system

Abstract

The system according to the embodiment aims to analyze employees' strengths and areas of interest and propose optimal tasks and projects. [Solution] A system according to an embodiment includes an analysis unit, a proposal unit, a transparency unit, and a decision-making unit. The analysis unit analyzes employees' strengths and areas of interest. The proposal unit proposes tasks and projects based on the data analyzed by the analysis unit. The transparency unit makes information within an organization transparent and stimulates communication between employees. The decision-making unit promotes decision-making.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the challenge of making it difficult to fully utilize employees' strengths and areas of interest and improve productivity across the organization.

[0005] The system according to the embodiment aims to analyze employees' strengths and areas of interest and propose optimal tasks and projects. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, a transparency unit, and a decision-making unit. The analysis unit analyzes employees' strengths and areas of interest. The proposal unit proposes tasks and projects based on the data analyzed by the analysis unit. The transparency unit makes information within the organization transparent and stimulates communication between employees. The decision-making unit promotes decision-making. [Effects of the Invention]

[0007] The system according to the embodiment can analyze employees' strengths and areas of interest and suggest optimal tasks and projects. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI organizational system according to an embodiment of the present invention addresses issues stemming from inefficient organizational structures and uniform educational systems in large Japanese companies. This AI organizational system utilizes generative AI to preserve the advantages of hierarchical organizations while incorporating the benefits of teal organizations, thereby drawing out employee autonomy and creativity and improving organizational productivity. It realizes a new workstyle in which each employee can achieve self-realization through collaboration with AI while contributing to organizational goals. For example, AI analyzes each employee's strengths and areas of interest and suggests optimal tasks and projects, promoting self-management. AI also increases transparency of information within the organization and stimulates communication among employees, eliminating silos between departments. AI mentors support the growth of each employee and clearly share organizational goals, aligning individual and organizational goals. Furthermore, AI facilitators promote flat decision-making and assemble optimal teams for each project, realizing a flexible organizational structure. AI coaches analyze employees' strengths and areas of interest and suggest optimal tasks and learning opportunities. AI mentors develop individual employees' career visions and learning plans, supporting their growth. The AI ​​Facilitator assists in meeting proceedings, fairly gathering everyone's opinions and promoting consensus building. The AI ​​Advisor aggregates and analyzes information within the organization and supports management decision-making. The AI ​​Coordinator assembles the optimal team for each project and flexibly reorganizes it according to the task. The AI ​​Monitor continuously monitors individual and team performance and provides appropriate feedback. The AI ​​Translator clearly explains management's policies to employees, helping them understand the organization's goals. The AI ​​Harmonizer detects emotional conflicts among employees and promotes constructive communication. The AI ​​Innovator collects and evaluates ideas from employees and provides promising suggestions to management. The AI ​​Organizer optimizes employees' working styles and work-life balance, improving productivity and happiness. In this way, the AI ​​organizational system aims to eliminate the inefficiency and rigidity prevalent in large Japanese companies and foster an organizational culture in which each employee can work autonomously and creatively.By realizing a new way of working in which AI and humans work together, we can dramatically improve organizational performance while increasing employee engagement and happiness. In the future, we hope that the AI ​​organizational system will become widespread in Japanese corporate society, accelerating the transformation to teal organizations. This will strengthen the international competitiveness of Japanese companies and stimulate innovation. Furthermore, by establishing a work style that allows for autonomy and creativity, it will also contribute to the realization of an inclusive society where diverse talent can thrive. The AI ​​organizational system will bring about change in Japan's organizational culture and be the first step toward opening up a sustainable and prosperous future. This will enable the AI ​​organizational system to bring out the autonomy and creativity of employees and improve productivity throughout the organization.

[0029] An AI organization system according to an embodiment includes an analysis unit, a proposal unit, a transparency unit, and a decision-making unit. The analysis unit analyzes employees' strengths and areas of interest. Employee strengths include, but are not limited to, technical skills and leadership abilities. The analysis unit collects data, such as employees' past work history, skill sets, and interests, and the generation AI analyzes the data. The generation AI can analyze employees' strengths and areas of interest using, for example, natural language processing or machine learning algorithms. The analysis unit can also estimate employees' emotions and adjust the timing of analysis based on the estimated employee emotions. For example, if an employee is stressed, the generation AI can delay the timing of analysis and perform the analysis in a relaxed state. Furthermore, if an employee is relaxed, the generation AI can immediately start analysis, allowing for efficient data collection. The proposal unit proposes optimal tasks and projects for each employee based on the data analyzed by the analysis unit. The proposal unit can propose optimal tasks and projects based on, for example, the employee's skill set and the importance of the project. The suggestion unit can also estimate an employee's emotions and adjust the way the suggestion is presented based on the estimated employee's emotions. For example, if an employee is feeling stressed, the generation AI can provide a simple and easy-to-understand suggestion. If an employee is feeling relaxed, the generation AI can provide a suggestion that includes detailed information. The transparency unit makes information within the organization transparent and stimulates communication between employees. The transparency unit collects information, such as the progress of work in each department and the progress of projects, and the generation AI analyzes it. The generation AI can make information transparent using, for example, natural language processing or machine learning algorithms. The transparency unit can also estimate an employee's emotions and adjust the way the information is displayed based on the estimated employee's emotions. For example, if an employee is feeling stressed, the generation AI can provide a simple, highly visible display. If an employee is relaxed, the generation AI can provide a display that includes detailed information. The decision-making unit promotes flat decision-making.The decision-making unit, for example, supports the progress of meetings, fairly gathers everyone's opinions, and promotes consensus building. The decision-making unit can use the generation AI to monitor the progress of meetings in real time and provide appropriate feedback. The decision-making unit can also estimate employees' emotions and adjust decision-making methods based on the estimated employee emotions. For example, if an employee is feeling stressed, the generation AI can provide a simple and quick decision-making method. On the other hand, if an employee is relaxed, the generation AI can provide a decision-making method based on detailed information. As a result, the AI ​​organization system according to the embodiment can bring out the autonomy and creativity of employees and improve the productivity of the entire organization.

[0030] The analysis unit can collect data on employees' past work history, skill sets, and interests and analyze it using the generative AI. For example, the analysis unit collects data on employees' past work history, skill sets, and interests and analyzes it using the generative AI. The generative AI can analyze employees' strengths and areas of interest using natural language processing or machine learning algorithms. For example, the generative AI can analyze employees' past project history and identify the factors behind successful projects. The generative AI can also analyze employees' skill sets and suggest optimal tasks and projects. Furthermore, the generative AI can analyze employees' interests and identify areas of interest and projects they want to work on. This allows for an accurate understanding of employees' strengths and areas of interest and suggests optimal tasks and projects. Some or all of the above-mentioned processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs data on employees' past work history, skill sets, and interests into the generative AI, which then outputs the analysis results.

[0031] The suggestion unit can suggest optimal tasks and projects for each employee based on the data analyzed by the analysis unit. The suggestion unit can suggest optimal tasks and projects based on, for example, the employee's skill set and the importance of the project. The suggestion unit can use a generation AI to suggest optimal tasks and projects based on the employee's strengths and areas of interest. For example, the generation AI can analyze the employee's skill set and identify tasks to which the employee can contribute most effectively. The generation AI can also analyze the importance of projects and suggest projects that are most important to the employee. Furthermore, the generation AI can analyze the employee's interests and suggest projects that the employee is interested in. This makes it possible to suggest optimal tasks and projects based on the employee's strengths and areas of interest. Some or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit inputs the data analyzed by the analysis unit into the generation AI, which then suggests optimal tasks and projects.

[0032] The transparency department can collect information on the progress of work and projects in each department and analyze it using the generation AI. For example, the transparency department collects information on the progress of work and projects in each department and analyzes it using the generation AI. The generation AI can make information transparent using, for example, natural language processing or machine learning algorithms. For example, the generation AI can analyze the progress of work in each department and identify delays and problems. The generation AI can also analyze the progress of projects and identify risks and issues in the projects. Furthermore, the generation AI can eliminate information gaps between departments and stimulate communication between employees. This makes information within the organization transparent and stimulates communication between employees. Some or all of the above-mentioned processing in the transparency department is performed using the generation AI. For example, the transparency department inputs information on the progress of work and projects in each department into the generation AI, which then outputs the analysis results.

[0033] The decision-making unit can support the progress of meetings, fairly gather everyone's opinions, and promote consensus building. The decision-making unit can, for example, support the progress of meetings, fairly gather everyone's opinions, and promote consensus building. The decision-making unit can use generation AI to monitor the progress of meetings in real time and provide appropriate feedback. For example, the generation AI can analyze statements made during meetings in real time and automatically summarize the key points. The generation AI can also display the progress of meetings in real time and manage the timing and order of statements. Furthermore, the generation AI can automatically generate meeting minutes and distribute them to participants after the meeting. This promotes flat decision-making and realizes a flexible organizational structure. Some or all of the above-mentioned processes in the decision-making unit are performed using generation AI. For example, the decision-making unit inputs the progress of meetings into the generation AI, which then provides feedback in real time.

[0034] The Transparency Department can stimulate communication between employees and eliminate information gaps between departments. For example, the Transparency Department can stimulate communication between employees and eliminate information gaps between departments. The Transparency Department can use a generation AI to collect and analyze information on the progress of each department's work and projects. The generation AI can make information transparent using, for example, natural language processing or machine learning algorithms. For example, the generation AI can analyze the progress of each department's work and identify delays and problems. The generation AI can also analyze the progress of a project and identify project risks and issues. Furthermore, the generation AI can eliminate information gaps between departments and stimulate communication between employees. This can eliminate silos between departments and promote information sharing throughout the organization. Some or all of the above-mentioned processes in the Transparency Department are performed using a generation AI. For example, the Transparency Department inputs information on the progress of each department's work and projects into a generation AI, which then outputs the analysis results.

[0035] The decision-making unit can organize teams for each project and achieve a flexible organizational structure. For example, the decision-making unit can organize teams for each project and achieve a flexible organizational structure. The decision-making unit can use a generative AI to analyze project requirements and employee skill sets and organize an optimal team. For example, the generative AI can analyze project requirements and identify the required skill sets. The generative AI can also analyze employee skill sets and select the optimal members for the project. Furthermore, the generative AI can monitor the progress of the project and reorganize the team as necessary. This allows the optimal team to be organized for each project and achieve a flexible organizational structure. Some or all of the above-mentioned processing in the decision-making unit is performed using a generative AI. For example, the decision-making unit inputs data on project requirements and employee skill sets into the generative AI, which then organizes the optimal team.

[0036] The analysis unit can analyze the employee's past project success rate and select an analysis method. The analysis unit, for example, analyzes the employee's past project success rate and selects the optimal analysis method. The generation AI can, for example, analyze the employee's past project success rate and select the most effective analysis method. For example, the generation AI can prioritize analysis of data on projects with a high employee success rate and identify success factors. The generation AI can also analyze in detail data on projects with a low employee success rate and extract areas for improvement. Furthermore, the generation AI can select the optimal analysis method based on the employee's project success rate. This makes it possible to select the optimal analysis method based on the employee's past project success rate. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on the employee's past project success rate into the generation AI, which selects the optimal analysis method.

[0037] The analysis unit can filter data based on the employee's current workload and stress level. The analysis unit filters data based on, for example, the employee's current workload and stress level. For example, if the employee's workload is high, the generation AI can filter out less important data and narrow the analysis target. Furthermore, if the employee's stress level is high, the generation AI can prioritize analysis of data for stress reduction. Furthermore, if the employee's workload is low, the generation AI can perform detailed data analysis and provide comprehensive results. This allows for filtering data according to the employee's workload and stress level, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on the employee's workload and stress level into the generation AI, which then filters the data.

[0038] The analysis unit can prioritize analysis of highly relevant data based on the employee's geographic location information. For example, the analysis unit prioritizes analysis of highly relevant data based on the employee's geographic location information. For example, if the employee is in a specific area, the generation AI can prioritize analysis of data related to that area. Furthermore, if the employee is on a business trip, the generation AI can prioritize analysis of data from the business trip destination. Furthermore, if the employee is working remotely, the generation AI can prioritize analysis of data around the employee's home. This makes it possible to prioritize analysis of highly relevant data based on the employee's geographic location information. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on the employee's geographic location information into the generation AI, which then prioritizes analysis of highly relevant data.

[0039] The analysis unit can analyze employees' social media activities and collect related data. For example, the analysis unit analyzes employees' social media activities and collects related data. For example, the generation AI can analyze employees' social media activities and identify work-related trends. The generation AI can also collect information useful for work based on employees' social media posts. Furthermore, the generation AI can analyze employees' social media networks and identify experts related to work. This makes it possible to collect related data based on employees' social media activities and use it for analysis. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on employees' social media activities into the generation AI, which then collects related data.

[0040] The suggestion unit can adjust the level of detail of the proposal based on the importance of the task or project. The suggestion unit adjusts the level of detail of the proposal based on, for example, the importance of the task or project. The generation AI can, for example, analyze the importance of the task or project and make detailed suggestions for tasks with high importance. The generation AI can also make concise suggestions for tasks with low importance. Furthermore, the generation AI can adjust the level of detail of the proposal according to the importance of the project. This allows the level of detail of the proposal to be adjusted according to the importance of the task or project, making it possible to make effective suggestions. Some or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs data on the importance of the task or project into the generation AI, and the generation AI adjusts the level of detail of the proposal.

[0041] The suggestion unit can apply a suggestion algorithm according to the category of the task or project. For example, the suggestion unit applies a suggestion algorithm according to the category of the task or project. For example, the generation AI can apply a creative suggestion algorithm to a creative project. Furthermore, the generation AI can apply a technical suggestion algorithm to a technical project. Furthermore, the generation AI can apply a suggestion algorithm specialized for marketing to a marketing project. This allows the optimal suggestion algorithm to be applied according to the category of the task or project, making effective suggestions. Some or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs data on the category of the task or project into the generation AI, and the generation AI applies the optimal suggestion algorithm.

[0042] The proposal unit can determine the priority of proposals based on the submission dates of tasks and projects. The proposal unit determines the priority of proposals based on, for example, the submission dates of tasks and projects. The generation AI can, for example, prioritize proposals for tasks with upcoming deadlines. The generation AI can also postpone proposals for projects with more distant submission dates. Furthermore, the generation AI can dynamically adjust the priority of proposals based on the submission dates. This makes it possible to determine the priority of proposals based on the submission dates of tasks and projects and make effective proposals. Some or all of the above-mentioned processing in the proposal unit is performed using the generation AI. For example, the proposal unit inputs data on the submission dates of tasks and projects into the generation AI, and the generation AI determines the priority of proposals.

[0043] The suggestion unit can adjust the order of proposals based on the relevance of tasks and projects. The suggestion unit adjusts the order of proposals based on, for example, the relevance of tasks and projects. The generation AI can, for example, prioritize proposals for highly relevant tasks. The generation AI can also postpone proposals for less relevant tasks. Furthermore, the generation AI can dynamically adjust the order of proposals based on the relevance of tasks and projects. This allows the order of proposals to be adjusted based on the relevance of tasks and projects, making effective proposals. Some or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs data on the relevance of tasks and projects into the generation AI, and the generation AI adjusts the order of proposals.

[0044] The transparency unit can optimize the current information transparency method by referring to past information transparency data. The transparency unit, for example, optimizes the current information transparency method by referring to past information transparency data. The generation AI can, for example, select the optimal transparency method based on past information transparency data. The generation AI can also analyze past data and identify areas for improvement in the transparency method. Furthermore, the generation AI can optimize the current transparency method by referring to past transparency data. This allows the current information transparency method to be optimized based on past data, making it possible to provide effective information. Some or all of the above-mentioned processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs past information transparency data into the generation AI, which then optimizes the current transparency method.

[0045] The transparency unit can update the work progress status of each department in real time and provide the latest information. The transparency unit, for example, can update the work progress status of each department in real time and provide the latest information. The generation AI, for example, can collect the work progress status of each department in real time and provide the latest information. The generation AI can also update the project progress status in real time and provide the latest information. Furthermore, the generation AI can monitor the work progress status of each department in real time and provide the latest information. This makes it possible to update the work progress status of each department in real time and provide the latest information. Some or all of the above-mentioned processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs data on the work progress status of each department into the generation AI, and the generation AI provides the latest information.

[0046] The transparency unit can provide information based on the geographic distribution of each department. The transparency unit, for example, provides information based on the geographic distribution of each department. The generation AI, for example, can provide information by region, taking into account the geographic distribution of each department. The generation AI can also integrate information between geographically distant departments and provide it in a centralized manner. Furthermore, the generation AI can select the optimal information provision method based on the geographic distribution of each department. This allows information to be provided based on the geographic distribution of each department, enabling effective information sharing. Some or all of the above-mentioned processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs data on the geographic distribution of each department into the generation AI, and the generation AI provides the information.

[0047] The transparency unit can improve the accuracy of the information by referring to related external data. The transparency unit can, for example, improve the accuracy of the information by referring to related external data. The generation AI can, for example, improve the accuracy of the information by referring to external market data. The generation AI can also improve the accuracy of the information by referring to external competitive information. Furthermore, the generation AI can improve the accuracy of the information by referring to external economic data. This makes it possible to improve the accuracy of the information by referring to related external data. Some or all of the above-mentioned processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs external data into the generation AI, and the generation AI improves the accuracy of the information.

[0048] The decision-making unit can select the optimal decision-making method by referring to past decision-making data. The decision-making unit, for example, selects the optimal decision-making method by referring to past decision-making data. The generation AI can, for example, select the optimal decision-making method based on past successful decision-making data. The generation AI can also analyze past unsuccessful decision-making data and identify areas for improvement. Furthermore, the generation AI can optimize the current decision-making method by referring to past decision-making data. This makes it possible to select the optimal decision-making method based on past data and make effective decisions. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs past decision-making data into the generation AI, which then selects the optimal decision-making method.

[0049] The decision-making unit can monitor the progress of the meeting in real time and provide appropriate feedback. The decision-making unit can, for example, monitor the progress of the meeting in real time and provide appropriate feedback. The generation AI can, for example, monitor the progress of the meeting in real time and provide appropriate feedback. The generation AI can also aggregate opinions during the meeting in real time and support decision-making. Furthermore, the generation AI can analyze the progress of the meeting in real time and propose optimal decisions. This makes it possible to monitor the progress of the meeting in real time and provide appropriate feedback. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs data on the progress of the meeting into the generation AI, and the generation AI provides feedback.

[0050] The decision-making unit can make decisions based on the geographic distribution of projects. The decision-making unit makes decisions based on, for example, the geographic distribution of projects. The generation AI can, for example, take the geographic distribution of projects into account and make optimal decisions for each region. The generation AI can also integrate information between geographically distant projects and make centralized decisions. Furthermore, the generation AI can select the optimal decision-making method based on the geographic distribution of projects. This allows optimal decisions to be made based on the geographic distribution of projects. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs data on the geographic distribution of projects into the generation AI, and the generation AI makes decisions.

[0051] The decision-making unit can improve the accuracy of decision-making by referring to relevant external data. The decision-making unit can, for example, improve the accuracy of decision-making by referring to relevant external data. The generation AI can, for example, improve the accuracy of decision-making by referring to external market data. The generation AI can also improve the accuracy of decision-making by referring to external competitive information. Furthermore, the generation AI can improve the accuracy of decision-making by referring to external economic data. This makes it possible to improve the accuracy of decision-making by referring to relevant external data. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs external data into the generation AI, which then improves the accuracy of decision-making.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The Suggestion Department can also suggest long-term tasks and projects based on employees' career goals. For example, if an employee wants to acquire a specific skill, the department can suggest a project that will utilize that skill. If an employee is aiming for a leadership position in the future, the department can suggest a task that will help them hone their leadership skills. Furthermore, the department can suggest appropriate mentors and training programs based on employees' career goals. This can support employees' career growth and improve the skill level of the entire organization.

[0054] To promote knowledge sharing within the organization, the Transparency Department can also create a database of employee expertise and experience and make it accessible to other employees. For example, it can aggregate information about employees with expertise in a particular technology or project, allowing other employees to search and use that information. The Transparency Department can also suggest relevant knowledge and resources based on the projects employees have previously participated in and the skills they have acquired. This can promote knowledge sharing within the organization and help employees improve their skills. The Transparency Department can also collect employee feedback and identify areas for improvement across the organization. This can increase organizational transparency and improve employee engagement.

[0055] The analytics department can also analyze employees' social media activities to collect work-related trends and information. For example, it can analyze information and comments shared by employees on social media to identify trends and ideas that are useful for work. It can also analyze employees' social media networks to identify experts and resources related to work. It can also suggest work-related information based on employees' social media activities. This makes it possible to use employees' social media activities to collect work-related information and use it for analysis.

[0056] The Transparency Department can also provide information based on the geographic distribution of employees. For example, providing regional information to employees working remotely can improve work efficiency. Providing information about business trip destinations to employees on business trips can also help ensure smooth work progress. Furthermore, by integrating and centralizing information between geographically separated departments, information sharing throughout the organization can be promoted. This allows for optimal information provision based on the geographic distribution of employees, achieving effective information sharing.

[0057] The proposal department can also determine the priority of proposals based on the submission dates of tasks and projects. For example, by giving priority to proposals for tasks with upcoming deadlines, efficient task management can be supported. Also, by postponing proposals for projects with more distant submission dates, optimal resource allocation can be achieved. Furthermore, by dynamically adjusting the priority of proposals based on the submission dates, it is possible to flexibly respond to changing business environments. This allows for effective proposals to be made based on the submission dates of tasks and projects.

[0058] The Transparency Department can also improve the accuracy of information by referencing related external data. For example, by referencing external market data, the latest trends and movements related to business can be grasped. Furthermore, by referencing external competitor information, information can be provided for formulating competitive strategies. Furthermore, by referencing external economic data, business plans can be formulated according to economic conditions. In this way, the accuracy of information can be improved by utilizing related external data, and effective decision-making can be supported.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The analysis department analyzes employees' strengths and areas of interest. Data such as employees' past work history, skill sets, and interests is collected and analyzed by the generative AI. The generative AI uses natural language processing and machine learning algorithms to analyze employees' strengths and areas of interest. It can also estimate employees' emotions and adjust the timing of analysis based on the estimated emotions. Step 2: The proposal department suggests tasks and projects that are best suited to each employee based on the data analyzed by the analysis department. It makes suggestions based on the employee's skill set and the importance of the project, estimates the employee's emotions, and can also adjust the way the suggestions are presented based on the estimated emotions. Step 3: The Transparency Department makes information within the organization transparent and stimulates communication between employees. Information such as the progress of work and projects in each department is collected and analyzed by the Generative AI. It can also estimate employee emotions and adjust the way information is displayed based on the estimated emotions. Step 4: The decision-making department promotes flat decision-making. It supports the progress of meetings, fairly gathers everyone's opinions, and promotes consensus building. It uses generative AI to monitor the progress of meetings in real time and provide appropriate feedback. It can also estimate employee emotions and adjust decision-making methods based on the estimated emotions.

[0061] (Example 2) An AI organizational system according to an embodiment of the present invention addresses issues stemming from inefficient organizational structures and uniform educational systems in large Japanese companies. This AI organizational system utilizes generative AI to preserve the advantages of hierarchical organizations while incorporating the benefits of teal organizations, thereby drawing out employee autonomy and creativity and improving organizational productivity. It realizes a new workstyle in which each employee can achieve self-realization through collaboration with AI while contributing to organizational goals. For example, AI analyzes each employee's strengths and areas of interest and suggests optimal tasks and projects, promoting self-management. AI also increases transparency of information within the organization and stimulates communication among employees, eliminating silos between departments. AI mentors support the growth of each employee and clearly share organizational goals, aligning individual and organizational goals. Furthermore, AI facilitators promote flat decision-making and assemble optimal teams for each project, realizing a flexible organizational structure. AI coaches analyze employees' strengths and areas of interest and suggest optimal tasks and learning opportunities. AI mentors develop individual employees' career visions and learning plans, supporting their growth. The AI ​​Facilitator assists in meeting proceedings, fairly gathering everyone's opinions and promoting consensus building. The AI ​​Advisor aggregates and analyzes information within the organization and supports management decision-making. The AI ​​Coordinator assembles the optimal team for each project and flexibly reorganizes it according to the task. The AI ​​Monitor continuously monitors individual and team performance and provides appropriate feedback. The AI ​​Translator clearly explains management's policies to employees, helping them understand the organization's goals. The AI ​​Harmonizer detects emotional conflicts among employees and promotes constructive communication. The AI ​​Innovator collects and evaluates ideas from employees and provides promising suggestions to management. The AI ​​Organizer optimizes employees' working styles and work-life balance, improving productivity and happiness. In this way, the AI ​​organizational system aims to eliminate the inefficiency and rigidity prevalent in large Japanese companies and foster an organizational culture in which each employee can work autonomously and creatively.By realizing a new way of working in which AI and humans work together, we can dramatically improve organizational performance while increasing employee engagement and happiness. In the future, we hope that the AI ​​organizational system will become widespread in Japanese corporate society, accelerating the transformation to teal organizations. This will strengthen the international competitiveness of Japanese companies and stimulate innovation. Furthermore, by establishing a work style that allows for autonomy and creativity, it will also contribute to the realization of an inclusive society where diverse talent can thrive. The AI ​​organizational system will bring about change in Japan's organizational culture and be the first step toward opening up a sustainable and prosperous future. This will enable the AI ​​organizational system to bring out the autonomy and creativity of employees and improve productivity throughout the organization.

[0062] An AI organization system according to an embodiment includes an analysis unit, a proposal unit, a transparency unit, and a decision-making unit. The analysis unit analyzes employees' strengths and areas of interest. Employee strengths include, but are not limited to, technical skills and leadership abilities. The analysis unit collects data, such as employees' past work history, skill sets, and interests, and the generation AI analyzes the data. The generation AI can analyze employees' strengths and areas of interest using, for example, natural language processing or machine learning algorithms. The analysis unit can also estimate employees' emotions and adjust the timing of analysis based on the estimated employee emotions. For example, if an employee is stressed, the generation AI can delay the timing of analysis and perform the analysis in a relaxed state. Furthermore, if an employee is relaxed, the generation AI can immediately start analysis, allowing for efficient data collection. The proposal unit proposes optimal tasks and projects for each employee based on the data analyzed by the analysis unit. The proposal unit can propose optimal tasks and projects based on, for example, the employee's skill set and the importance of the project. The suggestion unit can also estimate an employee's emotions and adjust the way the suggestion is presented based on the estimated employee's emotions. For example, if an employee is feeling stressed, the generation AI can provide a simple and easy-to-understand suggestion. If an employee is feeling relaxed, the generation AI can provide a suggestion that includes detailed information. The transparency unit makes information within the organization transparent and stimulates communication between employees. The transparency unit collects information, such as the progress of work in each department and the progress of projects, and the generation AI analyzes it. The generation AI can make information transparent using, for example, natural language processing or machine learning algorithms. The transparency unit can also estimate an employee's emotions and adjust the way the information is displayed based on the estimated employee's emotions. For example, if an employee is feeling stressed, the generation AI can provide a simple, highly visible display. If an employee is relaxed, the generation AI can provide a display that includes detailed information. The decision-making unit promotes flat decision-making.The decision-making unit, for example, supports the progress of meetings, fairly gathers everyone's opinions, and promotes consensus building. The decision-making unit can use the generation AI to monitor the progress of meetings in real time and provide appropriate feedback. The decision-making unit can also estimate employees' emotions and adjust decision-making methods based on the estimated employee emotions. For example, if an employee is feeling stressed, the generation AI can provide a simple and quick decision-making method. On the other hand, if an employee is relaxed, the generation AI can provide a decision-making method based on detailed information. As a result, the AI ​​organization system according to the embodiment can bring out the autonomy and creativity of employees and improve the productivity of the entire organization.

[0063] The analysis unit can collect data on employees' past work history, skill sets, and interests and analyze it using the generative AI. For example, the analysis unit collects data on employees' past work history, skill sets, and interests and analyzes it using the generative AI. The generative AI can analyze employees' strengths and areas of interest using natural language processing or machine learning algorithms. For example, the generative AI can analyze employees' past project history and identify the factors behind successful projects. The generative AI can also analyze employees' skill sets and suggest optimal tasks and projects. Furthermore, the generative AI can analyze employees' interests and identify areas of interest and projects they want to work on. This allows for an accurate understanding of employees' strengths and areas of interest and suggests optimal tasks and projects. Some or all of the above-mentioned processing in the analysis unit is performed using the generative AI. For example, the analysis unit inputs data on employees' past work history, skill sets, and interests into the generative AI, which then outputs the analysis results.

[0064] The suggestion unit can suggest optimal tasks and projects for each employee based on the data analyzed by the analysis unit. The suggestion unit can suggest optimal tasks and projects based on, for example, the employee's skill set and the importance of the project. The suggestion unit can use a generation AI to suggest optimal tasks and projects based on the employee's strengths and areas of interest. For example, the generation AI can analyze the employee's skill set and identify tasks to which the employee can contribute most effectively. The generation AI can also analyze the importance of projects and suggest projects that are most important to the employee. Furthermore, the generation AI can analyze the employee's interests and suggest projects that the employee is interested in. This makes it possible to suggest optimal tasks and projects based on the employee's strengths and areas of interest. Some or all of the above-mentioned processing in the suggestion unit is performed using a generation AI. For example, the suggestion unit inputs the data analyzed by the analysis unit into the generation AI, which then suggests optimal tasks and projects.

[0065] The transparency department can collect information on the progress of work and projects in each department and analyze it using the generation AI. For example, the transparency department collects information on the progress of work and projects in each department and analyzes it using the generation AI. The generation AI can make information transparent using, for example, natural language processing or machine learning algorithms. For example, the generation AI can analyze the progress of work in each department and identify delays and problems. The generation AI can also analyze the progress of projects and identify risks and issues in the projects. Furthermore, the generation AI can eliminate information gaps between departments and stimulate communication between employees. This makes information within the organization transparent and stimulates communication between employees. Some or all of the above-mentioned processing in the transparency department is performed using the generation AI. For example, the transparency department inputs information on the progress of work and projects in each department into the generation AI, which then outputs the analysis results.

[0066] The decision-making unit can support the progress of meetings, fairly gather everyone's opinions, and promote consensus building. The decision-making unit can, for example, support the progress of meetings, fairly gather everyone's opinions, and promote consensus building. The decision-making unit can use generation AI to monitor the progress of meetings in real time and provide appropriate feedback. For example, the generation AI can analyze statements made during meetings in real time and automatically summarize the key points. The generation AI can also display the progress of meetings in real time and manage the timing and order of statements. Furthermore, the generation AI can automatically generate meeting minutes and distribute them to participants after the meeting. This promotes flat decision-making and realizes a flexible organizational structure. Some or all of the above-mentioned processes in the decision-making unit are performed using generation AI. For example, the decision-making unit inputs the progress of meetings into the generation AI, which then provides feedback in real time.

[0067] The Transparency Department can stimulate communication between employees and eliminate information gaps between departments. For example, the Transparency Department can stimulate communication between employees and eliminate information gaps between departments. The Transparency Department can use a generation AI to collect and analyze information on the progress of each department's work and projects. The generation AI can make information transparent using, for example, natural language processing or machine learning algorithms. For example, the generation AI can analyze the progress of each department's work and identify delays and problems. The generation AI can also analyze the progress of a project and identify project risks and issues. Furthermore, the generation AI can eliminate information gaps between departments and stimulate communication between employees. This can eliminate silos between departments and promote information sharing throughout the organization. Some or all of the above-mentioned processes in the Transparency Department are performed using a generation AI. For example, the Transparency Department inputs information on the progress of each department's work and projects into a generation AI, which then outputs the analysis results.

[0068] The decision-making unit can organize teams for each project and achieve a flexible organizational structure. For example, the decision-making unit can organize teams for each project and achieve a flexible organizational structure. The decision-making unit can use a generative AI to analyze project requirements and employee skill sets and organize an optimal team. For example, the generative AI can analyze project requirements and identify the required skill sets. The generative AI can also analyze employee skill sets and select the optimal members for the project. Furthermore, the generative AI can monitor the progress of the project and reorganize the team as necessary. This allows the optimal team to be organized for each project and achieve a flexible organizational structure. Some or all of the above-mentioned processing in the decision-making unit is performed using a generative AI. For example, the decision-making unit inputs data on project requirements and employee skill sets into the generative AI, which then organizes the optimal team.

[0069] The analysis unit can estimate the employee's emotions and adjust the timing of analysis based on the estimated employee emotions. The analysis unit, for example, estimates the employee's emotions and adjusts the timing of analysis based on the estimated employee emotions. The generation AI can estimate the employee's emotions using, for example, facial expression recognition or voice analysis. For example, if the employee is feeling stressed, the generation AI can delay the timing of analysis and perform the analysis in a relaxed state. Furthermore, if the employee is relaxed, the generation AI can immediately start the analysis and collect data efficiently. Furthermore, if the employee is in a hurry, the generation AI can quickly perform the analysis and provide results in a short time. This allows the timing of analysis to be adjusted according to the employee's emotions and collect data efficiently. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs employee emotion data into the generation AI, and the generation AI adjusts the timing of the analysis.

[0070] The analysis unit can analyze the employee's past project success rate and select an analysis method. The analysis unit, for example, analyzes the employee's past project success rate and selects the optimal analysis method. The generation AI can, for example, analyze the employee's past project success rate and select the most effective analysis method. For example, the generation AI can prioritize analysis of data on projects with a high employee success rate and identify success factors. The generation AI can also analyze in detail data on projects with a low employee success rate and extract areas for improvement. Furthermore, the generation AI can select the optimal analysis method based on the employee's project success rate. This makes it possible to select the optimal analysis method based on the employee's past project success rate. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on the employee's past project success rate into the generation AI, which selects the optimal analysis method.

[0071] The analysis unit can filter data based on the employee's current workload and stress level. The analysis unit filters data based on, for example, the employee's current workload and stress level. For example, if the employee's workload is high, the generation AI can filter out less important data and narrow the analysis target. Furthermore, if the employee's stress level is high, the generation AI can prioritize analysis of data for stress reduction. Furthermore, if the employee's workload is low, the generation AI can perform detailed data analysis and provide comprehensive results. This allows for filtering data according to the employee's workload and stress level, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on the employee's workload and stress level into the generation AI, which then filters the data.

[0072] The analysis unit can estimate an employee's emotions and prioritize the data to be analyzed based on the estimated employee emotions. The analysis unit, for example, estimates an employee's emotions and prioritizes the data to be analyzed based on the estimated employee emotions. The generation AI can estimate an employee's emotions using, for example, facial expression recognition or voice analysis. For example, if an employee is feeling stressed, the generation AI can prioritize analyzing data that helps reduce stress. Furthermore, if an employee is relaxed, the generation AI can prioritize analyzing data related to long-term projects. Furthermore, if an employee is in a hurry, the generation AI can prioritize analyzing data that will produce short-term results. This allows data to be prioritized according to the employee's emotions, enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs employee emotion data into the generation AI, which then prioritizes the data.

[0073] The analysis unit can prioritize analysis of highly relevant data based on the employee's geographic location information. For example, the analysis unit prioritizes analysis of highly relevant data based on the employee's geographic location information. For example, if the employee is in a specific area, the generation AI can prioritize analysis of data related to that area. Furthermore, if the employee is on a business trip, the generation AI can prioritize analysis of data from the business trip destination. Furthermore, if the employee is working remotely, the generation AI can prioritize analysis of data around the employee's home. This makes it possible to prioritize analysis of highly relevant data based on the employee's geographic location information. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on the employee's geographic location information into the generation AI, which then prioritizes analysis of highly relevant data.

[0074] The analysis unit can analyze employees' social media activities and collect related data. For example, the analysis unit analyzes employees' social media activities and collects related data. For example, the generation AI can analyze employees' social media activities and identify work-related trends. The generation AI can also collect information useful for work based on employees' social media posts. Furthermore, the generation AI can analyze employees' social media networks and identify experts related to work. This makes it possible to collect related data based on employees' social media activities and use it for analysis. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs data on employees' social media activities into the generation AI, which then collects related data.

[0075] The suggestion unit can estimate the employee's emotions and adjust the way the suggestion is expressed based on the estimated employee's emotions. The suggestion unit, for example, estimates the employee's emotions and adjusts the way the suggestion is expressed based on the estimated employee's emotions. The generation AI can estimate the employee's emotions using, for example, facial expression recognition or voice analysis. For example, if the employee is feeling stressed, the generation AI can make a simple and easy-to-understand suggestion. Furthermore, if the employee is relaxed, the generation AI can make a suggestion that includes detailed information. Furthermore, if the employee is in a hurry, the generation AI can make a short suggestion that focuses on the main points. This allows the suggestion's expression to be adjusted according to the employee's emotions, making it possible to make effective suggestions. Some or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs the employee's emotional data into the generation AI, which then adjusts the way the suggestion is expressed.

[0076] The suggestion unit can adjust the level of detail of the proposal based on the importance of the task or project. The suggestion unit adjusts the level of detail of the proposal based on, for example, the importance of the task or project. The generation AI can, for example, analyze the importance of the task or project and make detailed suggestions for tasks with high importance. The generation AI can also make concise suggestions for tasks with low importance. Furthermore, the generation AI can adjust the level of detail of the proposal according to the importance of the project. This allows the level of detail of the proposal to be adjusted according to the importance of the task or project, making it possible to make effective suggestions. Some or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs data on the importance of the task or project into the generation AI, and the generation AI adjusts the level of detail of the proposal.

[0077] The suggestion unit can apply a suggestion algorithm according to the category of the task or project. For example, the suggestion unit applies a suggestion algorithm according to the category of the task or project. For example, the generation AI can apply a creative suggestion algorithm to a creative project. Furthermore, the generation AI can apply a technical suggestion algorithm to a technical project. Furthermore, the generation AI can apply a suggestion algorithm specialized for marketing to a marketing project. This allows the optimal suggestion algorithm to be applied according to the category of the task or project, making effective suggestions. Some or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs data on the category of the task or project into the generation AI, and the generation AI applies the optimal suggestion algorithm.

[0078] The suggestion unit can estimate the employee's emotions and adjust the length of the suggestion based on the estimated employee's emotions. The suggestion unit, for example, estimates the employee's emotions and adjusts the length of the suggestion based on the estimated employee's emotions. The generation AI can estimate the employee's emotions using, for example, facial expression recognition or voice analysis. For example, if the employee is stressed, the generation AI can make a short, to-the-point suggestion. If the employee is relaxed, the generation AI can make a longer suggestion with detailed explanations. Furthermore, if the employee is in a hurry, the generation AI can make a short suggestion that can be quickly understood. This allows the length of the suggestion to be adjusted according to the employee's emotions, making it possible to make effective suggestions. Some or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs employee emotion data into the generation AI, which then adjusts the length of the suggestion.

[0079] The proposal unit can determine the priority of proposals based on the submission dates of tasks and projects. The proposal unit determines the priority of proposals based on, for example, the submission dates of tasks and projects. The generation AI can, for example, prioritize proposals for tasks with upcoming deadlines. The generation AI can also postpone proposals for projects with more distant submission dates. Furthermore, the generation AI can dynamically adjust the priority of proposals based on the submission dates. This makes it possible to determine the priority of proposals based on the submission dates of tasks and projects and make effective proposals. Some or all of the above-mentioned processing in the proposal unit is performed using the generation AI. For example, the proposal unit inputs data on the submission dates of tasks and projects into the generation AI, and the generation AI determines the priority of proposals.

[0080] The suggestion unit can adjust the order of proposals based on the relevance of tasks and projects. The suggestion unit adjusts the order of proposals based on, for example, the relevance of tasks and projects. The generation AI can, for example, prioritize proposals for highly relevant tasks. The generation AI can also postpone proposals for less relevant tasks. Furthermore, the generation AI can dynamically adjust the order of proposals based on the relevance of tasks and projects. This allows the order of proposals to be adjusted based on the relevance of tasks and projects, making effective proposals. Some or all of the above-mentioned processing in the suggestion unit is performed using the generation AI. For example, the suggestion unit inputs data on the relevance of tasks and projects into the generation AI, and the generation AI adjusts the order of proposals.

[0081] The transparency unit can estimate an employee's emotions and adjust the information display method based on the estimated employee emotions. The transparency unit, for example, estimates an employee's emotions and adjusts the information display method based on the estimated employee emotions. The generation AI can estimate an employee's emotions using, for example, facial expression recognition or voice analysis. For example, if an employee is feeling stressed, the generation AI can provide a simple, highly visible display method. Furthermore, if an employee is relaxed, the generation AI can provide a display method that includes detailed information. Furthermore, if an employee is in a hurry, the generation AI can provide a display method that focuses on the main points. This allows the information display method to be adjusted according to the employee's emotions, improving visibility. Some or all of the above-described processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs employee emotion data into the generation AI, which then adjusts the information display method.

[0082] The transparency unit can optimize the current information transparency method by referring to past information transparency data. The transparency unit, for example, optimizes the current information transparency method by referring to past information transparency data. The generation AI can, for example, select the optimal transparency method based on past information transparency data. The generation AI can also analyze past data and identify areas for improvement in the transparency method. Furthermore, the generation AI can optimize the current transparency method by referring to past transparency data. This allows the current information transparency method to be optimized based on past data, making it possible to provide effective information. Some or all of the above-mentioned processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs past information transparency data into the generation AI, which then optimizes the current transparency method.

[0083] The transparency unit can update the work progress status of each department in real time and provide the latest information. The transparency unit, for example, can update the work progress status of each department in real time and provide the latest information. The generation AI, for example, can collect the work progress status of each department in real time and provide the latest information. The generation AI can also update the project progress status in real time and provide the latest information. Furthermore, the generation AI can monitor the work progress status of each department in real time and provide the latest information. This makes it possible to update the work progress status of each department in real time and provide the latest information. Some or all of the above-mentioned processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs data on the work progress status of each department into the generation AI, and the generation AI provides the latest information.

[0084] The transparency unit can estimate the employee's emotions and prioritize information based on the estimated employee's emotions. The transparency unit, for example, can estimate the employee's emotions and prioritize information based on the estimated employee's emotions. The generation AI can estimate the employee's emotions using, for example, facial expression recognition or voice analysis. For example, if the employee is feeling stressed, the generation AI can prioritize displaying information of high importance. Furthermore, if the employee is relaxed, the generation AI can prioritize displaying detailed information. Furthermore, if the employee is in a hurry, the generation AI can prioritize displaying information that focuses on the main points. This allows information to be prioritized according to the employee's emotions, enabling effective information provision. Some or all of the above-described processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs employee emotion data into the generation AI, which then prioritizes the information.

[0085] The transparency unit can provide information based on the geographic distribution of each department. The transparency unit, for example, provides information based on the geographic distribution of each department. The generation AI, for example, can provide information by region, taking into account the geographic distribution of each department. The generation AI can also integrate information between geographically distant departments and provide it in a centralized manner. Furthermore, the generation AI can select the optimal information provision method based on the geographic distribution of each department. This allows information to be provided based on the geographic distribution of each department, enabling effective information sharing. Some or all of the above-mentioned processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs data on the geographic distribution of each department into the generation AI, and the generation AI provides the information.

[0086] The transparency unit can improve the accuracy of the information by referring to related external data. The transparency unit can, for example, improve the accuracy of the information by referring to related external data. The generation AI can, for example, improve the accuracy of the information by referring to external market data. The generation AI can also improve the accuracy of the information by referring to external competitive information. Furthermore, the generation AI can improve the accuracy of the information by referring to external economic data. This makes it possible to improve the accuracy of the information by referring to related external data. Some or all of the above-mentioned processing in the transparency unit is performed using the generation AI. For example, the transparency unit inputs external data into the generation AI, and the generation AI improves the accuracy of the information.

[0087] The decision-making unit can estimate the employee's emotions and adjust the decision-making method based on the estimated employee's emotions. The decision-making unit, for example, estimates the employee's emotions and adjusts the decision-making method based on the estimated employee's emotions. The generation AI can estimate the employee's emotions using, for example, facial expression recognition or voice analysis. For example, the generation AI can provide a simple and quick decision-making method if the employee is stressed. The generation AI can also provide a decision-making method based on detailed information if the employee is relaxed. Furthermore, the generation AI can provide a quick decision-making method if the employee is in a hurry. This allows the decision-making method to be adjusted according to the employee's emotions, making it possible to make effective decisions. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs employee emotion data into the generation AI, which then adjusts the decision-making method.

[0088] The decision-making unit can select the optimal decision-making method by referring to past decision-making data. The decision-making unit, for example, selects the optimal decision-making method by referring to past decision-making data. The generation AI can, for example, select the optimal decision-making method based on past successful decision-making data. The generation AI can also analyze past unsuccessful decision-making data and identify areas for improvement. Furthermore, the generation AI can optimize the current decision-making method by referring to past decision-making data. This makes it possible to select the optimal decision-making method based on past data and make effective decisions. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs past decision-making data into the generation AI, which then selects the optimal decision-making method.

[0089] The decision-making unit can monitor the progress of the meeting in real time and provide appropriate feedback. The decision-making unit can, for example, monitor the progress of the meeting in real time and provide appropriate feedback. The generation AI can, for example, monitor the progress of the meeting in real time and provide appropriate feedback. The generation AI can also aggregate opinions during the meeting in real time and support decision-making. Furthermore, the generation AI can analyze the progress of the meeting in real time and propose optimal decisions. This makes it possible to monitor the progress of the meeting in real time and provide appropriate feedback. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs data on the progress of the meeting into the generation AI, and the generation AI provides feedback.

[0090] The decision-making unit can estimate the employee's emotions and prioritize decision-making based on the estimated employee's emotions. The decision-making unit, for example, estimates the employee's emotions and prioritizes decision-making based on the estimated employee's emotions. The generation AI can estimate the employee's emotions using, for example, facial expression recognition or voice analysis. For example, if the employee is feeling stressed, the generation AI can prioritize decisions of higher importance. Furthermore, if the employee is relaxed, the generation AI can make decisions based on detailed information. Furthermore, if the employee is in a hurry, the generation AI can make decisions quickly. This allows decision-making to be prioritized according to the employee's emotions, resulting in effective decision-making. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs employee emotion data into the generation AI, which then prioritizes decision-making.

[0091] The decision-making unit can make decisions based on the geographic distribution of projects. The decision-making unit makes decisions based on, for example, the geographic distribution of projects. The generation AI can, for example, take the geographic distribution of projects into account and make optimal decisions for each region. The generation AI can also integrate information between geographically distant projects and make centralized decisions. Furthermore, the generation AI can select the optimal decision-making method based on the geographic distribution of projects. This allows optimal decisions to be made based on the geographic distribution of projects. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs data on the geographic distribution of projects into the generation AI, and the generation AI makes decisions.

[0092] The decision-making unit can improve the accuracy of decision-making by referring to relevant external data. The decision-making unit can, for example, improve the accuracy of decision-making by referring to relevant external data. The generation AI can, for example, improve the accuracy of decision-making by referring to external market data. The generation AI can also improve the accuracy of decision-making by referring to external competitive information. Furthermore, the generation AI can improve the accuracy of decision-making by referring to external economic data. This makes it possible to improve the accuracy of decision-making by referring to relevant external data. Some or all of the above-mentioned processing in the decision-making unit is performed using the generation AI. For example, the decision-making unit inputs external data into the generation AI, which then improves the accuracy of decision-making. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, suggestion unit, transparency unit, and decision-making unit, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit collects employee data using the camera 42 and microphone 38B of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit provides suggestions generated by the specific processing unit 290 of the data processing device 12 to the employees via the display 40A and speaker 40B of the smart device 14. The transparency unit displays information analyzed by the specific processing unit 290 of the data processing device 12 on the display 40A of the smart device 14. The decision-making unit supports the progress of the meeting via the control unit 46A of the smart device 14 and is monitored in real time by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, suggestion unit, transparency unit, and decision-making unit, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit collects employee data using the camera 42 and microphone 238 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit provides suggestions generated by the specific processing unit 290 of the data processing device 12 to the employee through the speaker 240 of the smart glasses 214. The transparency unit displays information analyzed by the specific processing unit 290 of the data processing device 12 on the display of the smart glasses 214. The decision-making unit supports the progress of the meeting via the control unit 46A of the smart glasses 214 and is monitored in real time by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, transparency unit, and decision-making unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit collects employee data using the camera 42 and microphone 238 of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit provides suggestions generated by the specific processing unit 290 of the data processing device 12 to the employees through the speaker 240 of the headset type terminal 314. The transparency unit displays information analyzed by the specific processing unit 290 of the data processing device 12 on the display 343 of the headset type terminal 314. The decision-making unit supports the progress of the meeting via the control unit 46A of the headset type terminal 314, and is monitored in real time by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, transparency unit, and decision-making unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the analysis unit collects employee data using the camera 42 and microphone 238 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit provides suggestions generated by the specific processing unit 290 of the data processing device 12 to the employees through the speaker 240 of the robot 414. The transparency unit displays information analyzed by the specific processing unit 290 of the data processing device 12 on the display of the robot 414. The decision-making unit supports the progress of the meeting through the control unit 46A of the robot 414 and is monitored in real time by the specific processing unit 290 of the data processing device 12.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis department can also collect employee health data and analyze it using generative AI. For example, data from employees' fitness trackers and smartwatches can be used to monitor employee health and estimate stress and fatigue levels. This can help adjust workloads based on employees' health status and prevent overwork. The suggestion department can also suggest refreshing breaks and exercise based on employees' health data. For example, if an employee is feeling highly stressed, the generative AI can suggest relaxing tasks or projects. Furthermore, the transparency department can aggregate employee health data and visualize the health status of the entire organization. This can strengthen health management across the organization and improve employee productivity.

[0095] The Suggestion Department can also suggest long-term tasks and projects based on employees' career goals. For example, if an employee wants to acquire a specific skill, the department can suggest a project that will utilize that skill. If an employee is aiming for a leadership position in the future, the department can suggest a task that will help them hone their leadership skills. Furthermore, the department can suggest appropriate mentors and training programs based on employees' career goals. This can support employees' career growth and improve the skill level of the entire organization.

[0096] To promote knowledge sharing within the organization, the Transparency Department can also create a database of employee expertise and experience and make it accessible to other employees. For example, it can aggregate information about employees with expertise in a particular technology or project, allowing other employees to search and use that information. The Transparency Department can also suggest relevant knowledge and resources based on the projects employees have previously participated in and the skills they have acquired. This can promote knowledge sharing within the organization and help employees improve their skills. The Transparency Department can also collect employee feedback and identify areas for improvement across the organization. This can increase organizational transparency and improve employee engagement.

[0097] The decision-making unit can also estimate employee emotions and adjust the decision-making process based on the estimated employee emotions. For example, if an employee is feeling stressed, the generative AI can make quick decisions to reduce the employee's burden. If an employee is relaxed, the generative AI can make more careful decisions based on more detailed information. Furthermore, if an employee is in a hurry, the generative AI can provide a way to make decisions quickly. This allows the decision-making process to be adjusted according to the employee's emotions, resulting in effective decision-making.

[0098] The analytics department can also analyze employees' social media activities to collect work-related trends and information. For example, it can analyze information and comments shared by employees on social media to identify trends and ideas that are useful for work. It can also analyze employees' social media networks to identify experts and resources related to work. It can also suggest work-related information based on employees' social media activities. This makes it possible to use employees' social media activities to collect work-related information and use it for analysis.

[0099] The suggestion unit can also estimate an employee's emotions and adjust the timing of suggestions based on the estimated employee emotions. For example, if an employee is feeling stressed, the generation AI can delay the timing of suggestions and make suggestions in a relaxed state. Also, if an employee is relaxed, it can make suggestions immediately to support efficient task management. Furthermore, if an employee is in a hurry, the generation AI can make suggestions quickly to support quick decision-making. This allows the timing of suggestions to be adjusted according to the employee's emotions, making effective suggestions.

[0100] The Transparency Department can also provide information based on the geographic distribution of employees. For example, providing regional information to employees working remotely can improve work efficiency. Providing information about business trip destinations to employees on business trips can also help ensure smooth work progress. Furthermore, by integrating and centralizing information between geographically separated departments, information sharing throughout the organization can be promoted. This allows for optimal information provision based on the geographic distribution of employees, achieving effective information sharing.

[0101] The decision-making unit can also estimate employee emotions and prioritize decision-making based on the estimated employee emotions. For example, if an employee is feeling stressed, the generative AI can prioritize more important decisions, reducing the employee's burden. Also, if an employee is relaxed, they can make more careful decisions by making decisions based on more detailed information. Furthermore, if an employee is in a hurry, the generative AI can provide a way to make decisions quickly. This allows decision-making to be prioritized according to employee emotions, resulting in effective decision-making.

[0102] The proposal department can also determine the priority of proposals based on the submission dates of tasks and projects. For example, by giving priority to proposals for tasks with upcoming deadlines, efficient task management can be supported. Also, by postponing proposals for projects with more distant submission dates, optimal resource allocation can be achieved. Furthermore, by dynamically adjusting the priority of proposals based on the submission dates, it is possible to flexibly respond to changing business environments. This allows for effective proposals to be made based on the submission dates of tasks and projects.

[0103] The Transparency Department can also improve the accuracy of information by referencing related external data. For example, by referencing external market data, the latest trends and movements related to business can be grasped. Furthermore, by referencing external competitor information, information can be provided for formulating competitive strategies. Furthermore, by referencing external economic data, business plans can be formulated according to economic conditions. In this way, the accuracy of information can be improved by utilizing related external data, and effective decision-making can be supported.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The analysis department analyzes employees' strengths and areas of interest. Data such as employees' past work history, skill sets, and interests is collected and analyzed by the generative AI. The generative AI uses natural language processing and machine learning algorithms to analyze employees' strengths and areas of interest. It can also estimate employees' emotions and adjust the timing of analysis based on the estimated emotions. Step 2: The proposal department suggests tasks and projects that are best suited to each employee based on the data analyzed by the analysis department. It makes suggestions based on the employee's skill set and the importance of the project, estimates the employee's emotions, and can also adjust the way the suggestions are presented based on the estimated emotions. Step 3: The Transparency Department makes information within the organization transparent and stimulates communication between employees. Information such as the progress of work and projects in each department is collected and analyzed by the Generative AI. It can also estimate employee emotions and adjust the way information is displayed based on the estimated emotions. Step 4: The decision-making department promotes flat decision-making. It supports the progress of meetings, fairly gathers everyone's opinions, and promotes consensus building. It uses generative AI to monitor the progress of meetings in real time and provide appropriate feedback. It can also estimate employee emotions and adjust decision-making methods based on the estimated emotions.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis department that analyzes employees' strengths and areas of interest; a proposal unit that proposes tasks and projects based on the data analyzed by the analysis unit; The Transparency Department, which makes information within the organization transparent and stimulates communication between employees, a decision-making unit that facilitates decision-making; A system characterized by:

2. The analysis unit Collect data on employees' past work history, skill sets, and interests, and analyze it using generative AI.

2. The system of claim 1.

3. The proposal unit Based on the data analyzed by the analytics department, tasks and projects are proposed to each employee.

2. The system of claim 1.

4. The transparent portion is Collect information on the progress of each department's work and projects, and analyze it using generative AI.

2. The system of claim 1.

5. The decision-making unit Support the progress of meetings, fairly gather everyone's opinions, and promote consensus building 2. The system of claim 1.

6. The transparent portion is Activate communication between employees and eliminate information gaps between departments 2. The system of claim 1.

7. The decision-making unit Organize teams for each project to achieve a flexible organizational structure 2. The system of claim 1.

8. The analysis unit Estimate employee sentiment and adjust analysis timing based on estimated employee sentiment 2. The system of claim 1.

Images