system

Abstract

The system according to the embodiment aims to shorten the time required for considering requirements in system development and to prevent mistakes and rework. [Solution] A system according to an embodiment includes a collection unit, an organization / analysis unit, a sharing unit, a requirements examination unit, and a knowledge base update unit. The collection unit collects information through interviews, questionnaires, and observations. The organization / analysis unit organizes and analyzes the information collected by the collection unit using text mining or clustering. The sharing unit registers the information organized and analyzed by the organization / analysis unit in a shared folder or knowledge base. The requirements examination unit examines requirements based on the information shared by the sharing unit. The knowledge base update unit adds new insights obtained by the requirements examination unit to the knowledge base.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the time required for requirements review in system development is long, which can lead to mistakes and rework.

[0005] The system according to the embodiment aims to shorten the time required for considering requirements in system development and to prevent mistakes and rework. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an organization / analysis unit, a sharing unit, a requirements examination unit, and a knowledge base update unit. The collection unit collects information through interviews, questionnaires, and observations. The organization / analysis unit organizes and analyzes the information collected by the collection unit using text mining or clustering. The sharing unit registers the information organized and analyzed by the organization / analysis unit in a shared folder or knowledge base. The requirements examination unit examines requirements based on the information shared by the sharing unit. The knowledge base update unit adds new knowledge obtained by the requirements examination unit to the knowledge base. [Effects of the Invention]

[0007] The system according to the embodiment can shorten the time required for considering requirements in system development and prevent mistakes and rework. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The system according to an embodiment of the present invention shortens the requirements review period in system development and creates an environment free of errors and rework by externalizing and sharing the tacit knowledge held by developers and users of each internal system, as well as information previously only known to specific members. This system collects, organizes, analyzes, and converts the tacit knowledge held by developers and users into a shareable format, thereby streamlining requirements review in system development. For example, interviews, surveys, and observations are used to gather information such as developers' system design intentions and users' operational tips. The collected information is documented and registered in a shared folder or knowledge base. The information is categorized based on importance and relevance and organized in an easily searchable format. Based on the shared information, requirements review for system development is carried out, preventing errors and rework. The results of the requirements review are documented and shared with relevant parties. New insights gained during the requirements review process are added to the knowledge base and utilized in future developments. This mechanism shortens the requirements review period in system development and creates an environment free of errors and rework. This allows the system to shorten the time required for requirements review in system development and create an environment free of mistakes and rework.

[0029] The system according to the embodiment includes a collection unit, an organization / analysis unit, a sharing unit, a requirements review unit, and a knowledge base update unit. The collection unit collects information through interviews, questionnaires, and observations. The collection unit collects information such as the system design intentions and operational tips of developers and users. The collection unit can obtain detailed information about the developer's design intentions through interviews. The collection unit can also collect user operational tips using questionnaires. The collection unit can also observe user operation methods through observation to collect tacit knowledge. The organization / analysis unit organizes and analyzes the information collected by the collection unit using text mining or clustering. The organization / analysis unit can analyze the collected information using, for example, text mining technology to extract important keywords and phrases. The organization / analysis unit can also group related information using clustering technology. The organization / analysis unit can also classify information based on its importance and relevance, and organize it into a format that is easy to search. The sharing unit registers the information organized and analyzed by the organization / analysis unit in a shared folder or knowledge base. For example, the sharing unit documents the organized and analyzed information and stores it in a shared folder. The sharing unit can also register information in a knowledge base to make it easily accessible to relevant parties. Furthermore, the sharing unit can classify information based on its importance and relevance and organize it into a format that is easy to search. The requirements review unit examines requirements based on the information shared by the sharing unit. For example, the requirements review unit clarifies requirements based on the developer's design intent. The requirements review unit can also improve usability based on users' operational tips. Furthermore, the requirements review unit can prevent mistakes and rework by examining requirements while referring to the shared information. The knowledge base update unit adds new knowledge gained by the requirements review unit to the knowledge base. For example, the knowledge base update unit documents new knowledge gained during the requirements review process and registers it in the knowledge base. The knowledge base update unit can also periodically update the information in the knowledge base to provide the latest information.As a result, the system according to the embodiment can shorten the time required for considering requirements in system development and create an environment free from mistakes and rework.

[0030] During data collection, the collection department can change the information collection method depending on the specific project phase. For example, in the early stage of the project, the collection department may use a questionnaire to collect broad information. In the middle stage of the project, the collection department may also conduct interviews to obtain detailed information. Furthermore, in the later stage of the project, the collection department may also conduct observations to identify specific problems. In this way, information can be collected efficiently by using an information collection method appropriate for the project phase. Some or all of the above-mentioned processing in the collection department may be performed using, for example, AI, or may be performed without using AI. For example, the collection department may input the progress of the project into AI and have the AI ​​select an appropriate information collection method.

[0031] The collection unit can evaluate the reliability of information at the time of collection and prioritize collection of highly reliable information. The collection unit can evaluate the reliability of information based on, for example, the expertise and experience of the information provider. Furthermore, when the same information is obtained from multiple information sources, the collection unit can also determine that the information is highly reliable. Furthermore, the collection unit can evaluate the reliability of information based on the information provider's past statements and behavioral history. This allows accurate information to be obtained by preferentially collecting highly reliable information. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the information provider's past data into AI and have the AI ​​evaluate the reliability of the information.

[0032] During collection, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting information related to that area. In addition, if the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, if the user frequently visits a specific location, the collection unit can also prioritize collecting information related to that location. This allows highly relevant information to be collected efficiently by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into AI and have the AI ​​select highly relevant information.

[0033] At the time of collection, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect information based on their interests. Furthermore, the collection unit can collect related information from accounts and groups the user follows. In this way, by analyzing social media activities, information based on the user's interests can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​select related information.

[0034] The organizing and analysis unit can adjust the level of detail of the analysis based on the importance of the information during organization and analysis. For example, the organizing and analysis unit can perform a detailed analysis of highly important information to gain deeper insights. The organizing and analysis unit can also perform a simplified analysis of less important information to process it efficiently. Furthermore, the organizing and analysis unit can appropriately allocate analysis resources according to the importance of the information. This allows for efficient information analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the organizing and analysis unit may be performed using, or without, AI. For example, the organizing and analysis unit can input information importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0035] The organizing and analysis unit can apply different analytical methods depending on the category of information during organization and analysis. For example, the organizing and analysis unit can apply text mining to text data to extract patterns and trends. The organizing and analysis unit can also apply statistical analysis to numerical data to clarify data distributions and correlations. Furthermore, the organizing and analysis unit can apply image analysis to image data to extract visual features. This allows for more appropriate analysis results to be obtained by applying an analytical method depending on the category of information. Some or all of the above-mentioned processing in the organizing and analysis unit may be performed using, or without, AI. For example, the organizing and analysis unit can input information category data into AI and have the AI ​​select an appropriate analytical method.

[0036] During organization and analysis, the organization and analysis unit can determine the priority of analysis based on the time of information submission. For example, the organization and analysis unit may prioritize analysis of recently submitted information to grasp the latest situation. The organization and analysis unit may also lower the priority of information that was submitted earlier. Furthermore, the organization and analysis unit may adjust the analysis schedule based on the time of submission. In this way, by determining the priority based on the time of information submission, the latest information can be prioritized for analysis. Some or all of the above-mentioned processing in the organization and analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the organization and analysis unit may input data on the time of information submission into AI and have the AI ​​determine the analysis priority.

[0037] The organizing and analysis unit can adjust the order of analysis based on the relevance of the information during organization and analysis. For example, the organizing and analysis unit prioritizes analysis of highly relevant information to grasp the overall picture. The organizing and analysis unit can also postpone analysis of less relevant information. Furthermore, the organizing and analysis unit can appropriately allocate analysis resources based on the relevance of the information. This allows for efficient information analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the organizing and analysis unit may be performed using, or without, AI. For example, the organizing and analysis unit can input information relevance data into AI and have the AI ​​adjust the order of analysis.

[0038] The sharing unit can adjust the level of detail of the sharing based on the importance of the information when sharing. For example, the sharing unit shares information with a detailed description for highly important information. The sharing unit can also share information with a simplified description for less important information. Furthermore, the sharing unit can appropriately allocate sharing resources according to the importance of the information. This allows for efficient information sharing by adjusting the level of detail of the sharing based on the importance of the information. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information importance data into AI and have the AI ​​adjust the level of detail of the sharing.

[0039] The sharing unit can apply different sharing methods depending on the category of information when sharing. For example, the sharing unit applies document sharing to text data to provide detailed information. The sharing unit can also visually share numerical data using graphs and charts. Furthermore, the sharing unit can share image data using an image viewer to emphasize visual features. This enables more appropriate information sharing by applying a sharing method depending on the category of information. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information category data into AI and have the AI ​​select an appropriate sharing method.

[0040] The sharing unit can determine the priority of sharing based on the time of submission of information when sharing. For example, the sharing unit prioritizes sharing of recently submitted information to grasp the latest situation. The sharing unit can also lower the priority of information that was submitted earlier before sharing. Furthermore, the sharing unit can adjust the sharing schedule based on the time of submission. In this way, by determining the priority based on the time of submission of information, the latest information can be shared preferentially. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of sharing.

[0041] The sharing unit can adjust the order of sharing based on the relevance of the information when sharing. For example, the sharing unit prioritizes sharing of highly relevant information to grasp the overall picture. The sharing unit can also postpone sharing of less relevant information. Furthermore, the sharing unit can appropriately allocate sharing resources based on the relevance of the information. In this way, information can be shared efficiently by adjusting the order of sharing based on the relevance of the information. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information relevance data into AI and have the AI ​​adjust the order of sharing.

[0042] The requirements examination unit can clarify requirements by referring to past project data when examining requirements. For example, the requirements examination unit refers to requirements that were successful in past projects and applies them to the current project. The requirements examination unit can also refer to the data to avoid requirements that failed in past projects. Furthermore, the requirements examination unit can also determine the priority of requirements based on past project data. This makes it easier to clarify requirements by referring to past project data. Some or all of the above-mentioned processing in the requirements examination unit may be performed using, for example, AI, or may be performed without using AI. For example, the requirements examination unit can input past project data into AI and have the AI ​​clarify the requirements.

[0043] When examining requirements, the requirements examination department can apply different examination methods to each information category. For example, the requirements examination department applies user story mapping to functional requirements to clarify the requirements from the user's perspective. The requirements examination department can also apply performance testing to non-functional requirements to clarify system performance requirements. Furthermore, the requirements examination department can apply threat modeling to security requirements to identify potential risks. This enables more appropriate requirements examination by applying an examination method according to the information category. Some or all of the above-mentioned processing in the requirements examination department may be performed using, for example, AI, or may be performed without using AI. For example, the requirements examination department can input information category data into AI and have the AI ​​select an appropriate examination method.

[0044] When reviewing requirements, the requirements review department can determine the priority of requirements based on the time of submission of information. For example, the requirements review department prioritizes the most recent requirements based on the most recently submitted information. The requirements review department can also lower the priority of information that was submitted earlier. Furthermore, the requirements review department can adjust the schedule for reviewing requirements based on the time of submission. In this way, by determining the priority based on the time of submission of information, the most recent requirements can be prioritized for review. Some or all of the above-mentioned processing in the requirements review department may be performed using, for example, AI, or may be performed without using AI. For example, the requirements review department can input data on the time of submission of information into AI and have the AI ​​determine the priority of requirements.

[0045] The requirements review unit can adjust the order of requirements based on the relevance of information when reviewing requirements. For example, the requirements review unit prioritizes review of important requirements based on highly relevant information. The requirements review unit can also postpone review of less relevant information. Furthermore, the requirements review unit can appropriately allocate resources for reviewing requirements based on the relevance of information. This allows requirements to be reviewed efficiently by adjusting the order of requirements based on the relevance of information. Some or all of the above-described processing in the requirements review unit may be performed using, or without, AI, for example. For example, the requirements review unit can input information relevance data into AI and have the AI ​​adjust the order of requirements.

[0046] The knowledge base update unit can optimize the update algorithm by referring to past knowledge base data during an update. The knowledge base update unit, for example, selects an optimal update algorithm based on past knowledge base data. The knowledge base update unit can also analyze past data and optimize the frequency and timing of updates. Furthermore, the knowledge base update unit can determine update priorities based on past data. This allows the optimal update algorithm to be selected by referring to past knowledge base data. Some or all of the above-described processing in the knowledge base update unit may be performed using, or without, AI, for example. For example, the knowledge base update unit can input past knowledge base data into AI and have the AI ​​optimize the update algorithm.

[0047] The knowledge base update unit can apply different update methods to different information categories during updates. For example, the knowledge base update unit uses text mining to update text data. The knowledge base update unit can also use statistical analysis to update numerical data. Furthermore, the knowledge base update unit can also use image analysis to update image data. This allows the knowledge base to be updated efficiently by applying an update method according to the information category. Some or all of the above-described processing in the knowledge base update unit may be performed using, for example, AI, or may be performed without using AI. For example, the knowledge base update unit can input information category data into AI and have the AI ​​select an appropriate update method.

[0048] During an update, the knowledge base update unit can weight the update data in the knowledge base based on the time of submission of the information. For example, the knowledge base update unit prioritizes recently submitted information and reflects it in the knowledge base. The knowledge base update unit can also update information that was submitted earlier by assigning a lower weight to it. Furthermore, the knowledge base update unit can also determine the priority of the update data based on the time of submission. In this way, by weighting based on the time of submission of the information, the latest information can be preferentially reflected in the knowledge base. Some or all of the above-described processing in the knowledge base update unit may be performed using, for example, AI, or may be performed without using AI. For example, the knowledge base update unit can input information submission time data into AI and have the AI ​​perform weighting of the update data.

[0049] The knowledge base update unit can adjust the update order of the knowledge base based on the relevance of the information during an update. For example, the knowledge base update unit prioritizes updating highly relevant information to improve the accuracy of the knowledge base. The knowledge base update unit can also postpone updating less relevant information. Furthermore, the knowledge base update unit can appropriately allocate update resources based on the relevance of the information. This allows the knowledge base to be updated efficiently by adjusting the update order based on the relevance of the information. Some or all of the above-described processing in the knowledge base update unit may be performed using, or without, AI, for example. For example, the knowledge base update unit can input information relevance data into AI and have the AI ​​adjust the update order.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] During data collection, the collection department can change the information collection method depending on the specific project phase. For example, in the early stages of the project, a questionnaire can be used to collect broad information. In the middle stages of the project, interviews can be conducted to obtain detailed information. Furthermore, in the later stages of the project, observations can be conducted to identify specific problem areas. In this way, information can be collected efficiently by using an information collection method appropriate for the project phase. Some or all of the above-mentioned processing in the collection department may be performed using, for example, AI, or may be performed without using AI. For example, the collection department can input the progress of the project into AI and have it select an appropriate information collection method.

[0052] The collection unit can evaluate the reliability of information at the time of collection and prioritize collection of highly reliable information. For example, the reliability of information can be evaluated based on the expertise and experience of the information provider. In addition, when the same information is obtained from multiple information sources, it can also determine that the information is highly reliable. Furthermore, the reliability of information can be evaluated based on the information provider's past statements and behavioral history. This allows accurate information to be obtained by preferentially collecting highly reliable information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the information provider's past data into AI and have the AI ​​evaluate the reliability of the information.

[0053] During collection, the collection unit can prioritize collection of highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, information related to that area can be prioritized. Also, if the user is moving, relevant information can be collected based on the user's current location. Furthermore, if the user frequently visits a specific location, information related to that location can be prioritized. This allows highly relevant information to be collected efficiently by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into AI and have the AI ​​select highly relevant information.

[0054] During organization and analysis, the organization and analysis unit can adjust the level of analysis detail based on the importance of the information. For example, a detailed analysis can be performed on highly important information to gain deeper insights. A simplified analysis can also be performed on less important information to process it more efficiently. Furthermore, analysis resources can be appropriately allocated according to the importance of the information. By adjusting the level of analysis detail based on the importance of the information, information can be analyzed efficiently. Some or all of the above-described processing in the organization and analysis unit may be performed using, or without, AI. For example, the organization and analysis unit can input information importance data into AI and have the AI ​​adjust the level of analysis detail.

[0055] During organization and analysis, the organization and analysis unit can apply different analysis methods depending on the category of information. For example, text mining can be applied to text data to extract patterns and trends. Statistical analysis can also be applied to numerical data to clarify data distributions and correlations. Furthermore, image analysis can be applied to image data to extract visual features. By applying an analysis method according to the category of information, more appropriate analysis results can be obtained. Some or all of the above-described processing in the organization and analysis unit may be performed using, or without, AI. For example, the organization and analysis unit can input information category data into AI and have the AI ​​select an appropriate analysis method.

[0056] During organization and analysis, the organization and analysis unit can determine the priority of analysis based on the time of information submission. For example, it can prioritize analysis of recently submitted information to grasp the latest situation. It can also lower the priority of information that was submitted earlier. Furthermore, it can adjust the analysis schedule based on the time of submission. By determining priorities based on the time of information submission, it is possible to prioritize the latest information for analysis. Some or all of the above-described processing in the organization and analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the organization and analysis unit can input data on the time of information submission into AI and have the AI ​​determine the analysis priorities.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection department collects information through interviews, questionnaires, and observations. For example, they collect information such as the system's design intentions and operational tips held by developers and users. Design intentions can be heard in detail through interviews, and operational tips can also be collected using questionnaires. Furthermore, tacit knowledge can be collected by observing how users operate the system through observation. Step 2: The organization and analysis unit organizes and analyzes the information collected by the collection unit using text mining or clustering. For example, text mining techniques can be used to extract important keywords and phrases, and clustering techniques can be used to group related information. Furthermore, the information can be classified based on its importance and relevance, and organized into a format that is easy to search. Step 3: The sharing department registers the information organized and analyzed by the organization and analysis department in a shared folder or knowledge base. For example, the organized and analyzed information can be documented and saved in a shared folder. The information can also be registered in a knowledge base to make it easily accessible to those involved. The information can also be classified based on its importance and relevance, and organized in a format that makes it easy to search. Step 4: The requirements review department reviews the requirements based on the information shared by the sharing department. For example, they can clarify requirements based on the developer's design intent and improve usability based on the user's operational tips. Furthermore, by reviewing requirements while referring to the shared information, they can prevent mistakes and rework. Step 5: The knowledge base update department adds new knowledge gained by the requirements review department to the knowledge base. For example, new knowledge gained during the requirements review process is documented and registered in the knowledge base. The knowledge base information can also be updated periodically to provide the latest information.

[0059] (Example 2) The system according to an embodiment of the present invention shortens the requirements review period in system development and creates an environment free of errors and rework by externalizing and sharing the tacit knowledge held by developers and users of each internal system, as well as information previously only known to specific members. This system collects, organizes, analyzes, and converts the tacit knowledge held by developers and users into a shareable format, thereby streamlining requirements review in system development. For example, interviews, surveys, and observations are used to gather information such as developers' system design intentions and users' operational tips. The collected information is documented and registered in a shared folder or knowledge base. The information is categorized based on importance and relevance and organized in an easily searchable format. Based on the shared information, requirements review for system development is carried out, preventing errors and rework. The results of the requirements review are documented and shared with relevant parties. New insights gained during the requirements review process are added to the knowledge base and utilized in future developments. This mechanism shortens the requirements review period in system development and creates an environment free of errors and rework. This allows the system to shorten the time required for requirements review in system development and create an environment free of mistakes and rework.

[0060] The system according to the embodiment includes a collection unit, an organization / analysis unit, a sharing unit, a requirements review unit, and a knowledge base update unit. The collection unit collects information through interviews, questionnaires, and observations. The collection unit collects information such as the system design intentions and operational tips of developers and users. The collection unit can obtain detailed information about the developer's design intentions through interviews. The collection unit can also collect user operational tips using questionnaires. The collection unit can also observe user operation methods through observation to collect tacit knowledge. The organization / analysis unit organizes and analyzes the information collected by the collection unit using text mining or clustering. The organization / analysis unit can analyze the collected information using, for example, text mining technology to extract important keywords and phrases. The organization / analysis unit can also group related information using clustering technology. The organization / analysis unit can also classify information based on its importance and relevance, and organize it into a format that is easy to search. The sharing unit registers the information organized and analyzed by the organization / analysis unit in a shared folder or knowledge base. For example, the sharing unit documents the organized and analyzed information and stores it in a shared folder. The sharing unit can also register information in a knowledge base to make it easily accessible to relevant parties. Furthermore, the sharing unit can classify information based on its importance and relevance and organize it into a format that is easy to search. The requirements review unit examines requirements based on the information shared by the sharing unit. For example, the requirements review unit clarifies requirements based on the developer's design intent. The requirements review unit can also improve usability based on users' operational tips. Furthermore, the requirements review unit can prevent mistakes and rework by examining requirements while referring to the shared information. The knowledge base update unit adds new knowledge gained by the requirements review unit to the knowledge base. For example, the knowledge base update unit documents new knowledge gained during the requirements review process and registers it in the knowledge base. The knowledge base update unit can also periodically update the information in the knowledge base to provide the latest information.As a result, the system according to the embodiment can shorten the time required for considering requirements in system development and create an environment free from mistakes and rework.

[0061] The collection unit can estimate the user's emotions and adjust the content of interview or questionnaire questions based on the estimated user emotions. For example, if the user is nervous, the collection unit starts with simple questions to relax the user and gradually moves on to more detailed questions. Furthermore, if the user is relaxed, the collection unit can ask detailed questions from the beginning to efficiently collect information. Furthermore, if the user is in a hurry, the collection unit can prioritize important questions and collect necessary information in a short amount of time. This allows for more appropriate information to be collected by adjusting the content of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input facial expression data of the user during the interview into the generation AI and have the generation AI estimate the user's emotions.

[0062] During data collection, the collection department can change the information collection method depending on the specific project phase. For example, in the early stage of the project, the collection department may use a questionnaire to collect broad information. In the middle stage of the project, the collection department may also conduct interviews to obtain detailed information. Furthermore, in the later stage of the project, the collection department may also conduct observations to identify specific problems. In this way, information can be collected efficiently by using an information collection method appropriate for the project phase. Some or all of the above-mentioned processing in the collection department may be performed using, for example, AI, or may be performed without using AI. For example, the collection department may input the progress of the project into AI and have the AI ​​select an appropriate information collection method.

[0063] The collection unit can evaluate the reliability of information at the time of collection and prioritize collection of highly reliable information. The collection unit can evaluate the reliability of information based on, for example, the expertise and experience of the information provider. Furthermore, when the same information is obtained from multiple information sources, the collection unit can also determine that the information is highly reliable. Furthermore, the collection unit can evaluate the reliability of information based on the information provider's past statements and behavioral history. This allows accurate information to be obtained by preferentially collecting highly reliable information. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input the information provider's past data into AI and have the AI ​​evaluate the reliability of the information.

[0064] The collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important information to reduce the user's burden. Furthermore, when the user is relaxed, the collection unit can collect detailed information to grasp the overall picture. Furthermore, when the user is in a hurry, the collection unit can avoid time-consuming information collection and prioritize information that can be collected quickly. This allows for efficient information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input facial expression data of the user during an interview into the generation AI and have the generation AI estimate the user's emotions.

[0065] During collection, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting information related to that area. In addition, if the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, if the user frequently visits a specific location, the collection unit can also prioritize collecting information related to that location. This allows highly relevant information to be collected efficiently by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into AI and have the AI ​​select highly relevant information.

[0066] At the time of collection, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. The collection unit can also analyze the user's social media activity history and collect information based on their interests. Furthermore, the collection unit can collect related information from accounts and groups the user follows. In this way, by analyzing social media activities, information based on the user's interests can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​select related information.

[0067] The sorting and analysis unit can estimate the user's emotions and adjust the text mining algorithm based on the estimated user emotions. For example, if the user is nervous, the sorting and analysis unit can perform simple keyword extraction to reduce the burden. Alternatively, if the user is relaxed, the sorting and analysis unit can perform detailed text analysis to gain deeper insights. Furthermore, if the user is in a hurry, the sorting and analysis unit can use a simplified algorithm to obtain results quickly. This allows for more appropriate analysis results by adjusting the text mining algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sorting and analysis unit can be performed using AI, for example, or without AI. For example, the sorting and analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the text mining algorithm.

[0068] The organizing and analysis unit can adjust the level of detail of the analysis based on the importance of the information during organization and analysis. For example, the organizing and analysis unit can perform a detailed analysis of highly important information to gain deeper insights. The organizing and analysis unit can also perform a simplified analysis of less important information to process it efficiently. Furthermore, the organizing and analysis unit can appropriately allocate analysis resources according to the importance of the information. This allows for efficient information analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the organizing and analysis unit may be performed using, or without, AI. For example, the organizing and analysis unit can input information importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0069] The organizing and analysis unit can apply different analytical methods depending on the category of information during organization and analysis. For example, the organizing and analysis unit can apply text mining to text data to extract patterns and trends. The organizing and analysis unit can also apply statistical analysis to numerical data to clarify data distributions and correlations. Furthermore, the organizing and analysis unit can apply image analysis to image data to extract visual features. This allows for more appropriate analysis results to be obtained by applying an analytical method depending on the category of information. Some or all of the above-mentioned processing in the organizing and analysis unit may be performed using, or without, AI. For example, the organizing and analysis unit can input information category data into AI and have the AI ​​select an appropriate analytical method.

[0070] The organizing and analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the organizing and analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the organizing and analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the organizing and analysis unit can provide a display method that focuses on the main points. By adjusting the display method according to the user's emotions, more understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the organizing and analysis unit can be performed using AI, for example, or without AI. For example, the organizing and analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0071] During organization and analysis, the organization and analysis unit can determine the priority of analysis based on the time of information submission. For example, the organization and analysis unit may prioritize analysis of recently submitted information to grasp the latest situation. The organization and analysis unit may also lower the priority of information that was submitted earlier. Furthermore, the organization and analysis unit may adjust the analysis schedule based on the time of submission. In this way, by determining the priority based on the time of information submission, the latest information can be prioritized for analysis. Some or all of the above-mentioned processing in the organization and analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the organization and analysis unit may input data on the time of information submission into AI and have the AI ​​determine the analysis priority.

[0072] The organizing and analysis unit can adjust the order of analysis based on the relevance of the information during organization and analysis. For example, the organizing and analysis unit prioritizes analysis of highly relevant information to grasp the overall picture. The organizing and analysis unit can also postpone analysis of less relevant information. Furthermore, the organizing and analysis unit can appropriately allocate analysis resources based on the relevance of the information. This allows for efficient information analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the organizing and analysis unit may be performed using, or without, AI. For example, the organizing and analysis unit can input information relevance data into AI and have the AI ​​adjust the order of analysis.

[0073] The sharing unit can estimate a user's emotions and adjust the structure of a shared folder or knowledge base based on the estimated user emotions. For example, if the user is nervous, the sharing unit can provide a simple and intuitive folder structure. Furthermore, if the user is relaxed, the sharing unit can also perform detailed categorization and organize information. Furthermore, if the user is in a hurry, the sharing unit can adjust the folder structure so that important information can be found quickly. This facilitates information sharing by adjusting the folder structure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sharing unit can be performed using, for example, an AI, or without an AI. For example, the sharing unit can input the user's emotion data into the generation AI and have the generation AI adjust the folder structure.

[0074] The sharing unit can adjust the level of detail of the sharing based on the importance of the information when sharing. For example, the sharing unit shares information with a detailed description for highly important information. The sharing unit can also share information with a simplified description for less important information. Furthermore, the sharing unit can appropriately allocate sharing resources according to the importance of the information. This allows for efficient information sharing by adjusting the level of detail of the sharing based on the importance of the information. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information importance data into AI and have the AI ​​adjust the level of detail of the sharing.

[0075] The sharing unit can apply different sharing methods depending on the category of information when sharing. For example, the sharing unit applies document sharing to text data to provide detailed information. The sharing unit can also visually share numerical data using graphs and charts. Furthermore, the sharing unit can share image data using an image viewer to emphasize visual features. This enables more appropriate information sharing by applying a sharing method depending on the category of information. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information category data into AI and have the AI ​​select an appropriate sharing method.

[0076] The sharing unit can estimate the user's emotions and prioritize the information to be shared based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit can prioritize sharing important information to reduce the user's burden. Furthermore, if the user is relaxed, the sharing unit can share detailed information to grasp the overall picture. Furthermore, if the user is in a hurry, the sharing unit can avoid time-consuming information sharing and prioritize information that can be shared quickly. This allows information to be shared efficiently by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sharing unit can be performed using, for example, an AI, or without an AI. For example, the sharing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0077] The sharing unit can determine the priority of sharing based on the time of submission of information when sharing. For example, the sharing unit prioritizes sharing of recently submitted information to grasp the latest situation. The sharing unit can also lower the priority of information that was submitted earlier before sharing. Furthermore, the sharing unit can adjust the sharing schedule based on the time of submission. In this way, by determining the priority based on the time of submission of information, the latest information can be shared preferentially. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input data on the time of submission of information into AI and have the AI ​​determine the priority of sharing.

[0078] The sharing unit can adjust the order of sharing based on the relevance of the information when sharing. For example, the sharing unit prioritizes sharing of highly relevant information to grasp the overall picture. The sharing unit can also postpone sharing of less relevant information. Furthermore, the sharing unit can appropriately allocate sharing resources based on the relevance of the information. In this way, information can be shared efficiently by adjusting the order of sharing based on the relevance of the information. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information relevance data into AI and have the AI ​​adjust the order of sharing.

[0079] The requirements examination unit can estimate the user's emotions and adjust the requirements examination method based on the estimated user emotions. For example, if the user is nervous, the requirements examination unit starts with simple requirements to relax them and gradually moves on to more detailed requirements. Furthermore, if the user is relaxed, the requirements examination unit can efficiently proceed by first examining detailed requirements. Furthermore, if the user is in a hurry, the requirements examination unit can prioritize important requirements and clarify the requirements in a short time. This allows for efficient requirements clarification by adjusting the requirements examination method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the requirements examination unit can be performed using, for example, an AI. For example, the requirements examination unit can input the user's emotion data into the generation AI and have the generation AI adjust the requirements examination method.

[0080] The requirements examination unit can clarify requirements by referring to past project data when examining requirements. For example, the requirements examination unit refers to requirements that were successful in past projects and applies them to the current project. The requirements examination unit can also refer to the data to avoid requirements that failed in past projects. Furthermore, the requirements examination unit can also determine the priority of requirements based on past project data. This makes it easier to clarify requirements by referring to past project data. Some or all of the above-mentioned processing in the requirements examination unit may be performed using, for example, AI, or may be performed without using AI. For example, the requirements examination unit can input past project data into AI and have the AI ​​clarify the requirements.

[0081] When examining requirements, the requirements examination department can apply different examination methods to each information category. For example, the requirements examination department applies user story mapping to functional requirements to clarify the requirements from the user's perspective. The requirements examination department can also apply performance testing to non-functional requirements to clarify system performance requirements. Furthermore, the requirements examination department can apply threat modeling to security requirements to identify potential risks. This enables more appropriate requirements examination by applying an examination method according to the information category. Some or all of the above-mentioned processing in the requirements examination department may be performed using, for example, AI, or may be performed without using AI. For example, the requirements examination department can input information category data into AI and have the AI ​​select an appropriate examination method.

[0082] The requirements examination unit can estimate the user's emotions and determine the priority of requirement examination based on the estimated user emotions. For example, when the user is feeling stressed, the requirements examination unit prioritizes important requirements to reduce the user's burden. Furthermore, when the user is relaxed, the requirements examination unit can review detailed requirements and grasp the overall picture. Furthermore, when the user is in a hurry, the requirements examination unit can avoid time-consuming requirement examination and prioritize requirements that can be quickly examined. This allows requirements to be efficiently examined by determining the priority of requirement examination according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the requirements examination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the requirements examination unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of requirement examination.

[0083] When reviewing requirements, the requirements review department can determine the priority of requirements based on the time of submission of information. For example, the requirements review department prioritizes the most recent requirements based on the most recently submitted information. The requirements review department can also lower the priority of information that was submitted earlier. Furthermore, the requirements review department can adjust the schedule for reviewing requirements based on the time of submission. In this way, by determining the priority based on the time of submission of information, the most recent requirements can be prioritized for review. Some or all of the above-mentioned processing in the requirements review department may be performed using, for example, AI, or may be performed without using AI. For example, the requirements review department can input data on the time of submission of information into AI and have the AI ​​determine the priority of requirements.

[0084] The requirements review unit can adjust the order of requirements based on the relevance of information when reviewing requirements. For example, the requirements review unit prioritizes review of important requirements based on highly relevant information. The requirements review unit can also postpone review of less relevant information. Furthermore, the requirements review unit can appropriately allocate resources for reviewing requirements based on the relevance of information. This allows requirements to be reviewed efficiently by adjusting the order of requirements based on the relevance of information. Some or all of the above-described processing in the requirements review unit may be performed using, or without, AI, for example. For example, the requirements review unit can input information relevance data into AI and have the AI ​​adjust the order of requirements.

[0085] The knowledge base update unit can estimate the user's emotions and adjust the knowledge base update method based on the estimated user emotions. For example, if the user is nervous, the knowledge base update unit can provide a simple and intuitive update method. Furthermore, if the user is relaxed, the knowledge base update unit can provide an update method that includes detailed information. Furthermore, if the user is in a hurry, the knowledge base update unit can provide a quick update method. This allows the knowledge base to be updated efficiently by adjusting the update method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the knowledge base update unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the knowledge base update unit can input the user's emotion data into the generation AI and have the generation AI adjust the update method.

[0086] The knowledge base update unit can optimize the update algorithm by referring to past knowledge base data during an update. The knowledge base update unit, for example, selects an optimal update algorithm based on past knowledge base data. The knowledge base update unit can also analyze past data and optimize the frequency and timing of updates. Furthermore, the knowledge base update unit can determine update priorities based on past data. This allows the optimal update algorithm to be selected by referring to past knowledge base data. Some or all of the above-described processing in the knowledge base update unit may be performed using, or without, AI, for example. For example, the knowledge base update unit can input past knowledge base data into AI and have the AI ​​optimize the update algorithm.

[0087] The knowledge base update unit can apply different update methods to different information categories during updates. For example, the knowledge base update unit uses text mining to update text data. The knowledge base update unit can also use statistical analysis to update numerical data. Furthermore, the knowledge base update unit can also use image analysis to update image data. This allows the knowledge base to be updated efficiently by applying an update method according to the information category. Some or all of the above-described processing in the knowledge base update unit may be performed using, for example, AI, or may be performed without using AI. For example, the knowledge base update unit can input information category data into AI and have the AI ​​select an appropriate update method.

[0088] The knowledge base update unit can estimate the user's emotions and adjust the update frequency of the knowledge base based on the estimated user emotions. For example, if the user is feeling stressed, the knowledge base update unit can reduce the update frequency to reduce the burden on the user. Furthermore, if the user is relaxed, the knowledge base update unit can also increase the update frequency to provide the latest information. Furthermore, if the user is in a hurry, the knowledge base update unit can adjust the update frequency to enable quick updates. This allows the knowledge base to be updated efficiently by adjusting the update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the knowledge base update unit can be performed using, for example, an AI, or without an AI. For example, the knowledge base update unit can input the user's emotion data into the generation AI and have the generation AI adjust the update frequency.

[0089] During an update, the knowledge base update unit can weight the update data in the knowledge base based on the time of submission of the information. For example, the knowledge base update unit prioritizes recently submitted information and reflects it in the knowledge base. The knowledge base update unit can also update information that was submitted earlier by assigning a lower weight to it. Furthermore, the knowledge base update unit can also determine the priority of the update data based on the time of submission. In this way, by weighting based on the time of submission of the information, the latest information can be preferentially reflected in the knowledge base. Some or all of the above-described processing in the knowledge base update unit may be performed using, for example, AI, or may be performed without using AI. For example, the knowledge base update unit can input information submission time data into AI and have the AI ​​perform weighting of the update data.

[0090] The knowledge base update unit can adjust the update order of the knowledge base based on the relevance of the information during an update. For example, the knowledge base update unit prioritizes updating highly relevant information to improve the accuracy of the knowledge base. The knowledge base update unit can also postpone updating less relevant information. Furthermore, the knowledge base update unit can appropriately allocate update resources based on the relevance of the information. This allows the knowledge base to be updated efficiently by adjusting the update order based on the relevance of the information. Some or all of the above-described processing in the knowledge base update unit may be performed using, or without, AI, for example. For example, the knowledge base update unit can input information relevance data into AI and have the AI ​​adjust the update order. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, organization / analysis unit, sharing unit, requirements review unit, and knowledge base update unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12 via the control unit 46A. The organization / analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, organizes and analyzes the collected information using text mining or clustering techniques. The sharing unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, registers the organized and analyzed information in a shared folder or knowledge base. The requirements review unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, reviews requirements based on the shared information. The knowledge base update unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, adds new knowledge gained in the requirements review process to the knowledge base. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, organization / analysis unit, sharing unit, requirements review unit, and knowledge base update unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12 via the control unit 46A. The organization / analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, organizes and analyzes the collected information using text mining or clustering techniques. The sharing unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, registers the organized and analyzed information in a shared folder or knowledge base. The requirements review unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, reviews requirements based on the shared information. The knowledge base update unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, adds new knowledge obtained in the requirements review process to the knowledge base. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, organization / analysis unit, sharing unit, requirements review unit, and knowledge base update unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected information to the data processing device 12 via the control unit 46A. The organization / analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, organizes and analyzes the collected information using text mining or clustering techniques. The sharing unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, registers the organized and analyzed information in a shared folder or knowledge base. The requirements review unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, reviews requirements based on the shared information. The knowledge base update unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, adds new knowledge gained in the requirements review process to the knowledge base. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, organization / analysis unit, sharing unit, requirements review unit, and knowledge base update unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing device 12 by the control unit 46A. The organization / analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, organizes and analyzes the collected information using text mining or clustering techniques. The sharing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, registers the organized and analyzed information in a shared folder or knowledge base. The requirements review unit, realized, for example, by the specific processing unit 290 of the data processing device 12, reviews requirements based on the shared information. The knowledge base update unit, realized, for example, by the specific processing unit 290 of the data processing device 12, adds new knowledge obtained in the requirements review process to the knowledge base.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The collection unit can estimate the user's emotions and adjust the content of interview or questionnaire questions based on the estimated user emotions. For example, if the user is nervous, the collection unit can start with simple questions to relax the user and gradually move on to more detailed questions. Also, if the user is relaxed, detailed questions can be asked from the beginning to efficiently collect information. Furthermore, if the user is in a hurry, important questions can be prioritized to collect necessary information in a short amount of time. This allows for more appropriate information to be collected by adjusting the content of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input facial expression data of the user during the interview into the generation AI and have the generation AI estimate the user's emotions.

[0093] During data collection, the collection department can change the information collection method depending on the specific project phase. For example, in the early stages of the project, a questionnaire can be used to collect broad information. In the middle stages of the project, interviews can be conducted to obtain detailed information. Furthermore, in the later stages of the project, observations can be conducted to identify specific problem areas. In this way, information can be collected efficiently by using an information collection method appropriate for the project phase. Some or all of the above-mentioned processing in the collection department may be performed using, for example, AI, or may be performed without using AI. For example, the collection department can input the progress of the project into AI and have it select an appropriate information collection method.

[0094] The collection unit can evaluate the reliability of information at the time of collection and prioritize collection of highly reliable information. For example, the reliability of information can be evaluated based on the expertise and experience of the information provider. In addition, when the same information is obtained from multiple information sources, it can also determine that the information is highly reliable. Furthermore, the reliability of information can be evaluated based on the information provider's past statements and behavioral history. This allows accurate information to be obtained by preferentially collecting highly reliable information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the information provider's past data into AI and have the AI ​​evaluate the reliability of the information.

[0095] The collection unit can estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, important information can be collected first, reducing the burden on the user. Also, if the user is relaxed, detailed information can be collected to grasp the overall picture. Furthermore, if the user is in a hurry, time-consuming information collection can be avoided and information that can be collected quickly can be prioritized. This allows information to be collected efficiently by prioritizing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input facial expression data of the user during an interview into the generation AI and have the generation AI estimate the user's emotions.

[0096] During collection, the collection unit can prioritize collection of highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, information related to that area can be prioritized. Also, if the user is moving, relevant information can be collected based on the user's current location. Furthermore, if the user frequently visits a specific location, information related to that location can be prioritized. This allows highly relevant information to be collected efficiently by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into AI and have the AI ​​select highly relevant information.

[0097] The sorting and analysis unit can estimate the user's emotions and adjust the text mining algorithm based on the estimated user emotions. For example, if the user is nervous, simple keyword extraction can be performed to reduce the burden. Alternatively, if the user is relaxed, detailed text analysis can be performed to gain deeper insights. Furthermore, if the user is in a hurry, a simplified algorithm can be used to obtain results quickly. This allows for more appropriate analysis results by adjusting the text mining algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the sorting and analysis unit can be performed using AI, or without AI. For example, the sorting and analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the text mining algorithm.

[0098] During organization and analysis, the organization and analysis unit can adjust the level of analysis detail based on the importance of the information. For example, a detailed analysis can be performed on highly important information to gain deeper insights. A simplified analysis can also be performed on less important information to process it more efficiently. Furthermore, analysis resources can be appropriately allocated according to the importance of the information. By adjusting the level of analysis detail based on the importance of the information, information can be analyzed efficiently. Some or all of the above-described processing in the organization and analysis unit may be performed using, or without, AI. For example, the organization and analysis unit can input information importance data into AI and have the AI ​​adjust the level of analysis detail.

[0099] During organization and analysis, the organization and analysis unit can apply different analysis methods depending on the category of information. For example, text mining can be applied to text data to extract patterns and trends. Statistical analysis can also be applied to numerical data to clarify data distributions and correlations. Furthermore, image analysis can be applied to image data to extract visual features. By applying an analysis method according to the category of information, more appropriate analysis results can be obtained. Some or all of the above-described processing in the organization and analysis unit may be performed using, or without, AI. For example, the organization and analysis unit can input information category data into AI and have the AI ​​select an appropriate analysis method.

[0100] The organizing and analyzing unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. By adjusting the display method according to the user's emotions, analysis results can be provided that are easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the organizing and analyzing unit can be performed using AI, or without AI. For example, the organizing and analyzing unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0101] During organization and analysis, the organization and analysis unit can determine the priority of analysis based on the time of information submission. For example, it can prioritize analysis of recently submitted information to grasp the latest situation. It can also lower the priority of information that was submitted earlier. Furthermore, it can adjust the analysis schedule based on the time of submission. By determining priorities based on the time of information submission, it is possible to prioritize the latest information for analysis. Some or all of the above-described processing in the organization and analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the organization and analysis unit can input data on the time of information submission into AI and have the AI ​​determine the analysis priorities.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection department collects information through interviews, questionnaires, and observations. For example, they collect information such as the system's design intentions and operational tips held by developers and users. Design intentions can be heard in detail through interviews, and operational tips can also be collected using questionnaires. Furthermore, tacit knowledge can be collected by observing how users operate the system through observation. Step 2: The organization and analysis unit organizes and analyzes the information collected by the collection unit using text mining or clustering. For example, text mining techniques can be used to extract important keywords and phrases, and clustering techniques can be used to group related information. Furthermore, the information can be classified based on its importance and relevance, and organized into a format that is easy to search. Step 3: The sharing department registers the information organized and analyzed by the organization and analysis department in a shared folder or knowledge base. For example, the organized and analyzed information can be documented and saved in a shared folder. The information can also be registered in a knowledge base to make it easily accessible to those involved. The information can also be classified based on its importance and relevance, and organized in a format that makes it easy to search. Step 4: The requirements review department reviews the requirements based on the information shared by the sharing department. For example, they can clarify requirements based on the developer's design intent and improve usability based on the user's operational tips. Furthermore, by reviewing requirements while referring to the shared information, they can prevent mistakes and rework. Step 5: The knowledge base update department adds new knowledge gained by the requirements review department to the knowledge base. For example, new knowledge gained during the requirements review process is documented and registered in the knowledge base. The knowledge base information can also be updated periodically to provide the latest information.

[0104] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that collects information through interviews, questionnaires, or observations; an organization / analysis unit that organizes and analyzes the information collected by the collection unit using text mining or clustering; a sharing unit that registers the information organized and analyzed by the organization / analysis unit in a shared folder or knowledge base; a requirements review unit that reviews requirements based on the information shared by the sharing unit; and a knowledge base update unit that adds new knowledge obtained by the requirements review unit to the knowledge base.

2. The system according to claim 1 , wherein the collection unit estimates the user's emotions and adjusts the content of questions in an interview or questionnaire based on the estimated user emotions.

3. The collecting unit When gathering information, vary your information gathering methods depending on the specific project phase.

2. The system of claim 1.

4. The collecting unit When collecting information, evaluate its reliability and prioritize collection of reliable information.

2. The system of claim 1.

5. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

6. The system according to claim 1 , wherein the collection unit, at the time of collection, prioritizes collection of highly relevant information based on geographical location information of the user.

7. The collecting unit At the time of collection, we analyze your social media activity and collect relevant information.

2. The system of claim 1.

8. The organizing and analyzing unit Inferring user sentiment and adjusting text mining algorithms based on the inferred sentiment 2. The system of claim 1.

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