system

Abstract

The system according to the embodiment aims to efficiently summarize the content of employee communications and documents and register them in a human resources visualization tool. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, and a registration unit. The collection unit collects the contents of employees' emails or communication tools, as well as the contents of documents. The analysis unit analyzes the information collected by the collection unit, extracts important points, and summarizes them. The registration unit registers the information summarized by the analysis unit in a human resources visualization tool.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of efficiently summarizing employee communications and document content and registering it in a human resources visualization tool is time-consuming and leaves room for improvement.

[0005] The system according to the embodiment aims to efficiently summarize the content of employee communications and documents and register them in a human resources visualization tool. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a registration unit. The collection unit collects the contents of employees' emails or communication tools, and the contents of documents. The analysis unit analyzes the information collected by the collection unit, extracts important points, and summarizes them. The registration unit registers the information summarized by the analysis unit in the human resources visualization tool. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently summarize the content of employee communications and documents and register them in a human resources visualization tool. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The human resource visualization system according to an embodiment of the present invention inputs the contents of employees' emails, communication tools, and documents into a generation AI, which then automatically registers a summary of the input data in a human resource visualization tool. This human resource visualization system collects the contents of employees' emails, communication tools, and documents, inputs them into a generation AI for analysis and summarization, and automatically registers the summarized information in the human resource visualization tool. This allows for efficient collection and registration of accurate information about each employee, achieving effortless human resource visualization. For example, the contents of employees' emails, communication tools, and documents are collected. This requires a mechanism for automatically acquiring information from various data sources, such as email servers, chat tools, and document management systems. The collected information is then input into a generation AI. The generation AI analyzes the collected information and extracts and summarizes key points. For example, it can extract and summarize project progress and issues from email content. The summarized information is automatically registered in the human resource visualization tool. The information is then registered in association with each employee's profile, skill set, work history, and other information. This allows accurate information about each employee to be collected and registered efficiently. This system makes it possible to visualize human resources without much effort. For example, employee skills and work history are automatically updated, allowing managers to make appropriate personnel assignments and evaluations based on the latest information. Employees themselves can also check their own skills and work history, which can be useful for self-evaluation and career planning. In this way, the human resources visualization system can efficiently collect, analyze, and register employee information, making it possible to visualize human resources.

[0029] The human resource visualization system according to the embodiment includes a collection unit, an analysis unit, and a registration unit. The collection unit collects the contents of employees' emails or communication tools and the contents of documents. The collection unit can acquire information from, for example, an email server, a chat tool, or a document management system. For example, the collection unit can acquire email contents from an email server, chat contents from a chat tool, and document contents from a document management system. The analysis unit uses a generation AI to analyze the collected information, extract important points, and summarize them. For example, the analysis unit can use a generation AI to extract and summarize project progress and issues from email contents. The analysis unit can also use a generation AI to extract and summarize important conversation points from chat contents. The analysis unit can also use a generation AI to extract and summarize important information from document contents. For example, the generation AI can use a text generation AI (e.g., GPT-4 (registered trademark)) to analyze email contents, extract important points, and summarize them. The generation AI can also use BERT (Bidirectional Encoder Representations from Transformers) to analyze chat content, extract important conversational key points, and summarize them. The generation AI can also use multimodal generation AI to analyze document content, extract important information, and summarize it. The registration unit registers the information summarized by the analysis unit in the talent visualization tool. The registration unit can, for example, register the summarized information in association with each employee's profile, skill set, and work history. For example, the registration unit can register the summarized information in association with the employee's work history, qualifications, and project experience. The registration unit can also register the summarized information in association with the employee's skill set. The registration unit can also register the summarized information in association with the employee's work history. As a result, the talent visualization system according to the embodiment can efficiently collect, analyze, and register employee information, thereby achieving talent visualization.

[0030] The collection unit can acquire information from a mail server, a chat tool, or a document management system. The collection unit can, for example, acquire email content from a mail server. For example, the collection unit can acquire email content from a specific mail provider (e.g., Gmail). The collection unit can also acquire chat content from a chat tool. For example, the collection unit can acquire chat content from a specific chat app (e.g., Slack (registered trademark)). The collection unit can also acquire document content from a document management system. For example, the collection unit can acquire document content from a specific document management system (e.g., Google (registered trademark) Drive). This enables the collection unit to collect information from various data sources, thereby enabling comprehensive information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input email content acquired from a mail server into a generation AI, causing the generation AI to analyze the email content.

[0031] The analysis unit can analyze the collected information using the generation AI, extract important points, and summarize them. For example, the analysis unit can use the generation AI to analyze the content of collected emails, extract important points, and summarize them. For example, the analysis unit can use the generation AI to extract and summarize project progress and issues from the content of emails. The analysis unit can also use the generation AI to analyze the content of collected chat messages, extract important conversational key points, and summarize them. For example, the analysis unit can use the generation AI to extract and summarize important conversational key points from the content of chat messages. The analysis unit can also use the generation AI to analyze the content of collected documents, extract important information, and summarize them. For example, the analysis unit can use the generation AI to extract and summarize important information from the content of documents. As a result, the analysis unit uses the generation AI to improve the accuracy of information analysis and summarization. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected information into the generation AI, which can then analyze and summarize the information.

[0032] The registration unit can register the summarized information in the human resources visualization tool by associating it with each employee's profile, skill set, and work history. The registration unit can, for example, register the summarized information by associating it with each employee's profile, skill set, and work history. For example, the registration unit can register the summarized information by associating it with the employee's work history, qualifications, and project experience. The registration unit can also register the summarized information by associating it with the employee's skill set. The registration unit can also register the summarized information by associating it with the employee's work history. In this way, the registration unit can accurately associate and register employee information, thereby improving the accuracy of visualization. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the registration unit can input the summarized information into a generation AI, which can then associate the information.

[0033] Furthermore, the human resource visualization system includes a filtering unit that filters collected information to protect privacy. The filtering unit can, for example, anonymize personal information from the collected information. For example, the filtering unit can anonymize personal names and email addresses from the content of collected emails. The filtering unit can also anonymize personal names and chat IDs from the content of collected chats. The filtering unit can also anonymize personal information from the content of collected documents. For example, the filtering unit can anonymize personal names and addresses from the content of collected documents. This allows the filtering unit to collect, analyze, and register information while ensuring privacy protection. Some or all of the above-mentioned processing in the filtering unit may be performed using, or without, a generation AI. For example, the filtering unit can input collected information to a generation AI and have the generation AI anonymize personal information.

[0034] The human resource visualization system further includes a classification unit that classifies the summarized information. The classification unit can, for example, classify the summarized information by category. For example, the classification unit can classify the summarized information into categories such as project management, work history, and skill set. The classification unit can also classify the summarized information by importance. For example, the classification unit can classify the summarized information into high importance, medium importance, and low importance. The classification unit can also classify the summarized information chronologically. For example, the classification unit can classify the summarized information by date. This allows the classification unit to classify the information, making it easier to organize and search for the information. Some or all of the above-described processing in the classification unit may be performed using, or without, a generation AI. For example, the classification unit can input the summarized information to a generation AI and have the generation AI classify the information.

[0035] Furthermore, the human resource visualization system includes an association unit that associates the summarized information with each employee's profile, skill set, and work history. The association unit can, for example, associate the summarized information with each employee's profile, skill set, and work history. For example, the association unit can associate the summarized information with the employee's work history, qualifications, and project experience. The association unit can also associate the summarized information with the employee's skill set. The association unit can also associate the summarized information with the employee's work history. In this way, the association unit accurately associates information, thereby improving the visualization of employee skills and work history. Some or all of the above-described processing in the association unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the association unit can input summarized information into a generation AI and have the generation AI associate the information.

[0036] The collection unit can optimize the access frequency of each data source and improve information collection efficiency. For example, the collection unit can increase the information collection frequency from an email server during business hours and decrease it during non-business hours. For example, the collection unit can collect information from an email server once per hour during business hours and once per day during non-business hours. The collection unit can also collect information from a chat tool in real time to immediately obtain important messages. For example, the collection unit can always collect information from a chat tool in real time and immediately obtain important messages when they are sent. The collection unit can also increase the information collection frequency from a document management system for folders that are frequently updated. For example, the collection unit can collect information from specific folders in the document management system once per hour and from other folders once per day. This allows the collection unit to optimize the access frequency of each data source and improve information collection efficiency. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input the access frequency of each data source into the generation AI, and have the generation AI optimize the access frequency.

[0037] The collection unit can set priorities based on the importance of information and prioritize collecting important information. For example, the collection unit can prioritize collecting emails related to project progress. For example, the collection unit can prioritize collecting emails containing keywords related to project progress (e.g., "progress," "task," "completed," etc.). The collection unit can also prioritize collecting minutes of important meetings. For example, the collection unit can prioritize collecting documents containing keywords such as "meeting," "minutes," and "decisions." The collection unit can also prioritize collecting urgent chat messages. For example, the collection unit can prioritize collecting chat messages containing keywords such as "urgent," "urgent," and "important." This enables the collection unit to set priorities based on the importance of information and prioritize collecting important information, thereby enabling efficient information management. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the importance of information into the generation AI and have the generation AI set the priority of the information.

[0038] The collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. For example, when the user is in the office, the collection unit can prioritize collecting office-related information. For example, the collection unit can acquire the user's geographical location information from GPS data, and prioritize collecting office-related information when the user is in the office. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting information related to the business trip destination. For example, the collection unit can acquire the user's geographical location information from an IP address, and prioritize collecting information related to the business trip destination when the user is on a business trip. Furthermore, the collection unit can prioritize collecting information related to remote work when the user is at home. For example, the collection unit can acquire the user's geographical location information from Wi-Fi connection information, and prioritize collecting information related to remote work when the user is at home. As a result, the collection unit collects highly relevant information based on the user's geographical location information, thereby improving the accuracy of the information. Some or all of the above-described processing by the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, causing the generation AI to collect highly relevant information.

[0039] The collection unit can analyze the user's social media activities and collect related information. The collection unit can, for example, collect information related to a project shared by the user on social media. For example, the collection unit can analyze the user's social media activities and collect information related to the project. The collection unit can also collect information related to an event mentioned by the user on social media. For example, the collection unit can analyze the user's social media activities and collect information related to the event. The collection unit can also collect industry news that the user follows on social media. For example, the collection unit can analyze the user's social media activities and collect the industry news that the user follows. In this way, the collection unit collects related information based on the social media activities, thereby improving the comprehensiveness of the information. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can analyze information about important projects in detail. For example, the analysis unit can analyze information about project progress and issues in detail. The analysis unit can also analyze information about daily tasks concisely. For example, the analysis unit can briefly analyze information about daily tasks and extract key points. The analysis unit can also quickly analyze information about urgent issues. For example, the analysis unit can quickly analyze information about urgent issues and extract information that requires immediate action. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the information, enabling efficient information analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the information to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply an algorithm for analyzing progress to information related to project management. For example, the analysis unit can use an algorithm for analyzing project progress to perform a detailed analysis of the project progress. The analysis unit can also apply an algorithm for analyzing performance to information related to personnel evaluation. For example, the analysis unit can use an algorithm for analyzing information related to personnel evaluation to perform a detailed analysis of employee performance. The analysis unit can also apply an algorithm for analyzing customer satisfaction to information related to customer service. For example, the analysis unit can perform a detailed analysis of customer satisfaction using an algorithm for analyzing information related to customer service. In this way, the analysis unit can apply an analysis algorithm according to the category of information, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit can input the category of information into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the time of information submission. The analysis unit can, for example, prioritize analysis of information related to urgent issues. For example, the analysis unit can obtain the time of information submission from a timestamp and prioritize analysis of information related to urgent issues. The analysis unit can also give second priority to information related to periodic reports. For example, the analysis unit can obtain the time of information submission from the submission date and time and prioritize information related to periodic reports. The analysis unit can also analyze information related to past data last. For example, the analysis unit can obtain the time of information submission from the submission date and time and analyze information related to past data last. In this way, the analysis unit can determine the priority of analysis based on the time of information submission, thereby quickly analyzing information with high urgency. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the time of information submission to the generation AI and have the generation AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit can, for example, prioritize analysis of information related to the progress of a project. For example, the analysis unit can evaluate the relevance of information based on common keywords and prioritize analysis of information related to the progress of the project. The analysis unit can also secondarily prioritize information related to the performance of team members. For example, the analysis unit can evaluate the relevance of information based on related topics and secondarily prioritize information related to the performance of team members. The analysis unit can also analyze information related to customer service last. For example, the analysis unit can evaluate the relevance of information based on common keywords and analyze information related to customer service last. This enables the analysis unit to adjust the order of analysis based on the relevance of information, thereby enabling efficient information analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of information to the generation AI and have the generation AI adjust the order of analysis.

[0044] The registration unit can adjust the level of detail of the registration based on the importance of the information. For example, the registration unit can register information about important projects in detail. For example, the registration unit can register information about the progress of projects and issues in detail. The registration unit can also register information about daily tasks in a concise manner. For example, the registration unit can concisely register information about daily tasks and extract key points. The registration unit can also quickly register information about urgent issues. For example, the registration unit can quickly register information about urgent issues and extract information that requires immediate action. This allows the registration unit to adjust the level of detail of the registration according to the importance of the information, enabling efficient information registration. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input the importance of the information to the generation AI and have the generation AI adjust the level of detail of the registration.

[0045] The registration unit can apply different registration algorithms depending on the category of information. For example, the registration unit can apply an algorithm for registering progress to information related to project management. For example, the registration unit can register the project progress in detail using an algorithm for registering the project progress. The registration unit can also apply an algorithm for registering performance to information related to personnel evaluation. For example, the registration unit can register employee performance in detail using an algorithm for registering information related to personnel evaluation. The registration unit can also apply an algorithm for registering customer satisfaction to information related to customer service. For example, the registration unit can register customer satisfaction in detail using an algorithm for registering information related to customer service. In this way, the registration unit can apply a registration algorithm according to the category of information, thereby improving the accuracy of registration. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, a generation AI. For example, the registration unit can input the category of information to the generation AI and cause the generation AI to apply an appropriate registration algorithm.

[0046] The registration unit can adjust the order of registration based on the time of submission of information. The registration unit can, for example, prioritize registration of information related to urgent issues. For example, the registration unit can obtain the time of submission of information from a timestamp and prioritize registration of information related to urgent issues. The registration unit can also prioritize information related to periodic reports. For example, the registration unit can obtain the time of submission of information from the submission date and time and prioritize information related to periodic reports. The registration unit can also register information related to past data last. For example, the registration unit can obtain the time of submission of information from the submission date and time and register information related to past data last. In this way, the registration unit can adjust the order of registration based on the time of submission of information, thereby quickly registering information with high urgency. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input the time of submission of information to the generation AI and have the generation AI adjust the order of registration.

[0047] The registration unit can adjust the registration method based on the relevance of the information. For example, the registration unit can prioritize registering information related to the progress of a project. For example, the registration unit can evaluate the relevance of the information based on common keywords and prioritize registering information related to the progress of the project. The registration unit can also prioritize information related to the performance of team members. For example, the registration unit can evaluate the relevance of the information based on related topics and prioritize information related to the performance of team members. The registration unit can also register information related to customer correspondence last. For example, the registration unit can evaluate the relevance of the information based on common keywords and register information related to customer correspondence last. This allows the registration unit to adjust the registration method based on the relevance of the information, thereby enabling efficient information registration. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input the relevance of the information to the generation AI and cause the generation AI to adjust the registration method.

[0048] The filtering unit can adjust the level of filtering detail based on the importance of the information. For example, the filtering unit can filter information related to important projects in detail. For example, the filtering unit can filter information related to project progress and issues in detail. The filtering unit can also briefly filter information related to daily tasks. For example, the filtering unit can briefly filter information related to daily tasks to extract key points. The filtering unit can also quickly filter information related to urgent issues. For example, the filtering unit can quickly filter information related to urgent issues to extract information that requires immediate action. This enables the filtering unit to adjust the level of filtering detail according to the importance of the information, thereby enabling efficient information filtering. Some or all of the above-mentioned processing in the filtering unit may be performed using, or without, a generation AI. For example, the filtering unit can input the importance of the information to the generation AI and have the generation AI adjust the level of filtering detail.

[0049] The filtering unit can adjust the filtering order based on the time when the information was submitted. The filtering unit can, for example, prioritize filtering of information related to urgent issues. For example, the filtering unit can obtain the time when the information was submitted from a timestamp and prioritize filtering of information related to urgent issues. The filtering unit can also prioritize information related to periodic reports. For example, the filtering unit can obtain the time when the information was submitted from the submission date and time and prioritize information related to periodic reports. The filtering unit can also filter information related to past data last. For example, the filtering unit can obtain the time when the information was submitted from the submission date and time and filter information related to past data last. In this way, the filtering unit can adjust the filtering order based on the time when the information was submitted, thereby quickly filtering out information with high urgency. Some or all of the above-described processing in the filtering unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the filtering unit can input the time when the information was submitted to the generation AI and have the generation AI adjust the filtering order.

[0050] The classification unit can adjust the level of detail of the classification based on the importance of the information. For example, the classification unit can classify information related to important projects in detail. For example, the classification unit can classify information related to project progress and issues in detail. The classification unit can also briefly classify information related to daily tasks. For example, the classification unit can briefly classify information related to daily tasks and extract key points. The classification unit can also quickly classify information related to urgent issues. For example, the classification unit can quickly classify information related to urgent issues and extract information that requires immediate action. This enables the classification unit to efficiently classify information by adjusting the level of detail of the classification according to the importance of the information. Some or all of the above-mentioned processing in the classification unit may be performed using, or without, a generation AI. For example, the classification unit can input the importance of the information to the generation AI and have the generation AI adjust the level of detail of the classification.

[0051] The classification unit can adjust the order of classification based on the time of submission of the information. The classification unit can, for example, prioritize classification of information related to urgent issues. For example, the classification unit can obtain the time of submission of the information from a timestamp and prioritize classification of information related to urgent issues. The classification unit can also prioritize information related to periodic reports. For example, the classification unit can obtain the time of submission of the information from the submission date and time and prioritize information related to periodic reports. The classification unit can also classify information related to past data last. For example, the classification unit can obtain the time of submission of the information from the submission date and time and classify information related to past data last. In this way, the classification unit can quickly classify information with high urgency by adjusting the order of classification based on the time of submission of the information. Some or all of the above-described processing in the classification unit may be performed using, or without, a generation AI. For example, the classification unit can input the time of submission of the information to the generation AI and have the generation AI adjust the order of classification.

[0052] The association unit can adjust the level of detail of the association based on the importance of the information. For example, the association unit can associate information about important projects in detail. For example, the association unit can associate information about project progress and issues in detail. The association unit can also associate information about daily tasks in a concise manner. For example, the association unit can concisely associate information about daily tasks and extract key points. The association unit can also quickly associate information about urgent issues. For example, the association unit can quickly associate information about urgent issues and extract information that requires immediate action. This enables the association unit to adjust the level of detail of the association according to the importance of the information, thereby enabling efficient information association. Some or all of the above-mentioned processing in the association unit may be performed using, or without, a generation AI. For example, the association unit can input the importance of the information to the generation AI and have the generation AI adjust the level of detail of the association.

[0053] The associating unit can adjust the order of association based on the time of submission of the information. The associating unit can, for example, prioritize association of information related to urgent issues. For example, the associating unit can obtain the time of submission of the information from a timestamp and prioritize association of information related to urgent issues. The associating unit can also prioritize information related to periodic reports. For example, the associating unit can obtain the time of submission of the information from the submission date and time and prioritize information related to periodic reports. The associating unit can also associate information related to past data last. For example, the associating unit can obtain the time of submission of the information from the submission date and time and associate information related to past data last. In this way, the associating unit can adjust the order of association based on the time of submission of the information, thereby quickly associating information with high urgency. Some or all of the above-described processing in the associating unit may be performed using, or without, a generation AI. For example, the associating unit can input the time of submission of the information into the generation AI and have the generation AI adjust the order of association.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The collection unit can collect biometric data of the user and adjust the frequency of information collection based on the user's health condition. For example, the collection unit can measure the user's heart rate and blood pressure using a sensor, and reduce the collection frequency if the user's health condition is poor, thereby reducing the burden on the user. The collection unit can also monitor the user's sleep patterns and reduce the collection frequency if the user is not getting enough sleep. Furthermore, the collection unit can track the user's exercise volume and reduce the collection frequency if the user is not exercising enough, thereby promoting health. In this way, the collection unit can reduce the burden on the user and support their health by adjusting the frequency of information collection according to the user's health condition.

[0056] The analysis unit can learn the user's past behavioral patterns and determine analysis priorities based on predicted behavior. For example, the analysis unit can prioritize analysis of information that the user has frequently accessed in the past. The analysis unit can also prioritize analysis of information related to work that the user performs during a specific time period. Furthermore, the analysis unit can prioritize analysis of information related to a specific project during a period when the user is concentrating on that project. This allows the analysis unit to determine analysis priorities based on the user's behavioral patterns, enabling efficient information analysis.

[0057] The registration unit can adjust the timing of information registration taking into account the user's schedule. For example, the registration unit can refrain from registering information when the user is in a meeting or concentrating on a task, and register information during free time. The registration unit can also temporarily suspend information registration while the user is on vacation, and register all information at once after the user returns. Furthermore, the registration unit can register information after the user has finished work, thereby reducing the burden on the user during work hours. In this way, the registration unit can reduce the burden on the user by adjusting the timing of information registration according to the user's schedule.

[0058] The filtering unit can adjust the filtering criteria based on the user's job title and job content. For example, the filtering unit can prioritize filtering important management information for managers, and prioritize filtering information directly related to their work for general employees. The filtering unit can also prioritize filtering technical information for engineers, and prioritize filtering customer information for sales users. Furthermore, the filtering unit can prioritize filtering project progress information for project leaders. This allows the filtering unit to provide appropriate information by adjusting the filtering criteria according to the user's job title and job content.

[0059] The classification unit can adjust the classification criteria based on the user's business goals. For example, the classification unit can prioritize classification of information related to the business goals set by the user. The classification unit can also adjust the level of detail of classification according to the user's level of goal achievement. Furthermore, the classification unit can evaluate the importance of information based on the user's business goals and prioritize classification of important information. This allows the classification unit to adjust the classification criteria according to the user's business goals, enabling efficient information management.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection department collects the contents of employees' emails or communication tools, as well as the contents of documents. For example, the collection department can obtain email contents from an email server, chat contents from a chat tool, and document contents from a document management system. Step 2: The analysis unit uses the generation AI to analyze the collected information, extract key points, and summarize it. For example, the generation AI can use text generation AI (e.g., GPT-4) to analyze the content of emails, extract key points, and summarize them. It can also use BERT (Bidirectional Encoder Representations from Transformers) to analyze chat content, extract important conversational key points, and summarize them. Furthermore, it can use multimodal generation AI to analyze the content of documents, extract key information, and summarize them. Step 3: The registration unit registers the information summarized by the analysis unit in the human resources visualization tool. For example, the summarized information can be registered in association with each employee's profile, skill set, and work history. This allows information to be registered in association with an employee's work history, qualifications, project experience, skill set, and work history.

[0062] (Example 2) The human resource visualization system according to an embodiment of the present invention inputs the contents of employees' emails, communication tools, and documents into a generation AI, which then automatically registers a summary of the input data in a human resource visualization tool. This human resource visualization system collects the contents of employees' emails, communication tools, and documents, inputs them into a generation AI for analysis and summarization, and automatically registers the summarized information in the human resource visualization tool. This allows for efficient collection and registration of accurate information about each employee, achieving effortless human resource visualization. For example, the contents of employees' emails, communication tools, and documents are collected. This requires a mechanism for automatically acquiring information from various data sources, such as email servers, chat tools, and document management systems. The collected information is then input into a generation AI. The generation AI analyzes the collected information and extracts and summarizes key points. For example, it can extract and summarize project progress and issues from email content. The summarized information is automatically registered in the human resource visualization tool. The information is then registered in association with each employee's profile, skill set, work history, and other information. This allows accurate information about each employee to be collected and registered efficiently. This system makes it possible to visualize human resources without much effort. For example, employee skills and work history are automatically updated, allowing managers to make appropriate personnel assignments and evaluations based on the latest information. Employees themselves can also check their own skills and work history, which can be useful for self-evaluation and career planning. In this way, the human resources visualization system can efficiently collect, analyze, and register employee information, making it possible to visualize human resources.

[0063] The human resource visualization system according to the embodiment includes a collection unit, an analysis unit, and a registration unit. The collection unit collects the contents of employees' emails or communication tools, and the contents of documents. The collection unit can acquire information from, for example, an email server, a chat tool, or a document management system. For example, the collection unit can acquire email contents from an email server, chat contents from a chat tool, and document contents from a document management system. The analysis unit uses a generation AI to analyze the collected information, extract important points, and summarize them. For example, the analysis unit can use a generation AI to extract and summarize project progress and issues from email contents. The analysis unit can also use a generation AI to extract and summarize important conversation points from chat contents. The analysis unit can also use a generation AI to extract and summarize important information from document contents. For example, the generation AI can use a text generation AI (e.g., GPT-4) to analyze email contents, extract important points, and summarize them. The generation AI can also use BERT (Bidirectional Encoder Representations from Transformers) to analyze chat content, extract important conversational key points, and summarize them. The generation AI can also use multimodal generation AI to analyze document content, extract important information, and summarize it. The registration unit registers the information summarized by the analysis unit in the talent visualization tool. The registration unit can, for example, register the summarized information in association with each employee's profile, skill set, and work history. For example, the registration unit can register the summarized information in association with the employee's work history, qualifications, and project experience. The registration unit can also register the summarized information in association with the employee's skill set. The registration unit can also register the summarized information in association with the employee's work history. As a result, the talent visualization system according to the embodiment can efficiently collect, analyze, and register employee information, thereby achieving talent visualization.

[0064] The collection unit can acquire information from a mail server, a chat tool, or a document management system. The collection unit can, for example, acquire email content from a mail server. For example, the collection unit can acquire email content from a specific mail provider (e.g., Gmail). The collection unit can also acquire chat content from a chat tool. For example, the collection unit can acquire chat content from a specific chat app (e.g., Slack). The collection unit can also acquire document content from a document management system. For example, the collection unit can acquire document content from a specific document management system (e.g., Google Drive). This enables the collection unit to collect information from various data sources, thereby enabling comprehensive information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input email content acquired from a mail server into a generation AI, causing the generation AI to analyze the email content.

[0065] The analysis unit can analyze the collected information using the generation AI, extract important points, and summarize them. For example, the analysis unit can use the generation AI to analyze the content of collected emails, extract important points, and summarize them. For example, the analysis unit can use the generation AI to extract and summarize project progress and issues from the content of emails. The analysis unit can also use the generation AI to analyze the content of collected chat messages, extract important conversational key points, and summarize them. For example, the analysis unit can use the generation AI to extract and summarize important conversational key points from the content of chat messages. The analysis unit can also use the generation AI to analyze the content of collected documents, extract important information, and summarize them. For example, the analysis unit can use the generation AI to extract and summarize important information from the content of documents. As a result, the analysis unit uses the generation AI to improve the accuracy of information analysis and summarization. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected information into the generation AI, which can then analyze and summarize the information.

[0066] The registration unit can register the summarized information in the human resources visualization tool by associating it with each employee's profile, skill set, and work history. The registration unit can, for example, register the summarized information by associating it with each employee's profile, skill set, and work history. For example, the registration unit can register the summarized information by associating it with the employee's work history, qualifications, and project experience. The registration unit can also register the summarized information by associating it with the employee's skill set. The registration unit can also register the summarized information by associating it with the employee's work history. In this way, the registration unit can accurately associate and register employee information, thereby improving the accuracy of visualization. Some or all of the above-mentioned processing in the registration unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the registration unit can input the summarized information into a generation AI, which can then associate the information.

[0067] Furthermore, the human resource visualization system includes a filtering unit that filters collected information to protect privacy. The filtering unit can, for example, anonymize personal information from the collected information. For example, the filtering unit can anonymize personal names and email addresses from the content of collected emails. The filtering unit can also anonymize personal names and chat IDs from the content of collected chats. The filtering unit can also anonymize personal information from the content of collected documents. For example, the filtering unit can anonymize personal names and addresses from the content of collected documents. This allows the filtering unit to collect, analyze, and register information while ensuring privacy protection. Some or all of the above-mentioned processing in the filtering unit may be performed using, or without, a generation AI. For example, the filtering unit can input collected information to a generation AI and have the generation AI anonymize personal information.

[0068] The human resource visualization system further includes a classification unit that classifies the summarized information. The classification unit can, for example, classify the summarized information by category. For example, the classification unit can classify the summarized information into categories such as project management, work history, and skill set. The classification unit can also classify the summarized information by importance. For example, the classification unit can classify the summarized information into high importance, medium importance, and low importance. The classification unit can also classify the summarized information chronologically. For example, the classification unit can classify the summarized information by date. This allows the classification unit to classify the information, making it easier to organize and search for the information. Some or all of the above-described processing in the classification unit may be performed using, or without, a generation AI. For example, the classification unit can input the summarized information to a generation AI and have the generation AI classify the information.

[0069] Furthermore, the human resource visualization system includes an association unit that associates the summarized information with each employee's profile, skill set, and work history. The association unit can, for example, associate the summarized information with each employee's profile, skill set, and work history. For example, the association unit can associate the summarized information with the employee's work history, qualifications, and project experience. The association unit can also associate the summarized information with the employee's skill set. The association unit can also associate the summarized information with the employee's work history. In this way, the association unit accurately associates information, thereby improving the visualization of employee skills and work history. Some or all of the above-described processing in the association unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the association unit can input summarized information into a generation AI and have the generation AI associate the information.

[0070] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection frequency and reduce the burden on the user. For example, the collection unit can capture the user's facial expressions with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and adjust the collection frequency accordingly. The collection unit can also increase the collection frequency and acquire more detailed information if the user is relaxed. For example, the collection unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and adjust the collection frequency accordingly. The collection unit can also prioritize collecting only important information if the user is in a hurry. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and set the priority of the information to be collected. This allows the collection unit to adjust the timing of information collection according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI, and have the generation AI estimate the emotion and adjust the collection timing.

[0071] The collection unit can optimize the access frequency of each data source and improve information collection efficiency. For example, the collection unit can increase the information collection frequency from an email server during business hours and decrease it during non-business hours. For example, the collection unit can collect information from an email server once per hour during business hours and once per day during non-business hours. The collection unit can also collect information from a chat tool in real time to immediately obtain important messages. For example, the collection unit can always collect information from a chat tool in real time and immediately obtain important messages when they are sent. The collection unit can also increase the information collection frequency from a document management system for folders that are frequently updated. For example, the collection unit can collect information from specific folders in the document management system once per hour and from other folders once per day. This allows the collection unit to optimize the access frequency of each data source and improve information collection efficiency. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the collection unit can input the access frequency of each data source into the generation AI, and have the generation AI optimize the access frequency.

[0072] The collection unit can set priorities based on the importance of information and prioritize collecting important information. For example, the collection unit can prioritize collecting emails related to project progress. For example, the collection unit can prioritize collecting emails containing keywords related to project progress (e.g., "progress," "task," "completed," etc.). The collection unit can also prioritize collecting minutes of important meetings. For example, the collection unit can prioritize collecting documents containing keywords such as "meeting," "minutes," and "decisions." The collection unit can also prioritize collecting urgent chat messages. For example, the collection unit can prioritize collecting chat messages containing keywords such as "urgent," "urgent," and "important." This enables the collection unit to set priorities based on the importance of information and prioritize collecting important information, thereby enabling efficient information management. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the importance of information into the generation AI and have the generation AI set the priority of the information.

[0073] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting only information of high importance. For example, the collection unit can capture the user's facial expressions with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and prioritize collecting only information of high importance. The collection unit can also prioritize collecting detailed information if the user is relaxed. For example, the collection unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize collecting detailed information. The collection unit can also prioritize collecting information requiring a quick response if the user is in a hurry. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and prioritize collecting information requiring a quick response. This allows the collection unit to prioritize the information to be collected based on the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit may input user emotion data into the generation AI, causing the generation AI to estimate emotions and determine the priority of information.

[0074] The collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. For example, when the user is in the office, the collection unit can prioritize collecting office-related information. For example, the collection unit can acquire the user's geographical location information from GPS data, and prioritize collecting office-related information when the user is in the office. Furthermore, when the user is on a business trip, the collection unit can prioritize collecting information related to the business trip destination. For example, the collection unit can acquire the user's geographical location information from an IP address, and prioritize collecting information related to the business trip destination when the user is on a business trip. Furthermore, the collection unit can prioritize collecting information related to remote work when the user is at home. For example, the collection unit can acquire the user's geographical location information from Wi-Fi connection information, and prioritize collecting information related to remote work when the user is at home. As a result, the collection unit collects highly relevant information based on the user's geographical location information, thereby improving the accuracy of the information. Some or all of the above-described processing by the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, causing the generation AI to collect highly relevant information.

[0075] The collection unit can analyze the user's social media activities and collect related information. The collection unit can, for example, collect information related to a project shared by the user on social media. For example, the collection unit can analyze the user's social media activities and collect information related to the project. The collection unit can also collect information related to an event mentioned by the user on social media. For example, the collection unit can analyze the user's social media activities and collect information related to the event. The collection unit can also collect industry news that the user follows on social media. For example, the collection unit can analyze the user's social media activities and collect the industry news that the user follows. In this way, the collection unit collects related information based on the social media activities, thereby improving the comprehensiveness of the information. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related information.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a concise and to-the-point analysis result. For example, the analysis unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and provide a concise and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and provide a detailed analysis result. The analysis unit can also provide a quickly understandable analysis result if the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and provide the analysis result in a quickly understandable format. This allows the analysis unit to adjust the presentation method of the analysis according to the user's emotions, thereby providing an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input user emotion data into the generation AI, and have the generation AI estimate the emotion and adjust the method of expression for analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can analyze information about important projects in detail. For example, the analysis unit can analyze information about project progress and issues in detail. The analysis unit can also analyze information about daily tasks concisely. For example, the analysis unit can briefly analyze information about daily tasks and extract key points. The analysis unit can also quickly analyze information about urgent issues. For example, the analysis unit can quickly analyze information about urgent issues and extract information that requires immediate action. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the information, enabling efficient information analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the information to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply an algorithm for analyzing progress to information related to project management. For example, the analysis unit can use an algorithm for analyzing project progress to perform a detailed analysis of the project progress. The analysis unit can also apply an algorithm for analyzing performance to information related to personnel evaluation. For example, the analysis unit can use an algorithm for analyzing information related to personnel evaluation to perform a detailed analysis of employee performance. The analysis unit can also apply an algorithm for analyzing customer satisfaction to information related to customer service. For example, the analysis unit can perform a detailed analysis of customer satisfaction using an algorithm for analyzing information related to customer service. In this way, the analysis unit can apply an analysis algorithm according to the category of information, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI. For example, the analysis unit can input the category of information into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a short and concise analysis result. For example, the analysis unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and provide a detailed analysis result. The analysis unit can also provide the analysis result in a format that is easy to understand if the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and provide the analysis result in a format that is easy to understand. This allows the analysis unit to adjust the length of the analysis according to the user's emotions and provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input user emotion data into the generation AI, causing the generation AI to estimate the emotion and adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the time of information submission. The analysis unit can, for example, prioritize analysis of information related to urgent issues. For example, the analysis unit can obtain the time of information submission from a timestamp and prioritize analysis of information related to urgent issues. The analysis unit can also give second priority to information related to periodic reports. For example, the analysis unit can obtain the time of information submission from the submission date and time and prioritize information related to periodic reports. The analysis unit can also analyze information related to past data last. For example, the analysis unit can obtain the time of information submission from the submission date and time and analyze information related to past data last. In this way, the analysis unit can determine the priority of analysis based on the time of information submission, thereby quickly analyzing information with high urgency. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the time of information submission to the generation AI and have the generation AI determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit can, for example, prioritize analysis of information related to the progress of a project. For example, the analysis unit can evaluate the relevance of information based on common keywords and prioritize analysis of information related to the progress of the project. The analysis unit can also secondarily prioritize information related to the performance of team members. For example, the analysis unit can evaluate the relevance of information based on related topics and secondarily prioritize information related to the performance of team members. The analysis unit can also analyze information related to customer service last. For example, the analysis unit can evaluate the relevance of information based on common keywords and analyze information related to customer service last. This enables the analysis unit to adjust the order of analysis based on the relevance of information, thereby enabling efficient information analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of information to the generation AI and have the generation AI adjust the order of analysis.

[0082] The registration unit can estimate the user's emotion and adjust the registration method based on the estimated user emotion. For example, if the user is feeling stressed, the registration unit can provide a simple registration method. For example, the registration unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and provide a simple registration method. The registration unit can also provide a detailed registration method if the user is relaxed. For example, the registration unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and provide a detailed registration method. The registration unit can also provide a quick registration method if the user is in a hurry. For example, the registration unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and provide a quick registration method. This allows the registration unit to adjust the registration method according to the user's emotion, thereby providing a registration method appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit may input user emotion data into the generation AI, which may then adjust the emotion estimation and registration method.

[0083] The registration unit can adjust the level of detail of the registration based on the importance of the information. For example, the registration unit can register information about important projects in detail. For example, the registration unit can register information about the progress of projects and issues in detail. The registration unit can also register information about daily tasks in a concise manner. For example, the registration unit can concisely register information about daily tasks and extract key points. The registration unit can also quickly register information about urgent issues. For example, the registration unit can quickly register information about urgent issues and extract information that requires immediate action. This allows the registration unit to adjust the level of detail of the registration according to the importance of the information, enabling efficient information registration. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input the importance of the information to the generation AI and have the generation AI adjust the level of detail of the registration.

[0084] The registration unit can apply different registration algorithms depending on the category of information. For example, the registration unit can apply an algorithm for registering progress to information related to project management. For example, the registration unit can register the project progress in detail using an algorithm for registering the project progress. The registration unit can also apply an algorithm for registering performance to information related to personnel evaluation. For example, the registration unit can register employee performance in detail using an algorithm for registering information related to personnel evaluation. The registration unit can also apply an algorithm for registering customer satisfaction to information related to customer service. For example, the registration unit can register customer satisfaction in detail using an algorithm for registering information related to customer service. In this way, the registration unit can apply a registration algorithm according to the category of information, thereby improving the accuracy of registration. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, a generation AI. For example, the registration unit can input the category of information to the generation AI and cause the generation AI to apply an appropriate registration algorithm.

[0085] The registration unit can estimate the user's emotions and determine the registration priority based on the estimated user emotions. For example, if the user is feeling stressed, the registration unit can prioritize registering information of high importance. For example, the registration unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and prioritize registering information of high importance. Furthermore, if the user is relaxed, the registration unit can prioritize registering detailed information. For example, the registration unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize registering detailed information. Furthermore, if the user is in a hurry, the registration unit can prioritize registering information requiring a quick response. For example, the registration unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and prioritize registering information requiring a quick response. This allows the registration unit to prioritize registration based on the user's emotions, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit may input user emotion data into the generation AI, and have the generation AI estimate the emotion and determine the priority of registration.

[0086] The registration unit can adjust the order of registration based on the time of submission of information. The registration unit can, for example, prioritize registration of information related to urgent issues. For example, the registration unit can obtain the time of submission of information from a timestamp and prioritize registration of information related to urgent issues. The registration unit can also prioritize information related to periodic reports. For example, the registration unit can obtain the time of submission of information from the submission date and time and prioritize information related to periodic reports. The registration unit can also register information related to past data last. For example, the registration unit can obtain the time of submission of information from the submission date and time and register information related to past data last. In this way, the registration unit can adjust the order of registration based on the time of submission of information, thereby quickly registering information with high urgency. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, the generation AI. For example, the registration unit can input the time of submission of information to the generation AI and have the generation AI adjust the order of registration.

[0087] The registration unit can adjust the registration method based on the relevance of the information. For example, the registration unit can prioritize registering information related to the progress of a project. For example, the registration unit can evaluate the relevance of the information based on common keywords and prioritize registering information related to the progress of the project. The registration unit can also prioritize information related to the performance of team members. For example, the registration unit can evaluate the relevance of the information based on related topics and prioritize information related to the performance of team members. The registration unit can also register information related to customer correspondence last. For example, the registration unit can evaluate the relevance of the information based on common keywords and register information related to customer correspondence last. This allows the registration unit to adjust the registration method based on the relevance of the information, thereby enabling efficient information registration. Some or all of the above-mentioned processing in the registration unit may be performed using, or without, a generation AI. For example, the registration unit can input the relevance of the information to the generation AI and cause the generation AI to adjust the registration method.

[0088] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated user emotions. For example, if the user is feeling stressed, the filtering unit can filter only information of high importance. For example, the filtering unit can capture the user's facial expressions with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and filter only information of high importance. The filtering unit can also filter detailed information if the user is relaxed. For example, the filtering unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and filter detailed information. The filtering unit can also filter information requiring a quick response if the user is in a hurry. For example, the filtering unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and filter information requiring a quick response. This allows the filtering unit to adjust the filtering criteria according to the user's emotions, enabling appropriate filtering for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the filtering unit may be performed using, or without, the generation AI. For example, the filtering unit may input user emotion data into the generation AI, and have the generation AI estimate the emotion and adjust the filtering criteria.

[0089] The filtering unit can adjust the level of filtering detail based on the importance of the information. For example, the filtering unit can filter information related to important projects in detail. For example, the filtering unit can filter information related to project progress and issues in detail. The filtering unit can also briefly filter information related to daily tasks. For example, the filtering unit can briefly filter information related to daily tasks to extract key points. The filtering unit can also quickly filter information related to urgent issues. For example, the filtering unit can quickly filter information related to urgent issues to extract information that requires immediate action. This enables the filtering unit to adjust the level of filtering detail according to the importance of the information, thereby enabling efficient information filtering. Some or all of the above-mentioned processing in the filtering unit may be performed using, or without, a generation AI. For example, the filtering unit can input the importance of the information to the generation AI and have the generation AI adjust the level of filtering detail.

[0090] The filtering unit can estimate the user's emotions and determine filtering priorities based on the estimated user emotions. For example, if the user is feeling stressed, the filtering unit can prioritize filtering of information of high importance. For example, the filtering unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and prioritize filtering of information of high importance. The filtering unit can also prioritize filtering of detailed information if the user is relaxed. For example, the filtering unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize filtering of detailed information. The filtering unit can also prioritize filtering of information requiring a quick response if the user is in a hurry. For example, the filtering unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and prioritize filtering of information requiring a quick response. This allows the filtering unit to determine filtering priorities according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the filtering unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the filtering unit may input user emotion data into the generation AI, and have the generation AI estimate the emotion and determine the filtering priority.

[0091] The filtering unit can adjust the filtering order based on the time when the information was submitted. The filtering unit can, for example, prioritize filtering of information related to urgent issues. For example, the filtering unit can obtain the time when the information was submitted from a timestamp and prioritize filtering of information related to urgent issues. The filtering unit can also prioritize information related to periodic reports. For example, the filtering unit can obtain the time when the information was submitted from the submission date and time and prioritize information related to periodic reports. The filtering unit can also filter information related to past data last. For example, the filtering unit can obtain the time when the information was submitted from the submission date and time and filter information related to past data last. In this way, the filtering unit can adjust the filtering order based on the time when the information was submitted, thereby quickly filtering out information with high urgency. Some or all of the above-described processing in the filtering unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the filtering unit can input the time when the information was submitted to the generation AI and have the generation AI adjust the filtering order.

[0092] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated user emotions. For example, if the user is feeling stressed, the classification unit can provide concise and to-the-point classification criteria. For example, the classification unit can capture the user's facial expressions with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and provide concise and to-the-point classification criteria. The classification unit can also provide detailed classification criteria if the user is relaxed. For example, the classification unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and provide detailed classification criteria. The classification unit can also provide quickly understandable classification criteria if the user is in a hurry. For example, the classification unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and provide quickly understandable classification criteria. This allows the classification unit to adjust the classification criteria according to the user's emotions, thereby enabling appropriate classification for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the classification unit may be performed using, or without, the generation AI. For example, the classification unit may input user emotion data into the generation AI, causing the generation AI to estimate emotions and adjust classification criteria.

[0093] The classification unit can adjust the level of detail of the classification based on the importance of the information. For example, the classification unit can classify information related to important projects in detail. For example, the classification unit can classify information related to project progress and issues in detail. The classification unit can also briefly classify information related to daily tasks. For example, the classification unit can briefly classify information related to daily tasks and extract key points. The classification unit can also quickly classify information related to urgent issues. For example, the classification unit can quickly classify information related to urgent issues and extract information that requires immediate action. This enables the classification unit to efficiently classify information by adjusting the level of detail of the classification according to the importance of the information. Some or all of the above-mentioned processing in the classification unit may be performed using, or without, a generation AI. For example, the classification unit can input the importance of the information to the generation AI and have the generation AI adjust the level of detail of the classification.

[0094] The classification unit can estimate the user's emotions and determine classification priorities based on the estimated user emotions. For example, if the user is feeling stressed, the classification unit can prioritize classification of information with high importance. For example, the classification unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and prioritize classification of information with high importance. Furthermore, if the user is relaxed, the classification unit can prioritize classification of detailed information. For example, the classification unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize classification of detailed information. Furthermore, if the user is in a hurry, the classification unit can prioritize classification of information requiring a quick response. For example, the classification unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and prioritize classification of information requiring a quick response. This allows the classification unit to determine classification priorities according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the classification unit may be performed using, or without, the generation AI. For example, the classification unit may input user emotion data into the generation AI, and have the generation AI estimate the emotion and determine the classification priority.

[0095] The classification unit can adjust the order of classification based on the time of submission of the information. The classification unit can, for example, prioritize classification of information related to urgent issues. For example, the classification unit can obtain the time of submission of the information from a timestamp and prioritize classification of information related to urgent issues. The classification unit can also prioritize information related to periodic reports. For example, the classification unit can obtain the time of submission of the information from the submission date and time and prioritize information related to periodic reports. The classification unit can also classify information related to past data last. For example, the classification unit can obtain the time of submission of the information from the submission date and time and classify information related to past data last. In this way, the classification unit can quickly classify information with high urgency by adjusting the order of classification based on the time of submission of the information. Some or all of the above-described processing in the classification unit may be performed using, or without, a generation AI. For example, the classification unit can input the time of submission of the information to the generation AI and have the generation AI adjust the order of classification.

[0096] The association unit can estimate the user's emotion and adjust the association criteria based on the estimated user emotion. For example, if the user is feeling stressed, the association unit can provide concise and to-the-point association criteria. For example, the association unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and provide concise and to-the-point association criteria. The association unit can also provide detailed association criteria if the user is relaxed. For example, the association unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and provide detailed association criteria. The association unit can also provide quickly understandable association criteria if the user is in a hurry. For example, the association unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and provide quickly understandable association criteria. This allows the association unit to adjust the association criteria according to the user's emotion, thereby enabling appropriate association for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the association unit may be performed using, or without, the generation AI. For example, the association unit may input user emotion data into the generation AI, causing the generation AI to estimate emotions and adjust the association criteria.

[0097] The association unit can adjust the level of detail of the association based on the importance of the information. For example, the association unit can associate information about important projects in detail. For example, the association unit can associate information about project progress and issues in detail. The association unit can also associate information about daily tasks in a concise manner. For example, the association unit can concisely associate information about daily tasks and extract key points. The association unit can also quickly associate information about urgent issues. For example, the association unit can quickly associate information about urgent issues and extract information that requires immediate action. This enables the association unit to adjust the level of detail of the association according to the importance of the information, thereby enabling efficient information association. Some or all of the above-mentioned processing in the association unit may be performed using, or without, a generation AI. For example, the association unit can input the importance of the information to the generation AI and have the generation AI adjust the level of detail of the association.

[0098] The association unit can estimate the user's emotions and determine the priority of associations based on the estimated user emotions. For example, if the user is feeling stressed, the association unit can prioritize association of information of high importance. For example, the association unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and prioritize association of information of high importance. Furthermore, if the user is relaxed, the association unit can prioritize association of detailed information. For example, the association unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize association of detailed information. Furthermore, if the user is in a hurry, the association unit can prioritize association of information requiring a prompt response. For example, the association unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, determine whether the user is in a hurry, and prioritize association of information requiring a prompt response. This allows the association unit to prioritize associations according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., GPT-4) or a multimodal generation AI. Some or all of the above-described processing in the association unit may be performed using, or without, the generation AI. For example, the association unit may input user emotion data into the generation AI, causing the generation AI to estimate emotions and determine association priorities.

[0099] The associating unit can adjust the order of association based on the time of submission of the information. The associating unit can, for example, prioritize association of information related to urgent issues. For example, the associating unit can obtain the time of submission of the information from a timestamp and prioritize association of information related to urgent issues. The associating unit can also prioritize information related to periodic reports. For example, the associating unit can obtain the time of submission of the information from the submission date and time and prioritize information related to periodic reports. The associating unit can also associate information related to past data last. For example, the associating unit can obtain the time of submission of the information from the submission date and time and associate information related to past data last. In this way, the associating unit can adjust the order of association based on the time of submission of the information, thereby quickly associating information with high urgency. Some or all of the above-described processing in the associating unit may be performed using, or without, a generation AI. For example, the associating unit can input the time of submission of the information into the generation AI and have the generation AI adjust the order of association. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, registration unit, filtering unit, classification unit, and association unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit acquires information from a mail server, a chat tool, or a document management system via the communication I / F 44 of the smart device 14. The analysis unit analyzes and summarizes the information using a generation AI by the specific processing unit 290 of the data processing device 12. The registration unit registers the summarized information in a talent visualization tool via the control unit 46A of the smart device 14. The filtering unit anonymizes the collected information by the specific processing unit 290 of the data processing device 12. The classification unit classifies the summarized information by category by the specific processing unit 290 of the data processing device 12. The association unit associates the summarized information with each employee's profile, skill set, and work history via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, registration unit, filtering unit, classification unit, and association unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit acquires information from a mail server, a chat tool, or a document management system via the communication I / F 44 of the smart glasses 214. The analysis unit analyzes and summarizes the information using a generation AI by the specific processing unit 290 of the data processing device 12. The registration unit registers the summarized information in the talent visualization tool via the control unit 46A of the smart glasses 214. The filtering unit anonymizes the collected information by the specific processing unit 290 of the data processing device 12. The classification unit classifies the summarized information by category by the specific processing unit 290 of the data processing device 12. The association unit associates the summarized information with each employee's profile, skill set, and work history via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, registration unit, filtering unit, classification unit, and association unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit acquires information from a mail server, a chat tool, or a document management system via the communication I / F 44 of the headset type terminal 314. The analysis unit analyzes and summarizes the information using a generation AI by the specific processing unit 290 of the data processing device 12. The registration unit registers the summarized information in a talent visualization tool via the control unit 46A of the headset type terminal 314. The filtering unit anonymizes the collected information by the specific processing unit 290 of the data processing device 12. The classification unit classifies the summarized information by category by the specific processing unit 290 of the data processing device 12. The association unit associates the summarized information with each employee's profile, skill set, and work history via the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, registration unit, filtering unit, classification unit, and association unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit acquires information from a mail server, a chat tool, or a document management system via the communication I / F 44 of the robot 414. The analysis unit analyzes and summarizes the information using a generation AI by the specific processing unit 290 of the data processing device 12. The registration unit registers the summarized information in a talent visualization tool via the control unit 46A of the robot 414. The filtering unit anonymizes the collected information by the specific processing unit 290 of the data processing device 12. The classification unit classifies the summarized information by category by the specific processing unit 290 of the data processing device 12. The association unit associates the summarized information with each employee's profile, skill set, and work history via the control unit 46A of the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The collection unit can collect biometric data of the user and adjust the frequency of information collection based on the user's health condition. For example, the collection unit can measure the user's heart rate and blood pressure using a sensor, and reduce the collection frequency if the user's health condition is poor, thereby reducing the burden on the user. The collection unit can also monitor the user's sleep patterns and reduce the collection frequency if the user is not getting enough sleep. Furthermore, the collection unit can track the user's exercise volume and reduce the collection frequency if the user is not exercising enough, thereby promoting health. In this way, the collection unit can reduce the burden on the user and support their health by adjusting the frequency of information collection according to the user's health condition.

[0102] The analysis unit can learn the user's past behavioral patterns and determine analysis priorities based on predicted behavior. For example, the analysis unit can prioritize analysis of information that the user has frequently accessed in the past. The analysis unit can also prioritize analysis of information related to work that the user performs during a specific time period. Furthermore, the analysis unit can prioritize analysis of information related to a specific project during a period when the user is concentrating on that project. This allows the analysis unit to determine analysis priorities based on the user's behavioral patterns, enabling efficient information analysis.

[0103] The registration unit can adjust the timing of information registration taking into account the user's schedule. For example, the registration unit can refrain from registering information when the user is in a meeting or concentrating on a task, and register information during free time. The registration unit can also temporarily suspend information registration while the user is on vacation, and register all information at once after the user returns. Furthermore, the registration unit can register information after the user has finished work, thereby reducing the burden on the user during work hours. In this way, the registration unit can reduce the burden on the user by adjusting the timing of information registration according to the user's schedule.

[0104] The filtering unit can adjust the filtering criteria based on the user's job title and job content. For example, the filtering unit can prioritize filtering important management information for managers, and prioritize filtering information directly related to their work for general employees. The filtering unit can also prioritize filtering technical information for engineers, and prioritize filtering customer information for sales users. Furthermore, the filtering unit can prioritize filtering project progress information for project leaders. This allows the filtering unit to provide appropriate information by adjusting the filtering criteria according to the user's job title and job content.

[0105] The classification unit can adjust the classification criteria based on the user's business goals. For example, the classification unit can prioritize classification of information related to the business goals set by the user. The classification unit can also adjust the level of detail of classification according to the user's level of goal achievement. Furthermore, the classification unit can evaluate the importance of information based on the user's business goals and prioritize classification of important information. This allows the classification unit to adjust the classification criteria according to the user's business goals, enabling efficient information management.

[0106] The collection unit can estimate the user's emotions and adjust the information collection method based on the estimated user's emotions. For example, if the collection unit is feeling stressed, the collection unit can reduce the amount of information to be collected, thereby reducing the burden on the user. Also, if the user is relaxed, the collection unit can collect detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important information. In this way, the collection unit can reduce the burden on the user by adjusting the information collection method according to the user's emotions.

[0107] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can pause the analysis and resume it when the user is relaxed. The analysis unit can also perform a detailed analysis when the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can quickly perform the analysis and provide the results. In this way, the analysis unit can reduce the burden on the user by adjusting the timing of the analysis according to the user's emotions.

[0108] The registration unit can estimate the user's emotions and adjust the registration priority based on the estimated user's emotions. For example, if the user is feeling stressed, the registration unit can prioritize registering information of high importance. Also, if the user is relaxed, the registration unit can prioritize registering detailed information. Furthermore, if the user is in a hurry, the registration unit can prioritize registering information that requires a quick response. In this way, the registration unit can reduce the burden on the user by adjusting the registration priority according to the user's emotions.

[0109] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated user's emotions. For example, if the user is feeling stressed, the filtering unit can filter out only information of high importance. Also, if the user is relaxed, the filtering unit can filter out detailed information. Furthermore, if the user is in a hurry, the filtering unit can filter out information that requires a quick response. In this way, the filtering unit can reduce the burden on the user by adjusting the filtering criteria according to the user's emotions.

[0110] The classification unit can estimate the user's emotion and adjust the classification criteria based on the estimated user's emotion. For example, if the user is feeling stressed, the classification unit can provide concise and concise classification criteria. If the user is feeling relaxed, the classification unit can provide detailed classification criteria. Furthermore, if the user is in a hurry, the classification unit can provide classification criteria that can be quickly understood. In this way, the classification unit can adjust the classification criteria according to the user's emotion, thereby enabling appropriate classification for the user.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection department collects the contents of employees' emails or communication tools, as well as the contents of documents. For example, the collection department can obtain email contents from an email server, chat contents from a chat tool, and document contents from a document management system. Step 2: The analysis unit uses the generation AI to analyze the collected information, extract key points, and summarize it. For example, the generation AI can use text generation AI (e.g., GPT-4) to analyze the content of emails, extract key points, and summarize them. It can also use BERT (Bidirectional Encoder Representations from Transformers) to analyze chat content, extract important conversational key points, and summarize them. Furthermore, it can use multimodal generation AI to analyze the content of documents, extract key information, and summarize them. Step 3: The registration unit registers the information summarized by the analysis unit in the human resources visualization tool. For example, the summarized information can be registered in association with each employee's profile, skill set, and work history. This allows information to be registered in association with an employee's work history, qualifications, project experience, skill set, and work history.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects the contents of employees' emails and communication tools, as well as the contents of documents; an analysis unit that analyzes the information collected by the collection unit, extracts important points, and summarizes them; a registration unit that registers the information summarized by the analysis unit in a human resources visualization tool. A system characterized by:

2. The collecting unit Retrieve information from your email server, chat tool, or document management system 2. The system of claim 1.

3. The analysis unit Analyze collected information using generative AI, extract and summarize key points 2. The system of claim 1.

4. The registration unit The summarized information is linked to each employee's profile, skill set, and work history and registered in a human resources visualization tool.

2. The system of claim 1.

5. A filtering unit is provided to filter collected information for privacy protection.

2. The system of claim 1.

6. A classification unit for classifying the summarized information is provided.

2. The system of claim 1.

7. It has an association section that associates the summarized information with each employee's profile, skill set, and work history.

2. The system of claim 1.

8. The collecting unit Estimates user emotions and adjusts information collection timing based on the estimated user emotions.

2. The system of claim 1.

9. The collecting unit Optimize the frequency of access to each data source to improve information collection efficiency 2. The system of claim 1.

10. The collecting unit Set priorities based on the importance of the information and prioritize collection of important information 2. The system of claim 1.

Images