system

Abstract

The system according to this embodiment aims to efficiently select resources suitable for a project and improve the efficiency of project management. [Solution] The system according to the embodiment comprises an analysis unit, a selection unit, a negotiation unit, a proposal unit, a scheduling unit, and a management unit. The analysis unit analyzes employees' company history and skills. The selection unit selects the optimal resources based on the information analyzed by the analysis unit. The negotiation unit automates negotiations for the transfer of resources based on the resources selected by the selection unit. The proposal unit proposes external personnel. The scheduling unit creates the project schedule. The management unit manages the progress of the project.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to select resources suitable for a project and efficient project management is difficult. [[ID=3​​​​​​​​The system according to this embodiment comprises an analysis unit, a selection unit, a negotiation unit, a proposal unit, a scheduling unit, and a management unit. The analysis unit analyzes employees' company history and skills. The selection unit selects the optimal resources based on the information analyzed by the analysis unit. The negotiation unit automates resource transfer negotiations based on the resources selected by the selection unit. The proposal unit proposes external personnel. The scheduling unit creates the project schedule. The management unit manages the progress of the project. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently select resources suitable for a project and improve the efficiency of project management. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The project management system according to an embodiment of the present invention is a system that uses AI to analyze and propose the optimal resources for a project. The project management system uses AI to analyze employees' company history and skills and selects the most suitable resources for the project. Next, it automates resource transfer negotiations based on the selected resources. Furthermore, it includes functions for suggesting external personnel, creating schedules, and managing progress, thereby reducing the workload of the project manager. For example, the project management system uses AI to analyze employees' company history and skills. In this process, it analyzes employees' past project experience and skill sets in detail to identify the most suitable resources for the project. For example, it can select employees who are proficient in specific technologies or employees who have successfully completed similar projects in the past. Next, the project management system automates resource transfer negotiations based on the selected resources. The AI ​​automates the negotiation process regarding resource transfers, reducing time and costs. For example, it can consider important factors such as resource price, delivery time, quality, and service to make efficient arrangements. Furthermore, the project management system includes functions for suggesting external personnel, creating schedules, and managing progress. The AI ​​proposes the use of external resources and selects the most suitable external resources for the project. For example, external experts with specific skills can be proposed. Furthermore, project management systems create project schedules and manage progress. This allows for the identification and appropriate handling of scheduling risks and opportunities. This improves the efficiency of project management and reduces the workload. Project managers can quickly and reliably select the right people for complex projects, increasing the project's success rate. Resource management becomes more efficient, and overall project scheduling and progress monitoring become easier. Additionally, the organization can appropriately allocate its personnel and, when necessary, leverage external resources to build the optimal project team.This allows the project management system to efficiently select resources appropriate for the project, improve the efficiency of project management, and reduce the workload.

[0029] The project management system according to this embodiment comprises an analysis unit, a selection unit, a negotiation unit, a proposal unit, a scheduling unit, and a management unit. The analysis unit analyzes employees' company history and skills. For example, the analysis unit conducts a detailed analysis of employees' past project experience and skill sets. The analysis unit can use AI to analyze employees' company history and skills. The selection unit selects the optimal resources based on the information analyzed by the analysis unit. For example, the selection unit selects employees who are proficient in specific technologies or employees who have successfully completed similar projects in the past. The selection unit can use AI to select the optimal resources. The negotiation unit automates resource transfer negotiations based on the resources selected by the selection unit. For example, the negotiation unit considers important factors such as resource price, delivery date, quality, and service to make efficient arrangements. The negotiation unit can use AI to automate resource transfer negotiations. The proposal unit proposes external personnel. For example, the proposal unit proposes external experts with specific skills. The proposal department can use AI to propose external personnel. The scheduling department creates the project schedule. The scheduling department, for example, creates the project schedule and manages its progress. The scheduling department can use AI to create the project schedule. The management department manages the project progress. The management department, for example, manages the project progress, identifies and appropriately handles schedule risks and opportunities. The management department can use AI to manage the project progress. As a result, the project management system according to this embodiment can efficiently select resources suitable for the project, improve the efficiency of project management, and reduce the workload.

[0030] The analytics department analyzes employees' company history and skills. For example, it conducts a detailed analysis of employees' past project experience and skill sets. Specifically, it collects information stored in employee resumes and internal databases and analyzes this data using AI. The AI ​​uses natural language processing technology to extract important information from employee resumes and project reports and evaluate employees' skills and experience. For example, the AI ​​analyzes the types and scale of projects employees have handled in the past, the technologies and tools used, and the results achieved to create employee skill profiles. The AI ​​can also compare employees' skill sets with industry standards and the latest technology trends to identify skill gaps. This allows the analytics department to accurately understand employees' skills and experience and provide foundational information for selecting the most suitable resources for projects. Furthermore, the analytics department regularly updates employee skills and experience data and conducts analyses based on the latest information, always supporting the selection of optimal resources.

[0031] The selection department selects the most suitable resources based on the information analyzed by the analysis department. For example, the selection department selects employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. Specifically, it uses AI to evaluate the skill profiles of employees provided by the analysis department and selects the employees best suited to the project requirements. The AI ​​matches employee skills and experience based on the project requirements and goals, and lists the most suitable resources. For example, the AI ​​considers the project's technical requirements, deadlines, budget, etc., and selects the employees best suited to these requirements. The AI ​​also considers the employees' current workload and schedules to avoid overburdening resources. This allows the selection department to efficiently select the most suitable resources for the project and increase the project's success rate. Furthermore, the selection department monitors the performance of the selected resources and re-selects resources as needed, enabling flexible responses according to the project's progress.

[0032] The Negotiation Department automates resource transfer negotiations based on the resources selected by the Selection Department. The Negotiation Department considers key factors such as resource price, delivery time, quality, and service to ensure efficient arrangements. Specifically, it uses AI to automate resource transfer negotiations. Based on past negotiation data and market information, the AI ​​develops optimal negotiation strategies and proposes resource transfer terms. For example, the AI ​​analyzes the market price and supply status of resources in real time to conduct optimal price negotiations. Furthermore, the AI ​​considers requirements regarding resource quality and delivery time, balancing these factors to ensure efficient arrangements. In addition, the AI ​​monitors the progress of negotiations in real time and can modify the negotiation strategy as needed. This allows the Negotiation Department to conduct resource transfer negotiations efficiently and effectively, quickly securing the resources necessary for the project.

[0033] The proposal department proposes external personnel. For example, the proposal department proposes external experts with specific skills. Specifically, it uses AI to propose external personnel. The AI ​​searches and proposes external experts best suited to the project requirements from external talent databases and professional networks. For example, the AI ​​evaluates the profiles of external experts based on the project's technical requirements and skill sets, and lists the most suitable candidates. The AI ​​also analyzes the external experts' past project experience and evaluations to propose highly reliable personnel. Furthermore, the AI ​​also proposes contract terms and compensation for external experts, supporting efficient contract negotiation. This allows the proposal department to quickly propose the external personnel needed for the project and support the project's success.

[0034] The scheduling department creates project schedules. Specifically, it uses AI to create project schedules. The AI ​​develops an optimal schedule based on project requirements and resource utilization. For example, the AI ​​considers the dependencies and priorities of each project task to create an efficient schedule. The AI ​​can also monitor resource utilization and workload in real time and adjust the schedule accordingly. Furthermore, the AI ​​analyzes project progress and predicts potential schedule risks and delays. This allows the scheduling department to efficiently create project schedules and manage progress.

[0035] The management department manages the progress of the project. For example, the management department manages the project's progress, identifies and appropriately addresses scheduling risks and opportunities. Specifically, it uses AI to manage project progress. The AI ​​monitors the progress of each project task in real time and collects and analyzes progress data. For example, the AI ​​analyzes task completion status and resource utilization to predict scheduling risks and potential delays. The AI ​​can also suggest schedule adjustments and resource reallocations based on the project's progress data. Furthermore, the AI ​​visualizes the project's progress and reports it to stakeholders in real time. This allows the management department to efficiently manage project progress and appropriately address scheduling risks and opportunities.

[0036] The analysis unit can perform a detailed analysis of employees' past project experience and skill sets. For example, the analysis unit can perform a detailed analysis of employees' past project experience and skill sets. The analysis unit can use AI to perform a detailed analysis of employees' past project experience and skill sets. For example, the analysis unit can retrieve employees' past project experience from a database and analyze it using AI. The analysis unit can also use AI to analyze employees' skill sets and identify the most suitable resources for a project. This improves the accuracy of selecting the most suitable resources by performing a detailed analysis of employees' past project experience and skill sets. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs employees' past project experience and skill sets into the AI, and the AI ​​outputs the analysis results.

[0037] The selection department can select employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. For example, the selection department can select employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. The selection department can use AI to select employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. For example, the selection department can retrieve employees who are proficient in specific technologies from a database and select them using AI. Furthermore, the selection department can use AI to select employees who have experience successfully completing similar projects in the past, thereby identifying the optimal resources for the project. This improves the project's success rate by selecting employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. Some or all of the above-described processes in the selection department may be performed using AI or not. For example, the selection department inputs information on employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past into the AI, and the AI ​​outputs the selection results.

[0038] The negotiating department can make efficient arrangements by considering important factors such as resource price, delivery date, quality, and service. For example, the negotiating department can make efficient arrangements by considering important factors such as resource price, delivery date, quality, and service. The negotiating department can use AI to make efficient arrangements by considering important factors such as resource price, delivery date, quality, and service. For example, the negotiating department can retrieve resource prices from a database and use AI to set the optimal price. Furthermore, the negotiating department can use AI to consider important factors such as resource delivery date, quality, and service, and make efficient arrangements. This reduces project costs and time by considering important factors such as resource price, delivery date, quality, and service and making efficient arrangements. Some or all of the above processes in the negotiating department may be performed using AI or not. For example, the negotiating department inputs information such as resource price, delivery date, quality, and service into the AI, which then outputs the optimal arrangement.

[0039] The proposal department can propose external experts with specific skills. For example, the proposal department can propose external experts with specific skills. The proposal department can use AI to propose external experts with specific skills. For example, the proposal department can retrieve external experts with specific skills from a database and propose them using AI. The proposal department can also use AI to propose external experts with specific skills and identify the most suitable external resources for a project. This allows for the selection of the most suitable external resources for a project by proposing external experts with specific skills. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs information on external experts with specific skills into the AI, and the AI ​​outputs a proposal result.

[0040] The scheduling unit can create a project schedule. For example, the scheduling unit can create a project schedule. The scheduling unit can create a project schedule using AI. For example, the scheduling unit can retrieve a project schedule from a database and create it using AI. The scheduling unit can also create a project schedule using AI and manage its progress. This allows for the identification and appropriate handling of scheduling risks and opportunities by creating a project schedule. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit inputs the project schedule into the AI, and the AI ​​outputs the schedule.

[0041] The management department can manage the progress of a project. For example, the management department can manage the progress of a project. The management department can use AI to manage the progress of a project. For example, the management department can retrieve project progress from a database and manage it using AI. The management department can also use AI to manage project progress, identify schedule risks and opportunities, and handle them appropriately. This makes it easier to adjust the project schedule and monitor progress by managing project progress. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs project progress data into the AI, and the AI ​​outputs progress management results.

[0042] The analysis department can identify success and failure factors for projects when analyzing employees' past project experience and skill sets. For example, the analysis department can analyze past project data to identify commonalities in successful projects. The analysis department can use AI to analyze employees' past project experience and skill sets to identify success and failure factors for projects. For example, the analysis department inputs past project data into the AI, which then identifies success and failure factors. This provides guidance for future projects by identifying success and failure factors. Some or all of the above processes in the analysis department may be performed using AI or not.

[0043] The analysis unit can consider the rate of skill acquisition and growth curve when analyzing employees' skill sets. For example, the analysis unit can analyze employees' skill acquisition rates and identify employees who are growing quickly. The analysis unit can use AI to analyze employees' skill sets and consider the rate of skill acquisition and growth curve. For example, the analysis unit inputs employees' skill acquisition rates into the AI, and the AI ​​identifies employees who are growing quickly. By considering the rate of skill acquisition and growth curve, it is possible to propose career paths for employees. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0044] The analysis unit can perform employee analysis while considering the progress of other internal projects. For example, the analysis unit can check the progress of other projects to avoid resource duplication. The analysis unit can use AI to perform employee analysis while considering the progress of other internal projects. For example, the analysis unit inputs the progress of other projects into the AI, and the AI ​​adjusts the timing of the analysis. This avoids resource duplication and improves the accuracy of the analysis by considering the progress of other internal projects. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0045] The analysis unit can perform employee analysis by referencing external trends and technological developments. For example, the analysis unit can refer to the latest technological trends to evaluate employees' skill sets. The analysis unit can use AI to perform employee analysis by referencing external trends and technological developments. For example, the analysis unit inputs external trends and technological developments into the AI, and the AI ​​outputs the analysis results. This allows the analysis unit to evaluate employees' skill sets and suggest directions for skill improvement by referencing external trends and technological developments. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0046] The selection department can consider the latest trends and future potential of technology when selecting employees who are proficient in a particular technology. For example, the selection department may refer to the latest technology trends and select employees who are proficient in that technology. The selection department can also use AI to consider the latest trends and future potential of technology when selecting employees who are proficient in a particular technology. For example, the selection department may input the latest trends and future potential of technology into the AI, and the AI ​​may output the selection results. This allows for the selection of employees with technologies that will be important in the future by considering the latest trends and future potential of technology. Some or all of the above-described processes in the selection department may be performed using AI or not.

[0047] The selection department can evaluate the reproducibility of success when selecting employees who have successfully completed similar projects in the past. For example, the selection department can analyze data from past successful projects to evaluate the reproducibility of success. The selection department can use AI to evaluate the reproducibility of success when selecting employees who have successfully completed similar projects in the past. For example, the selection department can input data from past successful projects into the AI, and the AI ​​can evaluate the reproducibility of success. In this way, by evaluating the reproducibility of success, it is possible to select employees with high reproducibility. Some or all of the above processes in the selection department may be performed using AI or not.

[0048] The selection unit can make employee selections while considering the resource status of other internal projects. For example, the selection unit can check the resource status of other projects to avoid resource duplication. The selection unit can use AI to make employee selections while considering the resource status of other internal projects. For example, the selection unit inputs the resource status of other projects into the AI, and the AI ​​adjusts the timing of the selection. This improves the accuracy of the selection by avoiding resource duplication by considering the resource status of other internal projects. Some or all of the above processes in the selection unit may be performed using AI or not.

[0049] The selection department can make employee selections by referring to the opinions of external experts and consultants. For example, the selection department can adjust the employee selection criteria by referring to the opinions of external experts. The selection department can use AI to make employee selections by referring to the opinions of external experts and consultants. For example, the selection department inputs the opinions of external experts and consultants into the AI, and the AI ​​outputs the selection results. This improves the accuracy of selection by referring to the opinions of external experts and consultants. Some or all of the above processes in the selection department may be performed using AI or not.

[0050] The negotiation department can refer to past negotiation data to make the best arrangements when considering important factors such as the price, delivery time, quality, and service of resources. For example, the negotiation department can analyze past negotiation data to set the optimal price. The negotiation department can use AI to refer to past negotiation data to make the best arrangements when considering important factors such as the price, delivery time, quality, and service of resources. For example, the negotiation department can input past negotiation data into the AI, and the AI ​​can output the optimal arrangement. This improves the accuracy of negotiations by making the best arrangements by referring to past negotiation data. Some or all of the above processes in the negotiation department may be performed using AI or not.

[0051] The negotiation department can consider the resource status of other internal projects when negotiating resources. For example, the negotiation department can check the resource status of other projects to avoid resource duplication. The negotiation department can use AI to consider the resource status of other internal projects when negotiating resources. For example, the negotiation department can input the resource status of other projects into the AI, and the AI ​​can adjust the timing of negotiations. This allows for negotiations that consider the resource status of other internal projects, thereby avoiding resource duplication and improving the accuracy of negotiations. Some or all of the above processes in the negotiation department may be performed using AI or not.

[0052] The proposal department can consider the past performance and evaluation of external experts when proposing experts with specific skills. For example, the proposal department can evaluate the past performance of external experts and propose the most suitable expert. The proposal department can use AI to consider the past performance and evaluation of external experts when proposing experts with specific skills. For example, the proposal department inputs the past performance and evaluation of external experts into the AI, and the AI ​​outputs a proposal result. This allows the proposal of the most suitable external expert by considering the past performance and evaluation of experts. Some or all of the above processing in the proposal department may be performed using AI or not.

[0053] The proposal department can consider the resource status of other internal projects when proposing external experts. For example, the proposal department can check the resource status of other projects and adjust the proposals of external experts. The proposal department can use AI to consider the resource status of other internal projects when proposing external experts. For example, the proposal department can input the resource status of other projects into the AI, and the AI ​​can adjust the timing of proposals. This avoids resource duplication and improves the accuracy of proposals by considering the resource status of other internal projects. Some or all of the above processes in the proposal department may be performed using AI or not.

[0054] The scheduling unit can create an optimal project schedule by referencing past project schedule data. For example, the scheduling unit can analyze past project schedule data to create an optimal schedule. The scheduling unit can also use AI to create an optimal project schedule by referencing past project schedule data. For example, the scheduling unit inputs past project schedule data into the AI, and the AI ​​outputs an optimal schedule. This improves the accuracy of the schedule by creating an optimal schedule by referencing past project schedule data. Some or all of the above processes in the scheduling unit may be performed using AI or without AI.

[0055] The scheduling unit can create project schedules while considering the schedule status of other internal projects. For example, the scheduling unit can check the schedule status of other projects and avoid scheduling overlaps. The scheduling unit can use AI to create project schedules while considering the schedule status of other internal projects. For example, the scheduling unit inputs the schedule status of other projects into the AI, and the AI ​​adjusts the timing of the schedules. This avoids scheduling overlaps and improves the accuracy of the schedule by creating schedules while considering the schedule status of other internal projects. Some or all of the above processes in the scheduling unit may be performed using AI or not.

[0056] The management department can select the optimal management method by referring to past project progress data when managing project progress. For example, the management department can analyze past project progress data and select the optimal management method. The management department can also use AI to select the optimal management method by referring to past project progress data when managing project progress. For example, the management department inputs past project progress data into the AI, and the AI ​​outputs the optimal management method. This improves the accuracy of progress management by selecting the optimal management method by referring to past project progress data. Some or all of the above processes in the management department may be performed using AI or not.

[0057] The management department can manage project progress while considering the progress of other internal projects. For example, the management department can check the progress of other projects to avoid duplication of progress management. The management department can use AI to manage project progress while considering the progress of other internal projects. For example, the management department can input the progress of other projects into the AI, and the AI ​​can adjust the timing of progress management. This improves the accuracy of progress management by considering the progress of other internal projects. Some or all of the above processes in the management department may be performed using AI or not.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] A project management system can also include a project risk management function. The risk management department identifies project risks, assesses their impact, and proposes countermeasures. For example, the risk management department can retrieve potential risks that may arise during the project from a database and use AI to assess their impact. Furthermore, the risk management department can propose countermeasures to minimize the impact of risks. This allows for proper management of project risks and improves the project's success rate. Some or all of the above processes in the risk management department may be performed using AI or not.

[0060] A project management system can also include a project budget management function. The budget management department sets the project budget, monitors budget usage, and adjusts the budget. For example, the budget management department can retrieve the project budget from a database and monitor budget usage using AI. Furthermore, the budget management department can adjust the budget according to its usage. This allows for proper management of the project budget and prevents budget overruns. Some or all of the above processes in the budget management department may be performed using AI, or they may not.

[0061] A project management system can also include a project quality control function. The quality control department evaluates the quality of the project and proposes measures to improve it. For example, the quality control department can retrieve project quality data from a database and evaluate it using AI. Furthermore, the quality control department can propose measures to improve the quality. This allows for proper management of project quality and improvement. Some or all of the above processes in the quality control department may be performed using AI, or they may not.

[0062] A project management system can also include project communication management functions. The communication management unit provides tools to facilitate communication within the project team. For example, the communication management unit provides messaging tools and video conferencing tools within the project team to facilitate communication among team members. Furthermore, the communication management unit can provide a dashboard for sharing project progress. This facilitates communication within the project team and allows for efficient management of project progress. Some or all of the above processes in the communication management unit may be performed using AI, or not.

[0063] A project management system can also include a document management function for the project. The document management department centrally manages project-related documents and handles document version control and access permission management. For example, the document management department retrieves project-related documents from a database and uses AI to manage document version control. Furthermore, the document management department manages document access permissions, granting access only to necessary members. This ensures proper management of project-related documents and prevents loss or misuse. Some or all of the processes described above in the document management department may be performed using AI or not.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The analysis department analyzes employees' company history and skills. For example, it analyzes employees' past project experience and skill sets in detail. The analysis department can use AI to analyze employees' company history and skills. Step 2: The selection unit selects the optimal resources based on the information analyzed by the analysis unit. For example, it might select employees who are proficient in a particular technology or employees who have successfully completed similar projects in the past. The selection unit can use AI to select the optimal resources. Step 3: The Negotiation Department automates resource transfer negotiations based on the resources selected by the Selection Department. For example, it considers important factors such as resource price, delivery time, quality, and service to make efficient arrangements. The Negotiation Department can use AI to automate resource transfer negotiations. Step 4: The proposal department proposes external personnel. For example, it proposes external experts with specific skills. The proposal department can use AI to propose external personnel. Step 5: The scheduling unit creates the project schedule. For example, it creates a project schedule and manages its progress. The scheduling unit can use AI to create the project schedule. Step 6: The management department manages the project's progress. For example, they manage the project's progress, identify and appropriately address scheduling risks and opportunities. The management department can use AI to manage the project's progress.

[0066] (Example of form 2) The project management system according to an embodiment of the present invention is a system that uses AI to analyze and propose the optimal resources for a project. The project management system uses AI to analyze employees' company history and skills and selects the most suitable resources for the project. Next, it automates resource transfer negotiations based on the selected resources. Furthermore, it includes functions for suggesting external personnel, creating schedules, and managing progress, thereby reducing the workload of the project manager. For example, the project management system uses AI to analyze employees' company history and skills. In this process, it analyzes employees' past project experience and skill sets in detail to identify the most suitable resources for the project. For example, it can select employees who are proficient in specific technologies or employees who have successfully completed similar projects in the past. Next, the project management system automates resource transfer negotiations based on the selected resources. The AI ​​automates the negotiation process regarding resource transfers, reducing time and costs. For example, it can consider important factors such as resource price, delivery time, quality, and service to make efficient arrangements. Furthermore, the project management system includes functions for suggesting external personnel, creating schedules, and managing progress. The AI ​​proposes the use of external resources and selects the most suitable external resources for the project. For example, external experts with specific skills can be proposed. Furthermore, project management systems create project schedules and manage progress. This allows for the identification and appropriate handling of scheduling risks and opportunities. This improves the efficiency of project management and reduces the workload. Project managers can quickly and reliably select the right people for complex projects, increasing the project's success rate. Resource management becomes more efficient, and overall project scheduling and progress monitoring become easier. Additionally, the organization can appropriately allocate its personnel and, when necessary, leverage external resources to build the optimal project team.This allows the project management system to efficiently select resources appropriate for the project, improve the efficiency of project management, and reduce the workload.

[0067] The project management system according to this embodiment comprises an analysis unit, a selection unit, a negotiation unit, a proposal unit, a scheduling unit, and a management unit. The analysis unit analyzes employees' company history and skills. For example, the analysis unit conducts a detailed analysis of employees' past project experience and skill sets. The analysis unit can use AI to analyze employees' company history and skills. The selection unit selects the optimal resources based on the information analyzed by the analysis unit. For example, the selection unit selects employees who are proficient in specific technologies or employees who have successfully completed similar projects in the past. The selection unit can use AI to select the optimal resources. The negotiation unit automates resource transfer negotiations based on the resources selected by the selection unit. For example, the negotiation unit considers important factors such as resource price, delivery date, quality, and service to make efficient arrangements. The negotiation unit can use AI to automate resource transfer negotiations. The proposal unit proposes external personnel. For example, the proposal unit proposes external experts with specific skills. The proposal department can use AI to propose external personnel. The scheduling department creates the project schedule. The scheduling department, for example, creates the project schedule and manages its progress. The scheduling department can use AI to create the project schedule. The management department manages the project progress. The management department, for example, manages the project progress, identifies and appropriately handles schedule risks and opportunities. The management department can use AI to manage the project progress. As a result, the project management system according to this embodiment can efficiently select resources suitable for the project, improve the efficiency of project management, and reduce the workload.

[0068] The analytics department analyzes employees' company history and skills. For example, it conducts a detailed analysis of employees' past project experience and skill sets. Specifically, it collects information stored in employee resumes and internal databases and analyzes this data using AI. The AI ​​uses natural language processing technology to extract important information from employee resumes and project reports and evaluate employees' skills and experience. For example, the AI ​​analyzes the types and scale of projects employees have handled in the past, the technologies and tools used, and the results achieved to create employee skill profiles. The AI ​​can also compare employees' skill sets with industry standards and the latest technology trends to identify skill gaps. This allows the analytics department to accurately understand employees' skills and experience and provide foundational information for selecting the most suitable resources for projects. Furthermore, the analytics department regularly updates employee skills and experience data and conducts analyses based on the latest information, always supporting the selection of optimal resources.

[0069] The selection department selects the most suitable resources based on the information analyzed by the analysis department. For example, the selection department selects employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. Specifically, it uses AI to evaluate the skill profiles of employees provided by the analysis department and selects the employees best suited to the project requirements. The AI ​​matches employee skills and experience based on the project requirements and goals, and lists the most suitable resources. For example, the AI ​​considers the project's technical requirements, deadlines, budget, etc., and selects the employees best suited to these requirements. The AI ​​also considers the employees' current workload and schedules to avoid overburdening resources. This allows the selection department to efficiently select the most suitable resources for the project and increase the project's success rate. Furthermore, the selection department monitors the performance of the selected resources and re-selects resources as needed, enabling flexible responses according to the project's progress.

[0070] The Negotiation Department automates resource transfer negotiations based on the resources selected by the Selection Department. The Negotiation Department considers key factors such as resource price, delivery time, quality, and service to ensure efficient arrangements. Specifically, it uses AI to automate resource transfer negotiations. Based on past negotiation data and market information, the AI ​​develops optimal negotiation strategies and proposes resource transfer terms. For example, the AI ​​analyzes the market price and supply status of resources in real time to conduct optimal price negotiations. Furthermore, the AI ​​considers requirements regarding resource quality and delivery time, balancing these factors to ensure efficient arrangements. In addition, the AI ​​monitors the progress of negotiations in real time and can modify the negotiation strategy as needed. This allows the Negotiation Department to conduct resource transfer negotiations efficiently and effectively, quickly securing the resources necessary for the project.

[0071] The proposal department proposes external personnel. For example, the proposal department proposes external experts with specific skills. Specifically, it uses AI to propose external personnel. The AI ​​searches and proposes external experts best suited to the project requirements from external talent databases and professional networks. For example, the AI ​​evaluates the profiles of external experts based on the project's technical requirements and skill sets, and lists the most suitable candidates. The AI ​​also analyzes the external experts' past project experience and evaluations to propose highly reliable personnel. Furthermore, the AI ​​also proposes contract terms and compensation for external experts, supporting efficient contract negotiation. This allows the proposal department to quickly propose the external personnel needed for the project and support the project's success.

[0072] The scheduling department creates project schedules. Specifically, it uses AI to create project schedules. The AI ​​develops an optimal schedule based on project requirements and resource utilization. For example, the AI ​​considers the dependencies and priorities of each project task to create an efficient schedule. The AI ​​can also monitor resource utilization and workload in real time and adjust the schedule accordingly. Furthermore, the AI ​​analyzes project progress and predicts potential schedule risks and delays. This allows the scheduling department to efficiently create project schedules and manage progress.

[0073] The management department manages the progress of the project. For example, the management department manages the project's progress, identifies and appropriately addresses scheduling risks and opportunities. Specifically, it uses AI to manage project progress. The AI ​​monitors the progress of each project task in real time and collects and analyzes progress data. For example, the AI ​​analyzes task completion status and resource utilization to predict scheduling risks and potential delays. The AI ​​can also suggest schedule adjustments and resource reallocations based on the project's progress data. Furthermore, the AI ​​visualizes the project's progress and reports it to stakeholders in real time. This allows the management department to efficiently manage project progress and appropriately address scheduling risks and opportunities.

[0074] The analysis unit can perform a detailed analysis of employees' past project experience and skill sets. For example, the analysis unit can perform a detailed analysis of employees' past project experience and skill sets. The analysis unit can use AI to perform a detailed analysis of employees' past project experience and skill sets. For example, the analysis unit can retrieve employees' past project experience from a database and analyze it using AI. The analysis unit can also use AI to analyze employees' skill sets and identify the most suitable resources for a project. This improves the accuracy of selecting the most suitable resources by performing a detailed analysis of employees' past project experience and skill sets. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit inputs employees' past project experience and skill sets into the AI, and the AI ​​outputs the analysis results.

[0075] The selection department can select employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. For example, the selection department can select employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. The selection department can use AI to select employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. For example, the selection department can retrieve employees who are proficient in specific technologies from a database and select them using AI. Furthermore, the selection department can use AI to select employees who have experience successfully completing similar projects in the past, thereby identifying the optimal resources for the project. This improves the project's success rate by selecting employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. Some or all of the above-described processes in the selection department may be performed using AI or not. For example, the selection department inputs information on employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past into the AI, and the AI ​​outputs the selection results.

[0076] The negotiating department can make efficient arrangements by considering important factors such as resource price, delivery date, quality, and service. For example, the negotiating department can make efficient arrangements by considering important factors such as resource price, delivery date, quality, and service. The negotiating department can use AI to make efficient arrangements by considering important factors such as resource price, delivery date, quality, and service. For example, the negotiating department can retrieve resource prices from a database and use AI to set the optimal price. Furthermore, the negotiating department can use AI to consider important factors such as resource delivery date, quality, and service, and make efficient arrangements. This reduces project costs and time by considering important factors such as resource price, delivery date, quality, and service and making efficient arrangements. Some or all of the above processes in the negotiating department may be performed using AI or not. For example, the negotiating department inputs information such as resource price, delivery date, quality, and service into the AI, which then outputs the optimal arrangement.

[0077] The proposal department can propose external experts with specific skills. For example, the proposal department can propose external experts with specific skills. The proposal department can use AI to propose external experts with specific skills. For example, the proposal department can retrieve external experts with specific skills from a database and propose them using AI. The proposal department can also use AI to propose external experts with specific skills and identify the most suitable external resources for a project. This allows for the selection of the most suitable external resources for a project by proposing external experts with specific skills. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department inputs information on external experts with specific skills into the AI, and the AI ​​outputs a proposal result.

[0078] The scheduling unit can create a project schedule. For example, the scheduling unit can create a project schedule. The scheduling unit can create a project schedule using AI. For example, the scheduling unit can retrieve a project schedule from a database and create it using AI. The scheduling unit can also create a project schedule using AI and manage its progress. This allows for the identification and appropriate handling of scheduling risks and opportunities by creating a project schedule. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit inputs the project schedule into the AI, and the AI ​​outputs the schedule.

[0079] The management department can manage the progress of a project. For example, the management department can manage the progress of a project. The management department can use AI to manage the progress of a project. For example, the management department can retrieve project progress from a database and manage it using AI. The management department can also use AI to manage project progress, identify schedule risks and opportunities, and handle them appropriately. This makes it easier to adjust the project schedule and monitor progress by managing project progress. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs project progress data into the AI, and the AI ​​outputs progress management results.

[0080] The analysis unit can estimate employees' emotions and adjust the timing of the analysis based on the estimated emotions. For example, if an employee is feeling stressed, the analysis unit will postpone the analysis and perform it when the employee is relaxed. The analysis unit can use AI to estimate employees' emotions and adjust the timing of the analysis based on the estimated emotions. For example, the analysis unit inputs employee emotion data into the AI, and the AI ​​adjusts the timing of the analysis. By adjusting the timing of the analysis based on the employee's emotions, the burden on employees is reduced and the accuracy of the analysis is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis department can identify success and failure factors for projects when analyzing employees' past project experience and skill sets. For example, the analysis department can analyze past project data to identify commonalities in successful projects. The analysis department can use AI to analyze employees' past project experience and skill sets to identify success and failure factors for projects. For example, the analysis department inputs past project data into the AI, which then identifies success and failure factors. This provides guidance for future projects by identifying success and failure factors. Some or all of the above processes in the analysis department may be performed using AI or not.

[0082] The analysis unit can consider the rate of skill acquisition and growth curve when analyzing employees' skill sets. For example, the analysis unit can analyze employees' skill acquisition rates and identify employees who are growing quickly. The analysis unit can use AI to analyze employees' skill sets and consider the rate of skill acquisition and growth curve. For example, the analysis unit inputs employees' skill acquisition rates into the AI, and the AI ​​identifies employees who are growing quickly. By considering the rate of skill acquisition and growth curve, it is possible to propose career paths for employees. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0083] The analysis unit can estimate employees' emotions and determine analysis priorities based on those estimated emotions. For example, if an employee is stressed, the analysis unit will lower the priority of that analysis. The analysis unit can use AI to estimate employees' emotions and determine analysis priorities based on those estimated emotions. For example, the analysis unit inputs employee emotion data into the AI, and the AI ​​determines the analysis priorities. This reduces the burden on employees and improves the accuracy of the analysis by determining analysis priorities based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can perform employee analysis while considering the progress of other internal projects. For example, the analysis unit can check the progress of other projects to avoid resource duplication. The analysis unit can use AI to perform employee analysis while considering the progress of other internal projects. For example, the analysis unit inputs the progress of other projects into the AI, and the AI ​​adjusts the timing of the analysis. This avoids resource duplication and improves the accuracy of the analysis by considering the progress of other internal projects. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0085] The analysis unit can perform employee analysis by referencing external trends and technological developments. For example, the analysis unit can refer to the latest technological trends to evaluate employees' skill sets. The analysis unit can use AI to perform employee analysis by referencing external trends and technological developments. For example, the analysis unit inputs external trends and technological developments into the AI, and the AI ​​outputs the analysis results. This allows the analysis unit to evaluate employees' skill sets and suggest directions for skill improvement by referencing external trends and technological developments. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0086] The selection unit can estimate employees' emotions and adjust selection criteria based on those estimated emotions. For example, if an employee is stressed, the selection unit will relax the selection criteria. The selection unit can use AI to estimate employees' emotions and adjust selection criteria based on those estimated emotions. For example, the selection unit inputs employee emotion data into the AI, and the AI ​​adjusts the selection criteria. This reduces the burden on employees and improves selection accuracy by adjusting selection criteria based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The selection department can consider the latest trends and future potential of technology when selecting employees who are proficient in a particular technology. For example, the selection department may refer to the latest technology trends and select employees who are proficient in that technology. The selection department can also use AI to consider the latest trends and future potential of technology when selecting employees who are proficient in a particular technology. For example, the selection department may input the latest trends and future potential of technology into the AI, and the AI ​​may output the selection results. This allows for the selection of employees with technologies that will be important in the future by considering the latest trends and future potential of technology. Some or all of the above-described processes in the selection department may be performed using AI or not.

[0088] The selection department can evaluate the reproducibility of success when selecting employees who have successfully completed similar projects in the past. For example, the selection department can analyze data from past successful projects to evaluate the reproducibility of success. The selection department can use AI to evaluate the reproducibility of success when selecting employees who have successfully completed similar projects in the past. For example, the selection department can input data from past successful projects into the AI, and the AI ​​can evaluate the reproducibility of success. In this way, by evaluating the reproducibility of success, it is possible to select employees with high reproducibility. Some or all of the above processes in the selection department may be performed using AI or not.

[0089] The selection unit can estimate employees' emotions and determine selection priorities based on those estimated emotions. For example, if an employee is stressed, the selection unit will lower their selection priority. The selection unit can use AI to estimate employees' emotions and determine selection priorities based on those estimated emotions. For example, the selection unit inputs employee emotion data into the AI, and the AI ​​determines the selection priorities. This reduces the burden on employees and improves selection accuracy by determining selection priorities based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The selection unit can make employee selections while considering the resource status of other internal projects. For example, the selection unit can check the resource status of other projects to avoid resource duplication. The selection unit can use AI to make employee selections while considering the resource status of other internal projects. For example, the selection unit inputs the resource status of other projects into the AI, and the AI ​​adjusts the timing of the selection. This improves the accuracy of the selection by avoiding resource duplication by considering the resource status of other internal projects. Some or all of the above processes in the selection unit may be performed using AI or not.

[0091] The selection department can make employee selections by referring to the opinions of external experts and consultants. For example, the selection department can adjust the employee selection criteria by referring to the opinions of external experts. The selection department can use AI to make employee selections by referring to the opinions of external experts and consultants. For example, the selection department inputs the opinions of external experts and consultants into the AI, and the AI ​​outputs the selection results. This improves the accuracy of selection by referring to the opinions of external experts and consultants. Some or all of the above processes in the selection department may be performed using AI or not.

[0092] The negotiation department can estimate employees' emotions and adjust the negotiation process based on those estimates. For example, if an employee is feeling stressed, the department might postpone the negotiation and conduct it at a more relaxed time. The negotiation department can also use AI to estimate employees' emotions and adjust the negotiation process based on those estimates. For example, the department can input employee emotion data into the AI, which then adjusts the negotiation process. This reduces the burden on employees and improves the accuracy of negotiations by adjusting the negotiation process based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The negotiation department can refer to past negotiation data to make the best arrangements when considering important factors such as the price, delivery time, quality, and service of resources. For example, the negotiation department can analyze past negotiation data to set the optimal price. The negotiation department can use AI to refer to past negotiation data to make the best arrangements when considering important factors such as the price, delivery time, quality, and service of resources. For example, the negotiation department can input past negotiation data into the AI, and the AI ​​can output the optimal arrangement. This improves the accuracy of negotiations by making the best arrangements by referring to past negotiation data. Some or all of the above processes in the negotiation department may be performed using AI or not.

[0094] The negotiation department can estimate employees' emotions and determine negotiation priorities based on those estimated emotions. For example, if an employee is stressed, the negotiation department will lower the priority of that negotiation. The negotiation department can use AI to estimate employees' emotions and determine negotiation priorities based on those estimated emotions. For example, the negotiation department inputs employee emotion data into the AI, and the AI ​​determines the negotiation priorities. This reduces the burden on employees and improves the accuracy of negotiations by determining negotiation priorities based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The negotiation department can consider the resource status of other internal projects when negotiating resources. For example, the negotiation department can check the resource status of other projects to avoid resource duplication. The negotiation department can use AI to consider the resource status of other internal projects when negotiating resources. For example, the negotiation department can input the resource status of other projects into the AI, and the AI ​​can adjust the timing of negotiations. This allows for negotiations that consider the resource status of other internal projects, thereby avoiding resource duplication and improving the accuracy of negotiations. Some or all of the above processes in the negotiation department may be performed using AI or not.

[0096] The proposal department can estimate employees' emotions and adjust the way proposals are presented based on those emotions. For example, if an employee is feeling stressed, the proposal department will present simple and easy-to-understand proposals. The proposal department can use AI to estimate employees' emotions and adjust the way proposals are presented based on those emotions. For example, the proposal department inputs employee emotion data into the AI, which then adjusts the way proposals are presented. This reduces the burden on employees and improves the accuracy of proposals by adjusting the presentation based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The proposal department can consider the past performance and evaluation of external experts when proposing experts with specific skills. For example, the proposal department can evaluate the past performance of external experts and propose the most suitable expert. The proposal department can use AI to consider the past performance and evaluation of external experts when proposing experts with specific skills. For example, the proposal department inputs the past performance and evaluation of external experts into the AI, and the AI ​​outputs a proposal result. This allows the proposal of the most suitable external expert by considering the past performance and evaluation of experts. Some or all of the above processing in the proposal department may be performed using AI or not.

[0098] The proposal department can estimate employees' emotions and determine the priority of proposals based on those estimated emotions. For example, if an employee is feeling stressed, the proposal department will lower its priority. The proposal department can use AI to estimate employees' emotions and determine the priority of proposals based on those estimated emotions. For example, the proposal department inputs employee emotion data into the AI, and the AI ​​determines the priority of proposals. This reduces the burden on employees and improves the accuracy of proposals by determining the priority of proposals based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The proposal department can consider the resource status of other internal projects when proposing external experts. For example, the proposal department can check the resource status of other projects and adjust the proposals of external experts. The proposal department can use AI to consider the resource status of other internal projects when proposing external experts. For example, the proposal department can input the resource status of other projects into the AI, and the AI ​​can adjust the timing of proposals. This avoids resource duplication and improves the accuracy of proposals by considering the resource status of other internal projects. Some or all of the above processes in the proposal department may be performed using AI or not.

[0100] The scheduling unit can estimate employees' emotions and adjust the scheduling method based on those emotions. For example, if an employee is feeling stressed, the scheduling unit will set a more relaxed schedule. The scheduling unit can use AI to estimate employees' emotions and adjust the scheduling method based on those emotions. For example, the scheduling unit inputs employee emotion data into the AI, which then adjusts the scheduling method. This reduces the burden on employees and improves scheduling accuracy by adjusting the scheduling method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The scheduling unit can create an optimal project schedule by referencing past project schedule data. For example, the scheduling unit can analyze past project schedule data to create an optimal schedule. The scheduling unit can also use AI to create an optimal project schedule by referencing past project schedule data. For example, the scheduling unit inputs past project schedule data into the AI, and the AI ​​outputs an optimal schedule. This improves the accuracy of the schedule by creating an optimal schedule by referencing past project schedule data. Some or all of the above processes in the scheduling unit may be performed using AI or without AI.

[0102] The scheduling unit can estimate employees' emotions and determine schedule priorities based on those estimated emotions. For example, if an employee is feeling stressed, the scheduling unit will lower the priority of that schedule. The scheduling unit can use AI to estimate employees' emotions and determine schedule priorities based on those estimated emotions. For example, the scheduling unit inputs employee emotion data into the AI, and the AI ​​determines the schedule priorities. This reduces the burden on employees and improves scheduling accuracy by determining schedule priorities based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The scheduling unit can create project schedules while considering the schedule status of other internal projects. For example, the scheduling unit can check the schedule status of other projects and avoid scheduling overlaps. The scheduling unit can use AI to create project schedules while considering the schedule status of other internal projects. For example, the scheduling unit inputs the schedule status of other projects into the AI, and the AI ​​adjusts the timing of the schedules. This avoids scheduling overlaps and improves the accuracy of the schedule by creating schedules while considering the schedule status of other internal projects. Some or all of the above processes in the scheduling unit may be performed using AI or not.

[0104] The management department can estimate employees' emotions and adjust progress management methods based on those estimates. For example, if an employee is stressed, the management department can ease up on progress management. The management department can use AI to estimate employees' emotions and adjust progress management methods based on those estimates. For example, the management department can input employee emotion data into the AI, which then adjusts the progress management methods. This reduces the burden on employees and improves the accuracy of progress management by adjusting progress management methods based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The management department can select the optimal management method by referring to past project progress data when managing project progress. For example, the management department can analyze past project progress data and select the optimal management method. The management department can also use AI to select the optimal management method by referring to past project progress data when managing project progress. For example, the management department inputs past project progress data into the AI, and the AI ​​outputs the optimal management method. This improves the accuracy of progress management by selecting the optimal management method by referring to past project progress data. Some or all of the above processes in the management department may be performed using AI or not.

[0106] The management department can estimate employees' emotions and determine progress management priorities based on those estimated emotions. For example, if an employee is stressed, the management department will lower the priority of that progress management task. The management department can use AI to estimate employees' emotions and determine progress management priorities based on those estimated emotions. For example, the management department inputs employee emotion data into the AI, and the AI ​​determines the progress management priorities. This reduces the burden on employees and improves the accuracy of progress management by determining progress management priorities based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The management department can manage project progress while considering the progress of other internal projects. For example, the management department can check the progress of other projects to avoid duplication of progress management. The management department can use AI to manage project progress while considering the progress of other internal projects. For example, the management department can input the progress of other projects into the AI, and the AI ​​can adjust the timing of progress management. This improves the accuracy of progress management by considering the progress of other internal projects. Some or all of the above processes in the management department may be performed using AI or not.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] A project management system can also include a project risk management function. The risk management department identifies project risks, assesses their impact, and proposes countermeasures. For example, the risk management department can retrieve potential risks that may arise during the project from a database and use AI to assess their impact. Furthermore, the risk management department can propose countermeasures to minimize the impact of risks. This allows for proper management of project risks and improves the project's success rate. Some or all of the above processes in the risk management department may be performed using AI or not.

[0110] A project management system can also include a project budget management function. The budget management department sets the project budget, monitors budget usage, and adjusts the budget. For example, the budget management department can retrieve the project budget from a database and monitor budget usage using AI. Furthermore, the budget management department can adjust the budget according to its usage. This allows for proper management of the project budget and prevents budget overruns. Some or all of the above processes in the budget management department may be performed using AI, or they may not.

[0111] A project management system can also include a project quality control function. The quality control department evaluates the quality of the project and proposes measures to improve it. For example, the quality control department can retrieve project quality data from a database and evaluate it using AI. Furthermore, the quality control department can propose measures to improve the quality. This allows for proper management of project quality and improvement. Some or all of the above processes in the quality control department may be performed using AI, or they may not.

[0112] A project management system can also include project communication management functions. The communication management unit provides tools to facilitate communication within the project team. For example, the communication management unit provides messaging tools and video conferencing tools within the project team to facilitate communication among team members. Furthermore, the communication management unit can provide a dashboard for sharing project progress. This facilitates communication within the project team and allows for efficient management of project progress. Some or all of the above processes in the communication management unit may be performed using AI, or not.

[0113] A project management system can also include a document management function for the project. The document management department centrally manages project-related documents and handles document version control and access permission management. For example, the document management department retrieves project-related documents from a database and uses AI to manage document version control. Furthermore, the document management department manages document access permissions, granting access only to necessary members. This ensures proper management of project-related documents and prevents loss or misuse. Some or all of the processes described above in the document management department may be performed using AI or not.

[0114] A project management system can estimate employee emotions during project resource selection and adjust resource selection criteria based on those estimated emotions. For example, the selection department might relax selection criteria if an employee is experiencing stress. The selection department can use AI to estimate employee emotions and adjust selection criteria based on those estimated emotions. For example, the selection department inputs employee emotion data into the AI, which then adjusts the selection criteria. This reduces the burden on employees and improves selection accuracy by adjusting selection criteria based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] A project management system can estimate employees' emotions during project progress management and adjust progress management methods based on those estimated emotions. For example, the management department can ease progress management if an employee is feeling stressed. The management department can use AI to estimate employees' emotions and adjust progress management methods based on those estimated emotions. For example, the management department can input employee emotion data into the AI, which then adjusts the progress management methods. By adjusting progress management methods based on employee emotions, the burden on employees is reduced and the accuracy of progress management is improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] A project management system can estimate employee emotions when creating a project schedule and adjust the scheduling method based on those emotions. For example, if an employee is feeling stressed, the scheduling unit will set a more relaxed schedule. The scheduling unit can use AI to estimate employee emotions and adjust the scheduling method based on those emotions. For example, the scheduling unit inputs employee emotion data into the AI, which then adjusts the scheduling method. By adjusting the scheduling method based on employee emotions, the system reduces the burden on employees and improves the accuracy of the schedule. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The project management system can estimate employees' emotions when proposing a project and adjust the presentation of the proposal based on those emotions. For example, if an employee is feeling stressed, the proposal department will make a simple and easy-to-understand proposal. The proposal department can use AI to estimate employees' emotions and adjust the presentation of the proposal based on those emotions. For example, the proposal department can input employee emotion data into the AI, which will then adjust the presentation of the proposal. By adjusting the presentation of the proposal based on employees' emotions, the system reduces the burden on employees and improves the accuracy of proposals. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] Project management systems can estimate employees' emotions during project negotiations and adjust the negotiation process based on those estimated emotions. For example, the negotiation team might postpone negotiations if an employee is stressed, waiting until they are more relaxed. The negotiation team can also use AI to estimate employees' emotions and adjust the negotiation process based on those estimated emotions. For example, the negotiation team could input employee emotion data into the AI, which would then adjust the negotiation process. This reduces employee burden and improves negotiation accuracy by adjusting the negotiation process based on employee emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The analysis department analyzes employees' company history and skills. For example, it analyzes employees' past project experience and skill sets in detail. The analysis department can use AI to analyze employees' company history and skills. Step 2: The selection unit selects the optimal resources based on the information analyzed by the analysis unit. For example, it might select employees who are proficient in a particular technology or employees who have successfully completed similar projects in the past. The selection unit can use AI to select the optimal resources. Step 3: The Negotiation Department automates resource transfer negotiations based on the resources selected by the Selection Department. For example, it considers important factors such as resource price, delivery time, quality, and service to make efficient arrangements. The Negotiation Department can use AI to automate resource transfer negotiations. Step 4: The proposal department proposes external personnel. For example, it proposes external experts with specific skills. The proposal department can use AI to propose external personnel. Step 5: The scheduling unit creates the project schedule. For example, it creates a project schedule and manages its progress. The scheduling unit can use AI to create the project schedule. Step 6: The management department manages the project's progress. For example, they manage the project's progress, identify and appropriately address scheduling risks and opportunities. The management department can use AI to manage the project's progress.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the analysis unit, selection unit, negotiation unit, proposal unit, scheduling unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes employees' company history and skills. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the optimal resources based on the analyzed information. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates negotiations for the transfer of resources. The proposal unit is implemented by, for example, the control unit 46A of the smart device 14 and proposes external personnel. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a project schedule. The management unit is implemented by, for example, the control unit 46A of the smart device 14 and manages the progress of the project. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the analysis unit, selection unit, negotiation unit, proposal unit, scheduling unit, and management unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes employees' company history and skills. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the optimal resources based on the analyzed information. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates negotiations for the transfer of resources. The proposal unit is implemented by, for example, the control unit 46A of the smart glasses 214 and proposes external personnel. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a project schedule. The management unit is implemented by, for example, the control unit 46A of the smart glasses 214 and manages the progress of the project. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the analysis unit, selection unit, negotiation unit, proposal unit, scheduling unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes employees' company history and skills. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the optimal resources based on the analyzed information. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates negotiations for the transfer of resources. The proposal unit is implemented by, for example, the control unit 46A of the headset terminal 314 and proposes external personnel. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a project schedule. The management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and manages the progress of the project. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the analysis unit, selection unit, negotiation unit, proposal unit, scheduling unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes employees' company history and skills. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects the optimal resources based on the analyzed information. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates negotiations for the transfer of resources. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 and proposes external personnel. The scheduling unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a project schedule. The management unit is implemented by, for example, the control unit 46A of the robot 414 and manages the progress of the project. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The analysis department analyzes employees' company history and skills, A selection unit selects the optimal resource based on the information analyzed by the aforementioned analysis unit, A negotiation unit that automates resource transfer negotiations based on the resources selected by the aforementioned selection unit, The proposal department, which proposes external personnel, The scheduling department creates the project schedule, It includes a management department that manages the progress of the project. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze employees' past project experience and skill sets in detail. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is Select employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned negotiating body said, We will make efficient arrangements by considering important factors such as resource pricing, delivery time, quality, and service. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose external experts with specific skills. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned scheduling unit is Create a project schedule The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Manage project progress The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the emotions of employees and adjusts the timing of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing employees' past project experience and skill sets, identify the success and failure factors of projects. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing employees' skill sets, consider the rate of skill acquisition and the growth curve. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates employees' emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing employee data, the progress of other internal projects is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing employee data, we refer to external trends and technological developments. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned selection unit is We estimate the emotions of our employees and adjust the selection criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is When selecting employees with expertise in specific technologies, we consider the latest trends and future potential of those technologies. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is When selecting employees with experience successfully completing similar projects in the past, we evaluate the likelihood of replicating that success. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is The system estimates employees' emotions and determines selection priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is When selecting employees, we take into consideration the resource situation of other internal projects. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is When selecting employees, we take into account the opinions of external experts and consultants. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned negotiating body said, We estimate the emotions of our employees and adjust the negotiation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned negotiating body said, When considering key factors such as resource pricing, delivery time, quality, and service, we refer to past negotiation data to determine the best arrangement. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned negotiating body said, Estimate employees' emotions and determine negotiation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned negotiating body said, When negotiating resources, take into account the resource situation of other projects within the company. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, We estimate the emotions of our employees and adjust the way we present proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When proposing external experts with specific skills, consider their past performance and reputation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, The system estimates employees' emotions and prioritizes proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When proposing external experts, consider the resource situation of other internal projects. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned scheduling unit is We estimate employees' emotions and adjust the scheduling process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned scheduling unit is When creating a project schedule, refer to past project schedule data to create the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned scheduling unit is The system estimates employees' emotions and prioritizes schedules based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned scheduling unit is When creating a project schedule, take into account the schedule status of other projects within the company. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, Estimate employees' emotions and adjust progress management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, When managing project progress, refer to past project progress data to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, The system estimates employees' emotions and prioritizes progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, When managing project progress, take into account the progress of other projects within the company. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes employees' company history and skills, A selection unit selects the optimal resource based on the information analyzed by the aforementioned analysis unit, A negotiation unit that automates resource transfer negotiations based on the resources selected by the aforementioned selection unit, The proposal department, which proposes external personnel, The scheduling department creates the project schedule, It includes a management department that manages the progress of the project. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze employees' past project experience and skill sets in detail. The system according to feature 1.

3. The aforementioned selection unit is Select employees who are proficient in specific technologies or who have experience successfully completing similar projects in the past. The system according to feature 1.

4. The aforementioned negotiating body said, We will make efficient arrangements by considering important factors such as resource pricing, delivery time, quality, and service. The system according to feature 1.

5. The aforementioned proposal section is, We propose external experts with specific skills. The system according to feature 1.

6. The aforementioned scheduling unit is Create a project schedule The system according to feature 1.

7. The aforementioned management department, Manage project progress The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the emotions of employees and adjusts the timing of the analysis based on the estimated emotions. The system according to feature 1.

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