System

Abstract

An object of a system according to an embodiment is to quickly provide information to a person who newly participates in a project.SOLUTION: A system includes a material learning part and an information providing part. The material learning unit learns materials related to the project or past communication data. An information providing part quickly provides information to a person who newly enters the project on the basis of the information learned by the material learning 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 has the problem that it is difficult and time-consuming to quickly provide information to new project participants.

[0005] The system according to the embodiment aims to quickly provide information to people who are new to a project. [Means for solving the problem]

[0006] The system according to the embodiment includes a document learning unit and an information providing unit. The document learning unit learns documents or past communication data related to the project. The information providing unit quickly provides information to new people joining the project based on the information learned by the document learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly provide information to people who are new to a project. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 generative AI service according to an embodiment of the present invention is a system that learns project-related materials and past communication data and quickly provides information to people joining the project. This allows new people joining the project to quickly understand and become immediately effective, reduces the burden on those explaining, and improves the concentration of decision makers.

[0029] The generation AI service according to the embodiment includes a document learning unit and an information providing unit. The document learning unit learns project-related documents or past communication data. For example, the document learning unit learns project plans, progress reports, meeting minutes, etc. The document learning unit can also learn communication data such as emails, chat logs, and meeting recordings. The information providing unit quickly provides information to new project participants based on the information learned by the document learning unit. For example, the information providing unit allows the generation AI to instantly answer questions such as, "What is the purpose of this project?", "What is the current progress?", and "What challenges do you have?" The information providing unit can also provide information via real-time notifications, dashboard displays, email notifications, and other methods. This allows new project participants to quickly understand the project and become immediately effective.

[0030] The data learning unit can also learn from external news articles or industry reports related to the project. For example, the generative AI automatically collects external news articles and industry reports related to the project and adds them to the learning data. For example, it can filter news articles based on specific keywords and incorporate them as background information for the project. This allows for a deeper understanding of the project's background.

[0031] The data learning unit can automatically extract risk factors for a project based on the learned data and make proposals for risk management. For example, the data learning unit uses a generative AI to analyze project documents and communication data and automatically extract risk factors. For example, it identifies risk factors based on problems and issues that have arisen in past projects. The data learning unit also makes proposals for risk management. For example, it proposes risk avoidance measures, risk mitigation measures, risk acceptance measures, etc. This allows for efficient project risk management.

[0032] The material learning section also learns visual data related to the project, allowing for a deeper understanding of the project based on visual information. For example, the generative AI automatically collects visual data related to the project and adds it to the learning data. For example, it analyzes blueprints and graphs to understand detailed information about the project. The material learning section also deepens understanding of the project based on visual information. For example, it uses data visualization, infographics, presentation materials, etc. This allows for a deeper understanding of the project based on visual information.

[0033] The data learning unit can automatically extract success or failure cases of projects based on the learned data and provide them as lessons for other projects. For example, the data learning unit uses a generative AI to analyze project materials and communication data and automatically extract success or failure cases. For example, it identifies the factors that led to success or failure in past projects. The data learning unit also provides these as lessons for other projects. For example, it provides points to learn from success cases and points to learn from failure cases. This can be provided as lessons for other projects.

[0034] The information provision department can analyze the past experience and skill sets of people joining a project and provide customized information that is optimal for that person. For example, the information provision department uses a generation AI to analyze the past experience and skill sets of people joining a project, and then customizes and provides optimal information based on that information. For example, it prioritizes providing relevant materials to people with specific skills. The information provision department also provides information tailored to individual needs and personalized training plans. This allows new people joining a project to be provided with the optimal information, enabling them to quickly understand the process and become immediately effective.

[0035] The information provision department monitors the progress of the project in real time and can provide appropriate advice to new project members based on the latest information. For example, the information provision department uses a generation AI to monitor the progress of the project in real time and provide the latest information to new project members. For example, it automatically generates graphs and reports showing the progress. The information provision department also provides appropriate advice based on the latest information. For example, it provides information on how to proceed with the project, risk management, and problem-solving methods. This allows new project members to receive appropriate advice based on the latest information.

[0036] The information provision unit can automatically provide project-related training materials or video tutorials to new project participants. For example, the information provision unit builds a system in which generative AI automatically provides project-related training materials and video tutorials to new project participants. For example, it provides videos that explain the project's overview and basic operation methods. The information provision unit also provides online videos, recorded sessions, interactive videos, etc. This allows new project participants to quickly obtain the training materials and video tutorials they need.

[0037] The information provision department can collect feedback from other project members in real time in response to questions from people joining the project newcomers and provide optimal answers. For example, the information provision department will build a system in which a generation AI analyzes questions from people joining the project newcomers and collects feedback from other project members in real time. For example, it will provide optimal answers based on the content of the questions. The information provision department also collects real-time feedback, regular reviews, survey results, etc. This allows people joining the project newcomers to quickly obtain optimal answers.

[0038] The document learning unit can automatically update project progress or issues based on past documents and provide the latest information. For example, the document learning unit builds a system in which a generative AI analyzes past documents and automatically updates project progress and issues. For example, it provides the latest information based on progress reports and meeting minutes. The document learning unit also provides real-time data, the latest progress reports, the latest technical information, and more. This allows project progress and issues to be understood based on the latest information.

[0039] The material learning unit automatically generates visual elements when creating explanatory materials, making the explanations more effective. For example, the material learning unit builds a system that automatically generates visual elements when a generative AI creates explanatory materials. For example, it automatically generates infographics and dashboards to make explanations more effective. The material learning unit also generates visual elements such as graphs and charts and incorporates them into the explanatory materials. This allows the explanatory materials to be created visually effective.

[0040] The Material Learning Department can automatically incorporate success stories or best practices from other projects to create explanatory materials. For example, the Material Learning Department builds a system in which a generative AI automatically collects success stories and best practices from other projects and incorporates them into explanatory materials. For example, it makes specific proposals based on success stories. The Material Learning Department also incorporates best practices such as industry standards and recommended methods to create explanatory materials. This makes it possible to create explanatory materials that incorporate success stories and best practices.

[0041] The material learning unit can support output in different formats when creating explanatory materials. For example, the material learning unit builds a system that supports output in different formats when a generative AI creates explanatory materials. For example, output in the form of a presentation, report, video, etc. The material learning unit also supports output in visual formats such as posters and infographics. This allows explanatory materials to be output in different formats.

[0042] The Data Learning Department can centrally manage information from multiple projects and provide the most important information to decision makers on a priority basis. For example, the Data Learning Department will build a system in which a generative AI centrally manages information from multiple projects and provides the most important information to decision makers on a priority basis. For example, progress and issues will be displayed centrally. The Data Learning Department will also manage information using project management tools, databases, dashboards, etc. This will allow decision makers to quickly grasp the most important information.

[0043] The data learning unit can analyze the decision-maker's past decision-making history and make suggestions to support optimal decision-making. For example, the data learning unit builds a system in which generative AI analyzes the decision-maker's past decision-making history and makes suggestions to support optimal decision-making. For example, it makes suggestions based on past successes and failures. The data learning unit also makes suggestions using criteria such as risk assessment, cost-benefit analysis, and scenario planning. This can support decision-makers in making optimal decisions.

[0044] The data learning unit can report project progress or issues to decision makers in real time, supporting rapid decision-making. For example, the data learning unit builds a system in which the generation AI reports project progress and issues to decision makers in real time. For example, it automatically generates graphs and reports showing progress. The data learning unit also reports by regular reports, real-time notifications, dashboard displays, and other methods. This allows decision makers to make decisions quickly.

[0045] The data learning unit can automatically provide decision makers with success stories or best practices from other projects for reference. For example, the data learning unit will build a system in which generative AI automatically collects success stories and best practices from other projects and provides them to decision makers. For example, it will make specific proposals based on the success stories. The data learning unit will also provide analysis of success stories and methods for applying best practices. This allows decision makers to refer to success stories and best practices from other projects.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The resource learning unit can also learn from external news articles or industry reports related to the project. For example, the generation AI can automatically collect external news articles and industry reports related to the project and add them to the learning data. For example, it can filter news articles based on specific keywords and incorporate them as background information for the project. This allows for a deeper understanding of the project's background.

[0048] The data learning department can automatically extract risk factors for a project based on the learned data and make proposals for risk management. For example, the generative AI analyzes project documents and communication data to automatically extract risk factors. For example, it identifies risk factors based on problems and issues that have arisen in past projects. The data learning department also makes proposals for risk management. For example, it proposes risk avoidance measures, risk mitigation measures, risk acceptance measures, etc. This allows for efficient project risk management.

[0049] The material learning section also learns visual data related to the project, allowing for a deeper understanding of the project based on visual information. For example, the generative AI automatically collects visual data related to the project and adds it to the learning data. For example, it analyzes blueprints and graphs to understand detailed project information. The material learning section also deepens understanding of the project based on visual information. For example, it uses data visualization, infographics, presentation materials, etc. This allows for a deeper understanding of the project based on visual information.

[0050] The data learning unit can automatically extract examples of project success or failure based on the learned data and provide them as lessons for other projects. For example, the generative AI analyzes project materials and communication data to automatically extract examples of success or failure. For example, it identifies the factors that led to success or failure in past projects. The data learning unit also provides these as lessons for other projects. For example, it provides points to learn from success cases and points to learn from failure cases. This can be provided as lessons for other projects.

[0051] The information provision department can analyze the past experience and skill sets of people joining a project and provide customized information that is optimal for that person. For example, the generation AI can analyze the past experience and skill sets of people joining a project and provide customized information that is optimal for that person based on that information. For example, relevant materials can be provided preferentially to people with specific skills. The information provision department also provides information according to individual needs and personalized training plans. This allows new people joining a project to be provided with the most optimal information, enabling them to quickly understand the process and become immediately effective.

[0052] The information provision department monitors the progress of the project in real time and can provide appropriate advice to new project members based on the latest information. For example, the generation AI monitors the progress of the project in real time and provides the latest information to new project members. For example, it automatically generates graphs and reports showing the progress. The information provision department also provides appropriate advice based on the latest information. For example, it provides information on how to proceed with the project, risk management, and problem-solving methods. This allows new project members to receive appropriate advice based on the latest information.

[0053] The information provision department can automatically provide project-related training materials or video tutorials to new project participants. For example, a system can be built in which generative AI automatically provides project-related training materials and video tutorials to new project participants. For example, it can provide videos that explain the project's overview and basic operation methods. The information provision department can also provide online videos, recorded sessions, interactive videos, etc. This allows new project participants to quickly obtain the training materials and video tutorials they need.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The document learning section studies project-related documents or past communication data. For example, it studies documents such as project plans, progress reports, and meeting minutes. It can also study communication data such as emails, chat logs, and meeting recordings. Step 2: The information provision section quickly provides information to new project members based on the information learned by the material learning section. For example, the generation AI can instantly answer questions such as, "What is the purpose of this project?", "What is the current progress?", and "What challenges do you face?". Information can also be provided in the form of real-time notifications, dashboard displays, email notifications, and more.

[0056] (Example 2) The generative AI service according to an embodiment of the present invention is a system that learns project-related materials and past communication data and quickly provides information to people joining the project. This allows new people joining the project to quickly understand and become immediately effective, reduces the burden on those explaining, and improves the concentration of decision makers.

[0057] The generation AI service according to the embodiment includes a document learning unit and an information providing unit. The document learning unit learns project-related documents or past communication data. For example, the document learning unit learns project plans, progress reports, meeting minutes, etc. The document learning unit can also learn communication data such as emails, chat logs, and meeting recordings. The information providing unit quickly provides information to new project participants based on the information learned by the document learning unit. For example, the information providing unit allows the generation AI to instantly answer questions such as, "What is the purpose of this project?", "What is the current progress?", and "What challenges do you have?" The information providing unit can also provide information via real-time notifications, dashboard displays, email notifications, and other methods. This allows new project participants to quickly understand the project and become immediately effective.

[0058] The data learning unit can also learn from external news articles or industry reports related to the project. For example, the generative AI automatically collects external news articles and industry reports related to the project and adds them to the learning data. For example, it can filter news articles based on specific keywords and incorporate them as background information for the project. This allows for a deeper understanding of the project's background.

[0059] The data learning unit can automatically extract risk factors for a project based on the learned data and make proposals for risk management. For example, the data learning unit uses a generative AI to analyze project documents and communication data and automatically extract risk factors. For example, it identifies risk factors based on problems and issues that have arisen in past projects. The data learning unit also makes proposals for risk management. For example, it proposes risk avoidance measures, risk mitigation measures, risk acceptance measures, etc. This allows for efficient project risk management.

[0060] The data learning unit uses the emotion estimation function to analyze the changes in the emotions of project members from past communication data, making it possible to grasp the atmosphere or team dynamics of the project. For example, the data learning unit uses a generation AI to analyze past communication data and analyze the changes in the emotions of project members. For example, it calculates an emotion score from the content of emails and chats to grasp the atmosphere of the project. The data learning unit also uses the emotion estimation function to grasp the atmosphere and team dynamics of the project. For example, it evaluates the team's level of cooperation, stress level, motivation, etc. This makes it possible to grasp the atmosphere and team dynamics of the project.

[0061] The material learning section also learns visual data related to the project, allowing for a deeper understanding of the project based on visual information. For example, the generative AI automatically collects visual data related to the project and adds it to the learning data. For example, it analyzes blueprints and graphs to understand detailed information about the project. The material learning section also deepens understanding of the project based on visual information. For example, it uses data visualization, infographics, presentation materials, etc. This allows for a deeper understanding of the project based on visual information.

[0062] The data learning unit can automatically extract success or failure cases of projects based on the learned data and provide them as lessons for other projects. For example, the data learning unit uses a generative AI to analyze project materials and communication data and automatically extract success or failure cases. For example, it identifies the factors that led to success or failure in past projects. The data learning unit also provides these as lessons for other projects. For example, it provides points to learn from success cases and points to learn from failure cases. This can be provided as lessons for other projects.

[0063] The material learning unit can use the emotion estimation function to highlight particularly emotionally important parts of project-related materials, allowing new project participants to quickly grasp important points. For example, the material learning unit uses the generative AI to analyze project-related materials and identify particularly emotionally important parts using the emotion estimation function. For example, it highlights parts with high emotion scores. The material learning unit also provides the highlighted parts to new project participants. For example, it highlights them using different colors or font sizes. This allows new project participants to quickly grasp important points.

[0064] The information provision department can analyze the past experience and skill sets of people joining a project and provide customized information that is optimal for that person. For example, the information provision department uses a generation AI to analyze the past experience and skill sets of people joining a project, and then customizes and provides optimal information based on that information. For example, it prioritizes providing relevant materials to people with specific skills. The information provision department also provides information tailored to individual needs and personalized training plans. This allows new people joining a project to be provided with the optimal information, enabling them to quickly understand the process and become immediately effective.

[0065] The information provision department monitors the progress of the project in real time and can provide appropriate advice to new project members based on the latest information. For example, the information provision department uses a generation AI to monitor the progress of the project in real time and provide the latest information to new project members. For example, it automatically generates graphs and reports showing the progress. The information provision department also provides appropriate advice based on the latest information. For example, it provides information on how to proceed with the project, risk management, and problem-solving methods. This allows new project members to receive appropriate advice based on the latest information.

[0066] The information provision unit can use the emotion estimation function to analyze the initial emotional reactions of people newly entering a project and provide support to reduce stress or anxiety. For example, the information provision unit uses a generation AI to analyze the initial emotional reactions of people newly entering a project and provide support to reduce stress and anxiety. For example, it can suggest a relaxing environment based on the emotion score. The information provision unit also provides counseling and support programs. For example, it can use stress tests and relaxation techniques. This can reduce stress and anxiety for people newly entering a project.

[0067] The information provision unit can automatically provide project-related training materials or video tutorials to new project participants. For example, the information provision unit builds a system in which generative AI automatically provides project-related training materials and video tutorials to new project participants. For example, it provides videos that explain the project's overview and basic operation methods. The information provision unit also provides online videos, recorded sessions, interactive videos, etc. This allows new project participants to quickly obtain the training materials and video tutorials they need.

[0068] The information provision department can collect feedback from other project members in real time in response to questions from people joining the project newcomers and provide optimal answers. For example, the information provision department will build a system in which a generation AI analyzes questions from people joining the project newcomers and collects feedback from other project members in real time. For example, it will provide optimal answers based on the content of the questions. The information provision department also collects real-time feedback, regular reviews, survey results, etc. This allows people joining the project newcomers to quickly obtain optimal answers.

[0069] The information provision unit can use the emotion estimation function to monitor the emotions that new project participants have toward the project and provide an interactive guide to elicit positive emotions. For example, the information provision unit uses the emotion estimation function to build a system that monitors the emotions that new project participants have toward the project in real time. For example, it provides a guide to elicit positive emotions based on the emotion score. The information provision unit also provides interactive tutorials, interactive simulations, guided tours, and the like. This allows new project participants to work on the project with positive emotions.

[0070] The document learning unit can automatically update project progress or issues based on past documents and provide the latest information. For example, the document learning unit builds a system in which a generative AI analyzes past documents and automatically updates project progress and issues. For example, it provides the latest information based on progress reports and meeting minutes. The document learning unit also provides real-time data, the latest progress reports, the latest technical information, and more. This allows project progress and issues to be understood based on the latest information.

[0071] The material learning unit automatically generates visual elements when creating explanatory materials, making the explanations more effective. For example, the material learning unit builds a system that automatically generates visual elements when a generative AI creates explanatory materials. For example, it automatically generates infographics and dashboards to make explanations more effective. The material learning unit also generates visual elements such as graphs and charts and incorporates them into the explanatory materials. This allows the explanatory materials to be created visually effective.

[0072] The material learning unit can use the emotion estimation function to analyze the recipient's emotional response to past explanation materials and propose the most effective explanation method. For example, the material learning unit uses the emotion estimation function to analyze the recipient's emotional response to past explanation materials and build a system that proposes the most effective explanation method. For example, the explanation method is adjusted based on the emotion score. The material learning unit also evaluates the emotional responses of recipient project members, clients, stakeholders, etc. and proposes an explanation method that will elicit a positive response. This makes it possible to propose the most effective explanation method.

[0073] The Material Learning Department can automatically incorporate success stories or best practices from other projects to create explanatory materials. For example, the Material Learning Department builds a system in which a generative AI automatically collects success stories and best practices from other projects and incorporates them into explanatory materials. For example, it makes specific proposals based on success stories. The Material Learning Department also incorporates best practices such as industry standards and recommended methods to create explanatory materials. This makes it possible to create explanatory materials that incorporate success stories and best practices.

[0074] The material learning unit can support output in different formats when creating explanatory materials. For example, the material learning unit builds a system that supports output in different formats when a generative AI creates explanatory materials. For example, output in the form of a presentation, report, video, etc. The material learning unit also supports output in visual formats such as posters and infographics. This allows explanatory materials to be output in different formats.

[0075] The material learning unit can use the emotion estimation function to highlight particularly emotionally important parts of the explanatory materials, allowing the recipient to quickly grasp the important points. The material learning unit, for example, uses the emotion estimation function to build a system that identifies and highlights particularly emotionally important parts of the explanatory materials. For example, parts with high emotion scores are highlighted using color or font size. The material learning unit also enables the recipient to quickly grasp important points such as main conclusions, important data, and key messages. This allows the recipient to quickly grasp the important points.

[0076] The Data Learning Department can centrally manage information from multiple projects and provide the most important information to decision makers on a priority basis. For example, the Data Learning Department will build a system in which a generative AI centrally manages information from multiple projects and provides the most important information to decision makers on a priority basis. For example, progress and issues will be displayed centrally. The Data Learning Department will also manage information using project management tools, databases, dashboards, etc. This will allow decision makers to quickly grasp the most important information.

[0077] The data learning unit can analyze the decision-maker's past decision-making history and make suggestions to support optimal decision-making. For example, the data learning unit builds a system in which generative AI analyzes the decision-maker's past decision-making history and makes suggestions to support optimal decision-making. For example, it makes suggestions based on past successes and failures. The data learning unit also makes suggestions using criteria such as risk assessment, cost-benefit analysis, and scenario planning. This can support decision-makers in making optimal decisions.

[0078] The data learning unit can use the emotion estimation function to monitor the emotions that decision makers have toward the project and provide support to reduce stress or fatigue. For example, the data learning unit uses the emotion estimation function to build a system that monitors the emotions that decision makers have toward the project in real time. For example, the data learning unit provides support to reduce stress and fatigue based on the emotion score. The data learning unit also provides stress tests, rest programs, support programs, etc., thereby reducing the stress and fatigue of decision makers.

[0079] The data learning unit can report project progress or issues to decision makers in real time, supporting rapid decision-making. For example, the data learning unit builds a system in which the generation AI reports project progress and issues to decision makers in real time. For example, it automatically generates graphs and reports showing progress. The data learning unit also reports by regular reports, real-time notifications, dashboard displays, and other methods. This allows decision makers to make decisions quickly.

[0080] The data learning unit can automatically provide decision makers with success stories or best practices from other projects for reference. For example, the data learning unit will build a system in which generative AI automatically collects success stories and best practices from other projects and provides them to decision makers. For example, it will make specific proposals based on the success stories. The data learning unit will also provide analysis of success stories and methods for applying best practices. This allows decision makers to refer to success stories and best practices from other projects.

[0081] The data learning unit can use the emotion estimation function to analyze the emotions decision makers have toward the project and provide an interactive guide to elicit positive emotions. For example, the data learning unit uses the emotion estimation function to build a system that monitors the emotions decision makers have toward the project in real time. For example, it provides a guide to elicit positive emotions based on the emotion score. The data learning unit also provides interactive tutorials, interactive simulations, guided tours, etc., which enable decision makers to approach the project with positive emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The resource learning unit can also learn from external news articles or industry reports related to the project. For example, the generation AI can automatically collect external news articles and industry reports related to the project and add them to the learning data. For example, it can filter news articles based on specific keywords and incorporate them as background information for the project. This allows for a deeper understanding of the project's background.

[0084] The data learning department can automatically extract risk factors for a project based on the learned data and make proposals for risk management. For example, the generative AI analyzes project documents and communication data to automatically extract risk factors. For example, it identifies risk factors based on problems and issues that have arisen in past projects. The data learning department also makes proposals for risk management. For example, it proposes risk avoidance measures, risk mitigation measures, risk acceptance measures, etc. This allows for efficient project risk management.

[0085] The data learning department uses the emotion estimation function to analyze the changes in the emotions of project members from past communication data, making it possible to grasp the atmosphere of the project or team dynamics. For example, the generation AI analyzes past communication data and analyzes the changes in the emotions of project members. For example, it calculates an emotion score from the content of emails and chats to grasp the atmosphere of the project. The data learning department also uses the emotion estimation function to grasp the atmosphere of the project and team dynamics. For example, it evaluates the team's level of cooperation, stress level, motivation, etc. This makes it possible to grasp the atmosphere of the project and team dynamics.

[0086] The material learning section also learns visual data related to the project, allowing for a deeper understanding of the project based on visual information. For example, the generative AI automatically collects visual data related to the project and adds it to the learning data. For example, it analyzes blueprints and graphs to understand detailed project information. The material learning section also deepens understanding of the project based on visual information. For example, it uses data visualization, infographics, presentation materials, etc. This allows for a deeper understanding of the project based on visual information.

[0087] The data learning unit can automatically extract examples of project success or failure based on the learned data and provide them as lessons for other projects. For example, the generative AI analyzes project materials and communication data to automatically extract examples of success or failure. For example, it identifies the factors that led to success or failure in past projects. The data learning unit also provides these as lessons for other projects. For example, it provides points to learn from success cases and points to learn from failure cases. This can be provided as lessons for other projects.

[0088] The material learning unit can use the emotion estimation function to highlight particularly emotionally important parts of project-related materials, allowing new project participants to quickly grasp the key points. For example, the generative AI analyzes project-related materials and uses the emotion estimation function to identify particularly emotionally important parts. For example, it highlights parts with high emotion scores. The material learning unit then provides the highlighted parts to new project participants. For example, it highlights them using different colors or font sizes. This allows new project participants to quickly grasp the key points.

[0089] The information provision department can analyze the past experience and skill sets of people joining a project and provide customized information that is optimal for that person. For example, the generation AI can analyze the past experience and skill sets of people joining a project and provide customized information that is optimal for that person based on that information. For example, relevant materials can be provided preferentially to people with specific skills. The information provision department also provides information according to individual needs and personalized training plans. This allows new people joining a project to be provided with the most optimal information, enabling them to quickly understand the process and become immediately effective.

[0090] The information provision department monitors the progress of the project in real time and can provide appropriate advice to new project members based on the latest information. For example, the generation AI monitors the progress of the project in real time and provides the latest information to new project members. For example, it automatically generates graphs and reports showing the progress. The information provision department also provides appropriate advice based on the latest information. For example, it provides information on how to proceed with the project, risk management, and problem-solving methods. This allows new project members to receive appropriate advice based on the latest information.

[0091] The information provision unit can use the emotion estimation function to analyze the initial emotional reactions of people newly entering a project and provide support to reduce stress or anxiety. For example, the generation AI can analyze the initial emotional reactions of people newly entering a project and provide support to reduce stress and anxiety. For example, it can suggest a relaxing environment based on the emotion score. The information provision unit also provides counseling and support programs. For example, it can use stress tests and relaxation techniques. This can reduce stress and anxiety for people newly entering a project.

[0092] The information provision department can automatically provide project-related training materials or video tutorials to new project participants. For example, a system can be built in which generative AI automatically provides project-related training materials and video tutorials to new project participants. For example, it can provide videos that explain the project's overview and basic operation methods. The information provision department can also provide online videos, recorded sessions, interactive videos, etc. This allows new project participants to quickly obtain the training materials and video tutorials they need.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The document learning section studies project-related documents or past communication data. For example, it studies documents such as project plans, progress reports, and meeting minutes. It can also study communication data such as emails, chat logs, and meeting recordings. Step 2: The information provision section quickly provides information to new project members based on the information learned by the material learning section. For example, the generation AI can instantly answer questions such as, "What is the purpose of this project?", "What is the current progress?", and "What challenges do you face?". Information can also be provided in the form of real-time notifications, dashboard displays, email notifications, and more.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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. A resource learning department that studies project-related materials and past communication data; and an information providing unit that quickly provides information to new people joining the project based on the information learned by the material learning unit. A system characterized by:

2. The material learning unit Automatically extract risk factors for projects based on learned data and make proposals for risk management The system of claim 1 .

3. The material learning unit Learn visual data related to the project and deepen your understanding of the project based on visual information. The system of claim 1 .

4. The information providing unit Analyzing the past experience and skill sets of the new project participants and providing customized information that is best suited to each individual. The system of claim 1 .

5. The material learning unit Automatically update project status or issues based on historical data to provide up-to-date information 2. The system of claim 1.

6. The material learning unit Centralize information management across multiple projects and provide the most important information to decision makers on a priority basis. The system of claim 1 .

7. The material learning unit Using emotion estimation, we analyze the emotional transitions of project members from past communication data to understand the project atmosphere or team dynamics.

2. The system of claim 1.

8. The information providing unit Using emotion estimation, analyze the initial emotional reactions of the new project participants and provide support to reduce stress or anxiety.

2. The system of claim 1.

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