system

Abstract

The system according to this embodiment aims to efficiently collect and retrieve distributed business information, support problem solving, and automatically link business systems. [Solution] The system according to the embodiment comprises an information gathering unit, a problem solving unit, a knowledge base creation unit, and a system integration unit. The information gathering unit collects information. The problem solving unit detects problems based on the information collected by the information gathering unit and provides solutions. The knowledge base creation unit creates a knowledge base based on the solutions provided by the problem solving unit. The system integration unit integrates business systems based on the knowledge base created by the knowledge base creation unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 prior art, it is difficult to efficiently collect and search for distributed business information, identify an appropriate department for problem-solving, and link business systems, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and search for distributed business information, support problem-solving, and automatically link business systems.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an information gathering unit, a problem solving unit, a knowledge base creation unit, and a system integration unit. The information gathering unit collects information. The problem solving unit detects problems and provides solutions based on the information collected by the information gathering unit. The knowledge base creation unit creates a knowledge base based on the solutions provided by the problem solving unit. The system integration unit integrates business systems based on the knowledge base created by the knowledge base creation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and search distributed business information, support problem solving, and automatically link business systems. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied 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 integrated AI agent system according to an embodiment of the present invention is a system designed to dramatically improve the operational efficiency of a company. This system integrates four agent functions: information gathering, automated knowledge management, real-time support, and automated integration with business systems. The integrated AI agent system instantly solves various challenges faced by employees and simplifies daily work processes, thereby significantly improving the productivity of the entire organization. For example, the integrated AI agent system automatically collects all business-related information within the company, including terminology definitions, system usage instructions, and the location of analytical data storage, and provides this information to employees as a constantly updated knowledge base. This allows for quick acquisition of necessary information, significantly reducing effort and time. Furthermore, the integrated AI agent system detects and supports problems faced by employees during work in real time and provides optimal solutions. In addition, it has a function to automatically identify and escalate to the appropriate department if the appropriate contact is unknown. Moreover, the integrated AI agent system has a function to automatically create and maintain a knowledge base that summarizes the organizational structure, the functions and roles of each department, and key business operations in detail. This is particularly helpful for new employees and transferees, enabling them to quickly understand the overall picture of the organization and smoothly transition into their work. Finally, the integrated AI agent system centrally manages the use of multiple business systems and tools, automating their coordination. This agent improves operational efficiency by automating business processes and minimizing manual operations. As a result, the integrated AI agent system can instantly resolve various challenges faced by employees, simplify daily work processes, and significantly improve overall organizational productivity.

[0029] The integrated AI agent system according to this embodiment comprises an information gathering unit, a problem solving unit, a knowledge base creation unit, and a system integration unit. The information gathering unit collects information. For example, the information gathering unit automatically collects all business-related information within the company, terminology definitions, system usage methods, and the storage location of analytical data. The information gathering unit can collect information in formats such as text data, numerical data, and image data. The information gathering unit can also collect and integrate information from various departments within the company. For example, the information gathering unit collects and integrates project information, customer information, financial information, etc. The problem solving unit detects problems and provides solutions based on the information collected by the information gathering unit. For example, the problem solving unit can detect problems encountered during work in real time and provide optimal solutions. For example, the problem solving unit can detect problems such as system errors and business process delays and provide solutions. The problem solving unit can also automatically identify the appropriate department and escalate the issue when the appropriate contact is unknown. For example, the problem solving unit identifies the appropriate department and escalates the issue based on the assigned tasks and expertise. The Knowledge Base Creation Department creates knowledge bases based on solutions provided by the Problem Solving Department. For example, the Knowledge Base Creation Department automatically creates and maintains knowledge bases that detail organizational structure, the functions and roles of each department, and key business operations. The Knowledge Base Creation Department can create knowledge bases in various formats, such as FAQs, technical documents, and manuals. Furthermore, the Knowledge Base Creation Department can regularly update the content of the knowledge bases to ensure they always contain the latest information. The System Integration Department integrates business systems based on the knowledge bases created by the Knowledge Base Creation Department. For example, the System Integration Department centrally manages the usage of multiple business systems and tools and automates their integration. For example, the System Integration Department can integrate business systems such as ERP systems, CRM systems, and project management tools. The System Integration Department can also automate business system integration using API integration and automated scripts.As a result, the integrated AI agent system according to the embodiment can efficiently collect information, detect and resolve problems, create a knowledge base, and integrate with business systems.

[0030] The Information Gathering Department collects information. For example, it automatically collects all internal business-related information, terminology definitions, system usage instructions, and the location of analytical data storage. Specifically, the Information Gathering Department can collect information in the form of text data, numerical data, and image data provided by various departments within the company. For example, project information includes detailed information such as the progress of ongoing projects, assigned personnel, and deadlines; customer information includes customer contact information, past transaction history, and current contract details; and financial information includes detailed financial data such as revenue, expenditure, and budget. This information is automatically collected and integrated from various departments within the company. The Information Gathering Department can use natural language processing technology to analyze the meaning of text data and extract important information. It can also use image recognition technology to extract necessary information from image data. For example, it can extract text information from scanned documents or handwritten notes and save it as digital data. Furthermore, the Information Gathering Department centrally manages the collected information and can collaborate with other departments and systems as needed. For example, collected data can be stored on a cloud server and made accessible to the Problem Solving Department and the Knowledge Base Creation Department. Furthermore, the information gathering unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the information gathering unit to collect information efficiently and effectively, improving the overall system performance.

[0031] The Problem Solving Department detects problems and provides solutions based on information collected by the Information Gathering Department. Specifically, the Problem Solving Department detects problems encountered during work in real time and provides optimal solutions. For example, if a system error occurs, the Problem Solving Department analyzes the error log, identifies the cause of the error, and proposes a corrective method. Also, if a delay occurs in a business process, the Problem Solving Department can identify the cause of the delay and propose measures to improve the process. The Problem Solving Department uses AI to analyze collected data and detect problem patterns. For example, it uses machine learning algorithms to learn problem occurrence patterns from past data and respond quickly when similar problems occur. It also uses natural language processing technology to analyze user inquiries and provide appropriate solutions. Furthermore, the Problem Solving Department can automatically identify the appropriate department and escalate the issue when the appropriate contact is unknown. For example, the Problem Solving Department identifies the appropriate department and escalates the issue based on the assigned tasks and expertise. This allows the Problem Solving Department to solve problems quickly and accurately, improving operational efficiency. In addition, the Problem Solving Department can record the history of solutions to help solve problems in the future. This allows the problem-solving unit to continuously improve its problem-solving capabilities and enhance the overall reliability of the system.

[0032] The Knowledge Base Creation Department creates the knowledge base based on solutions provided by the Problem Solving Department. Specifically, the Knowledge Base Creation Department automatically creates and maintains a knowledge base that details the organizational structure, the functions and roles of each department, and key business operations. For example, the knowledge base can be created in the form of FAQs, technical documents, manuals, etc. Based on the solutions provided by the Problem Solving Department, the Knowledge Base Creation Department compiles detailed procedures and points to note and adds them to the knowledge base. This allows users to quickly refer to the knowledge base to check past problem solutions and address similar problems. The Knowledge Base Creation Department can also regularly update the content of the knowledge base to always provide the latest information. For example, if there are new business processes or system changes, the Knowledge Base Creation Department updates the knowledge base to reflect the latest information. Furthermore, the Knowledge Base Creation Department can collect feedback from users and improve the content of the knowledge base. For example, by allowing users to comment on and evaluate the content of the knowledge base, the Knowledge Base Creation Department can provide information that meets the needs of users. This allows the knowledge base creation department to always provide the latest and most useful information, helping users solve their problems.

[0033] The System Integration Department integrates business systems based on the knowledge base created by the Knowledge Base Creation Department. Specifically, the System Integration Department centrally manages the usage of multiple business systems and tools and automates their integration. For example, it can integrate business systems such as ERP systems, CRM systems, and project management tools. The System Integration Department can also automate the integration of business systems using API integration and automated scripts. This allows for smooth data exchange between different systems and improves business efficiency. For example, integrating an ERP system and a CRM system automatically synchronizes customer information and order information, preventing duplicate entries and data inconsistencies. In addition, integrating with a project management tool allows for real-time updates of project progress and task assignments, improving the overall work efficiency of the team. Furthermore, the System Integration Department monitors the integration status of business systems and can respond quickly if problems occur. For example, if an API integration fails or an automated script does not work correctly, the System Integration Department analyzes the error log, identifies the cause of the problem, and corrects it. The System Integration Department also continuously improves the integration of business systems and can flexibly respond to the introduction of new systems and tools. This allows the system integration department to efficiently and effectively integrate business systems, thereby improving the overall system performance.

[0034] The Information Gathering Department can automatically collect all internal business-related information, terminology definitions, system usage instructions, and data storage locations for analysis. For example, the Information Gathering Department can automatically collect all internal business-related information and always provide the most up-to-date information. It can also collect and integrate project information, customer information, financial information, etc. Furthermore, the Information Gathering Department can automatically collect terminology definitions and create glossaries including industry and internal terminology. In addition, the Information Gathering Department can automatically collect system usage instructions and provide operation manuals and training materials. For example, it can support system usage by collecting and providing system usage instructions to employees. The Information Gathering Department can also automatically collect data storage locations for analysis and manage storage locations such as databases and cloud storage. This allows for the automatic collection of all internal business-related information and the provision of always up-to-date information. Some or all of the above processes in the Information Gathering Department may be performed using AI, or not. For example, the Information Gathering Department can input all internal business-related information into a generating AI and have the generating AI perform the information collection and organization.

[0035] The problem-solving unit can detect problems encountered during work in real time and provide optimal solutions. For example, the problem-solving unit can detect problems encountered during work in real time and provide solutions quickly. For example, the problem-solving unit can detect problems such as system errors and business process delays and provide solutions. Furthermore, the problem-solving unit can also provide optimal solutions depending on the type and severity of the problem. For example, the problem-solving unit can provide a corrective program for system errors and a procedure manual for business process delays. In addition, the problem-solving unit can identify the cause of the problem and provide measures to prevent recurrence. For example, the problem-solving unit can identify the cause of a system error and provide measures to prevent recurrence. This enables the real-time detection of problems encountered during work and the provision of solutions quickly. Some or all of the above processes in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input problems encountered during work into a generating AI and have the generating AI perform problem detection and solution provision.

[0036] The problem-solving unit can automatically identify the appropriate department and escalate the issue when the correct contact point is unknown. For example, the problem-solving unit can automatically identify the appropriate department and respond quickly when the correct contact point is unknown. For example, the problem-solving unit can identify the appropriate department and escalate the issue based on the assigned duties and expertise. The problem-solving unit can also automatically execute escalation procedures and contact methods. For example, the problem-solving unit can automatically execute escalation procedures and contact the appropriate department. This allows the system to automatically identify the appropriate department and respond quickly even when the correct contact point is unknown. Some or all of the above processes in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information to generate AI to identify the appropriate department when the correct contact point is unknown, and have the generating AI perform department identification and escalation.

[0037] The Knowledge Base Creation Department can automatically create and maintain knowledge bases that detail organizational structure, the functions and roles of each department, and key business activities. For example, the Knowledge Base Creation Department can automatically create and maintain a knowledge base detailing the organizational structure. For example, it can create a knowledge base that includes departmental structure and hierarchical positions. Furthermore, the Knowledge Base Creation Department can create knowledge bases that detail the functions and roles of each department. For example, it can create knowledge bases that detail the functions and roles of departments such as sales and technology. In addition, the Knowledge Base Creation Department can create knowledge bases that detail key business activities. For example, it can create knowledge bases that detail key business activities such as daily operations and project operations. This enables the automatic creation and maintenance of knowledge bases that detail organizational structure and the functions and roles of each department. Some or all of the processes described above in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input information about the organizational structure and the functions and roles of each department into a generating AI, and have the generating AI create and maintain the knowledge base.

[0038] The System Integration Department can centrally manage the usage of multiple business systems and tools and automate their integration. For example, the System Integration Department can centrally manage the usage of multiple business systems and tools, thereby improving operational efficiency. For example, the System Integration Department can centrally manage business systems such as ERP systems, CRM systems, and project management tools. Furthermore, the System Integration Department can automate the integration of business systems using API integration and automated scripts. For example, the System Integration Department can automatically share data between different business systems using API integration. The System Integration Department can also automate business processes using automated scripts, minimizing manual operations. For example, the System Integration Department can automate data input and sharing using automated scripts. This improves operational efficiency by centrally managing the usage of multiple business systems and tools and automating their integration. Some or all of the above-described processes in the System Integration Department may be performed using AI, or not. For example, the System Integration Department can input information regarding business system integration into a generating AI and have the generating AI perform the automation of the integration.

[0039] The Information Gathering Department can analyze the company's past information gathering history and select the optimal gathering method. For example, the Information Gathering Department can identify the most efficient gathering method from past information gathering history and apply it to current information gathering. For example, the Information Gathering Department can analyze past information gathering history and prioritize the collection of information that takes a long time to gather. Furthermore, the Information Gathering Department can improve its gathering methods based on past information gathering history to achieve efficient information gathering. For example, the Information Gathering Department can optimize the collection frequency and means based on past information gathering history. This makes it possible to select the optimal gathering method and achieve efficient information gathering by analyzing past information gathering history. Some or all of the above processes in the Information Gathering Department may be performed using AI, for example, or without AI. For example, the Information Gathering Department can input past information gathering history into a generating AI and have the generating AI select the optimal gathering method.

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

[0041] The information gathering unit can prioritize collecting highly relevant information based on the user's geographical location information during information gathering. For example, if the user is in a specific region, the information gathering unit will prioritize collecting information related to that region. For example, the information gathering unit can filter and provide relevant information based on the user's geographical location information. Furthermore, if the user is on the move, the information gathering unit can collect and provide necessary information based on their current location. For example, the information gathering unit will collect and provide appropriate information based on the user's geographical location information. This allows for efficient collection of necessary information by prioritizing the collection of highly relevant information based on the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0042] The information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit can analyze a user's social media activity and collect information related to topics of interest. For example, the information gathering unit can collect relevant information based on the content of posts from accounts that the user follows. The information gathering unit can also analyze a user's social media activity history, collect necessary information, and provide it. For example, the information gathering unit can collect and provide appropriate information based on a user's social media activity. This allows for the efficient collection of relevant information by analyzing a user's social media activity. Some or all of the above-described processes in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input user social media activity data into a generating AI and have the generating AI collect relevant information.

[0043] The problem-solving unit can adjust the level of detail of the solution based on the importance of the problem. For example, the problem-solving unit can provide a detailed solution for a high-importance problem. For example, the problem-solving unit can provide a concise solution for a low-importance problem. The problem-solving unit can also adjust and provide the level of detail of the solution according to the importance of the problem. For example, the problem-solving unit can adjust the level of detail of the solution based on the scope of impact and urgency of the problem. This allows for the provision of an appropriate solution by adjusting the level of detail of the solution according to the importance of the problem. Some or all of the above processing in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information about the importance of the problem into a generating AI and have the generating AI perform the adjustment of the level of detail of the solution.

[0044] The problem-solving unit can apply different solution algorithms depending on the category of the problem. For example, the problem-solving unit can apply a technical solution algorithm to a technical problem. For example, the problem-solving unit can apply a business solution algorithm to a business problem. Furthermore, the problem-solving unit can apply a human resources solution algorithm to a human resources problem. For example, the problem-solving unit selects and applies an appropriate solution algorithm according to the category of the problem. This enables efficient problem solving by applying an appropriate solution algorithm according to the category of the problem. Some or all of the above processing in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information about the category of the problem into a generating AI and have the generating AI select and apply a solution algorithm.

[0045] The problem-solving unit can determine the priority of solutions based on when the problem occurred. For example, the problem-solving unit can provide solutions to recently occurring problems as a priority. For example, the problem-solving unit can provide solutions to problems that occurred in the past as a delay. The problem-solving unit can also adjust and provide the priority of solutions according to when the problem occurred. For example, the problem-solving unit can determine the priority of solutions based on the date and time or duration of the problem's occurrence. This enables rapid problem solving by determining the priority of solutions according to when the problem occurred. Some or all of the above processing in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information about when the problem occurred into a generating AI and have the generating AI perform the determination of the priority of solutions.

[0046] The problem-solving unit can adjust the order of solutions based on the relevance of the problems. For example, the problem-solving unit can prioritize providing solutions to highly relevant problems. For example, it can postpone providing solutions to less relevant problems. The problem-solving unit can also adjust and provide solutions in order according to the relevance of the problems. For example, the problem-solving unit can determine the order of solutions based on the same category or common causes of the problems. This allows for efficient problem solving by adjusting the order of solutions according to the relevance of the problems. Some or all of the above processing in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information about the relevance of the problems into a generating AI and have the generating AI adjust the order of solutions.

[0047] The knowledge base creation unit can select the optimal creation method by referring to past knowledge base data when creating a knowledge base. For example, the knowledge base creation unit can analyze past knowledge base data to identify the most efficient creation method. For example, the knowledge base creation unit can improve the information organization method based on past knowledge base data. The knowledge base creation unit can also refer to past knowledge base data to select a method for quickly providing the necessary information. For example, the knowledge base creation unit can optimize the information collection and organization methods based on past knowledge base data. This makes it possible to select the optimal creation method by referring to past knowledge base data and create a knowledge base efficiently. Some or all of the above processes in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input past knowledge base data into a generating AI and have the generating AI select the optimal creation method.

[0048] The knowledge base creation unit can customize the content of the knowledge base based on the organization's current situation when creating it. For example, the knowledge base creation unit can customize the content of the knowledge base based on the organization's current projects and operations. For example, the knowledge base creation unit can create a knowledge base that prioritizes providing necessary information, taking into account the organization's current situation. The knowledge base creation unit can also update the content of the knowledge base in response to changes in the organization, ensuring that the information is always up-to-date. For example, the knowledge base creation unit can appropriately customize the content of the knowledge base based on the organization's current situation. This ensures that the information is always up-to-date by customizing the content of the knowledge base based on the organization's current situation. Some or all of the above processes in the knowledge base creation unit may be performed using AI, or not. For example, the knowledge base creation unit can input information about the organization's current situation into a generating AI and have the generating AI perform the customization of the knowledge base content.

[0049] The knowledge base creation unit can create an optimal knowledge base by considering the organization's geographical location information during the knowledge base creation process. For example, the knowledge base creation unit can create a knowledge base that prioritizes the provision of relevant information based on the organization's geographical location information. For example, the knowledge base creation unit can create a knowledge base that quickly provides necessary information by considering the organization's geographical location information. Furthermore, the knowledge base creation unit can customize the content of the knowledge base based on the organization's geographical location information. For example, the knowledge base creation unit appropriately customizes the content of the knowledge base based on the organization's geographical location information. This allows for the priority provision of relevant information by considering the organization's geographical location information. Some or all of the above processes in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input the organization's geographical location information into a generating AI and have the generating AI create the knowledge base.

[0050] The knowledge base creation unit can improve the accuracy of the knowledge base by referring to relevant literature during the knowledge base creation process. For example, the knowledge base creation unit can enrich the content of the knowledge base by referring to relevant literature. For example, the knowledge base creation unit can improve the accuracy of the information in the knowledge base based on relevant literature. Furthermore, the knowledge base creation unit can update the content of the knowledge base with the latest information by referring to relevant literature. For example, the knowledge base creation unit can appropriately enrich the content of the knowledge base based on relevant literature. This allows the knowledge base to be enriched and its accuracy improved by referring to relevant literature. Some or all of the above processes in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input relevant literature into a generating AI and have the generating AI perform the knowledge base accuracy improvement.

[0051] The system integration unit can select the optimal integration method by referring to past integration data during system integration. For example, the system integration unit can analyze past integration data to identify the most efficient integration method. For example, the system integration unit can improve the integration method based on past integration data to achieve efficient system integration. The system integration unit can also refer to past integration data to select an integration method that provides the necessary information quickly. For example, the system integration unit optimizes the integration means and procedures based on past integration data. This enables efficient system integration by selecting the optimal integration method by referring to past integration data. Some or all of the above processes in the system integration unit may be performed using AI, for example, or without AI. For example, the system integration unit can input past integration data into a generating AI and have the generating AI select the optimal integration method.

[0052] The system integration unit can customize the means of integration based on the current status of each system during system integration. For example, the system integration unit can select the optimal means of integration based on the current operating status of each system. For example, the system integration unit can customize the integration method by considering the current status of each system, thereby achieving efficient system integration. Furthermore, the system integration unit can update the means of integration in response to changes in each system, always providing the optimal integration. For example, the system integration unit can appropriately customize the means of integration based on the operating status and load status of each system. This enables efficient system integration by customizing the means of integration based on the current status of each system. Some or all of the above-described processes in the system integration unit may be performed using AI, for example, or without AI. For example, the system integration unit can input information about the current status of each system into a generating AI and have the generating AI perform the customization of the means of integration.

[0053] The system integration unit can select the optimal integration method when integrating systems, taking into account the geographical location information of each system. For example, the system integration unit can select an integration method that prioritizes the provision of relevant information based on the geographical location information of each system. For example, the system integration unit can select an integration method that quickly provides necessary information, taking into account the geographical location information of each system. Furthermore, the system integration unit can customize the integration method based on the geographical location information of each system. For example, the system integration unit appropriately selects an integration method based on the geographical location information of each system. This allows for the priority provision of relevant information by considering the geographical location information of each system. Some or all of the above processing in the system integration unit may be performed using AI, for example, or without AI. For example, the system integration unit can input the geographical location information of each system into a generating AI and have the generating AI select the optimal integration method.

[0054] The system integration unit can improve the accuracy of system integration by referring to relevant literature during system integration. For example, the system integration unit can enrich the content of system integration by referring to relevant literature. For example, the system integration unit can improve the accuracy of system integration information based on relevant literature. Furthermore, the system integration unit can update the content of system integration with the latest information by referring to relevant literature. For example, the system integration unit can appropriately enrich the content of system integration based on relevant literature. In this way, by referring to relevant literature, the content of system integration can be enriched and its accuracy improved. Some or all of the above processing in the system integration unit may be performed using AI, for example, or without using AI. For example, the system integration unit can input relevant literature into a generating AI and have the generating AI perform the improvement of integration accuracy.

[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 integrated AI agent system can further analyze user behavior history and provide support optimized for each individual user. For example, the information gathering unit analyzes what kind of information users have frequently referenced in the past and prioritizes collecting similar information. The problem solving unit can propose the best solution based on the problems users have faced in the past and the solutions they have adopted. Furthermore, the knowledge base creation unit can add information that users need to the knowledge base based on their behavior history, prioritizing its addition. This makes it possible to provide more personalized support by utilizing user behavior history.

[0057] The integrated AI agent system can further adjust the timing of information delivery by taking the user's schedule into consideration. For example, the information gathering unit can refer to the user's calendar information and collect and provide relevant information before meetings or important events. The problem solving unit can also present solutions at the appropriate time, avoiding the user's busy periods. Furthermore, the knowledge base creation unit can prepare necessary information in advance based on the user's schedule and provide it at the appropriate time. This enables information delivery tailored to the user's schedule, further improving work efficiency.

[0058] The integrated AI agent system can further recognize user voice commands and support voice-based operation. For example, the information gathering unit can quickly collect and provide information instructed by the user via voice. The problem solving unit can detect problems reported by the user via voice in real time and provide solutions. Furthermore, the knowledge base creation unit can update the knowledge base based on information requested by the user via voice. As a result, by utilizing voice commands, users can operate the system hands-free, improving work efficiency.

[0059] The integrated AI agent system can further monitor the user's health status and provide support tailored to that status. For example, the information gathering unit collects the user's health data and provides information according to their health status. The problem solving unit can also suggest appropriate solutions considering the user's health status. Furthermore, the knowledge base creation unit can add health-related information to the knowledge base based on the user's health status. This enables support tailored to the user's health status and improves user health management.

[0060] The integrated AI agent system can further analyze the user's learning history and provide support tailored to their learning. For example, the information gathering unit collects and provides relevant information based on what the user has learned in the past. The problem solving unit can also suggest appropriate solutions, taking into account the user's learning history. Furthermore, the knowledge base creation unit can add information related to the learning content to the knowledge base based on the user's learning history. This allows for more effective support and improved learning efficiency by leveraging the user's learning history.

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

[0062] Step 1: The Information Gathering Department collects information. For example, it automatically collects all internal business-related information, terminology definitions, system usage instructions, and the location of analytical data storage. The Information Gathering Department can collect information in various formats, including text data, numerical data, and image data. It can also collect and integrate information from various departments within the company. For example, it collects and integrates project information, customer information, and financial information. Step 2: The Problem Solving Department detects problems and provides solutions based on the information collected by the Information Gathering Department. For example, it can detect problems encountered during work in real time and provide the optimal solution. It can detect and resolve issues such as system errors and delays in business processes. It can also automatically identify the appropriate department and escalate the issue if the correct contact point is unknown. Step 3: The Knowledge Base Creation Department creates a knowledge base based on the solutions provided by the Problem Solving Department. For example, it automatically creates and maintains a knowledge base that details the organizational structure, the functions and roles of each department, and key business operations. The knowledge base can be created in the form of FAQs, technical documents, manuals, etc. It can also regularly update the content of the knowledge base to always provide the latest information. Step 4: The System Integration Department integrates business systems based on the knowledge base created by the Knowledge Base Creation Department. For example, it centrally manages the usage of multiple business systems and tools and automates their integration. It can integrate business systems such as ERP systems, CRM systems, and project management tools. It can also automate the integration of business systems using API integration and automated scripts.

[0063] (Example of form 2) The integrated AI agent system according to an embodiment of the present invention is a system designed to dramatically improve the operational efficiency of a company. This system integrates four agent functions: information gathering, automated knowledge management, real-time support, and automated integration with business systems. The integrated AI agent system instantly solves various challenges faced by employees and simplifies daily work processes, thereby significantly improving the productivity of the entire organization. For example, the integrated AI agent system automatically collects all business-related information within the company, including terminology definitions, system usage instructions, and the location of analytical data storage, and provides this information to employees as a constantly updated knowledge base. This allows for quick acquisition of necessary information, significantly reducing effort and time. Furthermore, the integrated AI agent system detects and supports problems faced by employees during work in real time and provides optimal solutions. In addition, it has a function to automatically identify and escalate to the appropriate department if the appropriate contact is unknown. Moreover, the integrated AI agent system has a function to automatically create and maintain a knowledge base that summarizes the organizational structure, the functions and roles of each department, and key business operations in detail. This is particularly helpful for new employees and transferees, enabling them to quickly understand the overall picture of the organization and smoothly transition into their work. Finally, the integrated AI agent system centrally manages the use of multiple business systems and tools, automating their coordination. This agent improves operational efficiency by automating business processes and minimizing manual operations. As a result, the integrated AI agent system can instantly resolve various challenges faced by employees, simplify daily work processes, and significantly improve overall organizational productivity.

[0064] The integrated AI agent system according to this embodiment comprises an information gathering unit, a problem solving unit, a knowledge base creation unit, and a system integration unit. The information gathering unit collects information. For example, the information gathering unit automatically collects all business-related information within the company, terminology definitions, system usage methods, and the storage location of analytical data. The information gathering unit can collect information in formats such as text data, numerical data, and image data. The information gathering unit can also collect and integrate information from various departments within the company. For example, the information gathering unit collects and integrates project information, customer information, financial information, etc. The problem solving unit detects problems and provides solutions based on the information collected by the information gathering unit. For example, the problem solving unit can detect problems encountered during work in real time and provide optimal solutions. For example, the problem solving unit can detect problems such as system errors and business process delays and provide solutions. The problem solving unit can also automatically identify the appropriate department and escalate the issue when the appropriate contact is unknown. For example, the problem solving unit identifies the appropriate department and escalates the issue based on the assigned tasks and expertise. The Knowledge Base Creation Department creates knowledge bases based on solutions provided by the Problem Solving Department. For example, the Knowledge Base Creation Department automatically creates and maintains knowledge bases that detail organizational structure, the functions and roles of each department, and key business operations. The Knowledge Base Creation Department can create knowledge bases in various formats, such as FAQs, technical documents, and manuals. Furthermore, the Knowledge Base Creation Department can regularly update the content of the knowledge bases to ensure they always contain the latest information. The System Integration Department integrates business systems based on the knowledge bases created by the Knowledge Base Creation Department. For example, the System Integration Department centrally manages the usage of multiple business systems and tools and automates their integration. For example, the System Integration Department can integrate business systems such as ERP systems, CRM systems, and project management tools. The System Integration Department can also automate business system integration using API integration and automated scripts.As a result, the integrated AI agent system according to the embodiment can efficiently collect information, detect and resolve problems, create a knowledge base, and integrate with business systems.

[0065] The Information Gathering Department collects information. For example, it automatically collects all internal business-related information, terminology definitions, system usage instructions, and the location of analytical data storage. Specifically, the Information Gathering Department can collect information in the form of text data, numerical data, and image data provided by various departments within the company. For example, project information includes detailed information such as the progress of ongoing projects, assigned personnel, and deadlines; customer information includes customer contact information, past transaction history, and current contract details; and financial information includes detailed financial data such as revenue, expenditure, and budget. This information is automatically collected and integrated from various departments within the company. The Information Gathering Department can use natural language processing technology to analyze the meaning of text data and extract important information. It can also use image recognition technology to extract necessary information from image data. For example, it can extract text information from scanned documents or handwritten notes and save it as digital data. Furthermore, the Information Gathering Department centrally manages the collected information and can collaborate with other departments and systems as needed. For example, collected data can be stored on a cloud server and made accessible to the Problem Solving Department and the Knowledge Base Creation Department. Furthermore, the information gathering unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the information gathering unit to collect information efficiently and effectively, improving the overall system performance.

[0066] The Problem Solving Department detects problems and provides solutions based on information collected by the Information Gathering Department. Specifically, the Problem Solving Department detects problems encountered during work in real time and provides optimal solutions. For example, if a system error occurs, the Problem Solving Department analyzes the error log, identifies the cause of the error, and proposes a corrective method. Also, if a delay occurs in a business process, the Problem Solving Department can identify the cause of the delay and propose measures to improve the process. The Problem Solving Department uses AI to analyze collected data and detect problem patterns. For example, it uses machine learning algorithms to learn problem occurrence patterns from past data and respond quickly when similar problems occur. It also uses natural language processing technology to analyze user inquiries and provide appropriate solutions. Furthermore, the Problem Solving Department can automatically identify the appropriate department and escalate the issue when the appropriate contact is unknown. For example, the Problem Solving Department identifies the appropriate department and escalates the issue based on the assigned tasks and expertise. This allows the Problem Solving Department to solve problems quickly and accurately, improving operational efficiency. In addition, the Problem Solving Department can record the history of solutions to help solve problems in the future. This allows the problem-solving unit to continuously improve its problem-solving capabilities and enhance the overall reliability of the system.

[0067] The Knowledge Base Creation Department creates the knowledge base based on solutions provided by the Problem Solving Department. Specifically, the Knowledge Base Creation Department automatically creates and maintains a knowledge base that details the organizational structure, the functions and roles of each department, and key business operations. For example, the knowledge base can be created in the form of FAQs, technical documents, manuals, etc. Based on the solutions provided by the Problem Solving Department, the Knowledge Base Creation Department compiles detailed procedures and points to note and adds them to the knowledge base. This allows users to quickly refer to the knowledge base to check past problem solutions and address similar problems. The Knowledge Base Creation Department can also regularly update the content of the knowledge base to always provide the latest information. For example, if there are new business processes or system changes, the Knowledge Base Creation Department updates the knowledge base to reflect the latest information. Furthermore, the Knowledge Base Creation Department can collect feedback from users and improve the content of the knowledge base. For example, by allowing users to comment on and evaluate the content of the knowledge base, the Knowledge Base Creation Department can provide information that meets the needs of users. This allows the knowledge base creation department to always provide the latest and most useful information, helping users solve their problems.

[0068] The System Integration Department integrates business systems based on the knowledge base created by the Knowledge Base Creation Department. Specifically, the System Integration Department centrally manages the usage of multiple business systems and tools and automates their integration. For example, it can integrate business systems such as ERP systems, CRM systems, and project management tools. The System Integration Department can also automate the integration of business systems using API integration and automated scripts. This allows for smooth data exchange between different systems and improves business efficiency. For example, integrating an ERP system and a CRM system automatically synchronizes customer information and order information, preventing duplicate entries and data inconsistencies. In addition, integrating with a project management tool allows for real-time updates of project progress and task assignments, improving the overall work efficiency of the team. Furthermore, the System Integration Department monitors the integration status of business systems and can respond quickly if problems occur. For example, if an API integration fails or an automated script does not work correctly, the System Integration Department analyzes the error log, identifies the cause of the problem, and corrects it. The System Integration Department also continuously improves the integration of business systems and can flexibly respond to the introduction of new systems and tools. This allows the system integration department to efficiently and effectively integrate business systems, thereby improving the overall system performance.

[0069] The Information Gathering Department can automatically collect all internal business-related information, terminology definitions, system usage instructions, and data storage locations for analysis. For example, the Information Gathering Department can automatically collect all internal business-related information and always provide the most up-to-date information. It can also collect and integrate project information, customer information, financial information, etc. Furthermore, the Information Gathering Department can automatically collect terminology definitions and create glossaries including industry and internal terminology. In addition, the Information Gathering Department can automatically collect system usage instructions and provide operation manuals and training materials. For example, it can support system usage by collecting and providing system usage instructions to employees. The Information Gathering Department can also automatically collect data storage locations for analysis and manage storage locations such as databases and cloud storage. This allows for the automatic collection of all internal business-related information and the provision of always up-to-date information. Some or all of the above processes in the Information Gathering Department may be performed using AI, or not. For example, the Information Gathering Department can input all internal business-related information into a generating AI and have the generating AI perform the information collection and organization.

[0070] The problem-solving unit can detect problems encountered during work in real time and provide optimal solutions. For example, the problem-solving unit can detect problems encountered during work in real time and provide solutions quickly. For example, the problem-solving unit can detect problems such as system errors and business process delays and provide solutions. Furthermore, the problem-solving unit can also provide optimal solutions depending on the type and severity of the problem. For example, the problem-solving unit can provide a corrective program for system errors and a procedure manual for business process delays. In addition, the problem-solving unit can identify the cause of the problem and provide measures to prevent recurrence. For example, the problem-solving unit can identify the cause of a system error and provide measures to prevent recurrence. This enables the real-time detection of problems encountered during work and the provision of solutions quickly. Some or all of the above processes in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input problems encountered during work into a generating AI and have the generating AI perform problem detection and solution provision.

[0071] The problem-solving unit can automatically identify the appropriate department and escalate the issue when the correct contact point is unknown. For example, the problem-solving unit can automatically identify the appropriate department and respond quickly when the correct contact point is unknown. For example, the problem-solving unit can identify the appropriate department and escalate the issue based on the assigned duties and expertise. The problem-solving unit can also automatically execute escalation procedures and contact methods. For example, the problem-solving unit can automatically execute escalation procedures and contact the appropriate department. This allows the system to automatically identify the appropriate department and respond quickly even when the correct contact point is unknown. Some or all of the above processes in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information to generate AI to identify the appropriate department when the correct contact point is unknown, and have the generating AI perform department identification and escalation.

[0072] The Knowledge Base Creation Department can automatically create and maintain knowledge bases that detail organizational structure, the functions and roles of each department, and key business activities. For example, the Knowledge Base Creation Department can automatically create and maintain a knowledge base detailing the organizational structure. For example, it can create a knowledge base that includes departmental structure and hierarchical positions. Furthermore, the Knowledge Base Creation Department can create knowledge bases that detail the functions and roles of each department. For example, it can create knowledge bases that detail the functions and roles of departments such as sales and technology. In addition, the Knowledge Base Creation Department can create knowledge bases that detail key business activities. For example, it can create knowledge bases that detail key business activities such as daily operations and project operations. This enables the automatic creation and maintenance of knowledge bases that detail organizational structure and the functions and roles of each department. Some or all of the processes described above in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input information about the organizational structure and the functions and roles of each department into a generating AI, and have the generating AI create and maintain the knowledge base.

[0073] The System Integration Department can centrally manage the usage of multiple business systems and tools and automate their integration. For example, the System Integration Department can centrally manage the usage of multiple business systems and tools, thereby improving operational efficiency. For example, the System Integration Department can centrally manage business systems such as ERP systems, CRM systems, and project management tools. Furthermore, the System Integration Department can automate the integration of business systems using API integration and automated scripts. For example, the System Integration Department can automatically share data between different business systems using API integration. The System Integration Department can also automate business processes using automated scripts, minimizing manual operations. For example, the System Integration Department can automate data input and sharing using automated scripts. This improves operational efficiency by centrally managing the usage of multiple business systems and tools and automating their integration. Some or all of the above-described processes in the System Integration Department may be performed using AI, or not. For example, the System Integration Department can input information regarding business system integration into a generating AI and have the generating AI perform the automation of the integration.

[0074] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. For example, if the user is stressed, the information gathering unit can reduce the frequency of information gathering and collect only important information. For example, if the user is relaxed, the information gathering unit can collect detailed information and provide it to the user. Also, if the user is in a hurry, the information gathering unit can quickly collect necessary information and provide it immediately. For example, the information gathering unit adjusts the timing of information gathering according to the user's emotions and provides appropriate information. This makes it possible to collect more appropriate information by adjusting the timing of information gathering according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the timing of information gathering.

[0075] The Information Gathering Department can analyze the company's past information gathering history and select the optimal gathering method. For example, the Information Gathering Department can identify the most efficient gathering method from past information gathering history and apply it to current information gathering. For example, the Information Gathering Department can analyze past information gathering history and prioritize the collection of information that takes a long time to gather. Furthermore, the Information Gathering Department can improve its gathering methods based on past information gathering history to achieve efficient information gathering. For example, the Information Gathering Department can optimize the collection frequency and means based on past information gathering history. This makes it possible to select the optimal gathering method and achieve efficient information gathering by analyzing past information gathering history. Some or all of the above processes in the Information Gathering Department may be performed using AI, for example, or without AI. For example, the Information Gathering Department can input past information gathering history into a generating AI and have the generating AI select the optimal gathering method.

[0076] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit can prioritize collecting information related to the project the user is currently working on. For example, the information gathering unit can filter and provide relevant information based on the user's areas of interest. The information gathering unit can also collect and provide necessary information according to the progress of the user's project. For example, the information gathering unit can collect and provide appropriate information based on the progress of the user's project. This allows for efficient collection of necessary information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input information about the user's projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0077] The information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the information gathering unit can prioritize collecting and providing important information. For example, if the user is relaxed, the information gathering unit can collect and provide detailed information. Also, if the user is in a hurry, the information gathering unit can quickly collect necessary information and provide it immediately. For example, the information gathering unit determines the priority of information to collect according to the user's emotions and provides appropriate information. This allows for the priority collection of important information by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and information prioritization.

[0078] The information gathering unit can prioritize collecting highly relevant information based on the user's geographical location information during information gathering. For example, if the user is in a specific region, the information gathering unit will prioritize collecting information related to that region. For example, the information gathering unit can filter and provide relevant information based on the user's geographical location information. Furthermore, if the user is on the move, the information gathering unit can collect and provide necessary information based on their current location. For example, the information gathering unit will collect and provide appropriate information based on the user's geographical location information. This allows for efficient collection of necessary information by prioritizing the collection of highly relevant information based on the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0079] The information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit can analyze a user's social media activity and collect information related to topics of interest. For example, the information gathering unit can collect relevant information based on the content of posts from accounts that the user follows. The information gathering unit can also analyze a user's social media activity history, collect necessary information, and provide it. For example, the information gathering unit can collect and provide appropriate information based on a user's social media activity. This allows for the efficient collection of relevant information by analyzing a user's social media activity. Some or all of the above-described processes in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input user social media activity data into a generating AI and have the generating AI collect relevant information.

[0080] The problem-solving unit can estimate the user's emotions and adjust the way the problem-solving solution is presented based on the estimated emotions. For example, if the user is stressed, the problem-solving unit can provide a simple and easy-to-understand presentation. For example, if the user is relaxed, the problem-solving unit can provide a presentation that includes detailed explanations. Furthermore, if the user is in a hurry, the problem-solving unit can provide a quick solution. For example, the problem-solving unit adjusts the way the problem-solving solution is presented according to the user's emotions and provides an appropriate solution. This allows for the provision of more appropriate solutions by adjusting the way the problem-solving solution is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the problem-solving presentation.

[0081] The problem-solving unit can adjust the level of detail of the solution based on the importance of the problem. For example, the problem-solving unit can provide a detailed solution for a high-importance problem. For example, the problem-solving unit can provide a concise solution for a low-importance problem. The problem-solving unit can also adjust and provide the level of detail of the solution according to the importance of the problem. For example, the problem-solving unit can adjust the level of detail of the solution based on the scope of impact and urgency of the problem. This allows for the provision of an appropriate solution by adjusting the level of detail of the solution according to the importance of the problem. Some or all of the above processing in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information about the importance of the problem into a generating AI and have the generating AI perform the adjustment of the level of detail of the solution.

[0082] The problem-solving unit can apply different solution algorithms depending on the category of the problem. For example, the problem-solving unit can apply a technical solution algorithm to a technical problem. For example, the problem-solving unit can apply a business solution algorithm to a business problem. Furthermore, the problem-solving unit can apply a human resources solution algorithm to a human resources problem. For example, the problem-solving unit selects and applies an appropriate solution algorithm according to the category of the problem. This enables efficient problem solving by applying an appropriate solution algorithm according to the category of the problem. Some or all of the above processing in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information about the category of the problem into a generating AI and have the generating AI select and apply a solution algorithm.

[0083] The problem-solving unit can estimate the user's emotions and adjust the length of the solution based on the estimated emotions. For example, if the user is stressed, the problem-solving unit can provide a short, to-the-point solution. For example, if the user is relaxed, the problem-solving unit can provide a longer solution that includes detailed explanations. The problem-solving unit can also provide a quick solution if the user is in a hurry. For example, the problem-solving unit can adjust the length of the solution according to the user's emotions to provide an appropriate solution. By adjusting the length of the solution according to the user's emotions, a more appropriate solution can be provided. 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 problem-solving unit may be performed using AI, for example, or not using AI. For example, the problem-solving unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and solution length adjustment.

[0084] The problem-solving unit can determine the priority of solutions based on when the problem occurred. For example, the problem-solving unit can provide solutions to recently occurring problems as a priority. For example, the problem-solving unit can provide solutions to problems that occurred in the past as a delay. The problem-solving unit can also adjust and provide the priority of solutions according to when the problem occurred. For example, the problem-solving unit can determine the priority of solutions based on the date and time or duration of the problem's occurrence. This enables rapid problem solving by determining the priority of solutions according to when the problem occurred. Some or all of the above processing in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information about when the problem occurred into a generating AI and have the generating AI perform the determination of the priority of solutions.

[0085] The problem-solving unit can adjust the order of solutions based on the relevance of the problems. For example, the problem-solving unit can prioritize providing solutions to highly relevant problems. For example, it can postpone providing solutions to less relevant problems. The problem-solving unit can also adjust and provide solutions in order according to the relevance of the problems. For example, the problem-solving unit can determine the order of solutions based on the same category or common causes of the problems. This allows for efficient problem solving by adjusting the order of solutions according to the relevance of the problems. Some or all of the above processing in the problem-solving unit may be performed using AI, for example, or without AI. For example, the problem-solving unit can input information about the relevance of the problems into a generating AI and have the generating AI adjust the order of solutions.

[0086] The knowledge base creation unit can estimate the user's emotions and adjust the knowledge base creation method based on the estimated user emotions. For example, if the user is stressed, the knowledge base creation unit can create a simple and easy-to-understand knowledge base. For example, if the user is relaxed, the knowledge base creation unit can create a knowledge base containing detailed information. Also, if the user is in a hurry, the knowledge base creation unit can create a knowledge base that quickly provides the necessary information. For example, the knowledge base creation unit adjusts the knowledge base creation method according to the user's emotions to provide an appropriate knowledge base. This allows for the provision of a more appropriate knowledge base by adjusting the knowledge base creation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the method for creating the knowledge base.

[0087] The knowledge base creation unit can select the optimal creation method by referring to past knowledge base data when creating a knowledge base. For example, the knowledge base creation unit can analyze past knowledge base data to identify the most efficient creation method. For example, the knowledge base creation unit can improve the information organization method based on past knowledge base data. The knowledge base creation unit can also refer to past knowledge base data to select a method for quickly providing the necessary information. For example, the knowledge base creation unit can optimize the information collection and organization methods based on past knowledge base data. This makes it possible to select the optimal creation method by referring to past knowledge base data and create a knowledge base efficiently. Some or all of the above processes in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input past knowledge base data into a generating AI and have the generating AI select the optimal creation method.

[0088] The knowledge base creation unit can customize the content of the knowledge base based on the organization's current situation when creating it. For example, the knowledge base creation unit can customize the content of the knowledge base based on the organization's current projects and operations. For example, the knowledge base creation unit can create a knowledge base that prioritizes providing necessary information, taking into account the organization's current situation. The knowledge base creation unit can also update the content of the knowledge base in response to changes in the organization, ensuring that the information is always up-to-date. For example, the knowledge base creation unit can appropriately customize the content of the knowledge base based on the organization's current situation. This ensures that the information is always up-to-date by customizing the content of the knowledge base based on the organization's current situation. Some or all of the above processes in the knowledge base creation unit may be performed using AI, or not. For example, the knowledge base creation unit can input information about the organization's current situation into a generating AI and have the generating AI perform the customization of the knowledge base content.

[0089] The knowledge base creation unit can estimate the user's emotions and determine the priority of the knowledge base based on the estimated user emotions. For example, if the user is stressed, the knowledge base creation unit can create a knowledge base that prioritizes providing important information. For example, if the user is relaxed, the knowledge base creation unit can create a knowledge base that includes detailed information. Also, if the user is in a hurry, the knowledge base creation unit can create a knowledge base that quickly provides the necessary information. For example, the knowledge base creation unit determines the priority of the knowledge base according to the user's emotions and provides appropriate information. This ensures that important information is provided preferentially by determining the priority of the knowledge base according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input user emotion data into a generating AI, which can then perform emotion estimation and determine the priority of the knowledge base.

[0090] The knowledge base creation unit can create an optimal knowledge base by considering the organization's geographical location information during the knowledge base creation process. For example, the knowledge base creation unit can create a knowledge base that prioritizes the provision of relevant information based on the organization's geographical location information. For example, the knowledge base creation unit can create a knowledge base that quickly provides necessary information by considering the organization's geographical location information. Furthermore, the knowledge base creation unit can customize the content of the knowledge base based on the organization's geographical location information. For example, the knowledge base creation unit appropriately customizes the content of the knowledge base based on the organization's geographical location information. This allows for the priority provision of relevant information by considering the organization's geographical location information. Some or all of the above processes in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input the organization's geographical location information into a generating AI and have the generating AI create the knowledge base.

[0091] The knowledge base creation unit can improve the accuracy of the knowledge base by referring to relevant literature during the knowledge base creation process. For example, the knowledge base creation unit can enrich the content of the knowledge base by referring to relevant literature. For example, the knowledge base creation unit can improve the accuracy of the information in the knowledge base based on relevant literature. Furthermore, the knowledge base creation unit can update the content of the knowledge base with the latest information by referring to relevant literature. For example, the knowledge base creation unit can appropriately enrich the content of the knowledge base based on relevant literature. This allows the knowledge base to be enriched and its accuracy improved by referring to relevant literature. Some or all of the above processes in the knowledge base creation unit may be performed using AI, for example, or without AI. For example, the knowledge base creation unit can input relevant literature into a generating AI and have the generating AI perform the knowledge base accuracy improvement.

[0092] The system integration unit can estimate the user's emotions and adjust the system integration method based on the estimated user emotions. For example, if the user is stressed, the system integration unit can provide a simple and easy-to-understand system integration method. For example, if the user is relaxed, the system integration unit can provide a system integration method that includes detailed explanations. Furthermore, if the user is in a hurry, the system integration unit can provide a method for rapid system integration. For example, the system integration unit adjusts the system integration method according to the user's emotions to provide appropriate system integration. This allows for more appropriate system integration by adjusting the system integration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system integration unit may be performed using AI, for example, or without AI. For example, the system integration unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of the system integration method.

[0093] The system integration unit can select the optimal integration method by referring to past integration data during system integration. For example, the system integration unit can analyze past integration data to identify the most efficient integration method. For example, the system integration unit can improve the integration method based on past integration data to achieve efficient system integration. The system integration unit can also refer to past integration data to select an integration method that provides the necessary information quickly. For example, the system integration unit optimizes the integration means and procedures based on past integration data. This enables efficient system integration by selecting the optimal integration method by referring to past integration data. Some or all of the above processes in the system integration unit may be performed using AI, for example, or without AI. For example, the system integration unit can input past integration data into a generating AI and have the generating AI select the optimal integration method.

[0094] The system integration unit can customize the means of integration based on the current status of each system during system integration. For example, the system integration unit can select the optimal means of integration based on the current operating status of each system. For example, the system integration unit can customize the integration method by considering the current status of each system, thereby achieving efficient system integration. Furthermore, the system integration unit can update the means of integration in response to changes in each system, always providing the optimal integration. For example, the system integration unit can appropriately customize the means of integration based on the operating status and load status of each system. This enables efficient system integration by customizing the means of integration based on the current status of each system. Some or all of the above-described processes in the system integration unit may be performed using AI, for example, or without AI. For example, the system integration unit can input information about the current status of each system into a generating AI and have the generating AI perform the customization of the means of integration.

[0095] The system integration unit can estimate the user's emotions and determine the priority of system integrations based on the estimated user emotions. For example, if the user is stressed, the system integration unit will prioritize important system integrations. For example, if the user is relaxed, the system integration unit can perform system integrations that include detailed explanations. Also, if the user is in a hurry, the system integration unit can perform system integrations quickly. For example, the system integration unit determines the priority of system integrations according to the user's emotions and provides appropriate system integrations. This allows important system integrations to be prioritized by determining the priority of system integrations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system integration unit may be performed using AI, for example, or without AI. For example, the system integration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determination of system integration priorities.

[0096] The system integration unit can select the optimal integration method when integrating systems, taking into account the geographical location information of each system. For example, the system integration unit can select an integration method that prioritizes the provision of relevant information based on the geographical location information of each system. For example, the system integration unit can select an integration method that quickly provides necessary information, taking into account the geographical location information of each system. Furthermore, the system integration unit can customize the integration method based on the geographical location information of each system. For example, the system integration unit appropriately selects an integration method based on the geographical location information of each system. This allows for the priority provision of relevant information by considering the geographical location information of each system. Some or all of the above processing in the system integration unit may be performed using AI, for example, or without AI. For example, the system integration unit can input the geographical location information of each system into a generating AI and have the generating AI select the optimal integration method.

[0097] The system integration unit can improve the accuracy of system integration by referring to relevant literature during system integration. For example, the system integration unit can enrich the content of system integration by referring to relevant literature. For example, the system integration unit can improve the accuracy of system integration information based on relevant literature. Furthermore, the system integration unit can update the content of system integration with the latest information by referring to relevant literature. For example, the system integration unit can appropriately enrich the content of system integration based on relevant literature. In this way, by referring to relevant literature, the content of system integration can be enriched and its accuracy improved. Some or all of the above processing in the system integration unit may be performed using AI, for example, or without using AI. For example, the system integration unit can input relevant literature into a generating AI and have the generating AI perform the improvement of integration accuracy.

[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 integrated AI agent system can further analyze user behavior history and provide support optimized for each individual user. For example, the information gathering unit analyzes what kind of information users have frequently referenced in the past and prioritizes collecting similar information. The problem solving unit can propose the best solution based on the problems users have faced in the past and the solutions they have adopted. Furthermore, the knowledge base creation unit can add information that users need to the knowledge base based on their behavior history, prioritizing its addition. This makes it possible to provide more personalized support by utilizing user behavior history.

[0100] The integrated AI agent system can further adjust the timing of information delivery by taking the user's schedule into consideration. For example, the information gathering unit can refer to the user's calendar information and collect and provide relevant information before meetings or important events. The problem solving unit can also present solutions at the appropriate time, avoiding the user's busy periods. Furthermore, the knowledge base creation unit can prepare necessary information in advance based on the user's schedule and provide it at the appropriate time. This enables information delivery tailored to the user's schedule, further improving work efficiency.

[0101] The integrated AI agent system can further recognize user voice commands and support voice-based operation. For example, the information gathering unit can quickly collect and provide information instructed by the user via voice. The problem solving unit can detect problems reported by the user via voice in real time and provide solutions. Furthermore, the knowledge base creation unit can update the knowledge base based on information requested by the user via voice. As a result, by utilizing voice commands, users can operate the system hands-free, improving work efficiency.

[0102] The integrated AI agent system can further monitor the user's health status and provide support tailored to that status. For example, the information gathering unit collects the user's health data and provides information according to their health status. The problem solving unit can also suggest appropriate solutions considering the user's health status. Furthermore, the knowledge base creation unit can add health-related information to the knowledge base based on the user's health status. This enables support tailored to the user's health status and improves user health management.

[0103] The integrated AI agent system can further analyze the user's learning history and provide support tailored to their learning. For example, the information gathering unit collects and provides relevant information based on what the user has learned in the past. The problem solving unit can also suggest appropriate solutions, taking into account the user's learning history. Furthermore, the knowledge base creation unit can add information related to the learning content to the knowledge base based on the user's learning history. This allows for more effective support and improved learning efficiency by leveraging the user's learning history.

[0104] The integrated AI agent system can further estimate the user's emotions and adjust how information is displayed based on those emotions. For example, if the user is stressed, the information gathering unit can display information in a simple and easy-to-understand format. If the user is relaxed, the information gathering unit can display information in a format that includes detailed information. Furthermore, if the user is in a hurry, the information gathering unit can highlight important information. By adjusting how information is displayed according to the user's emotions, it becomes easier to understand the information and improves work efficiency.

[0105] The integrated AI agent system can further estimate the user's emotions and adjust the frequency of notifications based on those emotions. For example, if the user is stressed, the information gathering unit will reduce the frequency of notifications and send only important ones. If the user is relaxed, the information gathering unit can send detailed notifications more frequently. Furthermore, if the user is in a hurry, the information gathering unit can quickly send necessary notifications. This maximizes the effectiveness of notifications and improves work efficiency by adjusting the frequency of notifications according to the user's emotions.

[0106] The integrated AI agent system can further estimate the user's emotions and prioritize support based on those emotions. For example, if the user is stressed, the problem-solving unit will prioritize resolving important issues. If the user is relaxed, the problem-solving unit can provide solutions with detailed explanations. Furthermore, if the user is in a hurry, the problem-solving unit can provide solutions quickly. This allows for the rapid resolution of important issues by prioritizing support according to the user's emotions.

[0107] The integrated AI agent system can further estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, the knowledge base creation unit can provide simple, positive feedback if the user is stressed. If the user is relaxed, the knowledge base creation unit can provide detailed feedback. Furthermore, if the user is in a hurry, the knowledge base creation unit can provide quick feedback. This improves user satisfaction by adjusting the content of feedback according to the user's emotions.

[0108] The integrated AI agent system can further estimate the user's emotions and adjust the training content based on those emotions. For example, if the user is stressed, the knowledge base creation unit can provide simple and easy-to-understand training content. If the user is relaxed, the knowledge base creation unit can provide detailed training content. Furthermore, if the user is in a hurry, the knowledge base creation unit can provide content that allows for quick completion of the training. This allows for effective training by adjusting the training content according to the user's emotions.

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

[0110] Step 1: The Information Gathering Department collects information. For example, it automatically collects all internal business-related information, terminology definitions, system usage instructions, and the location of analytical data storage. The Information Gathering Department can collect information in various formats, including text data, numerical data, and image data. It can also collect and integrate information from various departments within the company. For example, it collects and integrates project information, customer information, and financial information. Step 2: The Problem Solving Department detects problems and provides solutions based on the information collected by the Information Gathering Department. For example, it can detect problems encountered during work in real time and provide the optimal solution. It can detect and resolve issues such as system errors and delays in business processes. It can also automatically identify the appropriate department and escalate the issue if the correct contact point is unknown. Step 3: The Knowledge Base Creation Department creates a knowledge base based on the solutions provided by the Problem Solving Department. For example, it automatically creates and maintains a knowledge base that details the organizational structure, the functions and roles of each department, and key business operations. The knowledge base can be created in the form of FAQs, technical documents, manuals, etc. It can also regularly update the content of the knowledge base to always provide the latest information. Step 4: The System Integration Department integrates business systems based on the knowledge base created by the Knowledge Base Creation Department. For example, it centrally manages the usage of multiple business systems and tools and automates their integration. It can integrate business systems such as ERP systems, CRM systems, and project management tools. It can also automate the integration of business systems using API integration and automated scripts.

[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 information gathering unit, problem solving unit, knowledge base creation unit, and system integration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the information gathering unit collects information using the camera 42 and microphone 38B of the smart device 14 and integrates the information using the control unit 46A. The problem solving unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to detect problems based on the collected information and provide solutions. The knowledge base creation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to create and maintain a knowledge base based on the solutions. The system integration unit is implemented in the control unit 46A of the smart device 14, for example, to automate the integration of business systems. 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 information gathering unit, problem solving unit, knowledge base creation unit, and system integration unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the information gathering unit collects information using the camera 42 and microphone 238 of the smart glasses 214 and integrates the information by the control unit 46A. The problem solving unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which detects problems based on the collected information and provides solutions. The knowledge base creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which creates and maintains a knowledge base based on the solutions. The system integration unit is implemented, for example, by the control unit 46A of the smart glasses 214, which automates the integration of business systems. The correspondence between each unit and the devices and control units is not limited to the examples 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 information gathering unit, problem solving unit, knowledge base creation unit, and system integration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the information gathering unit collects information using the camera 42 and microphone 238 of the headset terminal 314 and integrates the information using the control unit 46A. The problem solving unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which detects problems based on the collected information and provides solutions. The knowledge base creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates and maintains a knowledge base based on the solutions. The system integration unit is implemented by, for example, the control unit 46A of the headset terminal 314, which automates the integration of business systems. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[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 information gathering unit, problem solving unit, knowledge base creation unit, and system integration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the information gathering unit collects information using the camera 42 and microphone 238 of the robot 414 and integrates the information with the control unit 46A. The problem solving unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which detects problems based on the collected information and provides solutions. The knowledge base creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which creates and maintains a knowledge base based on the solutions. The system integration unit is implemented by, for example, the control unit 46A of the robot 414, which automates the integration of business systems. The correspondence between each unit and the devices and control units is not limited to the examples 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) The information gathering department collects information, A problem-solving unit detects problems and provides solutions based on the information collected by the aforementioned information collection unit, A knowledge base creation unit creates a knowledge base based on the solutions provided by the aforementioned problem-solving unit, The system includes a system integration unit that integrates business systems based on the knowledge base created by the aforementioned knowledge base creation unit. A system characterized by the following features. (Note 2) The aforementioned information gathering unit, Automatically collects all internal business-related information, terminology definitions, system usage instructions, and data storage locations for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned problem-solving unit, It detects problems encountered during work in real time and provides the optimal solution. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned problem-solving unit, If the appropriate department is unknown, the system will automatically identify the correct department and escalate the issue. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned knowledge base creation unit is: Automate the creation and maintenance of a knowledge base that details the organizational structure, the functions and roles of each department, and the main business activities. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned system integration unit is Centralized management of how to use multiple business systems and tools, and automation of their integration. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned information gathering unit, Analyze the company's past information gathering history and select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned information gathering unit, When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned information gathering unit, When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information gathering unit, When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned problem-solving unit, It estimates the user's emotions and adjusts the way problem-solving is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned problem-solving unit, Adjust the level of detail in the solution based on the importance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned problem-solving unit, Apply different problem-solving algorithms depending on the problem category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned problem-solving unit, It estimates the user's emotions and adjusts the length of the solution based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned problem-solving unit, Prioritize solutions based on when the problem occurred. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned problem-solving unit, Adjust the order of solutions based on the relevance of the problems. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned knowledge base creation unit is: We estimate user sentiment and adjust how the knowledge base is created based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned knowledge base creation unit is: When creating a knowledge base, refer to past knowledge base data to select the optimal creation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned knowledge base creation unit is: When creating a knowledge base, customize its content based on the organization's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned knowledge base creation unit is: It estimates user sentiment and prioritizes the knowledge base based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned knowledge base creation unit is: When creating a knowledge base, take into account the organization's geographical location to create an optimal knowledge base. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned knowledge base creation unit is: When creating a knowledge base, refer to relevant literature to improve the accuracy of the knowledge base. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned system integration unit is The system estimates the user's emotions and adjusts the system integration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned system integration unit is When integrating systems, the system selects the optimal integration method by referring to past integration data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned system integration unit is When integrating systems, the method of integration is customized based on the current status of each system. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned system integration unit is The system estimates user emotions and determines the priority of system integration based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned system integration unit is When integrating systems, the optimal integration method is selected by considering the geographical location information of each system. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned system integration unit is When integrating systems, we refer to relevant literature to improve the accuracy of the integration. 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. The information gathering department collects information, A problem-solving unit detects problems and provides solutions based on the information collected by the aforementioned information collection unit, A knowledge base creation unit creates a knowledge base based on the solutions provided by the aforementioned problem-solving unit, The system includes a system integration unit that integrates business systems based on the knowledge base created by the aforementioned knowledge base creation unit. A system characterized by the following features.

2. The aforementioned information gathering unit, Automatically collects all internal business-related information, terminology definitions, system usage instructions, and data storage locations for analysis. The system according to feature 1.

3. The aforementioned problem-solving unit, It detects problems encountered during work in real time and provides the optimal solution. The system according to feature 1.

4. The aforementioned problem-solving unit, If the appropriate department is unknown, the system will automatically identify the correct department and escalate the issue. The system according to feature 1.

5. The aforementioned knowledge base creation unit is: Automate the creation and maintenance of a knowledge base that details the organizational structure, the functions and roles of each department, and the main business activities. The system according to feature 1.

6. The aforementioned system integration unit is Centralized management of how to use multiple business systems and tools, and automation of their integration. The system according to feature 1.

7. The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned information gathering unit, Analyze the company's past information gathering history and select the most suitable collection method. The system according to feature 1.

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