System

Abstract

An object of the system according to the embodiment is to transmit information on a use situation of AI in an organization and to make communication smooth.SOLUTION: A system includes a working way monitoring part, a trouble analysis part, an information transmission part, and a communication support part. The working manner monitoring part monitors the working manner of the employee. The trouble analysis unit analyzes trouble of an organization. The information transmitting part transmits information on the use situation of the AI. The communication support unit makes communication smooth.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] With conventional technology, there is insufficient information sharing and utilization of AI within organizations, creating challenges in facilitating communication.

[0005] The system of the embodiment aims to disseminate information about the status of AI utilization within an organization and facilitate communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a work style monitoring unit, a concern analysis unit, an information transmission unit, and a communication support unit. The work style monitoring unit monitors employees' work styles. The concern analysis unit analyzes the concerns of the organization. The information transmission unit transmits information on the status of AI utilization. The communication support unit facilitates communication. [Effects of the Invention]

[0007] The system according to the embodiment can disseminate information about the status of AI utilization within an organization and facilitate communication. [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 information sharing system according to an embodiment of the present invention is a system that utilizes generative AI to automate information sharing and dissemination within an organization and facilitate communication. As a result, the information sharing system allows organizational members to efficiently share information and introduce appropriate AI solutions.

[0029] The information sharing system according to the embodiment includes a work style monitoring unit, a concern analysis unit, an information transmission unit, and a communication support unit. The work style monitoring unit monitors employees' work styles. For example, it monitors the status of remote work and office work and collects data. The work style monitoring unit can also monitor the use of flextime. For example, it records employees' arrival and departure times and analyzes their work patterns. The work style monitoring unit also monitors employees' work content and identifies the tasks they are performing. For example, it monitors the progress of tasks using a project management tool. The concern analysis unit analyzes organizational concerns. For example, it analyzes concerns entered by employees into ChatUI and proposes appropriate AI solutions. The concern analysis unit can also analyze work-related concerns and interpersonal concerns. For example, it analyzes concerns related to improving work efficiency and proposes appropriate tools. The concern analysis unit also analyzes career concerns and proposes appropriate career paths. For example, it proposes training programs for skill development. The information transmission unit transmits information on the status of AI utilization. For example, it provides a report on which teams are using which AI tools and how. The information dissemination department can also evaluate the frequency of AI tool use and the degree of business efficiency improvement. For example, it can quantitatively evaluate the effectiveness of introducing AI tools and share it with other teams. Furthermore, the information dissemination department regularly disseminates information on the status of AI utilization. For example, it can summarize the usage status of AI tools in a monthly report and share it with all employees. The communication support department facilitates communication. For example, it can automatically create meeting minutes and share them with relevant parties. The communication support department can also report project progress in real time. For example, it can automatically update the progress using a project management tool and notify relevant parties. Furthermore, the communication support department analyzes the content of emails and chats and extracts important information. For example, it can summarize the content of emails and highlight important points. As a result, the information sharing system according to the embodiment enables organizational members to efficiently share information and introduce appropriate AI solutions.For example, suggesting an appropriate AI tool when project management is not going well can help the project progress smoothly. Also, by regularly sharing information about the status of AI utilization, other teams and departments can learn from it and promote the use of AI throughout the organization.

[0030] The Work Style Monitoring Department can propose optimal task assignments by taking into account employees' skill sets and career paths. For example, the Work Style Monitoring Department analyzes employees' skill sets, and the generation AI proposes optimal task assignments. For example, to an employee with specific skills, tasks that allow them to utilize those skills are assigned. The Work Style Monitoring Department also considers employees' career paths and the generation AI proposes task assignments. For example, to an employee who wants to demonstrate leadership in the future, tasks that will foster leadership are assigned. Furthermore, the Work Style Monitoring Department uses the generation AI to propose optimal task assignments based on employees' skill sets and career paths. For example, to an employee who needs to improve their skills, tasks to improve their skills are assigned. In this way, the work style monitoring department can propose optimal task assignments by taking into account employees' skill sets and career paths.

[0031] The work style monitoring unit can analyze conversation content using voice recognition technology and provide insights that are useful for improving work efficiency. The work style monitoring unit, for example, uses voice recognition technology to analyze employee conversation content and provide insights that are useful for improving work efficiency. For example, it analyzes the content of meetings and extracts important points. The work style monitoring unit also analyzes the content of employee conference calls using voice recognition technology and provides insights that are useful for improving work efficiency. For example, it identifies frequently occurring issues and proposes improvement measures. Furthermore, the work style monitoring unit uses voice recognition technology to analyze employee conversation content in real time and provide insights that are useful for improving work efficiency. For example, it automatically records ideas that come up in conversation and shares them later. In this way, it is possible to analyze conversation content and provide insights that are useful for improving work efficiency.

[0032] The work style monitoring department can make specific proposals for improving operations by comparing them with benchmark data from different industries and job types. For example, the work style monitoring department can compare monitoring data on employee work styles with benchmark data from different industries and make specific proposals for improving operations. For example, it can propose improvement measures based on successful examples from other industries. The work style monitoring department can also compare monitoring data on employee work styles with benchmark data from different job types and make specific proposals for improving operations. For example, it can propose proposals to incorporate efficient work styles from other job types. Furthermore, the work style monitoring department can use the benchmark data to analyze monitoring data on employee work styles and make specific proposals for improving operations. For example, it can propose improvement measures based on best practices from other organizations. This makes it possible to make comparisons with benchmark data from different industries and job types and make specific proposals for improving operations.

[0033] The work style monitoring unit can work in conjunction with a health management system to comprehensively manage employees' health conditions. For example, the work style monitoring unit can link employee work style data with a health management system to comprehensively manage employees' health conditions. For example, it can issue an alert to employees who continue to work long hours to encourage them to take a rest. The work style monitoring unit can also work in conjunction with a health management system to manage health conditions based on employee work style data. For example, it can suggest a relaxation program to employees with high stress levels. Furthermore, the work style monitoring unit can integrate employee work style data with the health management system to provide comprehensive health management. For example, it can suggest an exercise program to employees who are not getting enough exercise. This allows the work style monitoring unit to work in conjunction with a health management system to comprehensively manage employees' health conditions.

[0034] The problem analysis unit can refer to similar past cases and provide specific solutions based on success cases and failure cases. For example, when analyzing an organization's problems, the problem analysis unit's generation AI can refer to similar past cases and provide specific solutions based on success cases. For example, it can propose project management methods that have been successful in the past. The problem analysis unit can also refer to similar past cases and provide specific solutions to the organization's problems. For example, it can propose improvement measures based on lessons learned from past failures. Furthermore, when analyzing an organization's problems, the problem analysis unit's generation AI can refer to similar past cases and provide specific solutions based on success cases and failure cases. For example, it can propose solutions based on the success factors of past projects. This makes it possible to refer to similar past cases and provide specific solutions.

[0035] The problem analysis unit uses natural language processing technology to dig deep into the background and root cause of the problem and propose a fundamental solution. For example, when an organization's problem is input, the problem analysis unit uses natural language processing technology to dig deep into the background and root cause of the problem and propose a fundamental solution. For example, it identifies the cause of a project delay and proposes a solution. The problem analysis unit also uses natural language processing technology to analyze the root cause of the organization's problem and proposes a fundamental solution. For example, it identifies the cause of a lack of communication in a team and proposes an improvement measure. Furthermore, when an organization's problem is input, the problem analysis unit uses natural language processing technology to dig deep into the background and root cause of the problem and proposes a fundamental solution. For example, it identifies inefficiencies in a business process and proposes an improvement measure. This makes it possible to dig deep into the background and root cause of the problem and propose a fundamental solution.

[0036] The problem analysis unit can refer to examples from different industries and regions and propose solutions from a global perspective. For example, when an organization's problems are input into the problem analysis unit, the generation AI can refer to examples from different industries and propose solutions from a global perspective. For example, it can propose solutions based on successful examples from other industries. Furthermore, when an organization's problems are input into the problem analysis unit, the generation AI can refer to examples from different industries and propose solutions from a global perspective. For example, it can propose solutions based on successful examples from other regions. Furthermore, when an organization's problems are input into the problem analysis unit, the generation AI can refer to examples from different industries and regions and propose solutions from a global perspective. For example, it can propose solutions based on international best practices. This makes it possible to refer to examples from different industries and regions and propose solutions from a global perspective.

[0037] The problem analysis unit can generate visual notes and mind maps to present solutions in a visually easy-to-understand format. For example, when an organization's problems are input, the problem analysis unit's generation AI generates visual notes to present solutions in a visually easy-to-understand format. For example, the solutions are shown using diagrams and icons. The problem analysis unit also generates mind maps to visually present solutions to the organization's problems. For example, the solution steps are displayed in mind map format. Furthermore, when an organization's problems are input, the problem analysis unit's generation AI generates visual notes and mind maps to present solutions in a visually easy-to-understand format. For example, the solution flow is visualized. This allows the problem analysis unit to generate visual notes and mind maps to present solutions in a visually easy-to-understand format.

[0038] The information dissemination unit can use data visualization technology to generate reports that are visually easy to understand. For example, when disseminating information about the AI ​​utilization status, the information dissemination unit uses data visualization technology to generate reports that are visually easy to understand. For example, data is visualized using graphs and charts. The information dissemination unit also uses data visualization technology to generate reports that visually display the AI ​​utilization status. For example, an interactive dashboard is created. Furthermore, when disseminating information about the AI ​​utilization status, the information dissemination unit uses data visualization technology to generate reports that are visually easy to understand. For example, data trends are visually shown. This makes it possible to generate reports that are visually easy to understand.

[0039] The information dissemination unit can compare with past data to identify trends and patterns. For example, when the information dissemination unit disseminates information about the status of AI utilization, the generating AI compares it with past data to identify trends and patterns. For example, it compares past data with current data to show growth rates. The information dissemination unit also compares past data with current data to identify trends and patterns. For example, it shows changes in the frequency of use of AI tools. Furthermore, when the information dissemination unit disseminates information about the status of AI utilization, the generating AI compares it with past data to identify trends and patterns. For example, it shows the effects of introducing AI by comparing it with past data. This makes it possible to identify trends and patterns by comparing it with past data.

[0040] The information dissemination department can provide insights from a global perspective by comparing data from different industries and regions. For example, when disseminating information about the status of AI utilization, the information dissemination department can provide insights from a global perspective by comparing the generating AI with data from different industries. For example, it can provide insights based on AI utilization cases in other industries. Furthermore, when disseminating information about the status of AI utilization, the generating AI can provide insights from a global perspective by comparing data from different industries and regions. For example, it can provide insights based on international best practices. This allows it to provide insights from a global perspective by comparing data from different industries and regions.

[0041] The information dissemination unit can provide an interactive dashboard, allowing users to explore data on their own. For example, when disseminating information about the AI ​​utilization status, the generating AI can provide an interactive dashboard, allowing users to explore data on their own. For example, detailed information can be accessed by clicking or zooming. The information dissemination unit can also provide an interactive dashboard, allowing users to explore data on their own. For example, specific data can be displayed using a filtering function. Furthermore, when disseminating information about the AI ​​utilization status, the generating AI can provide an interactive dashboard, allowing users to explore data on their own. For example, data visualization can be customized. This allows users to explore data on their own.

[0042] The communication support unit can use natural language processing technology to summarize the content of a conversation and extract important points. For example, in order to facilitate communication, the generation AI in the communication support unit uses natural language processing technology to summarize the content of a conversation and extract important points. For example, it automatically summarizes the main points of a meeting. The communication support unit also uses natural language processing technology to summarize the content of a conversation and extract important points. For example, it picks out important topics from a long conversation. Furthermore, in order to facilitate communication, the generation AI in the communication support unit uses natural language processing technology to summarize the content of a conversation and extract important points. For example, it automatically records ideas that come up in a conversation. This makes it possible to summarize the content of a conversation and extract important points.

[0043] The communication support unit can use speech recognition technology to transcribe the contents of a meeting in real time and automatically generate minutes. For example, to facilitate communication, the communication support unit uses a generation AI to transcribe the contents of a meeting in real time using speech recognition technology and automatically generate minutes. For example, the minutes are shared immediately after the meeting ends. The communication support unit also uses speech recognition technology to transcribe the contents of a meeting in real time and automatically generate minutes. For example, important remarks made during a meeting are automatically recorded. Furthermore, to facilitate communication, the communication support unit uses a generation AI to transcribe the contents of a meeting in real time using speech recognition technology and automatically generate minutes. For example, the main points of the meeting are automatically summarized. This allows the contents of a meeting to be transcribed in real time and minutes to be automatically generated.

[0044] The communication support unit can automatically translate into different languages ​​to support communication between international teams. For example, to facilitate communication, the generation AI automatically translates into different languages ​​to support communication between international teams. For example, the content of a meeting is translated in real time. The communication support unit also automatically translates into different languages ​​to support communication between international teams. For example, the content of emails and chats is automatically translated. For example, to facilitate communication, the communication support unit also automatically translates into different languages ​​to support communication between international teams. For example, documents are provided in multiple languages. This allows automatic translation into different languages ​​to support communication between international teams.

[0045] The communication support unit generates visual notes and mind maps, allowing information to be shared in a visually easy-to-understand format. For example, to facilitate communication, the generation AI of the communication support unit generates visual notes to share information in a visually easy-to-understand format. For example, the main points of a meeting are shown using diagrams and icons. The communication support unit also generates mind maps to visually share information to facilitate communication. For example, the progress of a project is displayed in mind map format. Furthermore, to facilitate communication, the generation AI of the communication support unit generates visual notes and mind maps to share information in a visually easy-to-understand format. For example, the relationships between tasks are visualized. This allows visual notes and mind maps to be generated, allowing information to be shared in a visually easy-to-understand format.

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

[0047] The information sharing system can further include a data analysis unit. The data analysis unit can analyze data within the organization and propose ways to improve and streamline operations. For example, it can analyze project progress data and identify bottlenecks. The data analysis unit can also analyze employee performance data and propose training and skill development opportunities. Furthermore, the data analysis unit can integrate data from across the organization and provide reports to management to support strategic decision-making. This makes it possible to utilize data from across the organization to propose ways to improve and streamline operations.

[0048] The information sharing system can further include a predictive analysis unit. The predictive analysis unit can predict future trends and risks based on past data and support strategic decision-making for the organization. For example, it can analyze sales data and predict future sales trends. The predictive analysis unit can also predict the risk of employee resignation and propose appropriate countermeasures. Furthermore, the predictive analysis unit can predict the probability of project success based on project progress data and support risk management. This makes it possible to predict future trends and risks and support strategic decision-making for the organization.

[0049] The information sharing system can further include a feedback collection unit. The feedback collection unit can collect feedback from employees and customers and identify areas for improvement in the organization. For example, it can conduct employee satisfaction surveys and propose ways to improve work styles. The feedback collection unit can also collect customer opinions and identify areas for improvement in products and services. Furthermore, the feedback collection unit can analyze the collected feedback and provide a report on areas for improvement across the entire organization. This makes it possible to identify areas for improvement in the organization by utilizing feedback from employees and customers.

[0050] The information sharing system can further include a knowledge management department. The knowledge management department can systematically manage knowledge within the organization and enable employees to quickly access the information they need. For example, it can create a database of project success stories and failure stories so that employees can refer to it. The knowledge management department can also manage contact information for employees with specialized knowledge so that they can quickly consult when needed. Furthermore, the knowledge management department can regularly hold knowledge sharing workshops and seminars to help employees improve their skills. This makes it possible to systematically manage knowledge within the organization and enable employees to quickly access the information they need.

[0051] The information sharing system can further include a resource management unit. The resource management unit can efficiently manage resources within an organization and propose optimal resource allocation. For example, it can appropriately allocate the human resources and equipment required for a project. The resource management unit can also monitor resource usage and make proposals to reduce waste. Furthermore, the resource management unit can predict future resource demands and create appropriate resource plans. This makes it possible to efficiently manage resources within an organization and propose optimal resource allocation.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The Work Style Monitoring Department monitors employee work styles. For example, it monitors the status of remote work and office work and collects data. It also monitors the use of flextime and records employees' arrival and departure times. It also uses project management tools to monitor task progress and understand the content of employees' work. Step 2: The Concern Analysis Department analyzes the concerns of the organization. For example, it analyzes concerns entered by employees into ChatUI and proposes appropriate AI solutions. It also analyzes work-related concerns and interpersonal concerns, and concerns about improving work efficiency and proposes appropriate tools. It also analyzes career concerns and proposes training programs for skill development. Step 3: The Information Dissemination Department will disseminate information on the status of AI utilization. For example, they will provide reports on which teams are using which AI tools and how. They will also evaluate the frequency of AI tool use and the degree of operational efficiency improvement, quantitatively evaluate the effects of implementation, and share this information with other teams. Furthermore, they will regularly disseminate information on the status of AI utilization, and share the status of AI tool usage with all employees in the form of monthly reports. Step 4: The communication support department facilitates communication. For example, it automatically creates meeting minutes and shares them with relevant parties. It also automatically updates progress using project management tools and notifies relevant parties. It also analyzes email and chat content, extracts and summarizes important information, and highlights key points.

[0054] (Example 2) The information sharing system according to an embodiment of the present invention is a system that utilizes generative AI to automate information sharing and dissemination within an organization and facilitate communication. As a result, the information sharing system allows organizational members to efficiently share information and introduce appropriate AI solutions.

[0055] The information sharing system according to the embodiment includes a work style monitoring unit, a concern analysis unit, an information transmission unit, and a communication support unit. The work style monitoring unit monitors employees' work styles. For example, it monitors the status of remote work and office work and collects data. The work style monitoring unit can also monitor the use of flextime. For example, it records employees' arrival and departure times and analyzes their work patterns. The work style monitoring unit also monitors employees' work content and identifies the tasks they are performing. For example, it monitors the progress of tasks using a project management tool. The concern analysis unit analyzes organizational concerns. For example, it analyzes concerns entered by employees into ChatUI and proposes appropriate AI solutions. The concern analysis unit can also analyze work-related concerns and interpersonal concerns. For example, it analyzes concerns related to improving work efficiency and proposes appropriate tools. The concern analysis unit also analyzes career concerns and proposes appropriate career paths. For example, it proposes training programs for skill development. The information transmission unit transmits information on the status of AI utilization. For example, it provides a report on which teams are using which AI tools and how. The information dissemination department can also evaluate the frequency of AI tool use and the degree of business efficiency improvement. For example, it can quantitatively evaluate the effectiveness of introducing AI tools and share it with other teams. Furthermore, the information dissemination department regularly disseminates information on the status of AI utilization. For example, it can summarize the usage status of AI tools in a monthly report and share it with all employees. The communication support department facilitates communication. For example, it can automatically create meeting minutes and share them with relevant parties. The communication support department can also report project progress in real time. For example, it can automatically update the progress using a project management tool and notify relevant parties. Furthermore, the communication support department analyzes the content of emails and chats and extracts important information. For example, it can summarize the content of emails and highlight important points. As a result, the information sharing system according to the embodiment enables organizational members to efficiently share information and introduce appropriate AI solutions.For example, suggesting an appropriate AI tool when project management is not going well can help the project progress smoothly. Also, by regularly sharing information about the status of AI utilization, other teams and departments can learn from it and promote the use of AI throughout the organization.

[0056] The work style monitoring unit uses the emotion estimation function to analyze employees' stress levels and motivation in real time and provide appropriate feedback. For example, when monitoring employees' work styles, the work style monitoring unit uses the generation AI to analyze employees' stress levels in real time. For example, it analyzes the content of employees' emails and chats to detect signs of stress. The work style monitoring unit also uses the generation AI to analyze employees' motivation in real time. For example, it analyzes employees' schedules and task progress to detect a decline in motivation. Furthermore, the work style monitoring unit uses the emotion estimation function to analyze employees' stress levels and motivation and provide appropriate feedback. For example, it suggests relaxation methods for employees with high stress levels. This makes it possible to analyze employees' stress levels and motivation in real time and provide appropriate feedback.

[0057] The Work Style Monitoring Department can propose optimal task assignments by taking into account employees' skill sets and career paths. For example, the Work Style Monitoring Department analyzes employees' skill sets, and the generation AI proposes optimal task assignments. For example, to an employee with specific skills, tasks that allow them to utilize those skills are assigned. The Work Style Monitoring Department also considers employees' career paths and the generation AI proposes task assignments. For example, to an employee who wants to demonstrate leadership in the future, tasks that will foster leadership are assigned. Furthermore, the Work Style Monitoring Department uses the generation AI to propose optimal task assignments based on employees' skill sets and career paths. For example, to an employee who needs to improve their skills, tasks to improve their skills are assigned. In this way, the work style monitoring department can propose optimal task assignments by taking into account employees' skill sets and career paths.

[0058] The work style monitoring unit can analyze conversation content using voice recognition technology and provide insights that are useful for improving work efficiency. The work style monitoring unit, for example, uses voice recognition technology to analyze employee conversation content and provide insights that are useful for improving work efficiency. For example, it analyzes the content of meetings and extracts important points. The work style monitoring unit also analyzes the content of employee conference calls using voice recognition technology and provides insights that are useful for improving work efficiency. For example, it identifies frequently occurring issues and proposes improvement measures. Furthermore, the work style monitoring unit uses voice recognition technology to analyze employee conversation content in real time and provide insights that are useful for improving work efficiency. For example, it automatically records ideas that come up in conversation and shares them later. In this way, it is possible to analyze conversation content and provide insights that are useful for improving work efficiency.

[0059] The work style monitoring department can make specific proposals for improving operations by comparing them with benchmark data from different industries and job types. For example, the work style monitoring department can compare monitoring data on employee work styles with benchmark data from different industries and make specific proposals for improving operations. For example, it can propose improvement measures based on successful examples from other industries. The work style monitoring department can also compare monitoring data on employee work styles with benchmark data from different job types and make specific proposals for improving operations. For example, it can propose proposals to incorporate efficient work styles from other job types. Furthermore, the work style monitoring department can use the benchmark data to analyze monitoring data on employee work styles and make specific proposals for improving operations. For example, it can propose improvement measures based on best practices from other organizations. This makes it possible to make comparisons with benchmark data from different industries and job types and make specific proposals for improving operations.

[0060] The work style monitoring unit can work in conjunction with a health management system to comprehensively manage employees' health conditions. For example, the work style monitoring unit can link employee work style data with a health management system to comprehensively manage employees' health conditions. For example, it can issue an alert to employees who continue to work long hours to encourage them to take a rest. The work style monitoring unit can also work in conjunction with a health management system to manage health conditions based on employee work style data. For example, it can suggest a relaxation program to employees with high stress levels. Furthermore, the work style monitoring unit can integrate employee work style data with the health management system to provide comprehensive health management. For example, it can suggest an exercise program to employees who are not getting enough exercise. This allows the work style monitoring unit to work in conjunction with a health management system to comprehensively manage employees' health conditions.

[0061] The work style monitoring unit can use the emotion estimation function to analyze employee work style data and automatically suggest events for team building and motivation improvement. The work style monitoring unit, for example, uses the emotion estimation function to analyze employee work style data and automatically suggest team building events. For example, it can suggest a refreshing event for a team with high stress. The work style monitoring unit also uses the emotion estimation function to analyze work style data and suggest appropriate events to improve employee motivation. For example, it can suggest a motivation-boosting seminar for employees with low motivation. Furthermore, the work style monitoring unit uses the emotion estimation function to automatically suggest events for team building and motivation improvement based on employee work style data. For example, it can suggest a workshop to increase team cohesion. This makes it possible to automatically suggest events for team building and motivation improvement.

[0062] The problem analysis unit uses the emotion estimation function to analyze the emotional nuances of the input problem and can propose more appropriate solutions. For example, when an organization's problem is input, the generation AI uses the emotion estimation function to analyze the emotional nuances. For example, if the problem content includes stress or anxiety, the generation AI proposes a corresponding solution. The problem analysis unit also uses the emotion estimation function to analyze the emotional nuances of the input problem and proposes an appropriate solution. For example, it proposes a solution to a decrease in motivation. Furthermore, when an organization's problem is input, the generation AI uses the emotion estimation function to analyze the emotional nuances and proposes a more appropriate solution. For example, it proposes a solution to team discord. In this way, it is possible to analyze emotional nuances and propose more appropriate solutions.

[0063] The problem analysis unit can refer to similar past cases and provide specific solutions based on success cases and failure cases. For example, when analyzing an organization's problems, the problem analysis unit's generation AI can refer to similar past cases and provide specific solutions based on success cases. For example, it can propose project management methods that have been successful in the past. The problem analysis unit can also refer to similar past cases and provide specific solutions to the organization's problems. For example, it can propose improvement measures based on lessons learned from past failures. Furthermore, when analyzing an organization's problems, the problem analysis unit's generation AI can refer to similar past cases and provide specific solutions based on success cases and failure cases. For example, it can propose solutions based on the success factors of past projects. This makes it possible to refer to similar past cases and provide specific solutions.

[0064] The problem analysis unit uses natural language processing technology to dig deep into the background and root cause of the problem and propose a fundamental solution. For example, when an organization's problem is input, the problem analysis unit uses natural language processing technology to dig deep into the background and root cause of the problem and propose a fundamental solution. For example, it identifies the cause of a project delay and proposes a solution. The problem analysis unit also uses natural language processing technology to analyze the root cause of the organization's problem and proposes a fundamental solution. For example, it identifies the cause of a lack of communication in a team and proposes an improvement measure. Furthermore, when an organization's problem is input, the problem analysis unit uses natural language processing technology to dig deep into the background and root cause of the problem and proposes a fundamental solution. For example, it identifies inefficiencies in a business process and proposes an improvement measure. This makes it possible to dig deep into the background and root cause of the problem and propose a fundamental solution.

[0065] The problem analysis unit can refer to examples from different industries and regions and propose solutions from a global perspective. For example, when an organization's problems are input into the problem analysis unit, the generation AI can refer to examples from different industries and propose solutions from a global perspective. For example, it can propose solutions based on successful examples from other industries. Furthermore, when an organization's problems are input into the problem analysis unit, the generation AI can refer to examples from different industries and propose solutions from a global perspective. For example, it can propose solutions based on successful examples from other regions. Furthermore, when an organization's problems are input into the problem analysis unit, the generation AI can refer to examples from different industries and regions and propose solutions from a global perspective. For example, it can propose solutions based on international best practices. This makes it possible to refer to examples from different industries and regions and propose solutions from a global perspective.

[0066] The problem analysis unit can generate visual notes and mind maps to present solutions in a visually easy-to-understand format. For example, when an organization's problems are input, the problem analysis unit's generation AI generates visual notes to present solutions in a visually easy-to-understand format. For example, the solutions are shown using diagrams and icons. The problem analysis unit also generates mind maps to visually present solutions to the organization's problems. For example, the solution steps are displayed in mind map format. Furthermore, when an organization's problems are input, the problem analysis unit's generation AI generates visual notes and mind maps to present solutions in a visually easy-to-understand format. For example, the solution flow is visualized. This allows the problem analysis unit to generate visual notes and mind maps to present solutions in a visually easy-to-understand format.

[0067] The problem analysis unit can use the emotion estimation function to analyze problems and propose solutions that are easy to empathize with emotionally. The problem analysis unit, for example, uses the emotion estimation function to analyze the problems of an organization and propose solutions that are easy to empathize with emotionally. For example, it presents a solution that includes an empathetic message according to the content of the problem. The problem analysis unit also analyzes the problems of an organization using the emotion estimation function and proposes solutions that are easy to empathize with emotionally. For example, it understands the emotions behind the problem and provides a solution that corresponds to that. Furthermore, the problem analysis unit uses the emotion estimation function to analyze the problems of an organization and proposes solutions that are easy to empathize with emotionally. For example, it presents a solution that takes into account the emotional aspects of the problem. This makes it possible to propose solutions that are easy to empathize with emotionally.

[0068] The information transmission unit uses the emotion estimation function to analyze the emotional response of the information recipient and can provide the information at the optimal timing and format. For example, when transmitting information about the AI ​​utilization status, the generation AI uses the emotion estimation function to analyze the emotional response of the recipient. For example, the information is provided at a time when positive emotions are heightened. The information transmission unit also uses the emotion estimation function to analyze the emotional response of the information recipient and provide the information in the optimal format. For example, the report is created in a format that is easy to empathize with emotionally. Furthermore, when transmitting information about the AI ​​utilization status, the generation AI uses the emotion estimation function to analyze the emotional response and provide the information at the optimal timing and format. For example, the information is transmitted at a time when it is easy to emotionally accept. This makes it possible to analyze the emotional response of the information recipient and provide the information at the optimal timing and format.

[0069] The information dissemination unit can use data visualization technology to generate reports that are visually easy to understand. For example, when disseminating information about the AI ​​utilization status, the information dissemination unit uses data visualization technology to generate reports that are visually easy to understand. For example, data is visualized using graphs and charts. The information dissemination unit also uses data visualization technology to generate reports that visually display the AI ​​utilization status. For example, an interactive dashboard is created. Furthermore, when disseminating information about the AI ​​utilization status, the information dissemination unit uses data visualization technology to generate reports that are visually easy to understand. For example, data trends are visually shown. This makes it possible to generate reports that are visually easy to understand.

[0070] The information dissemination unit can compare with past data to identify trends and patterns. For example, when the information dissemination unit disseminates information about the status of AI utilization, the generating AI compares it with past data to identify trends and patterns. For example, it compares past data with current data to show growth rates. The information dissemination unit also compares past data with current data to identify trends and patterns. For example, it shows changes in the frequency of use of AI tools. Furthermore, when the information dissemination unit disseminates information about the status of AI utilization, the generating AI compares it with past data to identify trends and patterns. For example, it shows the effects of introducing AI by comparing it with past data. This makes it possible to identify trends and patterns by comparing it with past data.

[0071] The information dissemination department can provide insights from a global perspective by comparing data from different industries and regions. For example, when disseminating information about the status of AI utilization, the information dissemination department can provide insights from a global perspective by comparing the generating AI with data from different industries. For example, it can provide insights based on AI utilization cases in other industries. Furthermore, when disseminating information about the status of AI utilization, the generating AI can provide insights from a global perspective by comparing data from different industries and regions. For example, it can provide insights based on international best practices. This allows it to provide insights from a global perspective by comparing data from different industries and regions.

[0072] The information dissemination unit can provide an interactive dashboard, allowing users to explore data on their own. For example, when disseminating information about the AI ​​utilization status, the generating AI can provide an interactive dashboard, allowing users to explore data on their own. For example, detailed information can be accessed by clicking or zooming. The information dissemination unit can also provide an interactive dashboard, allowing users to explore data on their own. For example, specific data can be displayed using a filtering function. Furthermore, when disseminating information about the AI ​​utilization status, the generating AI can provide an interactive dashboard, allowing users to explore data on their own. For example, data visualization can be customized. This allows users to explore data on their own.

[0073] The information dissemination unit can use the emotion estimation function to collect users' emotional reactions to the AI ​​utilization situation and continuously improve the content of the information dissemination. For example, the information dissemination unit can use the emotion estimation function to collect users' emotional reactions to the AI ​​utilization situation and continuously improve the content of the information dissemination. For example, it prioritizes disseminating information that has a high number of positive reactions. The information dissemination unit also collects users' emotional reactions and uses the emotion estimation function to improve the content of the information dissemination. For example, it modifies information that has a high number of negative reactions. Furthermore, the information dissemination unit can use the emotion estimation function to collect users' emotional reactions to the AI ​​utilization situation and continuously improve the content of the information dissemination. For example, it adjusts the format of the information based on the emotion score. In this way, it is possible to collect users' emotional reactions and continuously improve the content of the information dissemination.

[0074] The communication support unit can use the emotion estimation function to analyze the tone and emotions of a conversation and provide appropriate feedback. For example, in order to facilitate communication, the generation AI in the communication support unit uses the emotion estimation function to analyze the tone of a conversation and provide appropriate feedback. For example, if the conversation is tense, the generation AI makes suggestions to relax the conversation. The communication support unit also uses the emotion estimation function to analyze the emotions of a conversation and provide appropriate feedback. For example, feedback that emphasizes positive emotions. Furthermore, in order to facilitate communication, the generation AI uses the emotion estimation function to analyze the tone and emotions of a conversation and provide appropriate feedback. For example, feedback that shows emotional empathy. This makes it possible to analyze the tone and emotions of a conversation and provide appropriate feedback.

[0075] The communication support unit can use natural language processing technology to summarize the content of a conversation and extract important points. For example, in order to facilitate communication, the generation AI in the communication support unit uses natural language processing technology to summarize the content of a conversation and extract important points. For example, it automatically summarizes the main points of a meeting. The communication support unit also uses natural language processing technology to summarize the content of a conversation and extract important points. For example, it picks out important topics from a long conversation. Furthermore, in order to facilitate communication, the generation AI in the communication support unit uses natural language processing technology to summarize the content of a conversation and extract important points. For example, it automatically records ideas that come up in a conversation. This makes it possible to summarize the content of a conversation and extract important points.

[0076] The communication support unit can use speech recognition technology to transcribe the contents of a meeting in real time and automatically generate minutes. For example, to facilitate communication, the communication support unit uses a generation AI to transcribe the contents of a meeting in real time using speech recognition technology and automatically generate minutes. For example, the minutes are shared immediately after the meeting ends. The communication support unit also uses speech recognition technology to transcribe the contents of a meeting in real time and automatically generate minutes. For example, important remarks made during a meeting are automatically recorded. Furthermore, to facilitate communication, the communication support unit uses a generation AI to transcribe the contents of a meeting in real time using speech recognition technology and automatically generate minutes. For example, the main points of the meeting are automatically summarized. This allows the contents of a meeting to be transcribed in real time and minutes to be automatically generated.

[0077] The communication support unit can automatically translate into different languages ​​to support communication between international teams. For example, to facilitate communication, the generation AI automatically translates into different languages ​​to support communication between international teams. For example, the content of a meeting is translated in real time. The communication support unit also automatically translates into different languages ​​to support communication between international teams. For example, the content of emails and chats is automatically translated. For example, to facilitate communication, the communication support unit also automatically translates into different languages ​​to support communication between international teams. For example, documents are provided in multiple languages. This allows automatic translation into different languages ​​to support communication between international teams.

[0078] The communication support unit generates visual notes and mind maps, allowing information to be shared in a visually easy-to-understand format. For example, to facilitate communication, the generation AI of the communication support unit generates visual notes to share information in a visually easy-to-understand format. For example, the main points of a meeting are shown using diagrams and icons. The communication support unit also generates mind maps to visually share information to facilitate communication. For example, the progress of a project is displayed in mind map format. Furthermore, to facilitate communication, the generation AI of the communication support unit generates visual notes and mind maps to share information in a visually easy-to-understand format. For example, the relationships between tasks are visualized. This allows visual notes and mind maps to be generated, allowing information to be shared in a visually easy-to-understand format.

[0079] The communication support unit can use the emotion estimation function to analyze the user's emotional response to the content of the communication and suggest a communication method that is likely to be emotionally relatable. The communication support unit, for example, uses the emotion estimation function to analyze the user's emotional response to the content of the communication and suggest a communication method that is likely to be emotionally relatable. For example, it suggests language that elicits positive emotions. The communication support unit also analyzes the user's emotional response to the content of the communication using the emotion estimation function and suggests a method that is likely to be emotionally relatable. For example, it suggests feedback that shows empathy. Furthermore, the communication support unit uses the emotion estimation function to analyze the user's emotional response to the content of the communication and suggest a communication method that is likely to be emotionally relatable. For example, it suggests expressions that show emotional empathy. This makes it possible to suggest a communication method that is likely to be emotionally relatable.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The information sharing system can further include a data analysis unit. The data analysis unit can analyze data within the organization and propose ways to improve and streamline operations. For example, it can analyze project progress data and identify bottlenecks. The data analysis unit can also analyze employee performance data and propose training and skill development opportunities. Furthermore, the data analysis unit can integrate data from across the organization and provide reports to management to support strategic decision-making. This makes it possible to utilize data from across the organization to propose ways to improve and streamline operations.

[0082] The information sharing system can further include a predictive analysis unit. The predictive analysis unit can predict future trends and risks based on past data and support strategic decision-making for the organization. For example, it can analyze sales data and predict future sales trends. The predictive analysis unit can also predict the risk of employee resignation and propose appropriate countermeasures. Furthermore, the predictive analysis unit can predict the probability of project success based on project progress data and support risk management. This makes it possible to predict future trends and risks and support strategic decision-making for the organization.

[0083] The information sharing system can further include a feedback collection unit. The feedback collection unit can collect feedback from employees and customers and identify areas for improvement in the organization. For example, it can conduct employee satisfaction surveys and propose ways to improve work styles. The feedback collection unit can also collect customer opinions and identify areas for improvement in products and services. Furthermore, the feedback collection unit can analyze the collected feedback and provide a report on areas for improvement across the entire organization. This makes it possible to identify areas for improvement in the organization by utilizing feedback from employees and customers.

[0084] The information sharing system can further include a knowledge management department. The knowledge management department can systematically manage knowledge within the organization and enable employees to quickly access the information they need. For example, it can create a database of project success stories and failure stories so that employees can refer to it. The knowledge management department can also manage contact information for employees with specialized knowledge so that they can quickly consult when needed. Furthermore, the knowledge management department can regularly hold knowledge sharing workshops and seminars to help employees improve their skills. This makes it possible to systematically manage knowledge within the organization and enable employees to quickly access the information they need.

[0085] The information sharing system can further include a resource management unit. The resource management unit can efficiently manage resources within an organization and propose optimal resource allocation. For example, it can appropriately allocate the human resources and equipment required for a project. The resource management unit can also monitor resource usage and make proposals to reduce waste. Furthermore, the resource management unit can predict future resource demands and create appropriate resource plans. This makes it possible to efficiently manage resources within an organization and propose optimal resource allocation.

[0086] The information sharing system can also use an emotion estimation function to analyze employees' emotions and provide stress management and mental health support. For example, the content of employees' emails and chats can be analyzed to detect signs of stress. The emotion estimation function can also be used to analyze employees' emotions in real time and suggest relaxation methods for employees with high stress levels. Furthermore, the emotion estimation function can be used to analyze employees' emotions and provide resources for mental health support. This allows for the analysis of employees' emotions and the provision of stress management and mental health support.

[0087] The information sharing system can further use the emotion estimation function to analyze employee emotions and suggest events for team building and motivation improvement. For example, the emotion estimation function can be used to analyze employee emotions and suggest a refreshment event for a team experiencing high stress. The emotion estimation function can also be used to analyze employee emotions and suggest a motivation-boosting seminar for employees experiencing low motivation. Furthermore, the emotion estimation function can be used to analyze employee emotions and suggest a workshop to increase team cohesion. In this way, it is possible to analyze employee emotions and suggest events for team building and motivation improvement.

[0088] The information sharing system can further use the emotion estimation function to analyze employees' emotions and suggest areas for improving communication. For example, the emotion estimation function can be used to analyze employees' emotions and suggest improvement measures for a team that is not communicating smoothly. The emotion estimation function can also be used to analyze employees' emotions and provide advice on adjusting the tone and content of communication. Furthermore, the emotion estimation function can be used to analyze employees' emotions and suggest training programs to improve the quality of communication. This makes it possible to analyze employees' emotions and suggest areas for improving communication.

[0089] The information sharing system can further use an emotion estimation function to analyze employee emotions and evaluate performance. For example, the emotion estimation function can be used to analyze employee emotions and evaluate performance in high-stress situations. The emotion estimation function can also be used to analyze employee emotions and evaluate performance in high-motivation situations. The emotion estimation function can also be used to analyze employee emotions and provide performance evaluation feedback. This makes it possible to analyze employee emotions and evaluate performance.

[0090] The information sharing system can further use the emotion estimation function to analyze employee emotions and support leadership development. For example, the emotion estimation function can be used to analyze employee emotions and suggest a training program for demonstrating leadership. The emotion estimation function can also be used to analyze employee emotions and identify skills necessary for leadership development. The emotion estimation function can also be used to analyze employee emotions and provide feedback to support leadership development. In this way, it is possible to analyze employee emotions and support leadership development.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The Work Style Monitoring Department monitors employee work styles. For example, it monitors the status of remote work and office work and collects data. It also monitors the use of flextime and records employees' arrival and departure times. It also uses project management tools to monitor task progress and understand the content of employees' work. Step 2: The Concern Analysis Department analyzes the concerns of the organization. For example, it analyzes concerns entered by employees into ChatUI and proposes appropriate AI solutions. It also analyzes work-related concerns and interpersonal concerns, and concerns about improving work efficiency and proposes appropriate tools. It also analyzes career concerns and proposes training programs for skill development. Step 3: The Information Dissemination Department will disseminate information on the status of AI utilization. For example, they will provide reports on which teams are using which AI tools and how. They will also evaluate the frequency of AI tool use and the degree of operational efficiency improvement, quantitatively evaluate the effects of implementation, and share this information with other teams. Furthermore, they will regularly disseminate information on the status of AI utilization, and share the status of AI tool usage with all employees in the form of monthly reports. Step 4: The communication support department facilitates communication. For example, it automatically creates meeting minutes and shares them with relevant parties. It also automatically updates progress using project management tools and notifies relevant parties. It also analyzes email and chat content, extracts and summarizes important information, and highlights key points.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Work Style Monitoring Department monitors employees' work styles, A problem analysis department that analyzes the problems of the organization, The Information Dissemination Department disseminates information on the status of AI utilization, and a communication support unit that facilitates communication. A system characterized by:

2. The work style monitoring unit Analyze the employee's stress level and motivation in real time and provide appropriate feedback 2. The system of claim 1.

3. The work style monitoring unit Consider the employee's skill set and career path and propose optimal task assignments 2. The system of claim 1.

4. The work style monitoring unit Analyzes conversations using voice recognition technology and provides insights that help improve work efficiency 2. The system of claim 1.

5. The work style monitoring unit Compare with benchmark data from different industries and occupations and make specific suggestions for improving operations 2. The system of claim 1.

6. The work style monitoring unit In cooperation with the health management system, comprehensive management of the health status of the employee will be carried out.

2. The system of claim 1.

7. The work style monitoring unit Analyze the employee's work style data and automatically suggest events for team building and motivation improvement.

2. The system of claim 1.

8. The concern analysis unit Analyze the emotional nuances of the problem and propose more appropriate solutions 2. The system of claim 1.

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