System

Abstract

An object of a system according to an embodiment is to provide a practical English conversation based on the privacy of a company.SOLUTION: A system includes an in-company information understanding part, an English conversation simulation part, and a business scenario providing part. The inside-company intelligence understanding unit understands inside-company intelligence. An English conversation simulation part performs English conversation with the user on the basis of the internal information of the enterprise understood by the internal information understanding part. The business scenario providing unit provides a practical business scenario.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had the problem of making it difficult to practice practical English conversation based on the internal circumstances of a company.

[0005] The system according to the embodiment aims to provide practical English conversation based on the internal circumstances of a company. [Means for solving the problem]

[0006] The system according to the embodiment includes a corporate internal affairs understanding unit, an English conversation simulation unit, and a business scenario providing unit. The corporate internal affairs understanding unit understands the internal affairs of a company. The English conversation simulation unit engages in English conversation with a user based on the internal affairs of the company understood by the corporate internal affairs understanding unit. The business scenario providing unit provides practical business scenarios. [Effects of the Invention]

[0007] The system according to the embodiment can provide practical English conversation based on the internal circumstances of a company. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

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

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The English conversation learning system according to an embodiment of the present invention is a system in which a generation AI that understands the internal affairs of a company converses with a user in English and helps the user acquire practical business English. This allows the English conversation learning system to understand the internal affairs of a company and provide the user with practical business English based on that understanding.

[0029] An English conversation learning system according to an embodiment includes a corporate information understanding unit, an English conversation simulation unit, and a business scenario providing unit. The corporate information understanding unit understands the internal affairs of a company. For example, the corporate information understanding unit analyzes the company's business processes and the terminology used. The corporate information understanding unit can also analyze the content of interactions with business partners. The corporate information understanding unit can also analyze the content of internal documents and emails within a company. For example, the corporate information understanding unit analyzes project reports and inter-departmental emails to understand the progress of a project and the division of roles between departments. The corporate information understanding unit analyzes business manuals and procedure manuals to understand the specific procedures and tools used for each task. The corporate information understanding unit analyzes daily business communications and meeting minutes to understand the flow of work and important decisions. The English conversation simulation unit engages in English conversation with a user based on the internal affairs of the company understood by the corporate information understanding unit. For example, if a user asks a question such as, "What phrases can I use in meetings with business partners?", the English conversation simulation unit provides appropriate business English phrases in response to the question. The English conversation simulation unit can also point out the user's pronunciation and grammar errors and teach correct expressions. The English conversation simulation unit can also analyze the user's past conversation history, identify individual weaknesses, and provide focused instruction. For example, it can extract pronunciation and grammar errors and suggest areas for improvement. It can analyze the frequency of use of specific phrases and expressions and suggest appropriate alternative expressions. It can analyze the flow and structure of conversations and provide instruction on how to conduct more natural conversations. The business scenario providing unit provides practical business scenarios. For example, the business scenario providing unit generates scenarios such as meeting progress, presentation preparation, and negotiations with business partners, and conducts English conversations based on these scenarios. The business scenario providing unit can also add scenarios based on past successes and failures of actual companies. For example, it can reflect the success factors of a specific project in the scenario. It can reflect the causes of failure and areas for improvement in the scenario. It can add scenarios based on successes and failures to allow users to learn how to respond to realistic situations. In this way, the English conversation learning system according to the embodiment can understand the internal workings of a company and acquire practical business English based on that understanding.For example, users can learn English expressions that are useful in situations such as meetings with business partners, presentations, negotiations, etc. Furthermore, the feedback and customized learning plans provided by the generative AI allow users to efficiently improve their English skills.

[0030] The Corporate Information Understanding Department analyzes the contents of a company's internal documents and emails to understand detailed business processes, including non-public information. In the Corporate Information Understanding Department, for example, the Generative AI analyzes the contents of a company's internal documents and emails to understand the details of business processes. For example, it analyzes project reports and emails between departments to understand the progress of a project and the division of roles between departments. The Generative AI also analyzes a company's internal documents to understand the details of business processes. For example, it analyzes business manuals and procedure manuals to understand the specific steps for each task and the tools used. The Generative AI also analyzes a company's internal emails to understand the details of business processes. For example, it analyzes daily business communications and meeting minutes to understand the flow of work and important decisions. This allows for a detailed understanding of a company's business processes, enabling more practical English conversation skills.

[0031] The Corporate Information Understanding Department can analyze the content of interviews with company employees to understand actual work flows and issues. In the Corporate Information Understanding Department, for example, a generative AI analyzes the content of interviews with company employees to understand work flows and issues. For example, it extracts from the interviews the problems and areas for improvement that employees face on a daily basis, and uses this to improve business processes. The generative AI also analyzes the content of interviews with employees to understand work flows and issues. For example, it extracts from the interviews the factors that led to success or failure in a particular project, and uses this in future project management. The generative AI also analyzes the content of interviews with employees to understand work flows and issues. For example, it extracts employee opinions and suggestions from the interviews, and uses this to improve business processes and introduce new ideas. In this way, by analyzing the content of employee interviews, it is possible to understand actual work flows and issues.

[0032] When understanding a company's internal affairs, the Corporate Internal Affairs Understanding Department simultaneously analyzes information on competitors, enabling it to grasp industry-wide trends and best practices. For example, when the Generative AI understands a company's internal affairs, it simultaneously analyzes information on competitors to grasp industry-wide trends. For example, it analyzes competitors' performance reports and market analysis reports to understand industry trends. Furthermore, when the Generative AI understands a company's internal affairs, it simultaneously analyzes information on competitors to grasp best practices. For example, it analyzes competitors' success stories and business processes to help improve its own business operations. Furthermore, when the Generative AI understands a company's internal affairs, it simultaneously analyzes information on competitors to grasp industry-wide trends and best practices. For example, it analyzes the features of competitors' products and services and uses this in its own product development. In this way, by analyzing competitors' information, it is possible to grasp industry-wide trends and best practices.

[0033] The Corporate Information Understanding Department can analyze feedback and reviews from a company's customers and identify areas for business improvement from the customer's perspective. In the Corporate Information Understanding Department, for example, the generation AI analyzes feedback and reviews from a company's customers and identifies areas for business improvement from the customer's perspective. For example, it analyzes customer opinions and ratings and extracts areas for improvement in products and services. The generation AI also analyzes feedback and reviews from a company's customers and identifies areas for business improvement from the customer's perspective. For example, it analyzes customer complaints and requests and proposes measures to improve customer satisfaction. The generation AI also analyzes feedback and reviews from a company's customers and identifies areas for business improvement from the customer's perspective. For example, it analyzes customer reviews and uses them to improve the quality of products and services. In this way, by analyzing customer feedback, it is possible to identify areas for business improvement from the customer's perspective.

[0034] The English conversation simulation unit can analyze the user's past conversation history, identify individual weak points, and provide focused instruction. In the English conversation simulation unit, for example, the generation AI analyzes the user's past conversation history, identifies individual weak points, and provides focused instruction. For example, it extracts pronunciation and grammar errors and suggests areas for improvement. The generation AI also analyzes the user's past conversation history, identifies individual weak points, and provides focused instruction. For example, it analyzes the frequency of use of specific phrases and expressions and suggests appropriate alternative expressions. The generation AI also analyzes the user's past conversation history, identifies individual weak points, and provides focused instruction. For example, it analyzes the flow and structure of the conversation and provides instruction on how to conduct a more natural conversation. In this way, by analyzing the user's past conversation history, it is possible to identify individual weak points and provide focused instruction.

[0035] The English conversation simulation unit can present specific example sentences and situations when pointing out a user's pronunciation or grammar errors, and provide practical methods for improvement. For example, when the generation AI points out a user's pronunciation or grammar errors, the English conversation simulation unit presents specific example sentences and provides practical methods for improvement. For example, it presents examples of incorrect pronunciation and examples of correct pronunciation by comparing them. Furthermore, when the generation AI points out a user's pronunciation or grammar errors, it presents specific situations and provides practical methods for improvement. For example, it provides guidance using situations such as business meetings and presentations. Furthermore, when the generation AI points out a user's pronunciation or grammar errors, it presents specific example sentences and situations and provides practical methods for improvement. For example, it provides guidance using specific phrases from everyday conversations or business situations. In this way, by presenting specific example sentences and situations, it is possible to provide practical methods for improvement.

[0036] The English conversation simulation unit can improve international business skills by simultaneously teaching business manners and customs of different cultures when conversing in English with the user. For example, when the generating AI converses in English with the user, the English conversation simulation unit teaches business manners and customs of different cultures. For example, it explains the differences between American and Japanese business manners and provides guidance on appropriate ways to respond. The generating AI can also teach business manners and customs of different cultures when conversing in English with the user. For example, it can explain the differences between European and Asian business cultures and provide guidance on appropriate communication methods. The generating AI can also teach business manners and customs of different cultures when conversing in English with the user. For example, it can provide specific guidance on how to greet, exchange business cards, and conduct a meeting. This allows international business skills to be improved by teaching business manners and customs of different cultures.

[0037] When evaluating a user's English conversation skills, the English conversation simulation unit can compare the content of the conversation with other users and present a relative skill level. For example, when the generation AI evaluates a user's English conversation skills, the English conversation simulation unit compares the content of the conversation with other users and presents a relative skill level. For example, the content of conversations in the same scenario is compared and the difference in skill is clearly indicated. Furthermore, when the generation AI evaluates a user's English conversation skills, it compares the content of the conversation with other users and presents a relative skill level. For example, the skill level is evaluated based on the accuracy of pronunciation and grammar. Furthermore, when the generation AI evaluates a user's English conversation skills, it compares the content of the conversation with other users and presents a relative skill level. For example, the skill level is evaluated based on the fluency and naturalness of the conversation. In this way, by comparing the content of the conversation with other users, a relative skill level can be presented.

[0038] The business scenario providing unit can add scenarios based on past success stories and failure stories of actual companies to provide more realistic learning. For example, the business scenario providing unit adds scenarios based on past success stories of actual companies to the business scenarios provided by the generation AI. For example, it reflects the success factors of a specific project in the scenario. In addition, it adds scenarios based on past failure stories of actual companies to the business scenarios provided by the generation AI. For example, it reflects the causes of failure and areas for improvement in the scenario. In addition, it adds scenarios based on success stories and failure stories to the business scenarios provided by the generation AI so that users can learn how to respond in realistic situations. For example, it incorporates specific problem-solving techniques into the scenario. In this way, by adding scenarios based on past success stories and failure stories of actual companies, more realistic learning can be provided.

[0039] The business scenario providing unit can customize according to the user's work content and position, and generate individually tailored scenarios. For example, the business scenario providing unit customizes the business scenarios provided by the generation AI according to the user's work content. For example, it generates scenarios for sales representatives and scenarios for engineers. It also customizes the business scenarios provided by the generation AI according to the user's position. For example, it generates scenarios for managers and scenarios for new employees. It also customizes the business scenarios provided by the generation AI according to the user's work content and position, and generates individually tailored scenarios. For example, it generates a scenario related to a specific project. In this way, it is possible to generate individually tailored scenarios by customizing according to the user's work content and position.

[0040] The business scenario providing unit adds scenarios for different industries and occupations, allowing the user to acquire skills that can be used in a wide range of business situations. The business scenario providing unit, for example, adds scenarios for different industries to the business scenarios provided by the generation AI. For example, it generates scenarios for the IT industry, manufacturing, service industry, etc. It also adds scenarios for different occupations to the business scenarios provided by the generation AI. For example, it generates scenarios for sales, technical, and management positions. It also adds scenarios for different industries and occupations to the business scenarios provided by the generation AI, allowing the user to acquire skills that can be used in a wide range of business situations. For example, it generates a negotiation scenario with a business partner in a different industry. In this way, by adding scenarios for different industries and occupations, the user can acquire skills that can be used in a wide range of business situations.

[0041] The business scenario providing unit can add scenarios based on problems and issues that the user has actually encountered, allowing the user to learn practical solutions. For example, the business scenario providing unit adds scenarios based on problems that the user has actually encountered to business scenarios provided by the generation AI. For example, it reflects issues in a specific project in the scenario. It also adds scenarios based on issues that the user has actually encountered to business scenarios provided by the generation AI. For example, it generates scenarios for customer service and complaint handling. It also adds scenarios based on problems and issues that the user has actually encountered to business scenarios provided by the generation AI, allowing the user to learn practical solutions. For example, it incorporates problem-solving techniques and communication skills into the scenario. In this way, by adding scenarios based on problems and issues that the user has actually encountered, it is possible to allow the user to learn practical solutions.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The English conversation learning system can monitor the user's learning progress in real time and suggest breaks at appropriate times. For example, if the user has been studying for a long period of time, the system will prompt the user to take a short break. Also, if the system determines that the user's concentration is declining, it can suggest simple exercises or stretches to refresh themselves. Furthermore, it can suggest an optimal study schedule based on the user's study history, supporting efficient learning.

[0044] The English conversation learning system can be customized to suit the user's learning style. For example, for users who prefer visual learning, learning materials that make extensive use of diagrams and graphs can be provided. For users who prefer auditory learning, audio learning materials and podcasts can be provided. Furthermore, for users who prefer practical learning, role-playing scenarios can be provided to simulate actual business situations.

[0045] English conversation learning systems can incorporate gamification elements to maintain users' motivation to learn. For example, they can introduce a system that allows users to earn points and badges according to their learning progress. They can also provide a ranking function that encourages competition and cooperation with other users. Furthermore, the system can increase user motivation by providing congratulatory messages and rewards when certain goals are achieved.

[0046] The English conversation learning system can provide an individualized learning plan based on the user's learning history. For example, it can analyze past learning content and progress to identify the user's weaknesses. It can also customize the plan to suit the user's learning style and pace, proposing the optimal learning plan. It can also provide learning content tailored to the user's goals, supporting efficient learning.

[0047] An English conversation learning system can visualize a user's learning progress and provide feedback to maintain motivation. For example, it can display learning progress in graphs and charts, allowing users to see their own improvement. Furthermore, the system can motivate users by providing congratulatory messages and rewards when specific goals are achieved. Furthermore, it can provide a ranking function that encourages competition and cooperation with other users.

[0048] English conversation learning systems can be customized to suit a user's learning style and support effective learning. For example, for users who prefer visual learning, learning materials that make extensive use of diagrams and graphs can be provided. For users who prefer auditory learning, audio learning materials and podcasts can be provided. Furthermore, for users who prefer practical learning, role-playing scenarios can be provided to simulate actual business situations.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The Corporate Internal Understanding Department understands the internal affairs of the company. For example, the Corporate Internal Understanding Department analyzes the company's business processes and the terminology used. The Corporate Internal Understanding Department can also analyze the content of interactions with business partners. Furthermore, they analyze the content of the company's internal documents and emails, and analyze project reports and emails between departments to understand the progress of projects and the division of roles between departments. They analyze work manuals and procedure manuals to understand the specific procedures for each task and the tools used. They analyze daily business communications and meeting minutes to understand the flow of work and important decisions. Step 2: The English Conversation Simulation Unit engages in English conversation with the user based on the company's internal circumstances understood by the Corporate Internal Understanding Unit. For example, if the user asks, "What phrases can I use in a meeting with a client?", the unit will provide appropriate business English phrases in response to the question. It can also point out the user's pronunciation and grammar errors and teach correct expressions. It also analyzes the user's past conversation history, identifies individual weaknesses, and provides focused instruction. For example, it can extract pronunciation and grammar errors and suggest areas for improvement. It can analyze the frequency of use of specific phrases and expressions and suggest appropriate alternative expressions. It can also analyze the flow and structure of the conversation, providing instruction on how to conduct the conversation more naturally. Step 3: The business scenario provider provides practical business scenarios. For example, it generates scenarios for proceeding with a meeting, preparing a presentation, negotiating with a client, etc., and conducts English conversations based on these scenarios. It is also possible to add scenarios based on past successes and failures of actual companies. For example, it can reflect the success factors of a specific project in a scenario, or the causes of failure and areas for improvement in a scenario. Scenarios based on successes and failures can be added to allow users to learn how to respond to realistic situations.

[0051] (Example 2) The English conversation learning system according to an embodiment of the present invention is a system in which a generation AI that understands the internal affairs of a company converses with a user in English and helps the user acquire practical business English. This allows the English conversation learning system to understand the internal affairs of a company and provide the user with practical business English based on that understanding.

[0052] An English conversation learning system according to an embodiment includes a corporate information understanding unit, an English conversation simulation unit, and a business scenario providing unit. The corporate information understanding unit understands the internal affairs of a company. For example, the corporate information understanding unit analyzes the company's business processes and the terminology used. The corporate information understanding unit can also analyze the content of interactions with business partners. The corporate information understanding unit can also analyze the content of internal documents and emails within a company. For example, the corporate information understanding unit analyzes project reports and inter-departmental emails to understand the progress of a project and the division of roles between departments. The corporate information understanding unit analyzes business manuals and procedure manuals to understand the specific procedures and tools used for each task. The corporate information understanding unit analyzes daily business communications and meeting minutes to understand the flow of work and important decisions. The English conversation simulation unit engages in English conversation with a user based on the internal affairs of the company understood by the corporate information understanding unit. For example, if a user asks a question such as, "What phrases can I use in meetings with business partners?", the English conversation simulation unit provides appropriate business English phrases in response to the question. The English conversation simulation unit can also point out the user's pronunciation and grammar errors and teach correct expressions. The English conversation simulation unit can also analyze the user's past conversation history, identify individual weaknesses, and provide focused instruction. For example, it can extract pronunciation and grammar errors and suggest areas for improvement. It can analyze the frequency of use of specific phrases and expressions and suggest appropriate alternative expressions. It can analyze the flow and structure of conversations and provide instruction on how to conduct more natural conversations. The business scenario providing unit provides practical business scenarios. For example, the business scenario providing unit generates scenarios such as meeting progress, presentation preparation, and negotiations with business partners, and conducts English conversations based on these scenarios. The business scenario providing unit can also add scenarios based on past successes and failures of actual companies. For example, it can reflect the success factors of a specific project in the scenario. It can reflect the causes of failure and areas for improvement in the scenario. It can add scenarios based on successes and failures to allow users to learn how to respond to realistic situations. In this way, the English conversation learning system according to the embodiment can understand the internal workings of a company and acquire practical business English based on that understanding.For example, users can learn English expressions that are useful in situations such as meetings with business partners, presentations, negotiations, etc. Furthermore, the feedback and customized learning plans provided by the generative AI allow users to efficiently improve their English skills.

[0053] The Corporate Information Understanding Department analyzes the contents of a company's internal documents and emails to understand detailed business processes, including non-public information. In the Corporate Information Understanding Department, for example, the Generative AI analyzes the contents of a company's internal documents and emails to understand the details of business processes. For example, it analyzes project reports and emails between departments to understand the progress of a project and the division of roles between departments. The Generative AI also analyzes a company's internal documents to understand the details of business processes. For example, it analyzes business manuals and procedure manuals to understand the specific steps for each task and the tools used. The Generative AI also analyzes a company's internal emails to understand the details of business processes. For example, it analyzes daily business communications and meeting minutes to understand the flow of work and important decisions. This allows for a detailed understanding of a company's business processes, enabling more practical English conversation skills.

[0054] The Corporate Information Understanding Department can analyze the content of interviews with company employees to understand actual work flows and issues. In the Corporate Information Understanding Department, for example, a generative AI analyzes the content of interviews with company employees to understand work flows and issues. For example, it extracts from the interviews the problems and areas for improvement that employees face on a daily basis, and uses this to improve business processes. The generative AI also analyzes the content of interviews with employees to understand work flows and issues. For example, it extracts from the interviews the factors that led to success or failure in a particular project, and uses this in future project management. The generative AI also analyzes the content of interviews with employees to understand work flows and issues. For example, it extracts employee opinions and suggestions from the interviews, and uses this to improve business processes and introduce new ideas. In this way, by analyzing the content of employee interviews, it is possible to understand actual work flows and issues.

[0055] The corporate internal information understanding department can use the emotion estimation function to analyze the emotions of the company's employees and identify stress points in their work and sources of motivation. The corporate internal information understanding department, for example, uses the emotion estimation function to analyze the emotions of employees and identify stress points in their work. For example, it analyzes the content of employees' emails and chats to identify situations and causes of stress. It also uses the emotion estimation function to analyze employees' emotions and identify sources of motivation. For example, it analyzes employees' feedback and evaluation comments to identify factors that increase motivation. It also uses the emotion estimation function to analyze employees' emotions and identify stress points in their work and sources of motivation. For example, it analyzes employees' facial expressions and voices to monitor changes in their emotions in real time. In this way, it is possible to identify stress points in their work and sources of motivation by analyzing employees' emotions.

[0056] When understanding a company's internal affairs, the Corporate Internal Affairs Understanding Department simultaneously analyzes information on competitors, enabling it to grasp industry-wide trends and best practices. For example, when the Generative AI understands a company's internal affairs, it simultaneously analyzes information on competitors to grasp industry-wide trends. For example, it analyzes competitors' performance reports and market analysis reports to understand industry trends. Furthermore, when the Generative AI understands a company's internal affairs, it simultaneously analyzes information on competitors to grasp best practices. For example, it analyzes competitors' success stories and business processes to help improve its own business operations. Furthermore, when the Generative AI understands a company's internal affairs, it simultaneously analyzes information on competitors to grasp industry-wide trends and best practices. For example, it analyzes the features of competitors' products and services and uses this in its own product development. In this way, by analyzing competitors' information, it is possible to grasp industry-wide trends and best practices.

[0057] The Corporate Information Understanding Department can analyze feedback and reviews from a company's customers and identify areas for business improvement from the customer's perspective. In the Corporate Information Understanding Department, for example, the generation AI analyzes feedback and reviews from a company's customers and identifies areas for business improvement from the customer's perspective. For example, it analyzes customer opinions and ratings and extracts areas for improvement in products and services. The generation AI also analyzes feedback and reviews from a company's customers and identifies areas for business improvement from the customer's perspective. For example, it analyzes customer complaints and requests and proposes measures to improve customer satisfaction. The generation AI also analyzes feedback and reviews from a company's customers and identifies areas for business improvement from the customer's perspective. For example, it analyzes customer reviews and uses them to improve the quality of products and services. In this way, by analyzing customer feedback, it is possible to identify areas for business improvement from the customer's perspective.

[0058] The corporate internal information understanding department can use the emotion estimation function to analyze the emotions of the company's customers and identify areas for improvement in business processes that will lead to increased customer satisfaction. The corporate internal information understanding department, for example, uses the emotion estimation function to analyze customer emotions and identify areas for improvement in business processes that will lead to increased customer satisfaction. For example, it analyzes customer reviews and feedback to understand changes in emotions. It also uses the emotion estimation function to analyze customer emotions and identify areas for improvement in business processes that will lead to increased customer satisfaction. For example, it analyzes customer facial expressions and voices to monitor changes in emotions in real time. It also uses the emotion estimation function to analyze customer emotions and identify areas for improvement in business processes that will lead to increased customer satisfaction. For example, it extracts areas for improvement in business processes based on customer emotion scores. In this way, it is possible to identify areas for improvement in business processes that will lead to increased customer satisfaction by analyzing customer emotions.

[0059] The English conversation simulation unit can analyze the user's past conversation history, identify individual weak points, and provide focused instruction. In the English conversation simulation unit, for example, the generation AI analyzes the user's past conversation history, identifies individual weak points, and provides focused instruction. For example, it extracts pronunciation and grammar errors and suggests areas for improvement. The generation AI also analyzes the user's past conversation history, identifies individual weak points, and provides focused instruction. For example, it analyzes the frequency of use of specific phrases and expressions and suggests appropriate alternative expressions. The generation AI also analyzes the user's past conversation history, identifies individual weak points, and provides focused instruction. For example, it analyzes the flow and structure of the conversation and provides instruction on how to conduct a more natural conversation. In this way, by analyzing the user's past conversation history, it is possible to identify individual weak points and provide focused instruction.

[0060] The English conversation simulation unit can present specific example sentences and situations when pointing out a user's pronunciation or grammar errors, and provide practical methods for improvement. For example, when the generation AI points out a user's pronunciation or grammar errors, the English conversation simulation unit presents specific example sentences and provides practical methods for improvement. For example, it presents examples of incorrect pronunciation and examples of correct pronunciation by comparing them. Furthermore, when the generation AI points out a user's pronunciation or grammar errors, it presents specific situations and provides practical methods for improvement. For example, it provides guidance using situations such as business meetings and presentations. Furthermore, when the generation AI points out a user's pronunciation or grammar errors, it presents specific example sentences and situations and provides practical methods for improvement. For example, it provides guidance using specific phrases from everyday conversations or business situations. In this way, by presenting specific example sentences and situations, it is possible to provide practical methods for improvement.

[0061] The English conversation simulation unit can use the emotion estimation function to analyze the user's emotions during conversation and suggest relaxation methods to reduce tension and anxiety. The English conversation simulation unit, for example, uses the emotion estimation function to analyze the user's emotions during conversation and suggest relaxation methods to reduce tension and anxiety. For example, it can suggest deep breathing or simple exercises to relax. The emotion estimation function can also be used to analyze the user's emotions during conversation and suggest relaxation methods to reduce tension and anxiety. For example, it can provide relaxing music or environmental sounds. The emotion estimation function can also be used to analyze the user's emotions during conversation and suggest relaxation methods to reduce tension and anxiety. For example, it can suggest positive autosuggestion or mental rehearsal methods. In this way, the English conversation simulation unit can analyze the user's emotions during conversation and suggest relaxation methods to reduce tension and anxiety.

[0062] The English conversation simulation unit can improve international business skills by simultaneously teaching business manners and customs of different cultures when conversing in English with the user. For example, when the generating AI converses in English with the user, the English conversation simulation unit teaches business manners and customs of different cultures. For example, it explains the differences between American and Japanese business manners and provides guidance on appropriate ways to respond. The generating AI can also teach business manners and customs of different cultures when conversing in English with the user. For example, it can explain the differences between European and Asian business cultures and provide guidance on appropriate communication methods. The generating AI can also teach business manners and customs of different cultures when conversing in English with the user. For example, it can provide specific guidance on how to greet, exchange business cards, and conduct a meeting. This allows international business skills to be improved by teaching business manners and customs of different cultures.

[0063] When evaluating a user's English conversation skills, the English conversation simulation unit can compare the content of the conversation with other users and present a relative skill level. For example, when the generation AI evaluates a user's English conversation skills, the English conversation simulation unit compares the content of the conversation with other users and presents a relative skill level. For example, the content of conversations in the same scenario is compared and the difference in skill is clearly indicated. Furthermore, when the generation AI evaluates a user's English conversation skills, it compares the content of the conversation with other users and presents a relative skill level. For example, the skill level is evaluated based on the accuracy of pronunciation and grammar. Furthermore, when the generation AI evaluates a user's English conversation skills, it compares the content of the conversation with other users and presents a relative skill level. For example, the skill level is evaluated based on the fluency and naturalness of the conversation. In this way, by comparing the content of the conversation with other users, a relative skill level can be presented.

[0064] The English conversation simulation unit can use the emotion estimation function to analyze the user's emotions during conversation and suggest methods to increase motivation to draw out positive emotions. The English conversation simulation unit, for example, uses the emotion estimation function to analyze the user's emotions during conversation and suggest methods to increase motivation to draw out positive emotions. For example, it asks questions that make the user recall successful experiences. It also uses the emotion estimation function to analyze the user's emotions during conversation and suggest methods to increase motivation to draw out positive emotions. For example, it provides a message praising the user's efforts. It also uses the emotion estimation function to analyze the user's emotions during conversation and suggest methods to increase motivation to draw out positive emotions. For example, it suggests an environment that allows the user to relax. In this way, it is possible to analyze the user's emotions during conversation and suggest methods to increase motivation to draw out positive emotions.

[0065] The business scenario providing unit can add scenarios based on past success stories and failure stories of actual companies to provide more realistic learning. For example, the business scenario providing unit adds scenarios based on past success stories of actual companies to the business scenarios provided by the generation AI. For example, it reflects the success factors of a specific project in the scenario. In addition, it adds scenarios based on past failure stories of actual companies to the business scenarios provided by the generation AI. For example, it reflects the causes of failure and areas for improvement in the scenario. In addition, it adds scenarios based on success stories and failure stories to the business scenarios provided by the generation AI so that users can learn how to respond in realistic situations. For example, it incorporates specific problem-solving techniques into the scenario. In this way, by adding scenarios based on past success stories and failure stories of actual companies, more realistic learning can be provided.

[0066] The business scenario providing unit can customize according to the user's work content and position, and generate individually tailored scenarios. For example, the business scenario providing unit customizes the business scenarios provided by the generation AI according to the user's work content. For example, it generates scenarios for sales representatives and scenarios for engineers. It also customizes the business scenarios provided by the generation AI according to the user's position. For example, it generates scenarios for managers and scenarios for new employees. It also customizes the business scenarios provided by the generation AI according to the user's work content and position, and generates individually tailored scenarios. For example, it generates a scenario related to a specific project. In this way, it is possible to generate individually tailored scenarios by customizing according to the user's work content and position.

[0067] The business scenario providing unit can use the emotion estimation function to analyze the emotions the user feels toward a scenario and provide a scenario that is easy to empathize with emotionally. The business scenario providing unit, for example, uses the emotion estimation function to analyze the emotions the user feels toward a scenario and provide a scenario that is easy to empathize with emotionally. For example, it selects a theme that is likely to interest the user. It also uses the emotion estimation function to analyze the emotions the user feels toward a scenario and provide a scenario that is easy to empathize with emotionally. For example, it generates a scenario that evokes positive emotions in the user. It also uses the emotion estimation function to analyze the emotions the user feels toward a scenario and provide a scenario that is easy to empathize with emotionally. For example, it incorporates characters and stories that the user can easily empathize with into the scenario. This makes it possible to analyze the emotions the user feels toward a scenario and provide a scenario that is easy to empathize with emotionally.

[0068] The business scenario providing unit adds scenarios for different industries and occupations, allowing the user to acquire skills that can be used in a wide range of business situations. The business scenario providing unit, for example, adds scenarios for different industries to the business scenarios provided by the generation AI. For example, it generates scenarios for the IT industry, manufacturing, service industry, etc. It also adds scenarios for different occupations to the business scenarios provided by the generation AI. For example, it generates scenarios for sales, technical, and management positions. It also adds scenarios for different industries and occupations to the business scenarios provided by the generation AI, allowing the user to acquire skills that can be used in a wide range of business situations. For example, it generates a negotiation scenario with a business partner in a different industry. In this way, by adding scenarios for different industries and occupations, the user can acquire skills that can be used in a wide range of business situations.

[0069] The business scenario providing unit can add scenarios based on problems and issues that the user has actually encountered, allowing the user to learn practical solutions. For example, the business scenario providing unit adds scenarios based on problems that the user has actually encountered to business scenarios provided by the generation AI. For example, it reflects issues in a specific project in the scenario. It also adds scenarios based on issues that the user has actually encountered to business scenarios provided by the generation AI. For example, it generates scenarios for customer service and complaint handling. It also adds scenarios based on problems and issues that the user has actually encountered to business scenarios provided by the generation AI, allowing the user to learn practical solutions. For example, it incorporates problem-solving techniques and communication skills into the scenario. In this way, by adding scenarios based on problems and issues that the user has actually encountered, it is possible to allow the user to learn practical solutions.

[0070] The business scenario providing unit can use the emotion estimation function to analyze the emotion the user feels in response to a scenario and provide a scenario for eliciting positive emotions. The business scenario providing unit, for example, uses the emotion estimation function to analyze the emotion the user feels in response to a scenario and provide a scenario for eliciting positive emotions. For example, it incorporates successful experiences into a scenario. It also uses the emotion estimation function to analyze the emotion the user feels in response to a scenario and provide a scenario for eliciting positive emotions. For example, it generates a scenario that makes the user feel a sense of accomplishment. It also uses the emotion estimation function to analyze the emotion the user feels in response to a scenario and provide a scenario for eliciting positive emotions. For example, it generates a scenario that gives the user confidence. In this way, it is possible to analyze the emotion the user feels in response to a scenario and provide a scenario for eliciting positive emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The English conversation learning system can monitor the user's learning progress in real time and suggest breaks at appropriate times. For example, if the user has been studying for a long period of time, the system will prompt the user to take a short break. Also, if the system determines that the user's concentration is declining, it can suggest simple exercises or stretches to refresh themselves. Furthermore, it can suggest an optimal study schedule based on the user's study history, supporting efficient learning.

[0073] The English conversation learning system can be customized to suit the user's learning style. For example, for users who prefer visual learning, learning materials that make extensive use of diagrams and graphs can be provided. For users who prefer auditory learning, audio learning materials and podcasts can be provided. Furthermore, for users who prefer practical learning, role-playing scenarios can be provided to simulate actual business situations.

[0074] English conversation learning systems can incorporate gamification elements to maintain users' motivation to learn. For example, they can introduce a system that allows users to earn points and badges according to their learning progress. They can also provide a ranking function that encourages competition and cooperation with other users. Furthermore, the system can increase user motivation by providing congratulatory messages and rewards when certain goals are achieved.

[0075] The English conversation learning system can estimate the user's emotions and provide feedback according to the user's learning progress. For example, if the user feels anxious or stressed about learning, the system can send an encouraging message. If the user has positive feelings about learning, the system can suggest further challenges. Furthermore, the system can provide learning content according to the user's emotions, maximizing the effectiveness of learning.

[0076] The English conversation learning system can estimate the user's emotions and optimize the learning environment. For example, if the user is nervous, it can provide relaxing music or environmental sounds. If the user wants to improve their concentration, it can provide music or environmental sounds that support concentration. Furthermore, it can adjust the lighting and temperature of the learning environment according to the user's emotions.

[0077] The English conversation learning system can estimate the user's emotions and suggest breaks based on the user's progress. For example, if the user feels tired, it can encourage them to take a short break. If the user feels stressed, it can suggest simple exercises or stretches to relax them. Furthermore, it can suggest an optimal learning schedule based on the user's emotions, supporting efficient learning.

[0078] The English conversation learning system can provide an individualized learning plan based on the user's learning history. For example, it can analyze past learning content and progress to identify the user's weaknesses. It can also customize the plan to suit the user's learning style and pace, proposing the optimal learning plan. It can also provide learning content tailored to the user's goals, supporting efficient learning.

[0079] An English conversation learning system can visualize a user's learning progress and provide feedback to maintain motivation. For example, it can display learning progress in graphs and charts, allowing users to see their own improvement. Furthermore, the system can motivate users by providing congratulatory messages and rewards when specific goals are achieved. Furthermore, it can provide a ranking function that encourages competition and cooperation with other users.

[0080] English conversation learning systems can be customized to suit a user's learning style and support effective learning. For example, for users who prefer visual learning, learning materials that make extensive use of diagrams and graphs can be provided. For users who prefer auditory learning, audio learning materials and podcasts can be provided. Furthermore, for users who prefer practical learning, role-playing scenarios can be provided to simulate actual business situations.

[0081] The English conversation learning system can estimate the user's emotions and provide feedback according to the user's learning progress. For example, if the user feels anxious or stressed about learning, the system can send an encouraging message. If the user has positive feelings about learning, the system can suggest further challenges. Furthermore, the system can provide learning content according to the user's emotions, maximizing the effectiveness of learning.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The Corporate Internal Understanding Department understands the internal affairs of the company. For example, the Corporate Internal Understanding Department analyzes the company's business processes and the terminology used. The Corporate Internal Understanding Department can also analyze the content of interactions with business partners. Furthermore, they analyze the content of the company's internal documents and emails, and analyze project reports and emails between departments to understand the progress of projects and the division of roles between departments. They analyze work manuals and procedure manuals to understand the specific procedures for each task and the tools used. They analyze daily business communications and meeting minutes to understand the flow of work and important decisions. Step 2: The English Conversation Simulation Unit engages in English conversation with the user based on the company's internal circumstances understood by the Corporate Internal Understanding Unit. For example, if the user asks, "What phrases can I use in a meeting with a client?", the unit will provide appropriate business English phrases in response to the question. It can also point out the user's pronunciation and grammar errors and teach correct expressions. It also analyzes the user's past conversation history, identifies individual weaknesses, and provides focused instruction. For example, it can extract pronunciation and grammar errors and suggest areas for improvement. It can analyze the frequency of use of specific phrases and expressions and suggest appropriate alternative expressions. It can also analyze the flow and structure of the conversation, providing instruction on how to conduct the conversation more naturally. Step 3: The business scenario provider provides practical business scenarios. For example, it generates scenarios for proceeding with a meeting, preparing a presentation, negotiating with a client, etc., and conducts English conversations based on these scenarios. It is also possible to add scenarios based on past successes and failures of actual companies. For example, it can reflect the success factors of a specific project in a scenario, or the causes of failure and areas for improvement in a scenario. Scenarios based on successes and failures can be added to allow users to learn how to respond to realistic situations.

[0084] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Corporate Internal Affairs Understanding Department, which understands the internal affairs of companies, an English conversation simulation unit that conducts English conversation with a user based on the internal circumstances of the company understood by the internal circumstances understanding unit; a business scenario providing unit that provides practical business scenarios; A system characterized by:

2. The Corporate Internal Affairs Understanding Department: Analyze the contents of a company's internal documents and emails to understand detailed business processes, including non-public information 2. The system of claim 1.

3. The Corporate Internal Affairs Understanding Department: Analyze interviews with company employees to understand actual work flows and issues 2. The system of claim 1.

4. The Corporate Internal Affairs Understanding Department: Analyze the emotions of your employees and identify stress points and sources of motivation at work 2. The system of claim 1.

5. The Corporate Internal Affairs Understanding Department: When understanding a company's internal affairs, simultaneously analyze competitor information and understand industry-wide trends and best practices.

2. The system of claim 1.

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