system

Abstract

The system according to this embodiment aims to analyze skill sets and emotional states within the workplace and to optimize personnel placement and project management. [Solution] The system according to the embodiment comprises an analysis unit, an emotion recognition unit, a processing unit, a proposal unit, a support unit, a promotion unit, and a decision-making unit. The analysis unit analyzes the skill set within the workplace. The emotion recognition unit analyzes the emotional state based on the skill set analyzed by the analysis unit. The processing unit uses natural language processing technology based on the emotional state analyzed by the emotion recognition unit. The proposal unit proposes the most suitable personnel based on the information obtained by the processing unit. The support unit provides communication support based on the personnel proposed by the proposal unit. The promotion unit promotes creative projects based on the information supported by the support unit. The decision-making unit supports decision-making based on the projects promoted by the promotion unit.

Description

Technical Field

[0004] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] <00000​​​​​​​​​​​The system according to this embodiment comprises an analysis unit, an emotion recognition unit, a processing unit, a proposal unit, a support unit, a promotion unit, and a decision-making unit. The analysis unit analyzes the skill sets within the workplace. The emotion recognition unit analyzes emotional states based on the skill sets analyzed by the analysis unit. The processing unit uses natural language processing technology based on the emotional states analyzed by the emotion recognition unit. The proposal unit proposes the most suitable personnel based on the information obtained by the processing unit. The support unit provides communication support based on the personnel proposed by the proposal unit. The promotion unit promotes creative projects based on the information supported by the support unit. The decision-making unit supports decision-making based on the projects promoted by the promotion unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze skill sets and emotional states within the workplace to enable optimal personnel allocation and project management. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​Inspiration Partner System according to an embodiment of the present invention is a system in which AI supports optimal personnel placement within the workplace through emotion recognition and skill analysis. This AI Inspiration Partner System analyzes the skill set within the workplace and, in combination with emotion recognition and natural language processing technology, supports communication assistance, promotion of creative projects, and decision-making for specific tasks and projects, thereby achieving increased work efficiency and improved creativity. First, the AI ​​analyzes the skill set within the workplace. In this process, it collects and analyzes employee skills, work performance, communication information, etc. For example, based on an employee's past project experience and skill set, it can identify the most suitable personnel for a specific task. Next, it proposes the most suitable personnel for a specific task or project by combining emotion recognition technology and natural language processing technology. For example, the AI ​​can analyze the emotional state of employees and place personnel in a way that allows them to perform optimally with minimal stress. It also uses natural language processing technology to support smooth communication among employees. Furthermore, the AI ​​supports communication assistance, promotion of creative projects, and decision-making. For example, the AI ​​monitors the progress of a project and provides necessary advice and support. It also generates creative ideas and supports the advancement of projects. In decision-making, AI analyzes data to support optimal decision-making. This system enables increased efficiency and creativity in operations. For example, by suggesting the most suitable personnel in real time, AI can prevent project stagnation and support smooth progress. Furthermore, by utilizing emotion recognition technology, it can reduce employee stress and provide a more comfortable working environment. This improves overall workplace productivity and increases creative output. Thus, the AI ​​Inspiration Partner System analyzes the skill sets within the workplace, combines emotion recognition and natural language processing technologies to suggest the most suitable personnel, and supports communication, creative project advancement, and decision-making, thereby achieving increased efficiency and creativity in operations.

[0029] The AI ​​inspiration partner system according to this embodiment comprises an analysis unit, an emotion recognition unit, a processing unit, a proposal unit, a support unit, a promotion unit, and a decision-making unit. The analysis unit analyzes the skill sets within the workplace. For example, the analysis unit collects employee skills, work performance, and communication information, which are then analyzed by the AI. For example, the analysis unit can identify the most suitable personnel for a specific task based on employees' past project experience and skill sets. The emotion recognition unit analyzes emotional states. For example, the emotion recognition unit uses the AI ​​to analyze employees' emotional states and assign personnel to ensure optimal performance under low stress. For example, the emotion recognition unit monitors employees' emotional states in real time and evaluates their stress levels. The processing unit uses natural language processing technology. For example, the processing unit uses natural language processing technology to support smoother communication among employees. For example, the processing unit uses text analysis technology to analyze the content of emails and chats between employees and proposes improvements to communication. The proposal unit proposes the most suitable personnel. The Proposal Department, for example, uses AI to propose the most suitable personnel in real time. For example, the Proposal Department identifies the best personnel for a particular project based on employees' skill sets and emotional states. The Support Department provides communication support. For example, the Support Department uses AI to monitor project progress and provide necessary advice and support. For example, the Support Department grasps project progress in real time and provides appropriate feedback. The Promotion Department drives creative projects. For example, the Promotion Department uses AI to generate creative ideas and support project advancement. For example, the Promotion Department supports brainstorming sessions and elicits new ideas. The Decision-Making Department supports decision-making. For example, the Decision-Making Department uses AI to analyze data and support optimal decision-making. For example, the Decision-Making Department makes optimal decisions based on project progress and resource utilization.As a result, the AI ​​inspiration partner system according to this embodiment can analyze the skill sets within the workplace, propose the most suitable personnel by combining emotion recognition and natural language processing technologies, and support communication, the promotion of creative projects, and decision-making, thereby improving work efficiency and creativity.

[0030] The analytics department analyzes the skill sets within the workplace. Specifically, it collects employee skills, work performance, and communication information, and AI analyzes this data. For example, based on an employee's past project experience and skill set, it can identify the most suitable person for a particular task. The analytics department collects data from employee resumes, work reports, and project management tools, and AI integrates and analyzes this data. The AI ​​uses natural language processing technology to extract skills and experience from text data and stores it in a database. Furthermore, the AI ​​evaluates employees' work performance and understands their skill improvement and acquisition of new skills. This allows the analytics department to gain a detailed understanding of employees' skill sets and quickly identify the most suitable person for a specific project or task. In addition, the analytics department analyzes employee communication information to evaluate the quality and frequency of communication within teams. This helps identify areas for improvement in team collaboration and communication, supporting efficient team formation.

[0031] The emotion recognition unit analyzes emotional states. Specifically, AI analyzes employees' emotional states and assigns personnel to positions where they can perform optimally with minimal stress. The emotion recognition unit analyzes employees' facial expressions, voice, and text data to monitor their emotional states in real time. For example, it captures employees' facial expressions with a camera and analyzes changes in facial expressions using image recognition technology. It also uses voice recognition technology to evaluate emotional states from employees' tone of voice and speaking style. Furthermore, it uses text analysis technology to extract emotional states from the content of employees' emails and chats. As a result, the emotion recognition unit can grasp employees' stress levels and motivation in real time and provide support at the appropriate time. For example, it can suggest relaxation advice and breaks to employees who are experiencing high stress levels. It can also suggest encouraging messages and goal setting revisions to employees whose motivation is low. In this way, the emotion recognition unit can appropriately manage employees' emotional states, reduce stress, and improve performance.

[0032] The Processing Department utilizes Natural Language Processing (NLP) technology. Specifically, it uses NLP to support smoother communication among employees. The Processing Department uses text analysis technology to analyze the content of emails and chats between employees and proposes improvements to communication. For example, the Processing Department analyzes the content of emails and chats to identify ambiguous or misleading expressions and proposes correcting them to appropriate expressions. The Processing Department also analyzes communication patterns among employees and evaluates the frequency and quality of communication. This allows the Processing Department to identify areas for improvement in communication and support more efficient communication. Furthermore, the Processing Department uses NLP to collect employee opinions and feedback to understand the progress and challenges of projects. This allows the Processing Department to facilitate project progress and support efficient work execution.

[0033] The proposal department proposes the most suitable personnel. Specifically, AI proposes the most suitable personnel in real time. The proposal department identifies the most suitable personnel for a particular project based on employees' skill sets and emotional states. The proposal department integrates data provided by the analysis department and the emotion recognition department, and the AI ​​analyzes this data to identify the most suitable personnel. For example, the proposal department lists the most suitable personnel based on the skill sets required for a particular project and proposes them to the project manager. The proposal department also considers employees' emotional states and places personnel in a way that allows them to perform at their best with minimal stress. This allows the proposal department to quickly identify the most suitable personnel to contribute to the success of a project and support efficient personnel placement. Furthermore, the proposal department considers employees' career paths and growth goals and proposes appropriate projects and tasks. This can improve employee motivation and support career growth.

[0034] The support department provides communication support. Specifically, AI monitors project progress and provides necessary advice and support. The support department grasps project progress in real time and provides appropriate feedback. For example, the support department collects data from project management tools, and AI analyzes this data to evaluate project progress. Based on project progress and resource utilization, the AI ​​provides appropriate advice and support. For example, if project progress is behind schedule, the support department identifies the cause of the delay and proposes solutions. Conversely, if project progress is on track, the support department evaluates the progress and provides advice for the next steps. In this way, the support department can facilitate project progress and support efficient work execution. Furthermore, the support department supports communication among employees and strengthens team collaboration. For example, the support department evaluates the quality and frequency of communication among employees and proposes areas for improvement. In this way, the support department can support efficient communication and strengthen team collaboration.

[0035] The Promotion Department drives creative projects. Specifically, AI generates creative ideas and supports project advancement. The Promotion Department supports brainstorming sessions and elicits new ideas. For example, the Promotion Department uses AI to analyze past project data and industry trends to generate new ideas. Using natural language processing technology, the AI ​​extracts creative ideas from text data and proposes them in brainstorming sessions. This allows the Promotion Department to elicit creative ideas and support project advancement. The Promotion Department also collects employee ideas, and AI analyzes these ideas to identify the most suitable ones. This allows the Promotion Department to unleash employee creativity and contribute to project success. Furthermore, the Promotion Department monitors the progress of creative projects and provides appropriate support. For example, the Promotion Department evaluates project progress and provides necessary resources and support. This allows the Promotion Department to support the success of creative projects and achieve increased operational efficiency and creativity.

[0036] The decision-making department supports decision-making. Specifically, AI analyzes data to support optimal decision-making. The decision-making department makes optimal decisions based on project progress and resource utilization. For example, the decision-making department collects data from project management tools, and AI analyzes this data to make optimal decisions. AI evaluates project progress and resource utilization to support optimal decision-making. For example, if a project is behind schedule, the decision-making department identifies the cause of the delay and proposes solutions. If the project is progressing smoothly, the decision-making department evaluates the progress and makes decisions to move to the next step. In this way, the decision-making department can streamline project progress and support efficient work execution. Furthermore, the decision-making department collects employee opinions and feedback to improve the quality of decision-making. For example, based on employee opinions and feedback, the decision-making department identifies areas for improvement in decision-making and makes appropriate decisions. In this way, the decision-making department can support efficient decision-making and achieve increased work efficiency and creativity.

[0037] The analytics department can collect employee skills, work performance, and communication information, which can then be analyzed by AI. For example, the analytics department can identify the most suitable person for a specific task based on an employee's past project experience and skill set. This allows for a more accurate analysis of skill sets by collecting employee skills, work performance, and communication information and having AI analyze it.

[0038] The Processing Department can use natural language processing technology to support smoother communication among employees. For example, the Processing Department can use natural language processing technology to support smoother communication among employees. For example, the Processing Department can use text analysis technology to analyze the content of emails and chats between employees and suggest areas for improvement in communication. The Processing Department can use natural language processing technology to support smoother communication among employees. For example, the Processing Department can use text analysis technology to analyze the content of emails and chats between employees and suggest areas for improvement in communication. This makes it possible to support smoother communication among employees by using natural language processing technology. Some or all of the above processing in the Processing Department may be performed using, for example, generative AI, or not using generative AI. For example, the Processing Department can input the content of emails and chats between employees into generative AI and have the generative AI suggest areas for improvement in communication.

[0039] The proposal department can use AI to propose the most suitable personnel in real time. For example, the proposal department uses AI to propose the most suitable personnel in real time. For example, the proposal department identifies the most suitable personnel for a particular project based on the employee's skill set and emotional state. The proposal department uses AI to propose the most suitable personnel in real time. For example, the proposal department identifies the most suitable personnel for a particular project based on the employee's skill set and emotional state. This makes it possible to propose the most suitable personnel in real time by using AI. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input the employee's skill set and emotional state into generative AI and have the generative AI propose the most suitable personnel.

[0040] The support department can use AI to monitor the project's progress and provide necessary advice and support. For example, the support department can use AI to monitor the project's progress and provide necessary advice and support. For example, the support department can grasp the project's progress in real time and provide appropriate feedback. For example, the support department can use AI to monitor the project's progress and provide necessary advice and support. For example, the support department can grasp the project's progress in real time and provide appropriate feedback. This makes it possible to monitor the project's progress and provide necessary advice and support by using AI. Some or all of the above processing in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input the project's progress into generative AI and have the generative AI provide the necessary advice and support.

[0041] The Promotion Department can use AI to generate creative ideas and support project advancement. For example, the Promotion Department can use AI to generate creative ideas and support project advancement. For example, the Promotion Department can support brainstorming sessions and elicit new ideas. For example, the Promotion Department can use AI to generate creative ideas and support project advancement. For example, the Promotion Department can support brainstorming sessions and elicit new ideas. This makes it possible to generate creative ideas and support project advancement by using AI. Some or all of the above processes in the Promotion Department may be performed using, for example, generative AI, or not using generative AI. For example, the Promotion Department can have generative AI perform the generation of creative ideas.

[0042] The decision-making unit can use AI to analyze data and support optimal decision-making. For example, the decision-making unit can use AI to analyze data and support optimal decision-making. For example, the decision-making unit can make optimal decisions based on the progress of the project and the utilization of resources. For example, the decision-making unit can use AI to analyze data and support optimal decision-making. For example, the decision-making unit can make optimal decisions based on the progress of the project and the utilization of resources. This makes it possible to analyze data and support optimal decision-making by using AI. Some or all of the above-described processes in the decision-making unit may be performed using, for example, generative AI, or not using generative AI. For example, the decision-making unit can have generative AI perform data analysis.

[0043] The analysis department can analyze employees' past project experience in detail and evaluate the relevance of specific skill sets. For example, the analysis department can use AI to collect employees' past project data and evaluate which projects were effective for specific skill sets. The analysis department can use AI to collect employees' past project data and evaluate which projects were effective for specific skill sets. For example, the analysis department can use AI to collect employees' past project data and evaluate which projects were effective for specific skill sets. This makes it possible to evaluate the relevance of specific skill sets by analyzing employees' past project experience in detail. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can input employees' past project data into generative AI and have the generative AI perform the evaluation of the relevance of specific skill sets.

[0044] The analysis department can analyze employees' communication patterns and optimize their roles within teams. For example, the analysis department can use AI to analyze employees' email and chat histories and evaluate the frequency and content of their communication. The analysis department can use AI to analyze employees' communication patterns and optimize their roles within teams. For example, the analysis department can use AI to analyze employees' email and chat histories and evaluate the frequency and content of their communication. This makes it possible to optimize employees' roles within teams by analyzing their communication patterns. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can have generative AI perform the analysis of employees' communication patterns.

[0045] The analysis department can analyze the characteristics of skill sets by region, taking into account the geographical location information of employees. For example, the analysis department can use AI to collect employees' geographical location information and evaluate the characteristics of skill sets by region. The analysis department can use AI to collect employees' geographical location information and evaluate the characteristics of skill sets by region. For example, the analysis department can use AI to collect employees' geographical location information and evaluate the characteristics of skill sets by region. This makes it possible to analyze the characteristics of skill sets by region by taking into account the geographical location information of employees. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input employees' geographical location information into a generative AI and have the generative AI perform an analysis of the characteristics of skill sets by region.

[0046] The analysis department can analyze employees' social media activities and obtain supplementary information about their skill sets. For example, the analysis department can analyze employees' social media activities and obtain supplementary information about their skill sets. For example, the analysis department can have AI collect employees' social media activities and evaluate supplementary information about their skill sets. The analysis department can analyze employees' social media activities and obtain supplementary information about their skill sets. For example, the analysis department can have AI collect employees' social media activities and evaluate supplementary information about their skill sets. This makes it possible to obtain supplementary information about skill sets by analyzing employees' social media activities. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can have generative AI perform the analysis of employees' social media activities.

[0047] The emotion recognition unit can analyze the characteristics of an emotion by considering the geographical background of the employee when recognizing that emotion. For example, the emotion recognition unit can analyze the characteristics of an emotion by considering the geographical background of the employee when recognizing that emotion. For example, the emotion recognition unit can use AI to collect the geographical background of the employee and analyze the characteristics of that emotion. This makes it possible to analyze the characteristics of an emotion more accurately by considering the geographical background of the employee. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0048] The emotion recognition unit can improve the accuracy of emotion analysis by referring to relevant literature of employees during emotion recognition. For example, the emotion recognition unit can improve the accuracy of emotion analysis by referring to relevant literature of employees during emotion recognition. For example, the emotion recognition unit can improve the accuracy of emotion analysis by having the AI ​​collect relevant literature of employees. This makes it possible to improve the accuracy of emotion analysis by referring to relevant literature of employees. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0049] The processing unit can improve the accuracy of analysis by referring to the employee's past communication history during natural language processing. For example, the processing unit can improve the accuracy of analysis by referring to the employee's past communication history during natural language processing. For example, the processing unit can have an AI collect the employee's past communication history to improve the accuracy of natural language processing. The processing unit can improve the accuracy of analysis by referring to the employee's past communication history during natural language processing. For example, the processing unit can have an AI collect the employee's past communication history to improve the accuracy of natural language processing. This makes it possible to improve the accuracy of natural language processing by referring to the employee's past communication history. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit can input the employee's past communication history into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0050] The processing unit can perform analysis while considering employees' specialized terminology and industry jargon during natural language processing. For example, the processing unit can have AI collect employees' specialized terminology and industry jargon and perform analysis using natural language processing. The processing unit can perform analysis while considering employees' specialized terminology and industry jargon. For example, the processing unit can have AI collect employees' specialized terminology and industry jargon and perform analysis using natural language processing. This makes it possible to improve the accuracy of natural language processing analysis by considering employees' specialized terminology and industry jargon. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit can have a generative AI perform the analysis of employees' specialized terminology and industry jargon.

[0051] The processing unit can analyze the characteristics of the analysis while considering the geographical background of employees during natural language processing. For example, the processing unit can analyze the characteristics of the analysis while considering the geographical background of employees during natural language processing. For example, the processing unit can have an AI collect the geographical background of employees and analyze the characteristics of natural language processing. The processing unit can analyze the characteristics of the analysis while considering the geographical background of employees during natural language processing. For example, the processing unit can have an AI collect the geographical background of employees and analyze the characteristics of natural language processing. This makes it possible to analyze the characteristics of natural language processing more accurately by considering the geographical background of employees. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit can have a generative AI perform the analysis of the geographical background of employees.

[0052] The processing unit can improve the accuracy of analysis by referring to relevant literature of employees during natural language processing. For example, the processing unit can improve the accuracy of analysis by referring to relevant literature of employees during natural language processing. For example, the processing unit can have an AI collect relevant literature of employees to improve the accuracy of natural language processing. The processing unit can improve the accuracy of analysis by referring to relevant literature of employees during natural language processing. For example, the processing unit can have an AI collect relevant literature of employees to improve the accuracy of natural language processing. This makes it possible to improve the accuracy of analysis in natural language processing by referring to relevant literature of employees. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit can have a generative AI perform the task of referring to relevant literature of employees.

[0053] The proposal department can identify the most suitable personnel by referring to employees' past project experience when making a proposal. For example, the proposal department can identify the most suitable personnel by referring to employees' past project experience when making a proposal. For example, the proposal department can use AI to collect employees' past project data and identify the most suitable personnel. The proposal department can identify the most suitable personnel by referring to employees' past project experience when making a proposal. For example, the proposal department can use AI to collect employees' past project data and identify the most suitable personnel. This makes it possible to identify the most suitable personnel by referring to employees' past project experience. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input employees' past project data into a generative AI and have the generative AI perform the identification of the most suitable personnel.

[0054] The proposal department can customize proposals by taking into account the communication style of the employees when making a proposal. For example, the proposal department can customize proposals by taking into account the communication style of the employees when making a proposal. For example, the proposal department can use AI to collect employee communication styles and customize proposals. The proposal department can customize proposals by taking into account the communication style of the employees when making a proposal. For example, the proposal department can use AI to collect employee communication styles and customize proposals. This makes it possible to customize proposals by taking employee communication styles into account. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can have a generative AI perform the customization of employee communication styles.

[0055] The proposal department can identify the most suitable personnel by considering the geographical location information of employees when making a proposal. For example, the proposal department can identify the most suitable personnel by considering the geographical location information of employees when making a proposal. For example, the proposal department can use AI to collect the geographical location information of employees and identify the most suitable personnel. The proposal department can identify the most suitable personnel by considering the geographical location information of employees when making a proposal. For example, the proposal department can use AI to collect the geographical location information of employees and identify the most suitable personnel. This makes it possible to identify the most suitable personnel by considering the geographical location information of employees. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input the geographical location information of employees into a generative AI and have the generative AI perform the identification of the most suitable personnel.

[0056] The proposal department can analyze employees' social media activity to supplement the proposal content when making a proposal. For example, the proposal department can analyze employees' social media activity to supplement the proposal content when making a proposal. For example, the proposal department can use AI to collect employees' social media activity and supplement the proposal content. The proposal department can analyze employees' social media activity to supplement the proposal content when making a proposal. For example, the proposal department can use AI to collect employees' social media activity and supplement the proposal content. This makes it possible to supplement the proposal content by analyzing employees' social media activity. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can have generative AI perform the analysis of employees' social media activity.

[0057] The support department can select the optimal support method by referring to the employee's past communication history when providing communication support. For example, the support department can select the optimal support method by referring to the employee's past communication history when providing communication support. For example, the support department can use AI to collect the employee's past communication history and select the optimal support method. The support department can select the optimal support method by referring to the employee's past communication history when providing communication support. For example, the support department can use AI to collect the employee's past communication history and select the optimal support method. This makes it possible to select the optimal support method by referring to the employee's past communication history. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without using a generative AI. For example, the support department can input the employee's past communication history into a generative AI and have the generative AI select the optimal support method.

[0058] The support department can provide communication support while taking into account the specialized terminology and industry jargon used by employees. For example, the support department can use AI to collect the specialized terminology and industry jargon used by employees and provide communication support. For example, the support department can use AI to collect the specialized terminology and industry jargon used by employees and provide communication support. This makes it possible to provide more appropriate communication support by taking into account the specialized terminology and industry jargon used by employees. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can have a generative AI collect the specialized terminology and industry jargon used by employees.

[0059] The support department can customize the support provided when offering communication assistance, taking into account the geographical background of the employees. For example, the support department can customize the support provided when offering communication assistance, taking into account the geographical background of the employees. For example, the support department can use AI to collect the geographical background of employees and customize the support provided. The support department can customize the support provided when offering communication assistance, taking into account the geographical background of the employees. For example, the support department can use AI to collect the geographical background of employees and customize the support provided. This makes it possible to customize the support provided by taking into account the geographical background of employees. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without using a generative AI. For example, the support department can have a generative AI collect the geographical background of employees.

[0060] The support department can improve the accuracy of its support by referring to relevant literature related to employees during communication support. For example, the support department can improve the accuracy of its support by referring to relevant literature related to employees during communication support. For example, the support department can use AI to collect relevant literature related to employees and improve the accuracy of its support. The support department can improve the accuracy of its support by referring to relevant literature related to employees during communication support. For example, the support department can use AI to collect relevant literature related to employees and improve the accuracy of its support. This makes it possible to improve the accuracy of support by referring to relevant literature related to employees. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can have a generative AI perform the task of referring to relevant literature related to employees.

[0061] The Promotion Department can select the optimal promotion method when promoting a creative project by referring to the past project experience of employees. For example, the Promotion Department selects the optimal promotion method when promoting a creative project by referring to the past project experience of employees. For example, the Promotion Department uses AI to collect past project data of employees and select the optimal promotion method. The Promotion Department selects the optimal promotion method when promoting a creative project by referring to the past project experience of employees. For example, the Promotion Department uses AI to collect past project data of employees and select the optimal promotion method. This makes it possible to select the optimal promotion method by referring to the past project experience of employees. Some or all of the above processing in the Promotion Department may be performed using, for example, a generative AI, or without using a generative AI. For example, the Promotion Department can input past project data of employees into a generative AI and have the generative AI select the optimal promotion method.

[0062] The Promotion Department can take into account the specialized terminology and industry jargon of employees when promoting creative projects. For example, the Promotion Department can use AI to collect the specialized terminology and industry jargon of employees and promote creative projects. The Promotion Department can take into account the specialized terminology and industry jargon of employees when promoting creative projects. For example, the Promotion Department can use AI to collect the specialized terminology and industry jargon of employees and promote creative projects. This makes it possible to promote creative projects more appropriately by taking into account the specialized terminology and industry jargon of employees. Some or all of the above processing in the Promotion Department may be performed using, for example, generative AI, or not using generative AI. For example, the Promotion Department can have generative AI perform the task of collecting the specialized terminology and industry jargon of employees.

[0063] The Promotion Department can customize the content of creative projects by taking into account the geographical backgrounds of employees. For example, the Promotion Department customizes the content of creative projects by taking into account the geographical backgrounds of employees. For example, the Promotion Department uses AI to collect employees' geographical backgrounds and customize the content of the project. For example, the Promotion Department uses AI to collect employees' geographical backgrounds and customize the content of the project. This makes it possible to customize the content of the project by taking into account the geographical backgrounds of employees. Some or all of the above processing in the Promotion Department may be performed using, for example, generative AI, or without generative AI. For example, the Promotion Department can have generative AI collect employees' geographical backgrounds.

[0064] The Promotion Department can improve the accuracy of promotion by referring to relevant literature of employees when promoting creative projects. For example, the Promotion Department can improve the accuracy of promotion by referring to relevant literature of employees when promoting creative projects. For example, the Promotion Department can have AI collect relevant literature of employees and improve the accuracy of promotion. The Promotion Department can improve the accuracy of promotion by referring to relevant literature of employees when promoting creative projects. For example, the Promotion Department can have AI collect relevant literature of employees and improve the accuracy of promotion. This makes it possible to improve the accuracy of promotion by referring to relevant literature of employees. Some or all of the above processing in the Promotion Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Promotion Department can have a generative AI perform the task of referring to relevant literature of employees.

[0065] The decision-making unit can select the optimal decision-making method by referring to the employee's past decision-making history when making a decision. For example, the decision-making unit can select the optimal decision-making method by referring to the employee's past decision-making history when making a decision. For example, the decision-making unit can use AI to collect the employee's past decision-making history and select the optimal decision-making method. The decision-making unit can select the optimal decision-making method by referring to the employee's past decision-making history when making a decision. For example, the decision-making unit can use AI to collect the employee's past decision-making history and select the optimal decision-making method. This makes it possible to select the optimal decision-making method by referring to the employee's past decision-making history. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the decision-making unit can input the employee's past decision-making history into a generative AI and have the generative AI perform the selection of the optimal decision-making method.

[0066] The decision-making unit can make decisions while taking into account the specialized terminology and industry jargon of employees. For example, the decision-making unit can make decisions while taking into account the specialized terminology and industry jargon of employees. For example, the decision-making unit can use AI to collect the specialized terminology and industry jargon of employees and then make a decision. The decision-making unit can make decisions while taking into account the specialized terminology and industry jargon of employees. For example, the decision-making unit can use AI to collect the specialized terminology and industry jargon of employees and then make a decision. This makes it possible to make more appropriate decisions by taking into account the specialized terminology and industry jargon of employees. Some or all of the above processing in the decision-making unit may be performed using, for example, generative AI, or not using generative AI. For example, the decision-making unit can have generative AI perform the task of collecting the specialized terminology and industry jargon of employees.

[0067] The decision-making unit can customize its decisions by taking into account the geographical backgrounds of its employees. For example, the decision-making unit can customize its decisions by taking into account the geographical backgrounds of its employees. For example, the decision-making unit can use AI to collect the geographical backgrounds of its employees and customize the decisions. For example, the decision-making unit can use AI to collect the geographical backgrounds of its employees and customize the decisions. This makes it possible to customize decisions by taking into account the geographical backgrounds of its employees. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the decision-making unit can have a generative AI collect the geographical backgrounds of its employees.

[0068] The decision-making unit can improve the accuracy of its decisions by referring to relevant literature on employees during the decision-making process. For example, the decision-making unit can improve the accuracy of its decisions by referring to relevant literature on employees during the decision-making process. For example, the decision-making unit can improve the accuracy of its decisions by having an AI collect relevant literature on employees. The decision-making unit can improve the accuracy of its decisions by referring to relevant literature on employees during the decision-making process. For example, the decision-making unit can improve the accuracy of its decisions by having an AI collect relevant literature on employees. This makes it possible to improve the accuracy of decisions by referring to relevant literature on employees. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision-making unit can have a generative AI perform the task of referring to relevant literature on employees.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] The AI ​​Inspiration Partner System can also be equipped with a Resource Management Department. This department assists in the optimal allocation of resources required for a project. For example, it monitors the project's progress and proposes necessary resources in real time. Furthermore, it can optimize resource allocation based on employees' skill sets and work performance. Additionally, it can adjust resource allocation according to the project's progress, supporting efficient project management. This prevents project stagnation and ensures smooth progress.

[0071] The AI ​​Inspiration Partner System can also be equipped with a Risk Management Department. This department predicts project risks and proposes measures to mitigate them. For example, it monitors project progress and detects potential risks in real time. It can also analyze past project data and propose optimal risk avoidance measures. Furthermore, it can adjust risk avoidance measures according to project progress, supporting efficient risk management. This minimizes project risks and ensures smooth project progress.

[0072] The AI ​​Inspiration Partner System can also include a Cost Management Department. This department provides support for optimally managing project costs. For example, it monitors project progress and performs real-time cost forecasting and management. It can also analyze past project data and propose optimal measures for cost reduction. Furthermore, it can adjust cost management measures according to project progress, supporting efficient cost management. This minimizes project costs and ensures smooth progress within budget.

[0073] The AI ​​Inspiration Partner System can also include a Quality Control Department. This department provides support for optimally managing project quality. For example, it monitors project progress and performs real-time quality forecasting and management. It can also analyze past project data and propose optimal measures for quality improvement. Furthermore, it can adjust quality control measures according to project progress, supporting efficient quality management. This ensures high project quality and smooth project progress.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The analysis department analyzes the skill sets within the workplace. For example, it collects employee skills, work performance, and communication information, which is then analyzed by AI. This allows for the identification of the best person for a specific task. Step 2: The emotion recognition unit analyzes the emotional state based on the skill set analyzed by the analysis unit. For example, AI monitors employees' emotional states in real time and evaluates their stress levels. This allows for the placement of personnel so that they can perform at their best in a low-stress environment. Step 3: The processing unit uses natural language processing technology based on the emotional state analyzed by the emotion recognition unit. For example, it uses natural language processing technology to support smoother communication among employees. This involves analyzing the content of emails and chats between employees and suggesting areas for improvement in communication. Step 4: The proposal department proposes the most suitable personnel based on the information obtained by the processing department. For example, AI proposes the most suitable personnel for a specific project in real time based on employees' skill sets and emotional states. Step 5: The support department provides communication support based on the personnel proposed by the proposal department. For example, AI monitors the project's progress and provides necessary advice and support. This allows for real-time monitoring of the project's progress and the provision of appropriate feedback. Step 6: The Promotion Department drives creative projects based on information supported by the Support Department. For example, AI generates creative ideas to support project progress. This supports brainstorming sessions and elicits new ideas. Step 7: The Decision-Making Department supports decision-making based on the project promoted by the Promotion Department. For example, AI analyzes data to support optimal decision-making. This allows for optimal decisions to be made based on the project's progress and resource utilization.

[0076] (Example of form 2) The AI ​​Inspiration Partner System according to an embodiment of the present invention is a system in which AI supports optimal personnel placement within the workplace through emotion recognition and skill analysis. This AI Inspiration Partner System analyzes the skill set within the workplace and, in combination with emotion recognition and natural language processing technology, supports communication assistance, promotion of creative projects, and decision-making for specific tasks and projects, thereby achieving increased work efficiency and improved creativity. First, the AI ​​analyzes the skill set within the workplace. In this process, it collects and analyzes employee skills, work performance, communication information, etc. For example, based on an employee's past project experience and skill set, it can identify the most suitable personnel for a specific task. Next, it proposes the most suitable personnel for a specific task or project by combining emotion recognition technology and natural language processing technology. For example, the AI ​​can analyze the emotional state of employees and place personnel in a way that allows them to perform optimally with minimal stress. It also uses natural language processing technology to support smooth communication among employees. Furthermore, the AI ​​supports communication assistance, promotion of creative projects, and decision-making. For example, the AI ​​monitors the progress of a project and provides necessary advice and support. It also generates creative ideas and supports the advancement of projects. In decision-making, AI analyzes data to support optimal decision-making. This system enables increased efficiency and creativity in operations. For example, by suggesting the most suitable personnel in real time, AI can prevent project stagnation and support smooth progress. Furthermore, by utilizing emotion recognition technology, it can reduce employee stress and provide a more comfortable working environment. This improves overall workplace productivity and increases creative output. Thus, the AI ​​Inspiration Partner System analyzes the skill sets within the workplace, combines emotion recognition and natural language processing technologies to suggest the most suitable personnel, and supports communication, creative project advancement, and decision-making, thereby achieving increased efficiency and creativity in operations.

[0077] The AI ​​inspiration partner system according to this embodiment comprises an analysis unit, an emotion recognition unit, a processing unit, a proposal unit, a support unit, a promotion unit, and a decision-making unit. The analysis unit analyzes the skill sets within the workplace. For example, the analysis unit collects employee skills, work performance, and communication information, which are then analyzed by the AI. For example, the analysis unit can identify the most suitable personnel for a specific task based on employees' past project experience and skill sets. The emotion recognition unit analyzes emotional states. For example, the emotion recognition unit uses the AI ​​to analyze employees' emotional states and assign personnel to ensure optimal performance under low stress. For example, the emotion recognition unit monitors employees' emotional states in real time and evaluates their stress levels. The processing unit uses natural language processing technology. For example, the processing unit uses natural language processing technology to support smoother communication among employees. For example, the processing unit uses text analysis technology to analyze the content of emails and chats between employees and proposes improvements to communication. The proposal unit proposes the most suitable personnel. The Proposal Department, for example, uses AI to propose the most suitable personnel in real time. For example, the Proposal Department identifies the best personnel for a particular project based on employees' skill sets and emotional states. The Support Department provides communication support. For example, the Support Department uses AI to monitor project progress and provide necessary advice and support. For example, the Support Department grasps project progress in real time and provides appropriate feedback. The Promotion Department drives creative projects. For example, the Promotion Department uses AI to generate creative ideas and support project advancement. For example, the Promotion Department supports brainstorming sessions and elicits new ideas. The Decision-Making Department supports decision-making. For example, the Decision-Making Department uses AI to analyze data and support optimal decision-making. For example, the Decision-Making Department makes optimal decisions based on project progress and resource utilization.As a result, the AI ​​inspiration partner system according to this embodiment can analyze the skill sets within the workplace, propose the most suitable personnel by combining emotion recognition and natural language processing technologies, and support communication, the promotion of creative projects, and decision-making, thereby improving work efficiency and creativity.

[0078] The analytics department analyzes the skill sets within the workplace. Specifically, it collects employee skills, work performance, and communication information, and AI analyzes this data. For example, based on an employee's past project experience and skill set, it can identify the most suitable person for a particular task. The analytics department collects data from employee resumes, work reports, and project management tools, and AI integrates and analyzes this data. The AI ​​uses natural language processing technology to extract skills and experience from text data and stores it in a database. Furthermore, the AI ​​evaluates employees' work performance and understands their skill improvement and acquisition of new skills. This allows the analytics department to gain a detailed understanding of employees' skill sets and quickly identify the most suitable person for a specific project or task. In addition, the analytics department analyzes employee communication information to evaluate the quality and frequency of communication within teams. This helps identify areas for improvement in team collaboration and communication, supporting efficient team formation.

[0079] The emotion recognition unit analyzes emotional states. Specifically, AI analyzes employees' emotional states and assigns personnel to positions where they can perform optimally with minimal stress. The emotion recognition unit analyzes employees' facial expressions, voice, and text data to monitor their emotional states in real time. For example, it captures employees' facial expressions with a camera and analyzes changes in facial expressions using image recognition technology. It also uses voice recognition technology to evaluate emotional states from employees' tone of voice and speaking style. Furthermore, it uses text analysis technology to extract emotional states from the content of employees' emails and chats. As a result, the emotion recognition unit can grasp employees' stress levels and motivation in real time and provide support at the appropriate time. For example, it can suggest relaxation advice and breaks to employees who are experiencing high stress levels. It can also suggest encouraging messages and goal setting revisions to employees whose motivation is low. In this way, the emotion recognition unit can appropriately manage employees' emotional states, reduce stress, and improve performance.

[0080] The Processing Department utilizes Natural Language Processing (NLP) technology. Specifically, it uses NLP to support smoother communication among employees. The Processing Department uses text analysis technology to analyze the content of emails and chats between employees and proposes improvements to communication. For example, the Processing Department analyzes the content of emails and chats to identify ambiguous or misleading expressions and proposes correcting them to appropriate expressions. The Processing Department also analyzes communication patterns among employees and evaluates the frequency and quality of communication. This allows the Processing Department to identify areas for improvement in communication and support more efficient communication. Furthermore, the Processing Department uses NLP to collect employee opinions and feedback to understand the progress and challenges of projects. This allows the Processing Department to facilitate project progress and support efficient work execution.

[0081] The proposal department proposes the most suitable personnel. Specifically, AI proposes the most suitable personnel in real time. The proposal department identifies the most suitable personnel for a particular project based on employees' skill sets and emotional states. The proposal department integrates data provided by the analysis department and the emotion recognition department, and the AI ​​analyzes this data to identify the most suitable personnel. For example, the proposal department lists the most suitable personnel based on the skill sets required for a particular project and proposes them to the project manager. The proposal department also considers employees' emotional states and places personnel in a way that allows them to perform at their best with minimal stress. This allows the proposal department to quickly identify the most suitable personnel to contribute to the success of a project and support efficient personnel placement. Furthermore, the proposal department considers employees' career paths and growth goals and proposes appropriate projects and tasks. This can improve employee motivation and support career growth.

[0082] The support department provides communication support. Specifically, AI monitors project progress and provides necessary advice and support. The support department grasps project progress in real time and provides appropriate feedback. For example, the support department collects data from project management tools, and AI analyzes this data to evaluate project progress. Based on project progress and resource utilization, the AI ​​provides appropriate advice and support. For example, if project progress is behind schedule, the support department identifies the cause of the delay and proposes solutions. Conversely, if project progress is on track, the support department evaluates the progress and provides advice for the next steps. In this way, the support department can facilitate project progress and support efficient work execution. Furthermore, the support department supports communication among employees and strengthens team collaboration. For example, the support department evaluates the quality and frequency of communication among employees and proposes areas for improvement. In this way, the support department can support efficient communication and strengthen team collaboration.

[0083] The Promotion Department drives creative projects. Specifically, AI generates creative ideas and supports project advancement. The Promotion Department supports brainstorming sessions and elicits new ideas. For example, the Promotion Department uses AI to analyze past project data and industry trends to generate new ideas. Using natural language processing technology, the AI ​​extracts creative ideas from text data and proposes them in brainstorming sessions. This allows the Promotion Department to elicit creative ideas and support project advancement. The Promotion Department also collects employee ideas, and AI analyzes these ideas to identify the most suitable ones. This allows the Promotion Department to unleash employee creativity and contribute to project success. Furthermore, the Promotion Department monitors the progress of creative projects and provides appropriate support. For example, the Promotion Department evaluates project progress and provides necessary resources and support. This allows the Promotion Department to support the success of creative projects and achieve increased operational efficiency and creativity.

[0084] The decision-making department supports decision-making. Specifically, AI analyzes data to support optimal decision-making. The decision-making department makes optimal decisions based on project progress and resource utilization. For example, the decision-making department collects data from project management tools, and AI analyzes this data to make optimal decisions. AI evaluates project progress and resource utilization to support optimal decision-making. For example, if a project is behind schedule, the decision-making department identifies the cause of the delay and proposes solutions. If the project is progressing smoothly, the decision-making department evaluates the progress and makes decisions to move to the next step. In this way, the decision-making department can streamline project progress and support efficient work execution. Furthermore, the decision-making department collects employee opinions and feedback to improve the quality of decision-making. For example, based on employee opinions and feedback, the decision-making department identifies areas for improvement in decision-making and makes appropriate decisions. In this way, the decision-making department can support efficient decision-making and achieve increased work efficiency and creativity.

[0085] The analytics department can collect employee skills, work performance, and communication information, which can then be analyzed by AI. For example, the analytics department can identify the most suitable person for a specific task based on an employee's past project experience and skill set. This allows for a more accurate analysis of skill sets by collecting employee skills, work performance, and communication information and having AI analyze it.

[0086] The emotion recognition unit uses AI to analyze employees' emotional states and can allocate personnel in a way that allows them to perform optimally under low stress. For example, the emotion recognition unit uses AI to analyze employees' emotional states and allocate personnel in a way that allows them to perform optimally under low stress. For example, the emotion recognition unit monitors employees' emotional states in real time and evaluates their stress levels. By analyzing employees' emotional states and allocating personnel in a way that allows them to perform optimally under low stress, it is possible to improve operational efficiency and creativity. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The Processing Department can use natural language processing technology to support smoother communication among employees. For example, the Processing Department can use natural language processing technology to support smoother communication among employees. For example, the Processing Department can use text analysis technology to analyze the content of emails and chats between employees and suggest areas for improvement in communication. The Processing Department can use natural language processing technology to support smoother communication among employees. For example, the Processing Department can use text analysis technology to analyze the content of emails and chats between employees and suggest areas for improvement in communication. This makes it possible to support smoother communication among employees by using natural language processing technology. Some or all of the above processing in the Processing Department may be performed using, for example, generative AI, or not using generative AI. For example, the Processing Department can input the content of emails and chats between employees into generative AI and have the generative AI suggest areas for improvement in communication.

[0088] The proposal department can use AI to propose the most suitable personnel in real time. For example, the proposal department uses AI to propose the most suitable personnel in real time. For example, the proposal department identifies the most suitable personnel for a particular project based on the employee's skill set and emotional state. The proposal department uses AI to propose the most suitable personnel in real time. For example, the proposal department identifies the most suitable personnel for a particular project based on the employee's skill set and emotional state. This makes it possible to propose the most suitable personnel in real time by using AI. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input the employee's skill set and emotional state into generative AI and have the generative AI propose the most suitable personnel.

[0089] The support department can use AI to monitor the project's progress and provide necessary advice and support. For example, the support department can use AI to monitor the project's progress and provide necessary advice and support. For example, the support department can grasp the project's progress in real time and provide appropriate feedback. For example, the support department can use AI to monitor the project's progress and provide necessary advice and support. For example, the support department can grasp the project's progress in real time and provide appropriate feedback. This makes it possible to monitor the project's progress and provide necessary advice and support by using AI. Some or all of the above processing in the support department may be performed using, for example, generative AI, or not using generative AI. For example, the support department can input the project's progress into generative AI and have the generative AI provide the necessary advice and support.

[0090] The Promotion Department can use AI to generate creative ideas and support project advancement. For example, the Promotion Department can use AI to generate creative ideas and support project advancement. For example, the Promotion Department can support brainstorming sessions and elicit new ideas. For example, the Promotion Department can use AI to generate creative ideas and support project advancement. For example, the Promotion Department can support brainstorming sessions and elicit new ideas. This makes it possible to generate creative ideas and support project advancement by using AI. Some or all of the above processes in the Promotion Department may be performed using, for example, generative AI, or not using generative AI. For example, the Promotion Department can have generative AI perform the generation of creative ideas.

[0091] The decision-making unit can use AI to analyze data and support optimal decision-making. For example, the decision-making unit can use AI to analyze data and support optimal decision-making. For example, the decision-making unit can make optimal decisions based on the progress of the project and the utilization of resources. For example, the decision-making unit can use AI to analyze data and support optimal decision-making. For example, the decision-making unit can make optimal decisions based on the progress of the project and the utilization of resources. This makes it possible to analyze data and support optimal decision-making by using AI. Some or all of the above-described processes in the decision-making unit may be performed using, for example, generative AI, or not using generative AI. For example, the decision-making unit can have generative AI perform data analysis.

[0092] The analysis department can estimate employees' emotions and adjust the skill set analysis method based on the estimated emotions. For example, if an employee is stressed, the AI ​​will simplify the skill set analysis to reduce stress. The analysis department can estimate employees' emotions and adjust the skill set analysis method based on the estimated emotions. For example, if an employee is stressed, the AI ​​will simplify the skill set analysis to reduce stress. By adjusting the skill set analysis method based on employees' emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The analysis department can analyze employees' past project experience in detail and evaluate the relevance of specific skill sets. For example, the analysis department can use AI to collect employees' past project data and evaluate which projects were effective for specific skill sets. The analysis department can use AI to collect employees' past project data and evaluate which projects were effective for specific skill sets. For example, the analysis department can use AI to collect employees' past project data and evaluate which projects were effective for specific skill sets. This makes it possible to evaluate the relevance of specific skill sets by analyzing employees' past project experience in detail. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can input employees' past project data into generative AI and have the generative AI perform the evaluation of the relevance of specific skill sets.

[0094] The analysis department can analyze employees' communication patterns and optimize their roles within teams. For example, the analysis department can use AI to analyze employees' email and chat histories and evaluate the frequency and content of their communication. The analysis department can use AI to analyze employees' communication patterns and optimize their roles within teams. For example, the analysis department can use AI to analyze employees' email and chat histories and evaluate the frequency and content of their communication. This makes it possible to optimize employees' roles within teams by analyzing their communication patterns. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the analysis department can have generative AI perform the analysis of employees' communication patterns.

[0095] The analytics department can estimate employees' emotions and determine the priority of skill sets based on those estimated emotions. For example, if an employee is feeling stressed, the AI ​​can adjust the priority of skill sets to reduce stress. The analytics department can estimate employees' emotions and determine the priority of skill sets based on those estimated emotions. For example, if an employee is feeling stressed, the AI ​​can adjust the priority of skill sets to reduce stress. This allows for more appropriate prioritization by determining the priority of skill sets based on employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The analysis department can analyze the characteristics of skill sets by region, taking into account the geographical location information of employees. For example, the analysis department can use AI to collect employees' geographical location information and evaluate the characteristics of skill sets by region. The analysis department can use AI to collect employees' geographical location information and evaluate the characteristics of skill sets by region. For example, the analysis department can use AI to collect employees' geographical location information and evaluate the characteristics of skill sets by region. This makes it possible to analyze the characteristics of skill sets by region by taking into account the geographical location information of employees. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis department can input employees' geographical location information into a generative AI and have the generative AI perform an analysis of the characteristics of skill sets by region.

[0097] The analysis department can analyze employees' social media activities and obtain supplementary information about their skill sets. For example, the analysis department can analyze employees' social media activities and obtain supplementary information about their skill sets. For example, the analysis department can have AI collect employees' social media activities and evaluate supplementary information about their skill sets. The analysis department can analyze employees' social media activities and obtain supplementary information about their skill sets. For example, the analysis department can have AI collect employees' social media activities and evaluate supplementary information about their skill sets. This makes it possible to obtain supplementary information about skill sets by analyzing employees' social media activities. Some or all of the above processing in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can have generative AI perform the analysis of employees' social media activities.

[0098] The emotion recognition unit can estimate employees' emotions and improve the accuracy of emotion recognition based on the estimated emotions. For example, the emotion recognition unit can estimate employees' emotions and improve the accuracy of emotion recognition based on the estimated emotions. For example, the emotion recognition unit can use AI to collect employee emotion data and adjust the emotion recognition algorithm. The emotion recognition unit can estimate employees' emotions and improve the accuracy of emotion recognition based on the estimated emotions. For example, the emotion recognition unit can use AI to collect employee emotion data and adjust the emotion recognition algorithm. This improves the accuracy of emotion recognition based on employee emotions, enabling more accurate emotion recognition. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The emotion recognition unit can predict changes in an employee's emotions by referring to their past emotional history when recognizing an emotion. For example, the emotion recognition unit can predict changes in an employee's emotions by referring to their past emotional history when recognizing an emotion. For example, the emotion recognition unit can predict changes in an employee's emotions by having an AI collect their past emotional history. The emotion recognition unit can predict changes in an employee's emotions by referring to their past emotional history when recognizing an emotion. For example, the emotion recognition unit can predict changes in an employee's emotions by having an AI collect their past emotional history. This makes it possible to predict changes in emotions by referring to an employee's past emotional history. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] The emotion recognition unit can perform emotion analysis while considering the employee's communication style during emotion recognition. For example, the emotion recognition unit can perform emotion analysis while considering the employee's communication style during emotion recognition. For example, the emotion recognition unit can use AI to collect employee communication styles and perform emotion analysis. The emotion recognition unit can perform emotion analysis while considering employee communication styles during emotion recognition. For example, the emotion recognition unit can use AI to collect employee communication styles and perform emotion analysis. This makes it possible to perform more accurate emotion analysis by considering employee communication styles. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0101] The emotion recognition unit can estimate an employee's emotions and adjust the method of displaying the emotion recognition results based on the estimated emotions. For example, if an employee is feeling stressed, the emotion recognition unit can display the emotion recognition results simply. The emotion recognition unit can estimate an employee's emotions and adjust the method of displaying the emotion recognition results based on the estimated emotions. For example, if an employee is feeling stressed, the emotion recognition unit can display the emotion recognition results simply. By adjusting the method of displaying the emotion recognition results based on the employee's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The emotion recognition unit can analyze the characteristics of an emotion by considering the geographical background of the employee when recognizing that emotion. For example, the emotion recognition unit can analyze the characteristics of an emotion by considering the geographical background of the employee when recognizing that emotion. For example, the emotion recognition unit can use AI to collect the geographical background of the employee and analyze the characteristics of that emotion. This makes it possible to analyze the characteristics of an emotion more accurately by considering the geographical background of the employee. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0103] The emotion recognition unit can improve the accuracy of emotion analysis by referring to relevant literature of employees during emotion recognition. For example, the emotion recognition unit can improve the accuracy of emotion analysis by referring to relevant literature of employees during emotion recognition. For example, the emotion recognition unit can improve the accuracy of emotion analysis by having the AI ​​collect relevant literature of employees. This makes it possible to improve the accuracy of emotion analysis by referring to relevant literature of employees. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The processing unit can estimate an employee's emotions and adjust the natural language processing algorithm based on the estimated emotions. For example, if an employee is stressed, the processing unit will simplify the natural language processing algorithm. This allows for more appropriate analysis by adjusting the natural language processing algorithm based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The processing unit can improve the accuracy of analysis by referring to the employee's past communication history during natural language processing. For example, the processing unit can improve the accuracy of analysis by referring to the employee's past communication history during natural language processing. For example, the processing unit can have an AI collect the employee's past communication history to improve the accuracy of natural language processing. The processing unit can improve the accuracy of analysis by referring to the employee's past communication history during natural language processing. For example, the processing unit can have an AI collect the employee's past communication history to improve the accuracy of natural language processing. This makes it possible to improve the accuracy of natural language processing by referring to the employee's past communication history. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit can input the employee's past communication history into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0106] The processing unit can perform analysis while considering employees' specialized terminology and industry jargon during natural language processing. For example, the processing unit can have AI collect employees' specialized terminology and industry jargon and perform analysis using natural language processing. The processing unit can perform analysis while considering employees' specialized terminology and industry jargon. For example, the processing unit can have AI collect employees' specialized terminology and industry jargon and perform analysis using natural language processing. This makes it possible to improve the accuracy of natural language processing analysis by considering employees' specialized terminology and industry jargon. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit can have a generative AI perform the analysis of employees' specialized terminology and industry jargon.

[0107] The processing unit can estimate an employee's emotions and adjust how the results of natural language processing are displayed based on the estimated emotions. For example, the processing unit can estimate an employee's emotions and adjust how the results of natural language processing are displayed based on the estimated emotions. For example, if an employee is stressed, the processing unit can have the AI ​​display the natural language processing results simply. This allows for more appropriate displays by adjusting how the results of natural language processing are displayed based on the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The processing unit can analyze the characteristics of the analysis while considering the geographical background of employees during natural language processing. For example, the processing unit can analyze the characteristics of the analysis while considering the geographical background of employees during natural language processing. For example, the processing unit can have an AI collect the geographical background of employees and analyze the characteristics of natural language processing. The processing unit can analyze the characteristics of the analysis while considering the geographical background of employees during natural language processing. For example, the processing unit can have an AI collect the geographical background of employees and analyze the characteristics of natural language processing. This makes it possible to analyze the characteristics of natural language processing more accurately by considering the geographical background of employees. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit can have a generative AI perform the analysis of the geographical background of employees.

[0109] The processing unit can improve the accuracy of analysis by referring to relevant literature of employees during natural language processing. For example, the processing unit can improve the accuracy of analysis by referring to relevant literature of employees during natural language processing. For example, the processing unit can have an AI collect relevant literature of employees to improve the accuracy of natural language processing. The processing unit can improve the accuracy of analysis by referring to relevant literature of employees during natural language processing. For example, the processing unit can have an AI collect relevant literature of employees to improve the accuracy of natural language processing. This makes it possible to improve the accuracy of analysis in natural language processing by referring to relevant literature of employees. Some or all of the above processing in the processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the processing unit can have a generative AI perform the task of referring to relevant literature of employees.

[0110] The proposal department can estimate employees' emotions and adjust the way proposals are expressed based on those estimated emotions. For example, if an employee is feeling stressed, the proposal department's AI will make a simple and clear proposal. The proposal department can estimate employees' emotions and adjust the way proposals are expressed based on those estimated emotions. For example, if an employee is feeling stressed, the proposal department's AI will make a simple and clear proposal. By adjusting the way proposals are expressed based on employees' emotions, more appropriate proposals become possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The proposal department can identify the most suitable personnel by referring to employees' past project experience when making a proposal. For example, the proposal department can identify the most suitable personnel by referring to employees' past project experience when making a proposal. For example, the proposal department can use AI to collect employees' past project data and identify the most suitable personnel. The proposal department can identify the most suitable personnel by referring to employees' past project experience when making a proposal. For example, the proposal department can use AI to collect employees' past project data and identify the most suitable personnel. This makes it possible to identify the most suitable personnel by referring to employees' past project experience. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input employees' past project data into a generative AI and have the generative AI perform the identification of the most suitable personnel.

[0112] The proposal department can customize proposals by taking into account the communication style of the employees when making a proposal. For example, the proposal department can customize proposals by taking into account the communication style of the employees when making a proposal. For example, the proposal department can use AI to collect employee communication styles and customize proposals. The proposal department can customize proposals by taking into account the communication style of the employees when making a proposal. For example, the proposal department can use AI to collect employee communication styles and customize proposals. This makes it possible to customize proposals by taking employee communication styles into account. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can have a generative AI perform the customization of employee communication styles.

[0113] The proposal department can estimate employees' emotions and determine the priority of proposals based on those estimated emotions. For example, if an employee is feeling stressed, the AI ​​will adjust the priority of proposals to alleviate that stress. The proposal department can estimate employees' emotions and determine the priority of proposals based on those estimated emotions. For example, if an employee is feeling stressed, the AI ​​will adjust the priority of proposals to alleviate that stress. This allows for more appropriate prioritization by determining the priority of proposals based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The proposal department can identify the most suitable personnel by considering the geographical location information of employees when making a proposal. For example, the proposal department can identify the most suitable personnel by considering the geographical location information of employees when making a proposal. For example, the proposal department can use AI to collect the geographical location information of employees and identify the most suitable personnel. The proposal department can identify the most suitable personnel by considering the geographical location information of employees when making a proposal. For example, the proposal department can use AI to collect the geographical location information of employees and identify the most suitable personnel. This makes it possible to identify the most suitable personnel by considering the geographical location information of employees. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input the geographical location information of employees into a generative AI and have the generative AI perform the identification of the most suitable personnel.

[0115] The proposal department can analyze employees' social media activity to supplement the proposal content when making a proposal. For example, the proposal department can analyze employees' social media activity to supplement the proposal content when making a proposal. For example, the proposal department can use AI to collect employees' social media activity and supplement the proposal content. The proposal department can analyze employees' social media activity to supplement the proposal content when making a proposal. For example, the proposal department can use AI to collect employees' social media activity and supplement the proposal content. This makes it possible to supplement the proposal content by analyzing employees' social media activity. Some or all of the above processing in the proposal department may be performed using, for example, generative AI, or without generative AI. For example, the proposal department can have generative AI perform the analysis of employees' social media activity.

[0116] The support department can estimate employees' emotions and adjust communication support methods based on the estimated emotions. For example, if an employee is feeling stressed, the support department can use AI to provide simple and clear communication support. The support department can estimate employees' emotions and adjust communication support methods based on the estimated emotions. For example, if an employee is feeling stressed, the support department can use AI to provide simple and clear communication support. By adjusting communication support methods based on employees' emotions, more appropriate support becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The support department can select the optimal support method by referring to the employee's past communication history when providing communication support. For example, the support department can select the optimal support method by referring to the employee's past communication history when providing communication support. For example, the support department can use AI to collect the employee's past communication history and select the optimal support method. The support department can select the optimal support method by referring to the employee's past communication history when providing communication support. For example, the support department can use AI to collect the employee's past communication history and select the optimal support method. This makes it possible to select the optimal support method by referring to the employee's past communication history. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without using a generative AI. For example, the support department can input the employee's past communication history into a generative AI and have the generative AI select the optimal support method.

[0118] The support department can provide communication support while taking into account the specialized terminology and industry jargon used by employees. For example, the support department can use AI to collect the specialized terminology and industry jargon used by employees and provide communication support. For example, the support department can use AI to collect the specialized terminology and industry jargon used by employees and provide communication support. This makes it possible to provide more appropriate communication support by taking into account the specialized terminology and industry jargon used by employees. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can have a generative AI collect the specialized terminology and industry jargon used by employees.

[0119] The support department can estimate employees' emotions and determine the priority of communication support based on the estimated emotions. For example, if an employee is feeling stressed, the AI ​​adjusts the priority of communication support to reduce stress. The support department can estimate employees' emotions and determine the priority of communication support based on the estimated emotions. For example, if an employee is feeling stressed, the AI ​​adjusts the priority of communication support to reduce stress. This allows for more appropriate prioritization of communication support by determining the priority of communication support based on employees' emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The support department can customize the support provided when offering communication assistance, taking into account the geographical background of the employees. For example, the support department can customize the support provided when offering communication assistance, taking into account the geographical background of the employees. For example, the support department can use AI to collect the geographical background of employees and customize the support provided. The support department can customize the support provided when offering communication assistance, taking into account the geographical background of the employees. For example, the support department can use AI to collect the geographical background of employees and customize the support provided. This makes it possible to customize the support provided by taking into account the geographical background of employees. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without using a generative AI. For example, the support department can have a generative AI collect the geographical background of employees.

[0121] The support department can improve the accuracy of its support by referring to relevant literature related to employees during communication support. For example, the support department can improve the accuracy of its support by referring to relevant literature related to employees during communication support. For example, the support department can use AI to collect relevant literature related to employees and improve the accuracy of its support. The support department can improve the accuracy of its support by referring to relevant literature related to employees during communication support. For example, the support department can use AI to collect relevant literature related to employees and improve the accuracy of its support. This makes it possible to improve the accuracy of support by referring to relevant literature related to employees. Some or all of the above processing in the support department may be performed using, for example, a generative AI, or without a generative AI. For example, the support department can have a generative AI perform the task of referring to relevant literature related to employees.

[0122] The Project Management Department can estimate employees' emotions and adjust the way creative projects are promoted based on those estimated emotions. For example, if an employee is feeling stressed, the Project Management Department can use AI to provide a simple and clear approach to promoting the project. This allows for more appropriate promotion by adjusting the approach to promoting creative projects based on employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The Promotion Department can select the optimal promotion method when promoting a creative project by referring to the past project experience of employees. For example, the Promotion Department selects the optimal promotion method when promoting a creative project by referring to the past project experience of employees. For example, the Promotion Department uses AI to collect past project data of employees and select the optimal promotion method. The Promotion Department selects the optimal promotion method when promoting a creative project by referring to the past project experience of employees. For example, the Promotion Department uses AI to collect past project data of employees and select the optimal promotion method. This makes it possible to select the optimal promotion method by referring to the past project experience of employees. Some or all of the above processing in the Promotion Department may be performed using, for example, a generative AI, or without using a generative AI. For example, the Promotion Department can input past project data of employees into a generative AI and have the generative AI select the optimal promotion method.

[0124] The Promotion Department can take into account the specialized terminology and industry jargon of employees when promoting creative projects. For example, the Promotion Department can use AI to collect the specialized terminology and industry jargon of employees and promote creative projects. The Promotion Department can take into account the specialized terminology and industry jargon of employees when promoting creative projects. For example, the Promotion Department can use AI to collect the specialized terminology and industry jargon of employees and promote creative projects. This makes it possible to promote creative projects more appropriately by taking into account the specialized terminology and industry jargon of employees. Some or all of the above processing in the Promotion Department may be performed using, for example, generative AI, or not using generative AI. For example, the Promotion Department can have generative AI perform the task of collecting the specialized terminology and industry jargon of employees.

[0125] The Promotion Department can estimate employees' emotions and determine the priority of creative projects based on those estimated emotions. For example, if an employee is feeling stressed, the Promotion Department's AI can adjust the priority of creative projects to alleviate that stress. The Promotion Department can estimate employees' emotions and determine the priority of creative projects based on those estimated emotions. For example, if an employee is feeling stressed, the Promotion Department's AI can adjust the priority of creative projects to alleviate that stress. This allows for more appropriate prioritization by determining the priority of creative projects based on employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The Promotion Department can customize the content of creative projects by taking into account the geographical backgrounds of employees. For example, the Promotion Department customizes the content of creative projects by taking into account the geographical backgrounds of employees. For example, the Promotion Department uses AI to collect employees' geographical backgrounds and customize the content of the project. For example, the Promotion Department uses AI to collect employees' geographical backgrounds and customize the content of the project. This makes it possible to customize the content of the project by taking into account the geographical backgrounds of employees. Some or all of the above processing in the Promotion Department may be performed using, for example, generative AI, or without generative AI. For example, the Promotion Department can have generative AI collect employees' geographical backgrounds.

[0127] The Promotion Department can improve the accuracy of promotion by referring to relevant literature of employees when promoting creative projects. For example, the Promotion Department can improve the accuracy of promotion by referring to relevant literature of employees when promoting creative projects. For example, the Promotion Department can have AI collect relevant literature of employees and improve the accuracy of promotion. The Promotion Department can improve the accuracy of promotion by referring to relevant literature of employees when promoting creative projects. For example, the Promotion Department can have AI collect relevant literature of employees and improve the accuracy of promotion. This makes it possible to improve the accuracy of promotion by referring to relevant literature of employees. Some or all of the above processing in the Promotion Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Promotion Department can have a generative AI perform the task of referring to relevant literature of employees.

[0128] The decision-making unit can estimate employees' emotions and adjust its decision-making process based on those emotions. For example, if an employee is feeling stressed, the AI ​​can provide a simple and clear decision-making method. The decision-making unit can estimate employees' emotions and adjust its decision-making process based on those emotions. For example, if an employee is feeling stressed, the AI ​​can provide a simple and clear decision-making method. This allows for more appropriate decision-making by adjusting the decision-making process based on employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The decision-making unit can select the optimal decision-making method by referring to the employee's past decision-making history when making a decision. For example, the decision-making unit can select the optimal decision-making method by referring to the employee's past decision-making history when making a decision. For example, the decision-making unit can use AI to collect the employee's past decision-making history and select the optimal decision-making method. The decision-making unit can select the optimal decision-making method by referring to the employee's past decision-making history when making a decision. For example, the decision-making unit can use AI to collect the employee's past decision-making history and select the optimal decision-making method. This makes it possible to select the optimal decision-making method by referring to the employee's past decision-making history. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the decision-making unit can input the employee's past decision-making history into a generative AI and have the generative AI perform the selection of the optimal decision-making method.

[0130] The decision-making unit can make decisions while taking into account the specialized terminology and industry jargon of employees. For example, the decision-making unit can make decisions while taking into account the specialized terminology and industry jargon of employees. For example, the decision-making unit can use AI to collect the specialized terminology and industry jargon of employees and then make a decision. The decision-making unit can make decisions while taking into account the specialized terminology and industry jargon of employees. For example, the decision-making unit can use AI to collect the specialized terminology and industry jargon of employees and then make a decision. This makes it possible to make more appropriate decisions by taking into account the specialized terminology and industry jargon of employees. Some or all of the above processing in the decision-making unit may be performed using, for example, generative AI, or not using generative AI. For example, the decision-making unit can have generative AI perform the task of collecting the specialized terminology and industry jargon of employees.

[0131] The decision-making unit can estimate employees' emotions and determine decision priorities based on those estimated emotions. For example, if an employee is feeling stressed, the AI ​​can adjust the decision priorities to reduce that stress. The decision-making unit can estimate employees' emotions and determine decision priorities based on those estimated emotions. For example, if an employee is feeling stressed, the AI ​​can adjust the decision priorities to reduce that stress. This allows for more appropriate prioritization by determining decision priorities based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The decision-making unit can customize its decisions by taking into account the geographical backgrounds of its employees. For example, the decision-making unit can customize its decisions by taking into account the geographical backgrounds of its employees. For example, the decision-making unit can use AI to collect the geographical backgrounds of its employees and customize the decisions. For example, the decision-making unit can use AI to collect the geographical backgrounds of its employees and customize the decisions. This makes it possible to customize decisions by taking into account the geographical backgrounds of its employees. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the decision-making unit can have a generative AI collect the geographical backgrounds of its employees.

[0133] The decision-making unit can improve the accuracy of its decisions by referring to relevant literature on employees during the decision-making process. For example, the decision-making unit can improve the accuracy of its decisions by referring to relevant literature on employees during the decision-making process. For example, the decision-making unit can improve the accuracy of its decisions by having an AI collect relevant literature on employees. The decision-making unit can improve the accuracy of its decisions by referring to relevant literature on employees during the decision-making process. For example, the decision-making unit can improve the accuracy of its decisions by having an AI collect relevant literature on employees. This makes it possible to improve the accuracy of decisions by referring to relevant literature on employees. Some or all of the above processing in the decision-making unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision-making unit can have a generative AI perform the task of referring to relevant literature on employees.

[0134] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0135] The AI ​​Inspiration Partner System can also include a feedback unit. This unit collects feedback from employees, and the AI ​​analyzes this feedback to improve the system. For example, the feedback unit collects feedback provided by employees after project completion, and the AI ​​analyzes this feedback to incorporate it into future projects. Furthermore, the feedback unit collects problems and areas for improvement that employees notice on a daily basis, and the AI ​​can adjust the system based on this information. In addition, the feedback unit can adjust the content of feedback based on the employee's emotional state to provide more appropriate feedback. As a result, the system is constantly improved based on the latest information, leading to increased employee satisfaction and work efficiency.

[0136] The AI ​​Inspiration Partner System can also include a training department. This department provides training programs aimed at improving employees' skills. For example, it analyzes employees' skill sets and proposes necessary training. It can also refer to employees' past training history to customize optimal training programs. Furthermore, it can adjust training content and methods considering employees' emotional states to provide more effective training. This promotes employee skill improvement, leading to increased work efficiency and creativity.

[0137] The AI ​​Inspiration Partner System can also include a Motivation Department. This department proposes measures to improve employee motivation. For example, it analyzes employees' emotional states and provides appropriate support to those experiencing low motivation. It can also evaluate employees' work performance and propose rewards and incentives to boost motivation. Furthermore, it can customize motivation-enhancing measures based on employees' emotional states to provide more effective support. This leads to increased employee motivation, improved work efficiency, and enhanced creativity.

[0138] The AI ​​Inspiration Partner System can also include a Health Management Department. This department monitors employees' health status and provides support for maintaining their well-being. For example, it collects employee health data, and AI analyzes this data to predict health risks. Furthermore, the Health Management Department can provide health maintenance advice while considering employees' emotional states. It can also customize health maintenance programs based on employees' health status to provide more effective support. This leads to improved employee health, increased work efficiency, and enhanced creativity.

[0139] The AI ​​Inspiration Partner System can also include a Career Development Department. This department proposes measures to support employees' career paths. For example, it analyzes employees' skill sets and work performance to suggest the optimal career path. Furthermore, it can customize career development measures considering employees' emotional states to provide more effective support. Additionally, it can refer to employees' past career histories to optimize their career paths. This promotes employee career development, leading to increased work efficiency and improved creativity.

[0140] The AI ​​Inspiration Partner System can also be equipped with a Resource Management Department. This department assists in the optimal allocation of resources required for a project. For example, it monitors the project's progress and proposes necessary resources in real time. Furthermore, it can optimize resource allocation based on employees' skill sets and work performance. Additionally, it can adjust resource allocation according to the project's progress, supporting efficient project management. This prevents project stagnation and ensures smooth progress.

[0141] The AI ​​Inspiration Partner System can also be equipped with a Risk Management Department. This department predicts project risks and proposes measures to mitigate them. For example, it monitors project progress and detects potential risks in real time. It can also analyze past project data and propose optimal risk avoidance measures. Furthermore, it can adjust risk avoidance measures according to project progress, supporting efficient risk management. This minimizes project risks and ensures smooth project progress.

[0142] The AI ​​Inspiration Partner System can also include a Cost Management Department. This department provides support for optimally managing project costs. For example, it monitors project progress and performs real-time cost forecasting and management. It can also analyze past project data and propose optimal measures for cost reduction. Furthermore, it can adjust cost management measures according to project progress, supporting efficient cost management. This minimizes project costs and ensures smooth progress within budget.

[0143] The AI ​​Inspiration Partner System can also include a Quality Control Department. This department provides support for optimally managing project quality. For example, it monitors project progress and performs real-time quality forecasting and management. It can also analyze past project data and propose optimal measures for quality improvement. Furthermore, it can adjust quality control measures according to project progress, supporting efficient quality management. This ensures high project quality and smooth project progress.

[0144] The AI ​​Inspiration Partner System can also include a Customer Management (CRM) department. This department provides support for optimally managing customer relationships. For example, it collects customer feedback, and the AI ​​analyzes that feedback to improve customer satisfaction. It can also analyze a customer's past transaction history and propose the most appropriate customer service. Furthermore, it can customize customer service strategies considering the customer's emotional state to provide more effective support. This leads to increased customer satisfaction and the building of long-term customer relationships.

[0145] The following briefly describes the processing flow for example form 2.

[0146] Step 1: The analysis department analyzes the skill sets within the workplace. For example, it collects employee skills, work performance, and communication information, which is then analyzed by AI. This allows for the identification of the best person for a specific task. Step 2: The emotion recognition unit analyzes the emotional state based on the skill set analyzed by the analysis unit. For example, AI monitors employees' emotional states in real time and evaluates their stress levels. This allows for the placement of personnel so that they can perform at their best in a low-stress environment. Step 3: The processing unit uses natural language processing technology based on the emotional state analyzed by the emotion recognition unit. For example, it uses natural language processing technology to support smoother communication among employees. This involves analyzing the content of emails and chats between employees and suggesting areas for improvement in communication. Step 4: The proposal department proposes the most suitable personnel based on the information obtained by the processing department. For example, AI proposes the most suitable personnel for a specific project in real time based on employees' skill sets and emotional states. Step 5: The support department provides communication support based on the personnel proposed by the proposal department. For example, AI monitors the project's progress and provides necessary advice and support. This allows for real-time monitoring of the project's progress and the provision of appropriate feedback. Step 6: The Promotion Department drives creative projects based on information supported by the Support Department. For example, AI generates creative ideas to support project progress. This supports brainstorming sessions and elicits new ideas. Step 7: The Decision-Making Department supports decision-making based on the project promoted by the Promotion Department. For example, AI analyzes data to support optimal decision-making. This allows for optimal decisions to be made based on the project's progress and resource utilization.

[0147] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0148] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0149] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0150] Each of the multiple elements described above, including the analysis unit, emotion recognition unit, processing unit, proposal unit, support unit, promotion unit, and decision-making unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14, which collects employee skills, work performance, and communication information, and has AI analyze it. The emotion recognition unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the emotional state of employees. The processing unit is implemented by the control unit 46A of the smart device 14, which supports communication among employees using natural language processing technology. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable personnel. The support unit is implemented by the control unit 46A of the smart device 14, which monitors the progress of the project and provides necessary advice and support. The promotion unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates creative ideas and supports the promotion of the project. The decision-making unit is implemented, for example, by the control unit 46A of the smart device 14, which analyzes data and supports optimal decision-making. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0152] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the analysis unit, emotion recognition unit, processing unit, proposal unit, support unit, promotion unit, and decision-making unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214, which collects employee skills, work performance, and communication information, and has AI analyze it. The emotion recognition unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the emotional state of employees. The processing unit is implemented, for example, by the control unit 46A of the smart glasses 214, which supports communication among employees using natural language processing technology. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable personnel. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214, which monitors the progress of the project and provides necessary advice and support. The promotion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates creative ideas and supports the promotion of the project. The decision-making unit is implemented, for example, by the control unit 46A of the smart glasses 214, which analyzes data and supports optimal decision-making. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0168] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the analysis unit, emotion recognition unit, processing unit, proposal unit, support unit, promotion unit, and decision-making unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314, which collects employee skills, work performance, and communication information, and has AI analyze it. The emotion recognition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the emotional state of employees. The processing unit is implemented by, for example, the control unit 46A of the headset terminal 314, which supports communication between employees using natural language processing technology. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most suitable personnel. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314, which monitors the progress of the project and provides necessary advice and support. The promotion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which generates creative ideas and supports project advancement. The decision-making unit is implemented, for example, by the control unit 46A of the headset terminal 314, which analyzes data and supports optimal decision-making. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0183] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0184] As shown in Figure 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.

[0185] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0186] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0187] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0189] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0190] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0191] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0192] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0193] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0194] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0195] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0196] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0197] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0198] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0199] Each of the multiple elements described above, including the analysis unit, emotion recognition unit, processing unit, proposal unit, support unit, promotion unit, and decision-making unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414, which collects employee skills, work performance, and communication information, and has the AI ​​analyze it. The emotion recognition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the emotional state of employees. The processing unit is implemented by, for example, the control unit 46A of the robot 414, which supports communication among employees using natural language processing technology. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most suitable personnel. The support unit is implemented by, for example, the control unit 46A of the robot 414, which monitors the progress of the project and provides necessary advice and support. The promotion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates creative ideas and supports the promotion of the project. The decision-making unit is implemented, for example, by the control unit 46A of the robot 414, which analyzes data and supports optimal decision-making. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0200] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0201] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0202] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0203] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0204] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0205] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0206] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0207] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0208] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0209] 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.

[0210] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0211] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0212] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0213] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0214] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0215] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0216] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0217] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0218] (Note 1) The analysis department analyzes the skill sets within the workplace, An emotion recognition unit analyzes the emotional state based on the skill set analyzed by the aforementioned analysis unit, A processing unit that uses natural language processing technology based on the emotional state analyzed by the emotion recognition unit, A proposal unit proposes the most suitable personnel based on the information obtained by the processing unit, A support department provides communication support based on the personnel proposed by the aforementioned proposal department, Based on the information supported by the aforementioned support department, the promotion department promotes creative projects, The system includes a decision-making unit that supports decision-making based on projects promoted by the aforementioned promotion unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is AI collects and analyzes employee skills, work performance, and communication information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The emotion recognition unit, Using AI to analyze employees' emotional states, we can assign personnel to situations where they can perform optimally with minimal stress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The processing unit described above is: We use natural language processing technology to support smoother communication among employees. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Using AI to suggest the most suitable personnel in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit, We use AI to monitor project progress and provide necessary advice and support. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned propulsion unit is, We use AI to generate creative ideas and support project advancement. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned decision-making unit, We use AI to analyze data and support optimal decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is We estimate employees' emotions and adjust the skill set analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is We conduct a detailed analysis of employees' past project experience and evaluate the relevance of specific skill sets. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is Analyze employee communication patterns and optimize roles within teams. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Estimate employees' emotions and prioritize skill sets based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is Analyze the characteristics of skill sets by region, taking into account the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Analyze employees' social media activity to obtain supplementary information on their skill sets. The system described in Appendix 1, characterized by the features described herein. (Note 15) The emotion recognition unit, We estimate employees' emotions and improve the accuracy of emotion recognition based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The emotion recognition unit, When recognizing emotions, the system predicts changes in emotions by referring to the employee's past emotional history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The emotion recognition unit, When recognizing emotions, sentiment analysis is performed while taking into account the communication style of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 18) The emotion recognition unit, Adjusting the method for estimating employee emotions and displaying emotion recognition results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The emotion recognition unit, When recognizing emotions, we analyze the characteristics of those emotions while considering the geographical background of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 20) The emotion recognition unit, When recognizing emotions, we improve the accuracy of emotion analysis by referring to relevant literature related to employees. The system described in Appendix 1, characterized by the features described herein. (Note 21) The processing unit described above is: The system estimates employees' emotions and adjusts the natural language processing algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The processing unit described above is: During natural language processing, we improve the accuracy of analysis by referencing employees' past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The processing unit described above is: During natural language processing, the analysis takes into account the employees' specialized terminology and industry jargon. The system described in Appendix 1, characterized by the features described herein. (Note 24) The processing unit described above is: Adjust the way we estimate employee emotions and display the results of natural language processing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The processing unit described above is: When performing natural language processing, we analyze the characteristics of the analysis by taking into account the geographical background of employees. The system described in Appendix 1, characterized by the features described herein. (Note 26) The processing unit described above is: During natural language processing, we improve the accuracy of analysis by referencing relevant literature from employees. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, We estimate the emotions of employees and adjust the way proposals are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, we refer to employees' past project experience to identify the most suitable candidates. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, customize the content of the proposal to take into account the communication style of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Estimate employees' emotions and prioritize proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, we take into account the geographical location of employees to identify the most suitable personnel. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, analyze employees' social media activity to supplement the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit, We estimate employees' emotions and adjust communication support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit, When providing communication support, the optimal support method is selected by referring to the employee's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, When providing communication support, we take into account the employees' specialized terminology and industry jargon. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned support unit, The system estimates employees' emotions and prioritizes communication support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit, When providing communication support, customize the support content to take into account the geographical background of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit, When providing communication support, we improve the accuracy of support by referring to relevant literature used by employees. The system described in Appendix 1, characterized by the features described herein. (Supplementary Note 39) The propulsion unit estimates the emotions of employees and adjusts the method of promoting creative projects based on the estimated emotions The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 40) The propulsion unit selects an optimal propulsion method by referring to the past project experience of employees when promoting creative projects The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 41) The propulsion unit promotes the project taking into account the technical terms and industry terms of the employees when promoting creative projects The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 42) The propulsion unit estimates the emotions of employees and determines the priority order of promoting creative projects based on the estimated emotions The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 43) The propulsion unit customizes the content of promotion by considering the geographical background of employees when promoting creative projects The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 44) The propulsion unit improves the accuracy of promotion by referring to the relevant literature of employees when promoting creative projects The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 45) The decision-making unit estimates the emotions of employees and adjusts the decision-making method based on the estimated emotions The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 46) The decision-making unit selects an optimal decision-making method by referring to the past decision-making history of employees when making decisions The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 47) The decision-making unit makes a decision considering the specialized terms and industry terms of employees at the time of decision-making The system according to Supplementary Note 1, characterized in that. (Supplementary Note 48) The decision-making unit estimates the emotions of employees and determines the priority order of decision-making based on the estimated emotions The system according to Supplementary Note 1, characterized in that. (Supplementary Note 49) The decision-making unit customizes the content of the decision-making by considering the geographical background of employees at the time of decision-making The system according to Supplementary Note 1, characterized in that. (Supplementary Note 50) The decision-making unit refers to the relevant documents of employees to improve the accuracy of decision-making at the time of decision-making The system according to Supplementary Note 1, characterized in that.

Explanation of Signs

[0219] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. The analysis department analyzes the skill sets within the workplace, An emotion recognition unit analyzes the emotional state based on the skill set analyzed by the aforementioned analysis unit, A processing unit that uses natural language processing technology based on the emotional state analyzed by the emotion recognition unit, A proposal unit proposes the most suitable personnel based on the information obtained by the processing unit, A support department provides communication support based on the personnel proposed by the aforementioned proposal department, Based on the information supported by the aforementioned support department, the promotion department promotes creative projects, The system includes a decision-making unit that supports decision-making based on projects promoted by the aforementioned promotion unit. A system characterized by the following features.

2. The aforementioned analysis unit is AI collects employee skills, work performance, and communication information and analyzes it. The system according to feature 1.

3. The emotion recognition unit, Using AI to analyze employees' emotional states, we will allocate personnel in a way that allows them to perform optimally under minimal stress. The system according to feature 1.

4. The processing unit described above is: We use natural language processing technology to support smoother communication among employees. The system according to feature 1.

5. The aforementioned proposal section is, Using AI to propose the most suitable personnel in real time. The system according to feature 1.

6. The aforementioned support unit, We use AI to monitor project progress and provide necessary advice and support. The system according to feature 1.

7. The aforementioned propulsion unit is, We use AI to generate creative ideas and support project advancement. The system according to feature 1.

8. The aforementioned decision-making unit, Using AI to analyze data and support optimal decision-making. The system according to feature 1.

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