System

Abstract

An object of a system according to an embodiment is to effectively share knowledge of employees and promote knowledge sharing.SOLUTION: A system includes a knowledge input part, an article generation part, and a contribution part. The knowledge input part inputs the knowledge of the employee. The article generation unit generates an article based on the knowledge input by the knowledge input unit. The posting unit posts the article generated by the article generation unit to the in-house technical knowledge sharing site.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies make it difficult to effectively share employee knowledge, and there is room for improvement in promoting knowledge sharing.

[0005] The system according to the embodiment aims to effectively share the knowledge of employees and promote knowledge sharing. [Means for solving the problem]

[0006] The system according to the embodiment includes a knowledge input unit, an article generation unit, and a posting unit. The knowledge input unit inputs employee knowledge. The article generation unit generates an article based on the knowledge input by the knowledge input unit. The posting unit posts the article generated by the article generation unit to an in-house technical knowledge sharing site. [Effects of the Invention]

[0007] The system according to the embodiment can effectively share the knowledge of employees and promote knowledge sharing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The knowledge sharing system according to an embodiment of the present invention uses a generation AI to write articles based on knowledge gained by employees in easy-to-understand expressions and formats, and posts the articles on an in-house technical knowledge sharing site. This allows the knowledge sharing system to promote knowledge sharing and speed up business improvement.

[0029] A knowledge sharing system according to an embodiment includes a knowledge input unit, an article generation unit, and a posting unit. The knowledge input unit inputs employee knowledge. For example, when an employee learns a new technology, the employee can input the knowledge to the knowledge input unit. The knowledge input unit can also accept voice input and text input. For example, when an employee inputs knowledge by voice, the knowledge can be converted into text using voice recognition technology. The article generation unit generates an article based on the knowledge input by the knowledge input unit. For example, the generation AI analyzes the knowledge input by the employee and generates an article using easy-to-understand expressions and formats. The generation AI can also reference a past knowledge database and automatically add related information to complement the article. For example, when an employee learns how to use a new library, the AI ​​can add past use cases and best practices related to that library to the article. The posting unit posts the article generated by the article generation unit to an internal technical knowledge sharing site. For example, the article generated by the generation AI can be automatically posted to an internal technical knowledge sharing site so that other employees can view the article. In this way, the knowledge sharing system can promote knowledge sharing and improve the speed of business improvement. For example, when an employee learns a new technology, they can immediately write up and share that knowledge, allowing other employees to quickly learn that technology as well.

[0030] The knowledge input unit can understand the intent of employees' statements and automatically generate appropriate questions to dig deeper into their knowledge. For example, the knowledge input unit uses natural language processing technology to enable the generation AI to analyze the content of employees' statements and understand their intent. For example, if an employee says, "I learned how to use a new library," the unit automatically generates questions to dig deeper into the specific methods of using that library and points to note. The knowledge input unit can also automatically generate appropriate questions in response to employees' statements to dig deeper into their knowledge. For example, when an employee learns new technology, the unit generates questions about the detailed use of that technology and application examples to dig deeper into their knowledge. This allows employees' knowledge to be digged deeper.

[0031] The knowledge input unit can reference the past knowledge database and automatically add relevant information to complement articles. In the knowledge input unit, for example, the generative AI searches the past knowledge database and automatically extracts relevant information. For example, when an employee learns how to use a new library, past use cases and best practices related to that library are added to the article. The knowledge input unit can also reference the past knowledge database and automatically add relevant information to complement articles. For example, when an employee learns a new technology, past research results and practical examples related to that technology are added to the article. This makes it possible to further enrich the content of the article.

[0032] The knowledge input unit can analyze voice input and generate articles directly from the voice. The knowledge input unit, for example, uses voice recognition technology to convert an employee's voice input into text and generates an article based on that text. For example, when an employee explains how to use a new technology by voice, that content is automatically generated as an article. The knowledge input unit can also analyze voice input and generate articles directly from the voice. For example, when an employee inputs knowledge by voice, the voice is analyzed and an article is automatically generated. This makes it possible to generate articles directly from voice input, thereby reducing the effort required for input.

[0033] The knowledge input unit can accept input in different languages ​​and generate multilingual articles. In the knowledge input unit, for example, the generation AI accepts input in different languages ​​and generates articles corresponding to those languages. For example, an English article can be generated based on content entered in English, and other employees can view the article. The knowledge input unit can also accept input in different languages ​​and generate multilingual articles. For example, knowledge entered by an employee in Japanese can be translated into other languages ​​such as English or Chinese to generate multilingual articles. In this way, accepting input in different languages ​​and generating multilingual articles makes it possible to share knowledge internationally.

[0034] The posting department can have the generation AI automatically review the content of the article before posting and correct any errors or unclear parts. The posting department, for example, uses natural language processing technology to have the generation AI automatically review the content of the article and correct any errors or unclear parts. For example, it detects grammatical errors and spelling mistakes and automatically corrects them. The posting department can also have the generation AI automatically review the content of the article and correct any errors or unclear parts. For example, if the content of the article is unclear, it makes corrections to clarify those parts. This can improve the quality of the article.

[0035] The posting unit allows the generation AI to automatically tag posted articles, improving searchability. For example, the posting unit allows the generation AI to analyze the content of posted articles and automatically assign appropriate tags. For example, tags are generated based on the keywords and topics of the article. The posting unit also allows the generation AI to automatically tag posted articles, improving searchability. For example, tags related to the content of the article are automatically assigned, improving searchability. This improves the searchability of articles.

[0036] The posting department can automatically summarize posted articles and distribute the summarized versions to employees via email or chat. For example, the posting department can build a system in which a generative AI automatically summarizes posted articles and distributes the summarized versions to employees via email or chat. For example, it can extract important points and generate a concise summary. The posting department can also automatically summarize posted articles and distribute the summarized versions to employees via email or chat. For example, it can send summarized versions of articles to employees via email to provide information efficiently. In this way, employees can efficiently obtain information by distributing summarized versions of articles.

[0037] The posting unit can visualize posted articles and display them as infographics. The posting unit, for example, builds a system in which a generation AI analyzes the content of posted articles and visualizes them as infographics. For example, important data and statistical information is displayed in graphs and charts. The posting unit can also visualize posted articles and display them as infographics. For example, it generates infographics to visually display the content of articles in an easy-to-understand manner. In this way, visualizing articles makes it easier to understand the information.

[0038] The knowledge sharing promotion department can use generative AI to analyze employees' past posting history and suggest related new knowledge. For example, the knowledge sharing promotion department can build a system in which generative AI analyzes employees' past posting history and automatically suggests related new knowledge. For example, it can suggest information about new technologies and trends based on the content of articles posted in the past. The knowledge sharing promotion department can also use generative AI to analyze employees' past posting history and suggest related new knowledge. For example, it can suggest related new knowledge based on the content of articles posted in the past by employees. This makes it possible to promote knowledge sharing by suggesting new knowledge based on employees' past posting history.

[0039] The knowledge sharing promotion unit can use generative AI to analyze employees' skill sets and automatically suggest appropriate knowledge sharing opportunities. For example, the knowledge sharing promotion unit builds a system in which generative AI analyzes employees' skill sets and automatically suggests appropriate knowledge sharing opportunities. For example, for an employee with a specific skill, it suggests knowledge sharing opportunities related to that skill. The knowledge sharing promotion unit can also use generative AI to analyze employees' skill sets and automatically suggest appropriate knowledge sharing opportunities. For example, it suggests related knowledge sharing opportunities based on the employee's skill set. This makes it possible to promote knowledge sharing by suggesting knowledge sharing opportunities based on the employee's skill set.

[0040] The knowledge sharing promotion department can use generation AI to link with internal chat tools and promote knowledge sharing in real time. For example, the knowledge sharing promotion department can use generation AI to link with internal chat tools and build a system that promotes knowledge sharing in real time. For example, information shared in chats can be automatically turned into articles and posted on a knowledge sharing site. The knowledge sharing promotion department can also use generation AI to link with internal chat tools and promote knowledge sharing in real time. For example, it can analyze the content of conversations in chat tools and automatically share related knowledge. In this way, by linking with chat tools, it can promote knowledge sharing in real time.

[0041] The knowledge sharing promotion department can use generative AI to link with in-house project management tools and automatically share knowledge related to projects. For example, the knowledge sharing promotion department can use generative AI to link with in-house project management tools and build a system that automatically shares knowledge related to projects. For example, it can automatically suggest related technical articles and documents depending on the progress of the project. The knowledge sharing promotion department can also use generative AI to link with in-house project management tools and automatically share knowledge related to projects. For example, it can provide necessary knowledge in a timely manner as the project progresses. This makes it possible to automatically share knowledge related to projects by linking with project management tools.

[0042] The business improvement speed improvement unit can use generation AI to analyze business processes and automatically suggest areas for improvement. For example, the business improvement speed improvement unit builds a system in which generation AI analyzes business processes and automatically suggests areas for improvement. For example, it identifies bottlenecks in business flows and makes specific suggestions for improving efficiency. The business improvement speed improvement unit can also use generation AI to analyze business processes and automatically suggest areas for improvement. For example, it can suggest specific improvement measures to improve business efficiency. In this way, by analyzing business processes and suggesting areas for improvement, it is possible to increase the speed of business improvement.

[0043] The business improvement speed improvement unit can use generation AI to analyze past business data and propose improvement measures based on success cases. For example, the business improvement speed improvement unit builds a system in which generation AI analyzes past business data and automatically proposes improvement measures based on success cases. For example, it analyzes past success cases and proposes improvement measures for similar situations. The business improvement speed improvement unit can also use generation AI to analyze past business data and propose improvement measures based on success cases. For example, it makes specific proposals for business improvement based on success cases from past projects. In this way, the speed of business improvement can be improved by proposing improvement measures based on past success cases.

[0044] The business improvement speed improvement unit can use the generation AI to automatically generate training programs for business improvement and provide them to employees. The business improvement speed improvement unit, for example, builds a system in which the generation AI automatically generates training programs for business improvement and provides them to employees. For example, it generates training programs for improving specific skills or knowledge. The business improvement speed improvement unit can also use the generation AI to automatically generate training programs for business improvement and provide them to employees. For example, it generates and provides training programs based on the skills and knowledge required by employees. In this way, by automatically generating training programs for business improvement and providing them to employees, the speed of business improvement can be improved.

[0045] The business improvement speed improvement unit can use generative AI to automatically suggest tools and resources for business improvement. The business improvement speed improvement unit, for example, builds a system in which generative AI automatically suggests tools and resources for business improvement. For example, it suggests tools and resources for streamlining specific business processes. The business improvement speed improvement unit can also use generative AI to automatically suggest tools and resources for business improvement. For example, it suggests specific tools and resources for streamlining business. In this way, by automatically suggesting tools and resources for business improvement, it is possible to improve the speed of business improvement.

[0046] The Department for Strengthening Collaboration with Other Departments can use generative AI to analyze knowledge data from other departments and propose common issues and solutions. For example, the Department for Strengthening Collaboration with Other Departments can build a system in which generative AI analyzes knowledge data from other departments and automatically proposes common issues and solutions. For example, it can identify common problems faced by different departments and propose solutions. The Department for Strengthening Collaboration with Other Departments can also use generative AI to analyze knowledge data from other departments and propose common issues and solutions. For example, it can identify common issues based on knowledge data from other departments and propose solutions. This makes it possible to strengthen collaboration with other departments by analyzing knowledge data from other departments and proposing common issues and solutions.

[0047] The Department for Strengthening Collaboration with Other Departments can use generation AI to analyze communication history with other departments and make proposals to strengthen collaboration. The Department for Strengthening Collaboration with Other Departments can, for example, build a system in which generation AI analyzes communication history with other departments and automatically makes proposals to strengthen collaboration. For example, specific proposals are made based on past communication data to strengthen collaboration. The Department for Strengthening Collaboration with Other Departments can also use generation AI to analyze communication history with other departments and make proposals to strengthen collaboration. For example, specific proposals are made based on communication history with other departments. In this way, by analyzing communication history with other departments and making proposals to strengthen collaboration, collaboration with other departments can be strengthened.

[0048] The Department for Strengthening Collaboration with Other Departments can use generative AI to automatically propose joint projects with other departments and promote collaboration. The Department for Strengthening Collaboration with Other Departments can, for example, build a system in which generative AI automatically proposes joint projects with other departments and promotes collaboration. For example, it can propose projects that different departments should work on together. The Department for Strengthening Collaboration with Other Departments can also use generative AI to automatically propose joint projects with other departments and promote collaboration. For example, it can propose joint projects with other departments and promote collaboration. In this way, by automatically proposing joint projects with other departments, it can promote collaboration with other departments.

[0049] The Department for Strengthening Collaboration with Other Departments can provide a platform for automatically sharing knowledge with other departments using generative AI and strengthening collaboration. The Department for Strengthening Collaboration with Other Departments, for example, builds a platform for automatically sharing knowledge with other departments using generative AI and strengthening collaboration. For example, it provides a system that integrates and shares knowledge data from different departments. The Department for Strengthening Collaboration with Other Departments can also provide a platform for automatically sharing knowledge with other departments using generative AI and strengthening collaboration. For example, it provides a platform for automatically sharing knowledge with other departments and strengthening collaboration. This makes it possible to strengthen collaboration with other departments by automatically sharing knowledge with other departments.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The knowledge input unit can understand the intent of employees' statements and automatically generate appropriate questions to dig deeper into their knowledge. For example, if an employee says, "I learned how to use a new library," the unit automatically generates questions to dig deeper into the specific methods of using that library and points to note. The knowledge input unit can also automatically generate appropriate questions in response to employees' statements to dig deeper into their knowledge. For example, when an employee learns a new technology, the unit generates questions about the detailed use of that technology and examples of its application to dig deeper into their knowledge. This allows the employee's knowledge to be digged deeper.

[0052] The knowledge input unit can accept input in different languages ​​and generate multilingual articles. For example, the generation AI can accept input in different languages ​​and generate articles corresponding to those languages. For example, it can generate an English article based on content entered in English, and make that article available to other employees. The knowledge input unit can also accept input in different languages ​​and generate multilingual articles. For example, it can translate knowledge entered by an employee in Japanese into other languages ​​such as English or Chinese, and generate a multilingual article. This allows for international knowledge sharing by accepting input in different languages ​​and generating multilingual articles.

[0053] The posting department can have the generation AI automatically review the content of the article before posting and correct any errors or unclear parts. For example, the generation AI can use natural language processing technology to automatically review the content of the article and correct any errors or unclear parts. For example, it can detect grammatical errors and spelling mistakes and automatically correct them. The posting department can also have the generation AI automatically review the content of the article and correct any errors or unclear parts. For example, if the content of the article is unclear, it can make corrections to clarify those parts. This can improve the quality of the article.

[0054] The posting unit allows the generation AI to automatically tag posted articles, improving searchability. For example, the generation AI analyzes the content of the posted article and automatically assigns appropriate tags. For example, tags are generated based on the keywords and topics of the article. The posting unit also allows the generation AI to automatically tag posted articles, improving searchability. For example, tags related to the content of the article are automatically assigned, improving searchability. This improves the searchability of articles.

[0055] The posting department can automatically summarize posted articles and distribute the summarized version to employees via email or chat. For example, a system can be built in which a generative AI automatically summarizes posted articles and distributes the summarized version to employees via email or chat. For example, it can extract important points and generate a concise summary. The posting department can also automatically summarize posted articles and distribute the summarized version to employees via email or chat. For example, it can send a summarized version of an article to employees via email, providing information efficiently. In this way, employees can obtain information efficiently by distributing a summarized version of the article.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The knowledge input unit inputs employee knowledge. For example, when an employee learns a new technology, they can input that knowledge into the knowledge input unit. The knowledge input unit can also accept voice input and text input. For example, if an employee inputs knowledge by voice, it can be converted into text using voice recognition technology. Step 2: The article generation unit generates articles based on the knowledge entered by the knowledge input unit. For example, the generation AI analyzes the knowledge entered by employees and generates articles in easy-to-understand expressions and formats. The generation AI can also reference a past knowledge database and automatically add related information to complement the article. For example, when an employee learns how to use a new library, it adds past use cases and best practices related to that library to the article. Step 3: The posting unit posts the articles generated by the article generation unit to an internal technical knowledge sharing site. For example, the article generated by the generation AI can be automatically posted to an internal technical knowledge sharing site so that other employees can view the article.

[0058] (Example 2) The knowledge sharing system according to an embodiment of the present invention uses a generation AI to write articles based on knowledge gained by employees in easy-to-understand expressions and formats, and posts the articles on an in-house technical knowledge sharing site. This allows the knowledge sharing system to promote knowledge sharing and speed up business improvement.

[0059] A knowledge sharing system according to an embodiment includes a knowledge input unit, an article generation unit, and a posting unit. The knowledge input unit inputs employee knowledge. For example, when an employee learns a new technology, the employee can input the knowledge to the knowledge input unit. The knowledge input unit can also accept voice input and text input. For example, when an employee inputs knowledge by voice, the knowledge can be converted into text using voice recognition technology. The article generation unit generates an article based on the knowledge input by the knowledge input unit. For example, the generation AI analyzes the knowledge input by the employee and generates an article using easy-to-understand expressions and formats. The generation AI can also reference a past knowledge database and automatically add related information to complement the article. For example, when an employee learns how to use a new library, the AI ​​can add past use cases and best practices related to that library to the article. The posting unit posts the article generated by the article generation unit to an internal technical knowledge sharing site. For example, the article generated by the generation AI can be automatically posted to an internal technical knowledge sharing site so that other employees can view the article. In this way, the knowledge sharing system can promote knowledge sharing and improve the speed of business improvement. For example, when an employee learns a new technology, they can immediately write up and share that knowledge, allowing other employees to quickly learn that technology as well.

[0060] The knowledge input unit can understand the intent of employees' statements and automatically generate appropriate questions to dig deeper into their knowledge. For example, the knowledge input unit uses natural language processing technology to enable the generation AI to analyze the content of employees' statements and understand their intent. For example, if an employee says, "I learned how to use a new library," the unit automatically generates questions to dig deeper into the specific methods of using that library and points to note. The knowledge input unit can also automatically generate appropriate questions in response to employees' statements to dig deeper into their knowledge. For example, when an employee learns new technology, the unit generates questions about the detailed use of that technology and application examples to dig deeper into their knowledge. This allows employees' knowledge to be digged deeper.

[0061] The knowledge input unit can reference the past knowledge database and automatically add relevant information to complement articles. In the knowledge input unit, for example, the generative AI searches the past knowledge database and automatically extracts relevant information. For example, when an employee learns how to use a new library, past use cases and best practices related to that library are added to the article. The knowledge input unit can also reference the past knowledge database and automatically add relevant information to complement articles. For example, when an employee learns a new technology, past research results and practical examples related to that technology are added to the article. This makes it possible to further enrich the content of the article.

[0062] The knowledge input unit can use the emotion estimation function to analyze the emotional state of employees and generate articles that elicit positive emotions. The knowledge input unit, for example, uses the emotion estimation function to analyze the emotional state of employees when they speak in real time. For example, it detects excitement or joy when an employee learns a new technology and generates a positive article that reflects that emotion. The knowledge input unit can also use the emotion estimation function to analyze the emotional state of employees and generate articles that elicit positive emotions. For example, when an employee learns a new technology, it generates an article that emphasizes the benefits and success stories of that technology. This can elicit positive emotions from employees and improve the quality of the articles.

[0063] The knowledge input unit can analyze voice input and generate articles directly from the voice. The knowledge input unit, for example, uses voice recognition technology to convert an employee's voice input into text and generates an article based on that text. For example, when an employee explains how to use a new technology by voice, that content is automatically generated as an article. The knowledge input unit can also analyze voice input and generate articles directly from the voice. For example, when an employee inputs knowledge by voice, the voice is analyzed and an article is automatically generated. This makes it possible to generate articles directly from voice input, thereby reducing the effort required for input.

[0064] The knowledge input unit can accept input in different languages ​​and generate multilingual articles. In the knowledge input unit, for example, the generation AI accepts input in different languages ​​and generates articles corresponding to those languages. For example, an English article can be generated based on content entered in English, and other employees can view the article. The knowledge input unit can also accept input in different languages ​​and generate multilingual articles. For example, knowledge entered by an employee in Japanese can be translated into other languages ​​such as English or Chinese to generate multilingual articles. In this way, accepting input in different languages ​​and generating multilingual articles makes it possible to share knowledge internationally.

[0065] The knowledge input unit can use the emotion estimation function to analyze the emotions of employees when they enter text in real time and make suggestions to optimize the input content. The knowledge input unit, for example, uses the emotion estimation function to analyze the emotions of employees when they enter text in real time and make suggestions to optimize the input content based on the emotions. For example, if negative emotions are detected, the knowledge input unit can suggest positive expressions. The knowledge input unit can also use the emotion estimation function to analyze the emotions of employees when they enter text in real time and make suggestions to optimize the input content. For example, if an employee is feeling stressed, the knowledge input unit can make suggestions to alleviate the stress. In this way, the quality of articles can be improved by optimizing the input content based on the employees' emotions.

[0066] The posting department can have the generation AI automatically review the content of the article before posting and correct any errors or unclear parts. The posting department, for example, uses natural language processing technology to have the generation AI automatically review the content of the article and correct any errors or unclear parts. For example, it detects grammatical errors and spelling mistakes and automatically corrects them. The posting department can also have the generation AI automatically review the content of the article and correct any errors or unclear parts. For example, if the content of the article is unclear, it makes corrections to clarify those parts. This can improve the quality of the article.

[0067] The posting unit allows the generation AI to automatically tag posted articles, improving searchability. For example, the posting unit allows the generation AI to analyze the content of posted articles and automatically assign appropriate tags. For example, tags are generated based on the keywords and topics of the article. The posting unit also allows the generation AI to automatically tag posted articles, improving searchability. For example, tags related to the content of the article are automatically assigned, improving searchability. This improves the searchability of articles.

[0068] The posting unit can use the emotion estimation function to analyze employees' emotional reactions to posted articles and improve the articles based on the feedback. The posting unit, for example, uses the emotion estimation function to collect employees' emotional reactions to posted articles in real time and improve the articles based on that data. For example, it emphasizes parts that receive a lot of positive reactions and modifies parts that receive a lot of negative reactions. The posting unit can also use the emotion estimation function to analyze employees' emotional reactions to posted articles and improve the articles based on the feedback. For example, it modifies the content of the article based on the employees' emotional reactions to improve the quality. In this way, the quality of the article can be improved by improving the article based on the employees' emotional reactions.

[0069] The posting department can automatically summarize posted articles and distribute the summarized versions to employees via email or chat. For example, the posting department can build a system in which a generative AI automatically summarizes posted articles and distributes the summarized versions to employees via email or chat. For example, it can extract important points and generate a concise summary. The posting department can also automatically summarize posted articles and distribute the summarized versions to employees via email or chat. For example, it can send summarized versions of articles to employees via email to provide information efficiently. In this way, employees can efficiently obtain information by distributing summarized versions of articles.

[0070] The posting unit can visualize posted articles and display them as infographics. The posting unit, for example, builds a system in which a generation AI analyzes the content of posted articles and visualizes them as infographics. For example, important data and statistical information is displayed in graphs and charts. The posting unit can also visualize posted articles and display them as infographics. For example, it generates infographics to visually display the content of articles in an easy-to-understand manner. In this way, visualizing articles makes it easier to understand the information.

[0071] The knowledge sharing promotion department can use generative AI to analyze employees' past posting history and suggest related new knowledge. For example, the knowledge sharing promotion department can build a system in which generative AI analyzes employees' past posting history and automatically suggests related new knowledge. For example, it can suggest information about new technologies and trends based on the content of articles posted in the past. The knowledge sharing promotion department can also use generative AI to analyze employees' past posting history and suggest related new knowledge. For example, it can suggest related new knowledge based on the content of articles posted in the past by employees. This makes it possible to promote knowledge sharing by suggesting new knowledge based on employees' past posting history.

[0072] The knowledge sharing promotion unit can use generative AI to analyze employees' skill sets and automatically suggest appropriate knowledge sharing opportunities. For example, the knowledge sharing promotion unit builds a system in which generative AI analyzes employees' skill sets and automatically suggests appropriate knowledge sharing opportunities. For example, for an employee with a specific skill, it suggests knowledge sharing opportunities related to that skill. The knowledge sharing promotion unit can also use generative AI to analyze employees' skill sets and automatically suggest appropriate knowledge sharing opportunities. For example, it suggests related knowledge sharing opportunities based on the employee's skill set. This makes it possible to promote knowledge sharing by suggesting knowledge sharing opportunities based on the employee's skill set.

[0073] The knowledge sharing promotion unit can use the emotion estimation function to propose a knowledge sharing method that increases employee motivation. The knowledge sharing promotion unit, for example, uses the emotion estimation function to analyze the emotional state of employees in real time and propose a knowledge sharing method that increases motivation. For example, it provides opportunities for knowledge sharing at times when positive emotions are strong. The knowledge sharing promotion unit can also use the emotion estimation function to propose a knowledge sharing method that increases employee motivation. For example, it provides opportunities for knowledge sharing at times when employees are highly motivated. This increases employee motivation and promotes knowledge sharing.

[0074] The knowledge sharing promotion department can use generation AI to link with internal chat tools and promote knowledge sharing in real time. For example, the knowledge sharing promotion department can use generation AI to link with internal chat tools and build a system that promotes knowledge sharing in real time. For example, information shared in chats can be automatically turned into articles and posted on a knowledge sharing site. The knowledge sharing promotion department can also use generation AI to link with internal chat tools and promote knowledge sharing in real time. For example, it can analyze the content of conversations in chat tools and automatically share related knowledge. In this way, by linking with chat tools, it can promote knowledge sharing in real time.

[0075] The knowledge sharing promotion department can use generative AI to link with in-house project management tools and automatically share knowledge related to projects. For example, the knowledge sharing promotion department can use generative AI to link with in-house project management tools and build a system that automatically shares knowledge related to projects. For example, it can automatically suggest related technical articles and documents depending on the progress of the project. The knowledge sharing promotion department can also use generative AI to link with in-house project management tools and automatically share knowledge related to projects. For example, it can provide necessary knowledge in a timely manner as the project progresses. This makes it possible to automatically share knowledge related to projects by linking with project management tools.

[0076] The knowledge sharing promotion unit can use the emotion estimation function to analyze the emotions employees feel when sharing knowledge and encourage them to share at the optimal timing. The knowledge sharing promotion unit, for example, uses the emotion estimation function to analyze the emotions employees feel when sharing knowledge in real time and build a system that encourages sharing at the optimal timing. For example, it provides opportunities for knowledge sharing when positive emotions are strong. The knowledge sharing promotion unit can also use the emotion estimation function to analyze the emotions employees feel when sharing knowledge and encourage sharing at the optimal timing. For example, it provides opportunities for knowledge sharing when employees are highly motivated. This makes it possible to promote knowledge sharing by encouraging knowledge sharing at the optimal timing based on employees' emotions.

[0077] The business improvement speed improvement unit can use generation AI to analyze business processes and automatically suggest areas for improvement. For example, the business improvement speed improvement unit builds a system in which generation AI analyzes business processes and automatically suggests areas for improvement. For example, it identifies bottlenecks in business flows and makes specific suggestions for improving efficiency. The business improvement speed improvement unit can also use generation AI to analyze business processes and automatically suggest areas for improvement. For example, it can suggest specific improvement measures to improve business efficiency. In this way, by analyzing business processes and suggesting areas for improvement, it is possible to increase the speed of business improvement.

[0078] The business improvement speed improvement unit can use generation AI to analyze past business data and propose improvement measures based on success cases. For example, the business improvement speed improvement unit builds a system in which generation AI analyzes past business data and automatically proposes improvement measures based on success cases. For example, it analyzes past success cases and proposes improvement measures for similar situations. The business improvement speed improvement unit can also use generation AI to analyze past business data and propose improvement measures based on success cases. For example, it makes specific proposals for business improvement based on success cases from past projects. In this way, the speed of business improvement can be improved by proposing improvement measures based on past success cases.

[0079] The business improvement speed improvement unit can use the emotion estimation function to analyze employees' stress levels and propose business improvement measures to reduce stress. The business improvement speed improvement unit, for example, uses the emotion estimation function to build a system that analyzes employees' stress levels in real time and proposes business improvement measures to reduce stress. For example, it identifies business processes that cause high stress and proposes improvement measures. The business improvement speed improvement unit can also use the emotion estimation function to analyze employees' stress levels and propose business improvement measures to reduce stress. For example, it makes specific proposals to improve business efficiency based on employees' stress levels. In this way, the business improvement speed can be improved by analyzing employees' stress levels and proposing business improvement measures to reduce stress.

[0080] The business improvement speed improvement unit can use the generation AI to automatically generate training programs for business improvement and provide them to employees. The business improvement speed improvement unit, for example, builds a system in which the generation AI automatically generates training programs for business improvement and provides them to employees. For example, it generates training programs for improving specific skills or knowledge. The business improvement speed improvement unit can also use the generation AI to automatically generate training programs for business improvement and provide them to employees. For example, it generates and provides training programs based on the skills and knowledge required by employees. In this way, by automatically generating training programs for business improvement and providing them to employees, the speed of business improvement can be improved.

[0081] The business improvement speed improvement unit can use generative AI to automatically suggest tools and resources for business improvement. The business improvement speed improvement unit, for example, builds a system in which generative AI automatically suggests tools and resources for business improvement. For example, it suggests tools and resources for streamlining specific business processes. The business improvement speed improvement unit can also use generative AI to automatically suggest tools and resources for business improvement. For example, it suggests specific tools and resources for streamlining business. In this way, by automatically suggesting tools and resources for business improvement, it is possible to improve the speed of business improvement.

[0082] The Department for Strengthening Collaboration with Other Departments can use generative AI to analyze knowledge data from other departments and propose common issues and solutions. For example, the Department for Strengthening Collaboration with Other Departments can build a system in which generative AI analyzes knowledge data from other departments and automatically proposes common issues and solutions. For example, it can identify common problems faced by different departments and propose solutions. The Department for Strengthening Collaboration with Other Departments can also use generative AI to analyze knowledge data from other departments and propose common issues and solutions. For example, it can identify common issues based on knowledge data from other departments and propose solutions. This makes it possible to strengthen collaboration with other departments by analyzing knowledge data from other departments and proposing common issues and solutions.

[0083] The Department for Strengthening Collaboration with Other Departments can use generation AI to analyze communication history with other departments and make proposals to strengthen collaboration. The Department for Strengthening Collaboration with Other Departments can, for example, build a system in which generation AI analyzes communication history with other departments and automatically makes proposals to strengthen collaboration. For example, specific proposals are made based on past communication data to strengthen collaboration. The Department for Strengthening Collaboration with Other Departments can also use generation AI to analyze communication history with other departments and make proposals to strengthen collaboration. For example, specific proposals are made based on communication history with other departments. In this way, by analyzing communication history with other departments and making proposals to strengthen collaboration, collaboration with other departments can be strengthened.

[0084] The collaboration strengthening department with other departments can use the emotion estimation function to identify emotional barriers in collaboration with other departments and make proposals to resolve them. The collaboration strengthening department with other departments can, for example, use the emotion estimation function to build a system that analyzes emotional barriers in collaboration with other departments in real time and makes proposals to resolve them. For example, it can make a proposal to avoid collaboration when negative emotions are strong. The collaboration strengthening department with other departments can also use the emotion estimation function to identify emotional barriers in collaboration with other departments and make proposals to resolve them. For example, it can identify emotional barriers in collaboration with other departments and propose solutions to resolve them. In this way, by identifying emotional barriers in collaboration with other departments and making proposals to resolve them, collaboration with other departments can be strengthened.

[0085] The Department for Strengthening Collaboration with Other Departments can use generative AI to automatically propose joint projects with other departments and promote collaboration. The Department for Strengthening Collaboration with Other Departments can, for example, build a system in which generative AI automatically proposes joint projects with other departments and promotes collaboration. For example, it can propose projects that different departments should work on together. The Department for Strengthening Collaboration with Other Departments can also use generative AI to automatically propose joint projects with other departments and promote collaboration. For example, it can propose joint projects with other departments and promote collaboration. In this way, by automatically proposing joint projects with other departments, it can promote collaboration with other departments.

[0086] The Department for Strengthening Collaboration with Other Departments can provide a platform for automatically sharing knowledge with other departments using generative AI and strengthening collaboration. The Department for Strengthening Collaboration with Other Departments, for example, builds a platform for automatically sharing knowledge with other departments using generative AI and strengthening collaboration. For example, it provides a system that integrates and shares knowledge data from different departments. The Department for Strengthening Collaboration with Other Departments can also provide a platform for automatically sharing knowledge with other departments using generative AI and strengthening collaboration. For example, it provides a platform for automatically sharing knowledge with other departments and strengthening collaboration. This makes it possible to strengthen collaboration with other departments by automatically sharing knowledge with other departments.

[0087] The collaboration strengthening unit with other departments can use the emotion estimation function to make suggestions for strengthening the emotionally positive elements in collaboration with other departments. The collaboration strengthening unit with other departments, for example, uses the emotion estimation function to build a system that analyzes the emotionally positive elements in collaboration with other departments in real time and makes suggestions for strengthening collaboration. For example, it makes suggestions to promote collaboration at times when positive emotions are strong. Furthermore, the collaboration strengthening unit with other departments can use the emotion estimation function to make suggestions for strengthening the emotionally positive elements in collaboration with other departments. For example, it makes specific suggestions for strengthening positive emotions in collaboration with other departments. In this way, collaboration with other departments can be strengthened by strengthening the emotionally positive elements in collaboration with other departments.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The knowledge input unit can understand the intent of employees' statements and automatically generate appropriate questions to dig deeper into their knowledge. For example, if an employee says, "I learned how to use a new library," the unit automatically generates questions to dig deeper into the specific methods of using that library and points to note. The knowledge input unit can also automatically generate appropriate questions in response to employees' statements to dig deeper into their knowledge. For example, when an employee learns a new technology, the unit generates questions about the detailed use of that technology and examples of its application to dig deeper into their knowledge. This allows the employee's knowledge to be digged deeper.

[0090] The knowledge input unit can accept input in different languages ​​and generate multilingual articles. For example, the generation AI can accept input in different languages ​​and generate articles corresponding to those languages. For example, it can generate an English article based on content entered in English, and make that article available to other employees. The knowledge input unit can also accept input in different languages ​​and generate multilingual articles. For example, it can translate knowledge entered by an employee in Japanese into other languages ​​such as English or Chinese, and generate a multilingual article. This allows for international knowledge sharing by accepting input in different languages ​​and generating multilingual articles.

[0091] The posting department can have the generation AI automatically review the content of the article before posting and correct any errors or unclear parts. For example, the generation AI can use natural language processing technology to automatically review the content of the article and correct any errors or unclear parts. For example, it can detect grammatical errors and spelling mistakes and automatically correct them. The posting department can also have the generation AI automatically review the content of the article and correct any errors or unclear parts. For example, if the content of the article is unclear, it can make corrections to clarify those parts. This can improve the quality of the article.

[0092] The posting unit allows the generation AI to automatically tag posted articles, improving searchability. For example, the generation AI analyzes the content of the posted article and automatically assigns appropriate tags. For example, tags are generated based on the keywords and topics of the article. The posting unit also allows the generation AI to automatically tag posted articles, improving searchability. For example, tags related to the content of the article are automatically assigned, improving searchability. This improves the searchability of articles.

[0093] The posting department can automatically summarize posted articles and distribute the summarized version to employees via email or chat. For example, a system can be built in which a generative AI automatically summarizes posted articles and distributes the summarized version to employees via email or chat. For example, it can extract important points and generate a concise summary. The posting department can also automatically summarize posted articles and distribute the summarized version to employees via email or chat. For example, it can send a summarized version of an article to employees via email, providing information efficiently. In this way, employees can obtain information efficiently by distributing a summarized version of the article.

[0094] The knowledge input unit can use the emotion estimation function to analyze the emotional state of employees and generate articles that elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotional state of employees when they speak in real time. For example, the emotion estimation function can detect excitement or joy when an employee learns a new technology and generate a positive article that reflects that emotion. The knowledge input unit can also use the emotion estimation function to analyze the emotional state of employees and generate articles that elicit positive emotions. For example, when an employee learns a new technology, an article is generated that emphasizes the benefits and success stories of that technology. This can elicit positive emotions from employees and improve the quality of the articles.

[0095] The knowledge input unit can use the emotion estimation function to analyze the emotions of employees when they enter text in real time and make suggestions to optimize the entered content. For example, the emotion estimation function can be used to analyze the emotions of employees when they enter text in real time and make suggestions to optimize the entered content based on the emotions. For example, if negative emotions are detected, the knowledge input unit can suggest positive expressions. The knowledge input unit can also use the emotion estimation function to analyze the emotions of employees when they enter text in real time and make suggestions to optimize the entered content. For example, if an employee is feeling stressed, the knowledge input unit can make suggestions to reduce the stress. In this way, the quality of articles can be improved by optimizing the entered content based on the employees' emotions.

[0096] The posting department can use the emotion estimation function to analyze employees' emotional reactions to posted articles and improve the articles based on the feedback. For example, the emotion estimation function can be used to collect employees' emotional reactions to posted articles in real time and improve the articles based on that data. For example, the parts that receive a lot of positive reactions can be emphasized and the parts that receive a lot of negative reactions can be revised. The posting department can also use the emotion estimation function to analyze employees' emotional reactions to posted articles and improve the articles based on the feedback. For example, the content of the article can be revised based on the employees' emotional reactions to improve the quality. In this way, the quality of the article can be improved by improving the article based on the employees' emotional reactions.

[0097] The knowledge sharing promotion unit can use the emotion estimation function to propose knowledge sharing methods that will increase employee motivation. For example, the emotion estimation function can be used to analyze the emotional state of employees in real time and propose knowledge sharing methods that will increase motivation. For example, knowledge sharing opportunities can be provided at times when positive emotions are strong. The knowledge sharing promotion unit can also use the emotion estimation function to propose knowledge sharing methods that will increase employee motivation. For example, knowledge sharing opportunities can be provided at times when employees are highly motivated. This can increase employee motivation and promote knowledge sharing.

[0098] The department for strengthening collaboration with other departments can use the emotion estimation function to identify emotional barriers to collaboration with other departments and make proposals to resolve them. For example, a system can be built that uses the emotion estimation function to analyze emotional barriers to collaboration with other departments in real time and make proposals to resolve them. For example, a proposal can be made to avoid collaboration when negative emotions are strong. The department for strengthening collaboration with other departments can also use the emotion estimation function to identify emotional barriers to collaboration with other departments and make proposals to resolve them. For example, the department can identify emotional barriers to collaboration with other departments and propose solutions to resolve them. In this way, by identifying emotional barriers to collaboration with other departments and making proposals to resolve them, collaboration with other departments can be strengthened.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The knowledge input unit inputs employee knowledge. For example, when an employee learns a new technology, they can input that knowledge into the knowledge input unit. The knowledge input unit can also accept voice input and text input. For example, if an employee inputs knowledge by voice, it can be converted into text using voice recognition technology. Step 2: The article generation unit generates articles based on the knowledge entered by the knowledge input unit. For example, the generation AI analyzes the knowledge entered by employees and generates articles in easy-to-understand expressions and formats. The generation AI can also reference a past knowledge database and automatically add related information to complement the article. For example, when an employee learns how to use a new library, it adds past use cases and best practices related to that library to the article. Step 3: The posting unit posts the articles generated by the article generation unit to an internal technical knowledge sharing site. For example, the article generated by the generation AI can be automatically posted to an internal technical knowledge sharing site so that other employees can view the article.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

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

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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, in order to avoid confusion and to 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.

[0167] 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]

[0168] 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 department that inputs employee knowledge, an article generation unit that generates an article based on the knowledge input by the knowledge input unit; a posting unit that posts the article generated by the article generating unit to an in-house technical knowledge sharing site. A system characterized by:

2. The knowledge input section is Analyzes voice input and generates articles directly from speech 2. The system of claim 1.

3. The submission department is Before posting, generative AI automatically reviews the content of articles and corrects any errors or unclear parts.

2. The system of claim 1.

4. The Knowledge Sharing Promotion Department Using generative AI to analyze employees' past posting history and suggest new relevant insights 2. The system of claim 1.

5. The Business Improvement Speed ​​Improvement Department Analyze business processes using generative AI and automatically suggest improvements 2. The system of claim 1.

6. The knowledge input section is Analyze employees' emotional state and generate articles that elicit positive emotions 2. The system of claim 1.

7. The submission department is Analyze employees' emotional reactions to posted articles and improve them based on their feedback 2. The system of claim 1.

8. The Knowledge Sharing Promotion Department Propose a method of knowledge sharing that will increase employee motivation 2. The system of claim 1.

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