System

Abstract

An object of a system according to an embodiment is to utilize a digital twin in order to improve work efficiency and reduce work time.SOLUTION: A system according to an embodiment includes a digital twin generation unit, an operation record collection unit, a work analysis unit, a learning unit, and a proposal unit. The digital twin generation unit generates a digital twin based on real-world data. The operation record collection unit collects an operation record of the digital twin generated by the digital twin generation unit. The business analysis unit analyzes the data collected by the operation record collection unit. The learning unit learns in order to efficiently perform the same business next time based on the data analyzed by the business analysis unit. The proposal unit proposes a method of converting the work into data and shortening the time on the basis of the content learned by the learning unit.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 does not adequately collect and analyze data to improve work efficiency and reduce time, leaving room for improvement.

[0005] The system according to the embodiment aims to utilize digital twins to improve work efficiency and reduce time. [Means for solving the problem]

[0006] The system according to the embodiment comprises a digital twin generation unit, an operation record collection unit, a business analysis unit, a learning unit, and a proposal unit. The digital twin generation unit generates a digital twin based on real-world data. The operation record collection unit collects operation records of the digital twin generated by the digital twin generation unit. The business analysis unit analyzes the data collected by the operation record collection unit. The learning unit learns based on the data analyzed by the business analysis unit in order to perform the same business efficiently next time. The proposal unit proposes methods for digitizing business operations and reducing time based on what has been learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize digital twins to improve work efficiency and reduce time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The business efficiency improvement system according to an embodiment of the present invention uses digital twin technology to create a digital twin based on real-world data and places it in a virtual office to improve business efficiency. This allows the business efficiency system to process more than twice as much work in the same amount of time.

[0029] A business efficiency improvement system according to an embodiment includes a digital twin generation unit, an action record collection unit, a task analysis unit, a learning unit, and a proposal unit. The digital twin generation unit generates a digital twin based on real-world data. For example, it collects behavioral data and task data of employees in an office and generates a digital twin based on the collected data. The action record collection unit collects the digital twin's action records. For example, it records the content and time of tasks performed by the digital twin. The task analysis unit analyzes the collected data. For example, it analyzes the efficiency and areas for improvement of tasks performed by the digital twin. The learning unit learns based on the analyzed data to perform the same tasks more efficiently next time. For example, the digital twin learns the procedures and methods used in the previous tasks. The proposal unit proposes methods for digitizing tasks and reducing time based on the learned content. For example, the digital twin analyzes task data and proposes efficient procedures and tool usage methods. As a result, the business efficiency system according to an embodiment uses the digital twin to improve business efficiency and reduce time. For example, the digital twin can manage meeting schedules and respond to emails, allowing employees to focus on more important tasks. In addition, digital twins can analyze business data and suggest efficient procedures, significantly reducing business processing time.

[0030] The digital twin generation unit can perform a detailed analysis of an employee's past work history and skill set to create an optimal work profile. For example, the digital twin generation unit registers an employee's past work history in a database, and the generation AI analyzes that data to create an optimal work profile. For example, tasks are assigned based on data from past successful projects. The digital twin generation unit also analyzes an employee's skill set in detail and builds a system that assigns tasks to the digital twin that are best suited to their skills. For example, an employee who is well-versed in a particular technology is assigned tasks that make use of that technology. The digital twin generation unit also performs an integrated analysis of work history and skill sets to create a work profile that makes the most of an employee's strengths. For example, an employee with strong leadership skills could be assigned the role of project manager. This allows for the creation of optimal work profiles based on an employee's past work history and skill set, thereby improving work efficiency.

[0031] The digital twin generation unit can simulate interactions with other digital twins within a virtual office and propose optimal team compositions. The digital twin generation unit, for example, builds a system that simulates interactions between digital twins within a virtual office and proposes optimal team compositions. For example, it analyzes the frequency of communication and cooperative relationships. The digital twin generation unit also collects digital twin interaction data, and the generation AI proposes optimal team compositions based on that data. For example, it places digital twins that get along well together in the same team. The digital twin generation unit also develops a system that dynamically adjusts the placement of digital twins based on the simulation results and achieves optimal team compositions. For example, it changes the team composition depending on the progress of the project. In this way, it is possible to propose optimal team compositions by simulating the interactions between digital twins within a virtual office.

[0032] The digital twin generation unit can integrate data from different industries and occupations to generate highly versatile digital twins. For example, the digital twin generation unit collects data from different industries, and the generation AI analyzes that data to generate highly versatile digital twins. For example, it integrates data from manufacturing and services. The digital twin generation unit also integrates data from different occupations to build a system that enables digital twins to handle a variety of tasks. For example, it integrates data from engineers and marketing personnel. The digital twin generation unit also analyzes data from different industries, extracts common business processes, and generates highly versatile digital twins. For example, it creates a versatile digital twin based on project management data. This makes it possible to generate highly versatile digital twins by integrating data from different industries and occupations.

[0033] The digital twin generation unit can be applied to support household chores and childcare within the home, thereby improving efficiency within the home. For example, the digital twin generation unit applies digital twins to support household chores and childcare within the home, building a system that improves efficiency within the home. For example, it automates household schedule management and childcare tasks. The digital twin generation unit also collects household data, and the generation AI analyzes that data to propose optimal household chore and childcare support. For example, it proposes efficient procedures for cleaning and cooking. The digital twin generation unit also develops a system in which the home's digital twin performs household chores and childcare tasks, improving efficiency within the home. For example, it automatically records childcare activities, reducing the burden of childcare. This can be applied to support household chores and childcare within the home, improving efficiency within the home.

[0034] The action record collection unit simultaneously collects audio data and visual data, enabling more detailed work analysis. For example, the action record collection unit simultaneously collects audio and visual data in addition to the digital twin's action records, building a system that performs more detailed work analysis. For example, it collects audio data from meetings and video data from work. The action record collection unit also uses voice recognition technology to analyze the audio data from the digital twin's work to identify areas for work efficiency and improvement. For example, it analyzes what is said during meetings and automatically generates minutes. The action record collection unit also analyzes visual data and integrates it with the digital twin's action records to perform a detailed analysis of work. For example, it analyzes hand movements during work to suggest efficient work procedures. In this way, by simultaneously collecting audio and visual data, more detailed work analysis can be performed.

[0035] The operation record collection unit can analyze the operation records in real time and provide immediate feedback to improve work efficiency. The operation record collection unit, for example, builds a system that analyzes the operation records of a digital twin in real time and provides immediate feedback to improve work efficiency. For example, it detects unnecessary movements during work and makes suggestions for improvement. The operation record collection unit also uses real-time analysis technology to immediately analyze the operation records of the digital twin and provides feedback to improve work efficiency. For example, it monitors the progress of work in real time and suggests efficient procedures. The operation record collection unit also develops a system in which the digital twin provides immediate feedback to improve work efficiency based on the results of the real-time analysis of the operation records. For example, it immediately detects errors during work and makes suggestions for corrections. In this way, by analyzing the operation records in real time, it is possible to provide immediate feedback to improve work efficiency.

[0036] The behavior record collection unit can be applied to different work environments, allowing for a wide range of work analysis. For example, the behavior record collection unit can apply the digital twin's behavior records to different work environments, such as remote work and on-site work, to build a system that performs a wide range of work analysis. For example, it can analyze work efficiency during remote work. The behavior record collection unit also collects data from different work environments, and the generation AI analyzes that data to identify areas for work efficiency and improvement. For example, it can analyze behavior records during on-site work to propose efficient work procedures. The behavior record collection unit can also develop a system that comprehensively analyzes the digital twin's behavior records in different work environments, such as remote work and on-site work, and makes proposals for improving work efficiency. For example, it can propose measures to improve communication during remote work. This allows for a wide range of work analysis to be performed by applying it to different work environments.

[0037] The motion record collection unit can be applied to a system that provides health management and fitness advice based on the motion records. For example, the motion record collection unit builds a system that provides health management and fitness advice based on the motion records of the digital twin. For example, it analyzes posture and movements while working to provide healthy advice. The motion record collection unit also analyzes the motion record data, and a generation AI provides fitness advice based on that data. For example, it detects lack of exercise and suggests an appropriate exercise program. The motion record collection unit also applies the motion records of the digital twin to a system that provides health management and fitness advice to improve the user's health. For example, it suggests stretching and exercises while working. In this way, the user's health can be improved by providing health management and fitness advice based on the motion records.

[0038] The learning unit can develop an algorithm that compares different business processes and automatically selects the most efficient method. For example, the learning unit builds a system that compares different business processes and develops an algorithm that automatically selects the most efficient method. For example, it selects the optimal business process based on past data. The learning unit also collects business process data, and the generation AI analyzes the data to propose the most efficient method. For example, it compares the efficiency of performing the same work in different ways. The learning unit also analyzes different business processes in real time and develops an algorithm that automatically selects the most efficient method. For example, it proposes the optimal method depending on the progress of the work. In this way, it is possible to improve business efficiency by comparing different business processes and automatically selecting the most efficient method.

[0039] The learning unit can share learning data on the cloud and create an environment in which multiple digital twins can learn simultaneously. The learning unit, for example, develops a system that shares learning data on the cloud and creates an environment in which multiple digital twins can learn simultaneously. For example, it installs a learning database on the cloud. The learning unit also updates the learning data on the cloud in real time, allowing multiple digital twins to learn the latest data simultaneously. For example, when a new business process is added, it is shared immediately. The learning unit also integrates learning data on the cloud and creates a system that provides an environment in which multiple digital twins can learn simultaneously. For example, different digital twins learn based on the same data. This makes it possible to create an environment in which multiple digital twins can learn simultaneously by sharing learning data on the cloud.

[0040] The learning department can apply this to education and training programs to improve learning efficiency. For example, the learning department will apply digital twin learning to education and training programs to build a system that improves learning efficiency. For example, digital twins will be introduced into in-house training programs. The learning department will also collect education data, and generative AI will analyze the data to propose optimal training programs. For example, training content will be provided according to the skill level of employees. The learning department will also develop a system in which digital twins implement education and training programs to improve learning efficiency. For example, training progress will be monitored in real time and appropriate feedback will be provided. This will allow learning efficiency to be improved by applying it to education and training programs.

[0041] The learning department can share the business processes learned by the digital twin with other digital twins and actual employees, improving overall business efficiency. For example, the learning department builds a system that shares the business processes learned by the digital twin with other digital twins and actual employees, improving overall business efficiency. For example, it develops a platform for sharing learning content. The learning department also shares learning data so that other digital twins and employees can perform their work based on that data. For example, it automatically generates and shares business process manuals. The learning department also develops a system that shares the business processes learned by the digital twin in real time, improving overall business efficiency. For example, it shares new business processes immediately when they are added. In this way, by sharing the business processes learned by the digital twin, overall business efficiency can be improved.

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

[0043] The work efficiency system can also include a health management unit that monitors the user's health condition and makes suggestions for health management. For example, it collects the user's heart rate and sleep data and analyzes their health condition. The health management unit can also suggest appropriate exercise and diet based on the user's health data. For example, it can suggest regular stretching and walking to a user who sits for long periods of time. The health management unit can also monitor the user's health condition in real time and issue an alert if an abnormality is detected. In this way, the work efficiency system can support the user's health management and provide a healthy work environment.

[0044] The digital twin generation unit can perform a detailed analysis of a user's past work history and skill sets to create an optimal work profile. For example, it can assign work based on data from past successful projects. The digital twin generation unit can also build a system that analyzes employees' skill sets in detail and assigns the digital twin work that is best suited to their skills. For example, an employee who is well-versed in a particular technology can be assigned work that makes use of that technology. The digital twin generation unit can also perform an integrated analysis of work history and skill sets to create a work profile that makes the most of an employee's strengths. For example, an employee with strong leadership skills can be assigned the role of project manager. This makes it possible to improve work efficiency by creating an optimal work profile based on an employee's past work history and skill sets.

[0045] The digital twin generation unit can simulate interactions with other digital twins within a virtual office and propose optimal team compositions. For example, a system can be built that simulates interactions between digital twins within a virtual office and proposes optimal team compositions. For example, it can analyze the frequency of communication and cooperative relationships. The digital twin generation unit also collects digital twin interaction data, and the generation AI proposes optimal team compositions based on that data. For example, it can place digital twins that get along well together in the same team. The digital twin generation unit can also develop a system that dynamically adjusts the placement of digital twins based on the simulation results and achieves optimal team compositions. For example, it can change the team composition depending on the progress of the project. This makes it possible to propose optimal team compositions by simulating the interactions between digital twins within a virtual office.

[0046] The digital twin generation unit can integrate data from different industries and occupations to generate highly versatile digital twins. For example, data from different industries is collected and the generation AI analyzes the data to generate highly versatile digital twins. For example, data from manufacturing and services is integrated. The digital twin generation unit also integrates data from different occupations to build a system that enables digital twins to handle a variety of tasks. For example, data from engineers and marketers is integrated. The digital twin generation unit also analyzes data from different industries, extracts common business processes, and generates highly versatile digital twins. For example, a versatile digital twin is created based on project management data. This makes it possible to generate highly versatile digital twins by integrating data from different industries and occupations.

[0047] The digital twin generation unit can be applied to support household chores and childcare within the home, thereby improving efficiency within the home. For example, digital twins can be applied to support household chores and childcare within the home to build a system that improves efficiency within the home. For example, household schedule management and childcare tasks can be automated. The digital twin generation unit also collects data within the home, and the generation AI analyzes that data to propose optimal support for household chores and childcare. For example, it can propose efficient procedures for cleaning and cooking. The digital twin generation unit also develops a system in which the home's digital twin performs household chores and childcare tasks, improving efficiency within the home. For example, it can automatically record childcare activities to reduce the burden of childcare. This can be applied to support household chores and childcare within the home to improve efficiency within the home.

[0048] The action record collection unit simultaneously collects audio and visual data, enabling more detailed work analysis. For example, in addition to the digital twin's action records, audio and visual data can also be collected simultaneously to build a system that performs more detailed work analysis. For example, audio data from meetings and video data from work are collected. The action record collection unit also uses voice recognition technology to analyze the audio data from the digital twin's work to identify areas for work efficiency and improvement. For example, it analyzes what is said during meetings and automatically generates minutes. The action record collection unit also analyzes visual data and integrates it with the digital twin's action records to perform a detailed analysis of work. For example, it analyzes hand movements during work to suggest efficient work procedures. In this way, by simultaneously collecting audio and visual data, more detailed work analysis can be performed.

[0049] The operation record collection unit can analyze operation records in real time and provide immediate feedback to improve work efficiency. For example, a system can be built that analyzes the operation records of digital twins in real time and provides immediate feedback to improve work efficiency. For example, it can detect unnecessary movements during work and make suggestions for improvement. The operation record collection unit can also use real-time analysis technology to instantly analyze the operation records of digital twins and provide feedback to improve work efficiency. For example, it can monitor the progress of work in real time and suggest efficient procedures. The operation record collection unit can also develop a system that uses the results of real-time analysis of operation records to enable the digital twin to provide immediate feedback to improve work efficiency. For example, it can instantly detect errors during work and make suggestions for corrections. In this way, by analyzing operation records in real time, it can provide immediate feedback to improve work efficiency.

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

[0051] Step 1: The digital twin generation unit generates a digital twin based on real-world data. For example, it collects data on the behavior and work of employees in the office and generates a digital twin based on that data. Step 2: The operation record collection unit collects the operation records of the digital twin. For example, it records the content and time of the work performed by the digital twin. Step 3: The Business Analysis Department analyzes the collected data, for example, analyzing the efficiency and areas for improvement of the business performed by the digital twin. Step 4: The learning unit learns from the analyzed data to perform the same task more efficiently the next time. For example, the digital twin learns the procedures and methods used in the previous task. Step 5: The proposal department proposes ways to digitize and shorten work times based on what has been learned. For example, the digital twin analyzes work data and proposes efficient procedures and tool usage methods.

[0052] (Example 2) The business efficiency improvement system according to an embodiment of the present invention uses digital twin technology to create a digital twin based on real-world data and places it in a virtual office to improve business efficiency. This allows the business efficiency system to process more than twice as much work in the same amount of time.

[0053] A business efficiency improvement system according to an embodiment includes a digital twin generation unit, an action record collection unit, a task analysis unit, a learning unit, and a proposal unit. The digital twin generation unit generates a digital twin based on real-world data. For example, it collects behavioral data and task data of employees in an office and generates a digital twin based on the collected data. The action record collection unit collects the digital twin's action records. For example, it records the content and time of tasks performed by the digital twin. The task analysis unit analyzes the collected data. For example, it analyzes the efficiency and areas for improvement of tasks performed by the digital twin. The learning unit learns based on the analyzed data to perform the same tasks more efficiently next time. For example, the digital twin learns the procedures and methods used in the previous tasks. The proposal unit proposes methods for digitizing tasks and reducing time based on the learned content. For example, the digital twin analyzes task data and proposes efficient procedures and tool usage methods. As a result, the business efficiency system according to an embodiment uses the digital twin to improve business efficiency and reduce time. For example, the digital twin can manage meeting schedules and respond to emails, allowing employees to focus on more important tasks. In addition, digital twins can analyze business data and suggest efficient procedures, significantly reducing business processing time.

[0054] The digital twin generation unit uses generative AI to estimate employee emotions and stress levels and can optimally assign tasks based on that. For example, the digital twin generation unit uses generative AI to analyze employee emotions and stress levels in real time and optimize the digital twin's task assignments based on that data. For example, employees with high stress levels are assigned less demanding tasks. The digital twin generation unit also collects employee emotion data, and the generative AI analyzes that data to propose optimal task assignments. For example, employees with strong positive emotions are assigned the role of team leader. The digital twin generation unit also builds a system in which the digital twin automatically adjusts employee task assignments based on emotions and stress levels. For example, employees with low stress levels are assigned important projects. This makes it possible to improve work efficiency by optimally assigning tasks based on employees' emotions and stress levels.

[0055] The digital twin generation unit can perform a detailed analysis of an employee's past work history and skill set to create an optimal work profile. For example, the digital twin generation unit registers an employee's past work history in a database, and the generation AI analyzes that data to create an optimal work profile. For example, tasks are assigned based on data from past successful projects. The digital twin generation unit also analyzes an employee's skill set in detail and builds a system that assigns tasks to the digital twin that are best suited to their skills. For example, an employee who is well-versed in a particular technology is assigned tasks that make use of that technology. The digital twin generation unit also performs an integrated analysis of work history and skill sets to create a work profile that makes the most of an employee's strengths. For example, an employee with strong leadership skills could be assigned the role of project manager. This allows for the creation of optimal work profiles based on an employee's past work history and skill set, thereby improving work efficiency.

[0056] The digital twin generation unit can simulate interactions with other digital twins within a virtual office and propose optimal team compositions. The digital twin generation unit, for example, builds a system that simulates interactions between digital twins within a virtual office and proposes optimal team compositions. For example, it analyzes the frequency of communication and cooperative relationships. The digital twin generation unit also collects digital twin interaction data, and the generation AI proposes optimal team compositions based on that data. For example, it places digital twins that get along well together in the same team. The digital twin generation unit also develops a system that dynamically adjusts the placement of digital twins based on the simulation results and achieves optimal team compositions. For example, it changes the team composition depending on the progress of the project. In this way, it is possible to propose optimal team compositions by simulating the interactions between digital twins within a virtual office.

[0057] The digital twin generation unit can integrate data from different industries and occupations to generate highly versatile digital twins. For example, the digital twin generation unit collects data from different industries, and the generation AI analyzes that data to generate highly versatile digital twins. For example, it integrates data from manufacturing and services. The digital twin generation unit also integrates data from different occupations to build a system that enables digital twins to handle a variety of tasks. For example, it integrates data from engineers and marketing personnel. The digital twin generation unit also analyzes data from different industries, extracts common business processes, and generates highly versatile digital twins. For example, it creates a versatile digital twin based on project management data. This makes it possible to generate highly versatile digital twins by integrating data from different industries and occupations.

[0058] The digital twin generation unit can be applied to support household chores and childcare within the home, thereby improving efficiency within the home. For example, the digital twin generation unit applies digital twins to support household chores and childcare within the home, building a system that improves efficiency within the home. For example, it automates household schedule management and childcare tasks. The digital twin generation unit also collects household data, and the generation AI analyzes that data to propose optimal household chore and childcare support. For example, it proposes efficient procedures for cleaning and cooking. The digital twin generation unit also develops a system in which the home's digital twin performs household chores and childcare tasks, improving efficiency within the home. For example, it automatically records childcare activities, reducing the burden of childcare. This can be applied to support household chores and childcare within the home, improving efficiency within the home.

[0059] The digital twin generation unit uses the emotion estimation function to enable the digital twin to respond according to the user's emotions. The digital twin generation unit, for example, uses the emotion estimation function to build a system in which the digital twin responds according to the user's emotions. For example, if the user is feeling stressed, the digital twin generation unit may suggest relaxing. The digital twin generation unit may also analyze the user's emotional data in real time, and the digital twin may respond optimally based on that data. For example, if the user is feeling strongly positive, the digital twin generation unit may offer a compliment. The digital twin generation unit may also use the emotion estimation function to develop a system in which the digital twin provides services according to the user's emotions. For example, if the user is tired, the digital twin may play relaxing music. In this way, by using the emotion estimation function, the digital twin may respond according to the user's emotions, improving user satisfaction.

[0060] The behavior record collection unit can use the generation AI to analyze emotional fluctuations and identify work-related stress factors. For example, the behavior record collection unit uses the generation AI to analyze emotional fluctuations from the digital twin's behavior records and build a system to identify work-related stress factors. For example, it analyzes whether a specific task causes stress. The behavior record collection unit also collects emotional data, and the generation AI analyzes that data to identify work-related stress factors. For example, it analyzes whether long meetings cause stress. The behavior record collection unit also develops a system in which the digital twin identifies work-related stress factors based on the emotional fluctuation data and proposes improvement measures. For example, it proposes replacing high-stress tasks with other tasks. In this way, by analyzing emotional fluctuations, work-related stress factors can be identified and work efficiency can be improved.

[0061] The action record collection unit simultaneously collects audio data and visual data, enabling more detailed work analysis. For example, the action record collection unit simultaneously collects audio and visual data in addition to the digital twin's action records, building a system that performs more detailed work analysis. For example, it collects audio data from meetings and video data from work. The action record collection unit also uses voice recognition technology to analyze the audio data from the digital twin's work to identify areas for work efficiency and improvement. For example, it analyzes what is said during meetings and automatically generates minutes. The action record collection unit also analyzes visual data and integrates it with the digital twin's action records to perform a detailed analysis of work. For example, it analyzes hand movements during work to suggest efficient work procedures. In this way, by simultaneously collecting audio and visual data, more detailed work analysis can be performed.

[0062] The operation record collection unit can analyze the operation records in real time and provide immediate feedback to improve work efficiency. The operation record collection unit, for example, builds a system that analyzes the operation records of a digital twin in real time and provides immediate feedback to improve work efficiency. For example, it detects unnecessary movements during work and makes suggestions for improvement. The operation record collection unit also uses real-time analysis technology to immediately analyze the operation records of the digital twin and provides feedback to improve work efficiency. For example, it monitors the progress of work in real time and suggests efficient procedures. The operation record collection unit also develops a system in which the digital twin provides immediate feedback to improve work efficiency based on the results of the real-time analysis of the operation records. For example, it immediately detects errors during work and makes suggestions for corrections. In this way, by analyzing the operation records in real time, it is possible to provide immediate feedback to improve work efficiency.

[0063] The behavior record collection unit can be applied to different work environments, allowing for a wide range of work analysis. For example, the behavior record collection unit can apply the digital twin's behavior records to different work environments, such as remote work and on-site work, to build a system that performs a wide range of work analysis. For example, it can analyze work efficiency during remote work. The behavior record collection unit also collects data from different work environments, and the generation AI analyzes that data to identify areas for work efficiency and improvement. For example, it can analyze behavior records during on-site work to propose efficient work procedures. The behavior record collection unit can also develop a system that comprehensively analyzes the digital twin's behavior records in different work environments, such as remote work and on-site work, and makes proposals for improving work efficiency. For example, it can propose measures to improve communication during remote work. This allows for a wide range of work analysis to be performed by applying it to different work environments.

[0064] The motion record collection unit can be applied to a system that provides health management and fitness advice based on the motion records. For example, the motion record collection unit builds a system that provides health management and fitness advice based on the motion records of the digital twin. For example, it analyzes posture and movements while working to provide healthy advice. The motion record collection unit also analyzes the motion record data, and a generation AI provides fitness advice based on that data. For example, it detects lack of exercise and suggests an appropriate exercise program. The motion record collection unit also applies the motion records of the digital twin to a system that provides health management and fitness advice to improve the user's health. For example, it suggests stretching and exercises while working. In this way, the user's health can be improved by providing health management and fitness advice based on the motion records.

[0065] The behavior record collection unit uses the emotion estimation function to analyze the user's emotional state from the behavior record and make business improvement suggestions based on the emotion. The behavior record collection unit, for example, uses the emotion estimation function to analyze the user's emotional state from the digital twin's behavior record and builds a system that makes business improvement suggestions based on the emotion. For example, it makes a suggestion to relax if the user is highly stressed. The behavior record collection unit also uses the generation AI to analyze the user's emotional state based on the behavior record data and makes business improvement suggestions based on the emotion. For example, it gives a compliment if the user has strong positive emotions. The behavior record collection unit also uses the emotion estimation function to develop a system in which the digital twin analyzes the user's emotional state in real time and makes business improvement suggestions based on the emotion. For example, it suggests taking a break if the user is tired. As a result, the emotion estimation function can be used to analyze the user's emotional state from the behavior record and make business improvement suggestions based on the emotion.

[0066] The learning unit can incorporate emotional feedback using generative AI and provide the optimal learning method according to the emotion. For example, the learning unit uses generative AI to incorporate emotional feedback when the digital twin learns, and build a system that provides the optimal learning method according to the emotion. For example, if positive emotions are strong, the learning content is increased. The learning unit also collects emotional data, and the generative AI analyzes that data to suggest the optimal learning method. For example, if stress is high, the learning content is reduced. The learning unit also develops a system that dynamically adjusts the way the digital twin learns based on emotional feedback. For example, it adjusts the pace of learning according to the user's emotional state. In this way, by incorporating emotional feedback, it is possible to provide the optimal learning method according to the emotion.

[0067] The learning unit can develop an algorithm that compares different business processes and automatically selects the most efficient method. For example, the learning unit builds a system that compares different business processes and develops an algorithm that automatically selects the most efficient method. For example, it selects the optimal business process based on past data. The learning unit also collects business process data, and the generation AI analyzes the data to propose the most efficient method. For example, it compares the efficiency of performing the same work in different ways. The learning unit also analyzes different business processes in real time and develops an algorithm that automatically selects the most efficient method. For example, it proposes the optimal method depending on the progress of the work. In this way, it is possible to improve business efficiency by comparing different business processes and automatically selecting the most efficient method.

[0068] The learning unit can share learning data on the cloud and create an environment in which multiple digital twins can learn simultaneously. The learning unit, for example, develops a system that shares learning data on the cloud and creates an environment in which multiple digital twins can learn simultaneously. For example, it installs a learning database on the cloud. The learning unit also updates the learning data on the cloud in real time, allowing multiple digital twins to learn the latest data simultaneously. For example, when a new business process is added, it is shared immediately. The learning unit also integrates learning data on the cloud and creates a system that provides an environment in which multiple digital twins can learn simultaneously. For example, different digital twins learn based on the same data. This makes it possible to create an environment in which multiple digital twins can learn simultaneously by sharing learning data on the cloud.

[0069] The learning department can apply this to education and training programs to improve learning efficiency. For example, the learning department will apply digital twin learning to education and training programs to build a system that improves learning efficiency. For example, digital twins will be introduced into in-house training programs. The learning department will also collect education data, and generative AI will analyze the data to propose optimal training programs. For example, training content will be provided according to the skill level of employees. The learning department will also develop a system in which digital twins implement education and training programs to improve learning efficiency. For example, training progress will be monitored in real time and appropriate feedback will be provided. This will allow learning efficiency to be improved by applying it to education and training programs.

[0070] The learning department can share the business processes learned by the digital twin with other digital twins and actual employees, improving overall business efficiency. For example, the learning department builds a system that shares the business processes learned by the digital twin with other digital twins and actual employees, improving overall business efficiency. For example, it develops a platform for sharing learning content. The learning department also shares learning data so that other digital twins and employees can perform their work based on that data. For example, it automatically generates and shares business process manuals. The learning department also develops a system that shares the business processes learned by the digital twin in real time, improving overall business efficiency. For example, it shares new business processes immediately when they are added. In this way, by sharing the business processes learned by the digital twin, overall business efficiency can be improved.

[0071] The learning unit can use the emotion estimation function to monitor the emotional state of the digital twin during learning and provide a less stressful learning environment. The learning unit, for example, uses the emotion estimation function to monitor the emotional state of the digital twin during learning and build a system that provides a less stressful learning environment. For example, the learning content is reduced when stress is high. The learning unit also analyzes emotional data during learning in real time and adjusts the learning environment so that the digital twin does not feel stressed. For example, relaxing music is played. The learning unit also uses the emotion estimation function to monitor the emotional state of the digital twin during learning and develop a system that provides a less stressful learning environment. For example, the pace of learning is adjusted. In this way, the emotion estimation function can be used to monitor the emotional state of the digital twin during learning and provide a less stressful learning environment.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The work efficiency system can also include a health management unit that monitors the user's health condition and makes suggestions for health management. For example, it collects the user's heart rate and sleep data and analyzes their health condition. The health management unit can also suggest appropriate exercise and diet based on the user's health data. For example, it can suggest regular stretching and walking to a user who sits for long periods of time. The health management unit can also monitor the user's health condition in real time and issue an alert if an abnormality is detected. In this way, the work efficiency system can support the user's health management and provide a healthy work environment.

[0074] The digital twin generation unit can estimate the user's emotions and provide feedback to improve the user's motivation based on the estimated emotions. For example, if the user is tired, it can suggest a break. If the user is highly motivated, it can send words of praise or encouraging messages. The digital twin generation unit can also suggest long-term strategies to maintain motivation based on the user's emotional data. For example, it can set regular goals or assign tasks that will give the user a sense of accomplishment. This can further improve work efficiency by improving the user's motivation based on their emotions.

[0075] The digital twin generation unit can perform a detailed analysis of a user's past work history and skill sets to create an optimal work profile. For example, it can assign work based on data from past successful projects. The digital twin generation unit can also build a system that analyzes employees' skill sets in detail and assigns the digital twin work that is best suited to their skills. For example, an employee who is well-versed in a particular technology can be assigned work that makes use of that technology. The digital twin generation unit can also perform an integrated analysis of work history and skill sets to create a work profile that makes the most of an employee's strengths. For example, an employee with strong leadership skills can be assigned the role of project manager. This makes it possible to improve work efficiency by creating an optimal work profile based on an employee's past work history and skill sets.

[0076] The digital twin generation unit can simulate interactions with other digital twins within a virtual office and propose optimal team compositions. For example, a system can be built that simulates interactions between digital twins within a virtual office and proposes optimal team compositions. For example, it can analyze the frequency of communication and cooperative relationships. The digital twin generation unit also collects digital twin interaction data, and the generation AI proposes optimal team compositions based on that data. For example, it can place digital twins that get along well together in the same team. The digital twin generation unit can also develop a system that dynamically adjusts the placement of digital twins based on the simulation results and achieves optimal team compositions. For example, it can change the team composition depending on the progress of the project. This makes it possible to propose optimal team compositions by simulating the interactions between digital twins within a virtual office.

[0077] The digital twin generation unit can integrate data from different industries and occupations to generate highly versatile digital twins. For example, data from different industries is collected and the generation AI analyzes the data to generate highly versatile digital twins. For example, data from manufacturing and services is integrated. The digital twin generation unit also integrates data from different occupations to build a system that enables digital twins to handle a variety of tasks. For example, data from engineers and marketers is integrated. The digital twin generation unit also analyzes data from different industries, extracts common business processes, and generates highly versatile digital twins. For example, a versatile digital twin is created based on project management data. This makes it possible to generate highly versatile digital twins by integrating data from different industries and occupations.

[0078] The digital twin generation unit can be applied to support household chores and childcare within the home, thereby improving efficiency within the home. For example, digital twins can be applied to support household chores and childcare within the home to build a system that improves efficiency within the home. For example, household schedule management and childcare tasks can be automated. The digital twin generation unit also collects data within the home, and the generation AI analyzes that data to propose optimal support for household chores and childcare. For example, it can propose efficient procedures for cleaning and cooking. The digital twin generation unit also develops a system in which the home's digital twin performs household chores and childcare tasks, improving efficiency within the home. For example, it can automatically record childcare activities to reduce the burden of childcare. This can be applied to support household chores and childcare within the home to improve efficiency within the home.

[0079] The digital twin generation unit uses the emotion estimation function to enable the digital twin to respond according to the user's emotions. For example, using the emotion estimation function, a system is built in which the digital twin responds according to the user's emotions. For example, if the user is feeling stressed, the digital twin suggests relaxing. The digital twin generation unit also analyzes the user's emotional data in real time, and the digital twin responds optimally based on that data. For example, if the user is feeling strongly positive, the digital twin generation unit uses the emotion estimation function to develop a system in which the digital twin provides services according to the user's emotions. For example, if the user is tired, the digital twin plays relaxing music. In this way, by using the emotion estimation function, the digital twin can respond according to the user's emotions, improving user satisfaction.

[0080] The behavior record collection unit can use a generation AI to analyze emotional fluctuations and identify work-related stress factors. For example, a system can be built using a generation AI to analyze emotional fluctuations from the digital twin's behavior records and identify work-related stress factors. For example, it can analyze whether a specific task causes stress. The behavior record collection unit also collects emotional data, and the generation AI analyzes that data to identify work-related stress factors. For example, it can analyze whether long meetings cause stress. The behavior record collection unit can also develop a system in which the digital twin identifies work-related stress factors based on the emotional fluctuation data and proposes improvement measures. For example, it can propose replacing high-stress tasks with other tasks. In this way, by analyzing emotional fluctuations, work-related stress factors can be identified and work efficiency can be improved.

[0081] The action record collection unit simultaneously collects audio and visual data, enabling more detailed work analysis. For example, in addition to the digital twin's action records, audio and visual data can also be collected simultaneously to build a system that performs more detailed work analysis. For example, audio data from meetings and video data from work are collected. The action record collection unit also uses voice recognition technology to analyze the audio data from the digital twin's work to identify areas for work efficiency and improvement. For example, it analyzes what is said during meetings and automatically generates minutes. The action record collection unit also analyzes visual data and integrates it with the digital twin's action records to perform a detailed analysis of work. For example, it analyzes hand movements during work to suggest efficient work procedures. In this way, by simultaneously collecting audio and visual data, more detailed work analysis can be performed.

[0082] The operation record collection unit can analyze operation records in real time and provide immediate feedback to improve work efficiency. For example, a system can be built that analyzes the operation records of digital twins in real time and provides immediate feedback to improve work efficiency. For example, it can detect unnecessary movements during work and make suggestions for improvement. The operation record collection unit can also use real-time analysis technology to instantly analyze the operation records of digital twins and provide feedback to improve work efficiency. For example, it can monitor the progress of work in real time and suggest efficient procedures. The operation record collection unit can also develop a system that uses the results of real-time analysis of operation records to enable the digital twin to provide immediate feedback to improve work efficiency. For example, it can instantly detect errors during work and make suggestions for corrections. In this way, by analyzing operation records in real time, it can provide immediate feedback to improve work efficiency.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The digital twin generation unit generates a digital twin based on real-world data. For example, it collects data on the behavior and work of employees in the office and generates a digital twin based on that data. Step 2: The operation record collection unit collects the operation records of the digital twin. For example, it records the content and time of the work performed by the digital twin. Step 3: The Business Analysis Department analyzes the collected data, for example, analyzing the efficiency and areas for improvement of the business performed by the digital twin. Step 4: The learning unit learns from the analyzed data to perform the same task more efficiently the next time. For example, the digital twin learns the procedures and methods used in the previous task. Step 5: The proposal department proposes ways to digitize and shorten work times based on what has been learned. For example, the digital twin analyzes work data and proposes efficient procedures and tool usage methods.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 digital twin generation unit that generates a digital twin based on real-world data; an operation record collection unit that collects operation records of the digital twin generated by the digital twin generation unit; a task analysis unit that analyzes the data collected by the operation record collection unit; a learning unit that learns based on the data analyzed by the task analysis unit in order to perform the same task efficiently next time; a proposal unit that proposes a method for converting work data and shortening time based on the content learned by the learning unit. A system characterized by:

2. The digital twin generation unit is Generative AI is used to estimate employee emotions and stress levels, and optimally allocate work based on that.

2. The system of claim 1.

3. The digital twin generation unit is Create optimal work profiles based on detailed analysis of employees' past work history and skill sets 2. The system of claim 1.

4. The digital twin generation unit is Simulate interactions with other digital twins in a virtual office and suggest optimal team configurations 2. The system of claim 1.

5. The digital twin generation unit is Integrate data from different industries and occupations to create a versatile digital twin 2. The system of claim 1.

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