System

Abstract

An object of a system according to an embodiment is to quickly and accurately recommend personnel most suitable for a project.SOLUTION: A system includes a plan generation part, an employee list analysis part, and a personnel recommendation part. The proposal generator receives a prompt from a user and generates a proposal. The employee list analysis part analyzes the employee list. The personnel recommending section recommends personnel most suitable for the project based on the information analyzed by the employee list analyzing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have made it difficult to quickly and accurately find the best people for a project.

[0005] The system according to the embodiment aims to quickly and accurately recommend the most suitable personnel for a project. [Means for solving the problem]

[0006] The system according to the embodiment includes a proposal creation unit, an employee list analysis unit, and a talent recommendation unit. The proposal creation unit receives prompts from a user and creates a proposal. The employee list analysis unit analyzes the employee list. The talent recommendation unit recommends the most suitable talent for the project based on the information analyzed by the employee list analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately recommend the most suitable personnel for a project. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The talent matching support system according to an embodiment of the present invention is a system that supports talent matching specifically within the SB Group. This system not only uses generative AI to support the creation of proposals and drafts for new or existing projects and ideas, but also has the function of recommending the most suitable talent across departments for promoting and executing projects. As a result, the talent matching support system efficiently supports projects and ideas within the SB Group, from proposal creation to execution, and enables the recruitment of the most suitable talent across departments.

[0029] The talent matching support system according to the embodiment includes a proposal generation unit, an employee list analysis unit, and a talent recommendation unit. The proposal generation unit receives a prompt from a user and generates a proposal. For example, the generation AI creates a proposal or a rough draft based on a project or idea input by the user. The generation AI receives a prompt containing instructions from the user as input information and generates the content of the proposal based on the user's instructions. For example, if a prompt such as "Please create a proposal for a new marketing strategy" is input, the generation AI generates a specific proposal based on the instructions. The employee list analysis unit analyzes the employee list within the SB Group. For example, it analyzes employees' skill sets, past project experience, department information, etc. The generation AI analyzes the employee list and recommends talent best suited to the project. For example, if a prompt such as "This project requires marketing expertise" is input, the generation AI analyzes the employee list and recommends employees with marketing expertise. The talent recommendation unit recommends talent best suited to the project based on the information analyzed by the employee list analysis unit. For example, for a project requiring cooperation between the engineering and sales departments, the generation AI recommends appropriate talent from both departments. As a result, the talent matching support system according to the embodiment can provide consistent support from creating a project proposal to recommending talent.

[0030] The proposal generation unit can learn from a user's past proposals and automatically generate a proposal that matches the user's preferences and style. For example, the proposal generation unit uses a generation AI to store the user's past proposals in a database and learn the user's preferences and style based on that data. For example, for a user who prefers a specific format or wording, the unit automatically generates a proposal that reflects that style. The proposal generation unit also analyzes the content and structure of proposals created by the user in the past, and the generation AI learns the patterns. For example, the unit generates a proposal that reflects the order and content details of specific sections. The proposal generation unit also uses the generation AI to create a template that reflects the user's preferences and style based on the user's past proposals, and automatically generates a new proposal based on that template. For example, for a user who prefers a specific design or layout, the unit provides a proposal that reflects that style. This makes it possible to automatically generate proposals that match the user's preferences and style.

[0031] The proposal generation unit can propose the optimal proposal structure and content based on the project's purpose and goals. In the proposal generation unit, for example, the generation AI receives the project's purpose and goals as input information and proposes the optimal proposal structure based on that. For example, if the project goal is to increase sales, the generation AI proposes a structure and content appropriate for that goal. In addition, the proposal generation unit collects relevant data and information based on the project's purpose and goals and proposes the content of the proposal based on that. For example, the generation AI generates a proposal that reflects market research data and the results of competitive analysis. In addition, the proposal generation unit analyzes the project's purpose and goals, selects the optimal proposal template based on that, and proposes specific content based on that template. For example, if the project goal is to develop a new product, the generation AI generates a proposal using a template appropriate for that goal. This makes it possible to propose the optimal proposal based on the project's purpose and goals.

[0032] The proposal generation unit can support the creation of proposals in different languages ​​and enable international projects. For example, the proposal generation unit provides multilingual templates to support the generation AI in creating proposals in different languages. For example, it automatically generates proposals in multiple languages, such as English, French, and Chinese. To support the creation of proposals in different languages, the generation AI is equipped with an automatic translation function that translates content entered by the user in real time. For example, it translates content entered in Japanese into English to generate a proposal. To support the creation of proposals in different languages, the proposal generation unit learns specialized terminology and expressions specific to each language and generates an optimal proposal based on that. For example, it suggests content that takes into account the business terminology and cultural background of each language. This supports the creation of proposals in different languages ​​and enables the creation of proposals in international projects.

[0033] The proposal generation unit can automatically generate visual elements and create visually appealing proposals. In the proposal generation unit, for example, the generation AI automatically generates graphs and charts based on project data and incorporates them into the proposal. For example, it generates graphs that visually display sales data and market analysis results. In addition, to automatically generate visual elements, the generation AI provides design templates and creates graphs and charts based on the template selected by the user. For example, a proposal is created using a design template for presentations. In addition, in order for the generation AI to automatically generate visual elements, the proposal generation unit analyzes data entered by the user and suggests optimal graphs and charts based on that. For example, it selects bar graphs or pie charts depending on the type of data and purpose. This makes it possible to create visually appealing proposals.

[0034] The employee list analysis unit can make recommendations by taking into account not only an employee's skill set and experience, but also their individual career goals and interests. For example, the generation AI analyzes an employee's skill set and experience, and then makes recommendations by taking into account their individual career goals and interests. For example, the employee list analysis unit recommends employees who have marketing skills and want to take on new projects. The employee list analysis unit also registers employees' career goals and interests in a database, and the generation AI recommends the most suitable candidates based on that information. For example, an employee who wants to demonstrate leadership in the future may be recommended as a project leader. The generation AI also develops a recommendation algorithm that takes into account an employee's skill set and experience, as well as their career goals and interests, and then recommends the most suitable candidates based on that algorithm. For example, an employee who is interested in a particular field may be recommended for a project in that field. This makes it possible to make recommendations that take into account an employee's career goals and interests.

[0035] The employee list analysis unit can analyze employees' performance data in past projects and recommend the most suitable personnel. For example, the generation AI analyzes employees' performance data in past projects and recommends the most suitable personnel based on the results. For example, it prioritizes recommending employees who have demonstrated high performance in the past. The employee list analysis unit also stores employee performance data in a database and develops an algorithm that allows the generation AI to recommend the most suitable personnel based on that data. For example, it recommends employees with specific skill sets and experience. The employee list analysis unit also builds a system that uses the generation AI to analyze employees' performance data in past projects and recommends the most suitable personnel based on the results. For example, it makes recommendations based on project success rate and contribution level. This makes it possible to recommend the most suitable personnel based on past performance data.

[0036] The employee list analysis unit analyzes employee networking data both inside and outside the company, and can recommend personnel with connections that are beneficial to the project. For example, the employee list analysis unit uses a generation AI to analyze employee networking data both inside and outside the company and recommend personnel with connections that are beneficial to the project. For example, it may recommend employees with connections in a specific industry or field. The employee list analysis unit also stores employee networking data in a database, and the generation AI develops an algorithm to recommend the most suitable personnel based on that data. For example, it may recommend employees with high influence within the industry. The employee list analysis unit also uses a generation AI to analyze employee networking data, and builds a system that recommends personnel with connections that are beneficial to the project based on the results. For example, it may recommend employees with connections in a specific field. This makes it possible to recommend personnel with connections that are beneficial to the project.

[0037] The employee list analysis unit analyzes employees' learning history and self-development activities, and can recommend personnel with the latest knowledge and skills. For example, the generation AI analyzes employees' learning history and self-development activities, and based on the results, recommends personnel with the latest knowledge and skills. For example, recommendations are made based on recent training and qualifications. The employee list analysis unit also stores employees' learning history and self-development activities in a database, and the generation AI develops an algorithm to recommend the most suitable personnel based on that data. For example, it recommends employees with the latest technology and knowledge. The employee list analysis unit also builds a system in which the generation AI analyzes employees' learning history and self-development activities, and based on the results, recommends personnel with the latest knowledge and skills. For example, it recommends employees who have taken recent training or courses. This makes it possible to recommend personnel with the latest knowledge and skills.

[0038] The talent recommendation department can analyze inter-departmental cooperative relationships and past success stories to propose optimal inter-departmental talent combinations. For example, the talent recommendation department uses a generation AI to store inter-departmental cooperative relationships and past success stories in a database and propose optimal inter-departmental talent combinations based on that data. For example, the talent recommendation department proposes similar combinations based on data from successful projects in the past. The talent recommendation department can also analyze inter-departmental cooperative relationships and success stories, and develop an algorithm that uses the generation AI to propose optimal talent combinations based on the results. For example, the talent recommendation department can make proposals based on cases where cooperation between specific departments was effective. The talent recommendation department can also build a system in which the generation AI analyzes inter-departmental cooperative relationships and success stories and proposes optimal talent combinations based on the results. For example, for a project that requires cooperation between the technical and sales departments, the department can recommend appropriate talent from both departments. This makes it possible to propose optimal inter-departmental talent combinations.

[0039] The talent recommendation department can recommend talent who can communicate smoothly, taking into account differences in culture and working styles between departments. For example, the talent recommendation department uses a generation AI to store differences in culture and working styles between departments in a database, and then recommends talent who can communicate smoothly based on that data. For example, it recommends talent who can adapt to the culture of a specific department. The talent recommendation department can also analyze differences in culture and working styles between departments, and the generation AI can develop an algorithm to recommend the most suitable talent based on the results. For example, it can prioritize recommending talent with strong communication skills. The talent recommendation department can also build a system where the generation AI analyzes differences in culture and working styles between departments, and then recommends talent who can communicate smoothly based on the results. For example, it can recommend appropriate talent for projects that require cooperation between different departments. This makes it possible to recommend talent who can communicate smoothly.

[0040] The talent recommendation department can analyze the success factors of cross-departmental projects and recommend talent based on those factors. For example, the talent recommendation department uses a generation AI to store the success factors of cross-departmental projects in a database and recommend the most suitable talent based on that data. For example, it can recommend talent with similar factors based on data from past successful projects. The talent recommendation department can also analyze the success factors of cross-departmental projects and develop an algorithm where the generation AI can recommend the most suitable talent based on the results. For example, it can recommend talent with specific skill sets or experience. The talent recommendation department can also build a system where the generation AI can analyze the success factors of cross-departmental projects and recommend the most suitable talent based on the results. For example, for a project that requires cooperation from the technical and sales departments, it can recommend appropriate talent from both departments. This makes it possible to recommend talent based on success factors.

[0041] The talent recommendation department visualizes the resources and skill sets for each department and can propose optimal resource allocation. For example, the talent recommendation department uses a generation AI to store the resources and skill sets for each department in a database and then proposes optimal resource allocation based on that data. For example, it recommends talent with the skill sets required for a specific project. The talent recommendation department also analyzes the resources and skill sets for each department and develops an algorithm that uses the generation AI to propose optimal resource allocation based on the results. For example, it recommends appropriate talent for departments that are short of resources. The talent recommendation department also builds a system that uses a generation AI to visualize the resources and skill sets for each department and then proposes optimal resource allocation based on the results. For example, it recommends talent from departments that have the resources required for a project. This makes it possible to propose optimal resource allocation.

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

[0043] The talent matching support system can further include a progress management section that monitors project progress in real time and reallocates talent as necessary. For example, if a project is behind schedule, the progress management section will recommend additional talent with appropriate skills. The progress management section also analyzes the project's progress, and the generation AI proposes optimal resource allocation. For example, if a specific task is delayed, the generation AI will propose allocating additional resources to that task. The progress management section also uses the generation AI to predict the project's success probability based on the project's progress, and makes necessary adjustments based on that result. For example, if the project's success probability is low, the generation AI will propose reallocating resources or changing task priorities. This enables flexible talent allocation according to the project's progress.

[0044] The talent matching support system can further include a health management department that monitors employees' health and recommends talent based on their health status. For example, it analyzes employees' health data and prioritizes recommending employees in good health. The health management department also develops an algorithm that allows the generation AI to recommend the most suitable talent based on the employee's health status. For example, it may recommend employees with low stress levels. The health management department also monitors employees' health status in real time, and the generation AI makes necessary adjustments based on the results. For example, it may suggest time off for employees whose health is deteriorating. This makes it possible to recommend talent that takes into account the employee's health status.

[0045] The talent matching support system can further include a learning analysis unit that analyzes employees' learning history and self-development activities to recommend talent with the latest knowledge and skills. For example, recommendations can be made based on recent training or qualifications. The learning analysis unit also stores employees' learning history and self-development activities in a database, and the generation AI develops an algorithm to recommend the most suitable talent based on that data. For example, it can recommend employees with the latest technology and knowledge. The learning analysis unit also uses the generation AI to analyze employees' learning history and self-development activities, and builds a system that recommends talent with the latest knowledge and skills based on the results. For example, it can recommend employees who have taken recent training or courses. This makes it possible to recommend talent with the latest knowledge and skills.

[0046] The talent matching support system can further include a network analysis unit that analyzes employees' networking data both inside and outside the company and recommends talent with connections that are beneficial to the project. For example, it can recommend employees with connections in a specific industry or field. The network analysis unit also stores employees' networking data in a database, and the generation AI develops an algorithm that recommends the most suitable talent based on that data. For example, it can recommend employees with a high level of influence within the industry. The network analysis unit also uses the generation AI to analyze employees' networking data and build a system that recommends talent with connections that are beneficial to the project based on the results. For example, it can recommend employees with connections in a specific field. This makes it possible to recommend talent with connections that are beneficial to the project.

[0047] The talent matching support system can further be equipped with a resource management section that visualizes the resources and skill sets of each department and proposes optimal resource allocation. For example, it can recommend talent with the skill sets required for a specific project. The resource management section can also analyze the resources and skill sets of each department, and develop an algorithm that uses the generative AI to propose optimal resource allocation based on the results. For example, it can recommend appropriate talent to departments that are short of resources. The resource management section can also build a system that uses the generative AI to visualize the resources and skill sets of each department and propose optimal resource allocation based on the results. For example, it can recommend talent from departments that have the resources required for a project. This makes it possible to propose optimal resource allocation.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The proposal generation unit receives a prompt from the user and generates a proposal. For example, the generation AI creates a proposal or rough draft based on the project or idea entered by the user. The generation AI receives a prompt containing instructions from the user as input information and generates the contents of the proposal based on that. For example, if the prompt is "Please create a proposal for a new marketing strategy," the generation AI will generate a specific proposal based on that instruction. Step 2: The employee list analysis unit analyzes the employee list within the SB Group. For example, it analyzes employee skill sets, past project experience, department information, etc. The generation AI analyzes the employee list and recommends the best personnel for the project. For example, if a prompt including the instruction "This project requires marketing expertise" is input, the generation AI analyzes the employee list and recommends employees with marketing expertise. Step 3: The Talent Recommendation Department recommends the best talent for the project based on the information analyzed by the Employee List Analysis Department. For example, for a project that requires cooperation from the engineering and sales departments, the department will recommend the most appropriate talent from both departments.

[0050] (Example 2) The talent matching support system according to an embodiment of the present invention is a system that supports talent matching specifically within the SB Group. This system not only uses generative AI to support the creation of proposals and drafts for new or existing projects and ideas, but also has the function of recommending the most suitable talent across departments for promoting and executing projects. As a result, the talent matching support system efficiently supports projects and ideas within the SB Group, from proposal creation to execution, and enables the recruitment of the most suitable talent across departments.

[0051] The talent matching support system according to the embodiment includes a proposal generation unit, an employee list analysis unit, and a talent recommendation unit. The proposal generation unit receives a prompt from a user and generates a proposal. For example, the generation AI creates a proposal or a rough draft based on a project or idea input by the user. The generation AI receives a prompt containing instructions from the user as input information and generates the content of the proposal based on the user's instructions. For example, if a prompt such as "Please create a proposal for a new marketing strategy" is input, the generation AI generates a specific proposal based on the instructions. The employee list analysis unit analyzes the employee list within the SB Group. For example, it analyzes employees' skill sets, past project experience, department information, etc. The generation AI analyzes the employee list and recommends talent best suited to the project. For example, if a prompt such as "This project requires marketing expertise" is input, the generation AI analyzes the employee list and recommends employees with marketing expertise. The talent recommendation unit recommends talent best suited to the project based on the information analyzed by the employee list analysis unit. For example, for a project requiring cooperation between the engineering and sales departments, the generation AI recommends appropriate talent from both departments. As a result, the talent matching support system according to the embodiment can provide consistent support from creating a project proposal to recommending talent.

[0052] The proposal generation unit can learn from a user's past proposals and automatically generate a proposal that matches the user's preferences and style. For example, the proposal generation unit uses a generation AI to store the user's past proposals in a database and learn the user's preferences and style based on that data. For example, for a user who prefers a specific format or wording, the unit automatically generates a proposal that reflects that style. The proposal generation unit also analyzes the content and structure of proposals created by the user in the past, and the generation AI learns the patterns. For example, the unit generates a proposal that reflects the order and content details of specific sections. The proposal generation unit also uses the generation AI to create a template that reflects the user's preferences and style based on the user's past proposals, and automatically generates a new proposal based on that template. For example, for a user who prefers a specific design or layout, the unit provides a proposal that reflects that style. This makes it possible to automatically generate proposals that match the user's preferences and style.

[0053] The proposal generation unit can propose the optimal proposal structure and content based on the project's purpose and goals. In the proposal generation unit, for example, the generation AI receives the project's purpose and goals as input information and proposes the optimal proposal structure based on that. For example, if the project goal is to increase sales, the generation AI proposes a structure and content appropriate for that goal. In addition, the proposal generation unit collects relevant data and information based on the project's purpose and goals and proposes the content of the proposal based on that. For example, the generation AI generates a proposal that reflects market research data and the results of competitive analysis. In addition, the proposal generation unit analyzes the project's purpose and goals, selects the optimal proposal template based on that, and proposes specific content based on that template. For example, if the project goal is to develop a new product, the generation AI generates a proposal using a template appropriate for that goal. This makes it possible to propose the optimal proposal based on the project's purpose and goals.

[0054] The proposal generation unit can use the emotion estimation function to analyze the user's emotional state and generate proposal content that will inspire the user with the most positive emotions. For example, the proposal generation unit can use the emotion estimation function to analyze the user's emotional state in real time when creating a proposal and generate content that will elicit positive emotions. For example, if the user is feeling stressed, the proposal generation unit can suggest content that will help the user relax. The proposal generation unit can also suggest specific content and expressions that will elicit positive emotions for the generation AI based on the user's emotional state. For example, the proposal generation unit can generate a proposal that includes success stories and positive messages that will make the user feel happy. The proposal generation unit can also use the emotion estimation function to analyze the user's emotional state and, based on the results, select templates and structures that will elicit positive emotions for the generation AI. For example, the proposal generation unit can suggest a proposal with a simple, easy-to-understand structure that will give the user a sense of security. This allows the generation of a proposal that will inspire positive emotions for the user.

[0055] The proposal generation unit can support the creation of proposals in different languages ​​and enable international projects. For example, the proposal generation unit provides multilingual templates to support the generation AI in creating proposals in different languages. For example, it automatically generates proposals in multiple languages, such as English, French, and Chinese. To support the creation of proposals in different languages, the generation AI is equipped with an automatic translation function that translates content entered by the user in real time. For example, it translates content entered in Japanese into English to generate a proposal. To support the creation of proposals in different languages, the proposal generation unit learns specialized terminology and expressions specific to each language and generates an optimal proposal based on that. For example, it suggests content that takes into account the business terminology and cultural background of each language. This supports the creation of proposals in different languages ​​and enables the creation of proposals in international projects.

[0056] The proposal generation unit can automatically generate visual elements and create visually appealing proposals. In the proposal generation unit, for example, the generation AI automatically generates graphs and charts based on project data and incorporates them into the proposal. For example, it generates graphs that visually display sales data and market analysis results. In addition, to automatically generate visual elements, the generation AI provides design templates and creates graphs and charts based on the template selected by the user. For example, a proposal is created using a design template for presentations. In addition, in order for the generation AI to automatically generate visual elements, the proposal generation unit analyzes data entered by the user and suggests optimal graphs and charts based on that. For example, it selects bar graphs or pie charts depending on the type of data and purpose. This makes it possible to create visually appealing proposals.

[0057] The proposal generation unit uses the emotion estimation function to provide feedback on the user's emotional response to the proposal content in real time, and can adjust the content based on the results. For example, the proposal generation unit uses the emotion estimation function to analyze the user's emotional response in real time when reviewing the proposal content, and adjust the content based on the results. For example, if the user is feeling anxious, the proposal generation unit makes a suggestion to revise that part. The proposal generation unit also makes specific suggestions for the generation AI to adjust the proposal content based on the user's emotional response. For example, it suggests expressions and structures that will make the user feel positive. The proposal generation unit also uses the emotion estimation function to provide feedback on the user's emotional response in real time, and the generation AI automatically adjusts the proposal content based on the results. For example, it adds success stories that make the user feel happy. This allows the proposal content to be adjusted based on the user's emotional response.

[0058] The employee list analysis unit can make recommendations by taking into account not only an employee's skill set and experience, but also their individual career goals and interests. For example, the generation AI analyzes an employee's skill set and experience, and then makes recommendations by taking into account their individual career goals and interests. For example, the employee list analysis unit recommends employees who have marketing skills and want to take on new projects. The employee list analysis unit also registers employees' career goals and interests in a database, and the generation AI recommends the most suitable candidates based on that information. For example, an employee who wants to demonstrate leadership in the future may be recommended as a project leader. The generation AI also develops a recommendation algorithm that takes into account an employee's skill set and experience, as well as their career goals and interests, and then recommends the most suitable candidates based on that algorithm. For example, an employee who is interested in a particular field may be recommended for a project in that field. This makes it possible to make recommendations that take into account an employee's career goals and interests.

[0059] The employee list analysis unit can analyze employees' performance data in past projects and recommend the most suitable personnel. For example, the generation AI analyzes employees' performance data in past projects and recommends the most suitable personnel based on the results. For example, it prioritizes recommending employees who have demonstrated high performance in the past. The employee list analysis unit also stores employee performance data in a database and develops an algorithm that allows the generation AI to recommend the most suitable personnel based on that data. For example, it recommends employees with specific skill sets and experience. The employee list analysis unit also builds a system that uses the generation AI to analyze employees' performance data in past projects and recommends the most suitable personnel based on the results. For example, it makes recommendations based on project success rate and contribution level. This makes it possible to recommend the most suitable personnel based on past performance data.

[0060] The employee list analysis unit uses the emotion estimation function to analyze the emotional state of the recommended personnel and can prioritize recommending personnel who are highly motivated for the project. The employee list analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the recommended personnel in real time and prioritize recommending personnel who are highly motivated for the project. For example, it recommends employees with positive emotions. The employee list analysis unit also uses the emotion estimation function to allow the generation AI to identify personnel who are highly motivated for the project based on the emotional state of the recommended personnel and prioritize recommend those personnel. For example, it recommends employees with high emotion scores. The employee list analysis unit also uses the emotion estimation function to analyze the emotional state of the recommended personnel and builds a system in which the generation AI recommends the most suitable personnel based on the results. For example, it prioritizes recommending personnel who are highly motivated for the project. This allows highly motivated personnel to be recommended preferentially.

[0061] The employee list analysis unit analyzes employee networking data both inside and outside the company, and can recommend personnel with connections that are beneficial to the project. For example, the employee list analysis unit uses a generation AI to analyze employee networking data both inside and outside the company and recommend personnel with connections that are beneficial to the project. For example, it may recommend employees with connections in a specific industry or field. The employee list analysis unit also stores employee networking data in a database, and the generation AI develops an algorithm to recommend the most suitable personnel based on that data. For example, it may recommend employees with high influence within the industry. The employee list analysis unit also uses a generation AI to analyze employee networking data, and builds a system that recommends personnel with connections that are beneficial to the project based on the results. For example, it may recommend employees with connections in a specific field. This makes it possible to recommend personnel with connections that are beneficial to the project.

[0062] The employee list analysis unit analyzes employees' learning history and self-development activities, and can recommend personnel with the latest knowledge and skills. For example, the generation AI analyzes employees' learning history and self-development activities, and based on the results, recommends personnel with the latest knowledge and skills. For example, recommendations are made based on recent training and qualifications. The employee list analysis unit also stores employees' learning history and self-development activities in a database, and the generation AI develops an algorithm to recommend the most suitable personnel based on that data. For example, it recommends employees with the latest technology and knowledge. The employee list analysis unit also builds a system in which the generation AI analyzes employees' learning history and self-development activities, and based on the results, recommends personnel with the latest knowledge and skills. For example, it recommends employees who have taken recent training or courses. This makes it possible to recommend personnel with the latest knowledge and skills.

[0063] The employee list analysis unit uses the emotion estimation function to analyze how the recommended personnel feel about the project and can recommend personnel who are emotionally compatible. For example, the employee list analysis unit uses the emotion estimation function to analyze in real time how the recommended personnel feel about the project and recommend emotionally compatible personnel based on the results. For example, it prioritizes recommending employees with positive emotions. The employee list analysis unit also identifies the personnel who are most compatible with the project based on the emotional state of the recommended personnel, and recommends those personnel. For example, it recommends employees with high emotion scores. The employee list analysis unit also uses the emotion estimation function to analyze how the recommended personnel feel about the project, and builds a system in which the generation AI recommends emotionally compatible personnel based on the results. For example, it prioritizes recommending personnel who are highly motivated about the project. This makes it possible to recommend emotionally compatible personnel.

[0064] The talent recommendation department can analyze inter-departmental cooperative relationships and past success stories to propose optimal inter-departmental talent combinations. For example, the talent recommendation department uses a generation AI to store inter-departmental cooperative relationships and past success stories in a database and propose optimal inter-departmental talent combinations based on that data. For example, the talent recommendation department proposes similar combinations based on data from successful projects in the past. The talent recommendation department can also analyze inter-departmental cooperative relationships and success stories, and develop an algorithm that uses the generation AI to propose optimal talent combinations based on the results. For example, the talent recommendation department can make proposals based on cases where cooperation between specific departments was effective. The talent recommendation department can also build a system in which the generation AI analyzes inter-departmental cooperative relationships and success stories and proposes optimal talent combinations based on the results. For example, for a project that requires cooperation between the technical and sales departments, the department can recommend appropriate talent from both departments. This makes it possible to propose optimal inter-departmental talent combinations.

[0065] The talent recommendation department can recommend talent who can communicate smoothly, taking into account differences in culture and working styles between departments. For example, the talent recommendation department uses a generation AI to store differences in culture and working styles between departments in a database, and then recommends talent who can communicate smoothly based on that data. For example, it recommends talent who can adapt to the culture of a specific department. The talent recommendation department can also analyze differences in culture and working styles between departments, and the generation AI can develop an algorithm to recommend the most suitable talent based on the results. For example, it can prioritize recommending talent with strong communication skills. The talent recommendation department can also build a system where the generation AI analyzes differences in culture and working styles between departments, and then recommends talent who can communicate smoothly based on the results. For example, it can recommend appropriate talent for projects that require cooperation between different departments. This makes it possible to recommend talent who can communicate smoothly.

[0066] The talent recommendation unit uses the emotion estimation function to analyze the emotional compatibility of talent across departments, and can build an emotionally harmonious team. For example, the talent recommendation unit uses the emotion estimation function to analyze the emotional compatibility of talent across departments in real time, and builds an emotionally harmonious team based on the results. For example, it preferentially recommends talent with positive emotions. The talent recommendation unit also identifies emotionally harmonious teams using a generation AI based on the emotional states of talent across departments, and recommends those talent. For example, it recommends talent with high emotion scores. The talent recommendation unit also uses the emotion estimation function to analyze the emotional compatibility of talent across departments, and builds a system in which a generation AI builds an emotionally harmonious team based on the results. For example, it preferentially recommends talent who are highly motivated for a project. This makes it possible to build an emotionally harmonious team.

[0067] The talent recommendation department can analyze the success factors of cross-departmental projects and recommend talent based on those factors. For example, the talent recommendation department uses a generation AI to store the success factors of cross-departmental projects in a database and recommend the most suitable talent based on that data. For example, it can recommend talent with similar factors based on data from past successful projects. The talent recommendation department can also analyze the success factors of cross-departmental projects and develop an algorithm where the generation AI can recommend the most suitable talent based on the results. For example, it can recommend talent with specific skill sets or experience. The talent recommendation department can also build a system where the generation AI can analyze the success factors of cross-departmental projects and recommend the most suitable talent based on the results. For example, for a project that requires cooperation from the technical and sales departments, it can recommend appropriate talent from both departments. This makes it possible to recommend talent based on success factors.

[0068] The talent recommendation department visualizes the resources and skill sets for each department and can propose optimal resource allocation. For example, the talent recommendation department uses a generation AI to store the resources and skill sets for each department in a database and then proposes optimal resource allocation based on that data. For example, it recommends talent with the skill sets required for a specific project. The talent recommendation department also analyzes the resources and skill sets for each department and develops an algorithm that uses the generation AI to propose optimal resource allocation based on the results. For example, it recommends appropriate talent for departments that are short of resources. The talent recommendation department also builds a system that uses a generation AI to visualize the resources and skill sets for each department and then proposes optimal resource allocation based on the results. For example, it recommends talent from departments that have the resources required for a project. This makes it possible to propose optimal resource allocation.

[0069] The talent recommendation unit uses the emotion estimation function to monitor in real time how talent from across departments feels about the project and makes adjustments as needed. The talent recommendation unit, for example, uses the emotion estimation function to monitor in real time how talent from across departments feels about the project and makes adjustments as needed based on the results. For example, it provides support to talent with negative emotions. The talent recommendation unit also uses the generation AI to identify the emotional suitability for the project based on the emotional states of talent from across departments and makes adjustments as needed based on the results. For example, it provides feedback to talent with low emotion scores. The talent recommendation unit also uses the emotion estimation function to monitor in real time how talent from across departments feels about the project and builds a system in which the generation AI makes adjustments as needed based on the results. For example, it provides support to talent with low motivation for the project. This makes it possible to monitor emotions toward the project in real time and make adjustments as needed.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The talent matching support system can further include a progress management section that monitors project progress in real time and reallocates talent as necessary. For example, if a project is behind schedule, the progress management section will recommend additional talent with appropriate skills. The progress management section also analyzes the project's progress, and the generation AI proposes optimal resource allocation. For example, if a specific task is delayed, the generation AI will propose allocating additional resources to that task. The progress management section also uses the generation AI to predict the project's success probability based on the project's progress, and makes necessary adjustments based on that result. For example, if the project's success probability is low, the generation AI will propose reallocating resources or changing task priorities. This enables flexible talent allocation according to the project's progress.

[0072] The talent matching support system can further include a health management department that monitors employees' health and recommends talent based on their health status. For example, it analyzes employees' health data and prioritizes recommending employees in good health. The health management department also develops an algorithm that allows the generation AI to recommend the most suitable talent based on the employee's health status. For example, it may recommend employees with low stress levels. The health management department also monitors employees' health status in real time, and the generation AI makes necessary adjustments based on the results. For example, it may suggest time off for employees whose health is deteriorating. This makes it possible to recommend talent that takes into account the employee's health status.

[0073] The talent matching support system can further include a learning analysis unit that analyzes employees' learning history and self-development activities to recommend talent with the latest knowledge and skills. For example, recommendations can be made based on recent training or qualifications. The learning analysis unit also stores employees' learning history and self-development activities in a database, and the generation AI develops an algorithm to recommend the most suitable talent based on that data. For example, it can recommend employees with the latest technology and knowledge. The learning analysis unit also uses the generation AI to analyze employees' learning history and self-development activities, and builds a system that recommends talent with the latest knowledge and skills based on the results. For example, it can recommend employees who have taken recent training or courses. This makes it possible to recommend talent with the latest knowledge and skills.

[0074] The talent matching support system can further include a network analysis unit that analyzes employees' networking data both inside and outside the company and recommends talent with connections that are beneficial to the project. For example, it can recommend employees with connections in a specific industry or field. The network analysis unit also stores employees' networking data in a database, and the generation AI develops an algorithm that recommends the most suitable talent based on that data. For example, it can recommend employees with a high level of influence within the industry. The network analysis unit also uses the generation AI to analyze employees' networking data and build a system that recommends talent with connections that are beneficial to the project based on the results. For example, it can recommend employees with connections in a specific field. This makes it possible to recommend talent with connections that are beneficial to the project.

[0075] The talent matching support system can further be equipped with a resource management section that visualizes the resources and skill sets of each department and proposes optimal resource allocation. For example, it can recommend talent with the skill sets required for a specific project. The resource management section can also analyze the resources and skill sets of each department, and develop an algorithm that uses the generative AI to propose optimal resource allocation based on the results. For example, it can recommend appropriate talent to departments that are short of resources. The resource management section can also build a system that uses the generative AI to visualize the resources and skill sets of each department and propose optimal resource allocation based on the results. For example, it can recommend talent from departments that have the resources required for a project. This makes it possible to propose optimal resource allocation.

[0076] The talent matching support system can further use an emotion estimation function to analyze the user's emotional state and generate proposal content that will inspire the user with the most positive emotions. For example, the emotion estimation function can be used to analyze the user's emotional state in real time when creating a proposal, generating content that will elicit positive emotions. For example, if the user is feeling stressed, the system can suggest content that will help the user relax. The proposal generation unit also suggests specific content and expressions that will elicit positive emotions from the generation AI based on the user's emotional state. For example, the system can generate proposals that include success stories and positive messages that will make the user feel happy. The proposal generation unit also uses the emotion estimation function to analyze the user's emotional state, and based on the results, selects templates and structures that will elicit positive emotions from the generation AI. For example, the system can suggest proposals with simple, easy-to-understand structures that will give the user a sense of security. This allows the system to generate proposals that inspire positive emotions in the user.

[0077] The talent matching support system can further use an emotion estimation function to provide real-time feedback on the user's emotional reactions to the contents of the proposal, and adjust the content based on the results. For example, the emotion estimation function can be used to analyze the user's emotional reactions in real time when reviewing the contents of the proposal, and adjust the content based on the results. For example, if the user is feeling anxious, the system can suggest modifying that part. The proposal generation unit can also provide specific suggestions for the generation AI to adjust the content of the proposal based on the user's emotional reactions. For example, the system can suggest expressions and structures that will make the user feel positive. The proposal generation unit can also provide real-time feedback on the user's emotional reactions using the emotion estimation function, and the generation AI can automatically adjust the content of the proposal based on the results. For example, the system can add success stories that make the user feel happy. This allows the content of the proposal to be adjusted based on the user's emotional reactions.

[0078] The talent matching support system can further use an emotion estimation function to analyze the emotional state of recommended talent and prioritize recommending talent who are highly motivated for the project. For example, the emotion estimation function can be used to analyze the emotional state of recommended talent in real time and prioritize recommending talent who are highly motivated for the project. For example, employees with positive emotions can be recommended. Furthermore, the employee list analysis unit uses the emotional state of the recommended talent to have the generation AI identify talent who are highly motivated for the project and prioritize recommend those talent. For example, employees with high emotion scores can be recommended. Furthermore, the employee list analysis unit uses the emotion estimation function to analyze the emotional state of recommended talent, and based on the results, a system can be constructed in which the generation AI recommends the most suitable talent. For example, talent who are highly motivated for the project can be prioritized. This allows highly motivated talent to be recommended preferentially.

[0079] The talent matching support system can further use an emotion estimation function to analyze the emotional compatibility of talent across departments and build emotionally harmonious teams. For example, the emotion estimation function can be used to analyze the emotional compatibility of talent across departments in real time, and an emotionally harmonious team can be built based on the results. For example, talent with positive emotions can be preferentially recommended. The talent recommendation unit can then use the emotional state of talent across departments to identify emotionally harmonious teams using a generation AI and recommend those talent. For example, talent with a high emotion score can be recommended. The talent recommendation unit can also use the emotion estimation function to analyze the emotional compatibility of talent across departments, and build a system in which the generation AI builds emotionally harmonious teams based on the results. For example, talent with high motivation for the project can be preferentially recommended. This makes it possible to build emotionally harmonious teams.

[0080] The talent matching support system further uses an emotion estimation function to monitor in real time how talent from across departments feels about a project, and can make adjustments as needed based on the results. For example, the emotion estimation function can be used to monitor in real time how talent from across departments feels about a project, and can make adjustments as needed based on the results. For example, support can be provided to talent with negative emotions. The talent recommendation department also uses a generation AI to identify the emotional suitability of talent from across departments for a project based on the emotional state of talent from across departments, and can make necessary adjustments based on the results. For example, feedback can be provided to talent with low emotion scores. The talent recommendation department also uses an emotion estimation function to monitor in real time how talent from across departments feels about a project, and can make necessary adjustments based on the results. For example, support can be provided to talent with low motivation for a project. This allows for real-time monitoring of talent toward a project and adjustments as needed.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The proposal generation unit receives a prompt from the user and generates a proposal. For example, the generation AI creates a proposal or rough draft based on the project or idea entered by the user. The generation AI receives a prompt containing instructions from the user as input information and generates the contents of the proposal based on that. For example, if the prompt is "Please create a proposal for a new marketing strategy," the generation AI will generate a specific proposal based on that instruction. Step 2: The employee list analysis unit analyzes the employee list within the SB Group. For example, it analyzes employee skill sets, past project experience, department information, etc. The generation AI analyzes the employee list and recommends the best personnel for the project. For example, if a prompt including the instruction "This project requires marketing expertise" is input, the generation AI analyzes the employee list and recommends employees with marketing expertise. Step 3: The Talent Recommendation Department recommends the best talent for the project based on the information analyzed by the Employee List Analysis Department. For example, for a project that requires cooperation from the engineering and sales departments, the department will recommend the most appropriate talent from both departments.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, a proposal generation unit that receives a prompt from a user and generates a proposal; an employee list analysis unit that analyzes the employee list; a talent recommendation unit that recommends the most suitable talent for the project based on the information analyzed by the employee list analysis unit. A system characterized by:

2. The proposal creation unit The system learns from the user's past proposals and automatically generates proposals that match the user's preferences and style.

2. The system of claim 1.

3. The proposal creation unit Support for creating proposals in different languages ​​and making the project possible 2. The system of claim 1.

4. The employee list analysis unit Recommendations are made based on individual career goals and interests, as well as employee skill sets and experience.

2. The system of claim 1.

5. The talent recommendation unit Analyze interdepartmental collaboration and past success stories to propose optimal interdepartmental talent combinations 2. The system of claim 1.

6. The proposal creation unit Analyzing the emotional state of the user and generating the content of a proposal that will evoke the most positive emotions in the user 2. The system of claim 1.

7. The employee list analysis unit Analyze the emotional state of the recommended personnel and prioritize recommending personnel who are highly motivated for the project.

2. The system of claim 1.

8. The talent recommendation unit Analyze the emotional compatibility of cross-functional talent and build emotionally harmonious teams 2. The system of claim 1.

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