system

Abstract

The system according to this embodiment aims to make it easier for employees to express their true feelings to their superiors and to improve psychological safety. [Solution] The system according to the embodiment comprises a free talk unit, an analysis unit, and a submission unit. The free talk unit allows employees to engage in free talk with an AI chat partner. The analysis unit analyzes the content of the conversation recorded by the free talk unit. The submission unit submits the summary report generated by the analysis unit to the supervisor.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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, it is difficult for employees to convey their true feelings to their superiors, and there is room for improvement in improving psychological safety and grasping on-site problems.

[0005] [[ID=_{39}]]The system according to the embodiment aims to make it easier for employees to convey their true feelings to their superiors and improve psychological safety.

Means for Solving the Problems

[0006] [[ID=_{46}]]The system according to the embodiment includes a free talk section, an analysis section, and a submission section. The free talk section allows employees to have free talks with an AI chat partner. The analysis section analyzes the conversation content recorded by the free talk section. The submission section submits the summary report generated by the analysis section to the superior. [Effects of the Invention]

[0007] The system according to this embodiment can make it easier for employees to express their true feelings to their superiors and improve psychological safety. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 ​​chat system according to an embodiment of the present invention is a system for solving the problem of employees being unable to express their true feelings to their superiors. This system provides a mechanism for employees to have a free conversation about work with an AI chat partner once a week. The specific content of the conversation is kept confidential, but a summary report is submitted to the superior. This mechanism improves the psychological safety of employees and makes it possible to gather information on issues in the workplace. The aim is to improve the rigid communication in large corporations and provide data that is useful for preventing employee turnover and for management decisions. For example, employees can have a free conversation about work with an AI chat partner once a week. For example, they can talk to the AI ​​about things that are difficult to tell their superiors directly, such as their desired assignment, dissatisfaction with their job, or requests for paid leave. During this time, employees can speak freely, and the AI ​​records the content. Next, the AI ​​analyzes the recorded conversation content and generates a summary report. Since the specific content of the conversation is kept confidential, the privacy of employees is protected. The summary report includes the challenges and dissatisfactions that employees face, as well as their future career vision. The generated summary report is submitted to the superior. Based on this report, the superior can understand the employee's current situation and challenges and take appropriate action. For example, measures can be considered to address employee dissatisfaction, and career plans can be reviewed. This system improves employee psychological safety. Employees can reduce stress by talking to the AI ​​about things they find difficult to tell their superiors directly. Supervisors can also gather information on on-the-ground issues by understanding employees' true feelings. This improves the rigid communication in large corporations and provides data that is useful for reducing employee turnover and making informed management decisions. For example, if an employee talks to the AI ​​about their dissatisfaction with a current project, the AI ​​summarizes the content and reports it to the supervisor. Based on this report, the supervisor can review the project's progress and consider ways to improve it. Also, if an employee talks about their future career vision, the supervisor can review the career plan based on that vision and provide appropriate support.Thus, the AI ​​chat system is a groundbreaking system that solves the problem of employees being unable to express their true feelings to their superiors, improves employee psychological safety, and gathers on-the-ground issues.

[0029] The AI ​​chat system according to this embodiment comprises a free talk unit, an analysis unit, and a submission unit. The free talk unit allows employees to engage in free talk with the AI ​​chat partner. When employees engage in free talk with the AI ​​chat partner, they can talk to the AI ​​about things that are difficult to communicate directly to their superiors, such as their desired assignment, job dissatisfaction, or requests for paid leave. The free talk unit allows employees to talk freely. In addition, the free talk unit can keep specific conversation content private in order to protect employee privacy. For example, the free talk unit can encrypt and store the conversation content. The analysis unit analyzes the conversation content recorded by the free talk unit. The analysis unit analyzes the conversation content using natural language processing technology, for example, and generates a summary report. The analysis unit extracts keywords from the conversation content and reflects them in the summary report. The analysis unit can also perform sentiment analysis of the conversation content to understand the employee's emotional state. For example, the analysis unit analyzes positive and negative expressions in the conversation content and evaluates the employee's emotional state. The submission unit submits the summary report generated by the analysis unit to the superior. The submitting department can, for example, send a summary report to their supervisor via email. Alternatively, the submitting department can save the summary report to a shared folder within the company, making it accessible to the supervisor. For example, the submitting department can generate the summary report in PDF format and send it as an email attachment. This allows the AI ​​chat system according to the embodiment to solve the problem of employees being unable to express their true feelings to their supervisors, thereby improving employee psychological safety.

[0030] The Free Talk section allows employees to engage in free conversation with an AI chat partner. When employees engage in free talk with the AI ​​chat partner, they can discuss topics that are difficult to communicate directly to their superiors, such as their preferred assignments, work complaints, or requests for paid leave. The Free Talk section allows employees to speak freely. Furthermore, to protect employee privacy, the Free Talk section can keep specific conversation content private. For example, the Free Talk section can encrypt and store conversation content. Specifically, the Free Talk section is designed to allow employees to engage in natural conversations, and the AI ​​uses a pre-trained, large-scale language model to generate appropriate responses. What employees say is converted into text data in real time and stored on a server in an encrypted state. The encryption uses the latest technology, making it impossible for third parties to access the data. In addition, the Free Talk section also tags and adds metadata for analyzing the conversation content. For example, it automatically adds tags indicating conversation topics and changes in emotion, facilitating subsequent processing by the analysis section. This allows the Free Talk Department to provide an environment where employees can speak their minds with peace of mind, reducing psychological burden by allowing them to discuss matters they might find difficult to convey directly to their superiors with the AI. Furthermore, the Free Talk Department regularly updates its system, incorporating the latest natural language processing technologies and security measures to maintain high reliability and security at all times. This allows employees to engage in free talk with confidence and improves the overall reliability of the system.

[0031] The analysis unit analyzes the conversation content recorded by the free talk unit. For example, the analysis unit uses natural language processing technology to analyze the conversation content and generate a summary report. Specifically, the analysis unit receives text data of the conversation content as input and uses natural language processing technology to extract keywords and understand the context. For example, it uses topic modeling technology to identify the main themes of the conversation and extract important keywords. It also uses sentiment analysis algorithms to analyze positive and negative expressions in the conversation content and evaluate the emotional state of employees. Based on this information, the analysis unit automatically generates a summary report. The summary report includes key topics, keywords, and the evaluation results of the emotional state, and is provided in a format that supervisors can quickly understand. Furthermore, the analysis unit can track changes in employees' emotional state and opinions by comparing them with past conversation data. For example, it can compare past and current data to analyze fluctuations in employees' stress levels and satisfaction levels. This allows the analysis unit to continuously monitor employees' psychological states and detect problems early. The analysis unit also has a function to automatically categorize the generated summary reports and save them to appropriate folders. This allows supervisors to quickly search for and access necessary information. The analysis department utilizes the latest natural language processing technology to accurately and efficiently analyze conversation content, thereby understanding employees' psychological states and supporting appropriate responses.

[0032] The submission department submits the summary report generated by the analysis department to the supervisor. The submission department can, for example, send the summary report to the supervisor via email. Alternatively, the submission department can save the summary report to a shared internal folder for supervisor access. Specifically, the submission department converts the summary report received from the analysis department into PDF or HTML format and sends it as an email attachment. The email contains an overview and key points of the summary report, allowing supervisors to quickly grasp its contents. The submission department also automatically uploads the summary report to the company's cloud storage, making it accessible to supervisors at any time. Appropriate access permissions are set for the cloud storage, ensuring that confidential information is not leaked. Furthermore, the submission department manages the submission history of summary reports and has a function to easily search past reports. This allows supervisors to compare past and current reports to understand changes in employee sentiment and opinions. The submission department can flexibly configure the report submission method and format, allowing for customization to meet the needs of supervisors. For example, the submission frequency can be adjusted, such as submitting reports weekly to certain supervisors and monthly to others. This allows the submission department to efficiently and reliably deliver the summary reports generated by the analysis department to their supervisors, providing important information to improve employee psychological safety.

[0033] The Free Talk Department allows employees to speak freely. For example, the Free Talk Department improves psychological safety by allowing employees to speak freely. For example, the Free Talk Department can reduce stress by allowing employees to speak freely. Furthermore, the Free Talk Department allows employees to talk freely, enabling them to discuss things with the AI ​​that they might find difficult to communicate directly to their superiors. This, in turn, improves psychological safety by allowing employees to speak freely. Some or all of the above-described processes in the Free Talk Department may be performed using AI, or not. For example, the Free Talk Department can use AI to record employee conversations and provide the data to the analysis department.

[0034] The analysis unit analyzes the conversation content and generates a summary report. The analysis unit analyzes the conversation content using, for example, natural language processing technology and generates a summary report. The analysis unit extracts, for example, keywords from the conversation content and reflects them in the summary report. The analysis unit can also perform sentiment analysis of the conversation content to understand the emotional state of employees. For example, the analysis unit analyzes positive and negative expressions in the conversation content and evaluates the emotional state of employees. This allows supervisors to understand the current situation of employees by analyzing the conversation content and generating a summary report. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to analyze the conversation content and generate a summary report.

[0035] The submitting department submits a summary report to its supervisor. For example, the submitting department can send the summary report to its supervisor via email. Alternatively, the submitting department can save the summary report in a shared folder within the company so that its supervisor can access it. For example, the submitting department can generate the summary report in PDF format and send it as an email attachment. This allows the supervisor to take appropriate action by submitting the summary report. Some or all of the above processes performed by the submitting department may be performed using AI, for example, or not. For example, the submitting department can use AI to generate the summary report and submit it to its supervisor.

[0036] The Free Talk section protects employee privacy. For example, the Free Talk section can encrypt and store conversation content. It can also store conversation content anonymously. For example, the Free Talk section can anonymize conversation content so that it cannot be linked to specific employees. This protects employee privacy and allows them to speak with confidence. Some or all of the above processing in the Free Talk section may be performed using AI, for example, or without AI. For example, the Free Talk section can use AI to encrypt and store conversation content.

[0037] The submitting department allows supervisors to take appropriate action based on the summary report. For example, the submitting department can send the summary report to the supervisor via email, allowing the supervisor to take appropriate action based on the summary report. Alternatively, the submitting department can save the summary report in a shared folder within the company, making it accessible to the supervisor. For example, the submitting department can generate the summary report in PDF format and send it as an email attachment. This allows the supervisor to resolve employee dissatisfaction by taking appropriate action based on the summary report. Some or all of the above processes in the submitting department may be performed using AI, for example, or not. For example, the submitting department can use AI to generate the summary report and submit it to the supervisor.

[0038] The free talk section generates optimal questions by referring to the employee's past conversation history. For example, the free talk section uses AI to generate relevant questions based on what the employee has said in the past. For example, the free talk section uses AI to generate follow-up questions based on topics the employee has shown interest in in the past. For example, the free talk section uses AI to adjust questions so as to avoid topics the employee has avoided in the past. This allows for the generation of more appropriate questions by referring to past conversation history. Some or all of the above processes in the free talk section may be performed using AI, or not. For example, the free talk section can use AI to analyze the employee's past conversation history and generate optimal questions.

[0039] The Free Talk Department suggests conversation topics based on the employee's current work situation. For example, the Free Talk Department's AI might suggest topics related to a project the employee is currently working on. For example, if the employee has a heavy workload, the AI ​​might suggest topics that could help reduce stress. For example, if the employee's work is progressing smoothly, the AI ​​might suggest topics related to future career development. By suggesting conversation topics based on the current work situation, more relevant conversations become possible. Some or all of the above processing in the Free Talk Department may be performed using AI, or not. For example, the Free Talk Department can use AI to analyze the employee's work situation and suggest the most appropriate conversation topics.

[0040] The free talk section provides highly relevant topics by taking into account the geographical location of employees. For example, if an employee is on a business trip, the free talk section will provide topics related to that region using AI. For example, if an employee is working from home, the free talk section will provide topics related to their home environment using AI. For example, if an employee is in the office, the free talk section will provide topics related to their office environment using AI. By considering geographical location, it is possible to provide more relevant topics. Some or all of the above processing in the free talk section may be performed using AI, for example, or without AI. For example, the free talk section can use AI to analyze an employee's geographical location and provide highly relevant topics.

[0041] The Free Talk Department analyzes employees' social media activity and provides relevant topics. For example, the Free Talk Department uses AI to provide topics based on topics that employees have recently shown interest in on social media. For example, the Free Talk Department uses AI to provide topics related to people and organizations that employees follow on social media. For example, the Free Talk Department uses AI to provide topics based on articles that employees have shared on social media. This allows for the provision of more relevant topics by analyzing social media activity. Some or all of the above processing in the Free Talk Department may be performed using AI, for example, or without AI. For example, the Free Talk Department can use AI to analyze employees' social media activity and provide relevant topics.

[0042] The analysis unit adjusts the level of detail in the summary based on the importance of the conversation content. For example, the AI ​​in the analysis unit adjusts to generate a detailed summary for important conversation content. For example, the AI ​​in the analysis unit adjusts to generate a simplified summary for less important conversation content. For example, the AI ​​in the analysis unit adjusts to adjust the length of the summary according to the importance of the conversation content. This results in the generation of a more appropriate summary by adjusting the level of detail in the summary based on the importance of the conversation 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 use AI to evaluate the importance of the conversation content and adjust the level of detail in the summary.

[0043] The analysis unit applies different analysis algorithms depending on the category of the conversation. For example, the analysis unit applies a business-specific analysis algorithm to business-related conversations. For example, the analysis unit applies a privacy-conscious analysis algorithm to private conversations. For example, the analysis unit applies an algorithm specialized in emotion analysis to conversations related to emotions. By applying an analysis algorithm appropriate to the category of the conversation, 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 use AI to classify the category of the conversation and apply an appropriate analysis algorithm.

[0044] The analysis unit determines the priority of summaries based on the submission timing of conversations. The analysis unit adjusts the AI ​​to, for example, prioritize the generation of summaries for conversations with high urgency. The analysis unit adjusts the AI ​​to, for example, prioritize the generation of summaries for conversations with approaching submission deadlines. The analysis unit adjusts the order of summaries based on submission timing. This results in the generation of more appropriate summaries by determining the priority of summaries based on submission timing. 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 use AI to evaluate the submission timing of conversations and determine the priority of summaries.

[0045] The analysis unit adjusts the order of summaries based on the relevance of the conversation. For example, the AI ​​in the analysis unit adjusts the generation of summaries to prioritize highly relevant conversational content. For example, the AI ​​in the analysis unit adjusts the generation of summaries to postpone less relevant conversational content. For example, the AI ​​in the analysis unit adjusts the order of summaries based on the relevance of the conversation. This results in the generation of more appropriate summaries by adjusting the order of summaries based on the relevance of the conversation. 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 use AI to evaluate the relevance of conversations and adjust the order of summaries.

[0046] The submission unit selects the optimal submission timing by referring to the supervisor's past response history. For example, the AI ​​adjusts the submission unit to submit during times when the supervisor has responded promptly in the past. For example, the AI ​​adjusts the submission unit to avoid submitting during times when the supervisor has been late in responding in the past. For example, the AI ​​adjusts the submission unit to select the optimal submission timing based on the supervisor's past response history. This ensures that a more appropriate submission timing is selected by referring to past response history. Some or all of the above processes in the submission unit may be performed using AI, for example, or without AI. For example, the submission unit can use AI to analyze the supervisor's past response history and select the optimal submission timing.

[0047] The submission department customizes how to notify supervisors based on the content of the summary report. For example, the AI ​​adjusts the submission department to provide immediate notifications for important content. For example, the AI ​​adjusts the submission department to provide periodic notifications for less important content. The AI ​​adjusts the submission department to customize the notification method based on the content of the summary report. This ensures that notifications are more appropriate by customizing the notification method based on the content of the summary report. Some or all of the above processes in the submission department may be performed using AI, for example, or not using AI. For example, the submission department can use AI to analyze the content of the summary report and customize how to notify supervisors.

[0048] The submission department selects the optimal submission method by considering the supervisor's geographical location. For example, if the supervisor is on a business trip, the AI ​​will adjust the submission to be sent via email. For example, if the supervisor is in the office, the AI ​​will adjust the submission to be sent in person. For example, the AI ​​will adjust the submission department to select the optimal submission method based on the supervisor's geographical location. This ensures that a more appropriate submission method is selected by considering geographical location. Some or all of the above processing in the submission department may be performed using AI, or not. For example, the submission department can use AI to analyze the supervisor's geographical location and select the optimal submission method.

[0049] The submission unit analyzes the supervisor's social media activity and proposes the optimal submission method. For example, if the supervisor frequently uses social media, the AI ​​will adjust the submission method to use the messaging function. If the supervisor does not use social media often, the AI ​​will adjust the submission method to use email. The submission unit will adjust the AI ​​to propose the optimal submission method based on the supervisor's social media activity. By analyzing social media activity, a more appropriate submission method will be proposed. Some or all of the above processing in the submission unit may be performed using AI, for example, or without AI. For example, the submission unit can use AI to analyze the supervisor's social media activity and propose the optimal submission method.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The analysis unit can adjust the level of detail in the summary based on the importance of the conversation content. For example, it can adjust to generate a detailed summary for important conversation content, and a simplified summary for less important conversation content. Furthermore, it can adjust the length of the summary according to the importance of the conversation content. By adjusting the level of detail in the summary based on the importance of the conversation content, a more appropriate summary can be generated.

[0052] The submission team can select the optimal submission timing by referring to the supervisor's past response history. For example, they can adjust the submission time to coincide with a period when the supervisor previously responded promptly. They can also adjust the submission time to avoid periods when the supervisor previously responded late. Furthermore, they can select the optimal submission timing based on the supervisor's past response history. This allows for the selection of a more appropriate submission timing by referring to past response history.

[0053] The free talk section can provide highly relevant topics by considering employees' geographical location. For example, if an employee is on a business trip, it can provide topics related to that region. Similarly, if an employee is working from home, it can provide topics related to their home environment. Furthermore, if an employee is in the office, it can provide topics related to their office environment. This allows for the provision of more relevant topics by considering geographical location.

[0054] The analysis unit can apply different analysis algorithms depending on the category of the conversation. For example, a business-specific analysis algorithm can be applied to business-related conversations. A privacy-conscious analysis algorithm can be applied to private conversations. Furthermore, an algorithm specifically designed for emotion analysis can be applied to conversations related to emotions. By applying the appropriate analysis algorithm for each conversation category, more accurate analysis becomes possible.

[0055] The free talk section can generate optimal questions by referencing employees' past conversation history. For example, it can generate relevant questions based on what employees have talked about in the past. It can also generate in-depth questions based on topics employees have shown interest in in the past. Furthermore, it can adjust questions to avoid topics that employees have avoided in the past. In this way, more appropriate questions can be generated by referring to past conversation history.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The Free Talk section allows employees to engage in free conversation with an AI chat partner. Employees can discuss topics that are difficult to communicate directly to their superiors, such as their preferred assignments, work-related frustrations, and requests for paid leave. The Free Talk section allows employees to speak freely, and conversation content can be kept private to protect their privacy. For example, conversation content can be encrypted and stored. Step 2: The analysis unit analyzes the conversation content recorded by the free talk unit. The analysis unit uses natural language processing technology to analyze the conversation content and generate a summary report. For example, it extracts keywords from the conversation content and reflects them in the summary report. It can also perform sentiment analysis of the conversation content to understand the emotional state of employees. For example, it analyzes positive and negative expressions to evaluate the emotional state of employees. Step 3: The submission team submits the summary report generated by the analysis team to their supervisor. The submission team can send the summary report to their supervisor via email. Alternatively, they can save the summary report to a shared folder within the company for the supervisor to access. For example, the summary report can be generated in PDF format and sent as an email attachment.

[0058] (Example of form 2) The AI ​​chat system according to an embodiment of the present invention is a system for solving the problem of employees being unable to express their true feelings to their superiors. This system provides a mechanism for employees to have a free conversation about work with an AI chat partner once a week. The specific content of the conversation is kept confidential, but a summary report is submitted to the superior. This mechanism improves the psychological safety of employees and makes it possible to gather information on issues in the workplace. The aim is to improve the rigid communication in large corporations and provide data that is useful for preventing employee turnover and for management decisions. For example, employees can have a free conversation about work with an AI chat partner once a week. For example, they can talk to the AI ​​about things that are difficult to tell their superiors directly, such as their desired assignment, dissatisfaction with their job, or requests for paid leave. During this time, employees can speak freely, and the AI ​​records the content. Next, the AI ​​analyzes the recorded conversation content and generates a summary report. Since the specific content of the conversation is kept confidential, the privacy of employees is protected. The summary report includes the challenges and dissatisfactions that employees face, as well as their future career vision. The generated summary report is submitted to the superior. Based on this report, the superior can understand the employee's current situation and challenges and take appropriate action. For example, measures can be considered to address employee dissatisfaction, and career plans can be reviewed. This system improves employee psychological safety. Employees can reduce stress by talking to the AI ​​about things they find difficult to tell their superiors directly. Supervisors can also gather information on on-the-ground issues by understanding employees' true feelings. This improves the rigid communication in large corporations and provides data that is useful for reducing employee turnover and making informed management decisions. For example, if an employee talks to the AI ​​about their dissatisfaction with a current project, the AI ​​summarizes the content and reports it to the supervisor. Based on this report, the supervisor can review the project's progress and consider ways to improve it. Also, if an employee talks about their future career vision, the supervisor can review the career plan based on that vision and provide appropriate support.Thus, the AI ​​chat system is a groundbreaking system that solves the problem of employees being unable to express their true feelings to their superiors, improves employee psychological safety, and gathers on-the-ground issues.

[0059] The AI ​​chat system according to this embodiment comprises a free talk unit, an analysis unit, and a submission unit. The free talk unit allows employees to engage in free talk with the AI ​​chat partner. When employees engage in free talk with the AI ​​chat partner, they can talk to the AI ​​about things that are difficult to communicate directly to their superiors, such as their desired assignment, job dissatisfaction, or requests for paid leave. The free talk unit allows employees to talk freely. In addition, the free talk unit can keep specific conversation content private in order to protect employee privacy. For example, the free talk unit can encrypt and store the conversation content. The analysis unit analyzes the conversation content recorded by the free talk unit. The analysis unit analyzes the conversation content using natural language processing technology, for example, and generates a summary report. The analysis unit extracts keywords from the conversation content and reflects them in the summary report. The analysis unit can also perform sentiment analysis of the conversation content to understand the employee's emotional state. For example, the analysis unit analyzes positive and negative expressions in the conversation content and evaluates the employee's emotional state. The submission unit submits the summary report generated by the analysis unit to the superior. The submitting department can, for example, send a summary report to their supervisor via email. Alternatively, the submitting department can save the summary report to a shared folder within the company, making it accessible to the supervisor. For example, the submitting department can generate the summary report in PDF format and send it as an email attachment. This allows the AI ​​chat system according to the embodiment to solve the problem of employees being unable to express their true feelings to their supervisors, thereby improving employee psychological safety.

[0060] The Free Talk section allows employees to engage in free conversation with an AI chat partner. When employees engage in free talk with the AI ​​chat partner, they can discuss topics that are difficult to communicate directly to their superiors, such as their preferred assignments, work complaints, or requests for paid leave. The Free Talk section allows employees to speak freely. Furthermore, to protect employee privacy, the Free Talk section can keep specific conversation content private. For example, the Free Talk section can encrypt and store conversation content. Specifically, the Free Talk section is designed to allow employees to engage in natural conversations, and the AI ​​uses a pre-trained, large-scale language model to generate appropriate responses. What employees say is converted into text data in real time and stored on a server in an encrypted state. The encryption uses the latest technology, making it impossible for third parties to access the data. In addition, the Free Talk section also tags and adds metadata for analyzing the conversation content. For example, it automatically adds tags indicating conversation topics and changes in emotion, facilitating subsequent processing by the analysis section. This allows the Free Talk Department to provide an environment where employees can speak their minds with peace of mind, reducing psychological burden by allowing them to discuss matters they might find difficult to convey directly to their superiors with the AI. Furthermore, the Free Talk Department regularly updates its system, incorporating the latest natural language processing technologies and security measures to maintain high reliability and security at all times. This allows employees to engage in free talk with confidence and improves the overall reliability of the system.

[0061] The analysis unit analyzes the conversation content recorded by the free talk unit. For example, the analysis unit uses natural language processing technology to analyze the conversation content and generate a summary report. Specifically, the analysis unit receives text data of the conversation content as input and uses natural language processing technology to extract keywords and understand the context. For example, it uses topic modeling technology to identify the main themes of the conversation and extract important keywords. It also uses sentiment analysis algorithms to analyze positive and negative expressions in the conversation content and evaluate the emotional state of employees. Based on this information, the analysis unit automatically generates a summary report. The summary report includes key topics, keywords, and the evaluation results of the emotional state, and is provided in a format that supervisors can quickly understand. Furthermore, the analysis unit can track changes in employees' emotional state and opinions by comparing them with past conversation data. For example, it can compare past and current data to analyze fluctuations in employees' stress levels and satisfaction levels. This allows the analysis unit to continuously monitor employees' psychological states and detect problems early. The analysis unit also has a function to automatically categorize the generated summary reports and save them to appropriate folders. This allows supervisors to quickly search for and access necessary information. The analysis department utilizes the latest natural language processing technology to accurately and efficiently analyze conversation content, thereby understanding employees' psychological states and supporting appropriate responses.

[0062] The submission department submits the summary report generated by the analysis department to the supervisor. The submission department can, for example, send the summary report to the supervisor via email. Alternatively, the submission department can save the summary report to a shared internal folder for supervisor access. Specifically, the submission department converts the summary report received from the analysis department into PDF or HTML format and sends it as an email attachment. The email contains an overview and key points of the summary report, allowing supervisors to quickly grasp its contents. The submission department also automatically uploads the summary report to the company's cloud storage, making it accessible to supervisors at any time. Appropriate access permissions are set for the cloud storage, ensuring that confidential information is not leaked. Furthermore, the submission department manages the submission history of summary reports and has a function to easily search past reports. This allows supervisors to compare past and current reports to understand changes in employee sentiment and opinions. The submission department can flexibly configure the report submission method and format, allowing for customization to meet the needs of supervisors. For example, the submission frequency can be adjusted, such as submitting reports weekly to certain supervisors and monthly to others. This allows the submission department to efficiently and reliably deliver the summary reports generated by the analysis department to their supervisors, providing important information to improve employee psychological safety.

[0063] The Free Talk Department allows employees to speak freely. For example, the Free Talk Department improves psychological safety by allowing employees to speak freely. For example, the Free Talk Department can reduce stress by allowing employees to speak freely. Furthermore, the Free Talk Department allows employees to talk freely, enabling them to discuss things with the AI ​​that they might find difficult to communicate directly to their superiors. This, in turn, improves psychological safety by allowing employees to speak freely. Some or all of the above-described processes in the Free Talk Department may be performed using AI, or not. For example, the Free Talk Department can use AI to record employee conversations and provide the data to the analysis department.

[0064] The analysis unit analyzes the conversation content and generates a summary report. The analysis unit analyzes the conversation content using, for example, natural language processing technology and generates a summary report. The analysis unit extracts, for example, keywords from the conversation content and reflects them in the summary report. The analysis unit can also perform sentiment analysis of the conversation content to understand the emotional state of employees. For example, the analysis unit analyzes positive and negative expressions in the conversation content and evaluates the emotional state of employees. This allows supervisors to understand the current situation of employees by analyzing the conversation content and generating a summary report. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to analyze the conversation content and generate a summary report.

[0065] The submitting department submits a summary report to its supervisor. For example, the submitting department can send the summary report to its supervisor via email. Alternatively, the submitting department can save the summary report in a shared folder within the company so that its supervisor can access it. For example, the submitting department can generate the summary report in PDF format and send it as an email attachment. This allows the supervisor to take appropriate action by submitting the summary report. Some or all of the above processes performed by the submitting department may be performed using AI, for example, or not. For example, the submitting department can use AI to generate the summary report and submit it to its supervisor.

[0066] The Free Talk section protects employee privacy. For example, the Free Talk section can encrypt and store conversation content. It can also store conversation content anonymously. For example, the Free Talk section can anonymize conversation content so that it cannot be linked to specific employees. This protects employee privacy and allows them to speak with confidence. Some or all of the above processing in the Free Talk section may be performed using AI, for example, or without AI. For example, the Free Talk section can use AI to encrypt and store conversation content.

[0067] The submitting department allows supervisors to take appropriate action based on the summary report. For example, the submitting department can send the summary report to the supervisor via email, allowing the supervisor to take appropriate action based on the summary report. Alternatively, the submitting department can save the summary report in a shared folder within the company, making it accessible to the supervisor. For example, the submitting department can generate the summary report in PDF format and send it as an email attachment. This allows the supervisor to resolve employee dissatisfaction by taking appropriate action based on the summary report. Some or all of the above processes in the submitting department may be performed using AI, for example, or not. For example, the submitting department can use AI to generate the summary report and submit it to the supervisor.

[0068] The free talk section estimates employees' emotions and adjusts the flow of the free talk based on the estimated emotions. For example, if an employee is feeling stressed, the free talk section's AI generates questions that will help them relax. For example, if an employee is excited, the free talk section's AI generates questions that will help them calm down. For example, if an employee is tired, the free talk section's AI generates simple and easy-to-answer questions. By adjusting the flow of the free talk according to the employee's emotions, more effective communication 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the free talk section may be performed using AI, for example, or without AI. For example, the free talk section can use AI to estimate employees' emotions and adjust the flow of the free talk.

[0069] The free talk section generates optimal questions by referring to the employee's past conversation history. For example, the free talk section uses AI to generate relevant questions based on what the employee has said in the past. For example, the free talk section uses AI to generate follow-up questions based on topics the employee has shown interest in in the past. For example, the free talk section uses AI to adjust questions so as to avoid topics the employee has avoided in the past. This allows for the generation of more appropriate questions by referring to past conversation history. Some or all of the above processes in the free talk section may be performed using AI, or not. For example, the free talk section can use AI to analyze the employee's past conversation history and generate optimal questions.

[0070] The Free Talk Department suggests conversation topics based on the employee's current work situation. For example, the Free Talk Department's AI might suggest topics related to a project the employee is currently working on. For example, if the employee has a heavy workload, the AI ​​might suggest topics that could help reduce stress. For example, if the employee's work is progressing smoothly, the AI ​​might suggest topics related to future career development. By suggesting conversation topics based on the current work situation, more relevant conversations become possible. Some or all of the above processing in the Free Talk Department may be performed using AI, or not. For example, the Free Talk Department can use AI to analyze the employee's work situation and suggest the most appropriate conversation topics.

[0071] The free talk section estimates employees' emotions and adjusts the timing of free talk based on the estimated emotions. For example, the free talk section uses AI to adjust the timing of free talk to start when employees are relaxed. For example, the free talk section uses AI to adjust the timing of free talk to start when employees are stressed. For example, the free talk section uses AI to adjust the timing of free talk to start when employees are concentrating. By adjusting the timing of free talk to start according to employees' emotions, more effective communication 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 free talk section may be performed using AI, for example, or without AI. For example, the free talk section can use AI to estimate employees' emotions and adjust the timing of free talk to start.

[0072] The free talk section provides highly relevant topics by taking into account the geographical location of employees. For example, if an employee is on a business trip, the free talk section will provide topics related to that region using AI. For example, if an employee is working from home, the free talk section will provide topics related to their home environment using AI. For example, if an employee is in the office, the free talk section will provide topics related to their office environment using AI. By considering geographical location, it is possible to provide more relevant topics. Some or all of the above processing in the free talk section may be performed using AI, for example, or without AI. For example, the free talk section can use AI to analyze an employee's geographical location and provide highly relevant topics.

[0073] The Free Talk Department analyzes employees' social media activity and provides relevant topics. For example, the Free Talk Department uses AI to provide topics based on topics that employees have recently shown interest in on social media. For example, the Free Talk Department uses AI to provide topics related to people and organizations that employees follow on social media. For example, the Free Talk Department uses AI to provide topics based on articles that employees have shared on social media. This allows for the provision of more relevant topics by analyzing social media activity. Some or all of the above processing in the Free Talk Department may be performed using AI, for example, or without AI. For example, the Free Talk Department can use AI to analyze employees' social media activity and provide relevant topics.

[0074] The analysis unit estimates the employee's emotions and adjusts the accuracy of the analysis based on the estimated emotions. For example, if the employee is relaxed, the AI ​​in the analysis unit adjusts the analysis to perform a detailed analysis. For example, if the employee is stressed, the AI ​​in the analysis unit adjusts the analysis to perform a simplified analysis. For example, if the employee is excited, the AI ​​in the analysis unit adjusts the analysis to take into account emotional fluctuations. By adjusting the accuracy of the analysis according to the employee's emotions, a more accurate analysis 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to estimate the employee's emotions and adjust the accuracy of the analysis.

[0075] The analysis unit adjusts the level of detail in the summary based on the importance of the conversation content. For example, the AI ​​in the analysis unit adjusts to generate a detailed summary for important conversation content. For example, the AI ​​in the analysis unit adjusts to generate a simplified summary for less important conversation content. For example, the AI ​​in the analysis unit adjusts to adjust the length of the summary according to the importance of the conversation content. This results in the generation of a more appropriate summary by adjusting the level of detail in the summary based on the importance of the conversation 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 use AI to evaluate the importance of the conversation content and adjust the level of detail in the summary.

[0076] The analysis unit applies different analysis algorithms depending on the category of the conversation. For example, the analysis unit applies a business-specific analysis algorithm to business-related conversations. For example, the analysis unit applies a privacy-conscious analysis algorithm to private conversations. For example, the analysis unit applies an algorithm specialized in emotion analysis to conversations related to emotions. By applying an analysis algorithm appropriate to the category of the conversation, 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 use AI to classify the category of the conversation and apply an appropriate analysis algorithm.

[0077] The analysis unit estimates the employee's emotions and adjusts the length of the summary based on the estimated emotions. For example, if the employee is relaxed, the AI ​​in the analysis unit adjusts the summary to generate a detailed one. For example, if the employee is stressed, the AI ​​in the analysis unit adjusts the summary to generate a simplified one. For example, if the employee is excited, the AI ​​in the analysis unit adjusts the summary to generate one that takes emotional fluctuations into account. This allows for the generation of more appropriate summaries by adjusting the length of the summary according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to estimate the employee's emotions and adjust the length of the summary.

[0078] The analysis unit determines the priority of summaries based on the submission timing of conversations. The analysis unit adjusts the AI ​​to, for example, prioritize the generation of summaries for conversations with high urgency. The analysis unit adjusts the AI ​​to, for example, prioritize the generation of summaries for conversations with approaching submission deadlines. The analysis unit adjusts the order of summaries based on submission timing. This results in the generation of more appropriate summaries by determining the priority of summaries based on submission timing. 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 use AI to evaluate the submission timing of conversations and determine the priority of summaries.

[0079] The analysis unit adjusts the order of summaries based on the relevance of the conversation. For example, the AI ​​in the analysis unit adjusts the generation of summaries to prioritize highly relevant conversational content. For example, the AI ​​in the analysis unit adjusts the generation of summaries to postpone less relevant conversational content. For example, the AI ​​in the analysis unit adjusts the order of summaries based on the relevance of the conversation. This results in the generation of more appropriate summaries by adjusting the order of summaries based on the relevance of the conversation. 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 use AI to evaluate the relevance of conversations and adjust the order of summaries.

[0080] The submission unit estimates the employee's emotions and adjusts the method of submitting the summary report based on the estimated emotions. For example, if the employee is relaxed, the AI ​​adjusts the submission unit to submit a detailed summary report. For example, if the employee is stressed, the AI ​​adjusts the submission unit to submit a simplified summary report. For example, if the employee is excited, the AI ​​adjusts the submission unit to submit a summary report that takes emotional fluctuations into account. This ensures that a more appropriate summary report is submitted by adjusting the submission method 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 submission unit may be performed using AI or not using AI. For example, the submission unit can use AI to estimate the employee's emotions and adjust the method of submitting the summary report.

[0081] The submission unit selects the optimal submission timing by referring to the supervisor's past response history. For example, the AI ​​adjusts the submission unit to submit during times when the supervisor has responded promptly in the past. For example, the AI ​​adjusts the submission unit to avoid submitting during times when the supervisor has been late in responding in the past. For example, the AI ​​adjusts the submission unit to select the optimal submission timing based on the supervisor's past response history. This ensures that a more appropriate submission timing is selected by referring to past response history. Some or all of the above processes in the submission unit may be performed using AI, for example, or without AI. For example, the submission unit can use AI to analyze the supervisor's past response history and select the optimal submission timing.

[0082] The submission department customizes how to notify supervisors based on the content of the summary report. For example, the AI ​​adjusts the submission department to provide immediate notifications for important content. For example, the AI ​​adjusts the submission department to provide periodic notifications for less important content. The AI ​​adjusts the submission department to customize the notification method based on the content of the summary report. This ensures that notifications are more appropriate by customizing the notification method based on the content of the summary report. Some or all of the above processes in the submission department may be performed using AI, for example, or not using AI. For example, the submission department can use AI to analyze the content of the summary report and customize how to notify supervisors.

[0083] The submission unit estimates the employee's emotions and prioritizes summary reports based on the estimated emotions. For example, if an employee is relaxed, the AI ​​adjusts the submission unit to prioritize detailed summary reports. For example, if an employee is stressed, the AI ​​adjusts the submission unit to prioritize simplified summary reports. For example, if an employee is agitated, the AI ​​adjusts the submission unit to prioritize summary reports that take emotional fluctuations into account. This ensures that more appropriate summary reports are submitted by prioritizing 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the submission unit may be performed using AI or not. For example, the submission unit can use AI to estimate employee emotions and determine the priority of summary reports.

[0084] The submission department selects the optimal submission method by considering the supervisor's geographical location. For example, if the supervisor is on a business trip, the AI ​​will adjust the submission to be sent via email. For example, if the supervisor is in the office, the AI ​​will adjust the submission to be sent in person. For example, the AI ​​will adjust the submission department to select the optimal submission method based on the supervisor's geographical location. This ensures that a more appropriate submission method is selected by considering geographical location. Some or all of the above processing in the submission department may be performed using AI, or not. For example, the submission department can use AI to analyze the supervisor's geographical location and select the optimal submission method.

[0085] The submission unit analyzes the supervisor's social media activity and proposes the optimal submission method. For example, if the supervisor frequently uses social media, the AI ​​will adjust the submission method to use the messaging function. If the supervisor does not use social media often, the AI ​​will adjust the submission method to use email. The submission unit will adjust the AI ​​to propose the optimal submission method based on the supervisor's social media activity. By analyzing social media activity, a more appropriate submission method will be proposed. Some or all of the above processing in the submission unit may be performed using AI, for example, or without AI. For example, the submission unit can use AI to analyze the supervisor's social media activity and propose the optimal submission method.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The Free Talk function can estimate an employee's emotions and adjust the tone of conversation based on that estimation. For example, if an employee is stressed, the Free Talk function can conduct the conversation in a gentle, relaxing tone. If an employee is agitated, the Free Talk function can conduct the conversation in a calming tone. Furthermore, if an employee is tired, the Free Talk function can ask simple, easy-to-answer questions in a gentle tone. By adjusting the tone of conversation according to the employee's emotions, more effective communication becomes possible.

[0088] The analysis unit can adjust the level of detail in the summary based on the importance of the conversation content. For example, it can adjust to generate a detailed summary for important conversation content, and a simplified summary for less important conversation content. Furthermore, it can adjust the length of the summary according to the importance of the conversation content. By adjusting the level of detail in the summary based on the importance of the conversation content, a more appropriate summary can be generated.

[0089] The submission team can select the optimal submission timing by referring to the supervisor's past response history. For example, they can adjust the submission time to coincide with a period when the supervisor previously responded promptly. They can also adjust the submission time to avoid periods when the supervisor previously responded late. Furthermore, they can select the optimal submission timing based on the supervisor's past response history. This allows for the selection of a more appropriate submission timing by referring to past response history.

[0090] The free talk section can provide highly relevant topics by considering employees' geographical location. For example, if an employee is on a business trip, it can provide topics related to that region. Similarly, if an employee is working from home, it can provide topics related to their home environment. Furthermore, if an employee is in the office, it can provide topics related to their office environment. This allows for the provision of more relevant topics by considering geographical location.

[0091] The analysis unit can apply different analysis algorithms depending on the category of the conversation. For example, a business-specific analysis algorithm can be applied to business-related conversations. A privacy-conscious analysis algorithm can be applied to private conversations. Furthermore, an algorithm specifically designed for emotion analysis can be applied to conversations related to emotions. By applying the appropriate analysis algorithm for each conversation category, more accurate analysis becomes possible.

[0092] The free talk function can estimate employees' emotions and adjust the flow of the free talk based on those estimates. For example, if an employee is feeling stressed, it can generate questions that will help them relax. If an employee is excited, it can generate questions that will help them calm down. Furthermore, if an employee is tired, it can generate simple and easy-to-answer questions. By adjusting the flow of the free talk according to the employee's emotions, more effective communication becomes possible.

[0093] The analysis unit can estimate employees' emotions and adjust the accuracy of the analysis based on those estimates. For example, if an employee is relaxed, the analysis can be adjusted to be more detailed. If an employee is stressed, the analysis can be simplified. Furthermore, if an employee is excited, the analysis can be adjusted to take emotional fluctuations into account. By adjusting the accuracy of the analysis according to the employee's emotions, more accurate analysis becomes possible.

[0094] The submission system can estimate an employee's emotions and adjust the method of submitting the summary report based on that estimation. For example, if an employee is relaxed, the system can adjust the submission to include a detailed summary report. If an employee is stressed, the system can adjust the submission to include a simplified summary report. Furthermore, if an employee is agitated, the system can adjust the submission to include a summary report that takes emotional fluctuations into account. By adjusting the submission method according to the employee's emotions, a more appropriate summary report will be submitted.

[0095] The submission system can estimate employees' emotions and prioritize summary reports based on those estimates. For example, if an employee is relaxed, it can prioritize submitting a detailed summary report. If an employee is stressed, it can prioritize submitting a simplified summary report. Furthermore, if an employee is agitated, it can prioritize submitting a summary report that takes emotional fluctuations into account. This ensures that more appropriate summary reports are submitted by prioritizing them according to the employee's emotions.

[0096] The free talk section can generate optimal questions by referencing employees' past conversation history. For example, it can generate relevant questions based on what employees have talked about in the past. It can also generate in-depth questions based on topics employees have shown interest in in the past. Furthermore, it can adjust questions to avoid topics that employees have avoided in the past. In this way, more appropriate questions can be generated by referring to past conversation history.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The Free Talk section allows employees to engage in free conversation with an AI chat partner. Employees can discuss topics that are difficult to communicate directly to their superiors, such as their preferred assignments, work-related frustrations, and requests for paid leave. The Free Talk section allows employees to speak freely, and conversation content can be kept private to protect their privacy. For example, conversation content can be encrypted and stored. Step 2: The analysis unit analyzes the conversation content recorded by the free talk unit. The analysis unit uses natural language processing technology to analyze the conversation content and generate a summary report. For example, it extracts keywords from the conversation content and reflects them in the summary report. It can also perform sentiment analysis of the conversation content to understand the emotional state of employees. For example, it analyzes positive and negative expressions to evaluate the emotional state of employees. Step 3: The submission team submits the summary report generated by the analysis team to their supervisor. The submission team can send the summary report to their supervisor via email. Alternatively, they can save the summary report to a shared folder within the company for the supervisor to access. For example, the summary report can be generated in PDF format and sent as an email attachment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the free talk section, analysis section, and submission section, is implemented by at least one of the smart device 14 and the data processing device 12. For example, the free talk section is implemented by the control unit 46A of the smart device 14, allowing employees to speak freely. The analysis section is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the conversation content using natural language processing technology and generates a summary report. The submission section is implemented by the specific processing unit 290 of the data processing device 12, allowing the summary report to be sent to a supervisor via email. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the free talk section, analysis section, and submission section, is implemented by at least one of the smart glasses 214 and the data processing device 12. For example, the free talk section is implemented by the control unit 46A of the smart glasses 214, allowing employees to speak freely. The analysis section is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the conversation content using natural language processing technology and generates a summary report. The submission section is implemented by the specific processing unit 290 of the data processing device 12, allowing the summary report to be sent to a supervisor via email. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the free talk section, analysis section, and submission section, is implemented by at least one of the headset terminal 314 and the data processing device 12. For example, the free talk section is implemented by the control unit 46A of the headset terminal 314, allowing employees to speak freely. The analysis section is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the conversation content using natural language processing technology and generates a summary report. The submission section is implemented by the specific processing unit 290 of the data processing device 12, allowing the summary report to be sent to a supervisor via email. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the free talk section, analysis section, and submission section, is implemented by, for example, at least one of the robot 414 and the data processing device 12. For example, the free talk section is implemented by the control unit 46A of the robot 414, allowing employees to speak freely. The analysis section is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the conversation content using natural language processing technology and generates a summary report. The submission section is implemented by, for example, the specific processing unit 290 of the data processing device 12, allowing the summary report to be sent to a supervisor via email. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The Free Talk Department, where employees engage in free conversation with an AI chat partner, An analysis unit that analyzes the content of the conversation recorded by the free talk unit, The system includes a submission unit that submits a summary report generated by the analysis unit to a supervisor. A system characterized by the following features. (Note 2) The aforementioned free talk section is Employees can speak freely. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the conversation content and generate a summary report. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned submission section, Submit a summary report to your supervisor. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned free talk section is Protecting employee privacy The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned submission section, The supervisor will take appropriate action based on the summary report. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned free talk section is The system estimates the emotions of the employees and adjusts the flow of the free talk based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned free talk section is The system generates the most appropriate questions by referencing the employee's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned free talk section is Suggest conversation topics based on the employees' current work situations. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned free talk section is The system estimates employees' emotions and adjusts the timing of free talk based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned free talk section is Providing relevant topics while considering employees' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned free talk section is Analyze employees' social media activity and provide relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates employees' emotions and adjusts the accuracy of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Adjust the level of detail in the summary based on the importance of the conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Estimate employees' emotions and adjust the length of the summary based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Prioritize summaries based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Adjust the order of summaries based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned submission section, We estimate employees' emotions and adjust the method of submitting summary reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned submission section, Refer to your supervisor's past response history to determine the optimal submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned submission section, Customize how you notify your supervisor based on the content of the summary report. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned submission section, The system estimates employee sentiment and prioritizes summary reports based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned submission section, Select the most suitable submission method, taking into account the supervisor's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned submission section, Analyze your boss's social media activity and propose the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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 Free Talk Department, where employees engage in free conversation with an AI chat partner, An analysis unit that analyzes the content of the conversation recorded by the free talk unit, The system includes a submission unit that submits a summary report generated by the analysis unit to a supervisor. A system characterized by the following features.

2. The aforementioned free talk section is Employees can speak freely. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the conversation content and generate a summary report. The system according to feature 1.

4. The aforementioned submission section, Submit a summary report to your supervisor. The system according to feature 1.

5. The aforementioned free talk section is Protecting employee privacy The system according to feature 1.

6. The aforementioned submission section, The supervisor will take appropriate action based on the summary report. The system according to feature 1.

7. The aforementioned free talk section is The system estimates the emotions of the employees and adjusts the flow of the free talk based on those estimated emotions. The system according to feature 1.

8. The aforementioned free talk section is The system generates the most appropriate questions by referencing the employee's past conversation history. The system according to feature 1.

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