system

Abstract

The system according to this embodiment aims to reduce the workload of project managers and improve the success rate of projects. [Solution] The system according to the embodiment comprises a reception unit, an optimization unit, a monitoring unit, a prediction unit, and an assignment unit. The reception unit receives input for the project's objectives and requirements. The optimization unit optimizes the project plan based on the information entered by the reception unit. The monitoring unit monitors the project's progress based on the project plan generated by the optimization unit. The prediction unit predicts risks based on the progress monitored by the monitoring unit. The assignment unit automatically assigns additional tasks based on the risks predicted by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the load on the project manager is large, and there are problems in the success rate of the project and cost reduction.

[0005] The system according to the embodiment aims to reduce the load on the project manager and improve the success rate of the project.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an optimization unit, a monitoring unit, a prediction unit, and an assignment unit. The reception unit receives input for the project's objectives and requirements. The optimization unit optimizes the project plan based on the information entered by the reception unit. The monitoring unit monitors the project's progress based on the project plan generated by the optimization unit. The prediction unit predicts risks based on the progress monitored by the monitoring unit. The assignment unit automatically assigns additional tasks based on the risks predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the workload of project managers and improve the success rate of projects. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0016] [First Embodiment] [[ID=A]]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 receiving 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 receiving 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 outsourcing and support agent according to an embodiment of the present invention is a system that reduces the workload of project managers and contributes to improving project success rates and reducing costs. This system uses a generating AI to optimize project plans and achieve efficient resource allocation. It supports the project initiation phase, such as defining project scope and creating project plans. The generating AI understands the project objectives and requirements entered by the user and automatically generates an optimal plan. This includes timelines, milestones, resource allocation, and risk analysis. The generating AI learns from past project data and identifies planning patterns with a high probability of success to provide the optimal project plan. Next, it monitors the project progress and performs risk prediction and automatic assignment of additional tasks. The generating AI monitors the project progress in real time and predicts risks. For example, it detects risks such as resource shortages and schedule delays early and proposes countermeasures. It also ensures smooth project progress by automatically assigning additional tasks as needed. Furthermore, the generating AI provides support even in the project closing phase. It assists with confirming and evaluating project deliverables and reviewing the project. This reduces the workload of project managers and improves project success rates throughout the entire project process. This system allows less experienced project managers, leaders handling multiple projects, corporate management and PMOs (Project Management Offices), and small and medium-sized enterprises (SMEs) and startups to efficiently manage projects. It solves challenges such as the difficulty of project planning, lack of experience, handling change requests, and communication problems, thereby increasing the project success rate. As a result, project management outsourcing and support agencies can reduce the burden on project managers throughout the entire project process and improve the project success rate.

[0029] The project management outsourcing and support agent according to this embodiment comprises a reception unit, an optimization unit, a monitoring unit, a forecasting unit, and an allocation unit. The reception unit receives input for the project's objectives and requirements. Project objectives and requirements include, but are not limited to, project goals, required resources, and deadlines. The reception unit provides, for example, an interface for the user to input project objectives and requirements. The interface can support multiple input methods, such as text input, voice input, and selection from a list. The optimization unit uses a generative AI to optimize the project plan based on the information entered by the reception unit. The optimization unit learns from past project data and identifies planning patterns with a high probability of success. The generative AI generates a project plan that includes a project timeline, milestones, resource allocation, and risk analysis. For example, the generative AI sets the project start date, end date, and key milestones, and optimizes resource allocation. The monitoring unit monitors the project's progress based on the project plan generated by the optimization unit. The monitoring unit monitors the project's progress in real time and detects anomalies early. The monitoring unit can provide a dashboard for monitoring the project's progress. The dashboard visually displays project progress, task completion status, resource usage, and more. The forecasting unit predicts risks based on the progress monitored by the monitoring unit. The forecasting unit can detect risks early, such as resource shortages or schedule delays. The forecasting unit can evaluate the probability and impact of risks and propose countermeasures. The assignment unit automatically assigns additional tasks based on the risks predicted by the forecasting unit. For example, the assignment unit automatically assigns additional tasks to compensate for resource shortages. The assignment unit can make optimal task assignments by considering task priorities and resource utilization. As a result, the project management outsourcing and support agent according to this embodiment can reduce the workload on the project manager and improve the project success rate throughout the entire project process.

[0030] The reception desk receives input for project objectives and requirements. These objectives and requirements include, but are not limited to, project goals, required resources, and deadlines. The reception desk provides an interface for users to input project objectives and requirements. The interface can support multiple input methods, such as text input, voice input, and selection from multiple-choice options. Specifically, text input allows users to enter project details using a keyboard. Voice input allows users to dictate project requirements using a microphone, which is then converted to text using speech recognition technology. Selection from multiple-choice options allows users to choose the appropriate item from pre-configured options. This allows users to input project information in the way that best suits them. Furthermore, the reception desk has the functionality to automatically categorize and organize the input information. For example, it categorizes information such as project goals, resources, and deadlines into appropriate categories to facilitate subsequent processing. The reception desk also checks the consistency of the input information and can notify the user of any missing or inconsistent information, prompting corrections. This allows the reception desk to accurately and efficiently collect necessary information in the early stages of a project, laying the foundation for project success.

[0031] The optimization unit uses generative AI to optimize the project plan based on the information entered by the reception unit. For example, the optimization unit learns from past project data to identify planning patterns with a high probability of success. The generative AI generates a project plan that includes the project timeline, milestones, resource allocation, and risk analysis. Specifically, the generative AI sets the project start date, end date, and key milestones, and optimizes resource allocation. For example, it determines the priority of tasks in each phase of the project and allocates resources efficiently. The generative AI also evaluates the probability and impact of risks based on past project data and proposes measures to minimize risks. Furthermore, the generative AI can simulate the progress of the project and evaluate the feasibility of the plan. This allows the optimization unit to provide the optimal plan to increase the success rate of the project. In addition, the optimization unit presents the plan generated by the generative AI to the user and provides an interface that allows the user to review the plan and make modifications as needed. This allows the user to adjust the project details based on the generated plan and finalize the optimal plan. The optimization unit can continuously improve the accuracy and reliability of the plan by collecting user feedback and using it as training data for the generative AI.

[0032] The monitoring unit monitors the project's progress based on the project plan generated by the optimization unit. For example, the monitoring unit monitors the project's progress in real time and detects anomalies early. The monitoring unit can provide a dashboard for monitoring project progress. The dashboard visually displays project progress, task completion status, resource usage, etc. Specifically, the dashboard displays the progress of each project task using color coding, allowing for an at-a-glance understanding of task completion rates and delays. It also displays resource usage in graphs and charts, allowing for real-time confirmation of resource surpluses and shortages. Furthermore, the monitoring unit has an alert function regarding project progress, and immediately notifies the user if an anomaly is detected. For example, if a task is delayed or a resource shortage occurs, the monitoring unit issues an alert and prompts the user to take action. This allows the monitoring unit to grasp the project's progress in real time, detect anomalies early, and take countermeasures. In addition, the monitoring unit can accumulate data on project progress and utilize it for subsequent project planning and risk prediction. This allows the monitoring unit to efficiently monitor the project's progress and improve its success rate.

[0033] The forecasting unit predicts risks based on progress monitored by the monitoring unit. The forecasting unit detects risks early, such as resource shortages or schedule delays. It can evaluate the probability and impact of risks and propose countermeasures. Specifically, the forecasting unit analyzes progress data provided by the monitoring unit to detect signs of risk. For example, if a task is behind schedule, it identifies the cause and evaluates the impact of the delay on the overall project. It also analyzes resource usage and predicts the likelihood of resource shortages. The forecasting unit can propose specific countermeasures for these risks. For example, for task delays, it might suggest allocating additional resources or changing task priorities. For resource shortages, it might suggest reallocating resources or securing additional resources. Furthermore, the forecasting unit quantitatively evaluates the probability and impact of risks and determines risk priorities. This allows users to respond quickly to the most critical risks. The forecasting unit can utilize historical project data and statistical information to improve the accuracy of risk predictions. This allows the forecasting unit to detect risks early based on the project's progress and propose appropriate countermeasures, thereby improving the project's success rate.

[0034] The assignment unit automatically assigns additional tasks based on the risks predicted by the forecasting unit. For example, the assignment unit automatically assigns additional tasks to compensate for resource shortages. The assignment unit can make optimal task assignments by considering task priorities and resource utilization. Specifically, the assignment unit determines the need for additional tasks based on the risk information provided by the forecasting unit. For example, if a resource shortage is predicted, it automatically generates tasks to secure additional resources and assigns them to the appropriate person. Also, if a task delay is predicted, it generates tasks to resolve the delay and assigns them considering priority. The assignment unit can monitor the task assignment status in real time and reassign tasks as needed. For example, if a person in charge lacks resources, it reassigns the task to another person. It also monitors the progress of tasks and, if progress is behind schedule, can allocate additional resources or change the task priority. This allows the assignment unit to respond flexibly according to the progress of the project and improve the project's success rate. Furthermore, the assignment unit can accumulate a history of task assignments and use it for subsequent project planning and risk forecasting. This allows the assignment unit to make optimal task assignments according to the project's progress, thereby improving the project's success rate.

[0035] The optimization unit can learn from past project data and identify planning patterns with a high probability of success. For example, the optimization unit collects past project data and learns from it using a generative AI. The generative AI analyzes project deliverables, progress data, resource usage, etc., to identify planning patterns with a high probability of success. For example, the generative AI extracts commonalities from successful projects and optimizes new project plans based on them. The optimization unit can also learn from data of failed projects and identify the causes of failure. In this way, by learning from past project data, it is possible to identify planning patterns with a high probability of success. Past project data includes, but is not limited to, project deliverables, progress data, resource usage, etc. Planning patterns with a high probability of success are identified using, for example, analysis of successful cases, statistical methods, etc. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input past project data into a generative AI and have the generative AI perform the identification of planning patterns with a high probability of success.

[0036] The monitoring unit can monitor the project's progress in real time. For example, the monitoring unit can monitor the project's progress in real time and detect anomalies early. The monitoring unit can provide a dashboard for monitoring the project's progress. The dashboard visually displays the project's progress, task completion status, resource usage, etc. For example, the monitoring unit can display the project's progress in graphs and charts, allowing for an at-a-glance understanding of the progress. The monitoring unit can also display task completion status in a list format, clearly identifying incomplete tasks. Furthermore, the monitoring unit can monitor resource usage in real time and detect resource shortages or excesses early. This allows for early detection of anomalies by monitoring the project's progress in real time. Real-time monitoring includes, but is not limited to, data update frequency and monitoring system response time. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or not. For example, the monitoring unit can input project progress data into AI and have the AI ​​perform anomaly detection.

[0037] The forecasting unit can detect risks such as resource shortages and schedule delays early. For example, the forecasting unit monitors the progress of a project and detects risks such as resource shortages and schedule delays early. The forecasting unit can evaluate the probability and impact of risks and propose countermeasures. For example, the forecasting unit monitors resource usage and proposes arranging additional resources if resource shortages are expected. The forecasting unit also monitors the progress of the schedule and proposes schedule adjustments if schedule delays are expected. Furthermore, the forecasting unit can evaluate the probability and impact of risks and determine the priority of risks. This allows for the early detection of risks such as resource shortages and schedule delays, enabling rapid countermeasures. Resource shortages include, but are not limited to, the required amount of resources and the amount of resources available. Schedule delays include, but are not limited to, the difference between planned and actual results and the causes of delays. Some or all of the above processing in the forecasting unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input project progress data into the AI ​​and have the AI ​​perform risk detection.

[0038] The assignment unit can automatically assign additional tasks as needed. For example, it can automatically assign additional tasks to compensate for resource shortages. The assignment unit can make optimal task assignments by considering task priorities and resource utilization. For example, if resource shortages are anticipated, the assignment unit can automatically arrange for additional resources. Also, if schedule delays are anticipated, the assignment unit can automatically adjust the schedule. Furthermore, the assignment unit can evaluate the probability and impact of risks and assign additional tasks based on risk priorities. This ensures smooth project progress by automatically assigning additional tasks as needed. Criteria for "as needed" include, but are not limited to, the results of progress evaluations and the occurrence of risks. Some or all of the above processes in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input project progress data into AI and have the AI ​​perform the assignment of additional tasks.

[0039] The optimization unit can generate a project plan that includes a timeline, milestones, resource allocation, and risk analysis. For example, the optimization unit sets the project start date, end date, and important milestones, and optimizes resource allocation. The optimization unit uses generation AI to generate a project plan that includes a timeline, milestones, resource allocation, and risk analysis. For example, the generation AI sets the project start date, end date, and important milestones, and optimizes resource allocation. The optimization unit can also analyze project risks and evaluate the probability and impact of those risks. This streamlines project planning by generating a project plan that includes a timeline, milestones, resource allocation, and risk analysis. The timeline includes, for example, the project start date, end date, and important milestones. Milestones include, for example, the completion date of important tasks and progress checkpoints. Resource allocation includes, for example, resource types and allocation criteria. Risk analysis includes, for example, methods for identifying risks and methods for evaluating their impact. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the optimization unit can input project planning data into a generation AI and have the generation AI generate the project plan.

[0040] The reception desk can analyze the input history of past project objectives and requirements and suggest the optimal input method. For example, the reception desk can automatically display project objectives and requirements that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest project objectives and requirements to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Input history includes, but is not limited to, past input data and analysis methods. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input method.

[0041] The reception desk can filter project objectives and requirements based on the user's current work status and areas of interest. For example, the reception desk can analyze the user's current work status and prioritize displaying relevant project objectives and requirements. It can also suggest highly relevant project objectives and requirements based on the user's areas of interest. Furthermore, the reception desk can consider the user's work schedule and suggest the optimal timing for input. This allows for the provision of highly relevant information by filtering based on the user's work status and areas of interest. Work status includes, but is not limited to, current tasks and work progress. Areas of interest include, but is not limited to, the user's past activities and topics of interest. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's work status data into AI and have the AI ​​perform the filtering.

[0042] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting project objectives and requirements. For example, the reception desk can suggest relevant project objectives and requirements based on the user's current location. The reception desk can also prioritize inputting region-specific requirements by considering the user's geographical location. Furthermore, the reception desk can suggest optimal project objectives and requirements based on the user's location information. This allows for the priority input of region-specific requirements by considering the user's geographical location. Geographical location information includes, but is not limited to, the method of acquiring location information and region-specific requirements. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into AI and have the AI ​​suggest highly relevant information.

[0043] The reception desk can analyze the user's social media activity and input relevant information when the user inputs project objectives and requirements. For example, the reception desk can analyze the user's social media activity and suggest relevant project objectives and requirements. It can also input optimal project objectives and requirements based on the user's interests on social media. Furthermore, the reception desk can input relevant information by referring to the content of the user's social media posts. This allows the reception desk to provide highly relevant information by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and activity frequency. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​suggest relevant information.

[0044] The optimization unit can apply different optimization algorithms depending on the project category when optimizing the project plan. For example, the optimization unit can select the optimal optimization algorithm depending on the project category. Furthermore, the optimization unit can apply different optimization algorithms based on the characteristics of the project. In addition, the optimization unit can adjust the algorithm for generating the optimal plan for each project category. This allows for the generation of an optimal project plan by applying the optimization algorithm according to the project category. The optimization algorithm includes, but is not limited to, the type of algorithm used and the criteria for its application. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input project category data into a generative AI and have the generative AI execute the application of the optimization algorithm.

[0045] The optimization unit can determine the priority of projects based on their submission dates when optimizing project plans. For example, the optimization unit can determine the priority of projects based on their submission dates. The optimization unit can also prioritize the optimization of projects with approaching deadlines. Furthermore, the optimization unit can adjust the level of detail of the plans according to the submission dates. This allows for the generation of plans that meet deadlines by determining the priority of projects based on their submission dates. The submission dates include, but are not limited to, the submission deadline and the importance of the submission. Some or all of the above processing in the optimization unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the optimization unit can input project submission date data into a generation AI and have the generation AI determine the priority of the plans.

[0046] The monitoring unit can apply different monitoring algorithms depending on the project category when monitoring the progress of a project. For example, the monitoring unit can select the optimal monitoring algorithm depending on the project category. Furthermore, the monitoring unit can apply different monitoring algorithms based on the characteristics of the project. In addition, the monitoring unit can provide the optimal monitoring method for each project category. This allows for the provision of the optimal monitoring method by applying monitoring algorithms according to the project category. The monitoring algorithm includes, but is not limited to, the type of algorithm used and the criteria for application. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input project category data into AI and have AI perform the application of monitoring algorithms.

[0047] The monitoring unit can perform monitoring of project progress while considering the geographical distribution of the project. For example, the monitoring unit can consider the geographical distribution of the project and provide the optimal monitoring method. Furthermore, the monitoring unit can efficiently monitor geographically dispersed projects. In addition, the monitoring unit can detect anomalies early based on the geographical distribution. Thus, efficient monitoring becomes possible by considering the geographical distribution of the project. Geographical distribution includes, but is not limited to, project location information and distribution evaluation methods. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input project geographical distribution data into AI and have the AI ​​perform the monitoring.

[0048] The monitoring unit can improve the accuracy of its monitoring by referring to relevant project documentation when monitoring the progress of the project. For example, the monitoring unit can improve the accuracy of its monitoring by referring to relevant project documentation. In addition, the monitoring unit can detect anomalies early based on the relevant documentation. Furthermore, the monitoring unit can utilize relevant documentation when monitoring the progress of the project. This improves the accuracy of monitoring by referring to relevant documentation. Relevant documentation includes, but is not limited to, the type of documentation and the method of reference. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input project-related documentation data into AI and have the AI ​​perform the monitoring.

[0049] The prediction unit can apply different prediction algorithms depending on the project category when predicting risk. For example, the prediction unit can select the optimal prediction algorithm depending on the project category. Furthermore, the prediction unit can apply different prediction algorithms based on the characteristics of the project. In addition, the prediction unit can provide the optimal risk prediction method for each project category. This allows for optimal risk prediction by applying prediction algorithms according to the project category. The prediction algorithm includes, but is not limited to, the type of algorithm used and the criteria for application. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input project category data into AI and have the AI ​​apply the prediction algorithm.

[0050] The prediction unit can determine risk priorities based on project submission deadlines when predicting risks. For example, the prediction unit can determine risk priorities based on project submission deadlines. The prediction unit can also prioritize predicting risks for projects with approaching deadlines. Furthermore, the prediction unit can adjust the importance of risks according to the submission deadline. This allows for measures to be taken to meet submission deadlines by determining risk priorities based on project submission deadlines. Risk prioritization includes, but is not limited to, risk importance and prioritization methods. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input project submission deadline data into AI and have the AI ​​perform the determination of risk priorities.

[0051] The assignment unit can apply different assignment algorithms depending on the project category when assigning additional tasks. For example, the assignment unit can select the optimal assignment algorithm depending on the project category. Furthermore, the assignment unit can apply different assignment algorithms based on the characteristics of the project. In addition, the assignment unit can provide the optimal task assignment method for each project category. This allows for optimal task assignment by applying the assignment algorithm according to the project category. The assignment algorithm includes, but is not limited to, the type of algorithm used and the criteria for its application. Some or all of the above processing in the assignment unit may be performed using AI, or not. For example, the assignment unit can input project category data into the AI ​​and have the AI ​​apply the assignment algorithm.

[0052] The assignment unit can consider the geographical distribution of projects when assigning additional tasks. For example, the assignment unit can consider the geographical distribution of projects and provide an optimal task assignment method. The assignment unit can also efficiently manage geographically dispersed projects. Furthermore, the assignment unit can determine task priorities based on geographical distribution. This enables efficient task assignment by considering the geographical distribution of projects. Geographical distribution includes, but is not limited to, project location information and distribution evaluation methods. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input project geographical distribution data into AI and have the AI ​​perform task assignment.

[0053] The assignment unit can improve the accuracy of assignments by referring to relevant project literature when assigning additional tasks. For example, the assignment unit can improve the accuracy of task assignments by referring to relevant project literature. The assignment unit can also provide the optimal task assignment method based on the relevant literature. Furthermore, the assignment unit can utilize relevant literature according to the progress of the project. This improves the accuracy of task assignments by referring to relevant literature. Relevant literature includes, but is not limited to, the type of literature and the method of reference. Some or all of the above processing in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input project relevant literature data into AI and have the AI ​​perform task assignments.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The optimization unit can apply different optimization algorithms depending on the project category when optimizing a project plan. For example, it can select the optimal optimization algorithm depending on the project category. It can also apply different optimization algorithms based on the characteristics of the project. Furthermore, it can adjust the algorithm for generating the optimal plan for each project category. This allows for the generation of an optimal project plan by applying the optimization algorithm according to the project category. The optimization algorithm includes, but is not limited to, the type of algorithm used and the criteria for application. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input project category data into a generative AI and have the generative AI execute the application of the optimization algorithm.

[0056] The prediction unit can apply different prediction algorithms depending on the project category when predicting risk. For example, it can select the optimal prediction algorithm depending on the project category. It can also apply different prediction algorithms based on the characteristics of the project. Furthermore, it can provide the optimal risk prediction method for each project category. This allows for optimal risk prediction by applying prediction algorithms according to the project category. The prediction algorithm includes, but is not limited to, the type of algorithm used and the criteria for application. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input project category data into AI and have AI perform the application of prediction algorithms.

[0057] The reception desk can analyze the input history of past project objectives and requirements and suggest the optimal input method. For example, it can automatically display project objectives and requirements that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest project objectives and requirements to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Input history includes, but is not limited to, past input data and analysis methods. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input method.

[0058] The monitoring unit can perform monitoring of project progress while considering the geographical distribution of the project. For example, it can provide the optimal monitoring method by considering the geographical distribution of the project. It can also efficiently monitor geographically dispersed projects. Furthermore, it can detect anomalies early based on the geographical distribution. Thus, efficient monitoring becomes possible by considering the geographical distribution of the project. Geographical distribution includes, but is not limited to, project location information and distribution evaluation methods. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input project geographical distribution data into AI and have the AI ​​perform the monitoring.

[0059] The assignment unit can consider the geographical distribution of projects when assigning additional tasks. For example, it can provide an optimal task assignment method by considering the geographical distribution of projects. It can also efficiently manage geographically dispersed projects. Furthermore, it can determine task priorities based on geographical distribution. This makes efficient task assignment possible by considering the geographical distribution of projects. Geographical distribution includes, but is not limited to, project location information and distribution evaluation methods. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input project geographical distribution data into AI and have the AI ​​perform task assignment.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives the project's objectives and requirements. These include, for example, project goals, required resources, and deadlines. The reception desk provides an interface for users to enter the project's objectives and requirements, supporting multiple input methods such as text input, voice input, and selection from multiple-choice options. Step 2: The optimization unit uses a generation AI to optimize the project plan based on the information entered by the reception unit. The optimization unit learns from past project data and identifies planning patterns with a high probability of success. The generation AI generates a project plan that includes the project timeline, milestones, resource allocation, and risk analysis. Step 3: The monitoring unit monitors the project progress based on the project plan generated by the optimization unit. The monitoring unit monitors the project progress in real time and detects anomalies early. The monitoring unit provides a dashboard for monitoring project progress, visually displaying project progress, task completion status, resource usage, etc. Step 4: The prediction unit predicts risks based on the progress monitored by the monitoring unit. The prediction unit detects risks such as resource shortages and schedule delays early, evaluates the probability and impact of the risks, and proposes countermeasures. Step 5: The allocation unit automatically assigns additional tasks based on the risks predicted by the forecasting unit. The allocation unit automatically assigns additional tasks to compensate for resource shortages, and makes optimal task assignments considering task priorities and resource utilization.

[0062] (Example of form 2) The project management outsourcing and support agent according to an embodiment of the present invention is a system that reduces the workload of project managers and contributes to improving project success rates and reducing costs. This system uses a generating AI to optimize project plans and achieve efficient resource allocation. It supports the project initiation phase, such as defining project scope and creating project plans. The generating AI understands the project objectives and requirements entered by the user and automatically generates an optimal plan. This includes timelines, milestones, resource allocation, and risk analysis. The generating AI learns from past project data and identifies planning patterns with a high probability of success to provide the optimal project plan. Next, it monitors the project progress and performs risk prediction and automatic assignment of additional tasks. The generating AI monitors the project progress in real time and predicts risks. For example, it detects risks such as resource shortages and schedule delays early and proposes countermeasures. It also ensures smooth project progress by automatically assigning additional tasks as needed. Furthermore, the generating AI provides support even in the project closing phase. It assists with confirming and evaluating project deliverables and reviewing the project. This reduces the workload of project managers and improves project success rates throughout the entire project process. This system allows less experienced project managers, leaders handling multiple projects, corporate management and PMOs (Project Management Offices), and small and medium-sized enterprises (SMEs) and startups to efficiently manage projects. It solves challenges such as the difficulty of project planning, lack of experience, handling change requests, and communication problems, thereby increasing the project success rate. As a result, project management outsourcing and support agencies can reduce the burden on project managers throughout the entire project process and improve the project success rate.

[0063] The project management outsourcing and support agent according to this embodiment comprises a reception unit, an optimization unit, a monitoring unit, a forecasting unit, and an allocation unit. The reception unit receives input for the project's objectives and requirements. Project objectives and requirements include, but are not limited to, project goals, required resources, and deadlines. The reception unit provides, for example, an interface for the user to input project objectives and requirements. The interface can support multiple input methods, such as text input, voice input, and selection from a list. The optimization unit uses a generative AI to optimize the project plan based on the information entered by the reception unit. The optimization unit learns from past project data and identifies planning patterns with a high probability of success. The generative AI generates a project plan that includes a project timeline, milestones, resource allocation, and risk analysis. For example, the generative AI sets the project start date, end date, and key milestones, and optimizes resource allocation. The monitoring unit monitors the project's progress based on the project plan generated by the optimization unit. The monitoring unit monitors the project's progress in real time and detects anomalies early. The monitoring unit can provide a dashboard for monitoring the project's progress. The dashboard visually displays project progress, task completion status, resource usage, and more. The forecasting unit predicts risks based on the progress monitored by the monitoring unit. The forecasting unit can detect risks early, such as resource shortages or schedule delays. The forecasting unit can evaluate the probability and impact of risks and propose countermeasures. The assignment unit automatically assigns additional tasks based on the risks predicted by the forecasting unit. For example, the assignment unit automatically assigns additional tasks to compensate for resource shortages. The assignment unit can make optimal task assignments by considering task priorities and resource utilization. As a result, the project management outsourcing and support agent according to this embodiment can reduce the workload on the project manager and improve the project success rate throughout the entire project process.

[0064] The reception desk receives input for project objectives and requirements. These objectives and requirements include, but are not limited to, project goals, required resources, and deadlines. The reception desk provides an interface for users to input project objectives and requirements. The interface can support multiple input methods, such as text input, voice input, and selection from multiple-choice options. Specifically, text input allows users to enter project details using a keyboard. Voice input allows users to dictate project requirements using a microphone, which is then converted to text using speech recognition technology. Selection from multiple-choice options allows users to choose the appropriate item from pre-configured options. This allows users to input project information in the way that best suits them. Furthermore, the reception desk has the functionality to automatically categorize and organize the input information. For example, it categorizes information such as project goals, resources, and deadlines into appropriate categories to facilitate subsequent processing. The reception desk also checks the consistency of the input information and can notify the user of any missing or inconsistent information, prompting corrections. This allows the reception desk to accurately and efficiently collect necessary information in the early stages of a project, laying the foundation for project success.

[0065] The optimization unit uses generative AI to optimize the project plan based on the information entered by the reception unit. For example, the optimization unit learns from past project data to identify planning patterns with a high probability of success. The generative AI generates a project plan that includes the project timeline, milestones, resource allocation, and risk analysis. Specifically, the generative AI sets the project start date, end date, and key milestones, and optimizes resource allocation. For example, it determines the priority of tasks in each phase of the project and allocates resources efficiently. The generative AI also evaluates the probability and impact of risks based on past project data and proposes measures to minimize risks. Furthermore, the generative AI can simulate the progress of the project and evaluate the feasibility of the plan. This allows the optimization unit to provide the optimal plan to increase the success rate of the project. In addition, the optimization unit presents the plan generated by the generative AI to the user and provides an interface that allows the user to review the plan and make modifications as needed. This allows the user to adjust the project details based on the generated plan and finalize the optimal plan. The optimization unit can continuously improve the accuracy and reliability of the plan by collecting user feedback and using it as training data for the generative AI.

[0066] The monitoring unit monitors the project's progress based on the project plan generated by the optimization unit. For example, the monitoring unit monitors the project's progress in real time and detects anomalies early. The monitoring unit can provide a dashboard for monitoring project progress. The dashboard visually displays project progress, task completion status, resource usage, etc. Specifically, the dashboard displays the progress of each project task using color coding, allowing for an at-a-glance understanding of task completion rates and delays. It also displays resource usage in graphs and charts, allowing for real-time confirmation of resource surpluses and shortages. Furthermore, the monitoring unit has an alert function regarding project progress, and immediately notifies the user if an anomaly is detected. For example, if a task is delayed or a resource shortage occurs, the monitoring unit issues an alert and prompts the user to take action. This allows the monitoring unit to grasp the project's progress in real time, detect anomalies early, and take countermeasures. In addition, the monitoring unit can accumulate data on project progress and utilize it for subsequent project planning and risk prediction. This allows the monitoring unit to efficiently monitor the project's progress and improve its success rate.

[0067] The forecasting unit predicts risks based on progress monitored by the monitoring unit. The forecasting unit detects risks early, such as resource shortages or schedule delays. It can evaluate the probability and impact of risks and propose countermeasures. Specifically, the forecasting unit analyzes progress data provided by the monitoring unit to detect signs of risk. For example, if a task is behind schedule, it identifies the cause and evaluates the impact of the delay on the overall project. It also analyzes resource usage and predicts the likelihood of resource shortages. The forecasting unit can propose specific countermeasures for these risks. For example, for task delays, it might suggest allocating additional resources or changing task priorities. For resource shortages, it might suggest reallocating resources or securing additional resources. Furthermore, the forecasting unit quantitatively evaluates the probability and impact of risks and determines risk priorities. This allows users to respond quickly to the most critical risks. The forecasting unit can utilize historical project data and statistical information to improve the accuracy of risk predictions. This allows the forecasting unit to detect risks early based on the project's progress and propose appropriate countermeasures, thereby improving the project's success rate.

[0068] The assignment unit automatically assigns additional tasks based on the risks predicted by the forecasting unit. For example, the assignment unit automatically assigns additional tasks to compensate for resource shortages. The assignment unit can make optimal task assignments by considering task priorities and resource utilization. Specifically, the assignment unit determines the need for additional tasks based on the risk information provided by the forecasting unit. For example, if a resource shortage is predicted, it automatically generates tasks to secure additional resources and assigns them to the appropriate person. Also, if a task delay is predicted, it generates tasks to resolve the delay and assigns them considering priority. The assignment unit can monitor the task assignment status in real time and reassign tasks as needed. For example, if a person in charge lacks resources, it reassigns the task to another person. It also monitors the progress of tasks and, if progress is behind schedule, can allocate additional resources or change the task priority. This allows the assignment unit to respond flexibly according to the progress of the project and improve the project's success rate. Furthermore, the assignment unit can accumulate a history of task assignments and use it for subsequent project planning and risk forecasting. This allows the assignment unit to make optimal task assignments according to the project's progress, thereby improving the project's success rate.

[0069] The optimization unit can learn from past project data and identify planning patterns with a high probability of success. For example, the optimization unit collects past project data and learns from it using a generative AI. The generative AI analyzes project deliverables, progress data, resource usage, etc., to identify planning patterns with a high probability of success. For example, the generative AI extracts commonalities from successful projects and optimizes new project plans based on them. The optimization unit can also learn from data of failed projects and identify the causes of failure. In this way, by learning from past project data, it is possible to identify planning patterns with a high probability of success. Past project data includes, but is not limited to, project deliverables, progress data, resource usage, etc. Planning patterns with a high probability of success are identified using, for example, analysis of successful cases, statistical methods, etc. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input past project data into a generative AI and have the generative AI perform the identification of planning patterns with a high probability of success.

[0070] The monitoring unit can monitor the project's progress in real time. For example, the monitoring unit can monitor the project's progress in real time and detect anomalies early. The monitoring unit can provide a dashboard for monitoring the project's progress. The dashboard visually displays the project's progress, task completion status, resource usage, etc. For example, the monitoring unit can display the project's progress in graphs and charts, allowing for an at-a-glance understanding of the progress. The monitoring unit can also display task completion status in a list format, clearly identifying incomplete tasks. Furthermore, the monitoring unit can monitor resource usage in real time and detect resource shortages or excesses early. This allows for early detection of anomalies by monitoring the project's progress in real time. Real-time monitoring includes, but is not limited to, data update frequency and monitoring system response time. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or not. For example, the monitoring unit can input project progress data into AI and have the AI ​​perform anomaly detection.

[0071] The forecasting unit can detect risks such as resource shortages and schedule delays early. For example, the forecasting unit monitors the progress of a project and detects risks such as resource shortages and schedule delays early. The forecasting unit can evaluate the probability and impact of risks and propose countermeasures. For example, the forecasting unit monitors resource usage and proposes arranging additional resources if resource shortages are expected. The forecasting unit also monitors the progress of the schedule and proposes schedule adjustments if schedule delays are expected. Furthermore, the forecasting unit can evaluate the probability and impact of risks and determine the priority of risks. This allows for the early detection of risks such as resource shortages and schedule delays, enabling rapid countermeasures. Resource shortages include, but are not limited to, the required amount of resources and the amount of resources available. Schedule delays include, but are not limited to, the difference between planned and actual results and the causes of delays. Some or all of the above processing in the forecasting unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input project progress data into the AI ​​and have the AI ​​perform risk detection.

[0072] The assignment unit can automatically assign additional tasks as needed. For example, it can automatically assign additional tasks to compensate for resource shortages. The assignment unit can make optimal task assignments by considering task priorities and resource utilization. For example, if resource shortages are anticipated, the assignment unit can automatically arrange for additional resources. Also, if schedule delays are anticipated, the assignment unit can automatically adjust the schedule. Furthermore, the assignment unit can evaluate the probability and impact of risks and assign additional tasks based on risk priorities. This ensures smooth project progress by automatically assigning additional tasks as needed. Criteria for "as needed" include, but are not limited to, the results of progress evaluations and the occurrence of risks. Some or all of the above processes in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input project progress data into AI and have the AI ​​perform the assignment of additional tasks.

[0073] The optimization unit can generate a project plan that includes a timeline, milestones, resource allocation, and risk analysis. For example, the optimization unit sets the project start date, end date, and important milestones, and optimizes resource allocation. The optimization unit uses generation AI to generate a project plan that includes a timeline, milestones, resource allocation, and risk analysis. For example, the generation AI sets the project start date, end date, and important milestones, and optimizes resource allocation. The optimization unit can also analyze project risks and evaluate the probability and impact of those risks. This streamlines project planning by generating a project plan that includes a timeline, milestones, resource allocation, and risk analysis. The timeline includes, for example, the project start date, end date, and important milestones. Milestones include, for example, the completion date of important tasks and progress checkpoints. Resource allocation includes, for example, resource types and allocation criteria. Risk analysis includes, for example, methods for identifying risks and methods for evaluating their impact. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the optimization unit can input project planning data into a generation AI and have the generation AI generate the project plan.

[0074] The reception desk can estimate the user's emotions and adjust the input method for project objectives and requirements based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of project objectives and requirements. This reduces the user's burden by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can analyze the input history of past project objectives and requirements and suggest the optimal input method. For example, the reception desk can automatically display project objectives and requirements that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest project objectives and requirements to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Input history includes, but is not limited to, past input data and analysis methods. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input method.

[0076] The reception desk can filter project objectives and requirements based on the user's current work status and areas of interest. For example, the reception desk can analyze the user's current work status and prioritize displaying relevant project objectives and requirements. It can also suggest highly relevant project objectives and requirements based on the user's areas of interest. Furthermore, the reception desk can consider the user's work schedule and suggest the optimal timing for input. This allows for the provision of highly relevant information by filtering based on the user's work status and areas of interest. Work status includes, but is not limited to, current tasks and work progress. Areas of interest include, but is not limited to, the user's past activities and topics of interest. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's work status data into AI and have the AI ​​perform the filtering.

[0077] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of project objectives and requirements to be entered. For example, if the user is stressed, the reception desk may prompt them to prioritize entering important objectives and requirements. If the user is relaxed, the reception desk may suggest entering detailed objectives and requirements. Furthermore, if the user is in a hurry, the reception desk may enable them to quickly enter the most important objectives and requirements. This allows for the priority of important information to be entered by prioritizing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when inputting project objectives and requirements. For example, the reception desk can suggest relevant project objectives and requirements based on the user's current location. The reception desk can also prioritize inputting region-specific requirements by considering the user's geographical location. Furthermore, the reception desk can suggest optimal project objectives and requirements based on the user's location information. This allows for the priority input of region-specific requirements by considering the user's geographical location. Geographical location information includes, but is not limited to, the method of acquiring location information and region-specific requirements. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into AI and have the AI ​​suggest highly relevant information.

[0079] The reception desk can analyze the user's social media activity and input relevant information when the user inputs project objectives and requirements. For example, the reception desk can analyze the user's social media activity and suggest relevant project objectives and requirements. It can also input optimal project objectives and requirements based on the user's interests on social media. Furthermore, the reception desk can input relevant information by referring to the content of the user's social media posts. This allows the reception desk to provide highly relevant information by analyzing the user's social media activity. Social media activity includes, but is not limited to, analysis of post content and activity frequency. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into AI and have the AI ​​suggest relevant information.

[0080] The optimization unit can estimate the user's emotions and adjust the project plan optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit can generate a simple and efficient plan. If the user is relaxed, the optimization unit can generate a detailed plan. Furthermore, if the user is in a hurry, the optimization unit can generate a plan that can be quickly implemented. In this way, by adjusting the optimization method according to the user's emotions, the optimal project plan can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The optimization unit can apply different optimization algorithms depending on the project category when optimizing the project plan. For example, the optimization unit can select the optimal optimization algorithm depending on the project category. Furthermore, the optimization unit can apply different optimization algorithms based on the characteristics of the project. In addition, the optimization unit can adjust the algorithm for generating the optimal plan for each project category. This allows for the generation of an optimal project plan by applying the optimization algorithm according to the project category. The optimization algorithm includes, but is not limited to, the type of algorithm used and the criteria for its application. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input project category data into a generative AI and have the generative AI execute the application of the optimization algorithm.

[0082] The optimization unit can determine the priority of projects based on their submission dates when optimizing project plans. For example, the optimization unit can determine the priority of projects based on their submission dates. The optimization unit can also prioritize the optimization of projects with approaching deadlines. Furthermore, the optimization unit can adjust the level of detail of the plans according to the submission dates. This allows for the generation of plans that meet deadlines by determining the priority of projects based on their submission dates. The submission dates include, but are not limited to, the submission deadline and the importance of the submission. Some or all of the above processing in the optimization unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the optimization unit can input project submission date data into a generation AI and have the generation AI determine the priority of the plans.

[0083] The monitoring unit can estimate the user's emotions and adjust the project progress monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a simple and highly visible monitoring method. If the user is relaxed, the monitoring unit can provide detailed monitoring information. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise monitoring method. In this way, by adjusting the monitoring method according to the user's emotions, the optimal monitoring method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The monitoring unit can apply different monitoring algorithms depending on the project category when monitoring the progress of a project. For example, the monitoring unit can select the optimal monitoring algorithm depending on the project category. Furthermore, the monitoring unit can apply different monitoring algorithms based on the characteristics of the project. In addition, the monitoring unit can provide the optimal monitoring method for each project category. This allows for the provision of the optimal monitoring method by applying monitoring algorithms according to the project category. The monitoring algorithm includes, but is not limited to, the type of algorithm used and the criteria for application. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input project category data into AI and have AI perform the application of monitoring algorithms.

[0085] The monitoring unit can perform monitoring of project progress while considering the geographical distribution of the project. For example, the monitoring unit can consider the geographical distribution of the project and provide the optimal monitoring method. Furthermore, the monitoring unit can efficiently monitor geographically dispersed projects. In addition, the monitoring unit can detect anomalies early based on the geographical distribution. Thus, efficient monitoring becomes possible by considering the geographical distribution of the project. Geographical distribution includes, but is not limited to, project location information and distribution evaluation methods. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input project geographical distribution data into AI and have the AI ​​perform the monitoring.

[0086] The monitoring unit can improve the accuracy of its monitoring by referring to relevant project documentation when monitoring the progress of the project. For example, the monitoring unit can improve the accuracy of its monitoring by referring to relevant project documentation. In addition, the monitoring unit can detect anomalies early based on the relevant documentation. Furthermore, the monitoring unit can utilize relevant documentation when monitoring the progress of the project. This improves the accuracy of monitoring by referring to relevant documentation. Relevant documentation includes, but is not limited to, the type of documentation and the method of reference. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input project-related documentation data into AI and have the AI ​​perform the monitoring.

[0087] The prediction unit can estimate the user's emotions and adjust the risk prediction method based on the estimated user emotions. For example, if the user is stressed, the prediction unit can provide a simple and easy-to-understand risk prediction method. If the user is relaxed, the prediction unit can provide a detailed risk prediction. Furthermore, if the user is in a hurry, the prediction unit can provide a concise risk prediction. In this way, by adjusting the risk prediction method according to the user's emotions, the system can provide the user with the most optimal risk prediction. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The prediction unit can apply different prediction algorithms depending on the project category when predicting risk. For example, the prediction unit can select the optimal prediction algorithm depending on the project category. Furthermore, the prediction unit can apply different prediction algorithms based on the characteristics of the project. In addition, the prediction unit can provide the optimal risk prediction method for each project category. This allows for optimal risk prediction by applying prediction algorithms according to the project category. The prediction algorithm includes, but is not limited to, the type of algorithm used and the criteria for application. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input project category data into AI and have the AI ​​apply the prediction algorithm.

[0089] The prediction unit can determine risk priorities based on project submission deadlines when predicting risks. For example, the prediction unit can determine risk priorities based on project submission deadlines. The prediction unit can also prioritize predicting risks for projects with approaching deadlines. Furthermore, the prediction unit can adjust the importance of risks according to the submission deadline. This allows for measures to be taken to meet submission deadlines by determining risk priorities based on project submission deadlines. Risk prioritization includes, but is not limited to, risk importance and prioritization methods. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input project submission deadline data into AI and have the AI ​​perform the determination of risk priorities.

[0090] The assignment unit can estimate the user's emotions and adjust the method of assigning additional tasks based on the estimated emotions. For example, if the user is stressed, the assignment unit can provide a simple and efficient task assignment method. If the user is relaxed, the assignment unit can provide a more detailed task assignment method. Furthermore, if the user is in a hurry, the assignment unit can provide a quickly executable task assignment method. This allows the system to provide the user with the optimal task assignment by adjusting the method of assigning additional tasks according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the assignment unit may be performed using AI or not using AI. For example, the assignment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The assignment unit can apply different assignment algorithms depending on the project category when assigning additional tasks. For example, the assignment unit can select the optimal assignment algorithm depending on the project category. Furthermore, the assignment unit can apply different assignment algorithms based on the characteristics of the project. In addition, the assignment unit can provide the optimal task assignment method for each project category. This allows for optimal task assignment by applying the assignment algorithm according to the project category. The assignment algorithm includes, but is not limited to, the type of algorithm used and the criteria for its application. Some or all of the above processing in the assignment unit may be performed using AI, or not. For example, the assignment unit can input project category data into the AI ​​and have the AI ​​apply the assignment algorithm.

[0092] The assignment unit can consider the geographical distribution of projects when assigning additional tasks. For example, the assignment unit can consider the geographical distribution of projects and provide an optimal task assignment method. The assignment unit can also efficiently manage geographically dispersed projects. Furthermore, the assignment unit can determine task priorities based on geographical distribution. This enables efficient task assignment by considering the geographical distribution of projects. Geographical distribution includes, but is not limited to, project location information and distribution evaluation methods. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input project geographical distribution data into AI and have the AI ​​perform task assignment.

[0093] The assignment unit can improve the accuracy of assignments by referring to relevant project literature when assigning additional tasks. For example, the assignment unit can improve the accuracy of task assignments by referring to relevant project literature. The assignment unit can also provide the optimal task assignment method based on the relevant literature. Furthermore, the assignment unit can utilize relevant literature according to the progress of the project. This improves the accuracy of task assignments by referring to relevant literature. Relevant literature includes, but is not limited to, the type of literature and the method of reference. Some or all of the above processing in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input project relevant literature data into AI and have the AI ​​perform task assignments.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can estimate the user's emotions and adjust the input method for project objectives and requirements based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. If the user is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized, allowing for quick input of project objectives and requirements. This reduces the user's burden by adjusting the input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The optimization unit can apply different optimization algorithms depending on the project category when optimizing a project plan. For example, it can select the optimal optimization algorithm depending on the project category. It can also apply different optimization algorithms based on the characteristics of the project. Furthermore, it can adjust the algorithm for generating the optimal plan for each project category. This allows for the generation of an optimal project plan by applying the optimization algorithm according to the project category. The optimization algorithm includes, but is not limited to, the type of algorithm used and the criteria for application. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit can input project category data into a generative AI and have the generative AI execute the application of the optimization algorithm.

[0097] The monitoring unit can estimate the user's emotions and adjust the project progress monitoring method based on the estimated user emotions. For example, if the user is stressed, it can provide a simple and highly visible monitoring method. If the user is relaxed, it can provide detailed monitoring information. Furthermore, if the user is in a hurry, it can provide a concise monitoring method. In this way, by adjusting the monitoring method according to the user's emotions, the optimal monitoring method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The prediction unit can apply different prediction algorithms depending on the project category when predicting risk. For example, it can select the optimal prediction algorithm depending on the project category. It can also apply different prediction algorithms based on the characteristics of the project. Furthermore, it can provide the optimal risk prediction method for each project category. This allows for optimal risk prediction by applying prediction algorithms according to the project category. The prediction algorithm includes, but is not limited to, the type of algorithm used and the criteria for application. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input project category data into AI and have AI perform the application of prediction algorithms.

[0099] The assignment unit can estimate the user's emotions and adjust the method of assigning additional tasks based on the estimated emotions. For example, if the user is stressed, it can provide a simple and efficient task assignment method. If the user is relaxed, it can provide a more detailed task assignment method. Furthermore, if the user is in a hurry, it can provide a task assignment method that can be executed quickly. In this way, by adjusting the method of assigning additional tasks according to the user's emotions, the optimal task assignment can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The reception desk can analyze the input history of past project objectives and requirements and suggest the optimal input method. For example, it can automatically display project objectives and requirements that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest project objectives and requirements to be used during a specific time period based on the user's past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method to the user. Input history includes, but is not limited to, past input data and analysis methods. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input method.

[0101] The monitoring unit can perform monitoring of project progress while considering the geographical distribution of the project. For example, it can provide the optimal monitoring method by considering the geographical distribution of the project. It can also efficiently monitor geographically dispersed projects. Furthermore, it can detect anomalies early based on the geographical distribution. Thus, efficient monitoring becomes possible by considering the geographical distribution of the project. Geographical distribution includes, but is not limited to, project location information and distribution evaluation methods. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input project geographical distribution data into AI and have the AI ​​perform the monitoring.

[0102] The prediction unit can estimate the user's emotions and adjust the risk prediction method based on the estimated user emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand risk prediction method. If the user is relaxed, it can provide a detailed risk prediction. Furthermore, if the user is in a hurry, it can provide a concise risk prediction. In this way, by adjusting the risk prediction method according to the user's emotions, the system can provide the user with the most optimal risk prediction. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The assignment unit can consider the geographical distribution of projects when assigning additional tasks. For example, it can provide an optimal task assignment method by considering the geographical distribution of projects. It can also efficiently manage geographically dispersed projects. Furthermore, it can determine task priorities based on geographical distribution. This makes efficient task assignment possible by considering the geographical distribution of projects. Geographical distribution includes, but is not limited to, project location information and distribution evaluation methods. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input project geographical distribution data into AI and have the AI ​​perform task assignment.

[0104] The optimization unit can estimate the user's emotions and adjust the project plan optimization method based on the estimated user emotions. For example, if the user is stressed, it can generate a simple and efficient plan. If the user is relaxed, it can generate a detailed plan. Furthermore, if the user is in a hurry, it can generate a plan that can be quickly implemented. In this way, by adjusting the optimization method according to the user's emotions, the optimal project plan can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using a generative AI, or not using a generative AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives the project's objectives and requirements. These include, for example, project goals, required resources, and deadlines. The reception desk provides an interface for users to enter the project's objectives and requirements, supporting multiple input methods such as text input, voice input, and selection from multiple-choice options. Step 2: The optimization unit uses a generation AI to optimize the project plan based on the information entered by the reception unit. The optimization unit learns from past project data and identifies planning patterns with a high probability of success. The generation AI generates a project plan that includes the project timeline, milestones, resource allocation, and risk analysis. Step 3: The monitoring unit monitors the project progress based on the project plan generated by the optimization unit. The monitoring unit monitors the project progress in real time and detects anomalies early. The monitoring unit provides a dashboard for monitoring project progress, visually displaying project progress, task completion status, resource usage, etc. Step 4: The prediction unit predicts risks based on the progress monitored by the monitoring unit. The prediction unit detects risks such as resource shortages and schedule delays early, evaluates the probability and impact of the risks, and proposes countermeasures. Step 5: The allocation unit automatically assigns additional tasks based on the risks predicted by the forecasting unit. The allocation unit automatically assigns additional tasks to compensate for resource shortages, and makes optimal task assignments considering task priorities and resource utilization.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, optimization unit, monitoring unit, prediction unit, and assignment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input project objectives and requirements. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the project plan using generated AI. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the progress of the project in real time. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts risks. The assignment unit is implemented by the control unit 46A of the smart device 14 and automatically assigns additional tasks. 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.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, optimization unit, monitoring unit, prediction unit, and assignment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input project objectives and requirements. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the project plan using generated AI. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the progress of the project in real time. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts risks. The assignment unit is implemented by the control unit 46A of the smart glasses 214 and automatically assigns additional tasks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, optimization unit, monitoring unit, prediction unit, and assignment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input project objectives and requirements. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the project plan using generated AI. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the progress of the project in real time. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts risks. The assignment unit is implemented by the control unit 46A of the headset terminal 314 and automatically assigns additional tasks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, optimization unit, monitoring unit, prediction unit, and assignment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input project objectives and requirements. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the project plan using generated AI. The monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the progress of the project in real time. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts risks. The assignment unit is implemented by the control unit 46A of the robot 414 and automatically assigns additional tasks. 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception area where you enter the project's objectives and requirements, An optimization unit optimizes the project plan based on the information entered by the reception unit, A monitoring unit monitors the progress of the project based on the project plan generated by the optimization unit, A prediction unit that predicts risk based on the progress monitored by the aforementioned monitoring unit, The system includes an assignment unit that automatically assigns additional tasks based on the risks predicted by the prediction unit. A system characterized by the following features. (Note 2) The optimization unit, Learn from past project data to identify planning patterns with a high probability of success. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, Monitor project progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Early detection of risks such as resource shortages and schedule delays. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned allocation unit is, Automatically assign additional tasks as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, Generate a project plan that includes timelines, milestones, resource allocation, and risk analysis. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates user sentiment and adjusts the input methods for project objectives and requirements based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the input history of past project objectives and requirements and propose the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering project objectives and requirements, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates user sentiment and determines the priority of project objectives and requirements based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering project objectives and requirements, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering project objectives and requirements, analyze users' social media activity and input relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The optimization unit, It estimates user sentiment and adjusts the optimization method of the project plan based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The optimization unit, When optimizing project plans, different optimization algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The optimization unit, When optimizing a project plan, prioritize the plan based on the project's submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned monitoring unit, We estimate user sentiment and adjust project progress monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, When monitoring project progress, different monitoring algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned monitoring unit, When monitoring project progress, the geographical distribution of the project should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, When monitoring project progress, refer to relevant project documentation to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, We estimate user sentiment and adjust risk prediction methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When predicting risk, different prediction algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When predicting risks, prioritize risks based on the project submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned allocation unit is, It estimates the user's emotions and adjusts how additional tasks are assigned based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned allocation unit is, When assigning additional tasks, different assignment algorithms are applied depending on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned allocation unit is, When assigning additional tasks, consider the geographical distribution of the project. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned allocation unit is, When assigning additional tasks, refer to relevant project literature to improve the accuracy of the assignments. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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. A reception area where you enter the project's objectives and requirements, An optimization unit optimizes the project plan based on the information entered by the reception unit, A monitoring unit monitors the progress of the project based on the project plan generated by the optimization unit, A prediction unit that predicts risk based on the progress monitored by the aforementioned monitoring unit, The system includes an assignment unit that automatically assigns additional tasks based on the risks predicted by the prediction unit. A system characterized by the following features.

2. The optimization unit, Learn from past project data to identify planning patterns with a high probability of success. The system according to feature 1.

3. The aforementioned monitoring unit, Monitor project progress in real time. The system according to feature 1.

4. The prediction unit, Early detection of risks such as resource shortages and schedule delays. The system according to feature 1.

5. The aforementioned allocation unit is, Automatically assign additional tasks as needed. The system according to feature 1.

6. The optimization unit, Generate a project plan that includes timelines, milestones, resource allocation, and risk analysis. The system according to feature 1.

7. The aforementioned reception unit is It estimates user sentiment and adjusts the input methods for project objectives and requirements based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned reception unit is We analyze the input history of past project objectives and requirements and propose the optimal input method. The system according to feature 1.

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