction history data into a generating AI and have the generating AI propose instruction methods.

[0040] The reception unit can filter instructions based on the user's current work status and areas of interest when receiving them. For example, the reception unit can prioritize receiving instructions related to projects the user is currently working on. The reception unit can also filter relevant instructions based on the user's areas of interest. The reception unit can also suggest appropriate instructions based on the user's work status. This allows for more appropriate instructions to be provided by filtering based on the user's current work status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's work status data into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location. For example, if the user is in the office, the reception desk can prioritize receiving office-related instructions. The reception desk can also prioritize receiving instructions that can be performed at the user's current location if the user is out of the office. The reception desk can also prioritize receiving instructions related to a specific location if the user is in that location. This allows for more appropriate instructions to be given by prioritizing highly relevant instructions while considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI prioritize receiving highly relevant instructions.

[0042] The reception unit can analyze the user's social media activity when receiving instructions and accept relevant instructions. For example, the reception unit can prioritize receiving tasks mentioned by the user on social media. The reception unit can also suggest relevant instructions based on the user's social media activity. The reception unit can also accept appropriate instructions based on the content of the user's social media posts. In this way, relevant instructions can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant instructions.

[0043] The execution unit can adjust the level of detail in its execution based on the importance of the task. For example, the execution unit can provide detailed procedures for important tasks. For example, the execution unit can provide detailed procedures for important tasks. The execution unit can also provide standard procedures for routine tasks. For example, the execution unit can provide standard procedures for routine tasks. The execution unit can also provide procedures that can be completed quickly for urgent tasks. For example, the execution unit can provide procedures that can be completed quickly for urgent tasks. By adjusting the level of detail in execution based on the importance of the task, more appropriate tasks can be performed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task importance data into a generating AI and have the generating AI adjust the level of detail in execution.

[0044] The execution unit can apply different execution algorithms depending on the category of the task during task execution. For example, in the case of accounting tasks, the execution unit can apply an algorithm specifically for accounting. Similarly, in the case of scheduling tasks, the execution unit can apply an algorithm specifically for scheduling. Similarly, in the case of email management tasks, the execution unit can apply an algorithm specifically for email. By applying different execution algorithms depending on the category of the task, more appropriate task execution can be achieved. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task category data into a generating AI and have the generating AI apply the execution algorithm.

[0045] The execution unit can determine the priority of tasks based on their submission deadlines. For example, the execution unit can prioritize tasks with approaching deadlines. The execution unit can also postpone tasks with distant submission deadlines. The execution unit can also perform tasks with unknown submission deadlines in between other tasks. This allows for more appropriate task execution by determining the priority of tasks based on their submission deadlines. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task submission data into a generating AI and have the generating AI determine the priority of tasks.

[0046] The execution unit can adjust the order of execution based on the relevance of tasks during task execution. For example, the execution unit can group related tasks together. The execution unit can also postpone tasks with low relevance. The execution unit can also prioritize tasks with high relevance. By adjusting the order of execution based on the relevance of tasks, more appropriate task execution can be achieved. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task relevance data into a generating AI and have the generating AI adjust the order of execution.

[0047] The collaboration unit can improve the accuracy of information sharing by considering the interrelationships between agents. For example, the collaboration unit can strengthen the collaboration between agents and improve the accuracy of information. The collaboration unit can also facilitate communication between agents and improve the efficiency of information sharing. The collaboration unit can also analyze the interrelationships between agents and propose the optimal method of information sharing. This improves the accuracy of information sharing by considering the interrelationships between agents. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input interrelationship data between agents into a generating AI and have the generating AI perform the improvement of sharing accuracy.

[0048] The collaboration unit can share information while considering the agent's attribute information. For example, the collaboration unit can share relevant information based on the agent's area of ​​expertise. The collaboration unit can also share appropriate information according to the agent's role. The collaboration unit can also analyze the agent's attribute information and propose the optimal method of information sharing. This allows for more appropriate information sharing by considering the agent's attribute information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input agent attribute information data into a generating AI and have the generating AI perform the information sharing.

[0049] The collaboration unit can share information while considering the geographical distribution of agents. For example, if an agent is nearby, the collaboration unit can directly share information. For example, if an agent is nearby, the collaboration unit can directly share information. The collaboration unit can also remotely share information if an agent is far away. For example, the collaboration unit can remotely share information if an agent is far away. The collaboration unit can also analyze the geographical distribution of agents and propose the optimal method of information sharing. For example, the collaboration unit can analyze the geographical distribution of agents and propose the optimal method of information sharing. This allows for more appropriate information sharing by considering the geographical distribution of agents. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the geographical distribution data of agents into a generating AI and have the generating AI perform the information sharing.

[0050] The collaboration unit can improve the accuracy of information sharing by referring to relevant literature during information sharing. For example, the collaboration unit can improve the accuracy of information by referring to relevant literature. The collaboration unit can also improve the efficiency of information sharing based on relevant literature. For example, the collaboration unit can improve the efficiency of information sharing based on relevant literature. The collaboration unit can also analyze relevant literature and propose the optimal information sharing method. For example, the collaboration unit can analyze relevant literature and propose the optimal information sharing method. As a result, the accuracy of information sharing is improved by referring to relevant literature. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input relevant literature data into a generating AI and have the generating AI perform information sharing.

[0051] The monitoring unit can predict current progress by referring to past progress data when monitoring progress. For example, the monitoring unit can predict current progress based on past progress data. The monitoring unit can also analyze past progress data and evaluate the current progress. For example, the monitoring unit can analyze past progress data and evaluate the current progress. The monitoring unit can also refer to past progress data and propose the optimal progress prediction method. For example, the monitoring unit can refer to past progress data and propose the optimal progress prediction method. This allows the current progress to be predicted by referring to past progress data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past progress data into a generating AI and have the generating AI perform progress prediction.

[0052] The monitoring unit can apply different monitoring methods to each category of work when monitoring progress. For example, in the case of accounting work, the monitoring unit can apply a monitoring method specifically for accounting. Similarly, in the case of scheduling work, the monitoring unit can apply a monitoring method specifically for scheduling. Similarly, in the case of email management work, the monitoring unit can apply a monitoring method specifically for email. By applying different monitoring methods to each category of work, more appropriate progress monitoring can be performed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input business category data into a generating AI and have the generating AI apply the monitoring method.

[0053] The monitoring unit can analyze changes in progress based on the submission dates of tasks when monitoring progress. For example, the monitoring unit can prioritize monitoring the progress of tasks with approaching submission dates. The monitoring unit can also periodically monitor the progress of tasks with distant submission dates. The monitoring unit can also periodically monitor the progress of tasks with unknown submission dates in between other tasks. This allows for more appropriate progress monitoring by analyzing changes in progress based on the submission dates of tasks. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task submission date data into a generating AI and have the generating AI perform an analysis of changes in progress.

[0054] The monitoring unit can analyze progress by referring to relevant market data when monitoring progress. For example, the monitoring unit can evaluate progress based on relevant market data. The monitoring unit can also analyze relevant market data and predict changes in progress. For example, the monitoring unit can analyze relevant market data and predict changes in progress. The monitoring unit can also refer to relevant market data and propose the optimal progress analysis method. For example, the monitoring unit can refer to relevant market data and propose the optimal progress analysis method. This allows for more accurate analysis of progress by referring to relevant market data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant market data into a generating AI and have the generating AI perform progress analysis.

[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 reception desk can analyze a user's past instruction history and suggest the most suitable instruction method. For example, it can automatically suggest instructions that the user has frequently given in the past. It can also prioritize suggesting instruction methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest instructions to be given during specific time periods based on the user's past instruction history. In this way, by analyzing the user's past instruction history, the system can suggest the most suitable instruction method.

[0057] The execution unit can adjust the level of detail in the execution of tasks based on their importance. For example, for important tasks, it can provide detailed procedures. For routine tasks, it can provide standard procedures. Furthermore, for urgent tasks, it can provide procedures that allow for quick completion. By adjusting the level of detail based on the importance of the task, more appropriate tasks can be performed.

[0058] The collaboration unit can improve the accuracy of information sharing by considering the interrelationships between agents. For example, it can strengthen collaboration between agents and improve the accuracy of information. It can also streamline communication between agents and improve the efficiency of information sharing. Furthermore, it can analyze the interrelationships between agents and propose the optimal information sharing method. In this way, the accuracy of information sharing is improved by considering the interrelationships between agents.

[0059] The monitoring unit can predict current progress by referring to past progress data when monitoring progress. For example, it can predict current progress based on past progress data. It can also analyze past progress data and evaluate the current progress. Furthermore, it can refer to past progress data and propose the optimal progress prediction method. In this way, current progress can be predicted by referring to past progress data.

[0060] The monitoring unit can analyze progress by referring to relevant market data when monitoring progress. For example, it can evaluate progress based on relevant market data. It can also analyze relevant market data and predict changes in progress. Furthermore, it can refer to relevant market data and propose the optimal progress analysis method. As a result, by referring to relevant market data, progress analysis can be performed more accurately.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives instructions from the user. These instructions may include voice instructions, text instructions, and gesture instructions. The reception desk uses voice recognition technology to convert the user's voice instructions into text. It can also directly receive text instructions and interpret the user's gesture instructions using gesture recognition technology. Step 2: The execution unit carries out tasks based on instructions received by the reception unit. Task execution includes scheduling, email management, accounting, and invoice issuance. For example, the execution unit integrates with a calendar application to automatically adjust schedules, integrates with an email client to automate the categorization and replying to incoming emails, and integrates with accounting software to automate the entry of transaction data and the creation of ledgers. Step 3: The Collaboration Department shares information about the tasks performed by the Execution Department with other agents. This information sharing includes task progress, task results, and task notes. For example, the Collaboration Department shares task progress with other agents in real time and task results to help improve operations. Furthermore, it shares task notes to achieve centralized information management. Step 4: The monitoring department monitors the progress of tasks based on information shared by the collaboration department and makes adjustments as needed. Monitoring progress includes the progress of tasks, the completion status of tasks, and the status of delays. For example, the monitoring department monitors the progress of tasks in real time and issues alerts if progress is behind schedule. It also monitors the completion status of tasks and reports on completed tasks. Finally, it monitors for delays and takes countermeasures if delays occur.

[0063] (Example of form 2) The BizAgent Hub system, according to an embodiment of the present invention, is an AI agent platform that supports business owners and entrepreneurs. This BizAgent Hub system allows users to select AI agents specializing in specific tasks, such as a "secretary" or an "accounting department," and assign them tasks. These agents work together to automate and streamline tasks such as information management, scheduling, and accounting. This allows users to save time and effort, providing an environment where they can focus on strategic business activities. For example, the BizAgent Hub system allows users to access and select AI agents specializing in the tasks they need. These agents might include a "secretary" for secretarial duties or an "accounting department" for accounting tasks. Users then assign specific tasks to these agents. The selected AI agents then perform the tasks based on the user's instructions. For example, the "secretary" handles scheduling and email management, while the "accounting department" handles accounting and invoice issuance. These agents collaborate, sharing information and working efficiently. Furthermore, the AI ​​agents monitor the progress of tasks in real time and make adjustments as needed. For example, if schedule changes or additional tasks arise, the AI ​​agent automatically responds and notifies the user. This mechanism enables users to automate and streamline their work, saving time and effort. It also provides users with an environment where they can focus on strategic business activities. For instance, managers of startups and small businesses no longer need to handle multiple tasks themselves, allowing them to concentrate on management and their core business. In this way, the BizAgent Hub system is an innovative platform that supports business owners and entrepreneurs, enabling an efficient and seamless future of work. Through this, the BizAgent Hub system can automate and streamline users' work.

[0064] The BizAgent Hub system according to this embodiment comprises a reception unit, an execution unit, a coordination unit, and a monitoring unit. The reception unit receives instructions from the user. Instructions from the user include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit converts the user's voice instructions into text using, for example, speech recognition technology. The reception unit can also directly receive text instructions. Furthermore, the reception unit can interpret the user's gesture instructions using gesture recognition technology. For example, the reception unit converts the user's voice instructions into text with high accuracy using speech recognition technology. Text instructions can be accepted as text entered by the user. Gesture recognition technology analyzes the user's hand movements and facial expressions and interprets them as instructions. The execution unit performs tasks based on the instructions received by the reception unit. Task execution includes, but is not limited to, scheduling, email management, accounting processing, and invoice issuance. For example, the execution unit coordinates with a calendar application to automatically adjust schedules. Furthermore, the execution unit can integrate with email clients to manage emails, automating the classification and replying to incoming emails. In addition, the execution unit can integrate with accounting software to automate the input of transaction data and the creation of ledgers. For example, the execution unit can integrate with a calendar application to automatically adjust user schedules; integrate with an email client to automatically classify incoming emails and reply as needed; and integrate with accounting software to automatically input transaction data and create ledgers. The integration unit shares information about tasks performed by the execution unit with other agents. This information sharing includes, but is not limited to, task progress, task results, and task notes. For example, the integration unit can share task progress with other agents to improve efficiency. It can also share task results with other agents to help improve operations. Furthermore, the integration unit can share task notes with other agents to achieve centralized information management.For example, the Collaboration Department shares the progress of tasks with other agents in real time. It also shares the results of tasks with other agents to help improve operations. It shares notes related to tasks with other agents to achieve centralized information management. The Monitoring Department monitors the progress of tasks based on the information shared by the Collaboration Department and makes adjustments as needed. Monitoring progress includes, but is not limited to, the progress of tasks, the completion status of tasks, and the status of delays. For example, the Monitoring Department monitors the progress of tasks in real time and issues alerts if progress is behind schedule. The Monitoring Department can also monitor the completion status of tasks and report on completed tasks. Furthermore, the Monitoring Department can monitor for delays and take countermeasures if delays occur. For example, the Monitoring Department monitors the progress of tasks in real time and issues alerts if progress is behind schedule. It monitors the completion status of tasks and reports on completed tasks. It monitors for delays and takes countermeasures if delays occur. As a result, the BizAgent Hub system according to this embodiment can efficiently receive user instructions, perform tasks, share information, and monitor and adjust progress.

[0065] The reception desk receives instructions from users. These instructions may include, but are not limited to, voice, text, and gesture instructions. For example, the reception desk converts voice instructions to text using speech recognition technology. This speech recognition technology uses a deep learning-based speech model to analyze user utterances with high accuracy. Voice instructions are further refined by noise cancellation technology to remove ambient noise and obtain clear audio data. Furthermore, speech recognition technology understands the context of user utterances and converts them into appropriate text using natural language processing technology. Text instructions can be accepted directly from the user. Text input can be done using a keyboard or touchscreen, enhancing user convenience. Gesture recognition technology analyzes user hand movements and facial expressions and interprets them as instructions. Gesture recognition uses cameras and sensors to capture user movements in real time and machine learning algorithms to recognize gestures. For example, hand movements and facial expressions can be analyzed and specific actions interpreted as instructions. This allows the reception desk to support diverse input methods and efficiently receive user instructions. Furthermore, the reception unit can record the user's instruction history and refer to past instructions to more accurately understand the user's intentions. This allows the reception unit to receive user instructions quickly and accurately, improving the overall efficiency of the system.

[0066] The execution unit carries out tasks based on instructions received by the reception unit. Tasks include, but are not limited to, scheduling, email management, accounting, and invoice issuance. To schedule, the execution unit integrates with a calendar application to automatically adjust appointments. The calendar application centrally manages the user's schedule and suggests optimal times to avoid overlaps and conflicts. Based on user instructions, the execution unit can adjust meeting schedules and send notifications to participants. Furthermore, to manage emails, the execution unit can integrate with an email client to automate the categorization and replying to incoming emails. The email client categorizes incoming emails and prioritizes them according to importance. Based on user instructions, the execution unit automatically replies to specific emails, streamlining email processing. Additionally, to perform accounting tasks, the execution unit can integrate with accounting software to automate transaction data entry and ledger creation. The accounting software improves the efficiency of accounting operations by automatically entering transaction data and creating ledgers. The execution unit can issue invoices and manage payments based on user instructions. This allows the execution unit to efficiently perform a variety of tasks based on user instructions, improving the overall productivity of the system. Furthermore, the execution unit can monitor the progress of tasks in real time and make adjustments as needed. This enables the execution unit to achieve both increased efficiency and improved quality.

[0067] The Collaboration Department shares information about tasks performed by the Execution Department with other agents. This information sharing includes, but is not limited to, task progress, task results, and task notes. The Collaboration Department shares task progress with other agents to improve efficiency. For example, by sharing task progress in real time, the Collaboration Department enables each agent to perform tasks based on the latest information. Task results can also be shared with other agents to help improve operations. For example, the Collaboration Department analyzes task results and shares areas for improvement with other agents to enhance the quality of operations. Furthermore, the Collaboration Department can share task notes with other agents to achieve centralized information management. For example, the Collaboration Department can save task notes to cloud storage, making them accessible to other agents. This enables the Collaboration Department to share and centrally manage information, improving efficiency and quality. Additionally, the Collaboration Department can implement security measures when sharing information. For example, encryption technology can be used to ensure data confidentiality and prevent unauthorized access. Furthermore, the liaison department can record the history of information sharing and track and audit the information. This allows the liaison department to share and centrally manage information safely and efficiently.

[0068] The monitoring department monitors the progress of tasks based on information shared by the collaboration department and makes adjustments as needed. Monitoring progress includes, but is not limited to, the progress, completion status, and delay status of tasks. The monitoring department monitors the progress of tasks in real time and issues alerts if progress is behind schedule. For example, the monitoring department visualizes the progress of tasks using graphs and charts so that the progress can be understood at a glance. If progress is behind schedule, it issues an alert to notify relevant parties and encourages a quick response. The monitoring department can also monitor the completion status of tasks and report on completed tasks. For example, the monitoring department can display the completion status of tasks in a list format and automatically generate reports for completed tasks. Furthermore, the monitoring department can monitor the delay status of tasks and take countermeasures if delays occur. For example, the monitoring department can analyze the cause of delays and take countermeasures to minimize delays. In this way, the monitoring department can improve the efficiency and quality of tasks by monitoring the progress of tasks in real time and making adjustments as needed. Furthermore, the monitoring department can regularly report on the progress of the work and provide relevant parties with the latest information. This allows the monitoring department to accurately grasp the progress of the work and take prompt and appropriate action.

[0069] The reception department can instruct the agent selected by the user to perform specific tasks. For example, the reception department can instruct the agent selected by the user to perform specific tasks such as data entry, report creation, and customer service. For example, the reception department can instruct the agent selected by the user to perform data entry. The reception department can also instruct the agent selected by the user to create reports. The reception department can also instruct the agent selected by the user to handle customer service. This allows for increased efficiency in operations by instructing the agent selected by the user to perform specific tasks. Some or all of the above processes in the reception department may be performed using AI, for example, or without AI. For example, the reception department can input specific task instructions for the agent selected by the user into a generating AI and have the generating AI execute the task instructions.

[0070] The execution unit can perform scheduling and email management. For example, to schedule, the execution unit can integrate with a calendar application to automatically adjust appointments. For example, the execution unit can integrate with a calendar application to automatically adjust the user's appointments. The execution unit can also integrate with an email client to automate the classification and replying to received emails. For example, the execution unit can integrate with an email client to automatically classify received emails and reply as needed. Furthermore, the execution unit can use a generation AI to perform scheduling and email management. For example, to schedule, the execution unit can input the user's appointments into the generation AI and have the generation AI perform the scheduling adjustments. Similarly, to manage emails, the execution unit can input received emails into the generation AI and have the generation AI classify and reply to the emails. This allows for increased efficiency in work by automating scheduling and email management.

[0071] The execution unit can perform accounting processing and invoice issuance. For example, to perform accounting processing, the execution unit can integrate with accounting software to automate the input of transaction data and the creation of ledgers. For example, the execution unit can integrate with accounting software to automatically input transaction data and create ledgers. The execution unit can also integrate with an invoice issuance system to automate the creation and transmission of invoices. For example, the execution unit can integrate with an invoice issuance system to automatically create and send invoices. Furthermore, the execution unit can use a generation AI to perform accounting processing and invoice issuance. For example, for accounting processing, the execution unit can input transaction data into the generation AI and have the generation AI create ledgers. The execution unit can also have the generation AI create invoices for invoice issuance. This allows for increased efficiency in accounting processing and invoice issuance.

[0072] The collaboration department enables agents to share information and efficiently carry out their work. For example, the collaboration department can share the progress of work with other agents to improve work efficiency. For example, the collaboration department can share the progress of work with other agents in real time. The collaboration department can also share the results of work with other agents to help improve work. For example, the collaboration department can share the results of work with other agents to help improve work. The collaboration department can also share notes related to work with other agents to achieve centralized information management. For example, the collaboration department can share notes related to work with other agents to achieve centralized information management. This allows agents to share information and improve work efficiency. Some or all of the above processes in the collaboration department may be performed using AI, or not. For example, the collaboration department can input the progress of work into a generating AI and have the generating AI perform information sharing.

[0073] The monitoring unit can monitor the progress of tasks in real time and make adjustments as needed. For example, the monitoring unit can monitor the progress of tasks in real time and issue alerts if the progress is behind schedule. The monitoring unit can also monitor the completion status of tasks and report on completed tasks. The monitoring unit can also monitor the delay status of tasks and take countermeasures if delays occur. This allows for increased efficiency in tasks by monitoring progress in real time and making adjustments as needed. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the progress status of tasks into a generating AI and have the generating AI monitor the progress status.

[0074] The reception desk can estimate the user's emotions and determine the priority of instructions based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can instruct the system to prioritize important tasks. For example, if the user is relaxed, the reception desk can instruct the system to prioritize tasks. For example, if the user is relaxed, the reception desk can instruct the system to prioritize tasks. For example, if the user is in a hurry, the reception desk can instruct the system to prioritize urgent tasks. For example, if the user is in a hurry, the reception desk can instruct the system to prioritize urgent tasks. This allows for more appropriate instructions to be given by determining the priority of instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0075] The reception desk can analyze the user's past instruction history and propose the optimal instruction method. For example, the reception desk can automatically propose instructions that the user has frequently given in the past. The reception desk can also prioritize suggesting instruction methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and propose instructions to be given during specific time periods based on the user's past instruction history. This allows the reception desk to propose the optimal instruction method by analyzing the user's past instruction history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past instruction history data into a generating AI and have the generating AI propose instruction methods.

[0076] The reception unit can filter instructions based on the user's current work status and areas of interest when receiving them. For example, the reception unit can prioritize receiving instructions related to projects the user is currently working on. The reception unit can also filter relevant instructions based on the user's areas of interest. The reception unit can also suggest appropriate instructions based on the user's work status. This allows for more appropriate instructions to be provided by filtering based on the user's current work status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's work status data into a generating AI and have the generating AI perform the filtering.

[0077] The reception desk can estimate the user's emotions and adjust the content of instructions based on the estimated emotions. For example, if the user is stressed, the reception desk can provide concise and clear instructions. For example, if the user is relaxed, the reception desk can provide detailed instructions. For example, if the user is in a hurry, the reception desk can provide instructions that can be executed quickly. For example, if the user is in a hurry, the reception desk can provide instructions that can be executed quickly. This allows for more appropriate instructions to be given by adjusting the content of instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location. For example, if the user is in the office, the reception desk can prioritize receiving office-related instructions. The reception desk can also prioritize receiving instructions that can be performed at the user's current location if the user is out of the office. The reception desk can also prioritize receiving instructions related to a specific location if the user is in that location. This allows for more appropriate instructions to be given by prioritizing highly relevant instructions while considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI prioritize receiving highly relevant instructions.

[0079] The reception unit can analyze the user's social media activity when receiving instructions and accept relevant instructions. For example, the reception unit can prioritize receiving tasks mentioned by the user on social media. The reception unit can also suggest relevant instructions based on the user's social media activity. The reception unit can also accept appropriate instructions based on the content of the user's social media posts. In this way, relevant instructions can be received by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the reception of relevant instructions.

[0080] The execution unit can estimate the user's emotions and adjust the way tasks are performed based on the estimated emotions. For example, if the user is stressed, the execution unit can provide a concise and quick way to perform the task. For example, if the user is stressed, the execution unit can provide a concise and quick way to perform the task. For example, if the user is relaxed, the execution unit can provide a detailed way to perform the task. For example, if the user is relaxed, the execution unit can provide a detailed way to perform the task. For example, if the user is in a hurry, the execution unit can provide a way to perform the task quickly. For example, if the user is in a hurry, the execution unit can provide a way to perform the task quickly. In this way, more appropriate task performance can be achieved by adjusting the way tasks are performed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is 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 processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0081] The execution unit can adjust the level of detail in its execution based on the importance of the task. For example, the execution unit can provide detailed procedures for important tasks. For example, the execution unit can provide detailed procedures for important tasks. The execution unit can also provide standard procedures for routine tasks. For example, the execution unit can provide standard procedures for routine tasks. The execution unit can also provide procedures that can be completed quickly for urgent tasks. For example, the execution unit can provide procedures that can be completed quickly for urgent tasks. By adjusting the level of detail in execution based on the importance of the task, more appropriate tasks can be performed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task importance data into a generating AI and have the generating AI adjust the level of detail in execution.

[0082] The execution unit can apply different execution algorithms depending on the category of the task during task execution. For example, in the case of accounting tasks, the execution unit can apply an algorithm specifically for accounting. Similarly, in the case of scheduling tasks, the execution unit can apply an algorithm specifically for scheduling. Similarly, in the case of email management tasks, the execution unit can apply an algorithm specifically for email. By applying different execution algorithms depending on the category of the task, more appropriate task execution can be achieved. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task category data into a generating AI and have the generating AI apply the execution algorithm.

[0083] The execution unit can estimate the user's emotions and adjust the speed of task execution based on the estimated emotions. For example, if the user is stressed, the execution unit can perform tasks quickly. For example, if the user is stressed, the execution unit can perform tasks quickly. The execution unit can also perform tasks at a normal speed if the user is relaxed. For example, if the user is relaxed, the execution unit can perform tasks at a normal speed. The execution unit can also perform tasks as quickly as possible if the user is in a hurry. For example, if the user is in a hurry, the execution unit can perform tasks as quickly as possible. This allows for more appropriate task execution by adjusting the speed of task execution based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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-described processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0084] The execution unit can determine the priority of tasks based on their submission deadlines. For example, the execution unit can prioritize tasks with approaching deadlines. The execution unit can also postpone tasks with distant submission deadlines. The execution unit can also perform tasks with unknown submission deadlines in between other tasks. This allows for more appropriate task execution by determining the priority of tasks based on their submission deadlines. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task submission data into a generating AI and have the generating AI determine the priority of tasks.

[0085] The execution unit can adjust the order of execution based on the relevance of tasks during task execution. For example, the execution unit can group related tasks together. The execution unit can also postpone tasks with low relevance. The execution unit can also prioritize tasks with high relevance. By adjusting the order of execution based on the relevance of tasks, more appropriate task execution can be achieved. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input task relevance data into a generating AI and have the generating AI adjust the order of execution.

[0086] The collaboration unit can estimate the user's emotions and adjust the method of information sharing based on the estimated user emotions. For example, if the user is stressed, the collaboration unit can provide a concise and clear method of information sharing. For example, if the user is stressed, the collaboration unit can provide a concise and clear method of information sharing. The collaboration unit can also provide a detailed method of information sharing if the user is relaxed. For example, if the user is relaxed, the collaboration unit can provide a detailed method of information sharing. The collaboration unit can also provide a method of sharing information quickly if the user is in a hurry. For example, if the collaboration unit can provide a method of sharing information quickly if the user is in a hurry. This allows for more appropriate information sharing by adjusting the method of information sharing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0087] The collaboration unit can improve the accuracy of information sharing by considering the interrelationships between agents. For example, the collaboration unit can strengthen the collaboration between agents and improve the accuracy of information. The collaboration unit can also facilitate communication between agents and improve the efficiency of information sharing. The collaboration unit can also analyze the interrelationships between agents and propose the optimal method of information sharing. This improves the accuracy of information sharing by considering the interrelationships between agents. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input interrelationship data between agents into a generating AI and have the generating AI perform the improvement of sharing accuracy.

[0088] The collaboration unit can share information while considering the agent's attribute information. For example, the collaboration unit can share relevant information based on the agent's area of ​​expertise. The collaboration unit can also share appropriate information according to the agent's role. The collaboration unit can also analyze the agent's attribute information and propose the optimal method of information sharing. This allows for more appropriate information sharing by considering the agent's attribute information. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input agent attribute information data into a generating AI and have the generating AI perform the information sharing.

[0089] The communication unit can estimate the user's emotions and determine the priority of information sharing based on the estimated emotions. For example, if the user is stressed, the communication unit can prioritize sharing important information. For example, if the user is stressed, the communication unit can prioritize sharing important information. The communication unit can also share information with normal priorities if the user is relaxed. For example, if the user is relaxed, the communication unit can share information with normal priorities. The communication unit can also prioritize sharing urgent information if the user is in a hurry. For example, if the communication unit is in a hurry, the communication unit can prioritize sharing urgent information. This allows for more appropriate information sharing by determining the priority of information sharing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0090] The collaboration unit can share information while considering the geographical distribution of agents. For example, if an agent is nearby, the collaboration unit can directly share information. For example, if an agent is nearby, the collaboration unit can directly share information. The collaboration unit can also remotely share information if an agent is far away. For example, the collaboration unit can remotely share information if an agent is far away. The collaboration unit can also analyze the geographical distribution of agents and propose the optimal method of information sharing. For example, the collaboration unit can analyze the geographical distribution of agents and propose the optimal method of information sharing. This allows for more appropriate information sharing by considering the geographical distribution of agents. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input the geographical distribution data of agents into a generating AI and have the generating AI perform the information sharing.

[0091] The collaboration unit can improve the accuracy of information sharing by referring to relevant literature during information sharing. For example, the collaboration unit can improve the accuracy of information by referring to relevant literature. The collaboration unit can also improve the efficiency of information sharing based on relevant literature. For example, the collaboration unit can improve the efficiency of information sharing based on relevant literature. The collaboration unit can also analyze relevant literature and propose the optimal information sharing method. For example, the collaboration unit can analyze relevant literature and propose the optimal information sharing method. As a result, the accuracy of information sharing is improved by referring to relevant literature. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI. For example, the collaboration unit can input relevant literature data into a generating AI and have the generating AI perform information sharing.

[0092] The monitoring unit can estimate the user's emotions and adjust the progress display method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a concise and clear progress display method. For example, if the user is stressed, the monitoring unit can provide a concise and clear progress display method. The monitoring unit can also provide a detailed progress display method if the user is relaxed. For example, if the user is relaxed, the monitoring unit can provide a detailed progress display method. The monitoring unit can also provide a method to quickly display the progress if the user is in a hurry. For example, if the monitoring unit is in a hurry, the monitoring unit can provide a method to quickly display the progress. By adjusting the progress display method based on the user's emotions, a more appropriate progress display can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0093] The monitoring unit can predict current progress by referring to past progress data when monitoring progress. For example, the monitoring unit can predict current progress based on past progress data. The monitoring unit can also analyze past progress data and evaluate the current progress. For example, the monitoring unit can analyze past progress data and evaluate the current progress. The monitoring unit can also refer to past progress data and propose the optimal progress prediction method. For example, the monitoring unit can refer to past progress data and propose the optimal progress prediction method. This allows the current progress to be predicted by referring to past progress data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past progress data into a generating AI and have the generating AI perform progress prediction.

[0094] The monitoring unit can apply different monitoring methods to each category of work when monitoring progress. For example, in the case of accounting work, the monitoring unit can apply a monitoring method specifically for accounting. Similarly, in the case of scheduling work, the monitoring unit can apply a monitoring method specifically for scheduling. Similarly, in the case of email management work, the monitoring unit can apply a monitoring method specifically for email. By applying different monitoring methods to each category of work, more appropriate progress monitoring can be performed. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input business category data into a generating AI and have the generating AI apply the monitoring method.

[0095] The monitoring unit can estimate the user's emotions and adjust the importance of progress status based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can prioritize displaying important progress status. For example, if the user is feeling stressed, the monitoring unit can prioritize displaying important progress status. For example, if the user is relaxed, the monitoring unit can prioritize displaying progress status. For example, if the user is in a hurry, the monitoring unit can prioritize displaying urgent progress status. For example, if the user is in a hurry, the monitoring unit can prioritize displaying urgent progress status. This allows for a more appropriate display of progress status by adjusting the importance of progress status based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0096] The monitoring unit can analyze changes in progress based on the submission dates of tasks when monitoring progress. For example, the monitoring unit can prioritize monitoring the progress of tasks with approaching submission dates. The monitoring unit can also periodically monitor the progress of tasks with distant submission dates. The monitoring unit can also periodically monitor the progress of tasks with unknown submission dates in between other tasks. This allows for more appropriate progress monitoring by analyzing changes in progress based on the submission dates of tasks. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task submission date data into a generating AI and have the generating AI perform an analysis of changes in progress.

[0097] The monitoring unit can analyze progress by referring to relevant market data when monitoring progress. For example, the monitoring unit can evaluate progress based on relevant market data. The monitoring unit can also analyze relevant market data and predict changes in progress. For example, the monitoring unit can analyze relevant market data and predict changes in progress. The monitoring unit can also refer to relevant market data and propose the optimal progress analysis method. For example, the monitoring unit can refer to relevant market data and propose the optimal progress analysis method. This allows for more accurate analysis of progress by referring to relevant market data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant market data into a generating AI and have the generating AI perform progress analysis.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception system can analyze the tone and speed of the user's voice when receiving voice instructions, and estimate the user's emotions. For example, if the user's voice is high-pitched and fast, it can estimate that the user is stressed and instruct them to prioritize important tasks. If the user's voice is low and slow, it can estimate that the user is relaxed and instruct them to process tasks with normal priority. Furthermore, if the user's voice is trembling, it can instruct them to prioritize urgent tasks. In this way, by estimating emotions based on the tone and speed of the user's voice and determining the priority of instructions accordingly, more appropriate instructions can be given.

[0100] The execution unit can estimate the user's emotions and adjust the way tasks are performed based on those emotions. For example, if the user is stressed, it can provide a concise and quick method for performing the task. If the user is relaxed, it can provide a detailed method. Furthermore, if the user is in a hurry, it can provide a method for performing the task quickly. By adjusting the method of performing tasks based on the user's emotions, more appropriate tasks can be performed.

[0101] The collaboration unit can estimate the user's emotions and adjust the method of information sharing based on those emotions. For example, if the user is stressed, it can provide a concise and clear method of information sharing. If the user is relaxed, it can provide a more detailed method of information sharing. Furthermore, if the user is in a hurry, it can provide a method of information sharing quickly. By adjusting the method of information sharing based on the user's emotions, more appropriate information sharing can be achieved.

[0102] The monitoring unit can estimate the user's emotions and adjust the progress display method based on the estimated emotions. For example, if the user is stressed, it can provide a concise and clear progress display method. If the user is relaxed, it can provide a detailed progress display method. Furthermore, if the user is in a hurry, it can provide a method to display the progress quickly. In this way, by adjusting the progress display method based on the user's emotions, a more appropriate progress display can be achieved.

[0103] The reception desk can estimate the user's emotions and adjust the instructions based on those emotions. For example, if the user is stressed, it can provide concise and clear instructions. If the user is relaxed, it can provide detailed instructions. Furthermore, if the user is in a hurry, it can provide instructions that can be executed quickly. In this way, by adjusting the instructions based on the user's emotions, more appropriate instructions can be provided.

[0104] The reception desk can analyze a user's past instruction history and suggest the most suitable instruction method. For example, it can automatically suggest instructions that the user has frequently given in the past. It can also prioritize suggesting instruction methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest instructions to be given during specific time periods based on the user's past instruction history. In this way, by analyzing the user's past instruction history, the system can suggest the most suitable instruction method.

[0105] The execution unit can adjust the level of detail in the execution of tasks based on their importance. For example, for important tasks, it can provide detailed procedures. For routine tasks, it can provide standard procedures. Furthermore, for urgent tasks, it can provide procedures that allow for quick completion. By adjusting the level of detail based on the importance of the task, more appropriate tasks can be performed.

[0106] The collaboration unit can improve the accuracy of information sharing by considering the interrelationships between agents. For example, it can strengthen collaboration between agents and improve the accuracy of information. It can also streamline communication between agents and improve the efficiency of information sharing. Furthermore, it can analyze the interrelationships between agents and propose the optimal information sharing method. In this way, the accuracy of information sharing is improved by considering the interrelationships between agents.

[0107] The monitoring unit can predict current progress by referring to past progress data when monitoring progress. For example, it can predict current progress based on past progress data. It can also analyze past progress data and evaluate the current progress. Furthermore, it can refer to past progress data and propose the optimal progress prediction method. In this way, current progress can be predicted by referring to past progress data.

[0108] The monitoring unit can analyze progress by referring to relevant market data when monitoring progress. For example, it can evaluate progress based on relevant market data. It can also analyze relevant market data and predict changes in progress. Furthermore, it can refer to relevant market data and propose the optimal progress analysis method. As a result, by referring to relevant market data, progress analysis can be performed more accurately.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives instructions from the user. These instructions may include voice instructions, text instructions, and gesture instructions. The reception desk uses voice recognition technology to convert the user's voice instructions into text. It can also directly receive text instructions and interpret the user's gesture instructions using gesture recognition technology. Step 2: The execution unit carries out tasks based on instructions received by the reception unit. Task execution includes scheduling, email management, accounting, and invoice issuance. For example, the execution unit integrates with a calendar application to automatically adjust schedules, integrates with an email client to automate the categorization and replying to incoming emails, and integrates with accounting software to automate the entry of transaction data and the creation of ledgers. Step 3: The Collaboration Department shares information about the tasks performed by the Execution Department with other agents. This information sharing includes task progress, task results, and task notes. For example, the Collaboration Department shares task progress with other agents in real time and task results to help improve operations. Furthermore, it shares task notes to achieve centralized information management. Step 4: The monitoring department monitors the progress of tasks based on information shared by the collaboration department and makes adjustments as needed. Monitoring progress includes the progress of tasks, the completion status of tasks, and the status of delays. For example, the monitoring department monitors the progress of tasks in real time and issues alerts if progress is behind schedule. It also monitors the completion status of tasks and reports on completed tasks. Finally, it monitors for delays and takes countermeasures if delays occur.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, execution unit, coordination unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives voice and text instructions from the user. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs tasks such as scheduling, email management, and accounting. The coordination unit is implemented by the control unit 46A of the smart device 14 and shares information about the tasks performed by the execution unit with other agents. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the progress of tasks and makes adjustments as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, execution unit, coordination unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives voice and text instructions from the user. The execution unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs tasks such as scheduling, email management, and accounting. The coordination unit is implemented by the control unit 46A of the smart glasses 214 and shares information about the tasks performed by the execution unit with other agents. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the progress of tasks and makes adjustments as needed. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, execution unit, coordination unit, and monitoring unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives voice and text instructions from the user. The execution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs tasks such as scheduling, email management, and accounting. The coordination unit is implemented by, for example, the control unit 46A of the headset terminal 314 and shares information about the tasks performed by the execution unit with other agents. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the progress of tasks and makes adjustments as necessary. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, execution unit, coordination unit, and monitoring unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives voice and text instructions from the user. The execution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs tasks such as scheduling, email management, and accounting. The coordination unit is implemented by, for example, the control unit 46A of the robot 414 and shares information about the tasks performed by the execution unit with other agents. The monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the progress of the tasks and makes adjustments as necessary. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that receives instructions from users, An execution unit that carries out tasks based on instructions received by the aforementioned reception unit, A collaboration unit that shares information regarding the tasks performed by the aforementioned execution unit with other agents, The system includes a monitoring unit that monitors the progress of operations based on information shared by the aforementioned collaboration unit and makes adjustments as necessary. A system characterized by the following features. (Note 2) The aforementioned reception unit is The user assigns specific tasks to the agent they have selected. The system described in Appendix 1, characterized by the features described herein. (Note 3) The execution unit is, Schedule adjustments and email management The system described in Appendix 1, characterized by the features described herein. (Note 4) The execution unit is, Perform accounting tasks and issue invoices. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Agents share information with each other and work efficiently. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, We monitor the progress of tasks in real time and make adjustments as needed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past instruction history and suggests the optimal instruction method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current work status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving instructions, the system prioritizes accepting instructions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving instructions, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The execution unit is, It estimates the user's emotions and adjusts how tasks are performed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The execution unit is, When performing tasks, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The execution unit is, When performing tasks, different execution algorithms are applied depending on the category of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The execution unit is, It estimates the user's emotions and adjusts the pace of task execution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The execution unit is, When carrying out tasks, prioritize tasks based on their submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 18) The execution unit is, When performing tasks, adjust the order of execution based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned linkage unit is, It estimates user emotions and adjusts the way information is shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, When sharing information, consider the relationships between agents to improve the accuracy of the sharing process. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, When sharing information, consider the agent's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, It estimates user sentiment and determines the priority of information sharing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, When sharing information, consider the geographical distribution of agents. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, When sharing information, refer to relevant literature to improve the accuracy of the sharing process. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, When monitoring progress, we predict current progress by referring to past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, When monitoring progress, apply different monitoring methods for each category of work. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the importance of progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, When monitoring progress, analyze changes in progress based on the submission date of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, When monitoring progress, analyze the progress by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives instructions from users, An execution unit that carries out tasks based on instructions received by the aforementioned reception unit, A collaboration unit that shares information regarding the tasks performed by the aforementioned execution unit with other agents, The system includes a monitoring unit that monitors the progress of operations based on information shared by the aforementioned collaboration unit and makes adjustments as necessary. A system characterized by the following features.

2. The aforementioned reception unit is The user assigns specific tasks to the agent they have selected. The system according to feature 1.

3. The execution unit is, Schedule adjustments and email management The system according to feature 1.

4. The execution unit is, Perform accounting tasks and issue invoices. The system according to feature 1.

5. The aforementioned linkage unit is, Agents share information with each other and work efficiently. The system according to feature 1.

6. The aforementioned monitoring unit, We monitor the progress of tasks in real time and make adjustments as needed. The system according to feature 1.

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

8. The aforementioned reception unit is It analyzes the user's past instruction history and suggests the optimal instruction method. The system according to feature 1.

Images