System

Abstract

An object of the system according to the embodiment is to efficiently collect, store, and summarize information on a project and create a report.SOLUTION: A system includes an information collection part, an information storage part, an information summarization part, and a report creation part. The information collection unit includes a generation AI. The information storage unit stores the information collected by the information collection unit. The information summarization unit summarizes the information stored by the information storage unit. The report creating section creates a report based on the information summarized by the information summarizing section.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 technologies have had the problem of taking a lot of time and effort to collect, store, summarize, and report on project information.

[0005] The system according to the embodiment aims to efficiently collect, store, and summarize information about a project and generate reports. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information storage unit, an information summarization unit, and a report creation unit. The information collection unit is equipped with a generation AI. The information storage unit stores information collected by the information collection unit. The information summarization unit summarizes the information stored by the information storage unit. The report creation unit creates a report based on the information summarized by the information summarization unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect, store, summarize, and generate reports on project information. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A project management system according to an embodiment of the present invention utilizes generative AI to significantly reduce the time required for project managers to prepare documents and perform reporting tasks. This system automatically collects, stores, and summarizes all project-related information, providing report content in a format that conforms to the project. This allows project managers and reporters to perform their work efficiently.

[0029] A project management system according to an embodiment includes an information collection unit, an information storage unit, an information summarization unit, and a report creation unit. The information collection unit is equipped with a generation AI and automatically collects all project-related information, such as project progress, related documents, email correspondence, and meeting minutes. The generation AI receives prompts containing instructions for collecting project-related information, and the generation AI collects the information based on the prompts. The information storage unit automatically stores and organizes the collected information. For example, collected documents and emails are organized into folders by project, allowing necessary information to be quickly retrieved. The information summarization unit summarizes the collected information and provides report content in a formatted format. For example, it extracts key points from meeting minutes and summarizes them concisely. It also summarizes project progress in graphs and tables to provide a visually easy-to-understand report. The report creation unit automatically creates a formatted report based on the summarized information. For example, it can generate reports in various formats, such as project progress reports, meeting minutes, and risk management reports. This allows project management systems to significantly reduce the time project managers spend on document preparation and reporting.

[0030] The information gathering unit can also collect relevant information from social media or industry news. For example, the generation AI collects information related to the project from social media. For example, it analyzes posts on Twitter and LinkedIn to collect trends and opinions related to the project. It also collects the latest information related to the project from industry news sites. For example, it automatically collects news articles about technological trends and market changes. It also integrates the information collected from social media and industry news into the project database to comprehensively grasp the overall information. For example, it reflects related news articles and posts in the progress of the project. In this way, collecting information from social media and industry news can improve the comprehensiveness of the information.

[0031] The information collection unit can implement an algorithm that evaluates the reliability of information and prioritizes the collection of highly reliable information. The information collection unit, for example, implements an algorithm that evaluates the reliability of information collected by the generation AI and prioritizes the collection of highly reliable information. For example, it prioritizes the collection of information from highly reliable sources. In addition, to evaluate the reliability of information, it calculates a reliability score for the information source and prioritizes the collection of highly reliable information. For example, it prioritizes the collection of official websites and expert opinions. In addition, it evaluates the reliability of the information collected by the generation AI in real time and stores the highly reliable information in a database. For example, it filters out unreliable information and collects only highly reliable information. This makes it possible to improve the reliability of the report content by preferentially collecting highly reliable information.

[0032] The information collection unit can simultaneously collect information in different languages ​​and provide information from an international perspective. For example, the generation AI simultaneously collects information in different languages ​​and provides information from an international perspective. For example, it collects information in multiple languages, such as English, French, and Chinese. It also automatically translates information in different languages ​​and provides information related to the project in multiple languages. For example, it translates and collects overseas news articles and technical reports. It also analyzes information sources in different languages ​​to collect information related to the project in order to collect information from an international perspective. For example, it collects minutes and reports of international conferences. This makes it possible to provide information from an international perspective by collecting information in different languages.

[0033] The information collection unit can share information collected between different projects and automatically link mutually related information. For example, the information collection unit shares information collected by the generation AI between different projects and automatically links mutually related information. For example, it links the progress and results of related projects. In addition, a system is built that automatically links mutually related information based on the information shared between different projects. For example, it links common issues and resources. In addition, by sharing information collected by the generation AI between different projects and linking mutually related information, information across the entire project is integrated. For example, it links related documents and emails. This makes it possible to share information between different projects and link mutually related information, thereby integrating information.

[0034] The information storage unit automatically updates information according to the progress of the project, and can always provide the latest information. The information storage unit, for example, automatically updates the information stored by the generation AI according to the progress of the project and provides the latest information. For example, it updates progress reports and meeting minutes in real time. In addition, a system is built that automatically organizes the information to be stored based on the progress of the project and provides the latest information. For example, it automatically changes the folder structure according to the progress. In addition, the information stored by the generation AI is dynamically updated according to the progress of the project and provides the latest information. For example, it automatically organizes documents and emails according to the progress. In this way, by updating information according to the progress of the project, it is possible to always provide the latest information.

[0035] The information storage unit can reorganize the information into an optimal folder structure based on the user's access history. For example, the generation AI analyzes the user's access history and reorganizes it into an optimal folder structure. For example, it prioritizes displaying frequently accessed information. Also, a system is constructed that reorganizes stored information into an optimal folder structure based on the user's access history. For example, it automatically organizes frequently accessed information. Also, the generation AI reorganizes stored information into an optimal folder structure based on the user's access history, allowing necessary information to be quickly retrieved. For example, it automatically creates folders based on the access history. In this way, by reorganizing the folder structure based on the user's access history, it is possible to improve the ease of retrieving information.

[0036] The information storage unit can synchronize information between different devices, making it accessible from anywhere. The information storage unit, for example, builds a system that synchronizes information stored by the generation AI between different devices, making it accessible from anywhere. For example, it synchronizes information between PCs, smartphones, and tablets. By synchronizing information between different devices, users can access the latest information from anywhere. For example, it synchronizes information using cloud storage. It also synchronizes information stored by the generation AI in real time, always providing the latest information between different devices. For example, it automatically updates information between devices. This allows users to access the latest information from anywhere by synchronizing information between different devices.

[0037] The information storage unit can share information stored between different projects and automatically link mutually related information. The information storage unit, for example, builds a system that shares information stored by the generation AI between different projects and automatically links mutually related information. For example, it links common issues and resources. It also automatically links mutually related information based on information shared between different projects. For example, it links related documents and emails. It also shares information stored by the generation AI between different projects and links mutually related information, thereby integrating information across the entire project. For example, it links the progress and results of related projects. This makes it possible to share information between different projects and link mutually related information, thereby integrating information.

[0038] The information summarization unit can generate multiple summaries from different perspectives and allow the user to select one. For example, the information summarization unit uses a generation AI to generate multiple summaries from different perspectives and allow the user to select one. For example, it generates summaries for management and summaries for field staff. It also builds a system that automatically generates summaries from different perspectives and allows the user to select the most appropriate one. For example, it provides summaries from a technical perspective and a business perspective. It also generates information to be summarized by the generation AI from multiple perspectives and allows the user to select one. For example, it summarizes the progress of a project from different perspectives and allows the user to select the most appropriate one. This allows the user to select the most appropriate summary by providing summaries from different perspectives.

[0039] The information summarization unit can provide a summary in the optimal format based on the user's past summarization history. For example, the information summarization unit uses a generation AI to analyze the user's past summarization history and provide a summary in the optimal format. For example, a summary is generated based on the summary format that the user has preferred in the past. Also, a system is constructed that optimizes the summary format based on the user's past summarization history. For example, the past summarization history is analyzed and a summary is provided in the optimal format. Also, the generation AI provides a summary in the optimal format based on the user's past summarization history, allowing the user to efficiently grasp information. For example, the summary format is automatically adjusted based on the past summarization history. As a result, information can be efficiently grasped by providing a summary in the optimal format based on the user's past summarization history.

[0040] The information summarization unit can automatically translate into different languages ​​and provide summaries from an international perspective. For example, the information summarization unit automatically translates the information summarized by the generation AI into different languages ​​and provides summaries from an international perspective. For example, summaries are provided in multiple languages ​​such as English, French, and Chinese. In addition, a system is built to collect feedback from an international perspective based on summaries automatically translated into different languages. For example, the translated summaries are posted on a multilingual platform. In addition, by automatically translating the information summarized by the generation AI into different languages ​​and providing summaries from an international perspective, information sharing from a global perspective is realized. For example, a report is made at an international conference based on summaries in different languages. In this way, summaries from an international perspective can be provided by automatically translating into different languages.

[0041] The information summarization unit can convert summarized information into visual notes or mind maps to make it easier to understand visually. For example, the information summarization unit converts information summarized by the generation AI into visual notes and displays them visually. For example, it shows important points with diagrams and icons. It also converts summarized information into mind map format and visually organizes related keywords and concepts. This allows users to understand the overall picture of the information at a glance. We will also develop tools that automatically generate visual notes and mind maps, allowing users to easily display summaries visually. For example, we will provide a function to visualize summary text with drag and drop. This will allow summarized information to be converted into visual notes or mind maps, making it easier to understand visually.

[0042] The report creation unit can generate multiple reports from different perspectives and allow the user to select from them. For example, the report creation unit allows the generation AI to generate multiple reports from different perspectives and allow the user to select from them. For example, it generates reports for management and reports for field personnel. It also builds a system that automatically generates reports from different perspectives and allows the user to select the most appropriate report. For example, it provides reports from a technical perspective and a business perspective. It also generates reports created by the generation AI from multiple perspectives and allows the user to select from them. For example, it reports the progress of a project from different perspectives and allows the user to select the most appropriate report. This allows the user to select the most appropriate report by providing reports from different perspectives.

[0043] The report creation unit can provide a report in the optimal format based on the user's past report history. In the report creation unit, for example, the generation AI analyzes the user's past report history and provides the report in the optimal format. For example, the report is generated based on the report format that the user has preferred in the past. Also, a system is constructed that optimizes the report format based on the user's past report history. For example, the past report history is analyzed and the report is provided in the optimal format. Also, the generation AI provides the report in the optimal format based on the user's past report history, allowing the user to efficiently understand information. For example, the report format is automatically adjusted based on the past report history. As a result, by providing the report in the optimal format based on the user's past report history, information can be efficiently understood.

[0044] The report creation unit can automatically translate into different languages ​​and provide reports from an international perspective. For example, the report creation unit automatically translates reports created by the generation AI into different languages ​​and provides reports from an international perspective. For example, reports are provided in multiple languages ​​such as English, French, and Chinese. In addition, a system is built to collect feedback from an international perspective based on reports automatically translated into different languages. For example, the translated reports are posted on a multilingual platform. In addition, by automatically translating reports created by the generation AI into different languages ​​and providing reports from an international perspective, information sharing from a global perspective is realized. For example, a report at an international conference is made based on reports in different languages. In this way, reports can be provided from an international perspective by automatically translating them into different languages.

[0045] The report creation unit can convert the created report into a visual note or mind map to make it easier to understand visually. For example, the report creation unit converts the report created by the generation AI into a visual note and displays it visually. For example, it shows important points with diagrams and icons. It also converts the created report into a mind map format to visually organize related keywords and concepts. This allows the overall picture of the information to be understood at a glance. It also develops tools that automatically generate visual notes and mind maps, allowing users to easily display reports visually. For example, it provides a function to visualize reports with drag and drop. This allows the created report to be converted into a visual note or mind map, making it easier to understand visually.

[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 project management system may further include a risk assessment unit. The risk assessment unit automatically assesses risks that may occur during the project and calculates the impact and probability of occurrence of the risks. For example, it analyzes risk patterns based on past project data and predicts the possibility of similar risks occurring. The risk assessment unit can also update the risk assessment in real time in response to changes in the project progress and the external environment. For example, it can monitor external economic indicators and market trends and adjust the risk assessment based on them. Furthermore, the risk assessment unit can notify the project manager of the risk assessment results and propose appropriate risk countermeasures. For example, if the risk increases, it can propose resource reallocation or schedule adjustment. This strengthens project risk management and improves the project success rate.

[0048] The information gathering department can automatically collect patent information and technical literature related to the project. For example, it searches for relevant patent information from patent databases and collects technical information useful for the project. It also collects the latest technical literature from academic paper databases to strengthen the technical support of the project. Furthermore, the information gathering department integrates the collected patent information and technical literature into the project database, making it easily accessible to project members. This strengthens the project's technical foundation and improves the project's success rate.

[0049] The Information Collection Department can automatically collect legal and regulatory information related to a project to strengthen project compliance. For example, it collects the latest legal and regulatory information from relevant legal and regulatory databases to identify laws and regulations that may affect the progress of the project. It also integrates the collected legal and regulatory information into the project database, making it easily accessible to project members. Furthermore, the Information Collection Department can monitor changes in legal and regulatory information in real time and notify the project manager. This strengthens project compliance and reduces the risk of legal and regulatory violations.

[0050] The information repository automatically archives information according to the project's progress, enabling efficient management of past information. For example, after a project is completed, related documents and emails can be archived so they can be used in future projects. Archived information is also stored in an easy-to-search format, allowing necessary information to be quickly retrieved. Furthermore, the information repository periodically reviews archived information and deletes unnecessary information, maintaining database efficiency. This allows for efficient management of past information and maximum utilization of project knowledge assets.

[0051] The information storage unit can automatically back up information according to the progress of a project, ensuring data safety. For example, when an important milestone in a project is reached, it can automatically create backups of related documents and emails. The backed-up information can also be stored in cloud storage, allowing data to be restored in the event of a disaster or system failure. Furthermore, the information storage unit can dynamically adjust the backup schedule according to the progress of the project. For example, if the project progresses at an accelerated pace, it can increase the frequency of backups. This makes it possible to back up data according to the progress of the project, ensuring data safety.

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

[0053] Step 1: The information collection unit is equipped with a generation AI and automatically collects all information related to the project, such as project progress, related documents, email correspondence, and meeting minutes. The input to the generation AI is a prompt containing instructions for collecting information related to the project, and the generation AI collects information based on the prompt. Step 2: The information storage unit automatically stores and organizes the collected information. For example, collected documents and emails are sorted into folders by project, and the necessary information is organized so that it can be retrieved quickly. Step 3: The information summarization section summarizes the collected information and presents the report in a format that fits the report. For example, extracting important points from meeting minutes and summarizing them concisely. Also, by summarizing the progress of the project in graphs and tables, the report is visually easy to understand. Step 4: The report generator automatically creates a report in a format appropriate to the summary information. For example, it can generate reports in a variety of formats, such as project progress reports, meeting minutes, and risk management reports.

[0054] (Example 2) A project management system according to an embodiment of the present invention utilizes generative AI to significantly reduce the time required for project managers to prepare documents and perform reporting tasks. This system automatically collects, stores, and summarizes all project-related information, providing report content in a format that conforms to the project. This allows project managers and reporters to perform their work efficiently.

[0055] A project management system according to an embodiment includes an information collection unit, an information storage unit, an information summarization unit, and a report creation unit. The information collection unit is equipped with a generation AI and automatically collects all project-related information, such as project progress, related documents, email correspondence, and meeting minutes. The generation AI receives prompts containing instructions for collecting project-related information, and the generation AI collects the information based on the prompts. The information storage unit automatically stores and organizes the collected information. For example, collected documents and emails are organized into folders by project, allowing necessary information to be quickly retrieved. The information summarization unit summarizes the collected information and provides report content in a formatted format. For example, it extracts key points from meeting minutes and summarizes them concisely. It also summarizes project progress in graphs and tables to provide a visually easy-to-understand report. The report creation unit automatically creates a formatted report based on the summarized information. For example, it can generate reports in various formats, such as project progress reports, meeting minutes, and risk management reports. This allows project management systems to significantly reduce the time project managers spend on document preparation and reporting.

[0056] The information collection unit can use the emotion estimation function to evaluate the emotional value of information and prioritize collecting positive information. For example, the information collection unit performs emotion analysis on the information collected by the generation AI and prioritizes collecting information with a high positive emotion score. For example, it prioritizes collecting project success stories and positive feedback. It also uses the emotion estimation function to evaluate the emotional value of the information to be collected in real time and prioritize collecting positive information. For example, it extracts positive content from meeting minutes and email exchanges. It also assigns an emotion score to the information collected by the generation AI and prioritizes storing information with a high positive emotion score in the database. For example, it prioritizes collecting project progress and success stories. This allows the quality of report content to be improved by prioritizing the collection of positive information.

[0057] The information gathering unit can also collect relevant information from social media or industry news. For example, the generation AI collects information related to the project from social media. For example, it analyzes posts on Twitter and LinkedIn to collect trends and opinions related to the project. It also collects the latest information related to the project from industry news sites. For example, it automatically collects news articles about technological trends and market changes. It also integrates the information collected from social media and industry news into the project database to comprehensively grasp the overall information. For example, it reflects related news articles and posts in the progress of the project. In this way, collecting information from social media and industry news can improve the comprehensiveness of the information.

[0058] The information collection unit can implement an algorithm that evaluates the reliability of information and prioritizes the collection of highly reliable information. The information collection unit, for example, implements an algorithm that evaluates the reliability of information collected by the generation AI and prioritizes the collection of highly reliable information. For example, it prioritizes the collection of information from highly reliable sources. In addition, to evaluate the reliability of information, it calculates a reliability score for the information source and prioritizes the collection of highly reliable information. For example, it prioritizes the collection of official websites and expert opinions. In addition, it evaluates the reliability of the information collected by the generation AI in real time and stores the highly reliable information in a database. For example, it filters out unreliable information and collects only highly reliable information. This makes it possible to improve the reliability of the report content by preferentially collecting highly reliable information.

[0059] The information collection unit can simultaneously collect information in different languages ​​and provide information from an international perspective. For example, the generation AI simultaneously collects information in different languages ​​and provides information from an international perspective. For example, it collects information in multiple languages, such as English, French, and Chinese. It also automatically translates information in different languages ​​and provides information related to the project in multiple languages. For example, it translates and collects overseas news articles and technical reports. It also analyzes information sources in different languages ​​to collect information related to the project in order to collect information from an international perspective. For example, it collects minutes and reports of international conferences. This makes it possible to provide information from an international perspective by collecting information in different languages.

[0060] The information collection unit can share information collected between different projects and automatically link mutually related information. For example, the information collection unit shares information collected by the generation AI between different projects and automatically links mutually related information. For example, it links the progress and results of related projects. In addition, a system is built that automatically links mutually related information based on the information shared between different projects. For example, it links common issues and resources. In addition, by sharing information collected by the generation AI between different projects and linking mutually related information, information across the entire project is integrated. For example, it links related documents and emails. This makes it possible to share information between different projects and link mutually related information, thereby integrating information.

[0061] The information collection unit monitors the user's emotional response to the collected information in real time, thereby improving the quality of the collected information. The information collection unit, for example, uses an emotion estimation function to monitor the user's emotional response to the collected information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. In addition, a system is constructed that improves the quality of the collected information based on the user's emotional response data. For example, it prioritizes the collection of information with a high number of positive emotional responses. In addition, a system is developed that collects emotion estimation data in real time and dynamically adjusts the quality of the collected information. For example, it adjusts the information to be collected according to changes in the user's emotions. In this way, the quality of the collected information can be improved by monitoring the user's emotional response.

[0062] The information storage unit can use the emotion estimation function to evaluate the emotional value of information and prioritize organizing positive information. For example, the information storage unit performs emotion analysis on the information stored by the generation AI and prioritizes organizing information with a high positive emotion score. For example, it prioritizes organizing success stories and positive feedback. The emotion estimation function can also be used to evaluate the emotional value of the information to be stored in real time and prioritize organizing positive information. For example, it can extract positive content from meeting minutes and email exchanges. The generation AI can also assign an emotion score to the information to be stored and prioritize sorting information with a high positive emotion score into folders. For example, it can prioritize organizing project progress and success stories. In this way, the utility value of information can be improved by prioritizing organizing positive information.

[0063] The information storage unit automatically updates information according to the progress of the project, and can always provide the latest information. The information storage unit, for example, automatically updates the information stored by the generation AI according to the progress of the project and provides the latest information. For example, it updates progress reports and meeting minutes in real time. In addition, a system is built that automatically organizes the information to be stored based on the progress of the project and provides the latest information. For example, it automatically changes the folder structure according to the progress. In addition, the information stored by the generation AI is dynamically updated according to the progress of the project and provides the latest information. For example, it automatically organizes documents and emails according to the progress. In this way, by updating information according to the progress of the project, it is possible to always provide the latest information.

[0064] The information storage unit can reorganize the information into an optimal folder structure based on the user's access history. For example, the generation AI analyzes the user's access history and reorganizes it into an optimal folder structure. For example, it prioritizes displaying frequently accessed information. Also, a system is constructed that reorganizes stored information into an optimal folder structure based on the user's access history. For example, it automatically organizes frequently accessed information. Also, the generation AI reorganizes stored information into an optimal folder structure based on the user's access history, allowing necessary information to be quickly retrieved. For example, it automatically creates folders based on the access history. In this way, by reorganizing the folder structure based on the user's access history, it is possible to improve the ease of retrieving information.

[0065] The information storage unit can synchronize information between different devices, making it accessible from anywhere. The information storage unit, for example, builds a system that synchronizes information stored by the generation AI between different devices, making it accessible from anywhere. For example, it synchronizes information between PCs, smartphones, and tablets. By synchronizing information between different devices, users can access the latest information from anywhere. For example, it synchronizes information using cloud storage. It also synchronizes information stored by the generation AI in real time, always providing the latest information between different devices. For example, it automatically updates information between devices. This allows users to access the latest information from anywhere by synchronizing information between different devices.

[0066] The information storage unit can share information stored between different projects and automatically link mutually related information. The information storage unit, for example, builds a system that shares information stored by the generation AI between different projects and automatically links mutually related information. For example, it links common issues and resources. It also automatically links mutually related information based on information shared between different projects. For example, it links related documents and emails. It also shares information stored by the generation AI between different projects and links mutually related information, thereby integrating information across the entire project. For example, it links the progress and results of related projects. This makes it possible to share information between different projects and link mutually related information, thereby integrating information.

[0067] The information storage unit can analyze the user's emotional response to the stored information and optimize the method for organizing the information. The information storage unit, for example, uses an emotion estimation function to analyze the user's emotional response to the stored information and optimize the method for organizing the information. For example, information with a high number of positive emotional responses is prioritized for organization. Furthermore, a system is constructed that optimizes the method for organizing the stored information based on the user's emotional response data. For example, information with a high emotional score is prioritized for sorting into folders. Furthermore, a system is developed that collects emotion estimation data in real time and dynamically adjusts the method for organizing the stored information. For example, the method for organizing the information is adjusted according to changes in the user's emotions. In this way, the method for organizing the information can be optimized by analyzing the user's emotional responses.

[0068] The information summarization unit can use the emotion estimation function to evaluate the emotional value of the information and emphasize positive elements. For example, the information summarization unit performs emotion analysis on the information to be summarized by the generation AI and emphasizes elements with a high positive emotion score. For example, success stories and positive feedback can be reflected in the summary text. The emotion estimation function can also be used to evaluate the emotional value of the information to be summarized in real time and emphasize positive elements. For example, positive content can be extracted from meeting minutes and email exchanges. The generation AI can also assign an emotion score to the information to be summarized and emphasize elements with a high positive emotion score. For example, project progress and success stories can be reflected in the summary text. This can improve the quality of the summary content by emphasizing positive elements.

[0069] The information summarization unit can generate multiple summaries from different perspectives and allow the user to select one. For example, the information summarization unit uses a generation AI to generate multiple summaries from different perspectives and allow the user to select one. For example, it generates summaries for management and summaries for field staff. It also builds a system that automatically generates summaries from different perspectives and allows the user to select the most appropriate one. For example, it provides summaries from a technical perspective and a business perspective. It also generates information to be summarized by the generation AI from multiple perspectives and allows the user to select one. For example, it summarizes the progress of a project from different perspectives and allows the user to select the most appropriate one. This allows the user to select the most appropriate summary by providing summaries from different perspectives.

[0070] The information summarization unit can provide a summary in the optimal format based on the user's past summarization history. For example, the information summarization unit uses a generation AI to analyze the user's past summarization history and provide a summary in the optimal format. For example, a summary is generated based on the summary format that the user has preferred in the past. Also, a system is constructed that optimizes the summary format based on the user's past summarization history. For example, the past summarization history is analyzed and a summary is provided in the optimal format. Also, the generation AI provides a summary in the optimal format based on the user's past summarization history, allowing the user to efficiently grasp information. For example, the summary format is automatically adjusted based on the past summarization history. As a result, information can be efficiently grasped by providing a summary in the optimal format based on the user's past summarization history.

[0071] The information summarization unit can automatically translate into different languages ​​and provide summaries from an international perspective. For example, the information summarization unit automatically translates the information summarized by the generation AI into different languages ​​and provides summaries from an international perspective. For example, summaries are provided in multiple languages ​​such as English, French, and Chinese. In addition, a system is built to collect feedback from an international perspective based on summaries automatically translated into different languages. For example, the translated summaries are posted on a multilingual platform. In addition, by automatically translating the information summarized by the generation AI into different languages ​​and providing summaries from an international perspective, information sharing from a global perspective is realized. For example, a report is made at an international conference based on summaries in different languages. In this way, summaries from an international perspective can be provided by automatically translating into different languages.

[0072] The information summarization unit can convert summarized information into visual notes or mind maps to make it easier to understand visually. For example, the information summarization unit converts information summarized by the generation AI into visual notes and displays them visually. For example, it shows important points with diagrams and icons. It also converts summarized information into mind map format and visually organizes related keywords and concepts. This allows users to understand the overall picture of the information at a glance. We will also develop tools that automatically generate visual notes and mind maps, allowing users to easily display summaries visually. For example, we will provide a function to visualize summary text with drag and drop. This will allow summarized information to be converted into visual notes or mind maps, making it easier to understand visually.

[0073] The information summarization unit can collect users' emotional reactions to the summarized information and improve the accuracy of the summary based on that information. For example, the information summarization unit collects users' emotional reactions to the summarized information in real time and improves the accuracy of the summary based on that data. For example, it prioritizes the adoption of summary sentences with a high number of positive reactions. It also uses an emotion estimation function to collect feedback on the summarized information and regenerates the summary sentence if there are a high number of negative reactions. It also analyzes the user's emotional reaction data and identifies areas for improvement in the summary sentence based on the results. For example, it makes suggestions to correct parts with low emotional scores. In this way, the accuracy of the summary can be improved by collecting users' emotional reactions.

[0074] The report creation unit can use the emotion estimation function to evaluate the emotional value of the report and emphasize positive elements. For example, the report creation unit performs emotion analysis on the report created by the generation AI and emphasizes elements with a high positive emotion score. For example, success stories and positive feedback can be reflected in the report. The emotion estimation function can also be used to evaluate the emotional value of the report to be created in real time and emphasize positive elements. For example, positive content can be extracted from meeting minutes and email exchanges. An emotion score can also be assigned to the report created by the generation AI and elements with a high positive emotion score can be emphasized. For example, project progress and success stories can be reflected in the report. This can improve the quality of the report by emphasizing positive elements.

[0075] The report creation unit can generate multiple reports from different perspectives and allow the user to select from them. For example, the report creation unit allows the generation AI to generate multiple reports from different perspectives and allow the user to select from them. For example, it generates reports for management and reports for field personnel. It also builds a system that automatically generates reports from different perspectives and allows the user to select the most appropriate report. For example, it provides reports from a technical perspective and a business perspective. It also generates reports created by the generation AI from multiple perspectives and allows the user to select from them. For example, it reports the progress of a project from different perspectives and allows the user to select the most appropriate report. This allows the user to select the most appropriate report by providing reports from different perspectives.

[0076] The report creation unit can provide a report in the optimal format based on the user's past report history. In the report creation unit, for example, the generation AI analyzes the user's past report history and provides the report in the optimal format. For example, the report is generated based on the report format that the user has preferred in the past. Also, a system is constructed that optimizes the report format based on the user's past report history. For example, the past report history is analyzed and the report is provided in the optimal format. Also, the generation AI provides the report in the optimal format based on the user's past report history, allowing the user to efficiently understand information. For example, the report format is automatically adjusted based on the past report history. As a result, by providing the report in the optimal format based on the user's past report history, information can be efficiently understood.

[0077] The report creation unit can automatically translate into different languages ​​and provide reports from an international perspective. For example, the report creation unit automatically translates reports created by the generation AI into different languages ​​and provides reports from an international perspective. For example, reports are provided in multiple languages ​​such as English, French, and Chinese. In addition, a system is built to collect feedback from an international perspective based on reports automatically translated into different languages. For example, the translated reports are posted on a multilingual platform. In addition, by automatically translating reports created by the generation AI into different languages ​​and providing reports from an international perspective, information sharing from a global perspective is realized. For example, a report at an international conference is made based on reports in different languages. In this way, reports can be provided from an international perspective by automatically translating them into different languages.

[0078] The report creation unit can convert the created report into a visual note or mind map to make it easier to understand visually. For example, the report creation unit converts the report created by the generation AI into a visual note and displays it visually. For example, it shows important points with diagrams and icons. It also converts the created report into a mind map format to visually organize related keywords and concepts. This allows the overall picture of the information to be understood at a glance. It also develops tools that automatically generate visual notes and mind maps, allowing users to easily display reports visually. For example, it provides a function to visualize reports with drag and drop. This allows the created report to be converted into a visual note or mind map, making it easier to understand visually.

[0079] The report creation unit can collect users' emotional reactions to the created report and improve the accuracy of the report based on that. The report creation unit, for example, collects users' emotional reactions to the created report in real time and improves the accuracy of the report based on that data. For example, it prioritizes the adoption of reports with a high number of positive reactions. It also uses an emotion estimation function to collect feedback on the created report and regenerates the report if there are a high number of negative reactions. It also analyzes the user's emotional reaction data and identifies areas for improvement in the report based on the results. For example, it makes suggestions to correct parts with low emotional scores. In this way, the accuracy of the report can be improved by collecting users' emotional reactions.

[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 project management system may further include a risk assessment unit. The risk assessment unit automatically assesses risks that may occur during the project and calculates the impact and probability of occurrence of the risks. For example, it analyzes risk patterns based on past project data and predicts the possibility of similar risks occurring. The risk assessment unit can also update the risk assessment in real time in response to changes in the project progress and the external environment. For example, it can monitor external economic indicators and market trends and adjust the risk assessment based on them. Furthermore, the risk assessment unit can notify the project manager of the risk assessment results and propose appropriate risk countermeasures. For example, if the risk increases, it can propose resource reallocation or schedule adjustment. This strengthens project risk management and improves the project success rate.

[0082] The information collection unit can estimate the user's emotions and adjust the method of collecting information based on the estimated emotions. For example, if the user is feeling stressed, the frequency of information collection can be reduced to reduce the user's burden. Also, if the user is excited, the frequency of information collection can be increased to keep the user engaged. Furthermore, the information collection unit can adjust the type of information to be collected according to the user's emotions. For example, if the user is feeling anxious, information that gives a sense of security can be preferentially collected. This makes it possible to collect information according to the user's emotions, thereby improving user satisfaction.

[0083] The information gathering department can automatically collect patent information and technical literature related to the project. For example, it searches for relevant patent information from patent databases and collects technical information useful for the project. It also collects the latest technical literature from academic paper databases to strengthen the technical support of the project. Furthermore, the information gathering department integrates the collected patent information and technical literature into the project database, making it easily accessible to project members. This strengthens the project's technical foundation and improves the project's success rate.

[0084] The information collection unit can estimate the user's emotions and adjust the reliability evaluation of information based on the estimated emotions. For example, if the user is feeling anxious, highly reliable information is preferentially collected to increase the user's sense of security. Also, if the user is excited, less reliable information is collected to continue to attract the user's interest. Furthermore, the information collection unit can dynamically adjust the reliability evaluation criteria according to the user's emotions. For example, if the user is relaxed, the reliability evaluation criteria are relaxed and a wider range of information is collected. This makes it possible to evaluate reliability according to the user's emotions, thereby improving the quality of information collection.

[0085] The Information Collection Department can automatically collect legal and regulatory information related to a project to strengthen project compliance. For example, it collects the latest legal and regulatory information from relevant legal and regulatory databases to identify laws and regulations that may affect the progress of the project. It also integrates the collected legal and regulatory information into the project database, making it easily accessible to project members. Furthermore, the Information Collection Department can monitor changes in legal and regulatory information in real time and notify the project manager. This strengthens project compliance and reduces the risk of legal and regulatory violations.

[0086] The information collection unit can estimate the user's emotions and adjust the method of information sharing between different projects based on the estimated emotions. For example, if the user is feeling stressed, the frequency of information sharing can be reduced to reduce the user's burden. Also, if the user is excited, the frequency of information sharing can be increased to keep the user engaged. Furthermore, the information collection unit can adjust the type of information to be shared based on the user's emotions. For example, if the user is feeling anxious, information that gives a sense of security can be shared preferentially. This makes it possible to share information according to the user's emotions, thereby improving user satisfaction.

[0087] The information repository automatically archives information according to the project's progress, enabling efficient management of past information. For example, after a project is completed, related documents and emails can be archived so they can be used in future projects. Archived information is also stored in an easy-to-search format, allowing necessary information to be quickly retrieved. Furthermore, the information repository periodically reviews archived information and deletes unnecessary information, maintaining database efficiency. This allows for efficient management of past information and maximum utilization of project knowledge assets.

[0088] The information storage unit can estimate the user's emotions and adjust the way information is organized based on the estimated emotions. For example, if the user is feeling stressed, the information organization can be simplified to reduce the user's burden. Also, if the user is excited, the information organization can be detailed to keep the user interested. Furthermore, the information storage unit can dynamically adjust the folder structure according to the user's emotions. For example, if the user is relaxed, the folder structure can be flexibly changed to improve information accessibility. This makes it possible to organize information according to the user's emotions, thereby improving the usefulness of the information.

[0089] The information storage unit can automatically back up information according to the progress of a project, ensuring data safety. For example, when an important milestone in a project is reached, it can automatically create backups of related documents and emails. The backed-up information can also be stored in cloud storage, allowing data to be restored in the event of a disaster or system failure. Furthermore, the information storage unit can dynamically adjust the backup schedule according to the progress of the project. For example, if the project progresses at an accelerated pace, it can increase the frequency of backups. This makes it possible to back up data according to the progress of the project, ensuring data safety.

[0090] The information storage unit can estimate the user's emotions and adjust access permissions to information based on the estimated emotions. For example, if the user is feeling stressed, the access permissions can be relaxed to allow quick access to necessary information. Alternatively, if the user is excited, the access permissions can be tightened to enhance information security. Furthermore, the information storage unit can dynamically adjust the access permission settings according to the user's emotions. For example, if the user is relaxed, the access permissions can be flexibly changed to improve the usefulness of the information. This makes it possible to adjust access permissions according to the user's emotions, thereby achieving both information security and convenience.

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

[0092] Step 1: The information collection unit is equipped with a generation AI and automatically collects all information related to the project, such as project progress, related documents, email correspondence, and meeting minutes. The input to the generation AI is a prompt containing instructions for collecting information related to the project, and the generation AI collects information based on the prompt. Step 2: The information storage unit automatically stores and organizes the collected information. For example, collected documents and emails are sorted into folders by project, and the necessary information is organized so that it can be retrieved quickly. Step 3: The information summarization section summarizes the collected information and presents the report in a format that fits the report. For example, extracting important points from meeting minutes and summarizing them concisely. Also, by summarizing the progress of the project in graphs and tables, the report is visually easy to understand. Step 4: The report generator automatically creates a report in a format appropriate to the summary information. For example, it can generate reports in a variety of formats, such as project progress reports, meeting minutes, and risk management reports.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 unit equipped with a generative AI, an information storage unit that stores the information collected by the information collection unit; an information summarizing unit that summarizes the information stored by the information storage unit; a report creation unit that creates a report based on the information summarized by the information summary unit. A system characterized by:

2. The information collecting unit Evaluate the emotional value of the information and prioritize collecting positive information 2. The system of claim 1.

3. The information collecting unit Collecting information in different languages ​​simultaneously to provide information from an international perspective 2. The system of claim 1.

4. The information storage unit Evaluate the emotional value of the information and prioritize positive information 2. The system of claim 1.

5. The information storage unit Analyzing the user's emotional response to the stored information and optimizing how the information is organized 2. The system of claim 1.

6. The information summarizing unit Evaluate the emotional value of the information and highlight the positive aspects 2. The system of claim 1.

7. The information summarizing unit Collecting a user's emotional response to the summarized information and improving the accuracy of the summary based thereon.

2. The system of claim 1.

8. The report creation unit Evaluate the emotional value of the report and highlight the positive aspects 2. The system of claim 1.

Images