System

Abstract

An object of a system according to an embodiment is to provide an appropriate virtual consultation space in consideration of company rules, company style, and communication tendency.SOLUTION: A system includes a learning unit, an analysis unit, a proposal unit, and a provision unit. The learning unit learns an in-house rule, a company style, and a communication tendency. The analysis unit analyzes the content of the consultation based on the information learned by the learning unit. The proposing section proposes a virtual consultation space based on the consultation contents analyzed by the analyzing section. The provision part provides the virtual consultation space proposed by the proposal part.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 challenge of making it difficult to provide an appropriate consultation space that took into account internal company rules, corporate culture, and communication trends.

[0005] The system according to the embodiment aims to provide an appropriate virtual consultation space that takes into consideration company rules, corporate culture, and communication trends. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, an analysis unit, a proposal unit, and a provision unit. The learning unit learns company rules, corporate culture, and communication trends. The analysis unit analyzes the content of the consultation based on the information learned by the learning unit. The proposal unit proposes a virtual consultation space based on the consultation content analyzed by the analysis unit. The provision unit provides the virtual consultation space proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an appropriate virtual consultation space that takes into consideration company rules, corporate culture, and communication trends. [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 virtual consultation space providing system according to an embodiment of the present invention is a system in which a generation AI that has learned company rules, company culture, and communication tendencies proposes and provides an appropriate virtual consultation space according to the consultation content. As a result, the virtual consultation space providing system can provide an appropriate virtual consultation space that takes into account company rules, company culture, and communication tendencies.

[0029] A virtual consultation space providing system according to an embodiment includes a learning unit, an analysis unit, a proposal unit, and a provision unit. The learning unit learns company rules, company culture, and communication trends. For example, the learning unit analyzes company documents and past communication data, and analyzes company rules and guidelines, as well as past email and chat histories. The learning unit analyzes company rules and guidelines, for example, to understand company culture and communication patterns. The learning unit also analyzes past email and chat histories, for example, to understand company culture and communication patterns. The analysis unit analyzes the consultation content based on the information learned by the learning unit. For example, the analysis unit analyzes the consultation content entered by the client and acquires necessary parameter information. For example, if the consultation content is "consultation about how to proceed with a new project," the analysis unit acquires parameters such as project details, information about related parties, and the client's position and experience. The proposal unit proposes a virtual consultation space based on the consultation content analyzed by the analysis unit. For example, if the consultation content is about how to proceed with a project, the proposal unit proposes a virtual conference room equipped with project management tools and related materials. For example, the proposal unit proposes a virtual space that simulates a casual communication space within the company so that the person seeking consultation can talk in a relaxed atmosphere. The provision unit provides the virtual consultation space proposed by the proposal unit. For example, the provision unit generates a virtual conference room and makes it accessible to the person seeking consultation. The provision unit also places necessary materials and tools in the virtual space, for example. As a result, the virtual consultation space provision system according to the embodiment can provide an appropriate virtual consultation space that takes into account company rules, corporate culture, and communication trends.

[0030] The learning department analyzes informal communication within the company to gain a deeper understanding of the company culture. For example, the learning department may record conversations during lunchtime and analyze the content to understand the informal communication patterns between employees. For example, it may extract topics that are discussed during lunchtime and frequently occurring keywords. The learning department may also analyze records of company events to learn what types of communication employees engage in and in what situations. For example, it may analyze photos and videos of events and analyze employees' facial expressions and behavior. The learning department may also analyze the history of informal chats and messages within the company to understand the communication styles and frequency between employees. For example, it may analyze the content and responses of chats on specific topics. In this way, analyzing informal communication within the company allows a deeper understanding of the company culture.

[0031] The learning department can analyze historical data within the company to learn about changes in the company culture. For example, the learning department can create a database of successful and unsuccessful cases of past projects and analyze that data to learn about changes in the company culture. For example, they can identify commonalities between successful projects and the causes of unsuccessful projects. The learning department can also analyze historical data within the company chronologically to understand changes in the company culture. For example, they can analyze the progress of projects and employee reactions at specific times. The learning department can also analyze documents and reports related to past projects to learn about changes in the company culture. For example, they can identify changes in how projects are progressed and communication styles. In this way, they can learn about changes in the company culture by analyzing historical data within the company.

[0032] The analysis unit can dig deep into the problems and issues behind the consultation content and perform analysis to identify the root cause. The analysis unit, for example, analyzes the consultation content and identifies the problems and issues behind it. For example, if the consultation content is "project progress is delayed," the analysis unit analyzes the details of the project to identify the cause. The analysis unit also references the consultation history of the person seeking advice, finds patterns, and identifies the root cause. For example, it compares past consultation content with current consultation content to identify common issues. The analysis unit also collects data related to the consultation content and performs analysis to identify the root cause. For example, it collects the progress of the project and the opinions of stakeholders to identify the cause of the problem. This makes it possible to dig deep into the problems and issues behind the consultation content and perform analysis to identify the root cause.

[0033] The analysis unit references the consultation history of the client and finds patterns, thereby enabling more accurate parameter information to be acquired. The analysis unit, for example, creates a database of the consultation history of the client and analyzes the data to find patterns. For example, it references the content of past consultations and solutions to acquire information useful for the current consultation. The analysis unit also analyzes the consultation history of the client in chronological order to find patterns. For example, it identifies the content of consultations and solutions at specific times. The analysis unit also clusters the consultation history of the client to find patterns. For example, it groups similar consultation content and solutions to acquire information useful for the current consultation. In this way, it is possible to reference the consultation history of the client and find patterns, thereby enabling more accurate parameter information to be acquired.

[0034] The suggestion unit can suggest interactive elements in the virtual space according to the consultation content. For example, the suggestion unit places a virtual assistant in the virtual space based on the consultation content to support the client. For example, if the consultation is about project management, the virtual assistant displays the progress of the project in real time. The suggestion unit also places an interactive whiteboard in the virtual space according to the consultation content to allow the client to freely draw ideas and take notes. For example, if the consultation is about brainstorming, the whiteboard is used to visualize ideas. The suggestion unit also places interactive tools (e.g., a task management tool or a calendar) in the virtual space based on the consultation content to enable the client to efficiently proceed with their work. For example, the task progress is updated in real time. This makes it possible to suggest interactive elements in the virtual space according to the consultation content.

[0035] The suggestion unit can refer to the client's past virtual space usage history and propose optimal virtual space layouts and functions. The suggestion unit, for example, creates a database of the client's past virtual space usage history and analyzes that data to propose optimal virtual space layouts and functions. For example, the suggestion unit makes suggestions based on tools and layouts used in the past. The suggestion unit also analyzes the client's past virtual space usage history in chronological order to propose optimal virtual space layouts and functions. For example, the suggestion unit makes suggestions based on tools and layouts used at specific times. The suggestion unit also clusters the client's past virtual space usage history and proposes optimal virtual space layouts and functions. For example, the suggestion unit makes suggestions based on data of clients with similar usage histories. In this way, the suggestion unit can refer to the client's past virtual space usage history and propose optimal virtual space layouts and functions.

[0036] The providing unit can monitor user interactions in the virtual space in real time and automatically adjust the settings of the virtual space as needed. For example, the providing unit builds a system that monitors user behavior in the virtual space in real time and automatically adjusts the settings of the virtual space as needed. For example, if a user frequently uses a particular tool, the tool is placed in a prominent position. The providing unit also analyzes user interaction data and automatically adjusts the settings of the virtual space. For example, the color and design of the virtual space are adjusted to allow the user to relax. The providing unit also develops a system that monitors user behavior in the virtual space in real time and dynamically adjusts the settings of the virtual space based on the interaction data. For example, the layout of the virtual space is changed according to the user's behavior. This makes it possible to monitor user interactions in the virtual space in real time and automatically adjust the settings of the virtual space as needed.

[0037] The providing unit can enhance data sharing and file management functions within the virtual space, thereby realizing efficient information exchange. The providing unit, for example, enhances data sharing functions within the virtual space, allowing users to efficiently exchange information. For example, it makes it easy to upload and download files. The providing unit also enhances file management functions within the virtual space, allowing users to efficiently manage information. For example, it provides file version management and access permission settings. The providing unit also develops a system that enhances data sharing and file management functions within the virtual space, allowing users to efficiently exchange information. For example, it provides real-time file sharing and collaborative editing functions. This enhances data sharing and file management functions within the virtual space, thereby realizing efficient information exchange.

[0038] The providing unit can make the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access. For example, the providing unit builds a system that makes the virtual space available on mobile devices, allowing users to access it anywhere. For example, the providing unit provides an app that allows users to access the virtual space on smartphones and tablets. The providing unit also develops a system that makes the virtual space available on VR devices, allowing users to have an immersive experience. For example, the providing unit allows users to access the virtual space using a VR headset. The providing unit also provides an interface that makes the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access. For example, the providing unit provides an optimal user interface depending on the device. This makes the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access.

[0039] The providing unit can provide voice recognition and voice assistant functions within the virtual space, enabling operation by voice. The providing unit, for example, provides a voice recognition function within the virtual space, allowing the user to operate by voice. For example, launching tools or searching for information with voice commands. The providing unit also provides a voice assistant function within the virtual space, allowing the user to receive support by voice. For example, the voice assistant explains the progress of a project or manages tasks. The providing unit also develops a system that provides voice recognition and voice assistant functions within the virtual space, enabling the user to operate by voice. For example, it supports navigation and information input by voice. In this way, providing voice recognition and voice assistant functions within the virtual space enables operation by voice.

[0040] The providing unit can summarize the consultation content into text and send it to the person seeking consultation. The providing unit, for example, builds a system that automatically converts the consultation content into text and sends it to the person seeking consultation. For example, after the consultation is finished, a text summarizing the main points is sent by email. The providing unit also converts the consultation content into text in real time so that the person seeking consultation can immediately check it. For example, the main points are automatically converted into text during the consultation and displayed on a screen. The providing unit also emphasizes important points and next action items when summarizing the consultation content into text and sending it to the person seeking consultation. For example, important parts are highlighted before sending. In this way, by summarizing the consultation content into text and sending it to the person seeking consultation, it becomes easier to record and check the consultation content.

[0041] The provision unit can provide information and advice that will be useful for the next consultation based on the content of the consultation. For example, the provision unit builds a system that analyzes the content of the consultation and automatically generates information and advice that will be useful for the next consultation. For example, it suggests related materials and references. The provision unit also provides advice that will be useful for the next consultation based on the content of the consultation. For example, it suggests solutions to the client's issues and next steps. The provision unit also develops a feedback system that records the content of the consultation and provides information that will be useful for the next consultation. For example, it refers to the content of past consultations and provides related information. This makes it possible to provide continuous support by providing information and advice that will be useful for the next consultation based on the content of the consultation.

[0042] The providing unit can store the consultation content in a database and accumulate information that will be useful for future consultations. The providing unit, for example, builds a system that automatically stores the consultation content in a database and accumulates information that will be useful for future consultations. For example, the consultation content is tagged to make it easier to search. The providing unit also stores the consultation content in a database and provides information that will be useful for future consultations. For example, it refers to past consultation content and provides related information. The providing unit also develops a feedback system that stores the consultation content in a database and accumulates information that will be useful for future consultations. For example, it provides advice based on past consultation content. This makes it possible to provide continuous support by storing the consultation content in a database and accumulating information that will be useful for future consultations.

[0043] The provision department can analyze the content of the consultation and propose improvements to the company's rules and guidelines. For example, the provision department builds a system that analyzes the content of the consultation and proposes improvements to the company's rules and guidelines. For example, it proposes a review of the rules based on the content of the consultation. The provision department also analyzes the content of the consultation and identifies improvements to the company's rules and guidelines. For example, it proposes improvements to the rules based on the opinions and feedback of the person who consulted. The provision department also develops a feedback system for analyzing the content of the consultation and proposing improvements to the company's rules and guidelines. For example, it identifies improvements to the rules based on the content of the consultation. This makes it possible to improve the company's rules and guidelines by analyzing the content of the consultation and proposing improvements to the company's rules and guidelines.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The analysis unit can dig deep into the problems and issues behind the consultation content and perform analysis to identify the root cause. For example, if the consultation content is "project progress is delayed," the analysis unit analyzes the details of the project to identify the cause. The analysis unit also references the consultation history of the person seeking advice, finds patterns, and identifies the root cause. For example, it compares past consultation content with current consultation content to identify common issues. The analysis unit also collects data related to the consultation content and performs analysis to identify the root cause. For example, it collects the progress of the project and the opinions of stakeholders to identify the cause of the problem. This allows the analysis to dig deep into the problems and issues behind the consultation content and identify the root cause.

[0046] The suggestion unit can suggest interactive elements in the virtual space according to the consultation content. For example, based on the consultation content, a virtual assistant can be placed in the virtual space to support the client. For example, if the consultation is about project management, the virtual assistant can display the project progress in real time. The suggestion unit can also place an interactive whiteboard in the virtual space according to the consultation content, allowing the client to freely draw ideas and take notes. For example, if the consultation is about brainstorming, the whiteboard can be used to visualize ideas. The suggestion unit can also place interactive tools (e.g., task management tools and calendars) in the virtual space according to the consultation content, allowing the client to work efficiently. For example, the task progress can be updated in real time. This makes it possible to suggest interactive elements in the virtual space according to the consultation content.

[0047] The providing unit can monitor user interactions in the virtual space in real time and automatically adjust the settings of the virtual space as needed. For example, a system can be constructed that monitors user behavior in the virtual space in real time and automatically adjusts the settings of the virtual space as needed. For example, if a user frequently uses a particular tool, the tool can be placed in a prominent position. The providing unit can also analyze user interaction data and automatically adjust the settings of the virtual space. For example, the color and design of the virtual space can be adjusted to help the user relax. The providing unit can also develop a system that monitors user behavior in the virtual space in real time and dynamically adjusts the settings of the virtual space based on the interaction data. For example, the layout of the virtual space can be changed according to user behavior. This makes it possible to monitor user interactions in the virtual space in real time and automatically adjust the settings of the virtual space as needed.

[0048] The providing unit can enhance data sharing and file management functions within the virtual space, thereby realizing efficient information exchange. For example, the providing unit can enhance data sharing functions within the virtual space, thereby enabling users to efficiently exchange information. For example, the providing unit can make it easy to upload and download files. The providing unit can also enhance file management functions within the virtual space, thereby enabling users to efficiently manage information. For example, the providing unit can provide file version management and access permission settings. The providing unit can also develop a system that enhances data sharing and file management functions within the virtual space, thereby enabling users to efficiently exchange information. For example, the providing unit can provide real-time file sharing and collaborative editing functions. This can enhance data sharing and file management functions within the virtual space, thereby realizing efficient information exchange.

[0049] The providing unit can make the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access. For example, a system can be built that makes the virtual space available on mobile devices, allowing users to access it anywhere. For example, an app can be provided that allows users to access the virtual space on smartphones and tablets. The providing unit can also develop a system that makes the virtual space available on VR devices, allowing users to have an immersive experience. For example, the virtual space can be accessed using a VR headset. The providing unit can also provide an interface that makes the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access. For example, it can provide an optimal user interface depending on the device. This makes the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The learning department studies internal rules, corporate culture, and communication trends. For example, the learning department analyzes internal documents and past communication data, internal rules and guidelines, and past email and chat history. This allows the department to understand the internal culture and communication patterns. Step 2: The analysis unit analyzes the consultation content based on the information learned by the learning unit. For example, it analyzes the consultation content entered by the person seeking advice and obtains the necessary parameter information. If the consultation content is "consultation about how to proceed with a new project," it obtains parameters such as project details, information about the people involved, and the person seeking advice's position and experience. Step 3: The proposal department proposes a virtual consultation space based on the consultation content analyzed by the analysis department. For example, if the consultation content is about how to proceed with a project, the proposal department will propose a virtual conference room equipped with project management tools and related materials. The proposal also proposes a virtual space that mimics an in-house casual communication space so that the person seeking advice can talk in a relaxed atmosphere. Step 4: The providing unit provides the virtual consultation space proposed by the proposing unit. For example, it generates a virtual conference room and makes it accessible to the client. Necessary materials and tools are also placed in the virtual space.

[0052] (Example 2) The virtual consultation space providing system according to an embodiment of the present invention is a system in which a generation AI that has learned company rules, company culture, and communication tendencies proposes and provides an appropriate virtual consultation space according to the consultation content. As a result, the virtual consultation space providing system can provide an appropriate virtual consultation space that takes into account company rules, company culture, and communication tendencies.

[0053] A virtual consultation space providing system according to an embodiment includes a learning unit, an analysis unit, a proposal unit, and a provision unit. The learning unit learns company rules, company culture, and communication trends. For example, the learning unit analyzes company documents and past communication data, and analyzes company rules and guidelines, as well as past email and chat histories. The learning unit analyzes company rules and guidelines, for example, to understand company culture and communication patterns. The learning unit also analyzes past email and chat histories, for example, to understand company culture and communication patterns. The analysis unit analyzes the consultation content based on the information learned by the learning unit. For example, the analysis unit analyzes the consultation content entered by the client and acquires necessary parameter information. For example, if the consultation content is "consultation about how to proceed with a new project," the analysis unit acquires parameters such as project details, information about related parties, and the client's position and experience. The proposal unit proposes a virtual consultation space based on the consultation content analyzed by the analysis unit. For example, if the consultation content is about how to proceed with a project, the proposal unit proposes a virtual conference room equipped with project management tools and related materials. For example, the proposal unit proposes a virtual space that simulates a casual communication space within the company so that the person seeking consultation can talk in a relaxed atmosphere. The provision unit provides the virtual consultation space proposed by the proposal unit. For example, the provision unit generates a virtual conference room and makes it accessible to the person seeking consultation. The provision unit also places necessary materials and tools in the virtual space, for example. As a result, the virtual consultation space provision system according to the embodiment can provide an appropriate virtual consultation space that takes into account company rules, corporate culture, and communication trends.

[0054] The learning department analyzes informal communication within the company to gain a deeper understanding of the company culture. For example, the learning department may record conversations during lunchtime and analyze the content to understand the informal communication patterns between employees. For example, it may extract topics that are discussed during lunchtime and frequently occurring keywords. The learning department may also analyze records of company events to learn what types of communication employees engage in and in what situations. For example, it may analyze photos and videos of events and analyze employees' facial expressions and behavior. The learning department may also analyze the history of informal chats and messages within the company to understand the communication styles and frequency between employees. For example, it may analyze the content and responses of chats on specific topics. In this way, analyzing informal communication within the company allows a deeper understanding of the company culture.

[0055] The learning department can analyze historical data within the company to learn about changes in the company culture. For example, the learning department can create a database of successful and unsuccessful cases of past projects and analyze that data to learn about changes in the company culture. For example, they can identify commonalities between successful projects and the causes of unsuccessful projects. The learning department can also analyze historical data within the company chronologically to understand changes in the company culture. For example, they can analyze the progress of projects and employee reactions at specific times. The learning department can also analyze documents and reports related to past projects to learn about changes in the company culture. For example, they can identify changes in how projects are progressed and communication styles. In this way, they can learn about changes in the company culture by analyzing historical data within the company.

[0056] The learning unit uses the emotion estimation function to analyze changes in employees' emotions from past communication data and can grasp emotional trends within the company. The learning unit, for example, analyzes past email and chat history and uses the emotion estimation function to grasp changes in employees' emotions. For example, it analyzes emotional fluctuations over the course of a specific project. The learning unit also analyzes recorded data of internal conferences and meetings and uses the emotion estimation function to grasp changes in employees' emotions. For example, it analyzes the content and tone of comments made during meetings to identify emotional fluctuations. The learning unit also analyzes videos and photos of internal events and uses the emotion estimation function to grasp changes in employees' emotions. For example, it analyzes facial expressions and behavior during the event to identify emotional fluctuations. In this way, by using the emotion estimation function, it is possible to analyze changes in employees' emotions from past communication data and grasp emotional trends within the company.

[0057] The analysis unit can dig deep into the problems and issues behind the consultation content and perform analysis to identify the root cause. The analysis unit, for example, analyzes the consultation content and identifies the problems and issues behind it. For example, if the consultation content is "project progress is delayed," the analysis unit analyzes the details of the project to identify the cause. The analysis unit also references the consultation history of the person seeking advice, finds patterns, and identifies the root cause. For example, it compares past consultation content with current consultation content to identify common issues. The analysis unit also collects data related to the consultation content and performs analysis to identify the root cause. For example, it collects the progress of the project and the opinions of stakeholders to identify the cause of the problem. This makes it possible to dig deep into the problems and issues behind the consultation content and perform analysis to identify the root cause.

[0058] The analysis unit references the consultation history of the client and finds patterns, thereby enabling more accurate parameter information to be acquired. The analysis unit, for example, creates a database of the consultation history of the client and analyzes the data to find patterns. For example, it references the content of past consultations and solutions to acquire information useful for the current consultation. The analysis unit also analyzes the consultation history of the client in chronological order to find patterns. For example, it identifies the content of consultations and solutions at specific times. The analysis unit also clusters the consultation history of the client to find patterns. For example, it groups similar consultation content and solutions to acquire information useful for the current consultation. In this way, it is possible to reference the consultation history of the client and find patterns, thereby enabling more accurate parameter information to be acquired.

[0059] The analysis unit can use the emotion estimation function to analyze the emotional state of the client and acquire parameter information based on the emotion. The analysis unit, for example, analyzes the input content of the client and grasps the emotional state using the emotion estimation function. For example, it analyzes the text of the consultation content and identifies positive and negative emotions. The analysis unit also analyzes the client's voice data and grasps the emotional state using the emotion estimation function. For example, it analyzes the tone and pitch of the client's voice and identifies emotional fluctuations. The analysis unit also analyzes the client's facial expression data and grasps the emotional state using the emotion estimation function. For example, it analyzes the client's facial expression and identifies emotional fluctuations. In this way, the emotion estimation function can be used to analyze the client's emotional state and acquire parameter information based on the emotion.

[0060] The suggestion unit can suggest interactive elements in the virtual space according to the consultation content. For example, the suggestion unit places a virtual assistant in the virtual space based on the consultation content to support the client. For example, if the consultation is about project management, the virtual assistant displays the progress of the project in real time. The suggestion unit also places an interactive whiteboard in the virtual space according to the consultation content to allow the client to freely draw ideas and take notes. For example, if the consultation is about brainstorming, the whiteboard is used to visualize ideas. The suggestion unit also places interactive tools (e.g., a task management tool or a calendar) in the virtual space based on the consultation content to enable the client to efficiently proceed with their work. For example, the task progress is updated in real time. This makes it possible to suggest interactive elements in the virtual space according to the consultation content.

[0061] The suggestion unit can refer to the client's past virtual space usage history and propose optimal virtual space layouts and functions. The suggestion unit, for example, creates a database of the client's past virtual space usage history and analyzes that data to propose optimal virtual space layouts and functions. For example, the suggestion unit makes suggestions based on tools and layouts used in the past. The suggestion unit also analyzes the client's past virtual space usage history in chronological order to propose optimal virtual space layouts and functions. For example, the suggestion unit makes suggestions based on tools and layouts used at specific times. The suggestion unit also clusters the client's past virtual space usage history and proposes optimal virtual space layouts and functions. For example, the suggestion unit makes suggestions based on data of clients with similar usage histories. In this way, the suggestion unit can refer to the client's past virtual space usage history and propose optimal virtual space layouts and functions.

[0062] The suggestion unit can use the emotion estimation function to suggest a virtual space with a relaxing effect according to the emotional state of the client. For example, the suggestion unit uses the emotion estimation function to analyze the emotional state of the client in real time and suggest a virtual space with a relaxing effect. For example, if the client is feeling stressed, the suggestion unit displays relaxing music or scenery. The suggestion unit also adjusts the colors and design in the virtual space according to the client's emotional state to enhance the relaxing effect. For example, calming colors and natural scenery may be incorporated. The suggestion unit also develops a system that suggests a virtual space with a relaxing effect based on the emotion estimation data. For example, the setting of the virtual space may be dynamically adjusted each time the client's emotion changes. In this way, the emotion estimation function can be used to suggest a virtual space with a relaxing effect according to the client's emotional state.

[0063] The providing unit can monitor user interactions in the virtual space in real time and automatically adjust the settings of the virtual space as needed. For example, the providing unit builds a system that monitors user behavior in the virtual space in real time and automatically adjusts the settings of the virtual space as needed. For example, if a user frequently uses a particular tool, the tool is placed in a prominent position. The providing unit also analyzes user interaction data and automatically adjusts the settings of the virtual space. For example, the color and design of the virtual space are adjusted to allow the user to relax. The providing unit also develops a system that monitors user behavior in the virtual space in real time and dynamically adjusts the settings of the virtual space based on the interaction data. For example, the layout of the virtual space is changed according to the user's behavior. This makes it possible to monitor user interactions in the virtual space in real time and automatically adjust the settings of the virtual space as needed.

[0064] The providing unit can enhance data sharing and file management functions within the virtual space, thereby realizing efficient information exchange. The providing unit, for example, enhances data sharing functions within the virtual space, allowing users to efficiently exchange information. For example, it makes it easy to upload and download files. The providing unit also enhances file management functions within the virtual space, allowing users to efficiently manage information. For example, it provides file version management and access permission settings. The providing unit also develops a system that enhances data sharing and file management functions within the virtual space, allowing users to efficiently exchange information. For example, it provides real-time file sharing and collaborative editing functions. This enhances data sharing and file management functions within the virtual space, thereby realizing efficient information exchange.

[0065] The providing unit can use the emotion estimation function to monitor the user's emotional state in the virtual space in real time and provide appropriate feedback. The providing unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional state in the virtual space in real time. For example, the providing unit analyzes the user's facial expressions and voice and calculates an emotion score. The providing unit also monitors the user's emotional state in real time and provides appropriate feedback based on the results. For example, if the user is feeling stressed, the providing unit makes suggestions to help the user relax. The providing unit also develops a system that monitors the user's emotional state in the virtual space in real time and provides appropriate feedback based on the emotion estimation data. For example, the feedback is adjusted each time the user's emotion changes. In this way, the emotion estimation function can be used to monitor the user's emotional state in the virtual space in real time and provide appropriate feedback.

[0066] The providing unit can make the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access. For example, the providing unit builds a system that makes the virtual space available on mobile devices, allowing users to access it anywhere. For example, the providing unit provides an app that allows users to access the virtual space on smartphones and tablets. The providing unit also develops a system that makes the virtual space available on VR devices, allowing users to have an immersive experience. For example, the providing unit allows users to access the virtual space using a VR headset. The providing unit also provides an interface that makes the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access. For example, the providing unit provides an optimal user interface depending on the device. This makes the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access.

[0067] The providing unit can provide voice recognition and voice assistant functions within the virtual space, enabling operation by voice. The providing unit, for example, provides a voice recognition function within the virtual space, allowing the user to operate by voice. For example, launching tools or searching for information with voice commands. The providing unit also provides a voice assistant function within the virtual space, allowing the user to receive support by voice. For example, the voice assistant explains the progress of a project or manages tasks. The providing unit also develops a system that provides voice recognition and voice assistant functions within the virtual space, enabling the user to operate by voice. For example, it supports navigation and information input by voice. In this way, providing voice recognition and voice assistant functions within the virtual space enables operation by voice.

[0068] The providing unit uses the emotion estimation function to provide customization options based on the user's emotions in the virtual space, thereby improving the user experience. For example, the providing unit uses the emotion estimation function to analyze the user's emotional state in the virtual space in real time and provide customization options based on the data. For example, the providing unit adjusts the settings of the virtual space to allow the user to relax. The providing unit also customizes the colors and designs in the virtual space according to the client's emotional state. For example, if the client has strong positive emotions, the providing unit suggests bright colors and fun designs. The providing unit also develops a system that suggests a customizable virtual space based on the emotion estimation data. For example, the setting of the virtual space is dynamically adjusted each time the client's emotions change. In this way, the emotion estimation function can be used to provide customization options based on the user's emotions in the virtual space, improving the user experience.

[0069] The providing unit can summarize the consultation content into text and send it to the person seeking consultation. The providing unit, for example, builds a system that automatically converts the consultation content into text and sends it to the person seeking consultation. For example, after the consultation is finished, a text summarizing the main points is sent by email. The providing unit also converts the consultation content into text in real time so that the person seeking consultation can immediately check it. For example, the main points are automatically converted into text during the consultation and displayed on a screen. The providing unit also emphasizes important points and next action items when summarizing the consultation content into text and sending it to the person seeking consultation. For example, important parts are highlighted before sending. In this way, by summarizing the consultation content into text and sending it to the person seeking consultation, it becomes easier to record and check the consultation content.

[0070] The provision unit can provide information and advice that will be useful for the next consultation based on the content of the consultation. For example, the provision unit builds a system that analyzes the content of the consultation and automatically generates information and advice that will be useful for the next consultation. For example, it suggests related materials and references. The provision unit also provides advice that will be useful for the next consultation based on the content of the consultation. For example, it suggests solutions to the client's issues and next steps. The provision unit also develops a feedback system that records the content of the consultation and provides information that will be useful for the next consultation. For example, it refers to the content of past consultations and provides related information. This makes it possible to provide continuous support by providing information and advice that will be useful for the next consultation based on the content of the consultation.

[0071] The providing unit can provide feedback based on the consultation content using the emotion estimation function. The providing unit, for example, uses the emotion estimation function to build a system that provides feedback based on the consultation content. For example, the providing unit analyzes the emotional state of the client and provides appropriate advice. The providing unit also analyzes the consultation content and provides feedback based on the emotion estimation data. For example, if the client is feeling stressed, the providing unit makes suggestions to help the client relax. The providing unit also uses the emotion estimation function to develop a system that provides feedback based on the consultation content. For example, the feedback is adjusted every time the client's emotions change. In this way, feedback based on the consultation content can be provided by using the emotion estimation function.

[0072] The providing unit can store the consultation content in a database and accumulate information that will be useful for future consultations. The providing unit, for example, builds a system that automatically stores the consultation content in a database and accumulates information that will be useful for future consultations. For example, the consultation content is tagged to make it easier to search. The providing unit also stores the consultation content in a database and provides information that will be useful for future consultations. For example, it refers to past consultation content and provides related information. The providing unit also develops a feedback system that stores the consultation content in a database and accumulates information that will be useful for future consultations. For example, it provides advice based on past consultation content. This makes it possible to provide continuous support by storing the consultation content in a database and accumulating information that will be useful for future consultations.

[0073] The provision department can analyze the content of the consultation and propose improvements to the company's rules and guidelines. For example, the provision department builds a system that analyzes the content of the consultation and proposes improvements to the company's rules and guidelines. For example, it proposes a review of the rules based on the content of the consultation. The provision department also analyzes the content of the consultation and identifies improvements to the company's rules and guidelines. For example, it proposes improvements to the rules based on the opinions and feedback of the person who consulted. The provision department also develops a feedback system for analyzing the content of the consultation and proposing improvements to the company's rules and guidelines. For example, it identifies improvements to the rules based on the content of the consultation. This makes it possible to improve the company's rules and guidelines by analyzing the content of the consultation and proposing improvements to the company's rules and guidelines.

[0074] The providing unit can provide feedback based on the consultation content using the emotion estimation function. The providing unit, for example, uses the emotion estimation function to build a system that provides feedback based on the consultation content. For example, the providing unit analyzes the emotional state of the client and provides appropriate advice. The providing unit also analyzes the consultation content and provides feedback based on the emotion estimation data. For example, if the client is feeling stressed, the providing unit makes suggestions to help the client relax. The providing unit also uses the emotion estimation function to develop a system that provides feedback based on the consultation content. For example, the feedback is adjusted every time the client's emotions change. In this way, feedback based on the consultation content can be provided by using the emotion estimation function.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The analysis unit can dig deep into the problems and issues behind the consultation content and perform analysis to identify the root cause. For example, if the consultation content is "project progress is delayed," the analysis unit analyzes the details of the project to identify the cause. The analysis unit also references the consultation history of the person seeking advice, finds patterns, and identifies the root cause. For example, it compares past consultation content with current consultation content to identify common issues. The analysis unit also collects data related to the consultation content and performs analysis to identify the root cause. For example, it collects the progress of the project and the opinions of stakeholders to identify the cause of the problem. This allows the analysis to dig deep into the problems and issues behind the consultation content and identify the root cause.

[0077] The suggestion unit can suggest interactive elements in the virtual space according to the consultation content. For example, based on the consultation content, a virtual assistant can be placed in the virtual space to support the client. For example, if the consultation is about project management, the virtual assistant can display the project progress in real time. The suggestion unit can also place an interactive whiteboard in the virtual space according to the consultation content, allowing the client to freely draw ideas and take notes. For example, if the consultation is about brainstorming, the whiteboard can be used to visualize ideas. The suggestion unit can also place interactive tools (e.g., task management tools and calendars) in the virtual space according to the consultation content, allowing the client to work efficiently. For example, the task progress can be updated in real time. This makes it possible to suggest interactive elements in the virtual space according to the consultation content.

[0078] The providing unit can monitor user interactions in the virtual space in real time and automatically adjust the settings of the virtual space as needed. For example, a system can be constructed that monitors user behavior in the virtual space in real time and automatically adjusts the settings of the virtual space as needed. For example, if a user frequently uses a particular tool, the tool can be placed in a prominent position. The providing unit can also analyze user interaction data and automatically adjust the settings of the virtual space. For example, the color and design of the virtual space can be adjusted to help the user relax. The providing unit can also develop a system that monitors user behavior in the virtual space in real time and dynamically adjusts the settings of the virtual space based on the interaction data. For example, the layout of the virtual space can be changed according to user behavior. This makes it possible to monitor user interactions in the virtual space in real time and automatically adjust the settings of the virtual space as needed.

[0079] The providing unit can enhance data sharing and file management functions within the virtual space, thereby realizing efficient information exchange. For example, the providing unit can enhance data sharing functions within the virtual space, thereby enabling users to efficiently exchange information. For example, the providing unit can make it easy to upload and download files. The providing unit can also enhance file management functions within the virtual space, thereby enabling users to efficiently manage information. For example, the providing unit can provide file version management and access permission settings. The providing unit can also develop a system that enhances data sharing and file management functions within the virtual space, thereby enabling users to efficiently exchange information. For example, the providing unit can provide real-time file sharing and collaborative editing functions. This can enhance data sharing and file management functions within the virtual space, thereby realizing efficient information exchange.

[0080] The providing unit can make the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access. For example, a system can be built that makes the virtual space available on mobile devices, allowing users to access it anywhere. For example, an app can be provided that allows users to access the virtual space on smartphones and tablets. The providing unit can also develop a system that makes the virtual space available on VR devices, allowing users to have an immersive experience. For example, the virtual space can be accessed using a VR headset. The providing unit can also provide an interface that makes the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access. For example, it can provide an optimal user interface depending on the device. This makes the virtual space available on mobile devices and VR devices, thereby increasing flexibility of access.

[0081] The analysis unit can use the emotion estimation function to analyze the emotional state of the client and acquire parameter information based on the emotion. For example, the analysis unit analyzes the client's input content and uses the emotion estimation function to grasp the emotional state. For example, the analysis unit analyzes the text of the consultation content and identifies positive and negative emotions. The analysis unit also analyzes the client's voice data and uses the emotion estimation function to grasp the emotional state. For example, the analysis unit analyzes the client's tone and pitch of voice and identifies emotional fluctuations. The analysis unit also analyzes the client's facial expression data and uses the emotion estimation function to grasp the emotional state. For example, the analysis unit analyzes the client's facial expression and identifies emotional fluctuations. In this way, the emotion estimation function can be used to analyze the client's emotional state and acquire parameter information based on the emotion.

[0082] The suggestion unit can use the emotion estimation function to suggest a virtual space with a relaxing effect according to the emotional state of the client. For example, the emotion estimation function can be used to analyze the emotional state of the client in real time and suggest a virtual space with a relaxing effect. For example, if the client is feeling stressed, relaxing music or scenery can be displayed. The suggestion unit can also adjust the colors and design in the virtual space according to the client's emotional state to enhance the relaxing effect. For example, calming colors and natural scenery can be incorporated. The suggestion unit can also develop a system that suggests a virtual space with a relaxing effect based on the emotion estimation data. For example, the settings of the virtual space can be dynamically adjusted each time the client's emotions change. In this way, the emotion estimation function can be used to suggest a virtual space with a relaxing effect according to the client's emotional state.

[0083] The providing unit can use the emotion estimation function to monitor the user's emotional state in the virtual space in real time and provide appropriate feedback. For example, a system is constructed that uses the emotion estimation function to monitor the user's emotional state in the virtual space in real time. For example, the system analyzes the user's facial expressions and voice and calculates an emotion score. The providing unit also monitors the user's emotional state in real time and provides appropriate feedback based on the results. For example, if the user is feeling stressed, the providing unit makes suggestions to help the user relax. The providing unit also develops a system that monitors the user's emotional state in the virtual space in real time and provides appropriate feedback based on the emotion estimation data. For example, the feedback is adjusted each time the user's emotion changes. In this way, the emotion estimation function can be used to monitor the user's emotional state in the virtual space in real time and provide appropriate feedback.

[0084] The providing unit can use the emotion estimation function to provide feedback based on the consultation content. For example, a system is constructed that uses the emotion estimation function to provide feedback based on the consultation content. For example, the emotional state of the client is analyzed and appropriate advice is provided. The providing unit also analyzes the consultation content and provides feedback based on the emotion estimation data. For example, if the client is feeling stressed, the system makes suggestions to help the client relax. The providing unit also uses the emotion estimation function to develop a system that provides feedback based on the consultation content. For example, the system adjusts the feedback each time the client's emotions change. In this way, feedback based on the consultation content can be provided by using the emotion estimation function.

[0085] The providing unit can use the emotion estimation function to provide customization options based on the user's emotions in the virtual space, thereby improving the user experience. For example, the emotion estimation function can be used to analyze the user's emotional state in the virtual space in real time and provide customization options based on the data. For example, the settings of the virtual space can be adjusted to allow the user to relax. The providing unit can also customize the colors and designs in the virtual space according to the client's emotional state. For example, if the client has strong positive emotions, bright colors and fun designs can be suggested. The providing unit can also develop a system that suggests customizable virtual spaces based on the emotion estimation data. For example, the settings of the virtual space can be dynamically adjusted each time the client's emotions change. In this way, the emotion estimation function can be used to provide customization options based on the user's emotions in the virtual space, improving the user experience.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The learning department studies internal rules, corporate culture, and communication trends. For example, the learning department analyzes internal documents and past communication data, internal rules and guidelines, and past email and chat history. This allows the department to understand the internal culture and communication patterns. Step 2: The analysis unit analyzes the consultation content based on the information learned by the learning unit. For example, it analyzes the consultation content entered by the person seeking advice and obtains the necessary parameter information. If the consultation content is "consultation about how to proceed with a new project," it obtains parameters such as project details, information about the people involved, and the person seeking advice's position and experience. Step 3: The proposal department proposes a virtual consultation space based on the consultation content analyzed by the analysis department. For example, if the consultation content is about how to proceed with a project, the proposal department will propose a virtual conference room equipped with project management tools and related materials. The proposal also proposes a virtual space that mimics an in-house casual communication space so that the person seeking advice can talk in a relaxed atmosphere. Step 4: The providing unit provides the virtual consultation space proposed by the proposing unit. For example, it generates a virtual conference room and makes it accessible to the client. Necessary materials and tools are also placed in the virtual space.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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 learning department studies company rules, corporate culture, and communication trends, an analysis unit that analyzes the content of the consultation based on the information learned by the learning unit; a proposal unit that proposes a virtual consultation space based on the consultation content analyzed by the analysis unit; a providing unit that provides the virtual consultation space proposed by the proposing unit. A system characterized by:

2. The learning unit Analyze informal internal communications to gain a deeper understanding of company culture 2. The system of claim 1.

3. The learning unit Analyzing historical data from within the company to learn about changes in company culture 2. The system of claim 1.

4. The learning unit Analyze changes in employee emotions from past communication data to understand emotional trends within the company 2. The system of claim 1.

5. The analysis unit Dig deep into the issues and problems behind the consultation and conduct analysis to identify the root cause 2. The system of claim 1.

Images