system

Abstract

The system according to this embodiment aims to grasp the overall picture of internal communication and to detect feelings of isolation at an early stage. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a proposal unit. The collection unit collects communication patterns from emails, chat logs, video conferencing data, etc. The analysis unit analyzes the data collected by the collection unit and visualizes relationship correlation diagrams and interaction frequencies. The detection unit detects signs of isolation based on the data analyzed by the analysis unit. The proposal unit provides response guidelines and recommendations for appropriate personnel management based on the signs of isolation detected by the detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the overall picture of in-company communication and detect a sense of isolation at an early stage.

[0005] The system according to the embodiment aims to grasp the overall picture of in-company communication and detect a sense of isolation at an early stage.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a detection unit, and a proposal unit. The collection unit collects communication patterns from emails, chat logs, video conferencing data, etc. The analysis unit analyzes the data collected by the collection unit and visualizes relationship correlation diagrams and interaction frequencies. The detection unit detects signs of isolation based on the data analyzed by the analysis unit. The proposal unit provides response guidelines and recommendations for appropriate personnel management based on the signs of isolation detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp the overall picture of internal communication and detect feelings of isolation early on. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system for grasping the overall picture of internal communication, making promotion and transfer criteria objective, and detecting feelings of isolation due to lack of communication at an early stage. This AI agent system collects communication patterns from emails, chat logs, video conferencing data, etc., performs natural language analysis using generative AI, and visualizes correlation diagrams of human relationships and the frequency of interactions. This clarifies internal relationships and provides objective information for decision-making. Furthermore, it detects signs of isolation from the frequency and content of interactions and provides response guidelines and recommendations for appropriate personnel management. This supports the early resolution of communication problems and the response to feelings of isolation. It also creates detailed reports on specific people or departments, including suggestions for necessary follow-up and mentoring. This strengthens effective organizational management and mental health support. Finally, it predicts the potential impact of transfers and organizational changes and evaluates the benefits and risks. This enables appropriate responses to organizational changes. This AI agent can visualize the internal human network and analyze the connections between team members. It can also strengthen team cohesion by detecting feelings of isolation at an early stage and suggesting appropriate follow-up. Furthermore, it can be used as a basis for promotion and transfer decisions, thus supporting transparent and fair organizational management. This allows the AI ​​agent system to grasp the overall picture of internal communication, make promotion and transfer criteria objective, and detect feelings of isolation due to a lack of communication early on.

[0029] The AI ​​agent system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a suggestion unit. The collection unit collects communication patterns from emails, chat logs, video conference data, etc. The collection unit can collect, for example, the frequency of email exchanges, the content of chats, and the participation status of video conferences. The collection unit can, for example, acquire email data from an email server, acquire chat logs from a chat application, and acquire conference data from a video conferencing system. The collection unit can, for example, collect data periodically. The analysis unit uses generative AI to analyze the data collected by the collection unit and visualize relationship diagrams and interaction frequencies. The analysis unit, for example, performs natural language processing to analyze the content of emails and chats. The analysis unit, for example, uses generative AI to analyze the content of emails and chats and extract keywords and phrases. The analysis unit, for example, analyzes the content of video conferences and analyzes the frequency of speakers and the content of their statements. The analysis unit, for example, uses generative AI to analyze the content of video conferences and visualizes the frequency of speakers and the content of their statements. The detection unit detects signs of isolation based on data analyzed by the analysis unit. The detection unit detects signs of isolation, for example, from the frequency and content of interactions. The detection unit detects signs of isolation, for example, when the frequency of interactions decreases or when exclusion from a particular group is observed. The detection unit detects signs of isolation by analyzing the frequency and content of interactions, for example, using generative AI. The proposal unit provides response guidelines and recommendations for appropriate human resource management based on the signs of isolation detected by the detection unit. The proposal unit proposes, for example, follow-up methods and mentoring procedures. The proposal unit creates, for example, detailed reports on specific people or departments, including suggestions for necessary follow-up and mentoring. The proposal unit predicts the potential impact of transfers or organizational changes and evaluates the benefits and risks. As a result, the AI ​​agent system according to the embodiment can grasp the overall picture of internal communication, make promotion and transfer criteria objective, and detect feelings of isolation due to lack of communication at an early stage.

[0030] The data collection unit collects communication patterns from sources such as emails, chat logs, and video conferencing data. Specifically, it can collect data such as the frequency of email exchanges, the content of chats, and participation status in video conferences. For example, the data collection unit retrieves email data from email servers, chat logs from chat applications, and conference data from video conferencing systems. This data is collected regularly and stored in a central database. Email data collection includes sender and recipient addresses, date and time of sending, subject, and content of the message. Chat log collection includes participants in the chat, date and time of message sending, and message content. Video conferencing data collection includes participants in the meeting, identification of speakers, content of statements, and duration of statements. This allows the data collection unit to gain a detailed understanding of internal communication patterns. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and detection units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit uses generative AI to analyze data collected by the data collection unit and visualize relationship diagrams and interaction frequencies. Specifically, it performs natural language processing to analyze the content of emails and chats. The generative AI analyzes the content of emails and chats and extracts keywords and phrases. For example, it extracts important topics and emotional tendencies from email content and identifies the flow of conversation and important agenda items from chat content. When analyzing the content of video conferences, it analyzes the frequency and content of speakers to understand the progress of the meeting and the content of the discussion. The generative AI analyzes the content of video conferences and visualizes the frequency and content of speakers. This allows the analysis unit to understand internal communication patterns in detail and visualize relationship diagrams and interaction frequencies. Furthermore, the analysis unit can also analyze long-term trends and patterns by utilizing past data and statistical information. For example, it can analyze fluctuations in communication at specific times or events based on past communication data and make future predictions. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The detection unit detects signs of isolation based on data analyzed by the analysis unit. Specifically, it detects signs of isolation from the frequency and content of interactions. For example, it detects signs of isolation when the frequency of interactions decreases or when exclusion from a particular group is observed. Generative AI is used to analyze the frequency and content of interactions and detect signs of isolation. The generative AI analyzes the frequency and content of interactions and learns patterns for detecting signs of isolation. For example, it detects signs of isolation when a particular employee's communication with other employees decreases or when exclusion from a particular group is observed. This allows the detection unit to understand internal communication patterns in detail and detect signs of isolation early. Furthermore, the detection unit can also analyze long-term trends and patterns by utilizing historical data and statistical information. For example, it can analyze fluctuations in signs of isolation at specific times or events based on past communication data and make future predictions. In addition, the detection unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. This allows the detection unit to not only grasp the situation in real time, but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0033] The Proposal Department provides response guidelines and recommendations for appropriate human resource management based on the signs of isolation detected by the Detection Department. Specifically, it proposes follow-up methods and mentoring procedures. For example, it creates detailed reports on specific individuals or departments, including suggestions for necessary follow-up and mentoring. The Proposal Department predicts the potential impact of transfers and organizational changes and evaluates the benefits and risks. Using generative AI, it generates optimal response guidelines and human resource management recommendations based on historical data and statistics. For example, if a specific employee is isolated, it proposes follow-up methods and mentoring procedures for that employee. It also makes suggestions for resolving communication issues in specific departments. This allows the Proposal Department to gain a detailed understanding of internal communication patterns and provide appropriate response guidelines and human resource management recommendations. Furthermore, the Proposal Department can also leverage historical data and statistics to analyze long-term trends and patterns. For example, it can analyze fluctuations in communication at specific times or events based on historical communication data and make future predictions. In addition, the Proposal Department can use anomaly detection algorithms to detect unusual patterns and anomalous data, issuing early warnings. This allows the proposal department to not only grasp the situation in real time, but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0034] The analysis unit can perform natural language analysis using generative AI and visualize relationship diagrams and interaction frequencies. For example, the analysis unit can use generative AI to analyze the content of emails and chats and extract keywords and phrases. For example, the analysis unit can use generative AI to analyze the content of video conferences and visualize the frequency of speakers and the content of their statements. For example, the analysis unit can use generative AI to analyze the content of emails and chats and create relationship diagrams. As a result, using generative AI improves the accuracy of natural language analysis and allows for accurate visualization of relationship diagrams and interaction frequencies. Generative AI includes, but is not limited to, large-scale language models. Natural language analysis includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis.

[0035] The proposal department may create detailed reports on specific individuals or departments and include suggestions for necessary follow-up and mentoring. For example, the proposal department may create detailed reports on specific individuals or departments and propose follow-up methods and mentoring procedures. For example, the proposal department may create detailed reports on specific individuals or departments and propose progress checks and regular meetings. For example, the proposal department may create detailed reports on specific individuals or departments and propose mentor selection criteria and mentoring frequency. This makes it possible to propose necessary follow-up and mentoring by creating detailed reports on specific individuals or departments. Detailed reports include, but are not limited to, the items in the report and the level of detail of the information included. Follow-up includes, but are not limited to, regular meetings and progress checks. Mentoring includes, but are not limited to, mentor selection criteria and mentoring frequency.

[0036] The proposal department can predict the potential impact of personnel changes and organizational restructuring, and evaluate the benefits and risks. For example, the proposal department can predict the potential impact of personnel changes and organizational restructuring, and evaluate the impact on changes in work efficiency and team morale. For example, the proposal department can predict the potential impact of personnel changes and organizational restructuring, and quantitatively evaluate the benefits and risks. For example, the proposal department can predict the potential impact of personnel changes and organizational restructuring, and qualitatively evaluate the benefits and risks. This enables appropriate responses to organizational changes by predicting the potential impact of personnel changes and organizational restructuring, and evaluating the benefits and risks. Potential impacts include, but are not limited to, changes in work efficiency and impacts on team morale. Benefits and risks include, but are not limited to, quantitative and qualitative evaluations.

[0037] The data collection unit can analyze the user's past communication history and select the optimal collection method. For example, the data collection unit prioritizes collecting data from communication tools that the user has frequently used in the past. For example, the data collection unit adjusts the collection frequency based on the user's past communication frequency. For example, the data collection unit analyzes the content of the user's past communications and prioritizes collecting important data. This enables efficient data collection by selecting the optimal collection method through analysis of the user's past communication history. Past communication history includes, but is not limited to, email history, chat logs, and video conference recordings. The optimal collection method includes, but is not limited to, collection methods and frequencies depending on the type of data.

[0038] The data collection unit can filter communication data based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting data related to the user's current projects. For example, the data collection unit filters relevant communication data based on the user's areas of interest. For example, the data collection unit adjusts the scope of data collected according to the progress of the user's projects. This allows for the priority collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Current projects include, but are not limited to, the progress of the project and the members involved. Areas of interest include, but are not limited to, the user's interests and areas of expertise.

[0039] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting communication data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will filter relevant communication data based on the user's geographical location. For example, if the user is on the move, the data collection unit will collect data related to their current location in real time. This allows for the priority collection of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, the user's current location and travel history.

[0040] The data collection unit can analyze users' social media activity and collect relevant data when collecting communication data. For example, the data collection unit can collect relevant data based on the content users post on social media. For example, the data collection unit can analyze users' interactions with their social media followers and friends and collect relevant data. For example, the data collection unit can adjust the scope of data collected based on the frequency of users' social media activity. This allows for the efficient collection of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, posts, follower counts, and likes.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the communication during the analysis. For example, the analysis unit performs a detailed analysis for high-importance communications. For example, the analysis unit performs a simplified analysis for low-importance communications. For example, the analysis unit determines the priority of the analysis according to importance. This allows for detailed analysis of important communications by adjusting the level of detail of the analysis based on the importance of the communication. The importance of a communication includes, but is not limited to, the impact on business operations and the positions of the members involved. The level of detail of the analysis includes, but is not limited to, detailed analysis items and the accuracy of the analysis results.

[0042] The analysis unit can apply different analysis algorithms depending on the communication category during analysis. For example, the analysis unit applies different analysis algorithms for each category such as email, chat, and video conferencing. For example, the analysis unit selects an appropriate analysis algorithm depending on the content of the communication. For example, the analysis unit applies the optimal analysis method considering the characteristics of each category. This makes it possible to perform optimal analysis that takes into account the characteristics of each category by applying different analysis algorithms depending on the communication category. Communication categories include, but are not limited to, business communications, casual conversations, and meetings. Analysis algorithms include, but are not limited to, clustering algorithms and classification algorithms.

[0043] The analysis unit can determine the priority of analysis based on the timing of the communication during the analysis. For example, the analysis unit may prioritize the analysis of recent communications. For example, the analysis unit may prioritize the analysis of important past communications. For example, the analysis unit may adjust the priority of analysis according to the timing of the communication. This allows for the prioritization of the analysis of the latest information by determining the priority of analysis based on the timing of the communication. The timing of the communication includes, but is not limited to, recent communications and past communications. The priority of analysis includes, but is not limited to, analyses of urgency and analyses of high importance.

[0044] The analysis unit can adjust the order of analysis based on the relevance of communications during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant communications. For example, the analysis unit postpones the analysis of less relevant communications. The analysis unit adjusts the order of analysis according to the relevance of communications. This allows for the prioritization of highly relevant communications by adjusting the order of analysis based on the relevance of communications. Relevance of communications includes, but is not limited to, related topics or common members. The order of analysis includes, but is not limited to, orders of relevance or importance.

[0045] The detection unit can improve detection accuracy by considering the interrelationships of communication during detection. The detection unit detects signs of isolation by comprehensively analyzing the frequency and content of communication, for example. The detection unit accurately detects signs of isolation by considering the interrelationships of communication, for example. The detection unit detects signs of isolation early by analyzing communication patterns, for example. This allows for accurate detection of signs of isolation by considering the interrelationships of communication. Interrelationships of communication include, but are not limited to, the frequency of interactions between members and common topics. Detection accuracy includes, but is not limited to, reducing false positives and improving the detection algorithm.

[0046] The detection unit can perform detection while considering the attribute information of the communication sender. For example, the detection unit can detect signs of isolation by considering the communication sender's job title and department. For example, the detection unit can detect signs of isolation by considering the communication sender's past behavioral history. For example, the detection unit can detect signs of isolation by comprehensively analyzing the communication sender's attribute information. In this way, signs of isolation can be accurately detected by considering the communication sender's attribute information. The sender's attribute information includes, but is not limited to, job title, department, and age. Detection includes, but is not limited to, the detection algorithm used and the accuracy of the detection.

[0047] The detection unit can perform detection while considering the geographical distribution of communication. For example, the detection unit can analyze the geographical distribution of communication to detect signs of isolation. For example, the detection unit can detect signs of isolation by considering communication in geographically distant locations. For example, the detection unit can detect signs of isolation early based on geographical distribution. This allows for early detection of signs of isolation by considering the geographical distribution of communication. Geographical distribution includes, but is not limited to, the user's location and travel history. Detection includes, but is not limited to, the detection algorithm used and the accuracy of the detection.

[0048] The detection unit can improve detection accuracy by referring to relevant literature in the communication during detection. For example, the detection unit accurately detects signs of isolation by referring to relevant literature. For example, the detection unit detects signs of isolation by comparing the content of the communication with relevant literature. For example, the detection unit detects signs of isolation early based on relevant literature. This makes it possible to accurately detect signs of isolation by referring to relevant literature in the communication. Relevant literature includes, but is not limited to, academic papers and technical reports. Detection accuracy includes, but is not limited to, reducing false positives and improving the detection algorithm.

[0049] The proposal department can adjust the level of detail in a proposal based on the importance of the communication. For example, the proposal department will provide detailed proposals for high-importance communications. For example, the proposal department will provide simplified proposals for low-importance communications. For example, the proposal department will determine the priority of proposals according to their importance. This allows for detailed proposals for important communications by adjusting the level of detail based on the importance of the communication. The importance of a communication includes, but is not limited to, the impact on business operations and the positions of the members involved. The level of detail in a proposal includes, but is not limited to, detailed proposal items and the precision of the proposal content.

[0050] The proposal unit can apply different proposal algorithms depending on the communication category when making a proposal. For example, the proposal unit applies different proposal algorithms for each category such as email, chat, and video conferencing. For example, the proposal unit selects an appropriate proposal algorithm depending on the content of the communication. For example, the proposal unit applies the optimal proposal method considering the characteristics of each category. This makes it possible to make optimal proposals that take into account the characteristics of each category by applying different proposal algorithms depending on the communication category. Communication categories include, but are not limited to, business communications, casual conversations, and meetings. Proposal algorithms include, but are not limited to, clustering algorithms and classification algorithms.

[0051] The proposal department can determine the priority of proposals based on the timing of the communication. For example, the proposal department may prioritize recent communications. For example, the proposal department may prioritize important past communications. For example, the proposal department may adjust the priority of proposals according to the timing of the communication. This allows for prioritizing the proposal of the latest information by determining the priority of proposals based on the timing of the communication. The timing of the communication includes, but is not limited to, recent communications and past communications. The priority of proposals includes, but is not limited to, urgent proposals and high-priority proposals.

[0052] The proposal department can adjust the order of proposals based on the relevance of the communications when making a proposal. For example, the proposal department will prioritize highly relevant communications. For example, the proposal department will postpone less relevant communications. The proposal department will adjust the order of proposals according to the relevance of the communications. This allows for prioritizing highly relevant proposals by adjusting the order of proposals based on the relevance of the communications. Relevance of communications includes, but is not limited to, related topics or common members. The order of proposals includes, but is not limited to, being ordered by relevance or importance.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit can analyze the user's past communication history and select the optimal collection method. For example, the data collection unit prioritizes collecting data from communication tools that the user has frequently used in the past. For example, the data collection unit adjusts the collection frequency based on the user's past communication frequency. For example, the data collection unit analyzes the content of the user's past communications and prioritizes collecting important data. This enables efficient data collection by selecting the optimal collection method through analysis of the user's past communication history. Past communication history includes, but is not limited to, email history, chat logs, and video conference recordings. The optimal collection method includes, but is not limited to, collection methods and frequencies depending on the type of data.

[0055] The data collection unit can filter communication data based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting data related to the user's current projects. For example, the data collection unit filters relevant communication data based on the user's areas of interest. For example, the data collection unit adjusts the scope of data collected according to the progress of the user's projects. This allows for the priority collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Current projects include, but are not limited to, the progress of the project and the members involved. Areas of interest include, but are not limited to, the user's interests and areas of expertise.

[0056] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting communication data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will filter relevant communication data based on the user's geographical location. For example, if the user is on the move, the data collection unit will collect data related to their current location in real time. This allows for the priority collection of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, the user's current location and travel history.

[0057] The data collection unit can analyze users' social media activity and collect relevant data when collecting communication data. For example, the data collection unit can collect relevant data based on the content users post on social media. For example, the data collection unit can analyze users' interactions with their social media followers and friends and collect relevant data. For example, the data collection unit can adjust the scope of data collected based on the frequency of users' social media activity. This allows for the efficient collection of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, posts, follower counts, and likes.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the communication during the analysis. For example, the analysis unit performs a detailed analysis for high-importance communications. For example, the analysis unit performs a simplified analysis for low-importance communications. For example, the analysis unit determines the priority of the analysis according to importance. This allows for detailed analysis of important communications by adjusting the level of detail of the analysis based on the importance of the communication. The importance of a communication includes, but is not limited to, the impact on business operations and the positions of the members involved. The level of detail of the analysis includes, but is not limited to, detailed analysis items and the accuracy of the analysis results.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The collection unit collects communication patterns from sources such as emails, chat logs, and video conferencing data. For example, it collects the frequency of email exchanges, the content of chats, and participation status in video conferences. The collection unit retrieves email data from email servers, chat logs from chat applications, and conference data from video conferencing systems. The collection unit can collect data periodically. Step 2: The analysis unit uses generative AI to analyze the data collected by the collection unit and visualize relationship diagrams and interaction frequencies. For example, it performs natural language processing to analyze the content of emails and chats and extract keywords and phrases. It also analyzes the content of video conferences to visualize the frequency of speakers and the content of their statements. Step 3: The detection unit detects signs of isolation based on the data analyzed by the analysis unit. For example, it detects signs of isolation from the frequency and content of interactions. It detects signs of isolation when the frequency of interactions decreases or when exclusion from a particular group is observed. It uses generative AI to analyze the frequency and content of interactions and detect signs of isolation. Step 4: The Proposal team provides response guidelines and recommendations for appropriate human resource management based on the signs of isolation detected by the Detection team. For example, they propose follow-up methods and mentoring procedures. They create detailed reports on specific individuals or departments, including suggestions for necessary follow-up and mentoring. They predict the potential impact of transfers or organizational changes and assess the benefits and risks.

[0061] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system for grasping the overall picture of internal communication, making promotion and transfer criteria objective, and detecting feelings of isolation due to lack of communication at an early stage. This AI agent system collects communication patterns from emails, chat logs, video conferencing data, etc., performs natural language analysis using generative AI, and visualizes correlation diagrams of human relationships and the frequency of interactions. This clarifies internal relationships and provides objective information for decision-making. Furthermore, it detects signs of isolation from the frequency and content of interactions and provides response guidelines and recommendations for appropriate personnel management. This supports the early resolution of communication problems and the response to feelings of isolation. It also creates detailed reports on specific people or departments, including suggestions for necessary follow-up and mentoring. This strengthens effective organizational management and mental health support. Finally, it predicts the potential impact of transfers and organizational changes and evaluates the benefits and risks. This enables appropriate responses to organizational changes. This AI agent can visualize the internal human network and analyze the connections between team members. It can also strengthen team cohesion by detecting feelings of isolation at an early stage and suggesting appropriate follow-up. Furthermore, it can be used as a basis for promotion and transfer decisions, thus supporting transparent and fair organizational management. This allows the AI ​​agent system to grasp the overall picture of internal communication, make promotion and transfer criteria objective, and detect feelings of isolation due to a lack of communication early on.

[0062] The AI ​​agent system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a suggestion unit. The collection unit collects communication patterns from emails, chat logs, video conference data, etc. The collection unit can collect, for example, the frequency of email exchanges, the content of chats, and the participation status of video conferences. The collection unit can, for example, acquire email data from an email server, acquire chat logs from a chat application, and acquire conference data from a video conferencing system. The collection unit can, for example, collect data periodically. The analysis unit uses generative AI to analyze the data collected by the collection unit and visualize relationship diagrams and interaction frequencies. The analysis unit, for example, performs natural language processing to analyze the content of emails and chats. The analysis unit, for example, uses generative AI to analyze the content of emails and chats and extract keywords and phrases. The analysis unit, for example, analyzes the content of video conferences and analyzes the frequency of speakers and the content of their statements. The analysis unit, for example, uses generative AI to analyze the content of video conferences and visualizes the frequency of speakers and the content of their statements. The detection unit detects signs of isolation based on data analyzed by the analysis unit. The detection unit detects signs of isolation, for example, from the frequency and content of interactions. The detection unit detects signs of isolation, for example, when the frequency of interactions decreases or when exclusion from a particular group is observed. The detection unit detects signs of isolation by analyzing the frequency and content of interactions, for example, using generative AI. The proposal unit provides response guidelines and recommendations for appropriate human resource management based on the signs of isolation detected by the detection unit. The proposal unit proposes, for example, follow-up methods and mentoring procedures. The proposal unit creates, for example, detailed reports on specific people or departments, including suggestions for necessary follow-up and mentoring. The proposal unit predicts the potential impact of transfers or organizational changes and evaluates the benefits and risks. As a result, the AI ​​agent system according to the embodiment can grasp the overall picture of internal communication, make promotion and transfer criteria objective, and detect feelings of isolation due to lack of communication at an early stage.

[0063] The data collection unit collects communication patterns from sources such as emails, chat logs, and video conferencing data. Specifically, it can collect data such as the frequency of email exchanges, the content of chats, and participation status in video conferences. For example, the data collection unit retrieves email data from email servers, chat logs from chat applications, and conference data from video conferencing systems. This data is collected regularly and stored in a central database. Email data collection includes sender and recipient addresses, date and time of sending, subject, and content of the message. Chat log collection includes participants in the chat, date and time of message sending, and message content. Video conferencing data collection includes participants in the meeting, identification of speakers, content of statements, and duration of statements. This allows the data collection unit to gain a detailed understanding of internal communication patterns. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and detection units. Additionally, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0064] The analysis unit uses generative AI to analyze data collected by the data collection unit and visualize relationship diagrams and interaction frequencies. Specifically, it performs natural language processing to analyze the content of emails and chats. The generative AI analyzes the content of emails and chats and extracts keywords and phrases. For example, it extracts important topics and emotional tendencies from email content and identifies the flow of conversation and important agenda items from chat content. When analyzing the content of video conferences, it analyzes the frequency and content of speakers to understand the progress of the meeting and the content of the discussion. The generative AI analyzes the content of video conferences and visualizes the frequency and content of speakers. This allows the analysis unit to understand internal communication patterns in detail and visualize relationship diagrams and interaction frequencies. Furthermore, the analysis unit can also analyze long-term trends and patterns by utilizing past data and statistical information. For example, it can analyze fluctuations in communication at specific times or events based on past communication data and make future predictions. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0065] The detection unit detects signs of isolation based on data analyzed by the analysis unit. Specifically, it detects signs of isolation from the frequency and content of interactions. For example, it detects signs of isolation when the frequency of interactions decreases or when exclusion from a particular group is observed. Generative AI is used to analyze the frequency and content of interactions and detect signs of isolation. The generative AI analyzes the frequency and content of interactions and learns patterns for detecting signs of isolation. For example, it detects signs of isolation when a particular employee's communication with other employees decreases or when exclusion from a particular group is observed. This allows the detection unit to understand internal communication patterns in detail and detect signs of isolation early. Furthermore, the detection unit can also analyze long-term trends and patterns by utilizing historical data and statistical information. For example, it can analyze fluctuations in signs of isolation at specific times or events based on past communication data and make future predictions. In addition, the detection unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. This allows the detection unit to not only grasp the situation in real time, but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The Proposal Department provides response guidelines and recommendations for appropriate human resource management based on the signs of isolation detected by the Detection Department. Specifically, it proposes follow-up methods and mentoring procedures. For example, it creates detailed reports on specific individuals or departments, including suggestions for necessary follow-up and mentoring. The Proposal Department predicts the potential impact of transfers and organizational changes and evaluates the benefits and risks. Using generative AI, it generates optimal response guidelines and human resource management recommendations based on historical data and statistics. For example, if a specific employee is isolated, it proposes follow-up methods and mentoring procedures for that employee. It also makes suggestions for resolving communication issues in specific departments. This allows the Proposal Department to gain a detailed understanding of internal communication patterns and provide appropriate response guidelines and human resource management recommendations. Furthermore, the Proposal Department can also leverage historical data and statistics to analyze long-term trends and patterns. For example, it can analyze fluctuations in communication at specific times or events based on historical communication data and make future predictions. In addition, the Proposal Department can use anomaly detection algorithms to detect unusual patterns and anomalous data, issuing early warnings. This allows the proposal department to not only grasp the situation in real time, but also to handle long-term trend analysis and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The analysis unit can perform natural language analysis using generative AI and visualize relationship diagrams and interaction frequencies. For example, the analysis unit can use generative AI to analyze the content of emails and chats and extract keywords and phrases. For example, the analysis unit can use generative AI to analyze the content of video conferences and visualize the frequency of speakers and the content of their statements. For example, the analysis unit can use generative AI to analyze the content of emails and chats and create relationship diagrams. As a result, using generative AI improves the accuracy of natural language analysis and allows for accurate visualization of relationship diagrams and interaction frequencies. Generative AI includes, but is not limited to, large-scale language models. Natural language analysis includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis.

[0068] The proposal department may create detailed reports on specific individuals or departments and include suggestions for necessary follow-up and mentoring. For example, the proposal department may create detailed reports on specific individuals or departments and propose follow-up methods and mentoring procedures. For example, the proposal department may create detailed reports on specific individuals or departments and propose progress checks and regular meetings. For example, the proposal department may create detailed reports on specific individuals or departments and propose mentor selection criteria and mentoring frequency. This makes it possible to propose necessary follow-up and mentoring by creating detailed reports on specific individuals or departments. Detailed reports include, but are not limited to, the items in the report and the level of detail of the information included. Follow-up includes, but are not limited to, regular meetings and progress checks. Mentoring includes, but are not limited to, mentor selection criteria and mentoring frequency.

[0069] The proposal department can predict the potential impact of personnel changes and organizational restructuring, and evaluate the benefits and risks. For example, the proposal department can predict the potential impact of personnel changes and organizational restructuring, and evaluate the impact on changes in work efficiency and team morale. For example, the proposal department can predict the potential impact of personnel changes and organizational restructuring, and quantitatively evaluate the benefits and risks. For example, the proposal department can predict the potential impact of personnel changes and organizational restructuring, and qualitatively evaluate the benefits and risks. This enables appropriate responses to organizational changes by predicting the potential impact of personnel changes and organizational restructuring, and evaluating the benefits and risks. Potential impacts include, but are not limited to, changes in work efficiency and impacts on team morale. Benefits and risks include, but are not limited to, quantitative and qualitative evaluations.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the data collection unit can advance the collection timing to collect near real-time data. For example, if the user is busy, the data collection unit can adjust the collection timing to avoid disrupting their work. By adjusting the collection timing based on the user's emotions, the user's burden can be reduced and near real-time data can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Data collection timing includes, but is not limited to, real-time collection or periodic collection.

[0071] The data collection unit can analyze the user's past communication history and select the optimal collection method. For example, the data collection unit prioritizes collecting data from communication tools that the user has frequently used in the past. For example, the data collection unit adjusts the collection frequency based on the user's past communication frequency. For example, the data collection unit analyzes the content of the user's past communications and prioritizes collecting important data. This enables efficient data collection by selecting the optimal collection method through analysis of the user's past communication history. Past communication history includes, but is not limited to, email history, chat logs, and video conference recordings. The optimal collection method includes, but is not limited to, collection methods and frequencies depending on the type of data.

[0072] The data collection unit can filter communication data based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting data related to the user's current projects. For example, the data collection unit filters relevant communication data based on the user's areas of interest. For example, the data collection unit adjusts the scope of data collected according to the progress of the user's projects. This allows for the priority collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Current projects include, but are not limited to, the progress of the project and the members involved. Areas of interest include, but are not limited to, the user's interests and areas of expertise.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit will prioritize collecting a wide range of data. For example, if the user is busy, the data collection unit will prioritize collecting work-related data. This allows for the priority collection of high-priority data by determining data priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Data priorities include, but are not limited to, high-priority or urgent data.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting communication data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will filter relevant communication data based on the user's geographical location. For example, if the user is on the move, the data collection unit will collect data related to their current location in real time. This allows for the priority collection of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, the user's current location and travel history.

[0075] The data collection unit can analyze users' social media activity and collect relevant data when collecting communication data. For example, the data collection unit can collect relevant data based on the content users post on social media. For example, the data collection unit can analyze users' interactions with their social media followers and friends and collect relevant data. For example, the data collection unit can adjust the scope of data collected based on the frequency of users' social media activity. This allows for the efficient collection of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, posts, follower counts, and likes.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is busy, the analysis unit provides concise analysis results. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The presentation of the analysis includes, but is not limited to, the type of graph and the level of detail of the information displayed.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the communication during the analysis. For example, the analysis unit performs a detailed analysis for high-importance communications. For example, the analysis unit performs a simplified analysis for low-importance communications. For example, the analysis unit determines the priority of the analysis according to importance. This allows for detailed analysis of important communications by adjusting the level of detail of the analysis based on the importance of the communication. The importance of a communication includes, but is not limited to, the impact on business operations and the positions of the members involved. The level of detail of the analysis includes, but is not limited to, detailed analysis items and the accuracy of the analysis results.

[0078] The analysis unit can apply different analysis algorithms depending on the communication category during analysis. For example, the analysis unit applies different analysis algorithms for each category such as email, chat, and video conferencing. For example, the analysis unit selects an appropriate analysis algorithm depending on the content of the communication. For example, the analysis unit applies the optimal analysis method considering the characteristics of each category. This makes it possible to perform optimal analysis that takes into account the characteristics of each category by applying different analysis algorithms depending on the communication category. Communication categories include, but are not limited to, business communications, casual conversations, and meetings. Analysis algorithms include, but are not limited to, clustering algorithms and classification algorithms.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will provide a short, concise analysis. For example, if the user is relaxed, the analysis unit will provide a detailed analysis. For example, if the user is busy, the analysis unit will provide a brief analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with an analysis of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The length of the analysis includes, but is not limited to, the number of pages of the analysis results or the level of detail of the information displayed.

[0080] The analysis unit can determine the priority of analysis based on the timing of the communication during the analysis. For example, the analysis unit may prioritize the analysis of recent communications. For example, the analysis unit may prioritize the analysis of important past communications. For example, the analysis unit may adjust the priority of analysis according to the timing of the communication. This allows for the prioritization of the analysis of the latest information by determining the priority of analysis based on the timing of the communication. The timing of the communication includes, but is not limited to, recent communications and past communications. The priority of analysis includes, but is not limited to, analyses of urgency and analyses of high importance.

[0081] The analysis unit can adjust the order of analysis based on the relevance of communications during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant communications. For example, the analysis unit postpones the analysis of less relevant communications. The analysis unit adjusts the order of analysis according to the relevance of communications. This allows for the prioritization of highly relevant communications by adjusting the order of analysis based on the relevance of communications. Relevance of communications includes, but is not limited to, related topics or common members. The order of analysis includes, but is not limited to, orders of relevance or importance.

[0082] The detection unit can estimate the user's emotions and adjust the detection criteria for signs of isolation based on the estimated user emotions. For example, if the user is stressed, the detection unit sets criteria for early detection of signs of isolation. For example, if the user is relaxed, the detection unit sets criteria for gradual detection of signs of isolation. For example, if the user is busy, the detection unit sets criteria for rapid detection of signs of isolation. By adjusting the detection criteria for signs of isolation based on the user's emotions, signs of isolation can be detected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Detection criteria for signs of isolation include, but are not limited to, decreased frequency of communication or exclusion from a particular group.

[0083] The detection unit can improve detection accuracy by considering the interrelationships of communication during detection. The detection unit detects signs of isolation by comprehensively analyzing the frequency and content of communication, for example. The detection unit accurately detects signs of isolation by considering the interrelationships of communication, for example. The detection unit detects signs of isolation early by analyzing communication patterns, for example. This allows for accurate detection of signs of isolation by considering the interrelationships of communication. Interrelationships of communication include, but are not limited to, the frequency of interactions between members and common topics. Detection accuracy includes, but is not limited to, reducing false positives and improving the detection algorithm.

[0084] The detection unit can perform detection while considering the attribute information of the communication sender. For example, the detection unit can detect signs of isolation by considering the communication sender's job title and department. For example, the detection unit can detect signs of isolation by considering the communication sender's past behavioral history. For example, the detection unit can detect signs of isolation by comprehensively analyzing the communication sender's attribute information. In this way, signs of isolation can be accurately detected by considering the communication sender's attribute information. The sender's attribute information includes, but is not limited to, job title, department, and age. Detection includes, but is not limited to, the detection algorithm used and the accuracy of the detection.

[0085] The detection unit can estimate the user's emotions and adjust the display order of the detection results based on the estimated emotions. For example, if the user is stressed, the detection unit will prioritize displaying important detection results. For example, if the user is relaxed, the detection unit will display detailed detection results. For example, if the user is busy, the detection unit will display concise detection results. In this way, by adjusting the display order of detection results based on the user's emotions, important detection results can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Display order includes, but is not limited to, order of importance or urgency.

[0086] The detection unit can perform detection while considering the geographical distribution of communication. For example, the detection unit can analyze the geographical distribution of communication to detect signs of isolation. For example, the detection unit can detect signs of isolation by considering communication in geographically distant locations. For example, the detection unit can detect signs of isolation early based on geographical distribution. This allows for early detection of signs of isolation by considering the geographical distribution of communication. Geographical distribution includes, but is not limited to, the user's location and travel history. Detection includes, but is not limited to, the detection algorithm used and the accuracy of the detection.

[0087] The detection unit can improve detection accuracy by referring to relevant literature in the communication during detection. For example, the detection unit accurately detects signs of isolation by referring to relevant literature. For example, the detection unit detects signs of isolation by comparing the content of the communication with relevant literature. For example, the detection unit detects signs of isolation early based on relevant literature. This makes it possible to accurately detect signs of isolation by referring to relevant literature in the communication. Relevant literature includes, but is not limited to, academic papers and technical reports. Detection accuracy includes, but is not limited to, reducing false positives and improving the detection algorithm.

[0088] The suggestion function can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide simple and highly visible suggestions. If the user is relaxed, the suggestion function will provide detailed suggestions. If the user is busy, the suggestion function will provide concise suggestions. By adjusting the presentation of suggestions based on the user's emotions, the suggestion function can provide suggestions that are highly visible to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The presentation of suggestions includes, but is not limited to, the type of graph and the level of detail of the information displayed.

[0089] The proposal department can adjust the level of detail in a proposal based on the importance of the communication. For example, the proposal department will provide detailed proposals for high-importance communications. For example, the proposal department will provide simplified proposals for low-importance communications. For example, the proposal department will determine the priority of proposals according to their importance. This allows for detailed proposals for important communications by adjusting the level of detail based on the importance of the communication. The importance of a communication includes, but is not limited to, the impact on business operations and the positions of the members involved. The level of detail in a proposal includes, but is not limited to, detailed proposal items and the precision of the proposal content.

[0090] The proposal unit can apply different proposal algorithms depending on the communication category when making a proposal. For example, the proposal unit applies different proposal algorithms for each category such as email, chat, and video conferencing. For example, the proposal unit selects an appropriate proposal algorithm depending on the content of the communication. For example, the proposal unit applies the optimal proposal method considering the characteristics of each category. This makes it possible to make optimal proposals that take into account the characteristics of each category by applying different proposal algorithms depending on the communication category. Communication categories include, but are not limited to, business communications, casual conversations, and meetings. Proposal algorithms include, but are not limited to, clustering algorithms and classification algorithms.

[0091] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function will provide detailed suggestions. If the user is busy, the suggestion function will provide brief suggestions. By adjusting the length of suggestions based on the user's emotions, the suggestion function can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The length of the suggestions includes, but is not limited to, the number of pages in the suggestions or the level of detail of the information displayed.

[0092] The proposal department can determine the priority of proposals based on the timing of the communication. For example, the proposal department may prioritize recent communications. For example, the proposal department may prioritize important past communications. For example, the proposal department may adjust the priority of proposals according to the timing of the communication. This allows for prioritizing the proposal of the latest information by determining the priority of proposals based on the timing of the communication. The timing of the communication includes, but is not limited to, recent communications and past communications. The priority of proposals includes, but is not limited to, urgent proposals and high-priority proposals.

[0093] The proposal department can adjust the order of proposals based on the relevance of the communications when making a proposal. For example, the proposal department will prioritize highly relevant communications. For example, the proposal department will postpone less relevant communications. The proposal department will adjust the order of proposals according to the relevance of the communications. This allows for prioritizing highly relevant proposals by adjusting the order of proposals based on the relevance of the communications. Relevance of communications includes, but is not limited to, related topics or common members. The order of proposals includes, but is not limited to, being ordered by relevance or importance.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis to ensure that important information is not missed. For example, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis to grasp the overall trend. For example, if the user is busy, the analysis unit can adjust the accuracy of the analysis to provide concise information. In this way, by adjusting the accuracy of the analysis based on the user's emotions, the system can provide the user with the most optimal information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Accuracy of the analysis includes, but is not limited to, detailed analysis items and the accuracy of the analysis results.

[0096] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit will collect a wide range of data. For example, if the user is busy, the data collection unit will prioritize collecting work-related data. This allows for the prioritization of high-priority data by adjusting the types of data collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Types of data include, but are not limited to, high-priority or urgent data.

[0097] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function can delay the timing of suggestions to reduce the user's burden. For example, if the user is relaxed, the suggestion function can advance the timing of suggestions to encourage a quick response. For example, if the user is busy, the suggestion function can adjust the timing of suggestions to avoid disrupting their work. In this way, by adjusting the timing of suggestions based on the user's emotions, the burden on the user can be reduced and a quick response can be encouraged. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Suggestion timing includes, but is not limited to, real-time suggestions or periodic suggestions.

[0098] The detection unit can estimate the user's emotions and adjust the detection frequency based on the estimated emotions. For example, if the user is stressed, the detection unit can increase the detection frequency to find problems early. For example, if the user is relaxed, the detection unit can adjust the detection frequency to grasp the overall trend. For example, if the user is busy, the detection unit can adjust the detection frequency to ensure that important issues are not missed. This allows problems to be discovered at the optimal time for the user by adjusting the detection frequency based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Detection frequency includes, but is not limited to, real-time detection or periodic detection.

[0099] The suggestion function can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide simple and easy-to-implement suggestions. For example, if the user is relaxed, the suggestion function will provide detailed suggestions. For example, if the user is busy, the suggestion function will provide concise suggestions. By adjusting the content of suggestions based on the user's emotions, the suggestion function can provide suggestions that are easy for the user to implement. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The content of suggestions includes, but is not limited to, easy-to-implement suggestions or detailed suggestions.

[0100] The data collection unit can analyze the user's past communication history and select the optimal collection method. For example, the data collection unit prioritizes collecting data from communication tools that the user has frequently used in the past. For example, the data collection unit adjusts the collection frequency based on the user's past communication frequency. For example, the data collection unit analyzes the content of the user's past communications and prioritizes collecting important data. This enables efficient data collection by selecting the optimal collection method through analysis of the user's past communication history. Past communication history includes, but is not limited to, email history, chat logs, and video conference recordings. The optimal collection method includes, but is not limited to, collection methods and frequencies depending on the type of data.

[0101] The data collection unit can filter communication data based on the user's current projects and areas of interest. For example, the data collection unit prioritizes collecting data related to the user's current projects. For example, the data collection unit filters relevant communication data based on the user's areas of interest. For example, the data collection unit adjusts the scope of data collected according to the progress of the user's projects. This allows for the priority collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Current projects include, but are not limited to, the progress of the project and the members involved. Areas of interest include, but are not limited to, the user's interests and areas of expertise.

[0102] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting communication data. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will filter relevant communication data based on the user's geographical location. For example, if the user is on the move, the data collection unit will collect data related to their current location in real time. This allows for the priority collection of highly relevant data by considering the user's geographical location. Geographical location information includes, but is not limited to, the user's current location and travel history.

[0103] The data collection unit can analyze users' social media activity and collect relevant data when collecting communication data. For example, the data collection unit can collect relevant data based on the content users post on social media. For example, the data collection unit can analyze users' interactions with their social media followers and friends and collect relevant data. For example, the data collection unit can adjust the scope of data collected based on the frequency of users' social media activity. This allows for the efficient collection of relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, posts, follower counts, and likes.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the communication during the analysis. For example, the analysis unit performs a detailed analysis for high-importance communications. For example, the analysis unit performs a simplified analysis for low-importance communications. For example, the analysis unit determines the priority of the analysis according to importance. This allows for detailed analysis of important communications by adjusting the level of detail of the analysis based on the importance of the communication. The importance of a communication includes, but is not limited to, the impact on business operations and the positions of the members involved. The level of detail of the analysis includes, but is not limited to, detailed analysis items and the accuracy of the analysis results.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The collection unit collects communication patterns from sources such as emails, chat logs, and video conferencing data. For example, it collects the frequency of email exchanges, the content of chats, and participation status in video conferences. The collection unit retrieves email data from email servers, chat logs from chat applications, and conference data from video conferencing systems. The collection unit can collect data periodically. Step 2: The analysis unit uses generative AI to analyze the data collected by the collection unit and visualize relationship diagrams and interaction frequencies. For example, it performs natural language processing to analyze the content of emails and chats and extract keywords and phrases. It also analyzes the content of video conferences to visualize the frequency of speakers and the content of their statements. Step 3: The detection unit detects signs of isolation based on the data analyzed by the analysis unit. For example, it detects signs of isolation from the frequency and content of interactions. It detects signs of isolation when the frequency of interactions decreases or when exclusion from a particular group is observed. It uses generative AI to analyze the frequency and content of interactions and detect signs of isolation. Step 4: The Proposal team provides response guidelines and recommendations for appropriate human resource management based on the signs of isolation detected by the Detection team. For example, they propose follow-up methods and mentoring procedures. They create detailed reports on specific individuals or departments, including suggestions for necessary follow-up and mentoring. They predict the potential impact of transfers or organizational changes and assess the benefits and risks.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects emails, chat logs, video conferencing data, etc. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI to visualize relationship correlation diagrams and interaction frequencies. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects signs of isolation based on the analyzed data. The proposal unit is implemented by the control unit 46A of the smart device 14 and provides response guidelines and recommendations for appropriate personnel management based on the detected signs of isolation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects emails, chat logs, video conferencing data, etc. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI to visualize relationship correlation diagrams and interaction frequencies. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects signs of isolation based on the analyzed data. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and provides response guidelines and recommendations for appropriate personnel management based on the detected signs of isolation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects emails, chat logs, video conferencing data, etc. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI to visualize relationship correlation diagrams and interaction frequencies. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects signs of isolation based on the analyzed data. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and provides response guidelines and recommendations for appropriate personnel management based on the detected signs of isolation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

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

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects emails, chat logs, video conferencing data, etc. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generated AI to visualize relationship correlation diagrams and interaction frequencies. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects signs of isolation based on the analyzed data. The proposal unit is implemented by the control unit 46A of the robot 414 and provides response guidelines and recommendations for appropriate personnel management based on the detected signs of isolation. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) The collection unit collects communication patterns from emails, chat logs, video conferencing data, etc. The data collected by the aforementioned collection unit is analyzed by an analysis unit that visualizes correlation diagrams of human relationships and the frequency of interactions, A detection unit that detects signs of isolation based on the data analyzed by the analysis unit, The system includes a proposal unit that provides response guidelines and recommendations for appropriate human resource management based on the signs of isolation detected by the detection unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Using generative AI to perform natural language analysis, we visualize relationship diagrams and interaction frequencies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Create a detailed report on a specific person or department, including suggestions for necessary follow-up and mentoring. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Predict the potential impact of personnel changes and organizational restructuring, and evaluate the benefits and risks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of communication data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past communication history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting communication data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting communication data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting communication data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the communication. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the communication category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the communication occurred. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the communication. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is The system estimates the user's emotions and adjusts the criteria for detecting signs of isolation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is During detection, the accuracy of the detection is improved by considering the interrelationships of communication. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is During detection, the attribute information of the communication sender is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is It estimates the user's emotions and adjusts the display order of the detection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is During detection, the geographical distribution of communications is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is During detection, we refer to relevant literature on communication to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the communication. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the communication category. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the communication occurred. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the communication. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects communication patterns from emails, chat logs, video conferencing data, etc. The data collected by the aforementioned collection unit is analyzed by an analysis unit that visualizes correlation diagrams of human relationships and the frequency of interactions, A detection unit that detects signs of isolation based on data analyzed by the analysis unit, The system includes a proposal unit that provides response guidelines and recommendations for appropriate human resource management based on the signs of isolation detected by the detection unit. A system characterized by the following features.

2. The aforementioned analysis unit, Using generative AI to perform natural language analysis, we visualize correlation diagrams of human relationships and the frequency of interactions. The system according to feature 1.

3. The aforementioned proposal section is, Create a detailed report on a specific person or department, including suggestions for necessary follow-up and mentoring. The system according to feature 1.

4. The aforementioned proposal section is, Predict the potential impact of personnel changes and organizational restructuring, and evaluate the benefits and risks. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of communication data collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past communication history and select the optimal data collection method. The system according to feature 1.

7. The aforementioned collection unit is When collecting communication data, filter it based on the user's current projects and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

Images