System

Abstract

An object of the system according to the embodiment is to analyze information on past similar cases and review comments and generate a point of concern.SOLUTION: A system includes an information collection part, a review analysis part, and a point of concern generation part. The information collection unit collects information on a past similar case. The review analysis unit analyzes review comments of managers and specialists based on the information collected by the information collection unit. The point-of-concern generation unit generates a point of concern based on the information analyzed by the review analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] Conventional technology has the problem that it is difficult to effectively utilize information on similar past cases and review comments to identify concerns.

[0005] The system according to the embodiment aims to generate concerns by analyzing information on similar cases in the past and review comments. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a review analysis unit, and a concern generation unit. The information collection unit collects information on similar past cases. The review analysis unit analyzes review comments from managers and specialists based on the information collected by the information collection unit. The concern generation unit generates concerns based on the information analyzed by the review analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information on similar cases in the past and review comments to generate points of concern. [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 AI ​​system according to the embodiment of the present invention learns information from similar past projects, analyzes review comments from managers and specialists, and generates concerns based on the "intuition" of the managers and specialists. This enables the AI ​​system to support project risk management.

[0029] An AI system according to an embodiment includes an information collection unit, a review analysis unit, and a concern generation unit. The information collection unit collects information on similar past projects. For example, it collects data such as pre-implementation conditions, progress during implementation, and results after implementation. The information collection unit can also refer to external databases (e.g., industry reports and academic papers) to learn a wider range of information. The review analysis unit analyzes review comments from managers and specialists based on the information collected by the information collection unit. For example, it analyzes comments on project progress and risk warnings. The review analysis unit can also analyze the content of related meeting records and emails to understand the context and intent behind the review comments. The concern generation unit generates concerns based on the information analyzed by the review analysis unit. For example, when a new project plan is input, the generation AI refers to data and review comments from similar past projects and presents concerns such as "this plan has risks." The concern generation unit can also compare past successes and failures to identify high-risk elements. This enables the AI ​​system according to an embodiment to support project risk management. For example, the generation AI learns from past feedback to improve the accuracy of the concerns it presents. The generation AI analyzes the content of the feedback and automatically suggests areas for improvement. The generation AI uses emotion estimation functionality to analyze the emotional tone of the feedback and prioritizes analysis of feedback that contains negative emotions.

[0030] The information gathering unit can refer to external databases and learn a wider range of information based on industry reports and academic papers. For example, the generation AI automatically collects industry reports and academic papers and integrates them with information on similar cases. For example, it refers to academic papers on the introduction of new technology to evaluate technical risks. The information gathering unit also collects information from external databases in real time, and the generation AI uses that information to evaluate the risks of similar cases. For example, it reflects the latest market trends. The information gathering unit also automatically refers to relevant patent databases to learn the technical background of similar cases. For example, it evaluates technical risks based on patent information. In this way, by referring to external databases, the generation AI can learn a wider range of information and improve the accuracy of risk assessment.

[0031] The information gathering unit can automatically classify the success and failure factors of past projects and perform risk assessments based on each factor. For example, the generation AI in the information gathering unit analyzes data from past projects and automatically classifies success and failure factors. For example, it identifies factors based on the progress and results of the project. The information gathering unit also has the generation AI perform risk assessments for new projects based on the success and failure factors. For example, it prioritizes evaluation of projects with many success factors. The information gathering unit also has the generation AI learn from data from past projects and improve the accuracy of risk assessments. For example, it issues a warning for projects with many failure factors. In this way, by automatically classifying success and failure factors and performing risk assessments based on that, the accuracy of risk management is improved.

[0032] The information gathering unit collects project data from different industries and fields, allowing it to incorporate knowledge from different fields. For example, the information gathering unit allows the generation AI to collect project data from different industries and fields and integrate it with information on similar projects. For example, project data from the medical field can be applied to projects in the technology field. The information gathering unit also incorporates knowledge from different fields, allowing the generation AI to set new standards for risk assessment. For example, it performs risk assessment based on success factors from different industries. The information gathering unit also allows the generation AI to learn project data from different fields and improve the accuracy of risk assessment. For example, it can issue warnings based on failure factors from different industries. In this way, the accuracy of risk assessment is improved by incorporating knowledge from different fields.

[0033] The information collection unit monitors the progress of the project in real time and sequentially learns the progress data, thereby enabling dynamic risk assessment. In the information collection unit, for example, the generation AI monitors the progress of the project in real time and sequentially learns the progress data. For example, risk assessment is dynamically performed as the project progresses. In addition, the information collection unit allows the generation AI to perform dynamic risk assessment based on the progress data collected in real time. For example, a warning is issued for projects whose progress is behind schedule. In addition, the information collection unit allows the generation AI to sequentially learn the progress data, thereby improving the accuracy of risk assessment. For example, the risk assessment criteria are dynamically adjusted based on the progress data. In this way, the project progress is monitored in real time and dynamic risk assessment is performed, thereby improving the accuracy of risk management.

[0034] The review analysis unit can also analyze the content of meeting records and emails to understand the context and intention behind the review comments. For example, the review analysis unit analyzes the content of related meeting records and emails so that the generation AI can understand the context and intention behind the review comments. For example, it identifies the intention of the comment based on the meeting records. The review analysis unit also analyzes the content of meeting records and emails so that the generation AI can grasp the background information of the review comments. For example, it understands the intention of the comment based on the content of the email. The review analysis unit also learns the content of meeting records and emails related to the generation AI to improve the analysis accuracy of the review comments. For example, it understands the background of the comment based on the meeting records. This improves analysis accuracy by understanding the context and intention behind the review comments.

[0035] The review analysis unit can extract frequently occurring keywords and phrases from review comments and evaluate their impact on the success or failure of a project. For example, the review analysis unit allows the generation AI to extract frequently occurring keywords and phrases from review comments and evaluate their impact on the success or failure of a project. For example, it identifies keywords related to risk. The review analysis unit also allows the generation AI to identify factors that will lead to a project's success or failure based on frequently occurring keywords and phrases. For example, it extracts keywords related to success. The review analysis unit also allows the generation AI to learn keywords and phrases from review comments and evaluate their impact on the success or failure of a project. For example, it identifies phrases related to failure. In this way, the accuracy of risk management is improved by extracting frequently occurring keywords and phrases and evaluating their impact on the success or failure of a project based on them.

[0036] The review analysis unit can automatically translate comments written in different languages ​​and perform multilingual analysis. For example, the review analysis unit uses a generation AI to automatically translate review comments written in different languages ​​and perform multilingual analysis. For example, it translates comments in English and French. The review analysis unit also uses an automatic translation function to have the generation AI analyze multilingual review comments. For example, it analyzes comments in different languages ​​in a unified manner. The review analysis unit also uses a generation AI to analyze multilingual review comments and integrates comments in different languages. For example, it performs analysis based on the translated comments. This allows comments written in different languages ​​to be automatically translated and perform multilingual analysis, improving the accuracy of the analysis.

[0037] The review analysis unit can visualize the analysis results of the review comments and provide an intuitively understandable dashboard. For example, the review analysis unit uses a generation AI to visualize the analysis results of the review comments and provide an intuitively understandable dashboard. For example, the review analysis unit displays the results using graphs and charts. Furthermore, the review analysis unit uses a generation AI to intuitively display the contents of the review comments based on the visualized analysis results. For example, the frequency of keywords is shown in a graph. Furthermore, the review analysis unit uses a generation AI to visualize the analysis results of the review comments and provide a dashboard that the user can intuitively understand. For example, emotional tone is shown using color. This visualizes the analysis results and provides an intuitively understandable dashboard, thereby helping the user understand.

[0038] The concern generation unit can compare past success cases and failure cases to identify high-risk elements. In the concern generation unit, for example, the generation AI compares past success cases and failure cases to identify high-risk elements. For example, it extracts elements that are common to failure cases. In addition, the concern generation unit identifies high-risk elements based on success cases and failure cases. For example, it evaluates elements that are missing from success cases as risks. In addition, the concern generation unit compares past cases to identify high-risk elements. For example, it warns of elements that are frequently seen in failure cases as risks. In this way, by comparing past success cases and failure cases and identifying high-risk elements, the accuracy of risk management is improved.

[0039] The concern generation unit can monitor the progress of a project in real time and dynamically assess risks. In the concern generation unit, for example, a generation AI monitors the progress of a project in real time and dynamically assesses risks. For example, it issues a warning for projects that are behind schedule. In addition, in the concern generation unit, the generation AI dynamically assesses risks based on progress data collected in real time. For example, it issues a warning for projects that are behind schedule. In addition, in the concern generation unit, the generation AI sequentially learns the progress data and improves the accuracy of risk assessment. For example, it dynamically adjusts the criteria for risk assessment based on the progress data. In this way, the progress of a project can be monitored in real time and dynamic risk assessment can be performed, improving the accuracy of risk management.

[0040] The concern generation unit can incorporate knowledge from different industries and fields to identify risk factors in different fields. For example, the generation AI in the concern generation unit incorporates knowledge from different industries and fields to identify risk factors in different fields. For example, risk factors from the medical field are applied to projects in the technology field. The concern generation unit also uses knowledge from different fields to have the generation AI set new standards for risk assessment. For example, it performs risk assessment based on success factors from different industries. The concern generation unit also has the generation AI learn from project data from different fields to improve the accuracy of risk assessment. For example, it issues warnings based on failure factors from different industries. In this way, the accuracy of risk assessment is improved by incorporating knowledge from different fields.

[0041] The concern generation unit can visualize the generation results of the concern points and provide an intuitively understandable dashboard. For example, the concern generation unit uses a generation AI to visualize the generation results of the concern points and provide an intuitively understandable dashboard. For example, the concern generation unit displays the content of the concern points intuitively based on the visualized generation results. For example, high-risk elements are indicated by color. The concern generation unit also uses a generation AI to visualize the generation results of the concern points and provide a dashboard that the user can intuitively understand. For example, emotional tone is indicated by color. This visualizes the generation results and provides an intuitively understandable dashboard, thereby helping the user understand.

[0042] The concern generation unit can learn from past feedback and improve the accuracy of the concerns to be presented. In the concern generation unit, for example, the generation AI learns from past feedback and improves the accuracy of the concerns to be presented. For example, the risk assessment criteria are adjusted based on past feedback. In addition, the concern generation unit improves the accuracy of the concerns to be presented by the generation AI based on past feedback. For example, the content of the feedback is learned and the accuracy of the risk assessment is improved. In addition, the concern generation unit improves the accuracy of the concerns to be presented by the generation AI learning from past feedback. For example, the risk assessment criteria are adjusted based on the content of the feedback. In this way, by learning from past feedback and improving the accuracy of the concerns to be presented, the accuracy of risk management is improved.

[0043] The concern generation unit can analyze the content of the feedback and automatically suggest improvements. In the concern generation unit, for example, a generation AI analyzes the content of the feedback and automatically suggests improvements. For example, improvements are identified based on the content of the feedback. In addition, in the concern generation unit, a generation AI automatically suggests improvements based on the content of the feedback. For example, the content of the feedback is learned and improvements are identified. In addition, in the concern generation unit, a generation AI analyzes the content of the feedback and automatically suggests improvements. For example, improvements are identified based on the content of the feedback. In this way, by analyzing the content of the feedback and automatically suggesting improvements, the accuracy of risk management is improved.

[0044] The concern generation unit can visualize the results of the presentation of the concerns and provide an intuitively understandable dashboard. In the concern generation unit, for example, the generation AI visualizes the results of the presentation of the concerns and provides an intuitively understandable dashboard. For example, it displays using graphs or charts. Furthermore, in the concern generation unit, the generation AI intuitively displays the content of the concerns based on the visualized presentation results. For example, it indicates high-risk elements in color. Furthermore, the concern generation unit visualizes the results of the presentation of the concerns and provides a dashboard that the user can intuitively understand. For example, it indicates emotional tone in color. In this way, the presentation results are visualized and a dashboard that can be intuitively understood is provided, thereby helping the user understand.

[0045] The concern generation unit can analyze the content of the feedback and automatically suggest improvements. In the concern generation unit, for example, a generation AI analyzes the content of the feedback and automatically suggests improvements. For example, improvements are identified based on the content of the feedback. In addition, in the concern generation unit, a generation AI automatically suggests improvements based on the content of the feedback. For example, the content of the feedback is learned and improvements are identified. In addition, in the concern generation unit, a generation AI analyzes the content of the feedback and automatically suggests improvements. For example, improvements are identified based on the content of the feedback. In this way, by analyzing the content of the feedback and automatically suggesting improvements, the accuracy of risk management is improved.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The information collection unit monitors the progress of the project in real time and sequentially learns the progress data, thereby enabling dynamic risk assessment. For example, the generation AI monitors the progress of the project in real time and sequentially learns the progress data. Risk assessment can be performed dynamically as the project progresses. The information collection unit also allows the generation AI to perform dynamic risk assessment based on the progress data collected in real time. A warning can be issued for projects whose progress is behind schedule. Furthermore, the information collection unit allows the generation AI to sequentially learn the progress data, improving the accuracy of risk assessment. The risk assessment criteria can be dynamically adjusted based on the progress data. This allows the project progress to be monitored in real time and dynamic risk assessment to be performed, improving the accuracy of risk management.

[0048] The review analysis unit can extract frequently occurring keywords and phrases from review comments and evaluate their impact on the success or failure of a project. For example, the generation AI can extract frequently occurring keywords and phrases from review comments and evaluate their impact on the success or failure of a project. Keywords related to risk can be identified. The review analysis unit also allows the generation AI to identify the factors that will lead to a project's success or failure based on frequently occurring keywords and phrases. Keywords related to success can be extracted. Furthermore, the review analysis unit allows the generation AI to learn the keywords and phrases from review comments and evaluate their impact on the success or failure of a project. Phrases related to failure can be identified. This improves the accuracy of risk management by extracting frequently occurring keywords and phrases and evaluating their impact on the success or failure of a project based on these keywords and phrases.

[0049] The information gathering unit can collect project data from different industries and fields and incorporate knowledge from other fields. For example, the generation AI collects project data from different industries and fields and integrates it with information from similar projects. Project data from the medical field can be applied to projects in the technology field. Furthermore, by incorporating knowledge from other fields, the information gathering unit allows the generation AI to set new standards for risk assessment. Risk assessment can be performed based on success factors from different industries. Furthermore, the information gathering unit allows the generation AI to learn project data from other fields and improve the accuracy of risk assessment. Warnings can be issued based on failure factors from different industries. In this way, the accuracy of risk assessment can be improved by incorporating knowledge from other fields.

[0050] The review analysis unit can automatically translate comments written in different languages ​​and perform multilingual analysis. For example, the generation AI can automatically translate review comments written in different languages ​​and perform multilingual analysis. Comments in English and French can be translated. The review analysis unit also uses the automatic translation function to have the generation AI perform multilingual review comment analysis. Comments in different languages ​​can be analyzed in a unified manner. Furthermore, the review analysis unit has the generation AI perform multilingual review comment analysis and integrate comments in different languages. Analysis can be performed based on the translated comments. This allows comments written in different languages ​​to be automatically translated and analyzed in multiple languages, improving the accuracy of the analysis.

[0051] The concern generation unit can learn from past feedback and improve the accuracy of the concerns it presents. For example, the generation AI learns from past feedback and improves the accuracy of the concerns it presents. The risk assessment criteria can be adjusted based on the past feedback. Furthermore, the concern generation unit can improve the accuracy of the risk assessment by learning the content of the feedback. Furthermore, the concern generation unit can improve the accuracy of the concerns it presents by learning from past feedback. The risk assessment criteria can be adjusted based on the content of the feedback. In this way, the generation AI learns from past feedback and improves the accuracy of the concerns it presents, thereby improving the accuracy of risk management.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The information gathering department collects information on similar past projects. For example, it collects data on pre-implementation conditions, progress during implementation, and results after implementation. The information gathering department can also refer to external databases (for example, industry reports and academic papers) to learn more extensive information. Step 2: The review analysis department analyzes the review comments from managers and specialists based on the information collected by the information collection department. For example, it analyzes comments on project progress and risk warnings. The review analysis department can also analyze the content of related meeting records and emails to understand the context and intent behind the review comments. Step 3: The concern generation unit generates concerns based on the information analyzed by the review analysis unit. For example, when a new project plan is entered, the generation AI references data from similar past projects and review comments to present concerns such as "this plan has risks." The concern generation unit can also compare past successes and failures to identify high-risk elements.

[0054] (Example 2) The AI ​​system according to the embodiment of the present invention learns information from similar past projects, analyzes review comments from managers and specialists, and generates concerns based on the "intuition" of the managers and specialists. This enables the AI ​​system to support project risk management.

[0055] An AI system according to an embodiment includes an information collection unit, a review analysis unit, and a concern generation unit. The information collection unit collects information on similar past projects. For example, it collects data such as pre-implementation conditions, progress during implementation, and results after implementation. The information collection unit can also refer to external databases (e.g., industry reports and academic papers) to learn a wider range of information. The review analysis unit analyzes review comments from managers and specialists based on the information collected by the information collection unit. For example, it analyzes comments on project progress and risk warnings. The review analysis unit can also analyze the content of related meeting records and emails to understand the context and intent behind the review comments. The concern generation unit generates concerns based on the information analyzed by the review analysis unit. For example, when a new project plan is input, the generation AI refers to data and review comments from similar past projects and presents concerns such as "this plan has risks." The concern generation unit can also compare past successes and failures to identify high-risk elements. This enables the AI ​​system according to an embodiment to support project risk management. For example, the generation AI learns from past feedback to improve the accuracy of the concerns it presents. The generation AI analyzes the content of the feedback and automatically suggests areas for improvement. The generation AI uses emotion estimation functionality to analyze the emotional tone of the feedback and prioritizes analysis of feedback that contains negative emotions.

[0056] The information gathering unit can refer to external databases and learn a wider range of information based on industry reports and academic papers. For example, the generation AI automatically collects industry reports and academic papers and integrates them with information on similar cases. For example, it refers to academic papers on the introduction of new technology to evaluate technical risks. The information gathering unit also collects information from external databases in real time, and the generation AI uses that information to evaluate the risks of similar cases. For example, it reflects the latest market trends. The information gathering unit also automatically refers to relevant patent databases to learn the technical background of similar cases. For example, it evaluates technical risks based on patent information. In this way, by referring to external databases, the generation AI can learn a wider range of information and improve the accuracy of risk assessment.

[0057] The information gathering unit can automatically classify the success and failure factors of past projects and perform risk assessments based on each factor. For example, the generation AI in the information gathering unit analyzes data from past projects and automatically classifies success and failure factors. For example, it identifies factors based on the progress and results of the project. The information gathering unit also has the generation AI perform risk assessments for new projects based on the success and failure factors. For example, it prioritizes evaluation of projects with many success factors. The information gathering unit also has the generation AI learn from data from past projects and improve the accuracy of risk assessments. For example, it issues a warning for projects with many failure factors. In this way, by automatically classifying success and failure factors and performing risk assessments based on that, the accuracy of risk management is improved.

[0058] The information collection unit can use the emotion estimation function to collect emotional data of stakeholders related to past projects and analyze the impact of emotional fluctuations on project success. In the information collection unit, for example, the generation AI collects emotional data of stakeholders related to past projects and analyzes emotional fluctuations. For example, it evaluates emotional fluctuations as the project progresses. The information collection unit also uses the emotion estimation function to identify project success factors based on the emotional data of stakeholders. For example, it evaluates projects with a high proportion of positive emotions as successful. The information collection unit also has the generation AI learn the emotional data and analyze the impact of emotional fluctuations on project success. For example, it issues a warning for projects with a high proportion of negative emotions. In this way, collecting emotional data and analyzing the impact of emotional fluctuations on project success improves the accuracy of risk assessment.

[0059] The information gathering unit collects project data from different industries and fields, allowing it to incorporate knowledge from different fields. For example, the information gathering unit allows the generation AI to collect project data from different industries and fields and integrate it with information on similar projects. For example, project data from the medical field can be applied to projects in the technology field. The information gathering unit also incorporates knowledge from different fields, allowing the generation AI to set new standards for risk assessment. For example, it performs risk assessment based on success factors from different industries. The information gathering unit also allows the generation AI to learn project data from different fields and improve the accuracy of risk assessment. For example, it can issue warnings based on failure factors from different industries. In this way, the accuracy of risk assessment is improved by incorporating knowledge from different fields.

[0060] The information collection unit monitors the progress of the project in real time and sequentially learns the progress data, thereby enabling dynamic risk assessment. In the information collection unit, for example, the generation AI monitors the progress of the project in real time and sequentially learns the progress data. For example, risk assessment is dynamically performed as the project progresses. In addition, the information collection unit allows the generation AI to perform dynamic risk assessment based on the progress data collected in real time. For example, a warning is issued for projects whose progress is behind schedule. In addition, the information collection unit allows the generation AI to sequentially learn the progress data, thereby improving the accuracy of risk assessment. For example, the risk assessment criteria are dynamically adjusted based on the progress data. In this way, the project progress is monitored in real time and dynamic risk assessment is performed, thereby improving the accuracy of risk management.

[0061] The information collection unit can use the emotion estimation function to monitor the emotional states of project team members in real time and evaluate the impact of emotional fluctuations on the progress of the project. For example, the information collection unit uses the generation AI to monitor the emotional states of project team members in real time and evaluate emotional fluctuations. For example, it analyzes the impact of emotional fluctuations on the progress of the project. The information collection unit also uses the emotion estimation function to perform risk assessment based on the emotional data of project team members. For example, it issues a warning for projects with a lot of negative emotions. The information collection unit also uses the generation AI to collect emotion data in real time and evaluate the impact of emotional fluctuations on the progress of the project. For example, it evaluates projects with a lot of positive emotions as successful. In this way, the accuracy of risk management is improved by monitoring emotional states in real time and evaluating the impact of emotional fluctuations on the progress of the project.

[0062] The review analysis unit can also analyze the content of meeting records and emails to understand the context and intention behind the review comments. For example, the review analysis unit analyzes the content of related meeting records and emails so that the generation AI can understand the context and intention behind the review comments. For example, it identifies the intention of the comment based on the meeting records. The review analysis unit also analyzes the content of meeting records and emails so that the generation AI can grasp the background information of the review comments. For example, it understands the intention of the comment based on the content of the email. The review analysis unit also learns the content of meeting records and emails related to the generation AI to improve the analysis accuracy of the review comments. For example, it understands the background of the comment based on the meeting records. This improves analysis accuracy by understanding the context and intention behind the review comments.

[0063] The review analysis unit can extract frequently occurring keywords and phrases from review comments and evaluate their impact on the success or failure of a project. For example, the review analysis unit allows the generation AI to extract frequently occurring keywords and phrases from review comments and evaluate their impact on the success or failure of a project. For example, it identifies keywords related to risk. The review analysis unit also allows the generation AI to identify factors that will lead to a project's success or failure based on frequently occurring keywords and phrases. For example, it extracts keywords related to success. The review analysis unit also allows the generation AI to learn keywords and phrases from review comments and evaluate their impact on the success or failure of a project. For example, it identifies phrases related to failure. In this way, the accuracy of risk management is improved by extracting frequently occurring keywords and phrases and evaluating their impact on the success or failure of a project based on them.

[0064] The review analysis unit can use the emotion estimation function to analyze the emotional tone of review comments and prioritize analysis of comments containing negative emotions. In the review analysis unit, for example, the generation AI analyzes the emotional tone of review comments and prioritizes analysis of comments containing negative emotions. For example, negative comments are identified. In addition, the review analysis unit uses the emotion estimation function to have the generation AI prioritize analysis of comments containing negative emotions. For example, negative comments related to risk are identified. In addition, the review analysis unit uses the generation AI to analyze the emotional tone and prioritize evaluation of comments containing negative emotions. For example, comments with strong negative emotions are analyzed. In this way, by prioritizing analysis of comments containing negative emotions, the accuracy of risk management is improved.

[0065] The review analysis unit can automatically translate comments written in different languages ​​and perform multilingual analysis. For example, the review analysis unit uses a generation AI to automatically translate review comments written in different languages ​​and perform multilingual analysis. For example, it translates comments in English and French. The review analysis unit also uses an automatic translation function to have the generation AI analyze multilingual review comments. For example, it analyzes comments in different languages ​​in a unified manner. The review analysis unit also uses a generation AI to analyze multilingual review comments and integrates comments in different languages. For example, it performs analysis based on the translated comments. This allows comments written in different languages ​​to be automatically translated and perform multilingual analysis, improving the accuracy of the analysis.

[0066] The review analysis unit can visualize the analysis results of the review comments and provide an intuitively understandable dashboard. For example, the review analysis unit uses a generation AI to visualize the analysis results of the review comments and provide an intuitively understandable dashboard. For example, the review analysis unit displays the results using graphs and charts. Furthermore, the review analysis unit uses a generation AI to intuitively display the contents of the review comments based on the visualized analysis results. For example, the frequency of keywords is shown in a graph. Furthermore, the review analysis unit uses a generation AI to visualize the analysis results of the review comments and provide a dashboard that the user can intuitively understand. For example, emotional tone is shown using color. This visualizes the analysis results and provides an intuitively understandable dashboard, thereby helping the user understand.

[0067] The review analysis unit can use the emotion estimation function to analyze the emotional tone of review comments and prioritize analysis of comments containing positive emotions. In the review analysis unit, for example, the generation AI analyzes the emotional tone of review comments and prioritizes analysis of comments containing positive emotions. For example, positive comments are identified. In addition, the review analysis unit uses the emotion estimation function to have the generation AI prioritize analysis of comments containing positive emotions. For example, positive comments related to success are identified. In addition, the review analysis unit uses the generation AI to analyze the emotional tone and prioritize evaluation of comments containing positive emotions. For example, comments with strong positive emotions are analyzed. This prioritizes analysis of comments containing positive emotions, improving the accuracy of risk management.

[0068] The concern generation unit can compare past success cases and failure cases to identify high-risk elements. In the concern generation unit, for example, the generation AI compares past success cases and failure cases to identify high-risk elements. For example, it extracts elements that are common to failure cases. In addition, the concern generation unit identifies high-risk elements based on success cases and failure cases. For example, it evaluates elements that are missing from success cases as risks. In addition, the concern generation unit compares past cases to identify high-risk elements. For example, it warns of elements that are frequently seen in failure cases as risks. In this way, by comparing past success cases and failure cases and identifying high-risk elements, the accuracy of risk management is improved.

[0069] The concern generation unit can monitor the progress of a project in real time and dynamically assess risks. In the concern generation unit, for example, a generation AI monitors the progress of a project in real time and dynamically assesses risks. For example, it issues a warning for projects that are behind schedule. In addition, in the concern generation unit, the generation AI dynamically assesses risks based on progress data collected in real time. For example, it issues a warning for projects that are behind schedule. In addition, in the concern generation unit, the generation AI sequentially learns the progress data and improves the accuracy of risk assessment. For example, it dynamically adjusts the criteria for risk assessment based on the progress data. In this way, the progress of a project can be monitored in real time and dynamic risk assessment can be performed, improving the accuracy of risk management.

[0070] The concern generation unit can use the emotion estimation function to consider the emotional states of project team members and identify emotional risk factors. In the concern generation unit, for example, the generation AI monitors the emotional states of project team members in real time and evaluates emotional fluctuations. For example, it analyzes the impact of emotional fluctuations on project progress. The concern generation unit also uses the emotion estimation function to perform risk assessment based on the emotional data of project team members. For example, it issues a warning for projects with a lot of negative emotions. In the concern generation unit, the generation AI collects emotional data in real time and evaluates the impact of emotional fluctuations on project progress. For example, it evaluates projects with a lot of positive emotions as successful. In this way, the accuracy of risk management is improved by considering emotional states and identifying emotional risk factors.

[0071] The concern generation unit can incorporate knowledge from different industries and fields to identify risk factors in different fields. For example, the generation AI in the concern generation unit incorporates knowledge from different industries and fields to identify risk factors in different fields. For example, risk factors from the medical field are applied to projects in the technology field. The concern generation unit also uses knowledge from different fields to have the generation AI set new standards for risk assessment. For example, it performs risk assessment based on success factors from different industries. The concern generation unit also has the generation AI learn from project data from different fields to improve the accuracy of risk assessment. For example, it issues warnings based on failure factors from different industries. In this way, the accuracy of risk assessment is improved by incorporating knowledge from different fields.

[0072] The concern generation unit can visualize the generation results of the concern points and provide an intuitively understandable dashboard. For example, the concern generation unit uses a generation AI to visualize the generation results of the concern points and provide an intuitively understandable dashboard. For example, the concern generation unit displays the content of the concern points intuitively based on the visualized generation results. For example, high-risk elements are indicated by color. The concern generation unit also uses a generation AI to visualize the generation results of the concern points and provide a dashboard that the user can intuitively understand. For example, emotional tone is indicated by color. This visualizes the generation results and provides an intuitively understandable dashboard, thereby helping the user understand.

[0073] The concern generation unit can use the emotion estimation function to consider the emotional states of project team members and identify emotional risk factors. In the concern generation unit, for example, the generation AI monitors the emotional states of project team members in real time and evaluates emotional fluctuations. For example, it analyzes the impact of emotional fluctuations on project progress. The concern generation unit also uses the emotion estimation function to perform risk assessment based on the emotional data of project team members. For example, it issues a warning for projects with a lot of negative emotions. In the concern generation unit, the generation AI collects emotional data in real time and evaluates the impact of emotional fluctuations on project progress. For example, it evaluates projects with a lot of positive emotions as successful. In this way, the accuracy of risk management is improved by considering emotional states and identifying emotional risk factors.

[0074] The concern generation unit can learn from past feedback and improve the accuracy of the concerns to be presented. In the concern generation unit, for example, the generation AI learns from past feedback and improves the accuracy of the concerns to be presented. For example, the risk assessment criteria are adjusted based on past feedback. In addition, the concern generation unit improves the accuracy of the concerns to be presented by the generation AI based on past feedback. For example, the content of the feedback is learned and the accuracy of the risk assessment is improved. In addition, the concern generation unit improves the accuracy of the concerns to be presented by the generation AI learning from past feedback. For example, the risk assessment criteria are adjusted based on the content of the feedback. In this way, by learning from past feedback and improving the accuracy of the concerns to be presented, the accuracy of risk management is improved.

[0075] The concern generation unit can analyze the content of the feedback and automatically suggest improvements. In the concern generation unit, for example, a generation AI analyzes the content of the feedback and automatically suggests improvements. For example, improvements are identified based on the content of the feedback. In addition, in the concern generation unit, a generation AI automatically suggests improvements based on the content of the feedback. For example, the content of the feedback is learned and improvements are identified. In addition, in the concern generation unit, a generation AI analyzes the content of the feedback and automatically suggests improvements. For example, improvements are identified based on the content of the feedback. In this way, by analyzing the content of the feedback and automatically suggesting improvements, the accuracy of risk management is improved.

[0076] The concern generation unit can use the emotion estimation function to analyze the emotional tone of the feedback and prioritize analysis of feedback that includes negative emotions. In the concern generation unit, for example, the generation AI analyzes the emotional tone of the feedback and prioritizes analysis of feedback that includes negative emotions. For example, negative feedback is identified. In addition, the concern generation unit uses the emotion estimation function to have the generation AI prioritize analysis of feedback that includes negative emotions. For example, negative feedback related to risk is identified. In addition, the concern generation unit uses the emotion estimation function to have the generation AI analyze the emotional tone and prioritize evaluation of feedback that includes negative emotions. For example, feedback with strong negative emotions is analyzed. In this way, by prioritized analysis of feedback that includes negative emotions, the accuracy of risk management is improved.

[0077] The concern generation unit can visualize the results of the presentation of the concerns and provide an intuitively understandable dashboard. In the concern generation unit, for example, the generation AI visualizes the results of the presentation of the concerns and provides an intuitively understandable dashboard. For example, it displays using graphs or charts. Furthermore, in the concern generation unit, the generation AI intuitively displays the content of the concerns based on the visualized presentation results. For example, it indicates high-risk elements in color. Furthermore, the concern generation unit visualizes the results of the presentation of the concerns and provides a dashboard that the user can intuitively understand. For example, it indicates emotional tone in color. In this way, the presentation results are visualized and a dashboard that can be intuitively understood is provided, thereby helping the user understand.

[0078] The concern generation unit can analyze the content of the feedback and automatically suggest improvements. In the concern generation unit, for example, a generation AI analyzes the content of the feedback and automatically suggests improvements. For example, improvements are identified based on the content of the feedback. In addition, in the concern generation unit, a generation AI automatically suggests improvements based on the content of the feedback. For example, the content of the feedback is learned and improvements are identified. In addition, in the concern generation unit, a generation AI analyzes the content of the feedback and automatically suggests improvements. For example, improvements are identified based on the content of the feedback. In this way, by analyzing the content of the feedback and automatically suggesting improvements, the accuracy of risk management is improved.

[0079] The concern generation unit can use the emotion estimation function to analyze the emotional tone of the feedback and prioritize analysis of feedback that includes positive emotions. In the concern generation unit, for example, the generation AI analyzes the emotional tone of the feedback and prioritizes analysis of feedback that includes positive emotions. For example, positive feedback is identified. In addition, the concern generation unit uses the emotion estimation function to have the generation AI prioritize analysis of feedback that includes positive emotions. For example, positive feedback related to success is identified. In addition, in the concern generation unit, the generation AI analyzes the emotional tone and prioritizes evaluation of feedback that includes positive emotions. For example, feedback with strong positive emotions is analyzed. In this way, by prioritized analysis of feedback that includes positive emotions, the accuracy of risk management is improved.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The information collection unit monitors the progress of the project in real time and sequentially learns the progress data, thereby enabling dynamic risk assessment. For example, the generation AI monitors the progress of the project in real time and sequentially learns the progress data. Risk assessment can be performed dynamically as the project progresses. The information collection unit also allows the generation AI to perform dynamic risk assessment based on the progress data collected in real time. A warning can be issued for projects whose progress is behind schedule. Furthermore, the information collection unit allows the generation AI to sequentially learn the progress data, improving the accuracy of risk assessment. The risk assessment criteria can be dynamically adjusted based on the progress data. This allows the project progress to be monitored in real time and dynamic risk assessment to be performed, improving the accuracy of risk management.

[0082] The review analysis unit can extract frequently occurring keywords and phrases from review comments and evaluate their impact on the success or failure of a project. For example, the generation AI can extract frequently occurring keywords and phrases from review comments and evaluate their impact on the success or failure of a project. Keywords related to risk can be identified. The review analysis unit also allows the generation AI to identify the factors that will lead to a project's success or failure based on frequently occurring keywords and phrases. Keywords related to success can be extracted. Furthermore, the review analysis unit allows the generation AI to learn the keywords and phrases from review comments and evaluate their impact on the success or failure of a project. Phrases related to failure can be identified. This improves the accuracy of risk management by extracting frequently occurring keywords and phrases and evaluating their impact on the success or failure of a project based on these keywords and phrases.

[0083] The information gathering unit can collect project data from different industries and fields and incorporate knowledge from other fields. For example, the generation AI collects project data from different industries and fields and integrates it with information from similar projects. Project data from the medical field can be applied to projects in the technology field. Furthermore, by incorporating knowledge from other fields, the information gathering unit allows the generation AI to set new standards for risk assessment. Risk assessment can be performed based on success factors from different industries. Furthermore, the information gathering unit allows the generation AI to learn project data from other fields and improve the accuracy of risk assessment. Warnings can be issued based on failure factors from different industries. In this way, the accuracy of risk assessment can be improved by incorporating knowledge from other fields.

[0084] The review analysis unit can automatically translate comments written in different languages ​​and perform multilingual analysis. For example, the generation AI can automatically translate review comments written in different languages ​​and perform multilingual analysis. Comments in English and French can be translated. The review analysis unit also uses the automatic translation function to have the generation AI perform multilingual review comment analysis. Comments in different languages ​​can be analyzed in a unified manner. Furthermore, the review analysis unit has the generation AI perform multilingual review comment analysis and integrate comments in different languages. Analysis can be performed based on the translated comments. This allows comments written in different languages ​​to be automatically translated and analyzed in multiple languages, improving the accuracy of the analysis.

[0085] The concern generation unit can learn from past feedback and improve the accuracy of the concerns it presents. For example, the generation AI learns from past feedback and improves the accuracy of the concerns it presents. The risk assessment criteria can be adjusted based on the past feedback. Furthermore, the concern generation unit can improve the accuracy of the risk assessment by learning the content of the feedback. Furthermore, the concern generation unit can improve the accuracy of the concerns it presents by learning from past feedback. The risk assessment criteria can be adjusted based on the content of the feedback. In this way, the generation AI learns from past feedback and improves the accuracy of the concerns it presents, thereby improving the accuracy of risk management.

[0086] The information collection unit can use the emotion estimation function to collect emotional data from stakeholders in past projects and analyze the impact of emotional fluctuations on project success. For example, the generation AI collects emotional data from stakeholders in past projects and analyzes emotional fluctuations. Emotional fluctuations can be evaluated as the project progresses. The information collection unit also uses the emotion estimation function to identify project success factors based on the emotional data from stakeholders. Projects with a high proportion of positive emotions can be evaluated as successful. Furthermore, the information collection unit has the generation AI learn the emotional data and analyze the impact of emotional fluctuations on project success. A warning can be issued for projects with a high proportion of negative emotions. In this way, collecting emotional data and analyzing the impact of emotional fluctuations on project success improves the accuracy of risk assessment.

[0087] The review analysis unit uses the emotion estimation function to analyze the emotional tone of review comments and prioritize analysis of comments containing negative emotions. For example, the generation AI analyzes the emotional tone of review comments and prioritizes analysis of comments containing negative emotions. Negative comments can be identified. The review analysis unit also uses the emotion estimation function to have the generation AI prioritize analysis of comments containing negative emotions. Negative comments related to risk can be identified. Furthermore, the review analysis unit has the generation AI analyze the emotional tone and prioritize evaluation of comments containing negative emotions. Comments with strong negative emotions can be analyzed. This prioritizes analysis of comments containing negative emotions, improving the accuracy of risk management.

[0088] The concern generation unit can use the emotion estimation function to consider the emotional state of project team members and identify emotional risk factors. For example, the generation AI can monitor the emotional state of project team members in real time and evaluate emotional fluctuations. The impact of emotional fluctuations on project progress can be analyzed. The concern generation unit also uses the emotion estimation function to perform risk assessment based on the emotional data of project team members. It can issue a warning for projects with a high level of negative emotions. Furthermore, the concern generation unit uses the generation AI to collect emotional data in real time and evaluate the impact of emotional fluctuations on project progress. It can evaluate projects with a high level of positive emotions as successful. This improves the accuracy of risk management by considering emotional states and identifying emotional risk factors.

[0089] The concern generation unit uses the emotion estimation function to analyze the emotional tone of the feedback and prioritize analysis of feedback that includes negative emotions. For example, the generation AI analyzes the emotional tone of the feedback and prioritizes analysis of feedback that includes negative emotions. Negative feedback can be identified. Furthermore, the concern generation unit uses the emotion estimation function to have the generation AI prioritize analysis of feedback that includes negative emotions. Negative feedback related to risk can be identified. Furthermore, the concern generation unit uses the emotion estimation function to have the generation AI analyze the emotional tone and prioritize evaluation of feedback that includes negative emotions. Feedback with strong negative emotions can be analyzed. This prioritizes analysis of feedback that includes negative emotions, improving the accuracy of risk management.

[0090] The concern generation unit uses the emotion estimation function to analyze the emotional tone of the feedback and can prioritize analyzing feedback that includes positive emotions. For example, the generation AI analyzes the emotional tone of the feedback and prioritizes analyzing feedback that includes positive emotions. Positive feedback can be identified. The concern generation unit also uses the emotion estimation function to have the generation AI prioritize analyzing feedback that includes positive emotions. Positive feedback associated with success can be identified. Furthermore, the concern generation unit uses the emotion estimation function to have the generation AI analyze the emotional tone and prioritize evaluating feedback that includes positive emotions. Feedback with strong positive emotions can be analyzed. This prioritizes analyzing feedback that includes positive emotions, improving the accuracy of risk management.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The information gathering department collects information on similar past projects. For example, it collects data on pre-implementation conditions, progress during implementation, and results after implementation. The information gathering department can also refer to external databases (for example, industry reports and academic papers) to learn more extensive information. Step 2: The review analysis department analyzes the review comments from managers and specialists based on the information collected by the information collection department. For example, it analyzes comments on project progress and risk warnings. The review analysis department can also analyze the content of related meeting records and emails to understand the context and intent behind the review comments. Step 3: The concern generation unit generates concerns based on the information analyzed by the review analysis unit. For example, when a new project plan is entered, the generation AI references data from similar past projects and review comments to present concerns such as "this plan has risks." The concern generation unit can also compare past successes and failures to identify high-risk elements.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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. [Explanation of symbols]

[0160] 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. An information gathering department that collects information on similar cases in the past, a review analysis unit that analyzes review comments from managers and specialists based on the information collected by the information collection unit; a concern generation unit that generates a concern based on the information analyzed by the review analysis unit. A system characterized by:

2. The information collecting unit Consult external databases to learn more extensive information from industry reports and academic papers 2. The system of claim 1.

3. The information collecting unit Automatically classify factors that contributed to success and failure in past projects and perform risk assessment based on each factor.

2. The system of claim 1.

4. The information collecting unit Collecting emotional data from stakeholders on past projects and analyzing the impact of emotional fluctuations on project success 2. The system of claim 1.

5. The information collecting unit Collect project data from different industries and fields to incorporate knowledge from different fields 2. The system of claim 1.

Images