system

Abstract

The system according to this embodiment aims to reduce the man-hours required for creating business requirements definitions. [Solution] The system according to the embodiment comprises a collection unit, a setting unit, a meeting minutes creation unit, a reflection unit, and a generation unit. The collection unit creates a list of business requirements. The setting unit sets up detailed confirmation meetings based on the list of business requirements created by the collection unit. The meeting minutes creation unit creates minutes of the meetings set up by the setting unit. The reflection unit updates the list of business requirements based on the minutes created by the meeting minutes creation unit. The generation unit creates a business requirements screen definition document based on the list of business requirements updated by the reflection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes a lot of man-hours and is inefficient because the creation of business requirement definitions is done manually. <(

[0005] The system according to the embodiment aims to reduce the man-hours for creating business requirement definitions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a setting unit, a meeting minutes creation unit, a reflection unit, and a generation unit. The collection unit creates a list of business requirements. The setting unit sets up detailed confirmation meetings based on the list of business requirements created by the collection unit. The meeting minutes creation unit creates minutes for the meetings set up by the setting unit. The reflection unit updates the list of business requirements based on the minutes created by the meeting minutes creation unit. The generation unit creates a business requirements screen definition document based on the list of business requirements updated by the reflection unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the man-hours required to create business requirements definitions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that utilizes an AI agent to streamline the creation of system development requirements definitions for new service development in corporations. This system automates the process of creating a list of business requirements, scheduling detailed confirmation meetings, creating meeting minutes, reflecting the decisions in the list of business requirements, and creating a business requirements screen definition document. For example, the system automatically generates a list of business requirements and automatically schedules detailed confirmation meetings. The system automatically creates meeting minutes during the meeting and reflects the decisions in the list of business requirements. Furthermore, the system automatically creates a business requirements screen definition document. This streamlines the process of creating business requirements definitions and reduces the burden on employees. This mechanism can reduce the man-hours of departments creating business requirements for corporate operations and improve operational efficiency. By utilizing an AI agent, the process of creating business requirements definitions is automated, allowing employees to focus on more important tasks. As a result, the system can streamline the process of creating business requirements definitions and significantly reduce the man-hours of employees.

[0029] The system according to the embodiment comprises a collection unit, a setting unit, a minutes creation unit, an update unit, and a generation unit. The collection unit creates a list of business requirements. The collection unit automatically generates the list of business requirements using, for example, AI. The collection unit needs to clarify the specific content and format of the list of business requirements. For example, this includes the types of items and how to record them. The setting unit sets up detailed confirmation meetings based on the list of business requirements created by the collection unit. The setting unit automatically sets the schedule for the detailed confirmation meetings using, for example, AI. The setting unit needs to clarify the specific content and purpose of the detailed confirmation meetings. For example, this includes the agenda, participants, and how to conduct the meeting. The minutes creation unit creates minutes of the meetings set up by the setting unit. The minutes creation unit automatically creates minutes during the meeting using, for example, AI. The minutes creation unit needs to clarify the specific content and format of the minutes. For example, this includes the items to be recorded and the format. The update unit updates the list of business requirements based on the minutes created by the minutes creation unit. The reflection unit, for example, uses AI to reflect the matters decided in the meeting into the list of business requirements. The reflection unit needs to clarify the specific methods and criteria for reflection. For example, this includes the items to be reflected and the processes. The generation unit creates a business requirements screen definition document based on the list of business requirements updated by the reflection unit. The generation unit, for example, uses AI to automatically create the business requirements screen definition document. The generation unit needs to clarify the specific content and format of the business requirements screen definition document. For example, this includes the items to be included and the format. As a result, the system according to the embodiment can streamline the process of creating business requirements definitions and significantly reduce the workload of employees.

[0030] The data collection unit creates a list of business requirements. For example, the data collection unit automatically generates the list of business requirements using AI. Specifically, the AI ​​uses natural language processing technology to analyze past project data and related documents, extracting patterns and common items in the business requirements. This allows the AI ​​to quickly and accurately generate a list of business requirements for new projects. The data collection unit needs to clarify the specific content and format of the list of business requirements. This includes, for example, the types of items and how they are written. Item types include functional requirements, non-functional requirements, and constraints, while writing methods include bullet points and tables. Furthermore, the data collection unit manages versions of the list of business requirements and records the change history, enabling it to respond to changes or additions to requirements. This allows the data collection unit to efficiently collect and organize business requirements, enabling rapid requirements definition in the early stages of a project.

[0031] The scheduling department sets up detailed confirmation meetings based on the list of business requirements created by the collection department. The scheduling department can, for example, use AI to automatically schedule the detailed confirmation meetings. Specifically, the AI ​​analyzes participants' calendars and suggests the optimal date and time. The AI ​​also automatically selects the necessary participants based on the meeting's purpose and agenda and sends invitations. The scheduling department needs to clarify the specific content and purpose of the detailed confirmation meeting. This includes, for example, the agenda, participants, and method of proceeding. The agenda may include confirming business requirements, discussing revisions, and considering additional requirements, while the method of proceeding includes the role of the facilitator and how the discussion will proceed. Furthermore, the scheduling department distributes preparatory materials to participants before the meeting to prepare for efficient discussion. This allows the scheduling department to efficiently schedule and prepare for detailed confirmation meetings, ensuring the smooth progress of the project.

[0032] The minutes creation department creates meeting minutes for meetings set up by the setup department. For example, the minutes creation department can use AI to automatically create minutes during a meeting. Specifically, the AI ​​uses speech recognition technology to convert the meeting audio into text and extract important statements and decisions. The minutes creation department needs to clarify the specific content and format of the minutes. This includes, for example, the items to be included and the format. The items to be included may be the agenda, participants, statements, decisions, and action items, and the format may be bullet points or tables. Furthermore, the minutes creation department has the participants review the content of the minutes and makes corrections as needed. This allows the minutes creation department to accurately record the content of the meeting so that it can be referenced later.

[0033] The implementation department updates the list of business requirements based on the meeting minutes created by the meeting minutes creation department. The implementation department uses AI, for example, to reflect decisions made in meetings in the list of business requirements. Specifically, the AI ​​analyzes the content of the meeting minutes and automatically updates the relevant sections of the list of business requirements. The implementation department needs to clarify the specific methods and criteria for reflection. This includes, for example, the items to be reflected and the processes. Items to be reflected include additional requirements, modification requirements, and deletion requirements, and processes include the change approval flow and notification methods. Furthermore, the implementation department records the update history of the list of business requirements and notifies stakeholders of the changes. This allows the implementation department to reflect changes in business requirements quickly and accurately, ensuring smooth project progress.

[0034] The generation unit creates a business requirements screen definition document based on the business requirements list updated by the implementation unit. The generation unit can, for example, automatically create the business requirements screen definition document using AI. Specifically, the AI ​​analyzes the contents of the business requirements list and automatically arranges each item in the screen definition document. The generation unit needs to clarify the specific contents and format of the business requirements screen definition document. This includes, for example, the items to be included and the format. The items to be included include the screen layout, function description, and operating procedures, while the format includes the design and layout of the slides. Furthermore, the generation unit has stakeholders review the contents of the screen definition document and makes revisions as needed. This allows the generation unit to quickly and accurately create the business requirements screen definition document and support the progress of the project.

[0035] The data collection unit can automatically generate a list of business requirements. For example, the data collection unit can use AI to automatically generate the list of business requirements. The data collection unit needs to clarify the specific content and format of the list of business requirements, including, for example, the types of items and how they should be described. This automates and efficiently generates the list of business requirements. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can have AI perform the generation of the list of business requirements.

[0036] The setup unit can automatically schedule detailed confirmation meetings. For example, the setup unit can use AI to automatically schedule detailed confirmation meetings. The setup unit needs to clearly define the specific content and purpose of the detailed confirmation meeting, including, for example, the agenda, participants, and format. This automates and efficiently sets the meeting schedule. Some or all of the above-described processes in the setup unit may be performed using AI or not. For example, the setup unit can have AI perform the scheduling of detailed confirmation meetings.

[0037] The minutes creation department can automatically create meeting minutes during a meeting. For example, the minutes creation department can use AI to automatically create meeting minutes during a meeting. The minutes creation department needs to clearly define the specific content and format of the meeting minutes, including items to be included and formatting. This automates and streamlines the creation of meeting minutes. Some or all of the above processes in the minutes creation department may be performed using AI or not. For example, the minutes creation department can have AI perform the creation of meeting minutes.

[0038] The reflection unit can reflect the decisions made in meetings into the list of business requirements. The reflection unit can, for example, use AI to reflect the decisions made in meetings into the list of business requirements. The reflection unit needs to clarify the specific methods and criteria for reflection. For example, this includes the items to be reflected and the processes. This ensures that the decisions made in meetings are automatically reflected in the list of business requirements. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can have AI perform the updating of the list of business requirements.

[0039] The generation unit can automatically create business requirements screen definition documents. For example, the generation unit can use AI to automatically create business requirements screen definition documents. The generation unit needs to clarify the specific content and format of the business requirements screen definition document. This includes, for example, the items to be included and the format. This automates and efficiently creates the business requirements screen definition document. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI create the business requirements screen definition document.

[0040] The data collection unit can analyze past project data and automatically extract the optimal business requirements. For example, the data collection unit can extract business requirements from similar projects from past project data and use them as a reference. The data collection unit can analyze past project data and prioritize the extraction of business requirements from successful projects. Based on past project data, the data collection unit extracts business requirements that avoid those from unsuccessful projects. This improves the accuracy of business requirements by extracting the optimal business requirements based on past project data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the analysis of past project data.

[0041] The data collection unit can filter the list of business requirements based on the user's current project status and priorities when generating it. For example, the data collection unit can check the user's current project status and prioritize the generation of high-priority business requirements. The data collection unit can filter the necessary business requirements based on the progress of the user's project. The data collection unit selects and generates important business requirements based on the user's project priorities. This allows for the generation of more appropriate business requirements by filtering them based on the user's project status and priorities. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform filtering based on project status and priorities.

[0042] The data collection unit can prioritize generating highly relevant requirements by considering the user's geographical location when generating a list of business requirements. For example, the data collection unit can prioritize generating business requirements related to nearby projects based on the user's current location. The data collection unit can generate region-specific business requirements based on the user's geographical location. The data collection unit can generate business requirements that minimize travel time by considering the user's geographical location. As a result, more relevant business requirements are generated by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the generation of business requirements that take geographical location into consideration.

[0043] The data collection unit can analyze a user's social media activity and generate relevant requirements when generating a list of business requirements. For example, the data collection unit can generate business requirements related to projects of interest from a user's social media activity. The data collection unit can analyze a user's social media posts and extract necessary business requirements. The data collection unit generates relevant business requirements based on the user's social media activity history. As a result, more relevant business requirements are generated by analyzing the user's social media activity. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the analysis of social media activity.

[0044] The scheduling unit can analyze past meeting data and automatically select the optimal meeting time. For example, the scheduling unit can extract time slots convenient for participants from past meeting data and select the optimal meeting time. The scheduling unit can analyze past meeting data and prioritize selecting time slots for successful meetings. Based on past meeting data, the scheduling unit selects time slots that avoid those for unsuccessful meetings. In this way, efficient meetings are scheduled by selecting the optimal meeting time based on past meeting data. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform the analysis of past meeting data.

[0045] The scheduling unit can adjust meeting schedules based on participants' project status and priorities. For example, it can check participants' current project status and adjust meeting schedules to match high-priority projects. It can adjust the required meeting time based on participants' project progress. It can prioritize important meetings based on participants' project priorities. This ensures that more appropriate meetings are scheduled by adjusting them based on participants' project status and priorities. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform adjustments based on project status and priorities.

[0046] The scheduling unit can select the optimal time for a meeting by considering the geographical location of the participants. For example, the scheduling unit can select a time when nearby participants are likely to gather based on their current location. The scheduling unit can also select a time when travel time is minimized based on the geographical location of the participants. The scheduling unit selects the optimal time for an online meeting by considering the geographical location of the participants. As a result, by setting up a meeting while considering the geographical location of the participants, the meeting will be set up at a more appropriate time. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform the task of setting up a meeting while considering geographical location.

[0047] The scheduling unit can analyze participants' social media activity and reflect relevant information when scheduling meetings. For example, it can schedule meetings related to topics of interest based on participants' social media activity. It can analyze participants' social media posts and reflect the necessary meeting content. It can schedule relevant meetings based on participants' social media activity history. This ensures that more relevant meetings are scheduled by analyzing participants' social media activity. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform the analysis of social media activity.

[0048] The minutes creation department can analyze past minutes data and automatically select the optimal minutes format. For example, the minutes creation department can extract formats that are convenient for participants from past minutes data and select the optimal minutes format. The minutes creation department can analyze past minutes data and prioritize the selection of successful minutes formats. Based on past minutes data, the minutes creation department selects formats that avoid unsuccessful ones. In this way, by selecting the optimal format based on past minutes data, efficient minutes are created. Some or all of the above processes in the minutes creation department may be performed using AI or not. For example, the minutes creation department can have AI perform the analysis of past minutes data.

[0049] The minutes creation unit can adjust the level of detail based on the content and importance of the meeting when creating the minutes. For example, if the meeting content is important, the minutes creation unit will create detailed minutes. If the meeting content is general, the minutes creation unit can create concise minutes. If the meeting content is urgent, the minutes creation unit will create minutes that get straight to the point. By adjusting the level of detail of the minutes according to the content and importance of the meeting, more appropriate minutes are created. Some or all of the above processes in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can have AI perform the adjustment of level of detail based on the content and importance of the meeting.

[0050] The minutes creation unit can select the most appropriate expression method when creating meeting minutes, taking into account the attribute information of the meeting participants. For example, if the participants are engineers, the minutes creation unit can create minutes that use a lot of technical terminology. If the participants are executives, the minutes creation unit can create minutes that use a lot of business terminology. If the participants are from multiple nationalities, the minutes creation unit can create minutes that are available in multiple languages. By creating minutes while taking into account the attribute information of the participants, more appropriate minutes are produced. Some or all of the above processes in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can have AI perform the task of selecting an expression method that takes into account the attribute information of the participants.

[0051] The minutes creation unit can supplement the content of the minutes by referring to relevant literature and materials when creating them. For example, the minutes creation unit can refer to literature related to the content of the meeting and supplement the content of the minutes. The minutes creation unit can refer to materials used in the meeting and record the content of the minutes in detail. The minutes creation unit can refer to past minutes related to the meeting agenda and supplement the content. As a result, more detailed minutes are created by creating minutes by referring to relevant literature and materials. Some or all of the above processes in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can have AI perform the task of referring to relevant literature and materials.

[0052] The update unit can analyze past update history and automatically select the optimal update procedure. For example, the update unit can extract successful update procedures from past update history and select the optimal update procedure. The update unit can analyze past update history and select procedures to avoid failed update procedures. The update unit selects an efficient update procedure based on past update history. As a result, efficient updates are performed by selecting the optimal update procedure based on past update history. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can have AI perform the analysis of past update history.

[0053] The update unit can determine update priorities based on the content and importance of meetings when updating the list of business requirements. For example, if the meeting content is important, the update unit will prioritize updating the list of business requirements. If the meeting content is general, the update unit can postpone updating the list of business requirements. If the meeting content is urgent, the update unit will update the list of business requirements immediately. This ensures more appropriate updates by determining update priorities according to the content and importance of meetings. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can have AI perform the determination of update priorities based on the content and importance of meetings.

[0054] The update unit can supplement the content of the business requirements list by referring to relevant project data when updating the list of business requirements. For example, the update unit can refer to relevant project data to supplement the content of the business requirements list. The update unit can refer to the project progress to describe the content of the business requirements list in more detail. The update unit can refer to the project priority to adjust the content of the business requirements list. As a result, updating the business requirements list by referring to relevant project data reflects more detailed content. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can have AI perform the referencing of project data.

[0055] The update unit can supplement the content of the business requirements list by referring to relevant literature and materials when updating it. For example, the update unit can refer to relevant literature to supplement the content of the business requirements list. The update unit can refer to materials related to the business requirements to describe the content in detail. The update unit can refer to historical data related to the business requirements to supplement the content. As a result, updating the business requirements list by referring to relevant literature and materials will reflect more detailed content. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can have AI perform the referencing of literature and materials.

[0056] The generation unit can analyze past screen definition document data and automatically select the optimal format. For example, the generation unit can extract successful formats from past screen definition document data and select the optimal format. The generation unit can analyze past screen definition document data and select formats that avoid failed formats. The generation unit selects an efficient format based on past screen definition document data. As a result, an efficient screen definition document is created by selecting the optimal format based on past screen definition document data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the analysis of past screen definition document data.

[0057] The generation unit can adjust the level of detail in the business requirements screen definition document based on the project content and importance. For example, if the project content is important, the generation unit will create a detailed screen definition document. If the project content is general, the generation unit can create a concise screen definition document. If the project content is urgent, the generation unit will create a screen definition document that focuses on the essentials. By adjusting the level of detail in the screen definition document according to the project content and importance, a more appropriate screen definition document is created. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the adjustment of the level of detail based on the project content and importance.

[0058] The generation unit can supplement the content of the business requirements screen definition document by referring to relevant project data when generating it. For example, the generation unit can refer to relevant project data to supplement the content of the business requirements screen definition document. The generation unit can refer to the project progress to describe the content of the business requirements screen definition document in more detail. The generation unit can refer to the project priority to adjust the content of the business requirements screen definition document. As a result, a more detailed screen definition document is created by creating the screen definition document by referring to relevant project data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the referencing of project data.

[0059] The generation unit can supplement the content of the business requirements screen definition document by referring to relevant literature and materials when generating it. For example, the generation unit can refer to relevant literature to supplement the content of the business requirements screen definition document. The generation unit can refer to materials related to business requirements to describe the content in detail. The generation unit can refer to historical data related to business requirements to supplement the content. As a result, a more detailed screen definition document is created by creating the screen definition document by referring to relevant literature and materials. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the task of referring to literature and materials.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze past project data and automatically extract optimal business requirements. For example, it can extract business requirements from similar projects from past project data and use them as a reference. It can prioritize the extraction of business requirements from successful projects and avoid those from unsuccessful projects. This improves the accuracy of business requirements by extracting optimal requirements based on past project data.

[0062] The setup unit can analyze past meeting data and automatically select the optimal meeting time. For example, it can extract time slots convenient for participants from past meeting data and select the optimal meeting time. It can prioritize the selection of time slots for successful meetings and avoid the selection of time slots for unsuccessful meetings. In this way, by selecting the optimal meeting time based on past meeting data, efficient meetings can be scheduled.

[0063] The meeting minutes creation department can analyze past meeting minutes data and automatically select the optimal meeting minutes format. For example, it can extract formats that are convenient for participants from past meeting minutes data and select the most suitable format. It can prioritize the selection of formats used in successful meetings and avoid formats used in unsuccessful meetings. As a result, by selecting the optimal format based on past meeting minutes data, efficient meeting minutes are created.

[0064] The update unit can analyze past update history and automatically select the optimal update procedure. For example, it can extract successful update procedures from past update history and select the optimal procedure. It can also select procedures that avoid failed update procedures, thus selecting an efficient update procedure. In this way, by selecting the optimal update procedure based on past update history, updates are performed efficiently.

[0065] The generation unit can analyze past screen definition document data and automatically select the optimal format. For example, it can extract successful formats from past screen definition document data and select the optimal format. It can also select formats that avoid failed formats, thus selecting an efficient format. As a result, by selecting the optimal format based on past screen definition document data, an efficient screen definition document is created.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit creates a list of business requirements. The data collection unit can, for example, use AI to automatically generate the list of business requirements. The data collection unit needs to clarify the specific content and format of the list of business requirements. This includes, for example, the types of items and how they should be recorded. Step 2: The setup unit sets up detailed confirmation meetings based on the list of business requirements created by the collection unit. The setup unit can, for example, use AI to automatically set the schedule for the detailed confirmation meetings. The setup unit needs to clarify the specific content and purpose of the detailed confirmation meetings. This includes, for example, the agenda, participants, and how the meeting will proceed. Step 3: The minutes creation unit creates meeting minutes for the meeting set up by the setup unit. The minutes creation unit may, for example, use AI to automatically create minutes during the meeting. The minutes creation unit needs to clarify the specific content and format of the minutes. This includes, for example, the items to be included and the format. Step 4: The implementation team updates the list of business requirements based on the meeting minutes created by the minutes creation team. The implementation team, for example, uses AI to reflect the decisions made in the meeting into the list of business requirements. The implementation team needs to clarify the specific methods and criteria for the reflection. This includes, for example, the items to be reflected and the processes involved. Step 5: The generation unit creates a business requirements screen definition document based on the updated business requirements list from the reflection unit. The generation unit can, for example, use AI to automatically create the business requirements screen definition document. The generation unit needs to clarify the specific content and format of the business requirements screen definition document. This includes, for example, the items to be included and the format.

[0068] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes an AI agent to streamline the creation of system development requirements definitions for new service development in corporations. This system automates the process of creating a list of business requirements, scheduling detailed confirmation meetings, creating meeting minutes, reflecting the decisions in the list of business requirements, and creating a business requirements screen definition document. For example, the system automatically generates a list of business requirements and automatically schedules detailed confirmation meetings. The system automatically creates meeting minutes during the meeting and reflects the decisions in the list of business requirements. Furthermore, the system automatically creates a business requirements screen definition document. This streamlines the process of creating business requirements definitions and reduces the burden on employees. This mechanism can reduce the man-hours of departments creating business requirements for corporate operations and improve operational efficiency. By utilizing an AI agent, the process of creating business requirements definitions is automated, allowing employees to focus on more important tasks. As a result, the system can streamline the process of creating business requirements definitions and significantly reduce the man-hours of employees.

[0069] The system according to the embodiment comprises a collection unit, a setting unit, a minutes creation unit, an update unit, and a generation unit. The collection unit creates a list of business requirements. The collection unit automatically generates the list of business requirements using, for example, AI. The collection unit needs to clarify the specific content and format of the list of business requirements. For example, this includes the types of items and how to record them. The setting unit sets up detailed confirmation meetings based on the list of business requirements created by the collection unit. The setting unit automatically sets the schedule for the detailed confirmation meetings using, for example, AI. The setting unit needs to clarify the specific content and purpose of the detailed confirmation meetings. For example, this includes the agenda, participants, and how to conduct the meeting. The minutes creation unit creates minutes of the meetings set up by the setting unit. The minutes creation unit automatically creates minutes during the meeting using, for example, AI. The minutes creation unit needs to clarify the specific content and format of the minutes. For example, this includes the items to be recorded and the format. The update unit updates the list of business requirements based on the minutes created by the minutes creation unit. The reflection unit, for example, uses AI to reflect the matters decided in the meeting into the list of business requirements. The reflection unit needs to clarify the specific methods and criteria for reflection. For example, this includes the items to be reflected and the processes. The generation unit creates a business requirements screen definition document based on the list of business requirements updated by the reflection unit. The generation unit, for example, uses AI to automatically create the business requirements screen definition document. The generation unit needs to clarify the specific content and format of the business requirements screen definition document. For example, this includes the items to be included and the format. As a result, the system according to the embodiment can streamline the process of creating business requirements definitions and significantly reduce the workload of employees.

[0070] The data collection unit creates a list of business requirements. For example, the data collection unit automatically generates the list of business requirements using AI. Specifically, the AI ​​uses natural language processing technology to analyze past project data and related documents, extracting patterns and common items in the business requirements. This allows the AI ​​to quickly and accurately generate a list of business requirements for new projects. The data collection unit needs to clarify the specific content and format of the list of business requirements. This includes, for example, the types of items and how they are written. Item types include functional requirements, non-functional requirements, and constraints, while writing methods include bullet points and tables. Furthermore, the data collection unit manages versions of the list of business requirements and records the change history, enabling it to respond to changes or additions to requirements. This allows the data collection unit to efficiently collect and organize business requirements, enabling rapid requirements definition in the early stages of a project.

[0071] The scheduling department sets up detailed confirmation meetings based on the list of business requirements created by the collection department. The scheduling department can, for example, use AI to automatically schedule the detailed confirmation meetings. Specifically, the AI ​​analyzes participants' calendars and suggests the optimal date and time. The AI ​​also automatically selects the necessary participants based on the meeting's purpose and agenda and sends invitations. The scheduling department needs to clarify the specific content and purpose of the detailed confirmation meeting. This includes, for example, the agenda, participants, and method of proceeding. The agenda may include confirming business requirements, discussing revisions, and considering additional requirements, while the method of proceeding includes the role of the facilitator and how the discussion will proceed. Furthermore, the scheduling department distributes preparatory materials to participants before the meeting to prepare for efficient discussion. This allows the scheduling department to efficiently schedule and prepare for detailed confirmation meetings, ensuring the smooth progress of the project.

[0072] The minutes creation department creates meeting minutes for meetings set up by the setup department. For example, the minutes creation department can use AI to automatically create minutes during a meeting. Specifically, the AI ​​uses speech recognition technology to convert the meeting audio into text and extract important statements and decisions. The minutes creation department needs to clarify the specific content and format of the minutes. This includes, for example, the items to be included and the format. The items to be included may be the agenda, participants, statements, decisions, and action items, and the format may be bullet points or tables. Furthermore, the minutes creation department has the participants review the content of the minutes and makes corrections as needed. This allows the minutes creation department to accurately record the content of the meeting so that it can be referenced later.

[0073] The implementation department updates the list of business requirements based on the meeting minutes created by the meeting minutes creation department. The implementation department uses AI, for example, to reflect decisions made in meetings in the list of business requirements. Specifically, the AI ​​analyzes the content of the meeting minutes and automatically updates the relevant sections of the list of business requirements. The implementation department needs to clarify the specific methods and criteria for reflection. This includes, for example, the items to be reflected and the processes. Items to be reflected include additional requirements, modification requirements, and deletion requirements, and processes include the change approval flow and notification methods. Furthermore, the implementation department records the update history of the list of business requirements and notifies stakeholders of the changes. This allows the implementation department to reflect changes in business requirements quickly and accurately, ensuring smooth project progress.

[0074] The generation unit creates a business requirements screen definition document based on the business requirements list updated by the implementation unit. The generation unit can, for example, automatically create the business requirements screen definition document using AI. Specifically, the AI ​​analyzes the contents of the business requirements list and automatically arranges each item in the screen definition document. The generation unit needs to clarify the specific contents and format of the business requirements screen definition document. This includes, for example, the items to be included and the format. The items to be included include the screen layout, function description, and operating procedures, while the format includes the design and layout of the slides. Furthermore, the generation unit has stakeholders review the contents of the screen definition document and makes revisions as needed. This allows the generation unit to quickly and accurately create the business requirements screen definition document and support the progress of the project.

[0075] The data collection unit can automatically generate a list of business requirements. For example, the data collection unit can use AI to automatically generate the list of business requirements. The data collection unit needs to clarify the specific content and format of the list of business requirements, including, for example, the types of items and how they should be described. This automates and efficiently generates the list of business requirements. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can have AI perform the generation of the list of business requirements.

[0076] The setup unit can automatically schedule detailed confirmation meetings. For example, the setup unit can use AI to automatically schedule detailed confirmation meetings. The setup unit needs to clearly define the specific content and purpose of the detailed confirmation meeting, including, for example, the agenda, participants, and format. This automates and efficiently sets the meeting schedule. Some or all of the above-described processes in the setup unit may be performed using AI or not. For example, the setup unit can have AI perform the scheduling of detailed confirmation meetings.

[0077] The minutes creation department can automatically create meeting minutes during a meeting. For example, the minutes creation department can use AI to automatically create meeting minutes during a meeting. The minutes creation department needs to clearly define the specific content and format of the meeting minutes, including items to be included and formatting. This automates and streamlines the creation of meeting minutes. Some or all of the above processes in the minutes creation department may be performed using AI or not. For example, the minutes creation department can have AI perform the creation of meeting minutes.

[0078] The reflection unit can reflect the decisions made in meetings into the list of business requirements. The reflection unit can, for example, use AI to reflect the decisions made in meetings into the list of business requirements. The reflection unit needs to clarify the specific methods and criteria for reflection. For example, this includes the items to be reflected and the processes. This ensures that the decisions made in meetings are automatically reflected in the list of business requirements. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can have AI perform the updating of the list of business requirements.

[0079] The generation unit can automatically create business requirements screen definition documents. For example, the generation unit can use AI to automatically create business requirements screen definition documents. The generation unit needs to clarify the specific content and format of the business requirements screen definition document. This includes, for example, the items to be included and the format. This automates and efficiently creates the business requirements screen definition document. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI create the business requirements screen definition document.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of generating the list of business requirements based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the generation of the list of business requirements to allow the user time to relax. If the user is relaxed, the data collection unit can generate the list of business requirements quickly and efficiently. If the user is in a hurry, the data collection unit can generate the list of business requirements immediately, enabling a quick response. By adjusting the timing of the generation of the list of business requirements according to the user's emotions, it is generated at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have an AI perform the estimation of the user's emotions.

[0081] The data collection unit can analyze past project data and automatically extract the optimal business requirements. For example, the data collection unit can extract business requirements from similar projects from past project data and use them as a reference. The data collection unit can analyze past project data and prioritize the extraction of business requirements from successful projects. Based on past project data, the data collection unit extracts business requirements that avoid those from unsuccessful projects. This improves the accuracy of business requirements by extracting the optimal business requirements based on past project data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the analysis of past project data.

[0082] The data collection unit can filter the list of business requirements based on the user's current project status and priorities when generating it. For example, the data collection unit can check the user's current project status and prioritize the generation of high-priority business requirements. The data collection unit can filter the necessary business requirements based on the progress of the user's project. The data collection unit selects and generates important business requirements based on the user's project priorities. This allows for the generation of more appropriate business requirements by filtering them based on the user's project status and priorities. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform filtering based on project status and priorities.

[0083] The data collection unit can estimate the user's emotions and determine the priority of the business requirements to be generated based on the estimated user emotions. For example, if the user is stressed, the data collection unit can postpone less important business requirements and adjust the priority. If the user is relaxed, the data collection unit can prioritize generating high-importance business requirements. If the user is in a hurry, the data collection unit will prioritize generating urgent business requirements. This way, more appropriate business requirements are generated by prioritizing business requirements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the estimation of the user's emotions.

[0084] The data collection unit can prioritize generating highly relevant requirements by considering the user's geographical location when generating a list of business requirements. For example, the data collection unit can prioritize generating business requirements related to nearby projects based on the user's current location. The data collection unit can generate region-specific business requirements based on the user's geographical location. The data collection unit can generate business requirements that minimize travel time by considering the user's geographical location. As a result, more relevant business requirements are generated by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the generation of business requirements that take geographical location into consideration.

[0085] The data collection unit can analyze a user's social media activity and generate relevant requirements when generating a list of business requirements. For example, the data collection unit can generate business requirements related to projects of interest from a user's social media activity. The data collection unit can analyze a user's social media posts and extract necessary business requirements. The data collection unit generates relevant business requirements based on the user's social media activity history. As a result, more relevant business requirements are generated by analyzing the user's social media activity. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the analysis of social media activity.

[0086] The setting unit can estimate the user's emotions and adjust the meeting schedule based on the estimated emotions. For example, if the user is stressed, the setting unit can delay the meeting schedule to allow the user time to relax. If the user is relaxed, the setting unit can quickly schedule the meeting and proceed efficiently. If the user is in a hurry, the setting unit can immediately schedule the meeting, enabling a quick response. This ensures that meetings are scheduled at a more appropriate time by adjusting the meeting schedule according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the setting unit may be performed using AI or not. For example, the setting unit can have AI perform the estimation of the user's emotions.

[0087] The scheduling unit can analyze past meeting data and automatically select the optimal meeting time. For example, the scheduling unit can extract time slots convenient for participants from past meeting data and select the optimal meeting time. The scheduling unit can analyze past meeting data and prioritize selecting time slots for successful meetings. Based on past meeting data, the scheduling unit selects time slots that avoid those for unsuccessful meetings. In this way, efficient meetings are scheduled by selecting the optimal meeting time based on past meeting data. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform the analysis of past meeting data.

[0088] The scheduling unit can adjust meeting schedules based on participants' project status and priorities. For example, it can check participants' current project status and adjust meeting schedules to match high-priority projects. It can adjust the required meeting time based on participants' project progress. It can prioritize important meetings based on participants' project priorities. This ensures that more appropriate meetings are scheduled by adjusting them based on participants' project status and priorities. Some or all of the above processes in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform adjustments based on project status and priorities.

[0089] The scheduling unit can estimate the user's emotions and prioritize meetings based on those emotions. For example, if the user is stressed, the scheduling unit can postpone less important meetings and adjust the priorities. If the user is relaxed, the scheduling unit can prioritize high-priority meetings. If the user is in a hurry, the scheduling unit can prioritize urgent meetings. This ensures that more appropriate meetings are scheduled by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform the estimation of the user's emotions.

[0090] The scheduling unit can select the optimal time for a meeting by considering the geographical location of the participants. For example, the scheduling unit can select a time when nearby participants are likely to gather based on their current location. The scheduling unit can also select a time when travel time is minimized based on the geographical location of the participants. The scheduling unit selects the optimal time for an online meeting by considering the geographical location of the participants. As a result, by setting up a meeting while considering the geographical location of the participants, the meeting will be set up at a more appropriate time. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform the task of setting up a meeting while considering geographical location.

[0091] The scheduling unit can analyze participants' social media activity and reflect relevant information when scheduling meetings. For example, it can schedule meetings related to topics of interest based on participants' social media activity. It can analyze participants' social media posts and reflect the necessary meeting content. It can schedule relevant meetings based on participants' social media activity history. This ensures that more relevant meetings are scheduled by analyzing participants' social media activity. Some or all of the above processing in the scheduling unit may be performed using AI or not. For example, the scheduling unit can have AI perform the analysis of social media activity.

[0092] The minutes creation unit can estimate the user's emotions and adjust the presentation of the minutes based on the estimated emotions. For example, if the user is nervous, the minutes creation unit can provide a simple and highly legible presentation. If the user is relaxed, the minutes creation unit can provide a presentation that includes detailed information. If the user is in a hurry, the minutes creation unit can provide a presentation that gets straight to the point. By adjusting the presentation of the minutes according to the user's emotions, more appropriate minutes are created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can have AI perform the estimation of the user's emotions.

[0093] The minutes creation department can analyze past minutes data and automatically select the optimal minutes format. For example, the minutes creation department can extract formats that are convenient for participants from past minutes data and select the optimal minutes format. The minutes creation department can analyze past minutes data and prioritize the selection of successful minutes formats. Based on past minutes data, the minutes creation department selects formats that avoid unsuccessful ones. In this way, by selecting the optimal format based on past minutes data, efficient minutes are created. Some or all of the above processes in the minutes creation department may be performed using AI or not. For example, the minutes creation department can have AI perform the analysis of past minutes data.

[0094] The minutes creation unit can adjust the level of detail based on the content and importance of the meeting when creating the minutes. For example, if the meeting content is important, the minutes creation unit will create detailed minutes. If the meeting content is general, the minutes creation unit can create concise minutes. If the meeting content is urgent, the minutes creation unit will create minutes that get straight to the point. By adjusting the level of detail of the minutes according to the content and importance of the meeting, more appropriate minutes are created. Some or all of the above processes in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can have AI perform the adjustment of level of detail based on the content and importance of the meeting.

[0095] The minutes creation unit can estimate the user's emotions and adjust the length of the minutes based on the estimated emotions. For example, if the user is nervous, the minutes creation unit can create short, concise minutes. If the user is relaxed, the minutes creation unit can create longer minutes with more detailed explanations. If the user is in a hurry, the minutes creation unit can create short minutes that can be read quickly. By adjusting the length of the minutes according to the user's emotions, more appropriate minutes are created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can have AI perform the estimation of the user's emotions.

[0096] The minutes creation unit can select the most appropriate expression method when creating meeting minutes, taking into account the attribute information of the meeting participants. For example, if the participants are engineers, the minutes creation unit can create minutes that use a lot of technical terminology. If the participants are executives, the minutes creation unit can create minutes that use a lot of business terminology. If the participants are from multiple nationalities, the minutes creation unit can create minutes that are available in multiple languages. By creating minutes while taking into account the attribute information of the participants, more appropriate minutes are produced. Some or all of the above processes in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can have AI perform the task of selecting an expression method that takes into account the attribute information of the participants.

[0097] The minutes creation unit can supplement the content of the minutes by referring to relevant literature and materials when creating them. For example, the minutes creation unit can refer to literature related to the content of the meeting and supplement the content of the minutes. The minutes creation unit can refer to materials used in the meeting and record the content of the minutes in detail. The minutes creation unit can refer to past minutes related to the meeting agenda and supplement the content. As a result, more detailed minutes are created by creating minutes by referring to relevant literature and materials. Some or all of the above processes in the minutes creation unit may be performed using AI or not. For example, the minutes creation unit can have AI perform the task of referring to relevant literature and materials.

[0098] The reflection unit can estimate the user's emotions and adjust the update method of the business requirements list based on the estimated user emotions. For example, if the user is stressed, the reflection unit can delay the update of the business requirements list to allow the user time to relax. If the user is relaxed, the reflection unit can update the business requirements list quickly and efficiently. If the user is in a hurry, the reflection unit can update the business requirements list immediately to enable a quick response. In this way, by adjusting the update method of the business requirements list according to the user's emotions, more appropriate updates are made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can have AI perform the estimation of the user's emotions.

[0099] The update unit can analyze past update history and automatically select the optimal update procedure. For example, the update unit can extract successful update procedures from past update history and select the optimal update procedure. The update unit can analyze past update history and select procedures to avoid failed update procedures. The update unit selects an efficient update procedure based on past update history. As a result, efficient updates are performed by selecting the optimal update procedure based on past update history. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can have AI perform the analysis of past update history.

[0100] The update unit can determine update priorities based on the content and importance of meetings when updating the list of business requirements. For example, if the meeting content is important, the update unit will prioritize updating the list of business requirements. If the meeting content is general, the update unit can postpone updating the list of business requirements. If the meeting content is urgent, the update unit will update the list of business requirements immediately. This ensures more appropriate updates by determining update priorities according to the content and importance of meetings. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can have AI perform the determination of update priorities based on the content and importance of meetings.

[0101] The reflection unit can estimate the user's emotions and adjust how the update content is displayed based on the estimated emotions. For example, if the user is tense, the reflection unit can provide a simple and highly visible display method. If the user is relaxed, the reflection unit can provide a display method that includes detailed information. If the user is in a hurry, the reflection unit can provide a display method that gets straight to the point. By adjusting how the update content is displayed according to the user's emotions, a more appropriate display is achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reflection unit may be performed using AI or not. For example, the reflection unit can have AI perform the estimation of the user's emotions.

[0102] The update unit can supplement the content of the business requirements list by referring to relevant project data when updating the list of business requirements. For example, the update unit can refer to relevant project data to supplement the content of the business requirements list. The update unit can refer to the project progress to describe the content of the business requirements list in more detail. The update unit can refer to the project priority to adjust the content of the business requirements list. As a result, updating the business requirements list by referring to relevant project data reflects more detailed content. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can have AI perform the referencing of project data.

[0103] The update unit can supplement the content of the business requirements list by referring to relevant literature and materials when updating it. For example, the update unit can refer to relevant literature to supplement the content of the business requirements list. The update unit can refer to materials related to the business requirements to describe the content in detail. The update unit can refer to historical data related to the business requirements to supplement the content. As a result, updating the business requirements list by referring to relevant literature and materials will reflect more detailed content. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can have AI perform the referencing of literature and materials.

[0104] The generation unit can estimate the user's emotions and adjust the presentation of the business requirements screen definition document based on the estimated user emotions. For example, if the user is tense, the generation unit can provide a simple and highly visible presentation. If the user is relaxed, the generation unit can provide a presentation that includes detailed information. If the user is in a hurry, the generation unit can provide a presentation that gets straight to the point. By adjusting the presentation of the business requirements screen definition document according to the user's emotions, a more appropriate screen definition document is created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the estimation of the user's emotions.

[0105] The generation unit can analyze past screen definition document data and automatically select the optimal format. For example, the generation unit can extract successful formats from past screen definition document data and select the optimal format. The generation unit can analyze past screen definition document data and select formats that avoid failed formats. The generation unit selects an efficient format based on past screen definition document data. As a result, an efficient screen definition document is created by selecting the optimal format based on past screen definition document data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the analysis of past screen definition document data.

[0106] The generation unit can adjust the level of detail in the business requirements screen definition document based on the project content and importance. For example, if the project content is important, the generation unit will create a detailed screen definition document. If the project content is general, the generation unit can create a concise screen definition document. If the project content is urgent, the generation unit will create a screen definition document that focuses on the essentials. By adjusting the level of detail in the screen definition document according to the project content and importance, a more appropriate screen definition document is created. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the adjustment of the level of detail based on the project content and importance.

[0107] The generation unit can estimate the user's emotions and adjust the length of the business requirements screen definition document based on the estimated user emotions. For example, if the user is tense, the generation unit can create a short, concise screen definition document. If the user is relaxed, the generation unit can create a longer screen definition document with detailed explanations. If the user is in a hurry, the generation unit can create a short screen definition document that can be read quickly. By adjusting the length of the screen definition document according to the user's emotions, a more appropriate screen definition document is created. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the estimation of the user's emotions.

[0108] The generation unit can supplement the content of the business requirements screen definition document by referring to relevant project data when generating it. For example, the generation unit can refer to relevant project data to supplement the content of the business requirements screen definition document. The generation unit can refer to the project progress to describe the content of the business requirements screen definition document in more detail. The generation unit can refer to the project priority to adjust the content of the business requirements screen definition document. As a result, a more detailed screen definition document is created by creating the screen definition document by referring to relevant project data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the referencing of project data.

[0109] The generation unit can supplement the content of the business requirements screen definition document by referring to relevant literature and materials when generating it. For example, the generation unit can refer to relevant literature to supplement the content of the business requirements screen definition document. The generation unit can refer to materials related to business requirements to describe the content in detail. The generation unit can refer to historical data related to business requirements to supplement the content. As a result, a more detailed screen definition document is created by creating the screen definition document by referring to relevant literature and materials. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can have AI perform the task of referring to literature and materials.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can estimate the user's emotions and adjust the timing of generating the list of business requirements based on those emotions. For example, if the user is stressed, the generation of the list of business requirements can be delayed to allow the user time to relax. If the user is relaxed, the list of business requirements can be generated quickly for efficient work. If the user is in a hurry, the list of business requirements can be generated immediately to enable a quick response. In this way, by adjusting the timing of the generation of the list of business requirements according to the user's emotions, it can be generated at a more appropriate time.

[0112] The data collection unit can analyze past project data and automatically extract optimal business requirements. For example, it can extract business requirements from similar projects from past project data and use them as a reference. It can prioritize the extraction of business requirements from successful projects and avoid those from unsuccessful projects. This improves the accuracy of business requirements by extracting optimal requirements based on past project data.

[0113] The setup unit can estimate the user's emotions and adjust the meeting schedule based on those emotions. For example, if the user is stressed, the meeting schedule can be delayed to allow time for the user to relax. If the user is relaxed, the meeting can be scheduled quickly to proceed efficiently. If the user is in a hurry, the meeting can be scheduled immediately to enable a quick response. In this way, by adjusting the meeting schedule according to the user's emotions, meetings are scheduled at a more appropriate time.

[0114] The setup unit can analyze past meeting data and automatically select the optimal meeting time. For example, it can extract time slots convenient for participants from past meeting data and select the optimal meeting time. It can prioritize the selection of time slots for successful meetings and avoid the selection of time slots for unsuccessful meetings. In this way, by selecting the optimal meeting time based on past meeting data, efficient meetings can be scheduled.

[0115] The meeting minutes creation function can estimate the user's emotions and adjust the presentation of the minutes based on those estimates. For example, if the user is nervous, it can provide a simple and highly legible presentation. If the user is relaxed, it can provide a presentation that includes detailed information. If the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the presentation of the minutes according to the user's emotions, more appropriate meeting minutes can be created.

[0116] The meeting minutes creation department can analyze past meeting minutes data and automatically select the optimal meeting minutes format. For example, it can extract formats that are convenient for participants from past meeting minutes data and select the most suitable format. It can prioritize the selection of formats used in successful meetings and avoid formats used in unsuccessful meetings. As a result, by selecting the optimal format based on past meeting minutes data, efficient meeting minutes are created.

[0117] The reflection unit can estimate the user's emotions and adjust the update method of the business requirements list based on the estimated emotions. For example, if the user is stressed, the update of the business requirements list will be delayed to allow the user time to relax. If the user is relaxed, the update of the business requirements list will be performed quickly to proceed efficiently. If the user is in a hurry, the update of the business requirements list will be performed immediately to enable a quick response. In this way, by adjusting the update method of the business requirements list according to the user's emotions, more appropriate updates will be performed.

[0118] The update unit can analyze past update history and automatically select the optimal update procedure. For example, it can extract successful update procedures from past update history and select the optimal procedure. It can also select procedures that avoid failed update procedures, thus selecting an efficient update procedure. In this way, by selecting the optimal update procedure based on past update history, updates are performed efficiently.

[0119] The generation unit can estimate the user's emotions and adjust the presentation of the business requirements screen definition document based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible presentation. If the user is relaxed, it can provide a presentation that includes detailed information. If the user is in a hurry, it can provide a presentation that gets straight to the point. By adjusting the presentation of the business requirements screen definition document according to the user's emotions, a more appropriate screen definition document can be created.

[0120] The generation unit can analyze past screen definition document data and automatically select the optimal format. For example, it can extract successful formats from past screen definition document data and select the optimal format. It can also select formats that avoid failed formats, thus selecting an efficient format. As a result, by selecting the optimal format based on past screen definition document data, an efficient screen definition document is created.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit creates a list of business requirements. The data collection unit can, for example, use AI to automatically generate the list of business requirements. The data collection unit needs to clarify the specific content and format of the list of business requirements. This includes, for example, the types of items and how they should be recorded. Step 2: The setup unit sets up detailed confirmation meetings based on the list of business requirements created by the collection unit. The setup unit can, for example, use AI to automatically set the schedule for the detailed confirmation meetings. The setup unit needs to clarify the specific content and purpose of the detailed confirmation meetings. This includes, for example, the agenda, participants, and how the meeting will proceed. Step 3: The minutes creation unit creates meeting minutes for the meeting set up by the setup unit. The minutes creation unit may, for example, use AI to automatically create minutes during the meeting. The minutes creation unit needs to clarify the specific content and format of the minutes. This includes, for example, the items to be included and the format. Step 4: The implementation team updates the list of business requirements based on the meeting minutes created by the minutes creation team. The implementation team, for example, uses AI to reflect the decisions made in the meeting into the list of business requirements. The implementation team needs to clarify the specific methods and criteria for the reflection. This includes, for example, the items to be reflected and the processes involved. Step 5: The generation unit creates a business requirements screen definition document based on the updated business requirements list from the reflection unit. The generation unit can, for example, use AI to automatically create the business requirements screen definition document. The generation unit needs to clarify the specific content and format of the business requirements screen definition document. This includes, for example, the items to be included and the format.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, setting unit, minutes creation unit, reflection unit, and generation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit automatically generates a list of business requirements by the control unit 46A of the smart device 14. The setting unit automatically sets the schedule for the detailed confirmation meeting by, for example, the specific processing unit 290 of the data processing unit 12. The minutes creation unit automatically creates minutes during the meeting by, for example, the control unit 46A of the smart device 14. The reflection unit updates the list of business requirements based on the minutes by, for example, the specific processing unit 290 of the data processing unit 12. The generation unit automatically creates a business requirements screen definition document by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, setting unit, minutes creation unit, reflection unit, and generation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit automatically generates a list of business requirements by the control unit 46A of the smart glasses 214. The setting unit automatically sets the schedule for detailed confirmation meetings by, for example, the specific processing unit 290 of the data processing unit 12. The minutes creation unit automatically creates minutes during a meeting by, for example, the control unit 46A of the smart glasses 214. The reflection unit updates the list of business requirements based on the minutes by, for example, the specific processing unit 290 of the data processing unit 12. The generation unit automatically creates a business requirements screen definition document by, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, setting unit, minutes creation unit, reflection unit, and generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit automatically generates a list of business requirements by the control unit 46A of the headset terminal 314. The setting unit automatically sets the schedule for the detailed confirmation meeting by, for example, the specific processing unit 290 of the data processing unit 12. The minutes creation unit automatically creates minutes during the meeting by, for example, the control unit 46A of the headset terminal 314. The reflection unit updates the list of business requirements based on the minutes by, for example, the specific processing unit 290 of the data processing unit 12. The generation unit automatically creates a business requirements screen definition document by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, setting unit, minutes creation unit, reflection unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit automatically generates a list of business requirements by the control unit 46A of the robot 414. The setting unit automatically sets the schedule for detailed confirmation meetings by, for example, the specific processing unit 290 of the data processing unit 12. The minutes creation unit automatically creates minutes during a meeting by, for example, the control unit 46A of the robot 414. The reflection unit updates the list of business requirements based on the minutes by, for example, the specific processing unit 290 of the data processing unit 12. The generation unit automatically creates a business requirements screen definition document by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The collection department creates a list of business requirements, A setting unit sets up a detailed confirmation meeting based on the list of business requirements created by the aforementioned collection unit, A minutes creation unit that creates minutes of the meeting set by the aforementioned setting unit, A reflection unit updates the list of business requirements based on the minutes created by the minutes creation unit, The system includes a generation unit that creates a business requirements screen definition document based on the business requirements list updated by the reflection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Automatically generate a list of business requirements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned set section is Automatically schedule a detailed confirmation meeting. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned minutes preparation department, Automatically create meeting minutes during the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reflection unit is, The matters decided at the meeting will be reflected in the list of business requirements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Automatically create business requirements screen definition documents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of generating the list of business requirements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is By analyzing past project data, the system automatically extracts the optimal business requirements. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When generating a list of business requirements, filtering is performed based on the user's current project status and priority. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user emotions and determines the priority of business requirements generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When generating a list of business requirements, the system prioritizes generating highly relevant requirements by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When generating a list of business requirements, the system analyzes users' social media activity and generates relevant requirements. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned set section is It estimates the user's emotions and adjusts the meeting schedule based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned set section is By analyzing past meeting data, the system automatically selects the optimal meeting time. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned set section is When scheduling meetings, make adjustments based on the participants' project status and priorities. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned set section is It estimates user emotions and prioritizes meetings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned set section is When scheduling a meeting, the optimal time is selected by considering the geographical location of the participants. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned set section is When scheduling meetings, analyze participants' social media activity and reflect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned minutes preparation department, The system estimates the user's emotions and adjusts the way the meeting minutes are written based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned minutes preparation department, By analyzing past meeting minutes data, the system automatically selects the optimal meeting minutes format. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned minutes preparation department, When creating meeting minutes, adjust the level of detail based on the content and importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned minutes preparation department, It estimates the user's emotions and adjusts the length of the meeting minutes based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned minutes preparation department, When creating meeting minutes, select the most appropriate expression method by considering the attribute information of the meeting participants. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned minutes preparation department, When creating meeting minutes, supplement the content by referring to relevant literature and materials. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reflection unit is, The system estimates the user's emotions and adjusts the update method for the list of business requirements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reflection unit is, It analyzes past update history and automatically selects the optimal update procedure. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reflection unit is, When updating the list of business requirements, prioritize updates based on the content and importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reflection unit is, It estimates the user's sentiment and adjusts how the update content is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reflection unit is, When updating the list of business requirements, refer to related project data to supplement the content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reflection unit is, When updating the list of business requirements, supplement the content by referring to relevant literature and materials. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is The system estimates the user's emotions and adjusts the way the business requirements screen definition document is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is It analyzes past screen definition data and automatically selects the optimal format. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is When generating the business requirements screen definition document, adjust the level of detail based on the project content and importance. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is The system estimates the user's emotions and adjusts the length of the business requirements screen definition document based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is When generating the business requirements screen definition document, the content is supplemented by referring to related project data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The generating unit is When generating the business requirements screen definition document, supplement the content by referring to relevant literature and materials. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department creates a list of business requirements, A setting unit sets up a detailed confirmation meeting based on the list of business requirements created by the aforementioned collection unit, A minutes creation unit that creates minutes of the meeting set by the aforementioned setting unit, A reflection unit updates the list of business requirements based on the minutes created by the minutes creation unit, The system includes a generation unit that creates a business requirements screen definition document based on the business requirements list updated by the reflection unit. A system characterized by the following features.

2. The aforementioned collection unit is Automatically generate a list of business requirements. The system according to feature 1.

3. The aforementioned set section is Automatically schedule a detailed confirmation meeting. The system according to feature 1.

4. The aforementioned minutes preparation department, Automatically create meeting minutes during the meeting. The system according to feature 1.

5. The aforementioned reflection unit is, The matters decided at the meeting will be reflected in the list of business requirements. The system according to feature 1.

6. The generating unit is Automatically create business requirements screen definition documents. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of generating the list of business requirements based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is By analyzing past project data, the system automatically extracts the optimal business requirements. The system according to feature 1.

9. The aforementioned collection unit is When generating a list of business requirements, filtering is performed based on the user's current project status and priority. The system according to feature 1.

10. The aforementioned collection unit is It estimates user emotions and determines the priority of business requirements generated based on those estimated emotions. The system according to feature 1.

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