System

Abstract

To provide a system according to an embodiment that uniformly manages the progress of projects and appropriately facilitates a proposal and a conference.SOLUTION: A system comprises a collection unit, an analysis unit, a proposal unit, and a facilitation unit. The collection unit collects the progress of projects. The analysis unit analyzes the data collected by the collection unit. The proposal unit carries out a proposal on the basis of an analysis result obtained by the analysis unit. The facilitation unit facilitates a conference on the basis the details of the proposal carried out by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem in that it was difficult to uniformly manage the progress of a project and make appropriate proposals or facilitate meetings.

[0005] The system according to the embodiment aims to uniformly manage the progress of projects and provide appropriate proposals and facilitate meetings. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a facilitation unit. The collection unit collects progress status of a project. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit. The facilitation unit facilitates the meeting based on the content proposed by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment can uniformly manage the progress of projects and provide appropriate proposals and facilitate meetings. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A project management office (PMO) business support system according to an embodiment of the present invention is a system that uses a generation AI to collect, analyze, and make proposals on the progress of a project, and facilitates a meeting. In this system, the generation AI collects and analyzes the progress of the project in each phase. For example, data such as the completion status of tasks and the usage status of resources is collected, and the generation AI analyzes it to grasp the progress of the project. Next, the generation AI makes proposals regarding the progress of the project based on the analysis results. For example, proposals are made to improve the efficiency of the project, such as reallocation of resources and change in the priority of tasks. Furthermore, the generation AI facilitates the meeting. For example, the meeting agenda is automatically generated to support the progress of the discussion. In addition, the meeting minutes are automatically generated and shared with the participants to improve the efficiency of the meeting. This system makes it possible to carry out work with a uniformly high quality even when there is variation in project management skills. In addition, the project progress can be grasped in real time and appropriate proposals can be made, thereby increasing the success rate of the project. As a result, the PMO business support system can grasp the project progress in real time and make appropriate proposals.

[0029] The PMO business support system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a facilitation unit. The collection unit collects the progress of the project. The collection unit can collect data such as the completion status of tasks and the usage status of resources. The collection unit can also collect the progress status in each phase of the project using the generation AI. For example, the collection unit automatically acquires data from a project management tool and grasps the progress status. The analysis unit analyzes the data collected by the collection unit. The analysis unit uses the generation AI to analyze the collected data and grasp the progress of the project. For example, the analysis unit evaluates the progress of tasks and the efficiency of resource use. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit uses the generation AI to make proposals for improving the efficiency of the project, such as reallocating resources and changing the priority of tasks. For example, the proposal unit proposes an optimal method of allocating resources to smooth the progress of the project. The facilitation unit facilitates meetings based on the contents proposed by the proposal unit. The facilitation department uses generation AI to automatically generate meeting agendas and support the progress of discussions. For example, the facilitation department automatically generates meeting minutes and shares them with participants. This enables the PMO business support system according to the embodiment to efficiently collect, analyze, propose, and facilitate the progress of projects.

[0030] The collection unit collects the progress of the project. The collection unit can collect data such as the completion status of tasks and the usage status of resources. Specifically, the data is automatically acquired from a project management tool or a task management system, and the progress of each task and the allocation status of resources are grasped in real time. The collection unit can also collect the progress in each phase of the project using the generation AI. The generation AI analyzes data from the project management tool and evaluates the progress of the task and the efficiency of resource usage. For example, the collection unit automatically acquires data from the project management tool and grasps the progress. The generation AI can monitor the progress of the project in real time and detect abnormal delays and excessive use of resources. Furthermore, the collection unit collects feedback and reports from project members and comprehensively evaluates the progress of the project. This allows the collection unit to efficiently collect the progress of the project and grasp the situation in real time. The collected data is stored in a central database and made accessible to the analysis unit and proposal unit. This allows the collection unit to accurately grasp the progress of the project and support efficient operation of the project.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses the generation AI to analyze the collected data and grasp the progress of the project. Specifically, the generation AI evaluates the progress of the task and the efficiency of resource use, and visualizes the overall progress of the project. For example, the analysis unit evaluates the progress of the task and the efficiency of resource use. The generation AI can predict the progress of the project based on past data and statistical information, and identify future risks and problems. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the progress of the project in real time. The analysis unit provides a dashboard for visualizing the progress of the project, allowing project members and managers to easily grasp the situation. This allows the analysis unit to accurately grasp the progress of the project and support efficient operation of the project.

[0032] The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit uses the generation AI to make proposals for improving the efficiency of the project, such as reallocating resources and changing the priority of tasks. Specifically, the generation AI calculates the optimal resource allocation method and task priority order based on the analysis results, and makes proposals to smooth the progress of the project. For example, the proposal unit proposes the optimal resource allocation method to smooth the progress of the project. The generation AI can monitor the progress of the project and the usage of resources in real time and make optimal proposals. Furthermore, the proposal unit can predict future risks and problems based on the progress of the project and the usage of resources, and propose appropriate measures. This allows the proposal unit to support efficient operation of the project and contribute to the success of the project. The proposal unit shares the contents of the proposal with project members and managers, and supports rapid decision-making. This allows the proposal unit to support efficient operation of the project and contribute to the success of the project.

[0033] The facilitation department facilitates the meeting based on the contents proposed by the proposal department. The facilitation department uses the generation AI to automatically generate a meeting agenda and support the progress of the discussion. Specifically, the generation AI automatically generates a meeting agenda based on the proposal contents and the progress of the project, and promotes efficient discussion. For example, the facilitation department automatically generates minutes of the meeting and shares them with the participants. The generation AI can monitor the progress of the meeting in real time and support the progress of the discussion. Furthermore, the facilitation department automatically generates an agenda for the next meeting based on the progress of the meeting and the contents of the discussion, and supports efficient meeting management. This allows the facilitation department to support efficient management of the meeting and smooth progress of the project. The facilitation department automatically generates an agenda for the next meeting based on the progress of the meeting and the contents of the discussion, and supports efficient meeting management. This allows the facilitation department to support efficient management of the meeting and smooth progress of the project.

[0034] The collection unit can collect data on the completion status of a task or the usage status of a resource. The collection unit, for example, collects the completion status of a task. The collection unit can evaluate the completion status of a task based on the progress of the task and the completion deadline. The collection unit can also collect the usage status of a resource. The collection unit can evaluate the usage status of a resource based on the type and usage amount of the resource. For example, the collection unit obtains task progress data from a project management tool and grasps the completion status of the task. The collection unit also obtains resource usage data from a resource management tool and grasps the usage status of the resource. In this way, the collection unit can grasp the progress of a project by collecting the completion status of a task and the usage status of a resource.

[0035] The analysis unit can analyze the collected data and grasp the progress of the project. For example, the analysis unit can analyze the collected task progress data and grasp the progress of the project. The analysis unit can evaluate the progress of the project based on the task progress and completion deadline. The analysis unit can also analyze the collected resource usage data and evaluate the resource usage efficiency. For example, the analysis unit graphs the progress of the project based on the task progress data and visually grasps it. The analysis unit can also evaluate the resource usage efficiency based on the resource usage data and propose an optimal resource allocation method. In this way, the analysis unit can grasp the progress of the project by analyzing the collected data.

[0036] The suggestion unit can propose resource reallocation or change in task priority. The suggestion unit, for example, proposes resource reallocation. The suggestion unit can propose an optimal resource allocation method based on the type and usage status of the resource. The suggestion unit can also propose a change in task priority. The suggestion unit can propose a change in task priority based on the importance and urgency of the task. For example, the suggestion unit proposes resource reallocation to improve resource usage efficiency. The suggestion unit also proposes a change in task priority to smooth the progress of the project. In this way, the suggestion unit can improve the efficiency of the project by proposing resource reallocation or change in task priority.

[0037] The facilitation unit can automatically generate a meeting agenda and support the progress of the discussion. The facilitation unit, for example, automatically generates a meeting agenda. The facilitation unit can automatically generate an agenda based on the topic of the meeting and the opinions of the participants. The facilitation unit can also support the progress of the discussion. The facilitation unit can grasp the progress of the meeting in real time and support the progress of the discussion. For example, the facilitation unit automatically organizes the meeting topics and generates an agenda. Also, the facilitation unit reflects the opinions of the participants in real time to smooth the progress of the discussion. In this way, the facilitation unit can automatically generate a meeting agenda and support the progress of the discussion, thereby making the meeting more efficient.

[0038] The facilitation department can automatically generate minutes of a meeting and share them with participants. The facilitation department, for example, automatically generates minutes of a meeting. The facilitation department can automatically generate minutes based on the contents of the meeting and remarks. The facilitation department can also share the minutes with participants. The facilitation department can automatically generate minutes and share them with participants via email or cloud services. For example, the facilitation department records the contents of a meeting in real time and generates minutes. The facilitation department also automatically organizes the minutes and shares them with participants. In this way, the facilitation department can automatically generate minutes of a meeting and share them with participants, thereby improving the efficiency of the meeting.

[0039] The collection department can analyze past project data and select an appropriate data collection method. The collection department, for example, analyzes past project data. The collection department can select the most effective data collection method based on past project data. The collection department can also analyze past examples of successful and unsuccessful data collection to derive the optimal method. For example, the collection department can adjust the timing and method of data collection by referring to the progress of past projects. The collection department can also optimize the frequency and means of data collection based on past data. In this way, the collection department can select the optimal data collection method by analyzing past data.

[0040] The collection unit can filter data based on the current phase of the project and resource status when collecting data. For example, in the initial phase of a project, the collection unit collects basic data and postpones detailed data. When resources are insufficient, the collection unit can prioritize collecting only important data. The collection unit can also adjust the type and amount of data to be collected according to the progress of the project. For example, the collection unit determines the priority of data to be collected according to the phase of the project. The collection unit also selects the type of data to be collected based on the usage status of resources. In this way, the collection unit can prioritize collecting important data by filtering data based on the phase of the project and resource status.

[0041] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the project. For example, the collection unit prioritizes collecting data related to the location of the project. The collection unit can prioritize collecting data on geographically close resources. The collection unit can also select an optimal data collection method based on geographical conditions. For example, the collection unit prioritizes collecting data related to the location of the project. The collection unit also prioritizes collecting data on geographically close resources. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information.

[0042] The collection unit can analyze social media activities of the project at the time of data collection and collect related data. For example, the collection unit collects and analyzes social media posts related to the project. The collection unit can grasp trends on social media and collect data related to the project. The collection unit can also evaluate the progress of the project based on social media feedback. For example, the collection unit collects and analyzes social media posts related to the project. The collection unit can grasp trends on social media and collect data related to the project. In this way, the collection unit can collect related data by analyzing social media activities.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can perform a simplified analysis on data with low importance. The analysis unit can also optimally allocate analysis resources according to the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit performs a simplified analysis on data with low importance. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies an algorithm that evaluates progress to task data. The analysis unit can apply an algorithm that evaluates usage efficiency to resource data. The analysis unit can also apply an algorithm that proposes a solution to issue data. For example, the analysis unit applies an algorithm that evaluates progress to task data. The analysis unit also applies an algorithm that evaluates usage efficiency to resource data. In this way, the analysis unit can provide appropriate analysis results by applying different analysis algorithms depending on the category of data.

[0045] During analysis, the analysis unit can determine an analysis priority order based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can analyze current data while referring to past data. The analysis unit can also optimally allocate analysis resources according to the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can analyze current data while referring to past data. In this way, the analysis unit can prioritize analysis of the most recent data by determining an analysis priority order based on the time when the data was collected.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit gives priority to analyzing highly relevant data. The analysis unit can postpone analyzing less relevant data. The analysis unit can also optimally allocate analysis resources according to the relevance of the data. For example, the analysis unit gives priority to analyzing highly relevant data. The analysis unit postpones analyzing less relevant data. In this way, the analysis unit can prioritize analyzing highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0047] The proposal unit can adjust the level of detail of the proposal based on the importance of the resource when making a proposal. For example, the proposal unit makes a detailed proposal for an important resource. The proposal unit can make a simplified proposal for a resource with a low importance. The proposal unit can also optimally allocate the proposed resources according to the importance of the resource. For example, the proposal unit makes a detailed proposal for an important resource. The proposal unit can make a simplified proposal for a resource with a low importance. In this way, the proposal unit can make a detailed proposal for an important resource by adjusting the level of detail of the proposal based on the importance of the resource.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the task when making a suggestion. For example, the suggestion unit makes a suggestion to change the priority of the task. The suggestion unit can suggest reallocation of resources. The suggestion unit can also make a suggestion to improve the way the task progresses. For example, the suggestion unit makes a suggestion to change the priority of the task. The suggestion unit can also suggest reallocation of resources. In this way, the suggestion unit can make an appropriate suggestion by applying different suggestion algorithms depending on the category of the task.

[0049] At the time of proposal, the suggestion unit can determine the priority order of the proposal based on the submission time of the task. For example, the suggestion unit gives priority to proposals for urgent tasks. The suggestion unit can make proposals quickly for tasks whose submission deadline is approaching. The suggestion unit can also optimally allocate proposal resources according to the submission time. For example, the suggestion unit gives priority to proposals for urgent tasks. The suggestion unit can make proposals quickly for tasks whose submission deadline is approaching. In this way, the suggestion unit can make proposals quickly for urgent tasks by determining the priority order of proposals based on the submission time of tasks.

[0050] The suggestion unit can adjust the order of proposals based on the relevance of tasks at the time of proposal. For example, the suggestion unit gives priority to proposals for highly relevant tasks. The suggestion unit can postpone proposals for less relevant tasks. The suggestion unit can also optimally allocate proposal resources according to the relevance of tasks. For example, the suggestion unit gives priority to proposals for highly relevant tasks. The suggestion unit postpones proposals for less relevant tasks. In this way, the suggestion unit can give priority to proposals for highly relevant tasks by adjusting the order of proposals based on the relevance of tasks.

[0051] When facilitating a meeting, the facilitation department can select an appropriate method of proceeding by referring to past meeting data. For example, the facilitation department selects the most effective method of proceeding based on past meeting data. The facilitation department can analyze examples of success and failure in past meetings and derive the optimal method of proceeding. The facilitation department can also adjust the method of proceeding by referring to the progress of past meetings. For example, the facilitation department selects the most effective method of proceeding based on past meeting data. The facilitation department can also analyze examples of success and failure in past meetings and derive the optimal method of proceeding. In this way, the facilitation department can select the optimal method of proceeding by referring to past meeting data.

[0052] When facilitating a meeting, the facilitation department can proceed with the meeting taking into consideration the attribute information of the participants. For example, the facilitation department adjusts the order of remarks depending on the participants' job titles. The facilitation department can adjust the explanation of the agenda depending on the participants' fields of expertise. The facilitation department can also adjust the speed of the meeting depending on the participants' years of experience. For example, the facilitation department adjusts the order of remarks depending on the participants' job titles. The facilitation department can also adjust the explanation of the agenda depending on the participants' fields of expertise. In this way, the facilitation department can proceed appropriately by taking into consideration the attribute information of the participants.

[0053] When facilitating a meeting, the facilitation department can proceed with the meeting taking into account the geographical distribution of the meeting. For example, when there are many remote participants, the facilitation department prioritizes an online method of proceeding. The facilitation department can adjust the order of remarks depending on the layout of the meeting rooms. The facilitation department can also proceed with the meeting taking into account the time of day for participants who are geographically distant. For example, when there are many remote participants, the facilitation department prioritizes an online method of proceeding. The facilitation department can also adjust the order of remarks depending on the layout of the meeting rooms. This allows the facilitation department to proceed with the meeting appropriately by taking into account the geographical distribution of the meeting.

[0054] The facilitation department can refer to related literature when facilitating a meeting to improve the accuracy of the progress. For example, the facilitation department collects literature related to the meeting agenda in advance to help with the progress. The facilitation department can refer to related literature during the progress to improve the accuracy of the discussion. The facilitation department can also share related literature after the meeting to deepen the understanding of participants. For example, the facilitation department collects literature related to the meeting agenda in advance to help with the progress. The facilitation department can also refer to related literature during the progress to improve the accuracy of the discussion. In this way, the facilitation department can improve the accuracy of the progress by referring to related literature.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The proposal department can also make proposals for risk management according to the progress of the project. For example, if there is a potential risk in an ongoing task, the proposal department can detect the risk early and propose appropriate measures. The proposal department can also evaluate the impact of risks and determine the priority of the risks. Furthermore, the proposal department can specifically propose countermeasures in the event that a risk occurs, thereby ensuring the stability of the project. In this way, the proposal department can increase the success rate of the project by making proposals for risk management.

[0057] The collection department can also collect data from external data sources in order to grasp the progress of the project. For example, the collection department can collect industry benchmark data and compare the progress of the project. The collection department can also collect project data of competitors and evaluate the progress of the project. Furthermore, the collection department can collect opinions and advice from external experts and use them to help with the progress of the project. In this way, the collection department can grasp the progress of the project more accurately by utilizing external data sources.

[0058] The analysis unit can detect anomalies in the data in order to grasp the progress of the project. For example, if there is an anomaly in task progress data, the analysis unit can detect the anomaly and identify the cause. Also, if there is an anomaly in resource usage data, the analysis unit can detect the anomaly and propose an optimal method of allocating resources. Furthermore, if there is an anomaly in the progress of the project, the analysis unit can detect the anomaly early on and propose appropriate measures. In this way, the analysis unit can accurately grasp the progress of the project by detecting anomalies in the data.

[0059] The proposal department can also make proposals for improving the skills of team members according to the progress of the project. For example, if a skill is lacking for a specific task, the proposal department can propose a training program for improving the skill. The proposal department can also perform skill matching of team members and propose optimal task allocation. Furthermore, the proposal department can provide resources for skill improvement and improve the performance of the entire team. In this way, the proposal department can improve the efficiency of the project by making proposals for improving the skills of team members.

[0060] The facilitation department can collect the opinions of participants in real time while the meeting is in progress and provide instant feedback. For example, the facilitation department can collect the opinions of participants in real time and adjust the direction of the discussion. The facilitation department can also make suggestions to make the discussion progress smoothly based on the opinions of the participants. Furthermore, the facilitation department can provide instant feedback on the opinions of participants and improve the quality of the discussion. In this way, the facilitation department can improve the efficiency of the meeting by collecting the opinions of participants in real time and providing instant feedback.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects the progress of the project. The collection unit can collect data such as task completion status and resource usage status. The collection unit can also collect the progress status in each phase of the project using the generation AI. For example, the collection unit automatically obtains data from a project management tool and grasps the progress status. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses the generation AI to analyze the collected data and understand the progress of the project. For example, the analysis unit evaluates the progress of tasks and the efficiency of resource usage. Step 3: The proposal unit makes proposals based on the analysis results obtained by the analysis unit. Using the generation AI, the proposal unit makes proposals to improve the efficiency of the project, such as reallocating resources and changing task priorities. For example, the proposal unit proposes the optimal method of allocating resources to make the project proceed smoothly. Step 4: The Facilitation Department facilitates the meeting based on the content proposed by the Proposal Department. The Facilitation Department uses generative AI to automatically generate the meeting agenda and support the progress of the discussion. For example, the Facilitation Department automatically generates meeting minutes and shares them with participants.

[0063] (Example 2) A project management office (PMO) business support system according to an embodiment of the present invention is a system that uses a generation AI to collect, analyze, and make proposals on the progress of a project, and facilitates a meeting. In this system, the generation AI collects and analyzes the progress of the project in each phase. For example, data such as the completion status of tasks and the usage status of resources is collected, and the generation AI analyzes it to grasp the progress of the project. Next, the generation AI makes proposals regarding the progress of the project based on the analysis results. For example, proposals are made to improve the efficiency of the project, such as reallocation of resources and change in the priority of tasks. Furthermore, the generation AI facilitates the meeting. For example, the meeting agenda is automatically generated to support the progress of the discussion. In addition, the meeting minutes are automatically generated and shared with the participants to improve the efficiency of the meeting. This system makes it possible to carry out work with a uniformly high quality even when there is variation in project management skills. In addition, the project progress can be grasped in real time and appropriate proposals can be made, thereby increasing the success rate of the project. As a result, the PMO business support system can grasp the project progress in real time and make appropriate proposals.

[0064] The PMO business support system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a facilitation unit. The collection unit collects the progress of the project. The collection unit can collect data such as the completion status of tasks and the usage status of resources. The collection unit can also collect the progress status in each phase of the project using the generation AI. For example, the collection unit automatically acquires data from a project management tool and grasps the progress status. The analysis unit analyzes the data collected by the collection unit. The analysis unit uses the generation AI to analyze the collected data and grasp the progress of the project. For example, the analysis unit evaluates the progress of tasks and the efficiency of resource use. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit uses the generation AI to make proposals for improving the efficiency of the project, such as reallocating resources and changing the priority of tasks. For example, the proposal unit proposes an optimal method of allocating resources to smooth the progress of the project. The facilitation unit facilitates meetings based on the contents proposed by the proposal unit. The facilitation department uses generation AI to automatically generate meeting agendas and support the progress of discussions. For example, the facilitation department automatically generates meeting minutes and shares them with participants. This enables the PMO business support system according to the embodiment to efficiently collect, analyze, propose, and facilitate the progress of projects.

[0065] The collection unit collects the progress of the project. The collection unit can collect data such as the completion status of tasks and the usage status of resources. Specifically, the data is automatically acquired from a project management tool or a task management system, and the progress of each task and the allocation status of resources are grasped in real time. The collection unit can also collect the progress in each phase of the project using the generation AI. The generation AI analyzes data from the project management tool and evaluates the progress of the task and the efficiency of resource usage. For example, the collection unit automatically acquires data from the project management tool and grasps the progress. The generation AI can monitor the progress of the project in real time and detect abnormal delays and excessive use of resources. Furthermore, the collection unit collects feedback and reports from project members and comprehensively evaluates the progress of the project. This allows the collection unit to efficiently collect the progress of the project and grasp the situation in real time. The collected data is stored in a central database and made accessible to the analysis unit and proposal unit. This allows the collection unit to accurately grasp the progress of the project and support efficient operation of the project.

[0066] The analysis unit analyzes the data collected by the collection unit. The analysis unit uses the generation AI to analyze the collected data and grasp the progress of the project. Specifically, the generation AI evaluates the progress of the task and the efficiency of resource use, and visualizes the overall progress of the project. For example, the analysis unit evaluates the progress of the task and the efficiency of resource use. The generation AI can predict the progress of the project based on past data and statistical information, and identify future risks and problems. Furthermore, the analysis unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the progress of the project in real time. The analysis unit provides a dashboard for visualizing the progress of the project, allowing project members and managers to easily grasp the situation. This allows the analysis unit to accurately grasp the progress of the project and support efficient operation of the project.

[0067] The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The proposal unit uses the generation AI to make proposals for improving the efficiency of the project, such as reallocating resources and changing the priority of tasks. Specifically, the generation AI calculates the optimal resource allocation method and task priority order based on the analysis results, and makes proposals to smooth the progress of the project. For example, the proposal unit proposes the optimal resource allocation method to smooth the progress of the project. The generation AI can monitor the progress of the project and the usage of resources in real time and make optimal proposals. Furthermore, the proposal unit can predict future risks and problems based on the progress of the project and the usage of resources, and propose appropriate measures. This allows the proposal unit to support efficient operation of the project and contribute to the success of the project. The proposal unit shares the contents of the proposal with project members and managers, and supports rapid decision-making. This allows the proposal unit to support efficient operation of the project and contribute to the success of the project.

[0068] The facilitation department facilitates the meeting based on the contents proposed by the proposal department. The facilitation department uses the generation AI to automatically generate a meeting agenda and support the progress of the discussion. Specifically, the generation AI automatically generates a meeting agenda based on the proposal contents and the progress of the project, and promotes efficient discussion. For example, the facilitation department automatically generates minutes of the meeting and shares them with the participants. The generation AI can monitor the progress of the meeting in real time and support the progress of the discussion. Furthermore, the facilitation department automatically generates an agenda for the next meeting based on the progress of the meeting and the contents of the discussion, and supports efficient meeting management. This allows the facilitation department to support efficient management of the meeting and smooth progress of the project. The facilitation department automatically generates an agenda for the next meeting based on the progress of the meeting and the contents of the discussion, and supports efficient meeting management. This allows the facilitation department to support efficient management of the meeting and smooth progress of the project.

[0069] The collection unit can collect data on the completion status of a task or the usage status of a resource. The collection unit, for example, collects the completion status of a task. The collection unit can evaluate the completion status of a task based on the progress of the task and the completion deadline. The collection unit can also collect the usage status of a resource. The collection unit can evaluate the usage status of a resource based on the type and usage amount of the resource. For example, the collection unit obtains task progress data from a project management tool and grasps the completion status of the task. The collection unit also obtains resource usage data from a resource management tool and grasps the usage status of the resource. In this way, the collection unit can grasp the progress of a project by collecting the completion status of a task and the usage status of a resource.

[0070] The analysis unit can analyze the collected data and grasp the progress of the project. For example, the analysis unit can analyze the collected task progress data and grasp the progress of the project. The analysis unit can evaluate the progress of the project based on the task progress and completion deadline. The analysis unit can also analyze the collected resource usage data and evaluate the resource usage efficiency. For example, the analysis unit graphs the progress of the project based on the task progress data and visually grasps it. The analysis unit can also evaluate the resource usage efficiency based on the resource usage data and propose an optimal resource allocation method. In this way, the analysis unit can grasp the progress of the project by analyzing the collected data.

[0071] The suggestion unit can propose resource reallocation or change in task priority. The suggestion unit, for example, proposes resource reallocation. The suggestion unit can propose an optimal resource allocation method based on the type and usage status of the resource. The suggestion unit can also propose a change in task priority. The suggestion unit can propose a change in task priority based on the importance and urgency of the task. For example, the suggestion unit proposes resource reallocation to improve resource usage efficiency. The suggestion unit also proposes a change in task priority to smooth the progress of the project. In this way, the suggestion unit can improve the efficiency of the project by proposing resource reallocation or change in task priority.

[0072] The facilitation unit can automatically generate a meeting agenda and support the progress of the discussion. The facilitation unit, for example, automatically generates a meeting agenda. The facilitation unit can automatically generate an agenda based on the topic of the meeting and the opinions of the participants. The facilitation unit can also support the progress of the discussion. The facilitation unit can grasp the progress of the meeting in real time and support the progress of the discussion. For example, the facilitation unit automatically organizes the meeting topics and generates an agenda. Also, the facilitation unit reflects the opinions of the participants in real time to smooth the progress of the discussion. In this way, the facilitation unit can automatically generate a meeting agenda and support the progress of the discussion, thereby making the meeting more efficient.

[0073] The facilitation department can automatically generate minutes of a meeting and share them with participants. The facilitation department, for example, automatically generates minutes of a meeting. The facilitation department can automatically generate minutes based on the contents of the meeting and remarks. The facilitation department can also share the minutes with participants. The facilitation department can automatically generate minutes and share them with participants via email or cloud services. For example, the facilitation department records the contents of a meeting in real time and generates minutes. The facilitation department also automatically organizes the minutes and shares them with participants. In this way, the facilitation department can automatically generate minutes of a meeting and share them with participants, thereby improving the efficiency of the meeting.

[0074] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. The collection unit can estimate the user's emotion using an emotion estimation algorithm. For example, the collection unit analyzes the user's facial expression and voice to estimate the emotion. The collection unit can also adjust the timing of data collection based on the estimated user's emotion. The collection unit can reduce the frequency of data collection and reduce the burden on the user when the user is stressed. For example, the collection unit increases the frequency of data collection and collects detailed data when the user is relaxed. Also, the collection unit can prioritize collecting only important data when the user is in a hurry. Thereby, the collection unit can reduce the burden on the user by adjusting the timing of data collection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] The collection department can analyze past project data and select an appropriate data collection method. The collection department, for example, analyzes past project data. The collection department can select the most effective data collection method based on past project data. The collection department can also analyze past examples of successful and unsuccessful data collection to derive the optimal method. For example, the collection department can adjust the timing and method of data collection by referring to the progress of past projects. The collection department can also optimize the frequency and means of data collection based on past data. In this way, the collection department can select the optimal data collection method by analyzing past data.

[0076] The collection unit can filter data based on the current phase of the project and resource status when collecting data. For example, in the initial phase of a project, the collection unit collects basic data and postpones detailed data. When resources are insufficient, the collection unit can prioritize collecting only important data. The collection unit can also adjust the type and amount of data to be collected according to the progress of the project. For example, the collection unit determines the priority of data to be collected according to the phase of the project. The collection unit also selects the type of data to be collected based on the usage status of resources. In this way, the collection unit can prioritize collecting important data by filtering data based on the phase of the project and resource status.

[0077] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. The collection unit can estimate the user's emotion using an emotion estimation algorithm. For example, the collection unit analyzes the user's facial expression and voice to estimate the emotion. The collection unit can also determine the priority of data to be collected based on the estimated user's emotion. The collection unit can preferentially collect only important data when the user is stressed. For example, the collection unit preferentially collects detailed data when the user is relaxed. Also, the collection unit can preferentially collect data that can be collected quickly when the user is in a hurry. Thereby, the collection unit can preferentially collect important data by determining the priority of data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the project. For example, the collection unit prioritizes collecting data related to the location of the project. The collection unit can prioritize collecting data on geographically close resources. The collection unit can also select an optimal data collection method based on geographical conditions. For example, the collection unit prioritizes collecting data related to the location of the project. The collection unit also prioritizes collecting data on geographically close resources. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information.

[0079] The collection unit can analyze social media activities of the project at the time of data collection and collect related data. For example, the collection unit collects and analyzes social media posts related to the project. The collection unit can grasp trends on social media and collect data related to the project. The collection unit can also evaluate the progress of the project based on social media feedback. For example, the collection unit collects and analyzes social media posts related to the project. The collection unit can grasp trends on social media and collect data related to the project. In this way, the collection unit can collect related data by analyzing social media activities.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. The analysis unit can estimate the user's emotion using an emotion estimation algorithm. For example, the analysis unit analyzes the user's facial expression and voice to estimate the emotion. The analysis unit can also adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit can provide a simple and highly visible analysis result when the user is nervous. For example, the analysis unit can provide a detailed analysis result when the user is relaxed. Also, the analysis unit can provide an analysis result that covers the main points when the user is in a hurry. Thereby, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the expression method of the analysis based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can perform a simplified analysis on data with low importance. The analysis unit can also optimally allocate analysis resources according to the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit performs a simplified analysis on data with low importance. In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies an algorithm that evaluates progress to task data. The analysis unit can apply an algorithm that evaluates usage efficiency to resource data. The analysis unit can also apply an algorithm that proposes a solution to issue data. For example, the analysis unit applies an algorithm that evaluates progress to task data. The analysis unit also applies an algorithm that evaluates usage efficiency to resource data. In this way, the analysis unit can provide appropriate analysis results by applying different analysis algorithms depending on the category of data.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. The analysis unit can estimate the user's emotion using an emotion estimation algorithm. For example, the analysis unit analyzes the user's facial expression and voice to estimate the emotion. The analysis unit can also adjust the length of the analysis based on the estimated user's emotion. The analysis unit can provide a short and to the point analysis result when the user is in a hurry. For example, the analysis unit can provide a detailed analysis result when the user is relaxed. Also, the analysis unit can provide a visually stimulating analysis result when the user is excited. Thereby, the analysis unit can provide an analysis result of an appropriate length for the user by adjusting the length of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] During analysis, the analysis unit can determine an analysis priority order based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can analyze current data while referring to past data. The analysis unit can also optimally allocate analysis resources according to the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can analyze current data while referring to past data. In this way, the analysis unit can prioritize analysis of the most recent data by determining an analysis priority order based on the time when the data was collected.

[0085] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit gives priority to analyzing highly relevant data. The analysis unit can postpone analyzing less relevant data. The analysis unit can also optimally allocate analysis resources according to the relevance of the data. For example, the analysis unit gives priority to analyzing highly relevant data. The analysis unit postpones analyzing less relevant data. In this way, the analysis unit can prioritize analyzing highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0086] The suggestion unit can estimate the user's emotion and adjust the way of expressing the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. The suggestion unit can estimate the user's emotion using an emotion estimation algorithm. For example, the suggestion unit analyzes the user's facial expression and voice to estimate the emotion. The suggestion unit can also adjust the way of expressing the proposal based on the estimated user's emotion. The suggestion unit can provide a simple and highly visible proposal when the user is nervous. For example, the suggestion unit can provide a detailed proposal when the user is relaxed. Also, the suggestion unit can provide a proposal that covers the main points when the user is in a hurry. Thereby, the suggestion unit can provide a proposal that is easy for the user to understand by adjusting the way of expressing the proposal based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The proposal unit can adjust the level of detail of the proposal based on the importance of the resource when making a proposal. For example, the proposal unit makes a detailed proposal for an important resource. The proposal unit can make a simplified proposal for a resource with a low importance. The proposal unit can also optimally allocate the proposed resources according to the importance of the resource. For example, the proposal unit makes a detailed proposal for an important resource. The proposal unit can make a simplified proposal for a resource with a low importance. In this way, the proposal unit can make a detailed proposal for an important resource by adjusting the level of detail of the proposal based on the importance of the resource.

[0088] The suggestion unit can apply different suggestion algorithms depending on the category of the task when making a suggestion. For example, the suggestion unit makes a suggestion to change the priority of the task. The suggestion unit can suggest reallocation of resources. The suggestion unit can also make a suggestion to improve the way the task progresses. For example, the suggestion unit makes a suggestion to change the priority of the task. The suggestion unit can also suggest reallocation of resources. In this way, the suggestion unit can make an appropriate suggestion by applying different suggestion algorithms depending on the category of the task.

[0089] The suggestion unit can estimate the user's emotion and adjust the length of the proposal based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. The suggestion unit can estimate the user's emotion using an emotion estimation algorithm. For example, the suggestion unit analyzes the user's facial expression and voice to estimate the emotion. The suggestion unit can also adjust the length of the proposal based on the estimated user's emotion. The suggestion unit can provide a short and to the point proposal when the user is in a hurry. For example, the suggestion unit can provide a detailed proposal when the user is relaxed. Also, the suggestion unit can provide a visually stimulating proposal when the user is excited. Thereby, the suggestion unit can provide a proposal of an appropriate length for the user by adjusting the length of the proposal based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] At the time of proposal, the suggestion unit can determine the priority order of the proposal based on the submission time of the task. For example, the suggestion unit gives priority to proposals for urgent tasks. The suggestion unit can make proposals quickly for tasks whose submission deadline is approaching. The suggestion unit can also optimally allocate proposal resources according to the submission time. For example, the suggestion unit gives priority to proposals for urgent tasks. The suggestion unit can make proposals quickly for tasks whose submission deadline is approaching. In this way, the suggestion unit can make proposals quickly for urgent tasks by determining the priority order of proposals based on the submission time of tasks.

[0091] The suggestion unit can adjust the order of proposals based on the relevance of tasks at the time of proposal. For example, the suggestion unit gives priority to proposals for highly relevant tasks. The suggestion unit can postpone proposals for less relevant tasks. The suggestion unit can also optimally allocate proposal resources according to the relevance of tasks. For example, the suggestion unit gives priority to proposals for highly relevant tasks. The suggestion unit postpones proposals for less relevant tasks. In this way, the suggestion unit can give priority to proposals for highly relevant tasks by adjusting the order of proposals based on the relevance of tasks.

[0092] The facilitation unit can estimate the user's emotion and adjust the meeting agenda based on the estimated user's emotion. The facilitation unit, for example, estimates the user's emotion. The facilitation unit can estimate the user's emotion using an emotion estimation algorithm. For example, the facilitation unit analyzes the user's facial expression and voice to estimate the emotion. The facilitation unit can also adjust the meeting agenda based on the estimated user's emotion. The facilitation unit can provide a simple and highly visible agenda when the user is nervous. For example, the facilitation unit can provide a detailed agenda when the user is relaxed. Also, the facilitation unit can provide an agenda that covers the main points when the user is in a hurry. In this way, the facilitation unit can provide an appropriate agenda for the user by adjusting the meeting agenda based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] When facilitating a meeting, the facilitation department can select an appropriate method of proceeding by referring to past meeting data. For example, the facilitation department selects the most effective method of proceeding based on past meeting data. The facilitation department can analyze examples of success and failure in past meetings and derive the optimal method of proceeding. The facilitation department can also adjust the method of proceeding by referring to the progress of past meetings. For example, the facilitation department selects the most effective method of proceeding based on past meeting data. The facilitation department can also analyze examples of success and failure in past meetings and derive the optimal method of proceeding. In this way, the facilitation department can select the optimal method of proceeding by referring to past meeting data.

[0094] When facilitating a meeting, the facilitation department can proceed with the meeting taking into consideration the attribute information of the participants. For example, the facilitation department adjusts the order of remarks depending on the participants' job titles. The facilitation department can adjust the explanation of the agenda depending on the participants' fields of expertise. The facilitation department can also adjust the speed of the meeting depending on the participants' years of experience. For example, the facilitation department adjusts the order of remarks depending on the participants' job titles. The facilitation department can also adjust the explanation of the agenda depending on the participants' fields of expertise. In this way, the facilitation department can proceed appropriately by taking into consideration the attribute information of the participants.

[0095] The facilitation unit can estimate the user's emotion and adjust the proceeding order of the conference based on the estimated user's emotion. The facilitation unit, for example, estimates the user's emotion. The facilitation unit can estimate the user's emotion using an emotion estimation algorithm. For example, the facilitation unit analyzes the user's facial expression and voice to estimate the emotion. The facilitation unit can also adjust the proceeding order of the conference based on the estimated user's emotion. If the user is nervous, the facilitation unit can start with an agenda that allows the user to relax. For example, if the user is relaxed, the facilitation unit can start with an important agenda. If the user is in a hurry, the facilitation unit can start with an agenda that covers the main points. In this way, the facilitation unit can provide an appropriate proceeding order for the user by adjusting the proceeding order of the conference based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] When facilitating a meeting, the facilitation department can proceed with the meeting taking into account the geographical distribution of the meeting. For example, when there are many remote participants, the facilitation department prioritizes an online method of proceeding. The facilitation department can adjust the order of remarks depending on the layout of the meeting rooms. The facilitation department can also proceed with the meeting taking into account the time of day for participants who are geographically distant. For example, when there are many remote participants, the facilitation department prioritizes an online method of proceeding. The facilitation department can also adjust the order of remarks depending on the layout of the meeting rooms. This allows the facilitation department to proceed with the meeting appropriately by taking into account the geographical distribution of the meeting.

[0097] The facilitation department can refer to related literature when facilitating a meeting to improve the accuracy of the progress. For example, the facilitation department collects literature related to the meeting agenda in advance to help with the progress. The facilitation department can refer to related literature during the progress to improve the accuracy of the discussion. The facilitation department can also share related literature after the meeting to deepen the understanding of participants. For example, the facilitation department collects literature related to the meeting agenda in advance to help with the progress. The facilitation department can also refer to related literature during the progress to improve the accuracy of the discussion. In this way, the facilitation department can improve the accuracy of the progress by referring to related literature.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The proposal department can also make proposals for risk management according to the progress of the project. For example, if there is a potential risk in an ongoing task, the proposal department can detect the risk early and propose appropriate measures. The proposal department can also evaluate the impact of risks and determine the priority of the risks. Furthermore, the proposal department can specifically propose countermeasures in the event that a risk occurs, thereby ensuring the stability of the project. In this way, the proposal department can increase the success rate of the project by making proposals for risk management.

[0100] The collection department can also collect data from external data sources in order to grasp the progress of the project. For example, the collection department can collect industry benchmark data and compare the progress of the project. The collection department can also collect project data of competitors and evaluate the progress of the project. Furthermore, the collection department can collect opinions and advice from external experts and use them to help with the progress of the project. In this way, the collection department can grasp the progress of the project more accurately by utilizing external data sources.

[0101] The analysis unit can detect anomalies in the data in order to grasp the progress of the project. For example, if there is an anomaly in task progress data, the analysis unit can detect the anomaly and identify the cause. Also, if there is an anomaly in resource usage data, the analysis unit can detect the anomaly and propose an optimal method of allocating resources. Furthermore, if there is an anomaly in the progress of the project, the analysis unit can detect the anomaly early on and propose appropriate measures. In this way, the analysis unit can accurately grasp the progress of the project by detecting anomalies in the data.

[0102] The proposal department can also make proposals for improving the skills of team members according to the progress of the project. For example, if a skill is lacking for a specific task, the proposal department can propose a training program for improving the skill. The proposal department can also perform skill matching of team members and propose optimal task allocation. Furthermore, the proposal department can provide resources for skill improvement and improve the performance of the entire team. In this way, the proposal department can improve the efficiency of the project by making proposals for improving the skills of team members.

[0103] The facilitation department can collect the opinions of participants in real time while the meeting is in progress and provide instant feedback. For example, the facilitation department can collect the opinions of participants in real time and adjust the direction of the discussion. The facilitation department can also make suggestions to make the discussion progress smoothly based on the opinions of the participants. Furthermore, the facilitation department can provide instant feedback on the opinions of participants and improve the quality of the discussion. In this way, the facilitation department can improve the efficiency of the meeting by collecting the opinions of participants in real time and providing instant feedback.

[0104] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit simplifies the data collection method to reduce the burden on the user. Also, when the user is relaxed, the collection unit can select a method for collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can select a method for quickly collecting data. In this way, the collection unit can reduce the burden on the user and achieve efficient data collection by adjusting the data collection method based on the user's emotions.

[0105] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is nervous, the analysis unit provides simple and highly visible analysis results. When the user is relaxed, the analysis unit can provide detailed analysis results. When the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the display method of the analysis results based on the user's emotions.

[0106] The suggestion unit can estimate the user's emotion and adjust the content of the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit can provide a simple and easy-to-implement suggestion. When the user is relaxed, the suggestion unit can provide a detailed suggestion. When the user is in a hurry, the suggestion unit can provide a quickly implementable suggestion. In this way, the suggestion unit can provide an easy-to-implement suggestion for the user by adjusting the content of the suggestion based on the user's emotion.

[0107] The facilitation unit can estimate the user's emotions and adjust the conference proceeding method based on the estimated user's emotions. For example, if the user is nervous, the facilitation unit selects a proceeding method that allows the user to relax. Also, if the user is relaxed, the facilitation unit can select an efficient proceeding method. Furthermore, if the user is in a hurry, the facilitation unit can select a method that allows the conference to proceed quickly. In this way, the facilitation unit can provide a proceeding method that is appropriate for the user by adjusting the proceeding method of the conference based on the user's emotions.

[0108] The facilitation unit can estimate the user's emotions and adjust the meeting agenda based on the estimated user's emotions. For example, if the user is nervous, the facilitation unit can start with an agenda that will relax the user. Also, if the user is relaxed, the facilitation unit can start with an important agenda. Furthermore, if the user is in a hurry, the facilitation unit can start with an agenda that focuses on the main points. In this way, the facilitation unit can provide an appropriate agenda for the user by adjusting the meeting agenda based on the user's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects the progress of the project. The collection unit can collect data such as task completion status and resource usage status. The collection unit can also collect the progress status in each phase of the project using the generation AI. For example, the collection unit automatically obtains data from a project management tool and grasps the progress status. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses the generation AI to analyze the collected data and understand the progress of the project. For example, the analysis unit evaluates the progress of tasks and the efficiency of resource usage. Step 3: The proposal unit makes proposals based on the analysis results obtained by the analysis unit. Using the generation AI, the proposal unit makes proposals to improve the efficiency of the project, such as reallocating resources and changing task priorities. For example, the proposal unit proposes the optimal method of allocating resources to make the project proceed smoothly. Step 4: The Facilitation Department facilitates the meeting based on the content proposed by the Proposal Department. The Facilitation Department uses generative AI to automatically generate the meeting agenda and support the progress of the discussion. For example, the Facilitation Department automatically generates meeting minutes and shares them with participants.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and facilitation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14, and automatically acquires data from a project management tool. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes proposals such as resource reallocation and change in task priority order. The facilitation unit is realized, for example, by the control unit 46A of the smart device 14, and automatically generates a meeting agenda and creates minutes. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and facilitation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214, and automatically acquires data from a project management tool. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes proposals such as resource reallocation and change in task priority. The facilitation unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically generates a meeting agenda and creates minutes. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and facilitation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314, and automatically acquires data from a project management tool. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes proposals such as resource reallocation and change in task priority order. The facilitation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and automatically generates a meeting agenda and creates minutes. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and facilitation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414, and automatically acquires data from a project management tool. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes proposals such as resource reallocation and change in task priority order. The facilitation unit is realized, for example, by the control unit 46A of the robot 414, and automatically generates a meeting agenda and creates minutes. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection department that collects project progress information; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that makes a proposal based on an analysis result obtained by the analysis unit; A facilitation unit that facilitates the conference based on the content proposed by the proposal unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect task completion or resource usage data 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data to understand the progress of the project 2. The system of claim 1, (Appendix 4) The suggestion unit, Make suggestions for reallocating resources or changing task priorities 2. The system of claim 1, (Appendix 5) The facilitation department includes: Automatically generate meeting agendas to help facilitate discussions 2. The system of claim 1, (Appendix 6) The facilitation department includes: Automatically generate meeting minutes and share them with participants 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze past project data and choose the appropriate data collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter data collection based on the current phase of the project or resource availability 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collecting the most relevant data by considering the geographic location of the project 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, analyze the project's social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The suggestion unit, During proposals, adjust the detail of the proposal based on the importance of the resource 2. The system of claim 1, (Appendix 21) The suggestion unit, When making suggestions, we apply different suggestion algorithms depending on the task category. 2. The system of claim 1, (Appendix 22) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The suggestion unit, During proposals, prioritize proposals based on when tasks were submitted 2. The system of claim 1, (Appendix 24) The suggestion unit, During suggestions, adjust the order of suggestions based on task relevance 2. The system of claim 1, (Appendix 25) The facilitation department includes: Inferring user emotions and adjusting meeting agendas based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The facilitation department includes: When facilitating a meeting, choose the appropriate way to proceed by referring to past meeting data. 2. The system of claim 1, (Appendix 27) The facilitation department includes: When facilitating a meeting, proceed with the meeting taking into account the attributes of the participants. 2. The system of claim 1, (Appendix 28) The facilitation department includes: To estimate a user's emotion and adjust the order of progress of a meeting based on the estimated user's emotion 2. The system of claim 1, (Appendix 29) The facilitation department includes: When facilitating a meeting, take into account the geographical distribution of the meeting. 2. The system of claim 1, (Appendix 30) The facilitation department includes: When facilitating a meeting, refer to related literature to improve the accuracy of the proceedings. 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects project progress information; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that makes a proposal based on an analysis result obtained by the analysis unit; A facilitation unit that facilitates the conference based on the content proposed by the proposal unit. A system characterized by:

2. The collecting unit includes: Collect task completion or resource usage data 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data to understand the progress of the project 2. The system of claim 1 .

4. The suggestion unit, Make suggestions for reallocating resources or changing task priorities 2. The system of claim 1 .

5. The facilitation department includes: Automatically generate meeting agendas to help facilitate discussions 2. The system of claim 1 .

6. The facilitation department includes: Automatically generate meeting minutes and share them with participants 2. The system of claim 1 .

7. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

8. The collecting unit includes: Analyze past project data and choose the appropriate data collection method 2. The system of claim 1 .

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