system

Abstract

The system according to this embodiment aims to visualize organizational issues through dialogue with employees and to support employee growth. [Solution] The system according to this embodiment comprises a dialogue unit, an analysis unit, a visualization unit, and a support unit. The dialogue unit engages in dialogue with employees. The analysis unit analyzes the content of the dialogue collected by the dialogue unit. The visualization unit visualizes organizational issues based on the analysis results obtained by the analysis unit. The support unit supports employee growth based on the organizational issues visualized by the visualization 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 true feelings of employees with the one-sided response method of the questionnaire-type engagement survey.

[0005] The system according to the embodiment aims to visualize organizational issues through dialogue with employees and support the growth of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a dialogue unit, an analysis unit, a visualization unit, and a support unit. The dialogue unit engages in dialogue with employees. The analysis unit analyzes the content of the dialogue collected by the dialogue unit. The visualization unit visualizes organizational issues based on the analysis results obtained by the analysis unit. The support unit supports employee growth based on the organizational issues visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can visualize organizational challenges through dialogue with employees and support employee growth. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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-type engagement survey system according to an embodiment of the present invention is a system that elicits employees' true feelings and supports their growth. This system elicits true feelings through dialogue with employees, visualizes organizational issues by analyzing the content of those dialogues, and further supports their growth. This enables a flexible approach and fair analysis that is not bound by time or manpower, thereby eliciting employees' true feelings, supporting their growth, visualizing organizational issues, and improving productivity. For example, the AI ​​agent engages in dialogue with employees. Through dialogue with the AI ​​agent, employees can freely express their opinions and feelings. For example, if an employee tells the AI ​​agent about dissatisfaction with their work or areas for improvement, the AI ​​agent records the content. Next, the AI ​​agent analyzes the recorded dialogue content. The AI ​​agent analyzes the dialogue content using natural language processing technology and extracts the employee's true feelings and feelings. For example, if an employee says, "Recent projects are very stressful," the AI ​​agent identifies the cause of the stress and specific problems from that statement. Furthermore, the AI ​​agent visualizes organizational issues based on the extracted true feelings and feelings. The AI ​​agent aggregates the opinions and feelings of employees and clarifies the issues for the entire organization. For example, if multiple employees point out the same problem, that problem emerges as an organizational-wide issue. Finally, the AI ​​agent supports employee growth. Based on the employee's opinions and feelings, the AI ​​agent proposes individual growth plans. For example, if an employee wants to improve their skills, the AI ​​agent proposes an appropriate training program. In this way, the present invention enables the AI ​​agent to conduct dialogue with employees, analyze the content of those dialogues, and provide growth support, thereby eliciting employees' true feelings, supporting their growth, visualizing organizational issues, and improving productivity. As a result, the AI ​​agent-type engagement survey system can elicit employees' true feelings, support their growth, visualize organizational issues, and improve productivity.

[0029] The AI ​​agent-type engagement survey system according to this embodiment comprises a dialogue unit, an analysis unit, a visualization unit, and a support unit. The dialogue unit engages in dialogue with employees. The dialogue unit can collect employees' opinions and feelings, for example, through voice dialogue or text dialogue. The dialogue unit is designed to allow employees to freely express their opinions and feelings. For example, the dialogue unit provides an interface that allows employees to express dissatisfaction with their work or areas for improvement. The dialogue unit includes a recording unit that records employees' opinions and feelings. The recording unit can save employees' statements as text data. For example, the recording unit converts voice dialogue into text and saves it. The recording unit also has a function to classify employees' feelings. For example, the recording unit extracts feelings from employees' statements and classifies the type of feelings. The analysis unit analyzes the dialogue content collected by the dialogue unit. The analysis unit analyzes the dialogue content using natural language processing technology. For example, the analysis unit performs morphological analysis and grammatical analysis to understand the meaning of the dialogue content. The analysis unit has a function to extract employees' true feelings and emotions. For example, the analysis unit identifies the causes of stress and specific problems from employee statements. The visualization unit visualizes organizational issues based on the analysis results obtained by the analysis unit. The visualization unit includes a display unit that displays the aggregated information. The display unit visually displays organizational issues using graphs and dashboards. For example, the display unit displays problems pointed out by multiple employees in a graph, clarifying the issues for the entire organization. The support unit supports employee growth based on the organizational issues visualized by the visualization unit. The support unit includes a proposal unit that proposes individual growth plans. The proposal unit proposes appropriate training programs and career paths based on the employee's opinions and feelings. For example, if an employee wants to improve their skills, the proposal unit proposes an appropriate training program. As a result, the AI ​​agent-type engagement survey system according to this embodiment can elicit employees' true feelings, support their growth, visualize organizational issues, and improve productivity.

[0030] The dialogue unit engages in conversations with employees. For example, it can collect employee opinions and feelings through voice and text-based dialogue. Specifically, in voice dialogue, it converts what employees say through a microphone into text in real time using speech recognition technology, and in text dialogue, it collects text entered by employees via a chatbot. The dialogue unit is designed to allow employees to freely express their opinions and feelings. For example, the dialogue unit provides an interface where employees can express dissatisfaction with their work or suggest improvements. The interface is designed with ease of use in mind, allowing for intuitive operation. The dialogue unit also includes a recording unit to record employee opinions and feelings. The recording unit can save employee statements as text data. For example, the recording unit converts voice dialogues into text and saves it. Furthermore, the recording unit has a function to classify employee emotions. For example, the recording unit extracts emotions from employee statements and classifies them by type. Natural language processing technology is used to classify emotions into categories such as positive, negative, and neutral. This allows the Dialogue Department to meticulously record employee opinions and feelings, which can then be used for subsequent analysis and visualization. Furthermore, by accumulating a history of conversations with employees and referring to past conversations, the Dialogue Department can track changes in employee opinions and feelings. This enables the Dialogue Department to continuously monitor employee opinions and feelings, contributing to improved organizational engagement.

[0031] The Analysis Department analyzes the dialogue content collected by the Dialogue Department. The Analysis Department uses natural language processing technology to analyze the dialogue content. Specifically, it performs morphological and grammatical analysis to understand the meaning of the dialogue content. Morphological analysis divides employee statements into individual words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of sentences and identifies sentence elements such as subjects, predicates, and objects. The Analysis Department has the capability to extract employees' true feelings and emotions. For example, the Analysis Department identifies the causes of stress and specific problems from employee statements. Using sentiment analysis technology, it extracts the emotions contained in employee statements and classifies them into sentiment categories such as positive, negative, and neutral. Furthermore, the Analysis Department clusters employee statements to identify common themes and topics. This allows for the systematic organization of employee opinions and emotions, clarifying challenges and areas for improvement across the entire organization. By comparing past dialogue data with the statements of other employees, the Analysis Department can track changes in employee opinions and emotions and grasp trends. This allows the analytics department to conduct a detailed analysis of employee opinions and feelings, providing information to develop concrete measures to improve organizational engagement.

[0032] The visualization unit visualizes organizational issues based on the analysis results obtained by the analysis unit. The visualization unit is equipped with a display unit that displays aggregated information. The display unit visually displays organizational issues using graphs and dashboards. For example, the display unit displays problems pointed out by multiple employees in a graph, clarifying issues for the entire organization. Specifically, it uses bar graphs and pie charts to visually show the distribution of employee opinions and feelings. In addition, the dashboard allows for an at-a-glance understanding of the organization's engagement status based on data that is updated in real time. The visualization unit can display employee opinions and feelings over time, allowing for the understanding of trends in change. For example, it uses line graphs to visually show changes in employee satisfaction and stress levels. Furthermore, the visualization unit can filter data for specific departments or teams and display detailed analysis results. This allows the visualization unit to clarify not only issues for the entire organization but also issues for specific departments or teams, and provide information for formulating concrete countermeasures. The visualization unit provides an intuitive interface that users can operate, enabling them to quickly obtain the necessary information. This allows the visualization unit to provide information for formulating specific measures to improve organizational engagement, thereby contributing to increased productivity across the entire organization.

[0033] The Support Department assists employee growth based on organizational challenges visualized by the Visualization Department. The Support Department includes a Proposal Department that proposes individual growth plans. The Proposal Department suggests appropriate training programs and career paths based on employee opinions and feelings. For example, if an employee desires skill development, the Proposal Department suggests a suitable training program. Specifically, it proposes online courses, workshops, and mentoring programs based on the employee's skill set and career goals. The Proposal Department also clarifies the employee's career path and indicates a direction for future growth. For example, if an employee wants to improve their leadership skills, it provides leadership training programs and project management opportunities. To continuously support employee growth, the Support Department conducts regular feedback sessions to monitor progress. This allows the Support Department to promote employee growth and contribute to increased engagement across the organization. Furthermore, the Support Department provides flexible growth plans that reflect employee opinions and feelings, boosting employee motivation. This enables the Support Department to support employee growth and contribute to increased productivity across the organization.

[0034] The dialogue unit includes a recording unit that records employees' opinions and feelings. The recording unit can save employees' statements as text data. For example, the recording unit converts voice dialogues into text and saves it. The recording unit also has a function to classify employees' feelings. For example, the recording unit extracts feelings from employees' statements and classifies the type of feeling. This improves the accuracy of dialogue content analysis by recording employees' opinions and feelings. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input employee statements into a generating AI and have the generating AI perform the classification of feelings.

[0035] The analysis unit includes an analysis unit that analyzes dialogue content using natural language processing technology. The analysis unit performs morphological and grammatical analysis to understand the meaning of the dialogue content. For example, the analysis unit identifies the causes of stress and specific problems from the employee's statements. This improves the accuracy of dialogue content analysis by using natural language processing technology. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the employee's statements into a generating AI and have the generating AI perform the analysis of the dialogue content.

[0036] The visualization unit includes a display unit that displays aggregated information. The display unit visually displays organizational issues using graphs and dashboards. For example, the display unit displays problems pointed out by multiple employees in a graph, clarifying the issues for the entire organization. This makes it easier to visualize organizational issues by displaying aggregated information. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input aggregated information into a generating AI and have the generating AI execute the generation of graphs and dashboards.

[0037] The support department includes a proposal department that proposes individual growth plans. The proposal department proposes appropriate training programs and career paths based on employees' opinions and feelings. For example, if an employee wants to improve their skills, the proposal department proposes an appropriate training program. In this way, it supports employee growth by proposing individual growth plans. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input employees' opinions and feelings into a generating AI and have the generating AI produce growth plan proposals.

[0038] The dialogue unit selects the most appropriate dialogue content by referring to the employee's past dialogue history during a conversation. For example, the dialogue unit asks relevant questions based on what the employee has said in the past. For example, the dialogue unit customizes the dialogue content based on the employee's past interests. For example, the dialogue unit proposes solutions based on problems the employee has faced in the past. In this way, the dialogue unit can select the most appropriate dialogue content by referring to the employee's past dialogue history. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the employee's past dialogue history into a generating AI and have the generating AI select the most appropriate dialogue content.

[0039] The dialogue unit customizes the content of the conversation based on the employee's current work situation and areas of interest. For example, the dialogue unit asks questions related to the project the employee is currently working on. For example, the dialogue unit selects conversation topics based on the employee's areas of interest. For example, the dialogue unit adjusts the way the conversation progresses according to the employee's work situation. This allows for more effective conversations by customizing the content based on the employee's current work situation and areas of interest. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input data on the employee's work situation and areas of interest into a generating AI and have the generating AI perform the customization of the conversation content.

[0040] The dialogue unit prioritizes relevant dialogue content during conversations, taking into account the employee's geographical location. For example, if the employee is in the office, the dialogue unit prioritizes office-related dialogue. If the employee is on a business trip, the dialogue unit prioritizes dialogue related to the business trip destination. If the employee is working remotely, the dialogue unit prioritizes dialogue related to remote work. This allows the dialogue unit to prioritize relevant dialogue content by considering the employee's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the employee's geographical location information into a generating AI and have the generating AI select relevant dialogue content.

[0041] The dialogue unit analyzes the employee's social media activity during a dialogue and provides relevant dialogue content. For example, the dialogue unit selects dialogue content based on the employee's interests shown on social media. For example, the dialogue unit estimates the employee's current emotions from their social media activity and adjusts the dialogue content accordingly. For example, the dialogue unit adjusts the way the dialogue progresses based on the employee's social media posts. In this way, relevant dialogue content can be provided by analyzing the employee's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input employee social media activity data into a generating AI and have the generating AI select relevant dialogue content.

[0042] The analysis unit adjusts the level of detail in its analysis based on the importance of the dialogue content. For example, the analysis unit performs a detailed analysis on important dialogue content. For example, it performs a simplified analysis on general dialogue content. For example, it performs a rapid analysis on urgent dialogue content. By adjusting the level of detail in the analysis based on the importance of the dialogue content, more effective analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dialogue content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the analysis.

[0043] The analysis unit applies different analysis algorithms depending on the category of the dialogue content during analysis. For example, the analysis unit applies a stress analysis algorithm to dialogue content related to stress. For example, the analysis unit applies a business improvement analysis algorithm to dialogue content related to business improvement. For example, the analysis unit applies a skill improvement analysis algorithm to dialogue content related to skill improvement. By applying different analysis algorithms depending on the category of the dialogue content, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dialogue content category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis department determines the priority of analysis based on the submission date of the dialogue content. For example, the analysis department prioritizes the analysis of recently submitted dialogue content. For example, the analysis department postpones the analysis of previously submitted dialogue content. For example, the analysis department immediately analyzes dialogue content that is of high urgency. This allows for more effective analysis by determining the priority of analysis based on the submission date of the dialogue content. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input dialogue content submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit adjusts the order of analysis based on the relevance of the dialogue content during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant dialogue content. For example, the analysis unit postpones the analysis of less relevant dialogue content. The analysis unit adjusts the order of analysis according to the relevance of the dialogue content. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the dialogue content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dialogue content relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The visualization unit optimizes the current visualization method by referring to past visualization data during visualization. For example, the visualization unit selects the optimal display method based on past visualization data. For example, the visualization unit analyzes employee reactions from past visualization data and adjusts the display method. For example, the visualization unit improves the current visualization method by referring to past visualization data. In this way, the current visualization method can be optimized by referring to past visualization data. Some or all of the above processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past visualization data into a generating AI and have the generating AI perform the optimization of the current visualization method.

[0047] The visualization unit applies different visualization methods to each category of dialogue content during visualization. For example, for dialogue content related to stress, the visualization unit displays a graph showing the stress level. For example, for dialogue content related to business improvement, the visualization unit displays a chart showing areas for improvement. For example, for dialogue content related to skill development, the visualization unit displays a graph showing the skill level. By applying different visualization methods according to the category of dialogue content, more appropriate visualization becomes possible. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input dialogue content category data into a generating AI and have the generating AI execute the application of visualization methods.

[0048] The visualization unit analyzes changes in visualization based on the submission timing of the dialogue content during visualization. For example, the visualization unit prioritizes visualization of recently submitted dialogue content. For example, it postpones visualization of previously submitted dialogue content. The visualization unit analyzes changes in visualization according to the submission timing. This allows for more effective visualization by analyzing changes in visualization based on the submission timing of the dialogue content. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input dialogue content submission timing data into a generating AI and have the generating AI perform the analysis of changes in visualization.

[0049] The visualization unit performs visualization by referring to relevant market data related to the dialogue content. For example, the visualization unit performs visualization based on market data related to the dialogue content. For example, the visualization unit evaluates the importance of the dialogue content by referring to market data. For example, the visualization unit analyzes trends in the dialogue content based on market data. This makes it possible to perform more appropriate visualization by referring to relevant market data related to the dialogue content. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input relevant market data into a generating AI and have the generating AI perform the visualization.

[0050] The support department analyzes the employee's past growth history to select the optimal support method during the support process. For example, the support department proposes an optimal training program based on the employee's past growth history. For example, the support department selects a support method that matches the employee's current skill level based on their growth history. For example, the support department analyzes the employee's past growth history and adjusts the support method based on growth trends. This allows the support department to select the optimal support method by analyzing the employee's past growth history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the employee's past growth history data into a generating AI and have the generating AI select the optimal support method.

[0051] The support department customizes the means of support based on the employee's current work situation when providing assistance. For example, the support department proposes support methods related to the project the employee is currently working on. For example, the support department adjusts the means of support according to the employee's work situation. For example, the support department selects the optimal support method considering the employee's work situation. This makes it possible to provide more effective support by customizing the means of support based on the employee's current work situation. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input employee work situation data into a generating AI and have the generating AI perform the customization of the means of support.

[0052] The support department selects the optimal support method when providing assistance, taking into account the employee's geographical location. For example, if the employee is in the office, the support department will suggest office-related support methods. For example, if the employee is on a business trip, the support department will suggest support methods related to the business trip destination. For example, if the employee is working remotely, the support department will suggest support methods related to remote work. This allows the support department to select the optimal support method by considering the employee's geographical location. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input the employee's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0053] The support department analyzes the employee's social media activity and proposes support methods when providing assistance. For example, the support department selects a support method based on the employee's interests shown on social media. For example, the support department estimates the employee's current emotions from their social media activity and adjusts the support method accordingly. For example, the support department proposes support methods based on the employee's statements on social media. In this way, by analyzing the employee's social media activity, the support department can propose the most suitable support method. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input employee social media activity data into a generating AI and have the generating AI propose support methods.

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

[0055] The dialogue system can also be equipped with a function to translate employee conversations in real time. For example, the dialogue system can translate in real time to facilitate conversations between employees who speak different languages. The dialogue system can, for example, translate what an employee says in their native language into another language to facilitate the conversation. The dialogue system can, for example, translate what an employee says in a foreign language into their native language to aid understanding. This makes communication between employees who speak different languages ​​smoother and enables effective dialogue even in global organizations.

[0056] The dialogue unit can not only convert employee conversations from speech to text, but can also have the functionality to convert from text to speech. For example, the dialogue unit can convert text entered by an employee into speech and transmit it to other employees. The dialogue unit can, for example, play back the content entered by an employee as speech to maintain the naturalness of the conversation. The dialogue unit can, for example, convert the content entered by an employee as speech into text and record it. This enables bidirectional conversion between speech and text, improving the flexibility of conversations.

[0057] The analytics department can also incorporate features that consider the context of employee conversations when analyzing their content. For example, the analytics department can analyze how an employee's statements relate to the surrounding context. It can identify, for instance, whether an employee's statements are related to a specific project or task. It can also analyze, for example, how an employee's statements relate to past conversations. This allows for more accurate analysis by considering the context of the conversation.

[0058] The visualization unit can also include functions to display the frequency and patterns of employee conversations when visualizing their content. For example, the visualization unit can display a graph showing how often employees talk about a particular topic. The visualization unit can analyze and visually display patterns in employee conversations. The visualization unit can display changes in employee conversations over time. By visualizing the frequency and patterns of conversations, it becomes possible to understand the communication trends within the organization.

[0059] The support department can also have the function of suggesting team-building activities based on the content of employee conversations. For example, if an employee wants to improve teamwork, the support department can suggest appropriate team-building activities. If an employee wants to improve communication, the support department can suggest activities to improve communication skills. If an employee is feeling stressed, the support department can suggest relaxation activities. In this way, by suggesting appropriate team-building activities based on the content of employee conversations, team cohesion can be enhanced.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The dialogue unit engages in dialogue with employees. The dialogue unit can collect employee opinions and feelings through voice and text dialogue. It is designed to allow employees to freely express their opinions and feelings, providing an interface where they can, for example, state dissatisfaction with their work or suggest areas for improvement. The dialogue unit has a recording unit that records employee opinions and feelings, and the recording unit can save employee statements as text data. For example, it can convert voice dialogues into text and save them. The recording unit also has a function to classify employee emotions, extracting emotions from statements and classifying them by type. Step 2: The Analysis Department analyzes the dialogue content collected by the Dialogue Department. The Analysis Department uses natural language processing technology to analyze the dialogue content, performing morphological and grammatical analysis to understand the meaning of the dialogue. Furthermore, it has a function to extract employees' true feelings and emotions, identifying the causes of stress and specific problems from their statements. Step 3: The visualization unit visualizes organizational issues based on the analysis results obtained by the analysis unit. The visualization unit is equipped with a display unit that displays aggregated information, and visually displays organizational issues using graphs and dashboards. For example, it displays problems pointed out by multiple employees in a graph to clarify the issues for the entire organization. Step 4: The Support Department supports employee growth based on organizational challenges visualized by the Visualization Department. The Support Department includes a Proposal Department that proposes individual growth plans, suggesting appropriate training programs and career paths based on employee opinions and feelings. For example, if an employee wants to improve their skills, the Support Department will suggest an appropriate training program.

[0062] (Example of form 2) The AI ​​agent-type engagement survey system according to an embodiment of the present invention is a system that elicits employees' true feelings and supports their growth. This system elicits true feelings through dialogue with employees, visualizes organizational issues by analyzing the content of those dialogues, and further supports their growth. This enables a flexible approach and fair analysis that is not bound by time or manpower, thereby eliciting employees' true feelings, supporting their growth, visualizing organizational issues, and improving productivity. For example, the AI ​​agent engages in dialogue with employees. Through dialogue with the AI ​​agent, employees can freely express their opinions and feelings. For example, if an employee tells the AI ​​agent about dissatisfaction with their work or areas for improvement, the AI ​​agent records the content. Next, the AI ​​agent analyzes the recorded dialogue content. The AI ​​agent analyzes the dialogue content using natural language processing technology and extracts the employee's true feelings and feelings. For example, if an employee says, "Recent projects are very stressful," the AI ​​agent identifies the cause of the stress and specific problems from that statement. Furthermore, the AI ​​agent visualizes organizational issues based on the extracted true feelings and feelings. The AI ​​agent aggregates the opinions and feelings of employees and clarifies the issues for the entire organization. For example, if multiple employees point out the same problem, that problem emerges as an organizational-wide issue. Finally, the AI ​​agent supports employee growth. Based on the employee's opinions and feelings, the AI ​​agent proposes individual growth plans. For example, if an employee wants to improve their skills, the AI ​​agent proposes an appropriate training program. In this way, the present invention enables the AI ​​agent to conduct dialogue with employees, analyze the content of those dialogues, and provide growth support, thereby eliciting employees' true feelings, supporting their growth, visualizing organizational issues, and improving productivity. As a result, the AI ​​agent-type engagement survey system can elicit employees' true feelings, support their growth, visualize organizational issues, and improve productivity.

[0063] The AI ​​agent-type engagement survey system according to this embodiment comprises a dialogue unit, an analysis unit, a visualization unit, and a support unit. The dialogue unit engages in dialogue with employees. The dialogue unit can collect employees' opinions and feelings, for example, through voice dialogue or text dialogue. The dialogue unit is designed to allow employees to freely express their opinions and feelings. For example, the dialogue unit provides an interface that allows employees to express dissatisfaction with their work or areas for improvement. The dialogue unit includes a recording unit that records employees' opinions and feelings. The recording unit can save employees' statements as text data. For example, the recording unit converts voice dialogue into text and saves it. The recording unit also has a function to classify employees' feelings. For example, the recording unit extracts feelings from employees' statements and classifies the type of feelings. The analysis unit analyzes the dialogue content collected by the dialogue unit. The analysis unit analyzes the dialogue content using natural language processing technology. For example, the analysis unit performs morphological analysis and grammatical analysis to understand the meaning of the dialogue content. The analysis unit has a function to extract employees' true feelings and emotions. For example, the analysis unit identifies the causes of stress and specific problems from employee statements. The visualization unit visualizes organizational issues based on the analysis results obtained by the analysis unit. The visualization unit includes a display unit that displays the aggregated information. The display unit visually displays organizational issues using graphs and dashboards. For example, the display unit displays problems pointed out by multiple employees in a graph, clarifying the issues for the entire organization. The support unit supports employee growth based on the organizational issues visualized by the visualization unit. The support unit includes a proposal unit that proposes individual growth plans. The proposal unit proposes appropriate training programs and career paths based on the employee's opinions and feelings. For example, if an employee wants to improve their skills, the proposal unit proposes an appropriate training program. As a result, the AI ​​agent-type engagement survey system according to this embodiment can elicit employees' true feelings, support their growth, visualize organizational issues, and improve productivity.

[0064] The dialogue unit engages in conversations with employees. For example, it can collect employee opinions and feelings through voice and text-based dialogue. Specifically, in voice dialogue, it converts what employees say through a microphone into text in real time using speech recognition technology, and in text dialogue, it collects text entered by employees via a chatbot. The dialogue unit is designed to allow employees to freely express their opinions and feelings. For example, the dialogue unit provides an interface where employees can express dissatisfaction with their work or suggest improvements. The interface is designed with ease of use in mind, allowing for intuitive operation. The dialogue unit also includes a recording unit to record employee opinions and feelings. The recording unit can save employee statements as text data. For example, the recording unit converts voice dialogues into text and saves it. Furthermore, the recording unit has a function to classify employee emotions. For example, the recording unit extracts emotions from employee statements and classifies them by type. Natural language processing technology is used to classify emotions into categories such as positive, negative, and neutral. This allows the Dialogue Department to meticulously record employee opinions and feelings, which can then be used for subsequent analysis and visualization. Furthermore, by accumulating a history of conversations with employees and referring to past conversations, the Dialogue Department can track changes in employee opinions and feelings. This enables the Dialogue Department to continuously monitor employee opinions and feelings, contributing to improved organizational engagement.

[0065] The Analysis Department analyzes the dialogue content collected by the Dialogue Department. The Analysis Department uses natural language processing technology to analyze the dialogue content. Specifically, it performs morphological and grammatical analysis to understand the meaning of the dialogue content. Morphological analysis divides employee statements into individual words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of sentences and identifies sentence elements such as subjects, predicates, and objects. The Analysis Department has the capability to extract employees' true feelings and emotions. For example, the Analysis Department identifies the causes of stress and specific problems from employee statements. Using sentiment analysis technology, it extracts the emotions contained in employee statements and classifies them into sentiment categories such as positive, negative, and neutral. Furthermore, the Analysis Department clusters employee statements to identify common themes and topics. This allows for the systematic organization of employee opinions and emotions, clarifying challenges and areas for improvement across the entire organization. By comparing past dialogue data with the statements of other employees, the Analysis Department can track changes in employee opinions and emotions and grasp trends. This allows the analytics department to conduct a detailed analysis of employee opinions and feelings, providing information to develop concrete measures to improve organizational engagement.

[0066] The visualization unit visualizes organizational issues based on the analysis results obtained by the analysis unit. The visualization unit is equipped with a display unit that displays aggregated information. The display unit visually displays organizational issues using graphs and dashboards. For example, the display unit displays problems pointed out by multiple employees in a graph, clarifying issues for the entire organization. Specifically, it uses bar graphs and pie charts to visually show the distribution of employee opinions and feelings. In addition, the dashboard allows for an at-a-glance understanding of the organization's engagement status based on data that is updated in real time. The visualization unit can display employee opinions and feelings over time, allowing for the understanding of trends in change. For example, it uses line graphs to visually show changes in employee satisfaction and stress levels. Furthermore, the visualization unit can filter data for specific departments or teams and display detailed analysis results. This allows the visualization unit to clarify not only issues for the entire organization but also issues for specific departments or teams, and provide information for formulating concrete countermeasures. The visualization unit provides an intuitive interface that users can operate, enabling them to quickly obtain the necessary information. This allows the visualization unit to provide information for formulating specific measures to improve organizational engagement, thereby contributing to increased productivity across the entire organization.

[0067] The Support Department assists employee growth based on organizational challenges visualized by the Visualization Department. The Support Department includes a Proposal Department that proposes individual growth plans. The Proposal Department suggests appropriate training programs and career paths based on employee opinions and feelings. For example, if an employee desires skill development, the Proposal Department suggests a suitable training program. Specifically, it proposes online courses, workshops, and mentoring programs based on the employee's skill set and career goals. The Proposal Department also clarifies the employee's career path and indicates a direction for future growth. For example, if an employee wants to improve their leadership skills, it provides leadership training programs and project management opportunities. To continuously support employee growth, the Support Department conducts regular feedback sessions to monitor progress. This allows the Support Department to promote employee growth and contribute to increased engagement across the organization. Furthermore, the Support Department provides flexible growth plans that reflect employee opinions and feelings, boosting employee motivation. This enables the Support Department to support employee growth and contribute to increased productivity across the organization.

[0068] The dialogue unit includes a recording unit that records employees' opinions and feelings. The recording unit can save employees' statements as text data. For example, the recording unit converts voice dialogues into text and saves it. The recording unit also has a function to classify employees' feelings. For example, the recording unit extracts feelings from employees' statements and classifies the type of feeling. This improves the accuracy of dialogue content analysis by recording employees' opinions and feelings. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input employee statements into a generating AI and have the generating AI perform the classification of feelings.

[0069] The analysis unit includes an analysis unit that analyzes dialogue content using natural language processing technology. The analysis unit performs morphological and grammatical analysis to understand the meaning of the dialogue content. For example, the analysis unit identifies the causes of stress and specific problems from the employee's statements. This improves the accuracy of dialogue content analysis by using natural language processing technology. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the employee's statements into a generating AI and have the generating AI perform the analysis of the dialogue content.

[0070] The visualization unit includes a display unit that displays aggregated information. The display unit visually displays organizational issues using graphs and dashboards. For example, the display unit displays problems pointed out by multiple employees in a graph, clarifying the issues for the entire organization. This makes it easier to visualize organizational issues by displaying aggregated information. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input aggregated information into a generating AI and have the generating AI execute the generation of graphs and dashboards.

[0071] The support department includes a proposal department that proposes individual growth plans. The proposal department proposes appropriate training programs and career paths based on employees' opinions and feelings. For example, if an employee wants to improve their skills, the proposal department proposes an appropriate training program. In this way, it supports employee growth by proposing individual growth plans. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input employees' opinions and feelings into a generating AI and have the generating AI produce growth plan proposals.

[0072] The dialogue unit estimates the employee's emotions and adjusts the dialogue process based on the estimated emotions. For example, if the employee is stressed, the dialogue unit will proceed slowly to help them relax. If the employee is excited, the dialogue unit will proceed quickly to efficiently gather information. If the employee is tired, the dialogue unit will simplify the dialogue to reduce their burden. By adjusting the dialogue process according to the employee's emotions, a more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The dialogue unit selects the most appropriate dialogue content by referring to the employee's past dialogue history during a conversation. For example, the dialogue unit asks relevant questions based on what the employee has said in the past. For example, the dialogue unit customizes the dialogue content based on the employee's past interests. For example, the dialogue unit proposes solutions based on problems the employee has faced in the past. In this way, the dialogue unit can select the most appropriate dialogue content by referring to the employee's past dialogue history. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the employee's past dialogue history into a generating AI and have the generating AI select the most appropriate dialogue content.

[0074] The dialogue unit customizes the content of the conversation based on the employee's current work situation and areas of interest. For example, the dialogue unit asks questions related to the project the employee is currently working on. For example, the dialogue unit selects conversation topics based on the employee's areas of interest. For example, the dialogue unit adjusts the way the conversation progresses according to the employee's work situation. This allows for more effective conversations by customizing the content based on the employee's current work situation and areas of interest. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input data on the employee's work situation and areas of interest into a generating AI and have the generating AI perform the customization of the conversation content.

[0075] The dialogue unit estimates the employee's emotions and determines the priority of the dialogue based on the estimated emotions. For example, if the employee is stressed, the dialogue unit sets a high priority for the dialogue. For example, if the employee is relaxed, the dialogue unit sets a low priority for the dialogue. For example, if the employee is in a hurry, the dialogue unit sets a rapid priority for the dialogue. This allows for more appropriate dialogue by determining the priority of the dialogue according to the employee's emotions. 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. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The dialogue unit prioritizes relevant dialogue content during conversations, taking into account the employee's geographical location. For example, if the employee is in the office, the dialogue unit prioritizes office-related dialogue. If the employee is on a business trip, the dialogue unit prioritizes dialogue related to the business trip destination. If the employee is working remotely, the dialogue unit prioritizes dialogue related to remote work. This allows the dialogue unit to prioritize relevant dialogue content by considering the employee's geographical location. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the employee's geographical location information into a generating AI and have the generating AI select relevant dialogue content.

[0077] The dialogue unit analyzes the employee's social media activity during a dialogue and provides relevant dialogue content. For example, the dialogue unit selects dialogue content based on the employee's interests shown on social media. For example, the dialogue unit estimates the employee's current emotions from their social media activity and adjusts the dialogue content accordingly. For example, the dialogue unit adjusts the way the dialogue progresses based on the employee's social media posts. In this way, relevant dialogue content can be provided by analyzing the employee's social media activity. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input employee social media activity data into a generating AI and have the generating AI select relevant dialogue content.

[0078] The analysis department estimates employees' emotions and adjusts its analysis methods based on the estimated emotions. For example, if an employee is stressed, the analysis department conducts a detailed analysis to identify the cause of the stress. If an employee is relaxed, the analysis department conducts a simpler analysis to grasp the overall trend. If an employee is in a hurry, the analysis department conducts a rapid analysis to provide immediate feedback. This allows for more appropriate analysis by adjusting the analysis method according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, 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. Some or all of the above-described processes in the analysis department may be performed using AI or not. For example, the analysis department can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit adjusts the level of detail in its analysis based on the importance of the dialogue content. For example, the analysis unit performs a detailed analysis on important dialogue content. For example, it performs a simplified analysis on general dialogue content. For example, it performs a rapid analysis on urgent dialogue content. By adjusting the level of detail in the analysis based on the importance of the dialogue content, more effective analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dialogue content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the analysis.

[0080] The analysis unit applies different analysis algorithms depending on the category of the dialogue content during analysis. For example, the analysis unit applies a stress analysis algorithm to dialogue content related to stress. For example, the analysis unit applies a business improvement analysis algorithm to dialogue content related to business improvement. For example, the analysis unit applies a skill improvement analysis algorithm to dialogue content related to skill improvement. By applying different analysis algorithms depending on the category of the dialogue content, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dialogue content category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0081] The analysis department estimates employees' emotions and determines analysis priorities based on the estimated emotions. For example, if an employee is stressed, the analysis department sets a high priority. If an employee is relaxed, the analysis department sets a low priority. If an employee is in a hurry, the analysis department sets a rapid priority. This allows for more appropriate analysis by determining analysis priorities according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The analysis department determines the priority of analysis based on the submission date of the dialogue content. For example, the analysis department prioritizes the analysis of recently submitted dialogue content. For example, the analysis department postpones the analysis of previously submitted dialogue content. For example, the analysis department immediately analyzes dialogue content that is of high urgency. This allows for more effective analysis by determining the priority of analysis based on the submission date of the dialogue content. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input dialogue content submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit adjusts the order of analysis based on the relevance of the dialogue content during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant dialogue content. For example, the analysis unit postpones the analysis of less relevant dialogue content. The analysis unit adjusts the order of analysis according to the relevance of the dialogue content. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the dialogue content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input dialogue content relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0084] The visualization unit estimates the employee's emotions and adjusts the display method of the visualization based on the estimated employee's emotions. For example, if an employee is stressed, the visualization unit provides a simple and highly visible display method. For example, if an employee is relaxed, the visualization unit provides a display method that includes detailed information. For example, if an employee is in a hurry, the visualization unit provides a display method that gets straight to the point. By adjusting the display method of the visualization according to the employee's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The visualization unit optimizes the current visualization method by referring to past visualization data during visualization. For example, the visualization unit selects the optimal display method based on past visualization data. For example, the visualization unit analyzes employee reactions from past visualization data and adjusts the display method. For example, the visualization unit improves the current visualization method by referring to past visualization data. In this way, the current visualization method can be optimized by referring to past visualization data. Some or all of the above processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past visualization data into a generating AI and have the generating AI perform the optimization of the current visualization method.

[0086] The visualization unit applies different visualization methods to each category of dialogue content during visualization. For example, for dialogue content related to stress, the visualization unit displays a graph showing the stress level. For example, for dialogue content related to business improvement, the visualization unit displays a chart showing areas for improvement. For example, for dialogue content related to skill development, the visualization unit displays a graph showing the skill level. By applying different visualization methods according to the category of dialogue content, more appropriate visualization becomes possible. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input dialogue content category data into a generating AI and have the generating AI execute the application of visualization methods.

[0087] The visualization unit estimates the employee's emotions and adjusts the importance of the visualization based on the estimated employee's emotions. For example, if an employee is stressed, the visualization unit prioritizes displaying high-importance information. For example, if an employee is relaxed, the visualization unit displays low-importance information. For example, if an employee is in a hurry, the visualization unit quickly displays high-importance information. This allows for more appropriate information display by adjusting the importance of the visualization according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The visualization unit analyzes changes in visualization based on the submission timing of the dialogue content during visualization. For example, the visualization unit prioritizes visualization of recently submitted dialogue content. For example, it postpones visualization of previously submitted dialogue content. The visualization unit analyzes changes in visualization according to the submission timing. This allows for more effective visualization by analyzing changes in visualization based on the submission timing of the dialogue content. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input dialogue content submission timing data into a generating AI and have the generating AI perform the analysis of changes in visualization.

[0089] The visualization unit performs visualization by referring to relevant market data related to the dialogue content. For example, the visualization unit performs visualization based on market data related to the dialogue content. For example, the visualization unit evaluates the importance of the dialogue content by referring to market data. For example, the visualization unit analyzes trends in the dialogue content based on market data. This makes it possible to perform more appropriate visualization by referring to relevant market data related to the dialogue content. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input relevant market data into a generating AI and have the generating AI perform the visualization.

[0090] The support department estimates the employee's emotions and adjusts the support method based on the estimated emotions. For example, if an employee is stressed, the support department suggests a relaxing support method. For example, if an employee is relaxed, the support department suggests a proactive support method. For example, if an employee is in a hurry, the support department suggests a quick support method. By adjusting the support method according to the employee's emotions, more appropriate support becomes possible. 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. Some or all of the above processing in the support department may be performed using AI or not using AI. For example, the support department can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The support department analyzes the employee's past growth history to select the optimal support method during the support process. For example, the support department proposes an optimal training program based on the employee's past growth history. For example, the support department selects a support method that matches the employee's current skill level based on their growth history. For example, the support department analyzes the employee's past growth history and adjusts the support method based on growth trends. This allows the support department to select the optimal support method by analyzing the employee's past growth history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the employee's past growth history data into a generating AI and have the generating AI select the optimal support method.

[0092] The support department customizes the means of support based on the employee's current work situation when providing assistance. For example, the support department proposes support methods related to the project the employee is currently working on. For example, the support department adjusts the means of support according to the employee's work situation. For example, the support department selects the optimal support method considering the employee's work situation. This makes it possible to provide more effective support by customizing the means of support based on the employee's current work situation. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input employee work situation data into a generating AI and have the generating AI perform the customization of the means of support.

[0093] The support department estimates the emotions of employees and determines the priority of support based on the estimated emotions. For example, if an employee is stressed, the support department will set a high priority for support. For example, if an employee is relaxed, the support department will set a low priority for support. For example, if an employee is in a hurry, the support department will set a rapid priority for support. This allows for more appropriate support by determining the priority of support according to the employee's emotions. 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. Some or all of the above processing in the support department may be performed using AI or not using AI. For example, the support department can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The support department selects the optimal support method when providing assistance, taking into account the employee's geographical location. For example, if the employee is in the office, the support department will suggest office-related support methods. For example, if the employee is on a business trip, the support department will suggest support methods related to the business trip destination. For example, if the employee is working remotely, the support department will suggest support methods related to remote work. This allows the support department to select the optimal support method by considering the employee's geographical location. Some or all of the above processing in the support department may be performed using AI, for example, or without AI. For example, the support department can input the employee's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0095] The support department analyzes the employee's social media activity and proposes support methods when providing assistance. For example, the support department selects a support method based on the employee's interests shown on social media. For example, the support department estimates the employee's current emotions from their social media activity and adjusts the support method accordingly. For example, the support department proposes support methods based on the employee's statements on social media. In this way, by analyzing the employee's social media activity, the support department can propose the most suitable support method. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input employee social media activity data into a generating AI and have the generating AI propose support methods.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The dialogue system can also be equipped with a function to translate employee conversations in real time. For example, the dialogue system can translate in real time to facilitate conversations between employees who speak different languages. The dialogue system can, for example, translate what an employee says in their native language into another language to facilitate the conversation. The dialogue system can, for example, translate what an employee says in a foreign language into their native language to aid understanding. This makes communication between employees who speak different languages ​​smoother and enables effective dialogue even in global organizations.

[0098] The dialogue unit can not only convert employee conversations from speech to text, but can also have the functionality to convert from text to speech. For example, the dialogue unit can convert text entered by an employee into speech and transmit it to other employees. The dialogue unit can, for example, play back the content entered by an employee as speech to maintain the naturalness of the conversation. The dialogue unit can, for example, convert the content entered by an employee as speech into text and record it. This enables bidirectional conversion between speech and text, improving the flexibility of conversations.

[0099] The analytics department can also incorporate features that consider the context of employee conversations when analyzing their content. For example, the analytics department can analyze how an employee's statements relate to the surrounding context. It can identify, for instance, whether an employee's statements are related to a specific project or task. It can also analyze, for example, how an employee's statements relate to past conversations. This allows for more accurate analysis by considering the context of the conversation.

[0100] The visualization unit can also include functions to display the frequency and patterns of employee conversations when visualizing their content. For example, the visualization unit can display a graph showing how often employees talk about a particular topic. The visualization unit can analyze and visually display patterns in employee conversations. The visualization unit can display changes in employee conversations over time. By visualizing the frequency and patterns of conversations, it becomes possible to understand the communication trends within the organization.

[0101] The support department can also have the function of suggesting team-building activities based on the content of employee conversations. For example, if an employee wants to improve teamwork, the support department can suggest appropriate team-building activities. If an employee wants to improve communication, the support department can suggest activities to improve communication skills. If an employee is feeling stressed, the support department can suggest relaxation activities. In this way, by suggesting appropriate team-building activities based on the content of employee conversations, team cohesion can be enhanced.

[0102] The dialogue system can also estimate an employee's emotions and adjust the tone of the dialogue based on those estimates. For example, if an employee is feeling anxious, the dialogue system will soften the tone of the dialogue to provide reassurance. If an employee is excited, for example, the dialogue system will calm the tone of the dialogue to encourage a calm conversation. If an employee is tired, for example, the dialogue system will lighten the tone of the dialogue to reduce the burden. By adjusting the tone of the dialogue according to the employee's emotions, more appropriate dialogue becomes possible.

[0103] The dialogue system can also estimate an employee's emotions and adjust the content of the conversation based on those estimates. For example, if an employee is feeling stressed, the dialogue system can offer topics that help them relax. If an employee is excited, for example, the dialogue system can offer topics that help them calm down. If an employee is tired, for example, the dialogue system can offer concise and easy-to-understand topics. By adjusting the content of the conversation according to the employee's emotions, more appropriate conversations become possible.

[0104] The analytics department can estimate employees' emotions and adjust the depth of the analysis based on those estimates. For example, if an employee is stressed, the analytics department conducts a detailed analysis to identify the cause of the stress. If an employee is relaxed, for example, the analytics department conducts a simpler analysis to grasp the overall trend. If an employee is in a hurry, for example, the analytics department conducts a rapid analysis to provide immediate feedback. This allows for more appropriate analysis by adjusting the depth of the analysis according to the employee's emotions.

[0105] The visualization unit can also estimate employees' emotions and adjust the visualization style based on those emotions. For example, if an employee is stressed, the visualization unit provides a simple, easy-to-read graph. If an employee is relaxed, for example, the visualization unit provides a graph with detailed information. If an employee is in a hurry, for example, the visualization unit provides a concise graph. This allows for more appropriate information display by adjusting the visualization style according to the employee's emotions.

[0106] The support department can also estimate employees' emotions and adjust the timing of support based on those estimates. For example, if an employee is feeling stressed, the support department can provide immediate support. If an employee is relaxed, for example, the support department can provide planned support. If an employee is in a hurry, for example, the support department can provide rapid support. This allows for more appropriate support by adjusting the timing of support according to the employee's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The dialogue unit engages in dialogue with employees. The dialogue unit can collect employee opinions and feelings through voice and text dialogue. It is designed to allow employees to freely express their opinions and feelings, providing an interface where they can, for example, state dissatisfaction with their work or suggest areas for improvement. The dialogue unit has a recording unit that records employee opinions and feelings, and the recording unit can save employee statements as text data. For example, it can convert voice dialogues into text and save them. The recording unit also has a function to classify employee emotions, extracting emotions from statements and classifying them by type. Step 2: The Analysis Department analyzes the dialogue content collected by the Dialogue Department. The Analysis Department uses natural language processing technology to analyze the dialogue content, performing morphological and grammatical analysis to understand the meaning of the dialogue. Furthermore, it has a function to extract employees' true feelings and emotions, identifying the causes of stress and specific problems from their statements. Step 3: The visualization unit visualizes organizational issues based on the analysis results obtained by the analysis unit. The visualization unit is equipped with a display unit that displays aggregated information, and visually displays organizational issues using graphs and dashboards. For example, it displays problems pointed out by multiple employees in a graph to clarify the issues for the entire organization. Step 4: The Support Department supports employee growth based on organizational challenges visualized by the Visualization Department. The Support Department includes a Proposal Department that proposes individual growth plans, suggesting appropriate training programs and career paths based on employee opinions and feelings. For example, if an employee wants to improve their skills, the Support Department will suggest an appropriate training program.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the dialogue unit, analysis unit, visualization unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the smart device 14 and conducts voice and text dialogues with employees. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the dialogue using natural language processing technology. The visualization unit is implemented by the display 40A of the smart device 14 and visually displays organizational issues using graphs and dashboards. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes employee growth plans. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the dialogue unit, analysis unit, visualization unit, and support unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the smart glasses 214 and conducts voice and text dialogue with employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the dialogue using natural language processing technology. The visualization unit is implemented by, for example, the display of the smart glasses 214 and visually displays organizational issues using graphs and dashboards. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes employee growth plans. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the dialogue unit, analysis unit, visualization unit, and support unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the headset terminal 314 and conducts voice and text dialogue with employees. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the dialogue using natural language processing technology. The visualization unit is implemented by the display 343 of the headset terminal 314 and visually displays organizational issues using graphs and dashboards. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes employee growth plans. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the dialogue unit, analysis unit, visualization unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the dialogue unit is implemented by the control unit 46A of the robot 414 and conducts voice and text dialogue with employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the dialogue using natural language processing technology. The visualization unit is implemented by, for example, the display of the robot 414 and visually displays organizational issues using graphs and dashboards. The support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes employee growth plans. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The Dialogue Department, which engages in dialogue with employees, An analysis unit that analyzes the dialogue content collected by the aforementioned dialogue unit, A visualization unit visualizes organizational issues based on the analysis results obtained by the aforementioned analysis unit, A support unit that supports employee growth based on organizational issues visualized by the aforementioned visualization unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned dialogue unit, It includes a recording section for documenting employee opinions and feelings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It includes an analysis unit that analyzes dialogue content using natural language processing technology. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, It is equipped with a display unit that displays aggregated information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, We have a proposal department that proposes individualized growth plans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, The system estimates the employee's emotions and adjusts the conversation flow based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned dialogue unit, During a conversation, the system selects the most appropriate dialogue content by referring to the employee's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned dialogue unit, During conversations, the content of the dialogue is customized based on the employee's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned dialogue unit, The system estimates employee emotions and prioritizes dialogue based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned dialogue unit, During conversations, the system prioritizes relevant content by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned dialogue unit, During conversations, we analyze employees' social media activity and provide relevant conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate employee sentiment and adjust the analysis method based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the dialogue content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of the dialogue content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We estimate employee sentiment and prioritize analysis based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During the analysis, the priority of the analysis will be determined based on when the dialogue content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, the order of analysis is adjusted based on the relevance of the dialogue content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, The system estimates employee sentiment and adjusts the visualization display based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, When visualizing data, the current visualization method is optimized by referring to past visualization data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, When visualizing the data, different visualization methods are applied to each category of dialogue content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, We estimate employee sentiment and adjust the importance of visualization based on the estimated employee sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, When visualizing the data, analyze the changes in the visualization based on when the dialogue content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, During visualization, relevant market data related to the dialogue content is referenced. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned support unit, Estimate employees' emotions and adjust support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, When providing support, we analyze the employee's past growth history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, When providing support, customize the support methods based on the employee's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, The system estimates employee emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, When providing support, the most suitable support method will be selected considering the geographical location information of the employees. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, When providing support, we analyze employees' social media activity and propose ways to support them. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 Dialogue Department, which engages in dialogue with employees, An analysis unit that analyzes the dialogue content collected by the aforementioned dialogue unit, A visualization unit visualizes organizational issues based on the analysis results obtained by the aforementioned analysis unit, A support unit that supports employee growth based on organizational issues visualized by the aforementioned visualization unit, Equipped with A system characterized by the following features.

2. The aforementioned dialogue unit, It includes a recording section for documenting employee opinions and feelings. The system according to feature 1.

3. The aforementioned analysis unit is It includes an analysis unit that analyzes dialogue content using natural language processing technology. The system according to feature 1.

4. The aforementioned visualization unit, It is equipped with a display unit that displays aggregated information. The system according to feature 1.

5. The aforementioned support unit, We have a proposal department that proposes individualized growth plans. The system according to feature 1.

6. The aforementioned dialogue unit, The system estimates the employee's emotions and adjusts the conversation flow based on those estimated emotions. The system according to feature 1.

7. The aforementioned dialogue unit, During a conversation, the system selects the most appropriate dialogue content by referring to the employee's past conversation history. The system according to feature 1.

8. The aforementioned dialogue unit, During conversations, the content of the dialogue is customized based on the employee's current work situation and areas of interest. The system according to feature 1.

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