system

Abstract

The system according to this embodiment aims to prevent the personalization of tasks and knowledge and to enable the smooth sharing of information. [Solution] The system according to the embodiment comprises a reception unit, a storage unit, a bridging unit, and a resolution unit. The reception unit inputs work content and knowledge. The storage unit analyzes the information input by the reception unit and stores the work content and knowledge in a database. The bridging unit analyzes instructions from the setter and questions from the operator based on the information stored by the storage unit and provides appropriate answers. The resolution unit immediately resolves any unclear points on the operator's side based on the answers provided by the bridging unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to personalize business or knowledge and to smoothly share information when a person with specific knowledge or skills is absent or when the setter and the operator are different.

[0005] The system according to the embodiment aims to prevent the personalization of business and knowledge and to realize the smooth sharing of information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a storage unit, a bridging unit, and a resolution unit. The reception unit inputs business content and knowledge. The storage unit analyzes the information input by the reception unit and stores the business content and knowledge in a database. The bridging unit analyzes instructions from the setter and questions from the operator based on the information stored by the storage unit and provides appropriate answers. The resolution unit immediately resolves any unclear points on the operator's side based on the answers provided by the bridging unit. [Effects of the Invention]

[0007] The system according to this embodiment can prevent the personalization of tasks and knowledge, and enable the smooth sharing of information. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards 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 system according to an embodiment of the present invention is a system for solving the problem of reliance on specific individuals when a person with particular knowledge and skills is temporarily absent or leaves the company, causing a stagnation of their work and knowledge. In this system, when a person with specific knowledge and skills is absent, their work content and knowledge are pre-inputted into a generating AI. The generating AI analyzes the input information and stores the work content and knowledge in a database. Next, if the person who sets up the system and the person who operates it are different, the generating AI acts as a bridge to facilitate the smooth exchange of important information. The generating AI analyzes instructions from the person who sets up the system and questions from the person who operates it, and provides appropriate answers. Furthermore, if any ambiguities arise on the part of the operator, the generating AI immediately provides solutions. This mechanism ensures that work proceeds smoothly even when a person with specific knowledge and skills is absent, and that important information is exchanged smoothly even when the person who sets up the system and the person who operates it are different. Also, because the generating AI immediately provides solutions when ambiguities arise on the part of the operator, smooth operation of the company or project is achieved. As a result, the system ensures that work proceeds smoothly even when a person with specific knowledge and skills is absent, and that important information is exchanged smoothly even when the person who sets up the system and the person who operates it are different.

[0029] The system according to this embodiment comprises a reception unit, a storage unit, a bridging unit, and a resolution unit. The reception unit inputs business content and knowledge. The reception unit inputs information such as the progress of a specific project or technical know-how. The reception unit analyzes the input information using a generation AI and stores the business content and knowledge in a database. The storage unit analyzes the information input by the reception unit and stores the business content and knowledge in a database. The storage unit analyzes the information using, for example, text analysis, data mining, and machine learning algorithms. The storage unit stores the information in a database such as a relational database or a NoSQL database. The bridging unit analyzes instructions from setters and questions from operators based on the information stored by the storage unit and provides appropriate answers. The bridging unit analyzes instructions from setters and questions from operators using a generation AI and provides appropriate answers. The bridging unit provides answers based on criteria such as accuracy, relevance, and timeliness. The resolution unit immediately resolves any unclear points on the operator's side based on the answers provided by the bridging unit. The resolution unit uses generative AI to immediately resolve any ambiguities on the operational side. The resolution unit provides solutions based on criteria such as real-time response and rapid feedback. As a result, the system according to this embodiment can proceed without interruption even if a person with specific knowledge and skills is absent, and important information is exchanged smoothly even if the person who sets up the system and the person who operates it are different. Furthermore, even if ambiguities arise on the operational side, the generative AI provides immediate solutions, enabling the smooth operation of the company or project.

[0030] The reception desk inputs information about work content and knowledge. For example, the reception desk inputs information such as the progress of a specific project and technical know-how. Specifically, the reception desk can input information such as detailed project progress, technical challenges, solutions, and the tools and technology stack used through the user interface. This information can be input not only in text format but also in various formats such as images, videos, and audio. Furthermore, the reception desk uses generative AI to analyze the input information and save the work content and knowledge to a database. The generative AI utilizes natural language processing technology to analyze the input information based on context and extract important keywords and phrases. For example, if information about project progress is input, the generative AI analyzes that information and automatically classifies it into ongoing tasks, completed tasks, and unresolved issues. Also, if information about technical know-how is input, the generative AI analyzes that information and extracts relevant technologies, tools, and solution procedures. This allows the reception desk to efficiently analyze the input information and prepare it for storage in the database.

[0031] The data storage unit analyzes information entered by the reception unit and stores the work details and knowledge in a database. The data storage unit analyzes information using methods such as text analysis, data mining, and machine learning algorithms. Specifically, the data storage unit further scrutinizes the information analyzed by the generation AI to confirm the consistency and accuracy of the data. It uses text analysis technology to understand the context and meaning of the information and links related data. It uses data mining technology to extract useful patterns and trends from past data and gain new insights. It uses machine learning algorithms to classify and cluster the information, organizing and structuring the data. The data storage unit stores the information in databases such as relational databases and NoSQL databases. Relational databases store information in a table format, and data can be efficiently searched and retrieved using SQL queries. On the other hand, NoSQL databases have excellent scalability and flexibility, and can efficiently store and manage large amounts of data. As a result, the data storage unit can streamline information management throughout the entire system by appropriately analyzing the information provided by the reception unit and storing it in the database.

[0032] The bridging unit analyzes instructions from the system administrator and questions from the operator based on information stored by the storage unit, and provides appropriate answers. The bridging unit uses generative AI to analyze instructions from the system administrator and questions from the operator, and provides appropriate answers. Specifically, the generative AI uses natural language processing technology to analyze input from the system administrator and operator, and understand their intentions and requests. For example, if the system administrator wants to check the progress of a particular project, the generative AI analyzes that request and searches for and retrieves relevant information stored in the storage unit. Also, if the operator faces a technical problem, the generative AI analyzes the problem and provides relevant technical know-how and solutions stored in the storage unit. The bridging unit provides answers based on criteria such as accuracy, relevance, and timeliness. To ensure the accuracy of the answers provided, the generative AI cross-checks multiple sources of information and selects the most reliable information. To ensure relevance, it understands the context and background of the question and provides the most relevant information. To ensure timeliness, it searches for and retrieves information in real time and provides answers quickly. This allows the bridging unit to provide quick and accurate information to the setters and operators, thereby supporting the efficiency of their operations.

[0033] The resolution unit immediately resolves any ambiguities on the operational side based on the answers provided by the bridging unit. The resolution unit uses generative AI to immediately resolve ambiguities on the operational side. Specifically, the generative AI analyzes feedback and additional questions from operators and quickly provides the necessary information. For example, if an operator requests more detailed information regarding the provided answer, the generative AI analyzes the request and searches and retrieves the relevant information stored in the storage unit. Also, if an operator faces a new problem, the generative AI analyzes the problem and provides the optimal solution based on past data and similar cases. The resolution unit provides solutions based on criteria such as real-time response and rapid feedback. The generative AI processes information in real time and provides rapid feedback to operators. This allows operators to respond immediately when problems occur, minimizing disruption to operations. Furthermore, the resolution unit monitors the effectiveness of the provided solutions and makes improvements as needed. For example, it evaluates the accuracy and effectiveness of the solutions based on feedback from operators and adjusts the generative AI algorithm. This allows the resolution unit to always provide the optimal solution and improve the reliability and efficiency of the entire system.

[0034] The reception department inputs information such as the progress of a specific project and technical know-how. For example, the reception department inputs the progress of a specific project. The reception department inputs the project progress based on criteria such as progress reports and milestone achievement status. The reception department inputs technical know-how. The reception department inputs technical know-how based on criteria such as how to use a specific technology and troubleshooting procedures. This allows for the efficient input of project progress and technical know-how.

[0035] The storage unit analyzes the input information and saves the business content and knowledge to a database. The storage unit can analyze information using, for example, text analysis. It can also analyze information using data mining. It can also analyze information using machine learning algorithms. The storage unit saves information to a relational database. It can also save information to a NoSQL database. For example, the storage unit structures and saves information using a relational database. NoSQL databases offer scalability and flexibility, allowing for efficient storage of large amounts of data. This enables efficient storage of business content and knowledge.

[0036] The bridging unit analyzes instructions from the system designer and questions from the operator, and provides appropriate answers. For example, the bridging unit analyzes instructions from the system designer. The bridging unit can also analyze questions from the operator. The bridging unit uses generative AI to analyze instructions from the system designer and questions from the operator. The bridging unit provides answers based on criteria such as accuracy, relevance, and timeliness. For example, the bridging unit analyzes instructions from the system designer and provides appropriate answers. The bridging unit can also analyze questions from the operator and provide appropriate answers. The bridging unit uses generative AI to analyze instructions from the system designer and questions from the operator, and provides appropriate answers. This ensures that important information is smoothly exchanged between the system designer and the operator.

[0037] The solution unit immediately resolves any unclear points on the operational side. For example, the solution unit resolves unclear points on the operational side. The solution unit uses generative AI to resolve unclear points on the operational side. The solution unit provides solutions based on criteria such as real-time response and rapid feedback. For example, the solution unit resolves unclear points on the operational side. The solution unit uses generative AI to resolve unclear points on the operational side. The solution unit provides solutions based on criteria such as real-time response and rapid feedback. This allows for immediate solutions even when unclear points arise on the operational side.

[0038] The reception department, when inputting the progress status and technical know-how of a specific project, supplements the input content by referring to data from similar past projects. For example, the reception department can automatically supplement the input status based on past project data. The reception department can also supplement the input content by referring to the technical know-how of similar projects. The reception department can also optimize the input content based on successful examples from past projects. This allows the reception department to supplement the input content by referring to data from similar past projects. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past project data into a generating AI and have the generating AI perform the supplementation of the input content.

[0039] The reception desk provides input guides according to the inputter's level of expertise when they input information. For example, the reception desk may provide a simple input guide for beginners. The reception desk may also provide a detailed input guide for intermediate users. The reception desk may also provide a specialized input guide for advanced users. This ensures that input guides are provided according to the inputter's level of expertise. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the inputter's level of expertise into a generating AI and have the generating AI perform the task of providing input guides.

[0040] The reception unit prioritizes inputting highly relevant information based on the inputter's geographical location information when information is entered. For example, the reception unit may prioritize inputting relevant information based on the inputter's current location. The reception unit may also refer to the inputter's past location information and prioritize inputting highly relevant information. The reception unit may also analyze the inputter's geographical movement patterns and prioritize inputting the most appropriate information. This allows for the priority input of highly relevant information based on the inputter's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the inputter's geographical location information into a generating AI and have the generating AI prioritize highly relevant information.

[0041] The reception unit analyzes the user's social media activity when inputting information and inputs relevant information. For example, the reception unit analyzes the user's social media posts and prompts them to input relevant information. The reception unit can also refer to the activity of the user's social media followers and friends and prompt them to input relevant information. The reception unit can also analyze the user's social media trends and prompt them to input the most relevant information. This allows the reception unit to analyze the user's social media activity and input relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI input the relevant information.

[0042] The storage unit adjusts the level of detail in saving information based on its importance. For example, it saves important information in detail so that it can be easily referenced later. It can also save general information concisely and add details as needed. It can also save less important information in a simplified form to improve storage efficiency. This allows the level of detail in saving information to be adjusted based on its importance. Some or all of the above processes in the storage unit may be performed using AI, for example, or not. For example, the storage unit can input the importance of the information into a generating AI and have the generating AI determine the level of detail in saving.

[0043] The storage unit applies different storage algorithms depending on the category of information during storage. For example, the storage unit may use a compression algorithm to store technical information. The storage unit may also store project progress in a time-series database. The storage unit may also store know-how and knowledge in a searchable format. This allows for the application of different storage algorithms depending on the category of information. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit may input the category of information into a generating AI and have the generating AI execute the application of the storage algorithm.

[0044] The storage unit determines the storage priority based on the information submission date when saving. For example, the storage unit saves urgent information immediately and adds details later. The storage unit can also save periodic information according to a schedule. The storage unit can save historical information as an archive and refer to it as needed. This allows the storage unit to determine the storage priority based on the information submission date. Some or all of the above processes in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the information submission date into a generating AI and have the generating AI execute the storage priority.

[0045] The storage unit adjusts the order of saving information based on its relevance during the saving process. For example, the storage unit may prioritize saving highly relevant information so that it can be easily referenced later. The storage unit may also postpone saving less relevant information. The storage unit may also analyze the relevance of information and determine the optimal saving order. This allows the storage order to be adjusted based on the relevance of information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit may input the relevance of information into a generating AI and have the generating AI execute the saving order.

[0046] The bridging unit improves the accuracy of its answers by referring to similar past cases when analyzing instructions from the setter and questions from the operator. For example, the bridging unit provides the optimal answer based on similar past cases. The bridging unit can also improve the accuracy of its answers by referring to successful examples of similar cases. The bridging unit can also minimize the risk of answers by analyzing past failures. This allows it to improve the accuracy of answers by referring to similar past cases. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input past similar case data into a generating AI and have the generating AI perform the task of improving the accuracy of answers.

[0047] The bridging unit provides the optimal answer by considering the attribute information of the setter and the operator. For example, the bridging unit provides an appropriate answer by considering the expertise level of the setter and the operator. The bridging unit can also provide the optimal answer by considering the job titles and work content of the setter and the operator. The bridging unit can also provide the optimal answer by referring to the past interactions between the setter and the operator. This allows the bridging unit to provide the optimal answer by considering the attribute information of the setter and the operator. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input the attribute information of the setter and the operator into a generating AI and have the generating AI perform the task of providing the optimal answer.

[0048] The bridging unit analyzes instructions from the programmer and questions from the operator, taking geographical distribution into consideration when providing answers. For example, the bridging unit considers the geographical locations of the programmer and the operator to provide the optimal answer. The bridging unit can also consider geographical constraints to provide feasible answers. The bridging unit can also analyze geographical characteristics to provide the optimal answer. This allows the bridging unit to provide the optimal answer while considering geographical distribution. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input geographical distribution data into a generating AI and have the generating AI perform the task of providing the optimal answer.

[0049] The bridging unit improves the accuracy of its answers by referring to relevant literature when analyzing instructions from the setter and questions from the operator. For example, the bridging unit provides the optimal answer based on relevant literature. The bridging unit can also improve the accuracy of its answers by referring to the latest research findings. The bridging unit can also provide answers that include citations of relevant literature. This allows for improved accuracy of answers by referring to relevant literature. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the answers.

[0050] The solution unit provides the optimal solution by referring to past solutions when resolving unclear points on the operational side. For example, the solution unit provides the optimal solution based on past solutions. The solution unit can also improve the accuracy of the solution by referring to successful cases of similar cases. The solution unit can also minimize the risks of the solution by analyzing past failure cases. This allows it to provide the optimal solution by referring to past solutions. Some or all of the above processes in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input past solution data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0051] The solution unit provides the optimal solution by considering the operator's attribute information. For example, the solution unit provides an appropriate solution by considering the operator's level of expertise. The solution unit can also provide the optimal solution by considering the operator's position and job duties. The solution unit can also provide the optimal solution by referring to the operator's past interactions. This allows the solution unit to provide the optimal solution by considering the operator's attribute information. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input the operator's attribute information into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0052] The solution unit, when resolving unclear points on the operational side, provides solutions while considering geographical distribution. For example, the solution unit considers the geographical location of the operators and provides the optimal solution. The solution unit can also consider geographical constraints and provide feasible solutions. The solution unit can also analyze geographical characteristics and provide the optimal solution. This allows the solution unit to provide the optimal solution while considering geographical distribution. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input geographical distribution data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0053] The solution unit improves the accuracy of its solutions by referring to relevant literature when resolving unclear points on the operational side. For example, the solution unit provides the optimal solution based on relevant literature. The solution unit can also improve the accuracy of its solutions by referring to the latest research results. The solution unit can also provide solutions that include citations of relevant literature. This allows for improvement of the accuracy of the solutions by referring to relevant literature. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of the solutions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception department can supplement input data from similar past projects when entering the progress status and technical know-how of a specific project. For example, it can automatically supplement the input status based on past project data. It can also supplement the input content by referring to the technical know-how of similar projects. Furthermore, it can optimize the input content based on successful case studies from past projects. This allows for supplementation of input content by referring to data from similar past projects. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past project data into a generating AI and have the generating AI perform the supplementation of the input content.

[0056] The bridging unit can improve the accuracy of its responses by referring to similar past cases when analyzing instructions from the setter and questions from the operator. For example, it can provide the optimal response based on similar past cases. It can also improve the accuracy of its responses by referring to successful cases of similar situations. Furthermore, it can minimize the risk of responses by analyzing past failures. This allows for improved response accuracy by referring to similar past cases. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input past similar case data into a generating AI and have the generating AI perform the task of improving the accuracy of its responses.

[0057] The solution unit can provide the optimal solution by referring to past solutions when resolving unclear points on the operational side. For example, it can provide the optimal solution based on past solutions. It can also improve the accuracy of the solution by referring to successful cases of similar cases. Furthermore, it can minimize the risks of the solution by analyzing past failure cases. This allows it to provide the optimal solution by referring to past solutions. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input past solution data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0058] The reception desk can provide input guides to users according to their level of expertise when they input information. For example, it can provide simple input guides for beginners, detailed input guides for intermediate users, and even specialized input guides for advanced users. This ensures that input guides are tailored to the user's level of expertise. Some or all of the above-described processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's level of expertise into a generating AI and have the generating AI provide the input guides.

[0059] The storage unit can adjust the level of detail in saving information based on its importance. For example, important information can be saved in detail for easy reference later. General information can be saved concisely, with details added as needed. Furthermore, less important information can be saved in a simplified form to improve storage efficiency. This allows the level of detail in saving information to be adjusted based on its importance. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of the information into a generating AI and have the generating AI determine the level of detail in saving.

[0060] The bridging unit can provide the optimal answer by considering the attribute information of the setter and the operator. For example, it can provide an appropriate answer by considering the level of expertise of the setter and the operator. It can also provide the optimal answer by considering the job titles and work content of the setter and the operator. Furthermore, it can provide the optimal answer by referring to the past interactions between the setter and the operator. This allows the bridging unit to provide the optimal answer by considering the attribute information of the setter and the operator. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without using AI. For example, the bridging unit can input the attribute information of the setter and the operator into a generating AI and have the generating AI perform the task of providing the optimal answer.

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

[0062] Step 1: The reception department inputs information about the work and its knowledge. For example, it inputs information such as the progress of a specific project or technical know-how. The reception department uses a generation AI to analyze the input information and saves the work details and knowledge to a database. Step 2: The storage unit analyzes the information entered by the reception unit and saves the work details and knowledge to a database. The storage unit analyzes the information using text analysis, data mining, machine learning algorithms, etc., and saves the information to a database such as a relational database or a NoSQL database. Step 3: The bridging unit analyzes instructions from the programmer and questions from the operator based on the information stored by the storage unit and provides appropriate answers. The bridging unit uses generating AI to analyze instructions from the programmer and questions from the operator and provides appropriate answers based on criteria such as accuracy, relevance, and timeliness. Step 4: The Resolution Unit immediately resolves any ambiguities on the operational side based on the answers provided by the Bridging Unit. The Resolution Unit uses generative AI to provide solutions based on criteria such as real-time response and rapid feedback.

[0063] (Example of form 2) The system according to an embodiment of the present invention is a system for solving the problem of reliance on specific individuals when a person with particular knowledge and skills is temporarily absent or leaves the company, causing a stagnation of their work and knowledge. In this system, when a person with specific knowledge and skills is absent, their work content and knowledge are pre-inputted into a generating AI. The generating AI analyzes the input information and stores the work content and knowledge in a database. Next, if the person who sets up the system and the person who operates it are different, the generating AI acts as a bridge to facilitate the smooth exchange of important information. The generating AI analyzes instructions from the person who sets up the system and questions from the person who operates it, and provides appropriate answers. Furthermore, if any ambiguities arise on the part of the operator, the generating AI immediately provides solutions. This mechanism ensures that work proceeds smoothly even when a person with specific knowledge and skills is absent, and that important information is exchanged smoothly even when the person who sets up the system and the person who operates it are different. Also, because the generating AI immediately provides solutions when ambiguities arise on the part of the operator, smooth operation of the company or project is achieved. As a result, the system ensures that work proceeds smoothly even when a person with specific knowledge and skills is absent, and that important information is exchanged smoothly even when the person who sets up the system and the person who operates it are different.

[0064] The system according to this embodiment comprises a reception unit, a storage unit, a bridging unit, and a resolution unit. The reception unit inputs business content and knowledge. The reception unit inputs information such as the progress of a specific project or technical know-how. The reception unit analyzes the input information using a generation AI and stores the business content and knowledge in a database. The storage unit analyzes the information input by the reception unit and stores the business content and knowledge in a database. The storage unit analyzes the information using, for example, text analysis, data mining, and machine learning algorithms. The storage unit stores the information in a database such as a relational database or a NoSQL database. The bridging unit analyzes instructions from setters and questions from operators based on the information stored by the storage unit and provides appropriate answers. The bridging unit analyzes instructions from setters and questions from operators using a generation AI and provides appropriate answers. The bridging unit provides answers based on criteria such as accuracy, relevance, and timeliness. The resolution unit immediately resolves any unclear points on the operator's side based on the answers provided by the bridging unit. The resolution unit uses generative AI to immediately resolve any ambiguities on the operational side. The resolution unit provides solutions based on criteria such as real-time response and rapid feedback. As a result, the system according to this embodiment can proceed without interruption even if a person with specific knowledge and skills is absent, and important information is exchanged smoothly even if the person who sets up the system and the person who operates it are different. Furthermore, even if ambiguities arise on the operational side, the generative AI provides immediate solutions, enabling the smooth operation of the company or project.

[0065] The reception desk inputs information about work content and knowledge. For example, the reception desk inputs information such as the progress of a specific project and technical know-how. Specifically, the reception desk can input information such as detailed project progress, technical challenges, solutions, and the tools and technology stack used through the user interface. This information can be input not only in text format but also in various formats such as images, videos, and audio. Furthermore, the reception desk uses generative AI to analyze the input information and save the work content and knowledge to a database. The generative AI utilizes natural language processing technology to analyze the input information based on context and extract important keywords and phrases. For example, if information about project progress is input, the generative AI analyzes that information and automatically classifies it into ongoing tasks, completed tasks, and unresolved issues. Also, if information about technical know-how is input, the generative AI analyzes that information and extracts relevant technologies, tools, and solution procedures. This allows the reception desk to efficiently analyze the input information and prepare it for storage in the database.

[0066] The data storage unit analyzes information entered by the reception unit and stores the work details and knowledge in a database. The data storage unit analyzes information using methods such as text analysis, data mining, and machine learning algorithms. Specifically, the data storage unit further scrutinizes the information analyzed by the generation AI to confirm the consistency and accuracy of the data. It uses text analysis technology to understand the context and meaning of the information and links related data. It uses data mining technology to extract useful patterns and trends from past data and gain new insights. It uses machine learning algorithms to classify and cluster the information, organizing and structuring the data. The data storage unit stores the information in databases such as relational databases and NoSQL databases. Relational databases store information in a table format, and data can be efficiently searched and retrieved using SQL queries. On the other hand, NoSQL databases have excellent scalability and flexibility, and can efficiently store and manage large amounts of data. As a result, the data storage unit can streamline information management throughout the entire system by appropriately analyzing the information provided by the reception unit and storing it in the database.

[0067] The bridging unit analyzes instructions from the system administrator and questions from the operator based on information stored by the storage unit, and provides appropriate answers. The bridging unit uses generative AI to analyze instructions from the system administrator and questions from the operator, and provides appropriate answers. Specifically, the generative AI uses natural language processing technology to analyze input from the system administrator and operator, and understand their intentions and requests. For example, if the system administrator wants to check the progress of a particular project, the generative AI analyzes that request and searches for and retrieves relevant information stored in the storage unit. Also, if the operator faces a technical problem, the generative AI analyzes the problem and provides relevant technical know-how and solutions stored in the storage unit. The bridging unit provides answers based on criteria such as accuracy, relevance, and timeliness. To ensure the accuracy of the answers provided, the generative AI cross-checks multiple sources of information and selects the most reliable information. To ensure relevance, it understands the context and background of the question and provides the most relevant information. To ensure timeliness, it searches for and retrieves information in real time and provides answers quickly. This allows the bridging unit to provide quick and accurate information to the setters and operators, thereby supporting the efficiency of their operations.

[0068] The resolution unit immediately resolves any ambiguities on the operational side based on the answers provided by the bridging unit. The resolution unit uses generative AI to immediately resolve ambiguities on the operational side. Specifically, the generative AI analyzes feedback and additional questions from operators and quickly provides the necessary information. For example, if an operator requests more detailed information regarding the provided answer, the generative AI analyzes the request and searches and retrieves the relevant information stored in the storage unit. Also, if an operator faces a new problem, the generative AI analyzes the problem and provides the optimal solution based on past data and similar cases. The resolution unit provides solutions based on criteria such as real-time response and rapid feedback. The generative AI processes information in real time and provides rapid feedback to operators. This allows operators to respond immediately when problems occur, minimizing disruption to operations. Furthermore, the resolution unit monitors the effectiveness of the provided solutions and makes improvements as needed. For example, it evaluates the accuracy and effectiveness of the solutions based on feedback from operators and adjusts the generative AI algorithm. This allows the resolution unit to always provide the optimal solution and improve the reliability and efficiency of the entire system.

[0069] The reception department inputs information such as the progress of a specific project and technical know-how. For example, the reception department inputs the progress of a specific project. The reception department inputs the project progress based on criteria such as progress reports and milestone achievement status. The reception department inputs technical know-how. The reception department inputs technical know-how based on criteria such as how to use a specific technology and troubleshooting procedures. This allows for the efficient input of project progress and technical know-how.

[0070] The storage unit analyzes the input information and saves the business content and knowledge to a database. The storage unit can analyze information using, for example, text analysis. It can also analyze information using data mining. It can also analyze information using machine learning algorithms. The storage unit saves information to a relational database. It can also save information to a NoSQL database. For example, the storage unit structures and saves information using a relational database. NoSQL databases offer scalability and flexibility, allowing for efficient storage of large amounts of data. This enables efficient storage of business content and knowledge.

[0071] The bridging unit analyzes instructions from the system designer and questions from the operator, and provides appropriate answers. For example, the bridging unit analyzes instructions from the system designer. The bridging unit can also analyze questions from the operator. The bridging unit uses generative AI to analyze instructions from the system designer and questions from the operator. The bridging unit provides answers based on criteria such as accuracy, relevance, and timeliness. For example, the bridging unit analyzes instructions from the system designer and provides appropriate answers. The bridging unit can also analyze questions from the operator and provide appropriate answers. The bridging unit uses generative AI to analyze instructions from the system designer and questions from the operator, and provides appropriate answers. This ensures that important information is smoothly exchanged between the system designer and the operator.

[0072] The solution unit immediately resolves any unclear points on the operational side. For example, the solution unit resolves unclear points on the operational side. The solution unit uses generative AI to resolve unclear points on the operational side. The solution unit provides solutions based on criteria such as real-time response and rapid feedback. For example, the solution unit resolves unclear points on the operational side. The solution unit uses generative AI to resolve unclear points on the operational side. The solution unit provides solutions based on criteria such as real-time response and rapid feedback. This allows for immediate solutions even when unclear points arise on the operational side.

[0073] The reception desk estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. For example, if the user is stressed, the reception desk may delay the input timing to help them relax. If the user is concentrating, the reception desk may speed up the input timing to allow for more efficient information input. If the user is tired, the reception desk may adjust the input timing to allow for breaks. This allows the timing of information input to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception department, when inputting the progress status and technical know-how of a specific project, supplements the input content by referring to data from similar past projects. For example, the reception department can automatically supplement the input status based on past project data. The reception department can also supplement the input content by referring to the technical know-how of similar projects. The reception department can also optimize the input content based on successful examples from past projects. This allows the reception department to supplement the input content by referring to data from similar past projects. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past project data into a generating AI and have the generating AI perform the supplementation of the input content.

[0075] The reception desk provides input guides according to the inputter's level of expertise when they input information. For example, the reception desk may provide a simple input guide for beginners. The reception desk may also provide a detailed input guide for intermediate users. The reception desk may also provide a specialized input guide for advanced users. This ensures that input guides are provided according to the inputter's level of expertise. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the inputter's level of expertise into a generating AI and have the generating AI perform the task of providing input guides.

[0076] The reception desk estimates the user's emotions and determines the priority of the information to be entered based on the estimated emotions. For example, if the user is in a hurry, the reception desk may prioritize the input of important information. If the user is relaxed, the reception desk may also prioritize the input of detailed information. If the user is stressed, the reception desk may also prioritize the input of simple information. This allows the system to determine the priority of the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit prioritizes inputting highly relevant information based on the inputter's geographical location information when information is entered. For example, the reception unit may prioritize inputting relevant information based on the inputter's current location. The reception unit may also refer to the inputter's past location information and prioritize inputting highly relevant information. The reception unit may also analyze the inputter's geographical movement patterns and prioritize inputting the most appropriate information. This allows for the priority input of highly relevant information based on the inputter's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may input the inputter's geographical location information into a generating AI and have the generating AI prioritize highly relevant information.

[0078] The reception unit analyzes the user's social media activity when inputting information and inputs relevant information. For example, the reception unit analyzes the user's social media posts and prompts them to input relevant information. The reception unit can also refer to the activity of the user's social media followers and friends and prompt them to input relevant information. The reception unit can also analyze the user's social media trends and prompt them to input the most relevant information. This allows the reception unit to analyze the user's social media activity and input relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI input the relevant information.

[0079] The storage unit estimates the user's emotions and adjusts the format of the information stored based on the estimated emotions. For example, if the user is relaxed, the storage unit stores the information in a detailed format. If the user is in a hurry, the storage unit can also store the information in a concise format. If the user is stressed, the storage unit can also store the information in a visually easy-to-understand format. This allows the format of the information stored to be adjusted 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 storage unit may be performed using AI or not using AI. For example, the storage unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The storage unit adjusts the level of detail in saving information based on its importance. For example, it saves important information in detail so that it can be easily referenced later. It can also save general information concisely and add details as needed. It can also save less important information in a simplified form to improve storage efficiency. This allows the level of detail in saving information to be adjusted based on its importance. Some or all of the above processes in the storage unit may be performed using AI, for example, or not. For example, the storage unit can input the importance of the information into a generating AI and have the generating AI determine the level of detail in saving.

[0081] The storage unit applies different storage algorithms depending on the category of information during storage. For example, the storage unit may use a compression algorithm to store technical information. The storage unit may also store project progress in a time-series database. The storage unit may also store know-how and knowledge in a searchable format. This allows for the application of different storage algorithms depending on the category of information. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit may input the category of information into a generating AI and have the generating AI execute the application of the storage algorithm.

[0082] The storage unit estimates the user's emotions and determines the priority of information to store based on the estimated emotions. For example, if the user is in a hurry, the storage unit will prioritize storing important information. If the user is relaxed, the storage unit may also prioritize storing detailed information. If the user is stressed, the storage unit may also prioritize storing simple information. This allows the system to determine the priority of information to store according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, or not using AI. For example, the storage unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The storage unit determines the storage priority based on the information submission date when saving. For example, the storage unit saves urgent information immediately and adds details later. The storage unit can also save periodic information according to a schedule. The storage unit can save historical information as an archive and refer to it as needed. This allows the storage unit to determine the storage priority based on the information submission date. Some or all of the above processes in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the information submission date into a generating AI and have the generating AI execute the storage priority.

[0084] The storage unit adjusts the order of saving information based on its relevance during the saving process. For example, the storage unit may prioritize saving highly relevant information so that it can be easily referenced later. The storage unit may also postpone saving less relevant information. The storage unit may also analyze the relevance of information and determine the optimal saving order. This allows the storage order to be adjusted based on the relevance of information. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit may input the relevance of information into a generating AI and have the generating AI execute the saving order.

[0085] The bridging unit estimates the user's emotions and adjusts the way it expresses its responses based on the estimated emotions. For example, if the user is nervous, the bridging unit provides a simple and easily understandable response. If the user is relaxed, the bridging unit may also provide a response that includes detailed information. If the user is in a hurry, the bridging unit may also provide a concise response that gets straight to the point. This allows the bridging unit to adjust the way it expresses its responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the bridging unit may be performed using AI or not using AI. For example, the bridging unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The bridging unit improves the accuracy of its answers by referring to similar past cases when analyzing instructions from the setter and questions from the operator. For example, the bridging unit provides the optimal answer based on similar past cases. The bridging unit can also improve the accuracy of its answers by referring to successful examples of similar cases. The bridging unit can also minimize the risk of answers by analyzing past failures. This allows it to improve the accuracy of answers by referring to similar past cases. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input past similar case data into a generating AI and have the generating AI perform the task of improving the accuracy of answers.

[0087] The bridging unit provides the optimal answer by considering the attribute information of the setter and the operator. For example, the bridging unit provides an appropriate answer by considering the expertise level of the setter and the operator. The bridging unit can also provide the optimal answer by considering the job titles and work content of the setter and the operator. The bridging unit can also provide the optimal answer by referring to the past interactions between the setter and the operator. This allows the bridging unit to provide the optimal answer by considering the attribute information of the setter and the operator. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input the attribute information of the setter and the operator into a generating AI and have the generating AI perform the task of providing the optimal answer.

[0088] The bridging unit estimates the user's emotions and prioritizes responses based on the estimated emotions. For example, if the user is in a hurry, the bridging unit will prioritize important responses. If the user is relaxed, the bridging unit may also prioritize detailed responses. If the user is stressed, the bridging unit may also prioritize simple responses. This allows the system to prioritize responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the bridging unit may be performed using AI or not using AI. For example, the bridging unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The bridging unit analyzes instructions from the programmer and questions from the operator, taking geographical distribution into consideration when providing answers. For example, the bridging unit considers the geographical locations of the programmer and the operator to provide the optimal answer. The bridging unit can also consider geographical constraints to provide feasible answers. The bridging unit can also analyze geographical characteristics to provide the optimal answer. This allows the bridging unit to provide the optimal answer while considering geographical distribution. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input geographical distribution data into a generating AI and have the generating AI perform the task of providing the optimal answer.

[0090] The bridging unit improves the accuracy of its answers by referring to relevant literature when analyzing instructions from the setter and questions from the operator. For example, the bridging unit provides the optimal answer based on relevant literature. The bridging unit can also improve the accuracy of its answers by referring to the latest research findings. The bridging unit can also provide answers that include citations of relevant literature. This allows for improved accuracy of answers by referring to relevant literature. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the answers.

[0091] The solution unit estimates the user's emotions and adjusts how it provides solutions based on the estimated emotions. For example, if the user is nervous, the solution unit provides a simple and easily understandable solution. If the user is relaxed, the solution unit may also provide a solution that includes detailed information. If the user is in a hurry, the solution unit may also provide a concise solution that gets straight to the point. This allows the solution to be tailored according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the solution unit may be performed using AI or not using AI. For example, the solution unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The solution unit provides the optimal solution by referring to past solutions when resolving unclear points on the operational side. For example, the solution unit provides the optimal solution based on past solutions. The solution unit can also improve the accuracy of the solution by referring to successful cases of similar cases. The solution unit can also minimize the risks of the solution by analyzing past failure cases. This allows it to provide the optimal solution by referring to past solutions. Some or all of the above processes in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input past solution data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0093] The solution unit provides the optimal solution by considering the operator's attribute information. For example, the solution unit provides an appropriate solution by considering the operator's level of expertise. The solution unit can also provide the optimal solution by considering the operator's position and job duties. The solution unit can also provide the optimal solution by referring to the operator's past interactions. This allows the solution unit to provide the optimal solution by considering the operator's attribute information. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input the operator's attribute information into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0094] The solution unit estimates the user's emotions and determines the priority of solutions based on the estimated emotions. For example, if the user is in a hurry, the solution unit will prioritize providing important solutions. If the user is relaxed, the solution unit may also prioritize providing detailed solutions. If the user is stressed, the solution unit may also prioritize providing simple solutions. This allows the solution unit to prioritize solutions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the solution unit may be performed using AI, for example, or not using AI. For example, the solution unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The solution unit, when resolving unclear points on the operational side, provides solutions while considering geographical distribution. For example, the solution unit considers the geographical location of the operators and provides the optimal solution. The solution unit can also consider geographical constraints and provide feasible solutions. The solution unit can also analyze geographical characteristics and provide the optimal solution. This allows the solution unit to provide the optimal solution while considering geographical distribution. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input geographical distribution data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0096] The solution unit improves the accuracy of its solutions by referring to relevant literature when resolving unclear points on the operational side. For example, the solution unit provides the optimal solution based on relevant literature. The solution unit can also improve the accuracy of its solutions by referring to the latest research results. The solution unit can also provide solutions that include citations of relevant literature. This allows for improvement of the accuracy of the solutions by referring to relevant literature. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the accuracy of the solutions.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The reception desk can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is stressed, the input timing can be delayed to help them relax. If the user is concentrating, the input timing can be sped up to allow for more efficient information input. Furthermore, if the user is tired, the input timing can be adjusted to allow for breaks. This allows the timing of information input to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The reception department can supplement input data from similar past projects when entering the progress status and technical know-how of a specific project. For example, it can automatically supplement the input status based on past project data. It can also supplement the input content by referring to the technical know-how of similar projects. Furthermore, it can optimize the input content based on successful case studies from past projects. This allows for supplementation of input content by referring to data from similar past projects. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input past project data into a generating AI and have the generating AI perform the supplementation of the input content.

[0100] The storage unit can estimate the user's emotions and adjust the format of the information stored based on the estimated emotions. For example, if the user is relaxed, the information can be stored in a detailed format. If the user is in a hurry, the information can be stored in a concise format. Furthermore, if the user is stressed, the information can be stored in a visually easy-to-understand format. This allows the format of the information stored to be adjusted 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 storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The bridging unit can improve the accuracy of its responses by referring to similar past cases when analyzing instructions from the setter and questions from the operator. For example, it can provide the optimal response based on similar past cases. It can also improve the accuracy of its responses by referring to successful cases of similar situations. Furthermore, it can minimize the risk of responses by analyzing past failures. This allows for improved response accuracy by referring to similar past cases. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input past similar case data into a generating AI and have the generating AI perform the task of improving the accuracy of its responses.

[0102] The solution unit can provide the optimal solution by referring to past solutions when resolving unclear points on the operational side. For example, it can provide the optimal solution based on past solutions. It can also improve the accuracy of the solution by referring to successful cases of similar cases. Furthermore, it can minimize the risks of the solution by analyzing past failure cases. This allows it to provide the optimal solution by referring to past solutions. Some or all of the above processing in the solution unit may be performed using AI, for example, or without AI. For example, the solution unit can input past solution data into a generating AI and have the generating AI perform the task of providing the optimal solution.

[0103] The reception desk can provide input guides to users according to their level of expertise when they input information. For example, it can provide simple input guides for beginners, detailed input guides for intermediate users, and even specialized input guides for advanced users. This ensures that input guides are tailored to the user's level of expertise. Some or all of the above-described processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's level of expertise into a generating AI and have the generating AI provide the input guides.

[0104] The bridging unit can estimate the user's emotions and adjust the way it expresses its responses based on those emotions. For example, if the user is nervous, it can provide a simple and easily understandable response. If the user is relaxed, it can provide a response that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a concise response that gets straight to the point. This allows the bridging unit to adjust the way it expresses its responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without AI. For example, the bridging unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0105] The solution unit can estimate the user's emotions and adjust how it provides solutions based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easily understandable solution. If the user is relaxed, it can provide a solution that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise solution that gets straight to the point. This allows the solution to be tailored 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 solution unit may be performed using AI, for example, or not using AI. For example, the solution unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0106] The storage unit can adjust the level of detail in saving information based on its importance. For example, important information can be saved in detail for easy reference later. General information can be saved concisely, with details added as needed. Furthermore, less important information can be saved in a simplified form to improve storage efficiency. This allows the level of detail in saving information to be adjusted based on its importance. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of the information into a generating AI and have the generating AI determine the level of detail in saving.

[0107] The bridging unit can provide the optimal answer by considering the attribute information of the setter and the operator. For example, it can provide an appropriate answer by considering the level of expertise of the setter and the operator. It can also provide the optimal answer by considering the job titles and work content of the setter and the operator. Furthermore, it can provide the optimal answer by referring to the past interactions between the setter and the operator. This allows the bridging unit to provide the optimal answer by considering the attribute information of the setter and the operator. Some or all of the above processing in the bridging unit may be performed using AI, for example, or without using AI. For example, the bridging unit can input the attribute information of the setter and the operator into a generating AI and have the generating AI perform the task of providing the optimal answer.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The reception department inputs information about the work and its knowledge. For example, it inputs information such as the progress of a specific project or technical know-how. The reception department uses a generation AI to analyze the input information and saves the work details and knowledge to a database. Step 2: The storage unit analyzes the information entered by the reception unit and saves the work details and knowledge to a database. The storage unit analyzes the information using text analysis, data mining, machine learning algorithms, etc., and saves the information to a database such as a relational database or a NoSQL database. Step 3: The bridging unit analyzes instructions from the programmer and questions from the operator based on the information stored by the storage unit and provides appropriate answers. The bridging unit uses generating AI to analyze instructions from the programmer and questions from the operator and provides appropriate answers based on criteria such as accuracy, relevance, and timeliness. Step 4: The Resolution Unit immediately resolves any ambiguities on the operational side based on the answers provided by the Bridging Unit. The Resolution Unit uses generative AI to provide solutions based on criteria such as real-time response and rapid feedback.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the reception unit, storage unit, bridging unit, and resolution unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit inputs business content and knowledge using the reception device 38 of the smart device 14 and transmits the information to the generating AI by the control unit 46A. The storage unit analyzes the information using the specific processing unit 290 of the data processing unit 12 and stores it in the database 24. The bridging unit analyzes instructions from the setter and questions from the operator using the specific processing unit 290 of the data processing unit 12 and provides appropriate answers. The resolution unit immediately resolves any unclear points on the operator's side using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the reception unit, storage unit, bridging unit, and resolution unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit inputs work content and knowledge using the microphone 238 of the smart glasses 214 and transmits the information to the generating AI by the control unit 46A. The storage unit analyzes the information using the specific processing unit 290 of the data processing unit 12 and stores it in the database 24. The bridging unit analyzes instructions from the setter and questions from the operator using the specific processing unit 290 of the data processing unit 12 and provides appropriate answers. The resolution unit immediately resolves any unclear points on the operator's side using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the reception unit, storage unit, bridging unit, and resolution unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit inputs work content and knowledge using the microphone 238 of the headset terminal 314 and transmits the information to the generating AI by the control unit 46A. The storage unit analyzes the information using the specific processing unit 290 of the data processing unit 12 and stores it in the database 24. The bridging unit analyzes instructions from the setter and questions from the operator using the specific processing unit 290 of the data processing unit 12 and provides appropriate answers. The resolution unit immediately resolves any unclear points on the operator's side using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, storage unit, bridging unit, and resolution unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit inputs work content and knowledge using the microphone 238 of the robot 414 and transmits the information to the generating AI by the control unit 46A. The storage unit analyzes the information using the specific processing unit 290 of the data processing unit 12 and stores it in the database 24. The bridging unit analyzes instructions from the setter and questions from the operator using the specific processing unit 290 of the data processing unit 12 and provides appropriate answers. The resolution unit immediately resolves any unclear points on the operator's side using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The reception desk where you input job details and knowledge, The storage unit analyzes the information entered by the reception unit and saves the work details and knowledge to a database. Based on the information stored by the aforementioned storage unit, a bridging unit analyzes instructions from the setter and questions from the operator and provides appropriate answers. The system includes a resolution unit that immediately resolves any unclear points on the operational side based on the answers provided by the bridging unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Enter information such as the progress of a specific project and technical know-how. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned storage unit is The system analyzes the input information and saves the work details and knowledge to a database. The system described in Appendix 1, characterized by the features described herein. (Note 4) The bridging section is, We analyze instructions from the programmer and questions from the operator to provide appropriate answers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned solution unit is The operational side will immediately resolve any unclear points. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering the progress status or technical know-how of a specific project, the system supplements the input by referring to data from similar past projects. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering information, provide input guides according to the inputter's level of expertise. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information based on the geographical location of the person entering the information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned storage unit is It estimates the user's emotions and adjusts the format of the information stored based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned storage unit is When saving, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned storage unit is When saving, different saving algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned storage unit is It estimates the user's emotions and determines the priority of information to store based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned storage unit is When saving data, prioritize saving it based on when it was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned storage unit is When saving, adjust the saving order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The bridging section is, It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The bridging section is, When analyzing instructions from the programmer or questions from the operator, we improve the accuracy of our answers by referring to similar past cases. The system described in Appendix 1, characterized by the features described herein. (Note 20) The bridging section is, We will provide the optimal answer by considering the attribute information of the setter and the operator. The system described in Appendix 1, characterized by the features described herein. (Note 21) The bridging section is, The system estimates the user's emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The bridging section is, When analyzing instructions from the system administrator or questions from the operator, we provide answers that take geographical distribution into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The bridging section is, When analyzing instructions from the system administrator and questions from the operator, we refer to relevant literature to improve the accuracy of our answers. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned solution unit is It estimates the user's emotions and adjusts how solutions are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned solution unit is When resolving unclear points on the operational side, we refer to past case studies to provide the optimal solution. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned solution unit is We provide the optimal solution by taking into account the attributes of the operators. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned solution unit is It estimates the user's emotions and determines the priority of solutions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned solution unit is When resolving unclear points on the operational side, solutions are provided that take geographical distribution into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned solution unit is When resolving unclear points on the operational side, we refer to relevant literature to improve the accuracy of the solutions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you input job details and knowledge, The storage unit analyzes the information entered by the reception unit and saves the work details and knowledge to a database. Based on the information stored by the aforementioned storage unit, a bridging unit analyzes instructions from the setter and questions from the operator and provides appropriate answers. The system includes a resolution unit that immediately resolves any unclear points on the operational side based on the answers provided by the bridging unit. A system characterized by the following features.

2. The aforementioned reception unit is Enter information such as the progress of a specific project and technical know-how. The system according to feature 1.

3. The aforementioned storage unit is The system analyzes the input information and saves the work details and knowledge to a database. The system according to feature 1.

4. The bridging section is, We analyze instructions from the programmer and questions from the operator to provide appropriate answers. The system according to feature 1.

5. The aforementioned solution unit is The operational side will immediately resolve any unclear points. The system according to feature 1.

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

7. The aforementioned reception unit is When entering the progress status or technical know-how of a specific project, the system supplements the input by referring to data from similar past projects. The system according to feature 1.

8. The aforementioned reception unit is When entering information, provide input guides according to the inputter's level of expertise. The system according to feature 1.

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