system

Abstract

The system according to this embodiment aims to efficiently manage the Scrum process and respond quickly when problems occur. [Solution] The system according to the embodiment comprises a monitoring unit, an intervention unit, an automation unit, and a proposal unit. The monitoring unit monitors the progress status. The intervention unit intervenes when a problem occurs based on the progress status monitored by the monitoring unit. The automation unit automates the Scrum session. The proposal unit makes improvement suggestions based on the results of the session automated by the automation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently manage the Scrum process and quickly respond when a problem occurs.

[0005] The system according to the embodiment aims to efficiently manage the Scrum process and quickly respond when a problem occurs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an intervention unit, an automation unit, and a proposal unit. The monitoring unit monitors the progress status. The intervention unit intervenes when a problem occurs based on the progress status monitored by the monitoring unit. The automation unit automates the Scrum session. The proposal unit makes improvement suggestions based on the results of the session automated by the automation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage the Scrum process and respond quickly when problems occur. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 Scrum support system according to an embodiment of the present invention is a system in which AI assists the Scrum process. This Scrum support system automates and streamlines Scrum sessions such as sprint planning, daily scrum, sprint review, and sprint retrospective. The Scrum support system monitors the team's progress in real time, intervenes immediately when problems occur, and provides support to maximize the team's productivity. For example, the Scrum support system's AI agent observes the status of product development in real time and provides immediate feedback to the team when problems occur. This makes it possible to address problems while they are still small, preventing major delays and the occurrence of problems. Next, the Scrum support system uses AI to automate Scrum sessions such as sprint planning, daily scrum, sprint review, and sprint retrospective. This reduces the burden of manual management, allowing team members to focus on development. Furthermore, the Scrum support system's AI agent analyzes past data and the performance of the current process and provides customized improvement suggestions tailored to the team. This enables the implementation of effective improvement measures that meet the team's specific needs. The AI ​​agent continuously learns new data and applies that knowledge to its actions as a Scrum Master. This ensures that the latest best practices are always available to the team, accelerating product development. Scrum support systems are particularly effective when communication breakdowns and delays in progress are problematic in remote environments. AI automates each Scrum session and monitors progress in real time, alerting the team when problems arise and suggesting solutions. It also provides advice based on Scrum best practices to improve team performance. As a result, Scrum support systems can monitor the team's progress in real time, intervene immediately when problems occur, and provide support to maximize team productivity.

[0029] The Scrum support system according to this embodiment comprises a monitoring unit, an intervention unit, an automation unit, and a proposal unit. The monitoring unit monitors the progress status. Progress status includes, but is not limited to, the completion rate of tasks and adherence to the schedule. For example, the monitoring unit monitors the progress status of tasks in real time and evaluates the progress. The monitoring unit can also monitor adherence to the schedule and issue alerts if delays occur. Furthermore, the monitoring unit can evaluate quality and issue warnings if a decline in quality is observed. The intervention unit intervenes when problems occur based on the progress status monitored by the monitoring unit. For example, the intervention unit provides immediate feedback to the team when a problem occurs. For example, if a task is delayed, the intervention unit identifies the cause of the delay and proposes a solution. Furthermore, if a decline in quality is observed, the intervention unit can propose actions for quality improvement. Furthermore, if a communication breakdown occurs within the team, the intervention unit can propose measures to improve communication. The automation unit automates Scrum sessions. Scrum sessions include, but are not limited to, sprint planning, daily scrum, sprint review, and sprint retrospective. The automation unit can, for example, automate sprint planning, assigning tasks and setting schedules. It can also automate daily scrums and share team progress. Furthermore, the automation unit can automate sprint reviews and evaluate deliverables. The proposal unit makes improvement suggestions based on the results of sessions automated by the automation unit. The proposal unit can, for example, analyze historical data and the performance of current processes to make customized improvement suggestions tailored to the team. For example, the proposal unit can propose improvement measures based on historical data that are appropriate for the team's characteristics. It can also analyze the performance of current processes and make suggestions for efficiency improvements. Furthermore, the proposal unit can provide best practices tailored to the team's needs. As a result, the Scrum support system according to this embodiment can monitor progress, intervene when problems occur, automate Scrum sessions, and make improvement suggestions.

[0030] The monitoring department monitors the progress of the project. Progress includes, but is not limited to, tasks completed and schedule adherence. For example, the monitoring department monitors task progress in real time and evaluates the progress. Specifically, it records the progress of each task in detail, tracking the task's start date, end date, and ongoing status. This allows for a clear overview of the project's overall progress. The monitoring department also monitors schedule adherence and can issue alerts if delays occur. For example, if a task is not completed by its scheduled end date, the system automatically issues an alert and notifies relevant parties. Furthermore, the monitoring department can evaluate quality and issue warnings if a decline in quality is detected. Quality evaluation includes code review results and test results, and uses this data to detect quality declines. For example, if the bug frequency or test failure rate exceeds a certain threshold, the system issues a warning and notifies the need for quality improvement. This allows the monitoring department to monitor project progress and quality in real time and respond quickly when problems arise. Furthermore, the monitoring department can centrally manage this data and provide a dashboard to provide an overview of the entire project. The dashboard visually displays progress and quality evaluation results, making it easy for stakeholders to understand the situation. This allows the monitoring department to efficiently manage project progress and quality, enabling early detection and rapid response to problems.

[0031] The intervention team intervenes when problems arise based on progress monitored by the monitoring team. For example, the intervention team provides immediate feedback to the team when a problem occurs. Specifically, if a task is delayed, they identify the cause of the delay and propose solutions. For instance, if a task is behind schedule, the intervention team analyzes the cause of the delay and proposes specific solutions such as reallocating resources or reprioritizing tasks. The intervention team can also propose actions for quality improvement if a decline in quality is observed. For example, they analyze the results of code reviews and tests and propose specific action plans for quality improvement. Furthermore, the intervention team can propose measures to improve communication if there are communication breakdowns within the team. For example, if there is a lack of communication within the team, they propose measures such as holding regular meetings or introducing communication tools. This allows the intervention team to intervene quickly and appropriately in response to progress and quality issues, supporting the smooth progress of the project. In addition to providing this feedback and proposals to the team, the intervention team can also monitor implementation and provide additional support as needed. This allows the intervention team to support early detection and rapid resolution of problems, contributing to the success of the project.

[0032] The automation department automates Scrum sessions. Scrum sessions include, but are not limited to, sprint planning, daily scrums, sprint reviews, and sprint retrospectives. For example, the automation department can automate sprint planning, assigning tasks and setting schedules. Specifically, it automatically generates optimal task assignments and schedules based on historical data and team performance. The automation department can also automate daily scrums and share team progress. For example, it automatically collects each member's progress and issues and shares them during the daily scrum. Furthermore, the automation department can automate sprint reviews and evaluate deliverables. For example, it automatically collects sprint deliverables and evaluates them based on evaluation criteria. This allows the automation department to streamline and standardize Scrum sessions, improving team productivity. In addition, the automation department can customize these automated processes. For example, it can adjust the automation settings according to the team's characteristics and project requirements to achieve the optimal process. The automation department can also record the results of Scrum sessions for later reference. This allows the automation department to streamline and standardize Scrum sessions, thereby improving team productivity.

[0033] The proposals department makes improvement suggestions based on the results of sessions automated by the automation department. For example, the proposals department analyzes historical data and the performance of current processes to make customized improvement suggestions tailored to the team. Specifically, it analyzes past sprint data and team performance data to identify areas for improvement. For example, if a particular task is repeatedly delayed, it analyzes the cause and proposes solutions. The proposals department can also analyze the performance of current processes and make suggestions for efficiency improvements. For example, it can identify process bottlenecks and propose resource reallocation or process revisions. Furthermore, the proposals department can provide best practices tailored to the team's needs. For example, it can propose best practices suitable for the team based on examples from other successful projects. This allows the proposals department to provide concrete improvement suggestions to enhance team performance and contribute to project success. In addition, the proposals department can monitor the effects after these suggestions are implemented and make additional improvement suggestions as needed. This allows the proposals department to support continuous improvement and enhance team performance.

[0034] The observation unit can monitor progress in real time. For example, the observation unit can monitor the progress of a task in real time and evaluate the degree of progress. For example, the observation unit can update the task completion status in seconds and grasp the progress in real time. The observation unit can also monitor adherence to the schedule in real time and issue an alert immediately if a delay occurs. For example, the observation unit can update the schedule progress in minutes and detect delays in real time. Furthermore, the observation unit can evaluate quality in real time and issue a warning immediately if a decline in quality is observed. For example, the observation unit can update the quality evaluation criteria in seconds and detect a decline in quality in real time. This makes it possible to detect problems early by monitoring progress in real time. Some or all of the above processes in the observation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the observation unit can input the progress of a task into a generative AI and have the generative AI perform the evaluation of the degree of progress.

[0035] The learning unit can learn from past data. For example, the learning unit can learn from data from the past year to improve the accuracy of the system. For example, the learning unit can learn from data from past projects to predict the progress of those projects. The learning unit can also learn from data from a specific project and make improvement suggestions specific to that project. For example, the learning unit can predict the progress of a specific project based on its data and make appropriate improvement suggestions. Furthermore, the learning unit can continuously learn from past data to improve the accuracy of the system. For example, the learning unit can periodically learn from past data to improve the prediction accuracy of the system. In this way, the accuracy of the system improves by learning from past data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input past data into a generative AI and have the generative AI perform data learning.

[0036] The service provider can provide best practices. For example, the service provider can provide best practices based on success stories. For example, the service provider can analyze past success stories and provide best practices based on those stories. The service provider can also provide best practices based on standard methodologies. For example, the service provider can provide best practices suitable for a team based on industry standard methodologies. Furthermore, the service provider can provide customized best practices tailored to the characteristics of a team. For example, the service provider can analyze the characteristics of a team and provide best practices tailored to those characteristics. This improves the team's performance by providing best practices. Some or all of the above processes in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input success stories into generative AI and have the generative AI perform the provision of best practices.

[0037] The monitoring unit can monitor the status of product development in real time. For example, the monitoring unit can monitor the progress of product development in real time and evaluate the degree of progress. For example, the monitoring unit can update the progress of product development every second and grasp the progress in real time. The monitoring unit can also monitor the quality of product development in real time and issue an immediate warning if a decline in quality is observed. For example, the monitoring unit can update the quality evaluation criteria every second and detect a decline in quality in real time. This makes it easier to grasp the progress by monitoring the status of product development in real time. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input the progress of product development into a generative AI and have the generative AI perform an evaluation of the degree of progress.

[0038] The intervention unit can provide immediate feedback to the team when a problem occurs. For example, if a task is delayed, the intervention unit can identify the cause of the delay and propose a solution. The intervention unit can also propose actions to improve quality if a decline in quality is observed. Furthermore, if a communication breakdown occurs within the team, the intervention unit can propose measures to improve communication. This enables rapid problem resolution by providing immediate feedback when a problem occurs. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or not using generative AI. For example, the intervention unit can input the details of the problem into the generative AI and have the generative AI provide the feedback.

[0039] The automation unit can automate Scrum sessions such as sprint planning, daily scrums, sprint reviews, and sprint retrospectives. For example, the automation unit can automate sprint planning, assigning tasks and setting schedules. For instance, it can automatically determine task priorities and assign them to team members. The automation unit can also automate daily scrums and share team progress. For example, it can automatically collect progress reports from team members and present them as agenda items for the daily scrum. Furthermore, the automation unit can automate sprint reviews and evaluate deliverables. For example, it can automatically evaluate sprint deliverables and provide feedback. This reduces the burden of manual management by automating Scrum sessions. Some or all of the above processes in the automation unit may be performed using, for example, generative AI, or not. For example, the automation unit can input the progress of Scrum sessions into a generative AI and have the generative AI execute the automated process.

[0040] The proposal department can analyze past data and the performance of current processes to provide customized improvement suggestions tailored to the team. For example, the proposal department can propose improvement measures based on past data and tailored to the team's characteristics. For example, the proposal department can analyze data from past projects and make improvement suggestions based on successful cases in similar projects. The proposal department can also analyze the performance of current processes and make suggestions for efficiency improvements. For example, the proposal department can identify bottlenecks in current processes and propose solutions to improve them. Furthermore, the proposal department can provide best practices tailored to the team's needs. For example, the proposal department can analyze the team's characteristics and provide best practices tailored to those characteristics. This allows for the implementation of effective improvement measures tailored to the team's specific needs by providing customized improvement suggestions. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not. For example, the proposal department can input past data and the performance of current processes into a generative AI and have the generative AI execute the improvement suggestions.

[0041] The monitoring unit can adjust its monitoring focus based on the project's key milestones when monitoring progress. For example, as a key milestone approaches, the monitoring unit can increase monitoring frequency and check progress in detail. For example, the monitoring unit can frequently check the achievement status of milestones and gain a detailed understanding of the progress. The monitoring unit can also reduce monitoring frequency after a milestone is achieved and allow time for preparation for the next milestone. For example, the monitoring unit can periodically check the progress after a milestone is achieved and prepare for the next milestone. Furthermore, the monitoring unit can adjust its monitoring focus according to the importance of the milestones and optimize resource allocation. For example, the monitoring unit can focus its monitoring efforts and concentrate resources on high-priority milestones. This allows for optimal resource allocation by adjusting the monitoring focus based on the project's key milestones. Some or all of the above processes in the monitoring unit may be performed using, for example, generative AI, or not. For example, the monitoring unit can input project milestone data into a generative AI and have the generative AI adjust the monitoring focus.

[0042] The monitoring unit can apply different monitoring algorithms to team members depending on their skill sets when monitoring progress. For example, the monitoring unit can apply a monitoring algorithm that encourages self-management to highly skilled members. For instance, the monitoring unit can support highly skilled members in self-managing their task progress. The monitoring unit can also apply a monitoring algorithm that provides detailed feedback to less skilled members. For example, the monitoring unit can closely check the task progress of less skilled members and provide feedback as needed. Furthermore, the monitoring unit can apply a monitoring algorithm that provides appropriate support according to skill sets. For example, the monitoring unit can analyze each member's skill set and provide support accordingly. This ensures that appropriate support is provided by applying different monitoring algorithms according to the skill sets of team members. Some or all of the above processes in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input team member skill set data into a generative AI and have the generative AI execute the application of monitoring algorithms.

[0043] The monitoring unit can improve the accuracy of its monitoring by considering the geographical distribution of the project when monitoring progress. For example, the monitoring unit can monitor the progress of geographically dispersed team members in real time. For example, the monitoring unit can grasp the task progress of geographically dispersed team members in real time and evaluate their progress. The monitoring unit can also select appropriate communication methods according to the geographical distribution. For example, the monitoring unit can use video conferencing and chat tools to facilitate communication with geographically dispersed team members. Furthermore, the monitoring unit can change the focus of monitoring and allocate resources optimally by considering the geographical distribution. For example, the monitoring unit can focus monitoring and concentrate resources on geographically important locations. This improves the accuracy of monitoring by considering the geographical distribution of the project, enabling optimal resource allocation. Some or all of the above processes in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input the geographical distribution data of the project into generative AI and have the generative AI perform the improvement of monitoring accuracy.

[0044] The monitoring unit can improve the accuracy of its monitoring by referring to relevant external data when monitoring progress. For example, the monitoring unit can refer to external market data to evaluate the progress of the project. For example, the monitoring unit can grasp market trends in real time and reflect them in the project progress. The monitoring unit can also refer to external technology trends to confirm the direction of the project. For example, the monitoring unit can analyze the latest technology trends and use them to help the project progress. Furthermore, the monitoring unit can refer to external competitor information to evaluate the competitiveness of the project. For example, the monitoring unit can grasp the progress of competitors and compare it with the progress of its own project. By improving the accuracy of monitoring by referring to relevant external data, the evaluation of the project progress becomes more accurate. Some or all of the above processes in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input external data into generative AI and have the generative AI perform the improvement of monitoring accuracy.

[0045] The intervention unit can select the optimal intervention method by referring to similar past cases when a problem occurs. For example, the intervention unit can select the optimal intervention method based on similar past cases. For example, the intervention unit can analyze similar problems in past projects and select an intervention method by referring to their solutions. The intervention unit can also determine an intervention method by referring to successful examples of similar cases. For example, the intervention unit can select the optimal intervention method based on successful examples of past cases. Furthermore, the intervention unit can improve the intervention method by referring to failures in similar cases. For example, the intervention unit can analyze past failures and improve the intervention method based on the lessons learned. This enables rapid and effective problem solving by selecting the optimal intervention method by referring to similar past cases. Some or all of the above processes in the intervention unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the intervention unit can input data on similar past cases into a generative AI and have the generative AI select the optimal intervention method.

[0046] The intervention unit can apply different intervention methods depending on the role of each team member when a problem occurs. For example, the intervention unit can provide support to the team leader to help them exercise leadership. For example, the intervention unit can provide the team leader with advice on how to strengthen their leadership. The intervention unit can also provide technical support to development members. For example, the intervention unit can propose solutions to technical problems to development members. Furthermore, the intervention unit can support test members in improving the test process. For example, the intervention unit can provide test members with advice on how to streamline the test process. In this way, appropriate support can be provided by applying different intervention methods depending on the role of each team member. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or not using generative AI. For example, the intervention unit can input team member role data into generative AI and have the generative AI execute the application of intervention methods.

[0047] The intervention unit can determine the priority of interventions based on the project's progress when problems arise. For example, if progress is behind schedule, the intervention unit can intervene quickly to resolve the problem. For example, the intervention unit can provide prompt feedback to projects that are behind schedule to help resolve the problem. The intervention unit can also refrain from intervening and respect the autonomy of members if progress is on track. For example, the intervention unit can provide support to projects that are progressing well as needed. Furthermore, the intervention unit can determine the priority of interventions and allocate resources optimally according to the progress. For example, the intervention unit can prioritize resources for projects that are behind schedule and allocate resources sparingly for projects that are progressing well. This allows for optimal resource allocation by determining the priority of interventions based on the project's progress. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or not. For example, the intervention unit can input project progress data into the generative AI and have the generative AI determine the priority of interventions.

[0048] The intervention unit can improve the accuracy of its interventions by referring to relevant external resources when a problem occurs. For example, the intervention unit can refer to the opinions of external experts to determine the intervention method. For example, the intervention unit can select the optimal intervention method based on the opinions of external experts. The intervention unit can also refer to external technical documentation to provide technical support. For example, the intervention unit can propose solutions to technical problems based on external technical documentation. Furthermore, the intervention unit can refer to external best practices to improve its intervention methods. For example, the intervention unit can optimize its intervention methods based on external best practices. This allows for more effective problem solving by improving the accuracy of interventions by referring to relevant external resources. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or not using generative AI. For example, the intervention unit can input external resource data into generative AI and have the generative AI perform the task of improving the accuracy of the intervention.

[0049] The automation unit can adjust the level of detail of automation during Scrum sessions based on the project's progress. For example, if progress is behind schedule, the automation unit can increase the level of detail to accelerate progress. For example, the automation unit can provide a detailed automation process to projects that are behind schedule to accelerate progress. The automation unit can also lower the level of detail of automation when progress is on track, respecting the autonomy of the members. For example, the automation unit can provide a simplified automation process to projects that are on track. Furthermore, the automation unit can adjust the level of detail of automation according to the progress to optimally allocate resources. For example, the automation unit can provide a detailed automation process to projects that are behind schedule and a simplified automation process to projects that are on track. This allows for optimal resource allocation by adjusting the level of detail of automation based on the project's progress. Some or all of the above processes in the automation unit may be performed using, for example, generative AI, or not using generative AI. For example, the automation unit can input project progress data into the generating AI and have the generating AI adjust the level of detail of the automation.

[0050] The automation unit can apply different automation algorithms to team members depending on their skill sets when automating Scrum sessions. For example, the automation unit can apply an automation algorithm that encourages self-management to highly skilled members. For example, the automation unit can provide an automated process that encourages self-management to highly skilled members. The automation unit can also apply an automation algorithm that provides detailed feedback to less skilled members. For example, the automation unit can provide an automated process that provides detailed feedback to less skilled members. Furthermore, the automation unit can apply an automation algorithm that provides appropriate support depending on the skill set. For example, the automation unit can analyze each member's skill set and provide an automated process accordingly. This allows for the provision of appropriate support by applying different automation algorithms depending on the skill set of team members. Some or all of the above processes in the automation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the automation unit can input team member skill set data into a generative AI and have the generative AI execute the application of automation algorithms.

[0051] The automation unit can improve the accuracy of automation when automating Scrum sessions by considering the geographical distribution of the project. For example, the automation unit can monitor the progress of geographically dispersed team members in real time. For example, the automation unit can grasp the task progress of geographically dispersed team members in real time and evaluate their progress. The automation unit can also select appropriate communication methods according to the geographical distribution. For example, the automation unit can utilize video conferencing and chat tools to facilitate communication with geographically dispersed team members. Furthermore, the automation unit can adjust the focus of automation and optimize resource allocation by considering the geographical distribution. For example, the automation unit can focus automation on geographically important locations and concentrate resources there. This improves the accuracy of automation by considering the geographical distribution of the project, enabling optimal resource allocation. Some or all of the above processes in the automation unit may be performed using, for example, generative AI, or not using generative AI. For example, the automation unit can input project geographical distribution data into generative AI and have the generative AI perform the improvement of automation accuracy.

[0052] The automation unit can improve the accuracy of automation by referencing relevant external data when automating Scrum sessions. For example, the automation unit can refer to external market data to evaluate project progress. For example, the automation unit can grasp market trends in real time and reflect them in project progress. The automation unit can also refer to external technology trends to confirm the direction of the project. For example, the automation unit can analyze the latest technology trends and use them to help the project progress. Furthermore, the automation unit can refer to external competitor information to evaluate the competitiveness of the project. For example, the automation unit can grasp the progress of competitors and compare it with the progress of its own project. By improving the accuracy of automation by referencing relevant external data, the evaluation of project progress becomes more accurate. Some or all of the above processes in the automation unit may be performed using, for example, generative AI, or not using generative AI. For example, the automation unit can input external data into generative AI and have the generative AI perform the improvement of automation accuracy.

[0053] The proposal department can select the optimal proposal method by referring to past data when making improvement proposals. For example, the proposal department can make optimal improvement proposals based on past success stories. For example, the proposal department can analyze success stories from past projects and make improvement proposals based on those stories. The proposal department can also avoid risks when making improvement proposals based on past failure stories. For example, the proposal department can analyze past failure stories and make improvement proposals that avoid risks based on the lessons learned. Furthermore, the proposal department can analyze past data and make improvement proposals tailored to the characteristics of the team. For example, the proposal department can make improvement proposals tailored to the characteristics of the team based on past data. In this way, by referring to past data and selecting the optimal proposal method, effective improvement proposals become possible. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input past data into a generative AI and have the generative AI select the optimal proposal method.

[0054] The proposal department can apply different proposal methods depending on the role of each team member when making improvement suggestions. For example, the proposal department might make improvement suggestions to the team leader to strengthen their leadership. For example, the proposal department might make improvement suggestions to the team leader that provide advice on how to strengthen their leadership. The proposal department can also make improvement suggestions to development members to improve their technical skills. For example, the proposal department might make improvement suggestions to development members that provide training to improve their technical skills. Furthermore, the proposal department can also make improvement suggestions to test members to streamline the testing process. For example, the proposal department might make improvement suggestions to test members that provide advice on how to streamline the testing process. By applying different proposal methods depending on the role of each team member, appropriate support can be provided. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input team member role data into a generative AI and have the generative AI execute the application of the proposal method.

[0055] The proposal department can adjust the level of detail of improvement proposals based on the project's progress. For example, if a project is behind schedule, the proposal department can provide specific and detailed improvement proposals to accelerate progress. Alternatively, if a project is progressing smoothly, the proposal department can provide concise improvement proposals. For example, it can provide concise proposals to projects progressing smoothly, respecting the autonomy of team members. Furthermore, the proposal department can adjust the level of detail of proposals according to the project's progress to optimize resource allocation. For example, it can provide detailed improvement proposals to projects behind schedule and concise proposals to projects progressing smoothly. This allows for optimal resource allocation by adjusting the level of detail of proposals based on the project's progress. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or without one. For example, the proposal department can input project progress data into a generative AI and have the AI ​​adjust the level of detail of the proposals.

[0056] The proposal department can improve the accuracy of its proposals by referring to relevant external resources when making improvement suggestions. For example, the proposal department can improve the accuracy of its proposals by referring to the opinions of external experts. For example, the proposal department can make optimal improvement suggestions based on the opinions of external experts. The proposal department can also improve the accuracy of its technical suggestions by referring to external technical documents. For example, the proposal department can propose solutions to technical problems based on external technical documents. Furthermore, the proposal department can improve the accuracy of its suggestions by referring to external best practices. For example, the proposal department can make optimal improvement suggestions based on external best practices. In this way, by improving the accuracy of proposals by referring to relevant external resources, effective improvement suggestions become possible. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input external resource data into a generative AI and have the generative AI perform the improvement of the accuracy of the proposals.

[0057] The observation unit can adjust its observation focus based on the project's key milestones during observation. For example, as a key milestone approaches, the observation unit can increase the frequency of observations and check the progress in detail. For example, the observation unit can frequently check the achievement status of milestones and grasp the progress in detail. The observation unit can also reduce the frequency of observations after a milestone is achieved and allow for a preparation period for the next milestone. For example, the observation unit can regularly check the progress after a milestone is achieved and prepare for the next milestone. Furthermore, the observation unit can adjust its observation focus according to the importance of the milestones and allocate resources optimally. For example, the observation unit can focus its observations and concentrate resources on high-priority milestones. This allows for optimal resource allocation by adjusting the observation focus based on the project's key milestones. Some or all of the above processes in the observation unit may be performed using, for example, generative AI, or not using generative AI. For example, the observation unit can input project milestone data into generative AI and have the generative AI adjust the observation focus.

[0058] The observation unit can improve the accuracy of its observations by considering the geographical distribution of the project. For example, the observation unit can monitor the progress of geographically dispersed team members in real time. For example, the observation unit can grasp the task progress of geographically dispersed team members in real time and evaluate their progress. The observation unit can also select appropriate communication methods according to the geographical distribution. For example, the observation unit can utilize video conferencing and chat tools to facilitate communication with geographically dispersed team members. Furthermore, the observation unit can change the focus of its observations and optimally allocate resources by considering the geographical distribution. For example, the observation unit can focus its observations and concentrate resources on geographically important locations. This improves the accuracy of observations by considering the geographical distribution of the project, enabling optimal resource allocation. Some or all of the above processing in the observation unit may be performed using, for example, generative AI, or not using generative AI. For example, the observation unit can input the geographical distribution data of the project into the generative AI and have the generative AI perform the improvement of observation accuracy.

[0059] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze learning data from past projects and select the optimal learning algorithm based on that data. The learning unit can also analyze past learning data and improve the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data to improve accuracy. Furthermore, the learning unit can refer to past learning data to improve the accuracy of the learning algorithm. For example, the learning unit can evaluate and optimize the accuracy of the learning algorithm based on past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0060] The learning unit can weight the training data based on the project's progress during training. For example, if a project is behind schedule, the learning unit can weight important training data and prioritize training on that data. For example, the learning unit can weight important training data for projects that are behind schedule and prioritize training on those projects. The learning unit can also weight the training data evenly across all training data if the project is progressing smoothly. For example, the learning unit can weight the training data evenly across all training data for projects that are progressing smoothly. Furthermore, the learning unit can adjust the weighting of the training data according to the progress to optimally allocate resources. For example, the learning unit can weight important training data for projects that are behind schedule and evenly weight the training data for projects that are progressing smoothly. This allows for optimal resource allocation by weighting the training data based on the project's progress. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can input project progress data into the generating AI and have the generating AI perform weighting of the training data.

[0061] The service provider can select the optimal delivery method by referring to past data when providing best practices. For example, the service provider can provide the optimal best practices based on past success stories. For example, the service provider can analyze success stories from past projects and provide best practices based on those stories. The service provider can also provide best practices for avoiding risks based on past failure stories. For example, the service provider can analyze past failure stories and provide best practices for avoiding risks based on the lessons learned. Furthermore, the service provider can analyze past data and provide best practices tailored to the characteristics of the team. For example, the service provider can provide best practices tailored to the characteristics of the team based on past data. This makes it possible to provide effective best practices by selecting the optimal delivery method by referring to past data. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input past data into a generative AI and have the generative AI select the optimal delivery method.

[0062] The service provider can adjust the level of detail provided based on the project's progress when delivering best practices. For example, if a project is behind schedule, the service provider can provide specific and detailed best practices. For instance, the service provider can provide specific and detailed best practices to projects that are behind schedule to accelerate progress. Alternatively, if a project is progressing on schedule, the service provider can provide concise best practices. For example, the service provider can provide concise best practices to projects that are progressing on schedule, respecting the autonomy of the members. Furthermore, the service provider can adjust the level of detail provided according to the project's progress to optimally allocate resources. For example, the service provider can provide detailed best practices to projects that are behind schedule and concise best practices to projects that are progressing on schedule. This allows for optimal resource allocation by adjusting the level of detail provided based on the project's progress. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or not. For example, the service provider can input project progress data into a generative AI and have the generative AI adjust the level of detail provided.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The monitoring department can adjust the level of detail in monitoring a project according to its size. For example, in large-scale projects, the monitoring department can conduct detailed monitoring to grasp the progress in detail. Conversely, in small-scale projects, the monitoring department can conduct simplified monitoring to collect only the minimum necessary information. Furthermore, the monitoring department can change its monitoring focus according to the project's progress. For example, it can focus its monitoring on projects that are behind schedule to grasp the progress in detail. This allows for efficient monitoring by adjusting the level of detail according to the project's size and progress.

[0065] The observation team can adjust the frequency of observations according to the project's risk level when monitoring project progress. For example, in high-risk projects, the observation team can conduct frequent observations to gain a detailed understanding of the progress. Conversely, in low-risk projects, the observation team can reduce the frequency of observations and collect only the minimum necessary information. Furthermore, the observation team can change the focus of observations according to the project's risk level. For example, for high-risk projects, the observation team can focus on providing a detailed understanding of the progress. This allows for efficient observation by adjusting the frequency of observations according to the project's risk level.

[0066] The learning unit can adjust the level of detail in the training data according to the complexity of the project when learning about the project's progress. For example, in a complex project, the learning unit can use detailed training data to gain a detailed understanding of the progress. Conversely, in a simple project, the learning unit can use simplified training data to collect only the minimum necessary information. Furthermore, the learning unit can change its learning focus according to the complexity of the project. For example, it can focus its learning on complex projects to gain a detailed understanding of their progress. This allows for efficient learning by adjusting the level of detail in the training data according to the complexity of the project.

[0067] The service provider can adjust the level of detail provided when delivering project progress updates, according to the project's priority. For example, for high-priority projects, the service provider can provide detailed information, allowing for a thorough understanding of the progress. Conversely, for lower-priority projects, the service provider can provide simplified information, enabling users to gather only the essential information. Furthermore, the service provider can change the focus of its deliveries according to the project's priority. For example, for high-priority projects, the service provider can focus its deliveries to provide a detailed understanding of the progress. By adjusting the level of detail provided according to the project's priority, efficient service delivery becomes possible.

[0068] The intervention team can adjust the level of detail of their interventions according to the urgency of the project. For example, for high-urgency projects, the intervention team can conduct detailed interventions to gain a thorough understanding of the progress. For low-urgency projects, the intervention team can conduct simplified interventions to gather only the minimum necessary information. Furthermore, the intervention team can change the focus of their interventions according to the urgency of the project. For example, they can focus their interventions on high-urgency projects to gain a thorough understanding of the progress. This allows for efficient intervention by adjusting the level of detail according to the urgency of the project.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The monitoring department monitors the progress. Progress includes task completion, schedule adherence, and quality evaluation. The monitoring department monitors task progress in real time and evaluates the progress. It also monitors schedule adherence and can issue alerts if delays occur. Furthermore, it can evaluate quality and issue warnings if a decline in quality is observed. Step 2: The intervention team intervenes when problems arise based on the progress monitored by the monitoring team. The intervention team provides immediate feedback to the team when problems occur. For example, if a task is delayed, they identify the cause of the delay and propose solutions. They can also propose actions to improve quality if a decline in quality is observed. Furthermore, if there is a breakdown in team communication, they can propose measures to improve communication. Step 3: The automation team automates Scrum sessions. Scrum sessions include sprint planning, daily scrum, sprint review, and sprint retrospective. The automation team automates sprint planning, assigning tasks and setting schedules. They can also automate daily scrums to share team progress. Furthermore, they can automate sprint reviews to evaluate deliverables. Step 4: The proposal team makes improvement suggestions based on the results of the automated sessions conducted by the automation team. The proposal team analyzes historical data and the performance of the current process to make customized improvement suggestions tailored to the team. For example, they can propose improvement measures based on historical data that are suited to the team's characteristics. They can also analyze the performance of the current process and make suggestions for efficiency improvements. Furthermore, they can provide best practices that meet the team's needs.

[0071] (Example of form 2) The Scrum support system according to an embodiment of the present invention is a system in which AI assists the Scrum process. This Scrum support system automates and streamlines Scrum sessions such as sprint planning, daily scrum, sprint review, and sprint retrospective. The Scrum support system monitors the team's progress in real time, intervenes immediately when problems occur, and provides support to maximize the team's productivity. For example, the Scrum support system's AI agent observes the status of product development in real time and provides immediate feedback to the team when problems occur. This makes it possible to address problems while they are still small, preventing major delays and the occurrence of problems. Next, the Scrum support system uses AI to automate Scrum sessions such as sprint planning, daily scrum, sprint review, and sprint retrospective. This reduces the burden of manual management, allowing team members to focus on development. Furthermore, the Scrum support system's AI agent analyzes past data and the performance of the current process and provides customized improvement suggestions tailored to the team. This enables the implementation of effective improvement measures that meet the team's specific needs. The AI ​​agent continuously learns new data and applies that knowledge to its actions as a Scrum Master. This ensures that the latest best practices are always available to the team, accelerating product development. Scrum support systems are particularly effective when communication breakdowns and delays in progress are problematic in remote environments. AI automates each Scrum session and monitors progress in real time, alerting the team when problems arise and suggesting solutions. It also provides advice based on Scrum best practices to improve team performance. As a result, Scrum support systems can monitor the team's progress in real time, intervene immediately when problems occur, and provide support to maximize team productivity.

[0072] The Scrum support system according to this embodiment comprises a monitoring unit, an intervention unit, an automation unit, and a proposal unit. The monitoring unit monitors the progress status. Progress status includes, but is not limited to, the completion rate of tasks and adherence to the schedule. For example, the monitoring unit monitors the progress status of tasks in real time and evaluates the progress. The monitoring unit can also monitor adherence to the schedule and issue alerts if delays occur. Furthermore, the monitoring unit can evaluate quality and issue warnings if a decline in quality is observed. The intervention unit intervenes when problems occur based on the progress status monitored by the monitoring unit. For example, the intervention unit provides immediate feedback to the team when a problem occurs. For example, if a task is delayed, the intervention unit identifies the cause of the delay and proposes a solution. Furthermore, if a decline in quality is observed, the intervention unit can propose actions for quality improvement. Furthermore, if a communication breakdown occurs within the team, the intervention unit can propose measures to improve communication. The automation unit automates Scrum sessions. Scrum sessions include, but are not limited to, sprint planning, daily scrum, sprint review, and sprint retrospective. The automation unit can, for example, automate sprint planning, assigning tasks and setting schedules. It can also automate daily scrums and share team progress. Furthermore, the automation unit can automate sprint reviews and evaluate deliverables. The proposal unit makes improvement suggestions based on the results of sessions automated by the automation unit. The proposal unit can, for example, analyze historical data and the performance of current processes to make customized improvement suggestions tailored to the team. For example, the proposal unit can propose improvement measures based on historical data that are appropriate for the team's characteristics. It can also analyze the performance of current processes and make suggestions for efficiency improvements. Furthermore, the proposal unit can provide best practices tailored to the team's needs. As a result, the Scrum support system according to this embodiment can monitor progress, intervene when problems occur, automate Scrum sessions, and make improvement suggestions.

[0073] The monitoring department monitors the progress of the project. Progress includes, but is not limited to, tasks completed and schedule adherence. For example, the monitoring department monitors task progress in real time and evaluates the progress. Specifically, it records the progress of each task in detail, tracking the task's start date, end date, and ongoing status. This allows for a clear overview of the project's overall progress. The monitoring department also monitors schedule adherence and can issue alerts if delays occur. For example, if a task is not completed by its scheduled end date, the system automatically issues an alert and notifies relevant parties. Furthermore, the monitoring department can evaluate quality and issue warnings if a decline in quality is detected. Quality evaluation includes code review results and test results, and uses this data to detect quality declines. For example, if the bug frequency or test failure rate exceeds a certain threshold, the system issues a warning and notifies the need for quality improvement. This allows the monitoring department to monitor project progress and quality in real time and respond quickly when problems arise. Furthermore, the monitoring department can centrally manage this data and provide a dashboard to provide an overview of the entire project. The dashboard visually displays progress and quality evaluation results, making it easy for stakeholders to understand the situation. This allows the monitoring department to efficiently manage project progress and quality, enabling early detection and rapid response to problems.

[0074] The intervention team intervenes when problems arise based on progress monitored by the monitoring team. For example, the intervention team provides immediate feedback to the team when a problem occurs. Specifically, if a task is delayed, they identify the cause of the delay and propose solutions. For instance, if a task is behind schedule, the intervention team analyzes the cause of the delay and proposes specific solutions such as reallocating resources or reprioritizing tasks. The intervention team can also propose actions for quality improvement if a decline in quality is observed. For example, they analyze the results of code reviews and tests and propose specific action plans for quality improvement. Furthermore, the intervention team can propose measures to improve communication if there are communication breakdowns within the team. For example, if there is a lack of communication within the team, they propose measures such as holding regular meetings or introducing communication tools. This allows the intervention team to intervene quickly and appropriately in response to progress and quality issues, supporting the smooth progress of the project. In addition to providing this feedback and proposals to the team, the intervention team can also monitor implementation and provide additional support as needed. This allows the intervention team to support early detection and rapid resolution of problems, contributing to the success of the project.

[0075] The automation department automates Scrum sessions. Scrum sessions include, but are not limited to, sprint planning, daily scrums, sprint reviews, and sprint retrospectives. For example, the automation department can automate sprint planning, assigning tasks and setting schedules. Specifically, it automatically generates optimal task assignments and schedules based on historical data and team performance. The automation department can also automate daily scrums and share team progress. For example, it automatically collects each member's progress and issues and shares them during the daily scrum. Furthermore, the automation department can automate sprint reviews and evaluate deliverables. For example, it automatically collects sprint deliverables and evaluates them based on evaluation criteria. This allows the automation department to streamline and standardize Scrum sessions, improving team productivity. In addition, the automation department can customize these automated processes. For example, it can adjust the automation settings according to the team's characteristics and project requirements to achieve the optimal process. The automation department can also record the results of Scrum sessions for later reference. This allows the automation department to streamline and standardize Scrum sessions, thereby improving team productivity.

[0076] The proposals department makes improvement suggestions based on the results of sessions automated by the automation department. For example, the proposals department analyzes historical data and the performance of current processes to make customized improvement suggestions tailored to the team. Specifically, it analyzes past sprint data and team performance data to identify areas for improvement. For example, if a particular task is repeatedly delayed, it analyzes the cause and proposes solutions. The proposals department can also analyze the performance of current processes and make suggestions for efficiency improvements. For example, it can identify process bottlenecks and propose resource reallocation or process revisions. Furthermore, the proposals department can provide best practices tailored to the team's needs. For example, it can propose best practices suitable for the team based on examples from other successful projects. This allows the proposals department to provide concrete improvement suggestions to enhance team performance and contribute to project success. In addition, the proposals department can monitor the effects after these suggestions are implemented and make additional improvement suggestions as needed. This allows the proposals department to support continuous improvement and enhance team performance.

[0077] The observation unit can monitor progress in real time. For example, the observation unit can monitor the progress of a task in real time and evaluate the degree of progress. For example, the observation unit can update the task completion status in seconds and grasp the progress in real time. The observation unit can also monitor adherence to the schedule in real time and issue an alert immediately if a delay occurs. For example, the observation unit can update the schedule progress in minutes and detect delays in real time. Furthermore, the observation unit can evaluate quality in real time and issue a warning immediately if a decline in quality is observed. For example, the observation unit can update the quality evaluation criteria in seconds and detect a decline in quality in real time. This makes it possible to detect problems early by monitoring progress in real time. Some or all of the above processes in the observation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the observation unit can input the progress of a task into a generative AI and have the generative AI perform the evaluation of the degree of progress.

[0078] The learning unit can learn from past data. For example, the learning unit can learn from data from the past year to improve the accuracy of the system. For example, the learning unit can learn from data from past projects to predict the progress of those projects. The learning unit can also learn from data from a specific project and make improvement suggestions specific to that project. For example, the learning unit can predict the progress of a specific project based on its data and make appropriate improvement suggestions. Furthermore, the learning unit can continuously learn from past data to improve the accuracy of the system. For example, the learning unit can periodically learn from past data to improve the prediction accuracy of the system. In this way, the accuracy of the system improves by learning from past data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input past data into a generative AI and have the generative AI perform data learning.

[0079] The service provider can provide best practices. For example, the service provider can provide best practices based on success stories. For example, the service provider can analyze past success stories and provide best practices based on those stories. The service provider can also provide best practices based on standard methodologies. For example, the service provider can provide best practices suitable for a team based on industry standard methodologies. Furthermore, the service provider can provide customized best practices tailored to the characteristics of a team. For example, the service provider can analyze the characteristics of a team and provide best practices tailored to those characteristics. This improves the team's performance by providing best practices. Some or all of the above processes in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input success stories into generative AI and have the generative AI perform the provision of best practices.

[0080] The monitoring unit can monitor the status of product development in real time. For example, the monitoring unit can monitor the progress of product development in real time and evaluate the degree of progress. For example, the monitoring unit can update the progress of product development every second and grasp the progress in real time. The monitoring unit can also monitor the quality of product development in real time and issue an immediate warning if a decline in quality is observed. For example, the monitoring unit can update the quality evaluation criteria every second and detect a decline in quality in real time. This makes it easier to grasp the progress by monitoring the status of product development in real time. Some or all of the above processes in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit can input the progress of product development into a generative AI and have the generative AI perform an evaluation of the degree of progress.

[0081] The intervention unit can provide immediate feedback to the team when a problem occurs. For example, if a task is delayed, the intervention unit can identify the cause of the delay and propose a solution. The intervention unit can also propose actions to improve quality if a decline in quality is observed. Furthermore, if a communication breakdown occurs within the team, the intervention unit can propose measures to improve communication. This enables rapid problem resolution by providing immediate feedback when a problem occurs. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or not using generative AI. For example, the intervention unit can input the details of the problem into the generative AI and have the generative AI provide the feedback.

[0082] The automation unit can automate Scrum sessions such as sprint planning, daily scrums, sprint reviews, and sprint retrospectives. For example, the automation unit can automate sprint planning, assigning tasks and setting schedules. For instance, it can automatically determine task priorities and assign them to team members. The automation unit can also automate daily scrums and share team progress. For example, it can automatically collect progress reports from team members and present them as agenda items for the daily scrum. Furthermore, the automation unit can automate sprint reviews and evaluate deliverables. For example, it can automatically evaluate sprint deliverables and provide feedback. This reduces the burden of manual management by automating Scrum sessions. Some or all of the above processes in the automation unit may be performed using, for example, generative AI, or not. For example, the automation unit can input the progress of Scrum sessions into a generative AI and have the generative AI execute the automated process.

[0083] The proposal department can analyze past data and the performance of current processes to provide customized improvement suggestions tailored to the team. For example, the proposal department can propose improvement measures based on past data and tailored to the team's characteristics. For example, the proposal department can analyze data from past projects and make improvement suggestions based on successful cases in similar projects. The proposal department can also analyze the performance of current processes and make suggestions for efficiency improvements. For example, the proposal department can identify bottlenecks in current processes and propose solutions to improve them. Furthermore, the proposal department can provide best practices tailored to the team's needs. For example, the proposal department can analyze the team's characteristics and provide best practices tailored to those characteristics. This allows for the implementation of effective improvement measures tailored to the team's specific needs by providing customized improvement suggestions. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not. For example, the proposal department can input past data and the performance of current processes into a generative AI and have the generative AI execute the improvement suggestions.

[0084] The monitoring unit can estimate the emotions of team members and adjust the progress monitoring method based on the estimated emotions. For example, if a team member is stressed, the monitoring unit can increase the monitoring frequency to detect problems early. For example, the monitoring unit can frequently check the task progress of stressed team members and address problems before they occur. Conversely, if a team member is relaxed, the monitoring unit can reduce the monitoring frequency and respect the member's autonomy. For example, the monitoring unit can regularly check the task progress of relaxed team members and provide support as needed. Furthermore, if a team member is tired, the monitoring unit can simplify the monitoring method to reduce the burden. For example, the monitoring unit can check the task progress of tired team members in a simplified way to reduce the burden. This allows for more appropriate progress monitoring by adjusting the monitoring method according to the emotions of team members. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input emotional data of team members into a generative AI and have the generative AI adjust the method of monitoring progress.

[0085] The monitoring unit can adjust its monitoring focus based on the project's key milestones when monitoring progress. For example, as a key milestone approaches, the monitoring unit can increase monitoring frequency and check progress in detail. For example, the monitoring unit can frequently check the achievement status of milestones and gain a detailed understanding of the progress. The monitoring unit can also reduce monitoring frequency after a milestone is achieved and allow time for preparation for the next milestone. For example, the monitoring unit can periodically check the progress after a milestone is achieved and prepare for the next milestone. Furthermore, the monitoring unit can adjust its monitoring focus according to the importance of the milestones and optimize resource allocation. For example, the monitoring unit can focus its monitoring efforts and concentrate resources on high-priority milestones. This allows for optimal resource allocation by adjusting the monitoring focus based on the project's key milestones. Some or all of the above processes in the monitoring unit may be performed using, for example, generative AI, or not. For example, the monitoring unit can input project milestone data into a generative AI and have the generative AI adjust the monitoring focus.

[0086] The monitoring unit can apply different monitoring algorithms to team members depending on their skill sets when monitoring progress. For example, the monitoring unit can apply a monitoring algorithm that encourages self-management to highly skilled members. For instance, the monitoring unit can support highly skilled members in self-managing their task progress. The monitoring unit can also apply a monitoring algorithm that provides detailed feedback to less skilled members. For example, the monitoring unit can closely check the task progress of less skilled members and provide feedback as needed. Furthermore, the monitoring unit can apply a monitoring algorithm that provides appropriate support according to skill sets. For example, the monitoring unit can analyze each member's skill set and provide support accordingly. This ensures that appropriate support is provided by applying different monitoring algorithms according to the skill sets of team members. Some or all of the above processes in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input team member skill set data into a generative AI and have the generative AI execute the application of monitoring algorithms.

[0087] The monitoring unit can estimate the emotions of team members and adjust the display method of monitoring results based on the estimated emotions. For example, if a team member is stressed, the monitoring unit can provide a simple display method to avoid information overload. For instance, it can display only essential information to a stressed team member. Furthermore, if a team member is relaxed, the monitoring unit can provide a more detailed display method to enrich the information. For example, it can display detailed progress information to a relaxed team member. Additionally, if a team member is tired, the monitoring unit can provide a highly visible display method to facilitate understanding of the information. For example, it can display information to a tired team member using highly visible graphs or charts. This makes it easier to understand the information by adjusting the display method of monitoring results according to the emotions of the team members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the monitoring unit can input emotional data of team members into a generative AI and have the generative AI adjust how the monitoring results are displayed.

[0088] The monitoring unit can improve the accuracy of its monitoring by considering the geographical distribution of the project when monitoring progress. For example, the monitoring unit can monitor the progress of geographically dispersed team members in real time. For example, the monitoring unit can grasp the task progress of geographically dispersed team members in real time and evaluate their progress. The monitoring unit can also select appropriate communication methods according to the geographical distribution. For example, the monitoring unit can use video conferencing and chat tools to facilitate communication with geographically dispersed team members. Furthermore, the monitoring unit can change the focus of monitoring and allocate resources optimally by considering the geographical distribution. For example, the monitoring unit can focus monitoring and concentrate resources on geographically important locations. This improves the accuracy of monitoring by considering the geographical distribution of the project, enabling optimal resource allocation. Some or all of the above processes in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input the geographical distribution data of the project into generative AI and have the generative AI perform the improvement of monitoring accuracy.

[0089] The monitoring unit can improve the accuracy of its monitoring by referring to relevant external data when monitoring progress. For example, the monitoring unit can refer to external market data to evaluate the progress of the project. For example, the monitoring unit can grasp market trends in real time and reflect them in the project progress. The monitoring unit can also refer to external technology trends to confirm the direction of the project. For example, the monitoring unit can analyze the latest technology trends and use them to help the project progress. Furthermore, the monitoring unit can refer to external competitor information to evaluate the competitiveness of the project. For example, the monitoring unit can grasp the progress of competitors and compare it with the progress of its own project. By improving the accuracy of monitoring by referring to relevant external data, the evaluation of the project progress becomes more accurate. Some or all of the above processes in the monitoring unit may be performed using, for example, generative AI, or not using generative AI. For example, the monitoring unit can input external data into generative AI and have the generative AI perform the improvement of monitoring accuracy.

[0090] The intervention unit can estimate the emotions of team members and adjust the timing of intervention based on the estimated emotions. For example, if a team member is feeling stressed, the intervention unit can intervene early to resolve the problem. For example, the intervention unit can provide early feedback to a stressed team member to help resolve the problem. The intervention unit can also refrain from intervening if a team member is relaxed, respecting the member's autonomy. For example, the intervention unit can provide support to a relaxed team member as needed. Furthermore, if a team member is tired, the intervention unit can intervene at the appropriate time and provide support. For example, the intervention unit can provide feedback to a tired team member at the appropriate time to reduce their burden. In this way, appropriate support can be provided by adjusting the timing of intervention according to the emotions of team members. 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 intervention unit may be performed using, for example, generative AI, or not using generative AI. For example, the intervention unit can input emotional data from team members into a generating AI and have the AI ​​adjust the timing of the intervention.

[0091] The intervention unit can select the optimal intervention method by referring to similar past cases when a problem occurs. For example, the intervention unit can select the optimal intervention method based on similar past cases. For example, the intervention unit can analyze similar problems in past projects and select an intervention method by referring to their solutions. The intervention unit can also determine an intervention method by referring to successful examples of similar cases. For example, the intervention unit can select the optimal intervention method based on successful examples of past cases. Furthermore, the intervention unit can improve the intervention method by referring to failures in similar cases. For example, the intervention unit can analyze past failures and improve the intervention method based on the lessons learned. This enables rapid and effective problem solving by selecting the optimal intervention method by referring to similar past cases. Some or all of the above processes in the intervention unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the intervention unit can input data on similar past cases into a generative AI and have the generative AI select the optimal intervention method.

[0092] The intervention unit can apply different intervention methods depending on the role of each team member when a problem occurs. For example, the intervention unit can provide support to the team leader to help them exercise leadership. For example, the intervention unit can provide the team leader with advice on how to strengthen their leadership. The intervention unit can also provide technical support to development members. For example, the intervention unit can propose solutions to technical problems to development members. Furthermore, the intervention unit can support test members in improving the test process. For example, the intervention unit can provide test members with advice on how to streamline the test process. In this way, appropriate support can be provided by applying different intervention methods depending on the role of each team member. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or not using generative AI. For example, the intervention unit can input team member role data into generative AI and have the generative AI execute the application of intervention methods.

[0093] The intervention unit can estimate the emotions of team members and adjust the content of the intervention based on the estimated emotions. For example, if a team member is feeling stressed, the intervention unit can provide a relaxing environment. For example, the intervention unit can create a relaxing work environment for a stressed team member. The intervention unit can also provide challenging tasks if a team member is relaxed. For example, the intervention unit can assign challenging tasks to a relaxed team member. Furthermore, if a team member is tired, the intervention unit can encourage them to take a break. For example, the intervention unit can encourage a tired team member to take an appropriate break. In this way, appropriate support can be provided by adjusting the content of the intervention according to the emotions of the team members. 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 intervention unit may be performed using, for example, generative AI, or not using generative AI. For example, the intervention unit can input emotional data from team members into a generating AI and have the AI ​​adjust the intervention content.

[0094] The intervention unit can determine the priority of interventions based on the project's progress when problems arise. For example, if progress is behind schedule, the intervention unit can intervene quickly to resolve the problem. For example, the intervention unit can provide prompt feedback to projects that are behind schedule to help resolve the problem. The intervention unit can also refrain from intervening and respect the autonomy of members if progress is on track. For example, the intervention unit can provide support to projects that are progressing well as needed. Furthermore, the intervention unit can determine the priority of interventions and allocate resources optimally according to the progress. For example, the intervention unit can prioritize resources for projects that are behind schedule and allocate resources sparingly for projects that are progressing well. This allows for optimal resource allocation by determining the priority of interventions based on the project's progress. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or not. For example, the intervention unit can input project progress data into the generative AI and have the generative AI determine the priority of interventions.

[0095] The intervention unit can improve the accuracy of its interventions by referring to relevant external resources when a problem occurs. For example, the intervention unit can refer to the opinions of external experts to determine the intervention method. For example, the intervention unit can select the optimal intervention method based on the opinions of external experts. The intervention unit can also refer to external technical documentation to provide technical support. For example, the intervention unit can propose solutions to technical problems based on external technical documentation. Furthermore, the intervention unit can refer to external best practices to improve its intervention methods. For example, the intervention unit can optimize its intervention methods based on external best practices. This allows for more effective problem solving by improving the accuracy of interventions by referring to relevant external resources. Some or all of the above processes in the intervention unit may be performed using, for example, generative AI, or not using generative AI. For example, the intervention unit can input external resource data into generative AI and have the generative AI perform the task of improving the accuracy of the intervention.

[0096] The automation unit can estimate the emotions of team members and adjust the automation process based on the estimated emotions. For example, if a team member is stressed, the automation unit can simplify the automation process. For example, the automation unit can simplify the task automation process for a stressed team member to reduce their burden. The automation unit can also configure the automation process in detail if a team member is relaxed. For example, the automation unit can provide a detailed task automation process for a relaxed team member. Furthermore, if a team member is tired, the automation unit can optimize the automation process to reduce their burden. For example, the automation unit can provide an optimized task automation process for a tired team member. This allows for appropriate support to be provided by adjusting the automation process according to the emotions of team members. 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 automation unit may be performed using, for example, generative AI, or without generative AI. For example, the automation unit can input emotional data of team members into a generating AI and have the generating AI adjust the automation process.

[0097] The automation unit can adjust the level of detail of automation during Scrum sessions based on the project's progress. For example, if progress is behind schedule, the automation unit can increase the level of detail to accelerate progress. For example, the automation unit can provide a detailed automation process to projects that are behind schedule to accelerate progress. The automation unit can also lower the level of detail of automation when progress is on track, respecting the autonomy of the members. For example, the automation unit can provide a simplified automation process to projects that are on track. Furthermore, the automation unit can adjust the level of detail of automation according to the progress to optimally allocate resources. For example, the automation unit can provide a detailed automation process to projects that are behind schedule and a simplified automation process to projects that are on track. This allows for optimal resource allocation by adjusting the level of detail of automation based on the project's progress. Some or all of the above processes in the automation unit may be performed using, for example, generative AI, or not using generative AI. For example, the automation unit can input project progress data into the generating AI and have the generating AI adjust the level of detail of the automation.

[0098] The automation unit can apply different automation algorithms to team members depending on their skill sets when automating Scrum sessions. For example, the automation unit can apply an automation algorithm that encourages self-management to highly skilled members. For example, the automation unit can provide an automated process that encourages self-management to highly skilled members. The automation unit can also apply an automation algorithm that provides detailed feedback to less skilled members. For example, the automation unit can provide an automated process that provides detailed feedback to less skilled members. Furthermore, the automation unit can apply an automation algorithm that provides appropriate support depending on the skill set. For example, the automation unit can analyze each member's skill set and provide an automated process accordingly. This allows for the provision of appropriate support by applying different automation algorithms depending on the skill set of team members. Some or all of the above processes in the automation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the automation unit can input team member skill set data into a generative AI and have the generative AI execute the application of automation algorithms.

[0099] The automation unit can estimate the emotions of team members and adjust how the automation results are displayed based on the estimated emotions. For example, if a team member is stressed, the automation unit can provide a simple display method to avoid information overload. For instance, it can display only essential information to a stressed team member. Furthermore, if a team member is relaxed, the automation unit can provide a more detailed display method to enrich the information. For example, it can display detailed progress information to a relaxed team member. Additionally, if a team member is tired, the automation unit can provide a highly visible display method to facilitate understanding of the information. For example, it can display information to a tired team member using highly visible graphs or charts. This makes it easier to understand the information by adjusting how the automation results are displayed according to the emotions of the team members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the automation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the automation unit can input emotional data of team members into a generative AI and have the generative AI adjust how the automation results are displayed.

[0100] The automation unit can improve the accuracy of automation when automating Scrum sessions by considering the geographical distribution of the project. For example, the automation unit can monitor the progress of geographically dispersed team members in real time. For example, the automation unit can grasp the task progress of geographically dispersed team members in real time and evaluate their progress. The automation unit can also select appropriate communication methods according to the geographical distribution. For example, the automation unit can utilize video conferencing and chat tools to facilitate communication with geographically dispersed team members. Furthermore, the automation unit can adjust the focus of automation and optimize resource allocation by considering the geographical distribution. For example, the automation unit can focus automation on geographically important locations and concentrate resources there. This improves the accuracy of automation by considering the geographical distribution of the project, enabling optimal resource allocation. Some or all of the above processes in the automation unit may be performed using, for example, generative AI, or not using generative AI. For example, the automation unit can input project geographical distribution data into generative AI and have the generative AI perform the improvement of automation accuracy.

[0101] The automation unit can improve the accuracy of automation by referencing relevant external data when automating Scrum sessions. For example, the automation unit can refer to external market data to evaluate project progress. For example, the automation unit can grasp market trends in real time and reflect them in project progress. The automation unit can also refer to external technology trends to confirm the direction of the project. For example, the automation unit can analyze the latest technology trends and use them to help the project progress. Furthermore, the automation unit can refer to external competitor information to evaluate the competitiveness of the project. For example, the automation unit can grasp the progress of competitors and compare it with the progress of its own project. By improving the accuracy of automation by referencing relevant external data, the evaluation of project progress becomes more accurate. Some or all of the above processes in the automation unit may be performed using, for example, generative AI, or not using generative AI. For example, the automation unit can input external data into generative AI and have the generative AI perform the improvement of automation accuracy.

[0102] The suggestion department can estimate the emotions of team members and adjust the content of improvement suggestions based on the estimated emotions. For example, if a team member is feeling stressed, the suggestion department can suggest an improvement that provides a relaxing environment. For example, the suggestion department can suggest creating a relaxing work environment for a stressed team member. Also, if a team member is relaxed, the suggestion department can suggest providing a challenging task. For example, the suggestion department can suggest assigning a challenging task to a relaxed team member. Furthermore, if a team member is tired, the suggestion department can suggest encouraging them to take a break. For example, the suggestion department can suggest encouraging a tired team member to take an appropriate break. In this way, appropriate support can be provided by adjusting the content of improvement suggestions according to the emotions of team members. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal department may be performed using, for example, a generative AI, or without using a generative AI. For example, the proposal department can input emotional data of team members into a generative AI and have the generative AI adjust the content of the improvement proposals.

[0103] The proposal department can select the optimal proposal method by referring to past data when making improvement proposals. For example, the proposal department can make optimal improvement proposals based on past success stories. For example, the proposal department can analyze success stories from past projects and make improvement proposals based on those stories. The proposal department can also avoid risks when making improvement proposals based on past failure stories. For example, the proposal department can analyze past failure stories and make improvement proposals that avoid risks based on the lessons learned. Furthermore, the proposal department can analyze past data and make improvement proposals tailored to the characteristics of the team. For example, the proposal department can make improvement proposals tailored to the characteristics of the team based on past data. In this way, by referring to past data and selecting the optimal proposal method, effective improvement proposals become possible. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input past data into a generative AI and have the generative AI select the optimal proposal method.

[0104] The proposal department can apply different proposal methods depending on the role of each team member when making improvement suggestions. For example, the proposal department might make improvement suggestions to the team leader to strengthen their leadership. For example, the proposal department might make improvement suggestions to the team leader that provide advice on how to strengthen their leadership. The proposal department can also make improvement suggestions to development members to improve their technical skills. For example, the proposal department might make improvement suggestions to development members that provide training to improve their technical skills. Furthermore, the proposal department can also make improvement suggestions to test members to streamline the testing process. For example, the proposal department might make improvement suggestions to test members that provide advice on how to streamline the testing process. By applying different proposal methods depending on the role of each team member, appropriate support can be provided. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input team member role data into a generative AI and have the generative AI execute the application of the proposal method.

[0105] The suggestion department can estimate the emotions of team members and prioritize suggestions based on those emotions. For example, if a team member is stressed, the suggestion department will prioritize suggestions for stress reduction. For instance, it might suggest stress-reducing actions to a stressed team member. The suggestion department can also prioritize challenging suggestions if a team member is relaxed. For example, it might suggest a challenging task to a relaxed team member. Furthermore, if a team member is tired, the suggestion department can prioritize suggestions that encourage rest. For example, it might suggest that a tired team member take appropriate rest. This allows for appropriate support to be provided by prioritizing suggestions according to the emotions of team members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department can input emotional data from team members into a generative AI and have the AI ​​determine the priority of proposals.

[0106] The proposal department can adjust the level of detail of improvement proposals based on the project's progress. For example, if a project is behind schedule, the proposal department can provide specific and detailed improvement proposals to accelerate progress. Alternatively, if a project is progressing smoothly, the proposal department can provide concise improvement proposals. For example, it can provide concise proposals to projects progressing smoothly, respecting the autonomy of team members. Furthermore, the proposal department can adjust the level of detail of proposals according to the project's progress to optimize resource allocation. For example, it can provide detailed improvement proposals to projects behind schedule and concise proposals to projects progressing smoothly. This allows for optimal resource allocation by adjusting the level of detail of proposals based on the project's progress. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or without one. For example, the proposal department can input project progress data into a generative AI and have the AI ​​adjust the level of detail of the proposals.

[0107] The proposal department can improve the accuracy of its proposals by referring to relevant external resources when making improvement suggestions. For example, the proposal department can improve the accuracy of its proposals by referring to the opinions of external experts. For example, the proposal department can make optimal improvement suggestions based on the opinions of external experts. The proposal department can also improve the accuracy of its technical suggestions by referring to external technical documents. For example, the proposal department can propose solutions to technical problems based on external technical documents. Furthermore, the proposal department can improve the accuracy of its suggestions by referring to external best practices. For example, the proposal department can make optimal improvement suggestions based on external best practices. In this way, by improving the accuracy of proposals by referring to relevant external resources, effective improvement suggestions become possible. Some or all of the above processes in the proposal department may be performed using, for example, a generative AI, or not using a generative AI. For example, the proposal department can input external resource data into a generative AI and have the generative AI perform the improvement of the accuracy of the proposals.

[0108] The observation unit can estimate the emotions of team members and adjust its observation methods based on the estimated emotions. For example, if a team member is stressed, the observation unit can increase the frequency of observation to detect problems early. For example, the observation unit can frequently check the task progress of stressed team members and address problems before they arise. Conversely, if a team member is relaxed, the observation unit can reduce the frequency of observation and respect the member's autonomy. For example, the observation unit can regularly check the task progress of relaxed team members and provide support as needed. Furthermore, if a team member is tired, the observation unit can simplify its observation methods to reduce the burden. For example, the observation unit can check the task progress of tired team members in a simplified manner to reduce the burden. This allows for the provision of appropriate support by adjusting the observation methods according to the emotions of team members. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the observation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the observation unit can input emotional data of team members into a generative AI and have the generative AI adjust the observation method.

[0109] The observation unit can adjust its observation focus based on the project's key milestones during observation. For example, as a key milestone approaches, the observation unit can increase the frequency of observations and check the progress in detail. For example, the observation unit can frequently check the achievement status of milestones and grasp the progress in detail. The observation unit can also reduce the frequency of observations after a milestone is achieved and allow for a preparation period for the next milestone. For example, the observation unit can regularly check the progress after a milestone is achieved and prepare for the next milestone. Furthermore, the observation unit can adjust its observation focus according to the importance of the milestones and allocate resources optimally. For example, the observation unit can focus its observations and concentrate resources on high-priority milestones. This allows for optimal resource allocation by adjusting the observation focus based on the project's key milestones. Some or all of the above processes in the observation unit may be performed using, for example, generative AI, or not using generative AI. For example, the observation unit can input project milestone data into generative AI and have the generative AI adjust the observation focus.

[0110] The observation unit can estimate the emotions of team members and adjust the display method of the observation results based on the estimated emotions. For example, if a team member is stressed, the observation unit can provide a simple display method to avoid information overload. For instance, it can display only essential information to a stressed team member. Furthermore, if a team member is relaxed, the observation unit can provide a detailed display method to enrich the information. For example, it can display detailed progress information to a relaxed team member. Additionally, if a team member is tired, the observation unit can provide a highly visible display method to facilitate understanding of the information. For example, it can display information to a tired team member using highly visible graphs or charts. This makes it easier to understand the information by adjusting the display method of the observation results according to the emotions of the team members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the observation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the observation unit can input emotional data of team members into a generative AI and have the generative AI adjust how the observation results are displayed.

[0111] The observation unit can improve the accuracy of its observations by considering the geographical distribution of the project. For example, the observation unit can monitor the progress of geographically dispersed team members in real time. For example, the observation unit can grasp the task progress of geographically dispersed team members in real time and evaluate their progress. The observation unit can also select appropriate communication methods according to the geographical distribution. For example, the observation unit can utilize video conferencing and chat tools to facilitate communication with geographically dispersed team members. Furthermore, the observation unit can change the focus of its observations and optimally allocate resources by considering the geographical distribution. For example, the observation unit can focus its observations and concentrate resources on geographically important locations. This improves the accuracy of observations by considering the geographical distribution of the project, enabling optimal resource allocation. Some or all of the above processing in the observation unit may be performed using, for example, generative AI, or not using generative AI. For example, the observation unit can input the geographical distribution data of the project into the generative AI and have the generative AI perform the improvement of observation accuracy.

[0112] The learning unit can estimate the emotions of team members and select training data based on the estimated emotions. For example, if a team member is feeling stressed, the learning unit will select training data that promotes relaxation. For example, the learning unit will provide training data that promotes relaxation to a stressed team member. The learning unit can also select training data that is challenging if a team member is relaxed. For example, the learning unit will provide training data that is challenging to a relaxed team member. Furthermore, if a team member is tired, the learning unit can select training data that is simple and easy to understand. For example, the learning unit will provide training data that is simple and easy to understand to a tired team member. In this way, appropriate support can be provided by selecting training data according to the emotions of team members. 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-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input emotional data of team members into a generative AI and have the generative AI select the learning data.

[0113] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze learning data from past projects and select the optimal learning algorithm based on that data. The learning unit can also analyze past learning data and improve the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm based on past learning data to improve accuracy. Furthermore, the learning unit can refer to past learning data to improve the accuracy of the learning algorithm. For example, the learning unit can evaluate and optimize the accuracy of the learning algorithm based on past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input past learning data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0114] The learning unit can estimate the emotions of team members and adjust the frequency of learning based on the estimated emotions. For example, if a team member is feeling stressed, the learning unit can reduce the frequency of learning to alleviate the burden. For example, the learning unit can reduce the frequency of learning for a stressed team member to alleviate the burden. The learning unit can also increase the frequency of learning for a relaxed team member to promote skill improvement. For example, the learning unit can increase the frequency of learning for a relaxed team member to promote skill improvement. Furthermore, if a team member is tired, the learning unit can adjust the frequency of learning to provide appropriate rest. For example, the learning unit can adjust the frequency of learning for a tired team member to provide appropriate rest. In this way, appropriate support can be provided by adjusting the frequency of learning according to the emotions of team members. 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-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input emotional data of team members into a generative AI and have the generative AI adjust the learning frequency.

[0115] The learning unit can weight the training data based on the project's progress during training. For example, if a project is behind schedule, the learning unit can weight important training data and prioritize training on that data. For example, the learning unit can weight important training data for projects that are behind schedule and prioritize training on those projects. The learning unit can also weight the training data evenly across all training data if the project is progressing smoothly. For example, the learning unit can weight the training data evenly across all training data for projects that are progressing smoothly. Furthermore, the learning unit can adjust the weighting of the training data according to the progress to optimally allocate resources. For example, the learning unit can weight important training data for projects that are behind schedule and evenly weight the training data for projects that are progressing smoothly. This allows for optimal resource allocation by weighting the training data based on the project's progress. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without using generative AI. For example, the learning unit can input project progress data into the generating AI and have the generating AI perform weighting of the training data.

[0116] The service provider can estimate the emotions of team members and adjust how best practices are delivered based on those estimated emotions. For example, if a team member is feeling stressed, the service provider can suggest best practices that create a relaxing environment. For example, the service provider can suggest best practices that create a relaxing work environment for a stressed team member. The service provider can also suggest best practices that provide challenging tasks if a team member is relaxed. For example, the service provider can suggest best practices that assign challenging tasks to a relaxed team member. Furthermore, if a team member is tired, the service provider can suggest best practices that encourage rest. For example, the service provider can suggest best practices that encourage a tired team member to take appropriate rest. In this way, appropriate support can be provided by adjusting how best practices are delivered according to the emotions of team members. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the delivery unit can input emotional data of team members into a generative AI and have the generative AI adjust the method of providing best practices.

[0117] The service provider can select the optimal delivery method by referring to past data when providing best practices. For example, the service provider can provide the optimal best practices based on past success stories. For example, the service provider can analyze success stories from past projects and provide best practices based on those stories. The service provider can also provide best practices for avoiding risks based on past failure stories. For example, the service provider can analyze past failure stories and provide best practices for avoiding risks based on the lessons learned. Furthermore, the service provider can analyze past data and provide best practices tailored to the characteristics of the team. For example, the service provider can provide best practices tailored to the characteristics of the team based on past data. This makes it possible to provide effective best practices by selecting the optimal delivery method by referring to past data. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input past data into a generative AI and have the generative AI select the optimal delivery method.

[0118] The service provider can estimate the emotions of team members and prioritize best practices based on those estimated emotions. For example, if a team member is stressed, the service provider will prioritize best practices for stress reduction. For instance, it will suggest stress-reducing actions to the stressed team member. The service provider can also prioritize challenging best practices if a team member is relaxed. For example, it will suggest challenging tasks to the relaxed team member. Furthermore, if a team member is tired, the service provider can prioritize best practices that encourage rest. For example, it will suggest that the tired team member take appropriate rest. By prioritizing best practices according to the emotions of team members, appropriate support can be provided. 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 service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider can input emotional data of team members into a generative AI and have the AI ​​determine the priorities of best practices.

[0119] The service provider can adjust the level of detail provided based on the project's progress when delivering best practices. For example, if a project is behind schedule, the service provider can provide specific and detailed best practices. For instance, the service provider can provide specific and detailed best practices to projects that are behind schedule to accelerate progress. Alternatively, if a project is progressing on schedule, the service provider can provide concise best practices. For example, the service provider can provide concise best practices to projects that are progressing on schedule, respecting the autonomy of the members. Furthermore, the service provider can adjust the level of detail provided according to the project's progress to optimally allocate resources. For example, the service provider can provide detailed best practices to projects that are behind schedule and concise best practices to projects that are progressing on schedule. This allows for optimal resource allocation by adjusting the level of detail provided based on the project's progress. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or not. For example, the service provider can input project progress data into a generative AI and have the generative AI adjust the level of detail provided.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The monitoring department can adjust the level of detail in monitoring a project according to its size. For example, in large-scale projects, the monitoring department can conduct detailed monitoring to grasp the progress in detail. Conversely, in small-scale projects, the monitoring department can conduct simplified monitoring to collect only the minimum necessary information. Furthermore, the monitoring department can change its monitoring focus according to the project's progress. For example, it can focus its monitoring on projects that are behind schedule to grasp the progress in detail. This allows for efficient monitoring by adjusting the level of detail according to the project's size and progress.

[0122] The observation team can adjust the frequency of observations according to the project's risk level when monitoring project progress. For example, in high-risk projects, the observation team can conduct frequent observations to gain a detailed understanding of the progress. Conversely, in low-risk projects, the observation team can reduce the frequency of observations and collect only the minimum necessary information. Furthermore, the observation team can change the focus of observations according to the project's risk level. For example, for high-risk projects, the observation team can focus on providing a detailed understanding of the progress. This allows for efficient observation by adjusting the frequency of observations according to the project's risk level.

[0123] The learning unit can adjust the level of detail in the training data according to the complexity of the project when learning about the project's progress. For example, in a complex project, the learning unit can use detailed training data to gain a detailed understanding of the progress. Conversely, in a simple project, the learning unit can use simplified training data to collect only the minimum necessary information. Furthermore, the learning unit can change its learning focus according to the complexity of the project. For example, it can focus its learning on complex projects to gain a detailed understanding of their progress. This allows for efficient learning by adjusting the level of detail in the training data according to the complexity of the project.

[0124] The service provider can adjust the level of detail provided when delivering project progress updates, according to the project's priority. For example, for high-priority projects, the service provider can provide detailed information, allowing for a thorough understanding of the progress. Conversely, for lower-priority projects, the service provider can provide simplified information, enabling users to gather only the essential information. Furthermore, the service provider can change the focus of its deliveries according to the project's priority. For example, for high-priority projects, the service provider can focus its deliveries to provide a detailed understanding of the progress. By adjusting the level of detail provided according to the project's priority, efficient service delivery becomes possible.

[0125] The intervention team can adjust the level of detail of their interventions according to the urgency of the project. For example, for high-urgency projects, the intervention team can conduct detailed interventions to gain a thorough understanding of the progress. For low-urgency projects, the intervention team can conduct simplified interventions to gather only the minimum necessary information. Furthermore, the intervention team can change the focus of their interventions according to the urgency of the project. For example, they can focus their interventions on high-urgency projects to gain a thorough understanding of the progress. This allows for efficient intervention by adjusting the level of detail according to the urgency of the project.

[0126] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on that estimation. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency to detect problems early. Conversely, if the user is relaxed, the monitoring unit can decrease the monitoring frequency, respecting the user's autonomy. Furthermore, if the user is tired, the monitoring unit can adjust the monitoring frequency to reduce the user's burden. In this way, appropriate support can be provided by adjusting the monitoring frequency according to the user's emotions.

[0127] The observation unit can estimate the user's emotions and adjust its observation methods based on those estimates. For example, if the user is stressed, the observation unit can simplify its observation methods to reduce the user's burden. If the user is relaxed, the observation unit can conduct detailed observations to gain a thorough understanding of their progress. Furthermore, if the user is tired, the observation unit can adjust its observation methods to reduce the user's burden. In this way, appropriate support can be provided by adjusting the observation methods according to the user's emotions.

[0128] The learning unit can estimate the user's emotions and adjust the learning content based on those estimates. For example, if the user is stressed, the learning unit can provide relaxing learning content. If the user is relaxed, the learning unit can provide challenging learning content. Furthermore, if the user is tired, the learning unit can provide simple and easy-to-understand learning content. In this way, appropriate support can be provided by adjusting the learning content according to the user's emotions.

[0129] The service provider can estimate the user's emotions and adjust the delivery method based on those estimates. For example, if the user is stressed, the service provider can adopt a simple delivery method to avoid information overload. If the user is relaxed, the service provider can adopt a detailed delivery method to enrich the information. Furthermore, if the user is tired, the service provider can adopt a highly visual delivery method to make the information easier to understand. In this way, appropriate support can be provided by adjusting the delivery method according to the user's emotions.

[0130] The intervention unit can estimate the user's emotions and adjust the timing of intervention based on those estimates. For example, if the user is stressed, the intervention unit can intervene early to resolve the problem. Conversely, if the user is relaxed, the intervention unit can refrain from intervening, respecting the user's autonomy. Furthermore, if the user is tired, the intervention unit can intervene at the appropriate time to reduce the user's burden. In this way, appropriate support can be provided by adjusting the timing of intervention according to the user's emotions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The monitoring department monitors the progress. Progress includes task completion, schedule adherence, and quality evaluation. The monitoring department monitors task progress in real time and evaluates the progress. It also monitors schedule adherence and can issue alerts if delays occur. Furthermore, it can evaluate quality and issue warnings if a decline in quality is observed. Step 2: The intervention team intervenes when problems arise based on the progress monitored by the monitoring team. The intervention team provides immediate feedback to the team when problems occur. For example, if a task is delayed, they identify the cause of the delay and propose solutions. They can also propose actions to improve quality if a decline in quality is observed. Furthermore, if there is a breakdown in team communication, they can propose measures to improve communication. Step 3: The automation team automates Scrum sessions. Scrum sessions include sprint planning, daily scrum, sprint review, and sprint retrospective. The automation team automates sprint planning, assigning tasks and setting schedules. They can also automate daily scrums to share team progress. Furthermore, they can automate sprint reviews to evaluate deliverables. Step 4: The proposal team makes improvement suggestions based on the results of the automated sessions conducted by the automation team. The proposal team analyzes historical data and the performance of the current process to make customized improvement suggestions tailored to the team. For example, they can propose improvement measures based on historical data that are suited to the team's characteristics. They can also analyze the performance of the current process and make suggestions for efficiency improvements. Furthermore, they can provide best practices that meet the team's needs.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the monitoring unit, intervention unit, automation unit, proposal unit, observation unit, learning unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the progress in real time. The intervention unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and intervenes immediately when a problem occurs. The automation unit is implemented, for example, by the control unit 46A of the smart device 14 and automates the Scrum session. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes improvement suggestions. The observation unit is implemented, for example, by the control unit 46A of the smart device 14 and observes the progress in real time. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns from past data. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides best practices. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the monitoring unit, intervention unit, automation unit, proposal unit, observation unit, learning unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the progress in real time. The intervention unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and intervenes immediately when a problem occurs. The automation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and automates the Scrum session. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes improvement suggestions. The observation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and observes the progress in real time. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns from past data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides best practices. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the monitoring unit, intervention unit, automation unit, proposal unit, observation unit, learning unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the progress in real time. The intervention unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and intervenes immediately when a problem occurs. The automation unit is implemented, for example, by the control unit 46A of the headset terminal 314 and automates the Scrum session. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes improvement suggestions. The observation unit is implemented, for example, by the control unit 46A of the headset terminal 314 and observes the progress in real time. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns from past data. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides best practices. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the monitoring unit, intervention unit, automation unit, proposal unit, observation unit, learning unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the progress in real time. The intervention unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and intervenes immediately when a problem occurs. The automation unit is implemented, for example, by the control unit 46A of the robot 414 and automates the Scrum session. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes improvement suggestions. The observation unit is implemented, for example, by the control unit 46A of the robot 414 and observes the progress in real time. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and learns from past data. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides best practices. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) The monitoring department monitors the progress, An intervention unit intervenes when a problem occurs based on the progress monitored by the aforementioned monitoring unit, The automation unit automates Scrum sessions, A proposal unit that makes improvement suggestions based on the results of the automated session by the aforementioned automation unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with an observation unit that monitors progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a learning unit that learns from past data. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a department that provides best practices. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Monitor the product development status in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned intervention unit is Provide immediate feedback to the team when a problem occurs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned automation unit, Automate Scrum sessions such as sprint planning, daily scrum, sprint review, and sprint retrospective. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, We analyze past data and the performance of current processes to provide customized improvement suggestions tailored to the team. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, Estimate the emotions of team members and adjust progress monitoring methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, When monitoring progress, shift monitoring focus based on key project milestones. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, When monitoring progress, apply different monitoring algorithms depending on the skill set of the team members. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, The system estimates the emotions of team members and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, When monitoring progress, consider the geographical distribution of the project to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned monitoring unit, When monitoring progress, refer to relevant external data to improve monitoring accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned intervention unit is Estimate the emotions of team members and adjust the timing of interventions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned intervention unit is When a problem occurs, the optimal intervention method is selected by referring to similar past cases. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned intervention unit is When a problem occurs, apply different intervention methods depending on the role of the team member. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned intervention unit is Estimate the emotions of team members and adjust the intervention based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned intervention unit is When a problem occurs, prioritize interventions based on the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned intervention unit is When a problem occurs, refer to relevant external resources to improve the accuracy of interventions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automation unit, It estimates the emotions of team members and adjusts the automated process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, When automating Scrum sessions, adjust the level of detail of the automation based on the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, When automating Scrum sessions, apply different automation algorithms depending on the skill sets of the team members. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned automation unit, Adjust how you estimate the emotions of team members and how you display automated results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned automation unit, When automating Scrum sessions, consider the geographical distribution of projects to improve the accuracy of the automation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned automation unit, When automating Scrum sessions, refer to relevant external data to improve the accuracy of the automation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, The system estimates the emotions of team members and adjusts the content of improvement suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When proposing improvements, refer to past data to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making improvement suggestions, apply different suggestion methods depending on the role of each team member. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Estimate the emotions of team members and determine the priority of proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making improvement suggestions, adjust the level of detail based on the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making improvement suggestions, refer to relevant external resources to improve the accuracy of the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The observation unit is, Estimate the emotions of team members and adjust observation methods based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The observation unit is, During observation, shift the focus of observation based on key milestones in the project. The system described in Appendix 2, characterized by the features described herein. (Note 35) The observation unit is, The system estimates the emotions of team members and adjusts how observation results are displayed based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The observation unit is, During observation, consider the geographical distribution of the project to improve the accuracy of the observations. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned learning unit, The system estimates the emotions of team members and selects training data based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned learning unit, The system estimates the emotions of team members and adjusts the learning frequency based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned learning unit, During training, the training data is weighted based on the project's progress. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned supply unit is, Estimate the emotions of team members and adjust how best practices are delivered based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When providing best practices, we refer to past data to select the optimal delivery method. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned supply unit is, Estimate the emotions of team members and prioritize best practices based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned supply unit is, When providing best practices, adjust the level of detail based on the project's progress. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The monitoring department monitors the progress, An intervention unit intervenes when a problem occurs based on the progress monitored by the aforementioned monitoring unit, The automation unit automates Scrum sessions, A proposal unit that makes improvement suggestions based on the results of the automated session by the aforementioned automation unit, Equipped with A system characterized by the following features.

2. It is equipped with an observation unit that monitors progress in real time. The system according to feature 1.

3. It has a learning unit that learns from past data. The system according to feature 1.

4. We have a department that provides best practices. The system according to feature 1.

5. The aforementioned monitoring unit, Monitor the product development status in real time. The system according to feature 1.

6. The aforementioned intervention unit is Provide immediate feedback to the team when a problem occurs. The system according to feature 1.

7. The aforementioned automation unit, Automate Scrum sessions such as sprint planning, daily scrum, sprint review, and sprint retrospective. The system according to feature 1.

8. The aforementioned proposal section is, We analyze past data and the performance of current processes to provide customized improvement suggestions tailored to the team. The system according to feature 1.

9. The aforementioned monitoring unit, Estimate the emotions of team members and adjust progress monitoring methods based on those estimated emotions. The system according to feature 1.

10. The aforementioned monitoring unit, When monitoring progress, shift monitoring focus based on key project milestones. The system according to feature 1.

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