System

Abstract

An object of the system according to the embodiment is to provide a specific case and method related to the introduction of telecommuting.SOLUTION: A system according to an embodiment includes a case collection part, a case analysis part, and a case provision part. A case collection part collects cases of a company which has already adopted telecommuting. The case example analysis unit analyzes the case examples collected by the case example collection unit, and organizes operations and methods suitable for work at home. A case providing part provides the cases arranged by the case analysis part to the enterprise which does not adopt the work at home.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 was a lack of sharing of specific examples and methods for introducing telecommuting, which hindered the spread of telecommuting.

[0005] The system according to the embodiment aims to provide specific examples and methods for introducing telecommuting. [Means for solving the problem]

[0006] The system according to the embodiment includes a case collection unit, a case analysis unit, and a case provision unit. The case collection unit collects cases from companies that have already adopted telecommuting. The case analysis unit analyzes the cases collected by the case collection unit and organizes tasks and methods suitable for telecommuting. The case provision unit provides the cases organized by the case analysis unit to companies that have not yet adopted telecommuting. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific examples and methods for introducing telecommuting. [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 telecommuting promotion system according to an embodiment of the present invention collects examples of companies that have already implemented telecommuting and provides them to companies that have not yet implemented telecommuting. This allows the telecommuting promotion system to promote the introduction of telecommuting, alleviate congestion on crowded trains, and reduce commuting time.

[0029] The telecommuting promotion system according to the embodiment includes a case collection unit, a case analysis unit, and a case provision unit. The case collection unit collects cases from companies that have already adopted telecommuting. For example, the case collection unit uses a generation AI to analyze publicly available company information and interview results to extract specific telecommuting cases. The case collection unit can also use a generation AI to analyze the contents of internal company documents and emails to collect private information about telecommuting. The case collection unit can also use a generation AI to monitor the company's social media accounts and collect posts and comments about telecommuting in real time. The case analysis unit analyzes the cases collected by the case collection unit and organizes tasks and methods suitable for telecommuting. For example, the case analysis unit classifies the types of tasks and implementation methods suitable for telecommuting based on the information collected using the generation AI and proposes specific procedures and tools. The case analysis unit can also use a generation AI to conduct detailed analysis of the cases to clarify the factors that contribute to the success and failure of telecommuting. The case analysis unit can also use a generation AI to track the temporal evolution of the cases and analyze changes before and after the introduction of telecommuting. The case provision unit provides the cases organized by the case analysis unit to companies that have not yet adopted telecommuting. For example, the case provision unit uses generation AI to select appropriate cases based on the needs and business content of the companies to which it provides the cases and propose specific implementation methods. The case provision unit can also use generation AI to collect feedback after providing the cases and continuously improve the content provided. Furthermore, the case provision unit can use an emotion estimation function to analyze the emotions of employees at the recipient companies and make suggestions that elicit positive emotions. As a result, the telecommuting promotion system according to the embodiment can promote the adoption of telecommuting, alleviate congestion on crowded trains, and reduce commuting time.

[0030] The case collection unit can use the generation AI to analyze the contents of a company's internal documents and emails to collect non-public information about telecommuting. For example, the case collection unit uses the generation AI to analyze the contents of a company's internal documents and emails to collect non-public information about telecommuting. For example, the case collection unit analyzes project management tools and internal chat logs to extract specific implementation methods for telecommuting. The case collection unit also uses the generation AI to access the company's internal network to collect non-public information about telecommuting. For example, the case collection unit analyzes documents on an internal portal site or intranet to extract successful cases of telecommuting. The case collection unit also uses the generation AI to analyze the company's internal database to collect non-public information about telecommuting. For example, the case collection unit analyzes employee work reports and evaluation reports to evaluate the effectiveness of telecommuting. In this way, more specific cases of telecommuting can be collected by analyzing the company's internal information.

[0031] The case collection unit can use the generation AI to monitor a company's social media accounts and collect posts and comments related to working from home in real time. For example, the generation AI monitors a company's official social media accounts and collects posts and comments related to working from home in real time. For example, it analyzes posts on Twitter and LinkedIn to extract specific examples of working from home. The case collection unit also monitors the social media accounts of company employees and collects posts and comments related to working from home. For example, it analyzes posts in which employees share their experiences working from home and extracts success stories. The generation AI also monitors the company's social media accounts and tracks hashtags related to working from home. For example, it analyzes hashtags such as #workfromhome and #remotework and collects related posts. In this way, by monitoring social media, information related to working from home can be collected in real time.

[0032] The case collection department can use generative AI to collect telecommuting cases from different industries and provide cross-industry insights. For example, the case collection department uses generative AI to collect telecommuting cases from different industries and provide cross-industry insights. For example, it analyzes cases from the IT industry, manufacturing industry, service industry, etc. and extracts common success factors. The case collection department also uses generative AI to analyze public information from companies in different industries and collect specific telecommuting cases. For example, it compares and analyzes telecommuting implementation methods and tools by industry. The case collection department also uses generative AI to interview experts in different industries and collect telecommuting cases. For example, it provides cross-industry insights based on feedback from leaders and managers in each industry. In this way, cross-industry insights can be provided by collecting cases from different industries.

[0033] The case collection unit also uses generative AI to collect case studies from international companies, enabling it to provide telecommuting case studies from a global perspective. For example, the case collection unit uses generative AI to analyze public information from international companies to collect telecommuting case studies. For example, it analyzes case studies from companies in the United States, Europe, Asia, etc., and provides insights from a global perspective. The case collection unit also uses generative AI to interview personnel from international companies to collect telecommuting case studies. For example, it compares and analyzes methods for introducing telecommuting in accordance with the culture and regulations of each country. The case collection unit also uses generative AI to monitor the social media accounts of international companies and collect posts and comments related to telecommuting. For example, it analyzes successful telecommuting cases shared by companies in each country. In this way, by collecting case studies from international companies, it is possible to provide insights from a global perspective.

[0034] The case analysis unit uses the generation AI to conduct detailed analysis of cases and clarify the factors that led to success and failure in telecommuting. For example, the case analysis unit analyzes cases collected by the generation AI in detail to clarify the factors that led to success and failure in telecommuting. For example, it compares successful and unsuccessful cases and extracts common factors. In addition, the generation AI analyzes the text data of the cases and classifies the factors that led to success and failure. For example, it uses natural language processing technology to automatically extract positive and negative factors. In addition, the generation AI analyzes the metadata of the cases and identifies the factors that led to success and failure. For example, it performs analysis based on metadata such as the type of work, tools used, and implementation process. This clarifies the factors that led to success and failure, thereby effectively supporting the introduction of telecommuting.

[0035] The case analysis unit uses the generation AI to track changes in cases over time and analyze changes before and after the introduction of telecommuting. For example, the generation AI tracks changes in cases over time and analyzes changes before and after the introduction of telecommuting. For example, it analyzes changes in work efficiency and employee satisfaction before and after the introduction. The case analysis unit also uses the generation AI to create a timeline of the case and visualize the telecommuting introduction process. For example, it organizes the preparation stage before the introduction, the challenges during the introduction, and the results after the introduction in chronological order. The case analysis unit also uses the generation AI to analyze the temporal data of the case and compare performance before and after the introduction of telecommuting. For example, it evaluates work productivity and cost reduction effects along a timeline. This makes it possible to evaluate the effectiveness of telecommuting by analyzing changes before and after the introduction.

[0036] The case analysis unit can use the generation AI to modularize and organize cases so that they can be applied to different business processes. For example, the case analysis unit modularizes and organizes the cases collected by the generation AI so that they can be applied to different business processes. For example, it divides cases by type of work and saves them as reusable modules. The case analysis unit also uses the generation AI to extract common elements from the cases and modularize them. For example, it standardizes the tools and procedures required for working from home so that they can be applied to other business processes. The case analysis unit also uses the generation AI to analyze the metadata of the cases and modularize them. For example, it creates modules based on metadata such as the type of work, tools used, and implementation process. In this way, by modularizing the cases, they can be applied to different business processes.

[0037] The case analysis unit can use the generative AI to translate case studies into different languages ​​so that they can be applied to international companies. For example, the case analysis unit translates case studies collected by the generative AI into different languages ​​so that they can be applied to international companies. For example, translation into multiple languages ​​such as English, French, and Chinese. The case analysis unit also uses the generative AI to automatically translate the text data of the case studies and provide the case studies in different languages. For example, guidelines that can be applied to international companies are created based on the translated case studies. The case analysis unit also uses the generative AI to translate the audio and video data of the case studies and provide the case studies in different languages. For example, interview audio and video conference footage can be translated so that they can be applied to international companies. In this way, translating case studies into different languages ​​makes them applicable to international companies.

[0038] The case provision unit can use the generation AI to perform a detailed analysis of the business content and needs of the companies to which the data is provided, and customize and provide the most appropriate case study. For example, the case provision unit can perform a detailed analysis of the business content and needs of the companies to which the generation AI is provided, and customize and provide the most appropriate case study. For example, it can analyze the company's business processes and tools used to select an appropriate case study. The case provision unit also uses the generation AI to collect the company's needs in real time, and customize the most appropriate case study based on that data. For example, it can analyze the company's feedback and requests and adjust the case study. The case provision unit also uses the generation AI to analyze the company's business content, and provide case studies according to the type and scale of the business. For example, it can customize and provide case studies for small and medium-sized enterprises and large enterprises. This allows the most appropriate case study to be provided by performing a detailed analysis of the company's business content and needs.

[0039] The case provision department can use the generation AI to collect feedback after the case is provided and continuously improve the content provided. For example, the generation AI in the case provision department collects feedback after the case is provided and continuously improves the content provided. For example, it analyzes feedback from companies and updates the content of the case. The case provision department also uses the generation AI to collect feedback from companies in real time and improves the content provided based on that data. For example, it adjusts the case by reflecting the requests and opinions of the companies. The case provision department also uses the generation AI to monitor the effectiveness of the case after it is provided and improves the content provided. For example, it analyzes changes in the company's business efficiency and employee satisfaction and optimizes the case. In this way, the content provided can be continuously improved by collecting feedback.

[0040] The case provision unit can use the generative AI to diversify and provide case studies so that the generative AI can be applied to companies in different industries and regions. The case provision unit diversifies and provides case studies so that the generative AI can be applied to companies in different industries and regions. For example, it provides case studies from the IT industry, manufacturing industry, service industry, etc. The case provision unit also diversifies and provides case studies so that the generative AI can be applied to companies in different regions. For example, it provides case studies suitable for companies in the United States, Europe, Asia, etc. The case provision unit also analyzes the needs of companies in different industries and regions and provides case studies that correspond to them. For example, it provides case studies that correspond to the characteristics of each industry and the regulations of each region. This makes it possible to provide a diversified set of case studies so that the generative AI can be applied to companies in different industries and regions.

[0041] The case provision unit can use the generative AI to promote understanding by using visual and interactive content when providing cases. For example, the case provision unit promotes understanding by using visual and interactive content when providing cases. For example, it provides a visual map of the case or an interactive demo. The case provision unit also has the generative AI automatically generate visual content for the case and provide it to companies. For example, it creates flowcharts and graphs for the case to make it easier to understand visually. The case provision unit also has the generative AI create interactive content for the case and provide it to companies. For example, it provides a simulation of the case or an interactive guide. In this way, understanding can be promoted by using visual and interactive content.

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

[0043] The case collection department can also use generative AI to analyze the health data of company employees to evaluate the health impact of working from home. For example, it can collect data from employees' fitness trackers and smartwatches to analyze their exercise levels and sleep patterns while working from home. The case collection department can also use generative AI to analyze employees' health checkup results to evaluate the health risks of working from home. For example, it can track changes in blood pressure and weight to evaluate the health impact of working from home. Furthermore, the case collection department can use generative AI to analyze employees' stress levels and evaluate their mental health while working from home. For example, it can analyze stress check results and heart rate fluctuations to evaluate the mental health impact of working from home. This allows for the collection of more comprehensive case studies of working from home by evaluating the health impact of working from home.

[0044] The Case Analysis Department can also use generative AI to analyze the economic impact of working from home. For example, it can evaluate the reduction in commuting expenses and office space costs that result from working from home. The Case Analysis Department can also use generative AI to analyze changes in productivity due to working from home. For example, it can compare work efficiency and the quality of deliverables to evaluate the economic benefits of working from home. Furthermore, the Case Analysis Department can use generative AI to analyze changes in employee turnover rates due to working from home. For example, it can compare turnover rates before and after the introduction of working from home to evaluate the economic impact of working from home. In this way, by analyzing the economic impact of working from home, it can clarify the benefits to companies.

[0045] The Case Study Department can also use generative AI to provide legal advice on the introduction of telecommuting. For example, it analyzes the labor laws and regulations of each country and organizes the legal requirements for telecommuting. The Case Study Department can also use generative AI to provide templates for contracts and policies related to telecommuting. For example, it can create templates for labor contracts and privacy policies related to telecommuting and provide them to companies. Furthermore, the Case Study Department can use generative AI to evaluate legal risks related to telecommuting and propose methods for risk management. For example, it can evaluate risks related to data protection and working hour management and propose appropriate measures. This allows it to provide legal advice on the introduction of telecommuting to help companies avoid legal risks.

[0046] The case study department can also use generative AI to provide training programs on implementing telecommuting. For example, it can create online courses to teach employees how to use the skills and tools necessary for telecommuting. The case study department can also use generative AI to hold webinars and workshops on telecommuting. For example, it can hold webinars to introduce best practices and success stories for telecommuting. Furthermore, the case study department can use generative AI to provide individual consulting services on telecommuting. For example, it can provide customized training programs tailored to the needs of each company. This can help companies smoothly implement telecommuting by providing training programs on the implementation of telecommuting.

[0047] The Case Analysis Department can also use generative AI to analyze the environmental impact of telecommuting. For example, it can evaluate the reduction in energy consumption and carbon dioxide emissions resulting from telecommuting. The Case Analysis Department can also use generative AI to analyze the use of office space resulting from telecommuting. For example, it can evaluate the utilization rate of office space and the cost reduction effect. Furthermore, the Case Analysis Department can use generative AI to analyze changes in traffic volume resulting from telecommuting. For example, it can evaluate the reduction in traffic congestion due to commuting and the use of public transportation. By analyzing the environmental impact of telecommuting, it can support companies in considering introducing environmentally friendly telecommuting.

[0048] The case collection department can also use generative AI to collect employee productivity data regarding telecommuting and evaluate the effectiveness of telecommuting. For example, it can analyze employees' working hours and task completion status to evaluate changes in productivity. The case collection department can also use generative AI to collect employee performance data regarding telecommuting. For example, it can analyze work deliverables and evaluation reports to evaluate the effectiveness of telecommuting. Furthermore, the case collection department can use generative AI to collect employee feedback regarding telecommuting and identify factors that contribute to improved productivity. For example, it can analyze survey results and interview content to propose specific measures to improve productivity. This can help companies consider measures to improve productivity by evaluating the effectiveness of telecommuting.

[0049] The Case Study Department can also use generative AI to support the introduction of communication tools for working from home. For example, it can propose the optimal communication tools based on the needs of the company. The Case Study Department can also use generative AI to provide training on how to use communication tools. For example, it can create online courses to teach how to use video conferencing tools and chat tools. Furthermore, the Case Study Department can use generative AI to monitor the effectiveness of communication tools after their introduction and suggest areas for improvement. For example, it can analyze tool usage and employee feedback and suggest areas for improving communication. By supporting the introduction of communication tools, it can help companies smoothly implement working from home.

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

[0051] Step 1: The case collection department collects case studies from companies that have already adopted telecommuting. For example, the case collection department uses generative AI to analyze publicly available company information and interview results to extract specific examples of telecommuting. The case collection department can also use generative AI to analyze the contents of internal company documents and emails to collect private information about telecommuting. Furthermore, the case collection department can use generative AI to monitor companies' social media accounts and collect posts and comments about telecommuting in real time. Step 2: The Case Analysis Department analyzes the cases collected by the Case Collection Department and organizes tasks and methods suitable for telecommuting. For example, the Case Analysis Department classifies the types of tasks and methods suitable for telecommuting based on the information collected using generative AI, and proposes specific procedures and tools. The Case Analysis Department can also use generative AI to conduct detailed analysis of cases and clarify the factors that make telecommuting successful and unsuccessful. Furthermore, the Case Analysis Department can use generative AI to track changes in cases over time and analyze changes before and after the introduction of telecommuting. Step 3: The Case Study Provider provides the case studies organized by the Case Study Analysis Department to companies that have not yet adopted telecommuting. For example, the Case Study Provider uses generation AI to select appropriate case studies based on the needs and business operations of the companies to which it is providing the study, and proposes specific implementation methods. The Case Study Provider can also use generation AI to collect feedback after providing the case studies and continuously improve the content of the studies. Furthermore, the Case Study Provider can use emotion estimation functionality to analyze the emotions of the employees at the client companies and make suggestions that elicit positive emotions.

[0052] (Example 2) The telecommuting promotion system according to an embodiment of the present invention collects examples of companies that have already implemented telecommuting and provides them to companies that have not yet implemented telecommuting. This allows the telecommuting promotion system to promote the introduction of telecommuting, alleviate congestion on crowded trains, and reduce commuting time.

[0053] The telecommuting promotion system according to the embodiment includes a case collection unit, a case analysis unit, and a case provision unit. The case collection unit collects cases from companies that have already adopted telecommuting. For example, the case collection unit uses a generation AI to analyze publicly available company information and interview results to extract specific telecommuting cases. The case collection unit can also use a generation AI to analyze the contents of internal company documents and emails to collect private information about telecommuting. The case collection unit can also use a generation AI to monitor the company's social media accounts and collect posts and comments about telecommuting in real time. The case analysis unit analyzes the cases collected by the case collection unit and organizes tasks and methods suitable for telecommuting. For example, the case analysis unit classifies the types of tasks and implementation methods suitable for telecommuting based on the information collected using the generation AI and proposes specific procedures and tools. The case analysis unit can also use a generation AI to conduct detailed analysis of the cases to clarify the factors that contribute to the success and failure of telecommuting. The case analysis unit can also use a generation AI to track the temporal evolution of the cases and analyze changes before and after the introduction of telecommuting. The case provision unit provides the cases organized by the case analysis unit to companies that have not yet adopted telecommuting. For example, the case provision unit uses generation AI to select appropriate cases based on the needs and business content of the companies to which it provides the cases and propose specific implementation methods. The case provision unit can also use generation AI to collect feedback after providing the cases and continuously improve the content provided. Furthermore, the case provision unit can use an emotion estimation function to analyze the emotions of employees at the recipient companies and make suggestions that elicit positive emotions. As a result, the telecommuting promotion system according to the embodiment can promote the adoption of telecommuting, alleviate congestion on crowded trains, and reduce commuting time.

[0054] The case collection unit can use the generation AI to analyze the contents of a company's internal documents and emails to collect non-public information about telecommuting. For example, the case collection unit uses the generation AI to analyze the contents of a company's internal documents and emails to collect non-public information about telecommuting. For example, the case collection unit analyzes project management tools and internal chat logs to extract specific implementation methods for telecommuting. The case collection unit also uses the generation AI to access the company's internal network to collect non-public information about telecommuting. For example, the case collection unit analyzes documents on an internal portal site or intranet to extract successful cases of telecommuting. The case collection unit also uses the generation AI to analyze the company's internal database to collect non-public information about telecommuting. For example, the case collection unit analyzes employee work reports and evaluation reports to evaluate the effectiveness of telecommuting. In this way, more specific cases of telecommuting can be collected by analyzing the company's internal information.

[0055] The case collection unit can use the generation AI to monitor a company's social media accounts and collect posts and comments related to working from home in real time. For example, the generation AI monitors a company's official social media accounts and collects posts and comments related to working from home in real time. For example, it analyzes posts on Twitter and LinkedIn to extract specific examples of working from home. The case collection unit also monitors the social media accounts of company employees and collects posts and comments related to working from home. For example, it analyzes posts in which employees share their experiences working from home and extracts success stories. The generation AI also monitors the company's social media accounts and tracks hashtags related to working from home. For example, it analyzes hashtags such as #workfromhome and #remotework and collects related posts. In this way, by monitoring social media, information related to working from home can be collected in real time.

[0056] The case collection unit can use the emotion estimation function to analyze the emotions of company personnel and prioritize collecting cases with positive emotions. The case collection unit, for example, uses the emotion estimation function to analyze the emotions of company personnel and prioritize collecting cases with positive emotions. For example, it analyzes audio data from interviews and extracts cases with strong positive emotions. The case collection unit also uses the emotion estimation function to analyze the content of emails and chats from company personnel and collect cases with positive emotions. For example, it prioritizes analyzing messages with many expressions of gratitude and joy. The case collection unit also uses the emotion estimation function to analyze the facial expressions and voices of company personnel in real time and collect cases with positive emotions. For example, it analyzes video footage from video conferences and extracts cases with smiling faces and bright voice tones. In this way, by preferentially collecting cases with positive emotions, it is possible to collect many success cases.

[0057] The case collection department can use generative AI to collect telecommuting cases from different industries and provide cross-industry insights. For example, the case collection department uses generative AI to collect telecommuting cases from different industries and provide cross-industry insights. For example, it analyzes cases from the IT industry, manufacturing industry, service industry, etc. and extracts common success factors. The case collection department also uses generative AI to analyze public information from companies in different industries and collect specific telecommuting cases. For example, it compares and analyzes telecommuting implementation methods and tools by industry. The case collection department also uses generative AI to interview experts in different industries and collect telecommuting cases. For example, it provides cross-industry insights based on feedback from leaders and managers in each industry. In this way, cross-industry insights can be provided by collecting cases from different industries.

[0058] The case collection unit also uses generative AI to collect case studies from international companies, enabling it to provide telecommuting case studies from a global perspective. For example, the case collection unit uses generative AI to analyze public information from international companies to collect telecommuting case studies. For example, it analyzes case studies from companies in the United States, Europe, Asia, etc., and provides insights from a global perspective. The case collection unit also uses generative AI to interview personnel from international companies to collect telecommuting case studies. For example, it compares and analyzes methods for introducing telecommuting in accordance with the culture and regulations of each country. The case collection unit also uses generative AI to monitor the social media accounts of international companies and collect posts and comments related to telecommuting. For example, it analyzes successful telecommuting cases shared by companies in each country. In this way, by collecting case studies from international companies, it is possible to provide insights from a global perspective.

[0059] The case collection unit can use the emotion estimation function to collect employee emotions regarding telecommuting and identify cases that elicit positive emotions. The case collection unit, for example, uses the emotion estimation function to collect employee emotions regarding telecommuting and identify cases that elicit positive emotions. For example, the case collection unit analyzes employee survey results and extracts cases with a lot of positive feedback. The case collection unit also uses the emotion estimation function to analyze the content of employees' emails and chats regarding telecommuting and collects cases with positive emotions. For example, messages with a lot of expressions of gratitude and joy are prioritized for analysis. The case collection unit also uses the emotion estimation function to analyze employees' facial expressions and voices regarding telecommuting in real time and collect cases with positive emotions. For example, the case collection unit analyzes video conference footage and extracts cases with smiling faces and bright voice tones. In this way, by collecting employee emotions, cases that elicit positive emotions can be identified.

[0060] The case analysis unit uses the generation AI to conduct detailed analysis of cases and clarify the factors that led to success and failure in telecommuting. For example, the case analysis unit analyzes cases collected by the generation AI in detail to clarify the factors that led to success and failure in telecommuting. For example, it compares successful and unsuccessful cases and extracts common factors. In addition, the generation AI analyzes the text data of the cases and classifies the factors that led to success and failure. For example, it uses natural language processing technology to automatically extract positive and negative factors. In addition, the generation AI analyzes the metadata of the cases and identifies the factors that led to success and failure. For example, it performs analysis based on metadata such as the type of work, tools used, and implementation process. This clarifies the factors that led to success and failure, thereby effectively supporting the introduction of telecommuting.

[0061] The case analysis unit uses the generation AI to track changes in cases over time and analyze changes before and after the introduction of telecommuting. For example, the generation AI tracks changes in cases over time and analyzes changes before and after the introduction of telecommuting. For example, it analyzes changes in work efficiency and employee satisfaction before and after the introduction. The case analysis unit also uses the generation AI to create a timeline of the case and visualize the telecommuting introduction process. For example, it organizes the preparation stage before the introduction, the challenges during the introduction, and the results after the introduction in chronological order. The case analysis unit also uses the generation AI to analyze the temporal data of the case and compare performance before and after the introduction of telecommuting. For example, it evaluates work productivity and cost reduction effects along a timeline. This makes it possible to evaluate the effectiveness of telecommuting by analyzing changes before and after the introduction.

[0062] The case analysis unit can use the emotion estimation function to analyze the emotional aspects of the cases and prioritize and organize cases with positive emotions. The case analysis unit, for example, uses the emotion estimation function to analyze the emotional aspects of the cases and prioritize and organize cases with positive emotions. For example, it analyzes employee feedback and evaluation comments to extract positive cases. The case analysis unit also uses the emotion estimation function to analyze text data of the cases and calculate an emotion score. For example, it prioritizes and organizes cases with a high positive emotion score. The case analysis unit also uses the emotion estimation function to analyze audio data and video data of the cases and identify cases with positive emotions. For example, it analyzes audio of interviews and video of video conferences to extract positive cases. In this way, by prioritizing and organizing cases with positive emotions, it is possible to provide many success stories.

[0063] The case analysis unit can use the generation AI to modularize and organize cases so that they can be applied to different business processes. For example, the case analysis unit modularizes and organizes the cases collected by the generation AI so that they can be applied to different business processes. For example, it divides cases by type of work and saves them as reusable modules. The case analysis unit also uses the generation AI to extract common elements from the cases and modularize them. For example, it standardizes the tools and procedures required for working from home so that they can be applied to other business processes. The case analysis unit also uses the generation AI to analyze the metadata of the cases and modularize them. For example, it creates modules based on metadata such as the type of work, tools used, and implementation process. In this way, by modularizing the cases, they can be applied to different business processes.

[0064] The case analysis unit can use the generative AI to translate case studies into different languages ​​so that they can be applied to international companies. For example, the case analysis unit translates case studies collected by the generative AI into different languages ​​so that they can be applied to international companies. For example, translation into multiple languages ​​such as English, French, and Chinese. The case analysis unit also uses the generative AI to automatically translate the text data of the case studies and provide the case studies in different languages. For example, guidelines that can be applied to international companies are created based on the translated case studies. The case analysis unit also uses the generative AI to translate the audio and video data of the case studies and provide the case studies in different languages. For example, interview audio and video conference footage can be translated so that they can be applied to international companies. In this way, translating case studies into different languages ​​makes them applicable to international companies.

[0065] The case analysis unit can use the emotion estimation function to analyze the emotional responses of the case and propose a method for eliciting positive emotions. The case analysis unit, for example, uses the emotion estimation function to analyze the emotional responses of the case and propose a method for eliciting positive emotions. For example, the case analysis unit analyzes employee feedback and evaluation comments to identify factors that elicit positive emotions. The case analysis unit also uses the emotion estimation function to analyze text data of the case and calculate an emotion score. For example, the case analysis unit proposes a method for eliciting positive emotions based on cases with high positive emotion scores. The case analysis unit also uses the emotion estimation function to analyze audio data and video data of the case and propose a method for eliciting positive emotions. For example, the case analysis unit analyzes audio data and video footage of interviews to identify factors that elicit positive emotions. In this way, the case analysis unit can propose a method for eliciting positive emotions by analyzing emotional responses.

[0066] The case provision unit can use the generation AI to perform a detailed analysis of the business content and needs of the companies to which the data is provided, and customize and provide the most appropriate case study. For example, the case provision unit can perform a detailed analysis of the business content and needs of the companies to which the generation AI is provided, and customize and provide the most appropriate case study. For example, it can analyze the company's business processes and tools used to select an appropriate case study. The case provision unit also uses the generation AI to collect the company's needs in real time, and customize the most appropriate case study based on that data. For example, it can analyze the company's feedback and requests and adjust the case study. The case provision unit also uses the generation AI to analyze the company's business content, and provide case studies according to the type and scale of the business. For example, it can customize and provide case studies for small and medium-sized enterprises and large enterprises. This allows the most appropriate case study to be provided by performing a detailed analysis of the company's business content and needs.

[0067] The case provision department can use the generation AI to collect feedback after the case is provided and continuously improve the content provided. For example, the generation AI in the case provision department collects feedback after the case is provided and continuously improves the content provided. For example, it analyzes feedback from companies and updates the content of the case. The case provision department also uses the generation AI to collect feedback from companies in real time and improves the content provided based on that data. For example, it adjusts the case by reflecting the requests and opinions of the companies. The case provision department also uses the generation AI to monitor the effectiveness of the case after it is provided and improves the content provided. For example, it analyzes changes in the company's business efficiency and employee satisfaction and optimizes the case. In this way, the content provided can be continuously improved by collecting feedback.

[0068] The case providing unit can use the emotion estimation function to analyze the emotions of the person in charge of the client company and make suggestions that will elicit positive emotions. The case providing unit, for example, uses the emotion estimation function to analyze the emotions of the person in charge of the client company and make suggestions that will elicit positive emotions. For example, it analyzes the content of the person in charge's emails and chats and makes positive suggestions. The case providing unit also uses the emotion estimation function to analyze the facial expressions and voice of the person in charge of the client company in real time and make suggestions that will elicit positive emotions. For example, it analyzes video conference footage and makes positive suggestions. The case providing unit also uses the emotion estimation function to calculate an emotion score of the person in charge of the client company and make suggestions that will elicit positive emotions. For example, it prioritizes suggestions with a high emotion score. In this way, it is possible to make suggestions that will elicit positive emotions by analyzing the person in charge's emotions.

[0069] The case provision unit can use the generative AI to diversify and provide case studies so that the generative AI can be applied to companies in different industries and regions. The case provision unit diversifies and provides case studies so that the generative AI can be applied to companies in different industries and regions. For example, it provides case studies from the IT industry, manufacturing industry, service industry, etc. The case provision unit also diversifies and provides case studies so that the generative AI can be applied to companies in different regions. For example, it provides case studies suitable for companies in the United States, Europe, Asia, etc. The case provision unit also analyzes the needs of companies in different industries and regions and provides case studies that correspond to them. For example, it provides case studies that correspond to the characteristics of each industry and the regulations of each region. This makes it possible to provide a diversified set of case studies so that the generative AI can be applied to companies in different industries and regions.

[0070] The case provision unit can use the generative AI to promote understanding by using visual and interactive content when providing cases. For example, the case provision unit promotes understanding by using visual and interactive content when providing cases. For example, it provides a visual map of the case or an interactive demo. The case provision unit also has the generative AI automatically generate visual content for the case and provide it to companies. For example, it creates flowcharts and graphs for the case to make it easier to understand visually. The case provision unit also has the generative AI create interactive content for the case and provide it to companies. For example, it provides a simulation of the case or an interactive guide. In this way, understanding can be promoted by using visual and interactive content.

[0071] The case providing unit can use the emotion estimation function to analyze the emotions of the client company's employees and propose methods for eliciting positive emotions. For example, the case providing unit uses the emotion estimation function to analyze the emotions of the client company's employees and propose methods for eliciting positive emotions. For example, the case providing unit analyzes employee feedback and evaluation comments to identify factors that elicit positive emotions. The case providing unit also uses the emotion estimation function to analyze the content of emails and chats of the client company's employees and propose methods for eliciting positive emotions. For example, messages containing many expressions of gratitude and joy are analyzed preferentially. The case providing unit also uses the emotion estimation function to analyze the facial expressions and voices of the client company's employees in real time and propose methods for eliciting positive emotions. For example, the case providing unit analyzes video conference footage to extract examples of smiling faces and bright voice tones. This makes it possible to propose methods for eliciting positive emotions by analyzing employee emotions.

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

[0073] The case collection department can also use generative AI to analyze the health data of company employees to evaluate the health impact of working from home. For example, it can collect data from employees' fitness trackers and smartwatches to analyze their exercise levels and sleep patterns while working from home. The case collection department can also use generative AI to analyze employees' health checkup results to evaluate the health risks of working from home. For example, it can track changes in blood pressure and weight to evaluate the health impact of working from home. Furthermore, the case collection department can use generative AI to analyze employees' stress levels and evaluate their mental health while working from home. For example, it can analyze stress check results and heart rate fluctuations to evaluate the mental health impact of working from home. This allows for the collection of more comprehensive case studies of working from home by evaluating the health impact of working from home.

[0074] The Case Analysis Department can also use generative AI to analyze the economic impact of working from home. For example, it can evaluate the reduction in commuting expenses and office space costs that result from working from home. The Case Analysis Department can also use generative AI to analyze changes in productivity due to working from home. For example, it can compare work efficiency and the quality of deliverables to evaluate the economic benefits of working from home. Furthermore, the Case Analysis Department can use generative AI to analyze changes in employee turnover rates due to working from home. For example, it can compare turnover rates before and after the introduction of working from home to evaluate the economic impact of working from home. In this way, by analyzing the economic impact of working from home, it can clarify the benefits to companies.

[0075] The Case Study Department can also use generative AI to provide legal advice on the introduction of telecommuting. For example, it analyzes the labor laws and regulations of each country and organizes the legal requirements for telecommuting. The Case Study Department can also use generative AI to provide templates for contracts and policies related to telecommuting. For example, it can create templates for labor contracts and privacy policies related to telecommuting and provide them to companies. Furthermore, the Case Study Department can use generative AI to evaluate legal risks related to telecommuting and propose methods for risk management. For example, it can evaluate risks related to data protection and working hour management and propose appropriate measures. This allows it to provide legal advice on the introduction of telecommuting to help companies avoid legal risks.

[0076] The case collection unit can use the emotion estimation function to collect employee emotions regarding telecommuting and identify cases that elicit positive emotions. For example, it analyzes employee survey results and extracts cases with a lot of positive feedback. The case collection unit also uses the emotion estimation function to analyze the content of employees' emails and chats regarding telecommuting and collects cases with positive emotions. For example, it prioritizes analysis of messages with a lot of expressions of gratitude and joy. The case collection unit also uses the emotion estimation function to analyze employees' facial expressions and voices regarding telecommuting in real time and collects cases with positive emotions. For example, it analyzes video conference footage and extracts cases with smiling faces and a bright tone of voice. In this way, by collecting employee emotions, it is possible to identify cases that elicit positive emotions.

[0077] The case study department can also use generative AI to provide training programs on implementing telecommuting. For example, it can create online courses to teach employees how to use the skills and tools necessary for telecommuting. The case study department can also use generative AI to hold webinars and workshops on telecommuting. For example, it can hold webinars to introduce best practices and success stories for telecommuting. Furthermore, the case study department can use generative AI to provide individual consulting services on telecommuting. For example, it can provide customized training programs tailored to the needs of each company. This can help companies smoothly implement telecommuting by providing training programs on the implementation of telecommuting.

[0078] The Case Analysis Department can also use generative AI to analyze the environmental impact of telecommuting. For example, it can evaluate the reduction in energy consumption and carbon dioxide emissions resulting from telecommuting. The Case Analysis Department can also use generative AI to analyze the use of office space resulting from telecommuting. For example, it can evaluate the utilization rate of office space and the cost reduction effect. Furthermore, the Case Analysis Department can use generative AI to analyze changes in traffic volume resulting from telecommuting. For example, it can evaluate the reduction in traffic congestion due to commuting and the use of public transportation. By analyzing the environmental impact of telecommuting, it can support companies in considering introducing environmentally friendly telecommuting.

[0079] The case providing unit uses the emotion estimation function to analyze the emotions of the person in charge at the client company and make suggestions that will elicit positive emotions. For example, it analyzes the content of the person in charge's emails and chats and makes positive suggestions. The case providing unit also uses the emotion estimation function to analyze the facial expressions and voice of the person in charge at the client company in real time and makes suggestions that will elicit positive emotions. For example, it analyzes video conference footage and makes positive suggestions. The case providing unit also uses the emotion estimation function to calculate the emotion score of the person in charge at the client company and make suggestions that will elicit positive emotions. For example, it prioritizes suggestions with a high emotion score. In this way, it is possible to make suggestions that will elicit positive emotions by analyzing the person in charge's emotions.

[0080] The case collection department can also use generative AI to collect employee productivity data regarding telecommuting and evaluate the effectiveness of telecommuting. For example, it can analyze employees' working hours and task completion status to evaluate changes in productivity. The case collection department can also use generative AI to collect employee performance data regarding telecommuting. For example, it can analyze work deliverables and evaluation reports to evaluate the effectiveness of telecommuting. Furthermore, the case collection department can use generative AI to collect employee feedback regarding telecommuting and identify factors that contribute to improved productivity. For example, it can analyze survey results and interview content to propose specific measures to improve productivity. This can help companies consider measures to improve productivity by evaluating the effectiveness of telecommuting.

[0081] The Case Study Department can also use generative AI to support the introduction of communication tools for working from home. For example, it can propose the optimal communication tools based on the needs of the company. The Case Study Department can also use generative AI to provide training on how to use communication tools. For example, it can create online courses to teach how to use video conferencing tools and chat tools. Furthermore, the Case Study Department can use generative AI to monitor the effectiveness of communication tools after their introduction and suggest areas for improvement. For example, it can analyze tool usage and employee feedback and suggest areas for improving communication. By supporting the introduction of communication tools, it can help companies smoothly implement working from home.

[0082] The case analysis unit can use the emotion estimation function to analyze the emotional aspects of cases and prioritize cases with positive emotions. For example, it analyzes employee feedback and evaluation comments to extract positive cases. The case analysis unit also uses the emotion estimation function to analyze text data of cases and calculate an emotion score. For example, it prioritizes cases with a high positive emotion score. The case analysis unit also uses the emotion estimation function to analyze audio data and video data of cases and identify cases with positive emotions. For example, it analyzes audio of interviews and video of video conferences to extract positive cases. This allows it to prioritize cases with positive emotions and provide many success stories.

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

[0084] Step 1: The case collection department collects case studies from companies that have already adopted telecommuting. For example, the case collection department uses generative AI to analyze publicly available company information and interview results to extract specific examples of telecommuting. The case collection department can also use generative AI to analyze the contents of internal company documents and emails to collect private information about telecommuting. Furthermore, the case collection department can use generative AI to monitor companies' social media accounts and collect posts and comments about telecommuting in real time. Step 2: The Case Analysis Department analyzes the cases collected by the Case Collection Department and organizes tasks and methods suitable for telecommuting. For example, the Case Analysis Department classifies the types of tasks and methods suitable for telecommuting based on the information collected using generative AI, and proposes specific procedures and tools. The Case Analysis Department can also use generative AI to conduct detailed analysis of cases and clarify the factors that make telecommuting successful and unsuccessful. Furthermore, the Case Analysis Department can use generative AI to track changes in cases over time and analyze changes before and after the introduction of telecommuting. Step 3: The Case Study Provider provides the case studies organized by the Case Study Analysis Department to companies that have not yet adopted telecommuting. For example, the Case Study Provider uses generation AI to select appropriate case studies based on the needs and business operations of the companies to which it is providing the study, and proposes specific implementation methods. The Case Study Provider can also use generation AI to collect feedback after providing the case studies and continuously improve the content of the studies. Furthermore, the Case Study Provider can use emotion estimation functionality to analyze the emotions of the employees at the client companies and make suggestions that elicit positive emotions.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A case study collection department that collects case studies from companies that have already adopted telecommuting; a case analysis unit that analyzes the cases collected by the case collection unit and organizes tasks and methods suitable for telecommuting; a case providing unit that provides the cases organized by the case analysis unit to companies that do not adopt telecommuting A system characterized by:

2. The case collection unit Using generative AI, the contents of the company's internal documents and emails are analyzed to collect non-public information about telecommuting.

2. The system of claim 1.

3. The case collection unit Generative AI is used to monitor the company's social media accounts and collect posts and comments about working from home in real time.

2. The system of claim 1.

4. The case collection unit Analyzing the emotions of the company's personnel and preferentially collecting cases with positive emotions 2. The system of claim 1.

5. The case collection unit Using generative AI, we will also collect examples of telecommuting in the above different industries to provide cross-industry insights.

2. The system of claim 1.

6. The case collection unit Using generative AI, we will also collect examples of telecommuting from international companies and provide these examples from a global perspective.

2. The system of claim 1.

7. The case collection unit Collect employee sentiment about working from home and identify examples that elicit positive sentiment 2. The system of claim 1.

8. The case analysis unit Using generative AI, we will conduct a detailed analysis of the above cases to clarify the factors that make telecommuting successful and unsuccessful.

2. The system of claim 1.

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