system

Abstract

The system according to the embodiment aims to efficiently collect verbal complaints and dissatisfaction from employees and provide them to managers. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects verbal complaints or dissatisfaction from employees. The analysis unit analyzes the data collected by the collection unit. The summarization unit generates a summary report based on the data analyzed by the analysis unit. The provision unit provides the summary report generated by the summarization unit to management.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to efficiently collect employees' verbal complaints and dissatisfaction and provide them to management, which can lead to a communication gap.

[0005] The system according to the embodiment aims to efficiently collect verbal complaints and dissatisfaction from employees and provide them to managers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects employees' verbal complaints or grievances. The analysis unit analyzes the data collected by the collection unit. The summarization unit generates a summary report based on the data analyzed by the analysis unit. The provision unit provides the summary report generated by the summarization unit to a manager. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect verbal complaints and dissatisfaction from employees and provide them to managers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication gap elimination system according to an embodiment of the present invention collects employees' verbal complaints and dissatisfaction, analyzes them using AI, generates a summary report, and provides it to management. This system contributes to strengthening internal communication and improving management efficiency. It is particularly effective for companies with significant communication barriers, providing objective analysis and anonymity. For example, employees' verbal complaints and dissatisfaction are collected by recording what employees say freely and collecting it as text data. For example, employees' speech during meetings or breaks is recorded and the data is collected. Next, the collected data is analyzed by AI. The AI ​​analyzes the collected text data and summarizes the content of employees' complaints and dissatisfaction. For example, the AI ​​analyzes the text data and extracts frequently occurring keywords and phrases to identify trends in employee dissatisfaction. Based on the analyzed data, the AI ​​generates a summary report. This summary report concisely summarizes the content of employees' complaints and dissatisfaction and is provided to management. For example, a summary report generated by AI may include the main causes of employee dissatisfaction and areas for improvement. This summary report is provided to management, contributing to strengthening internal communication and improving management efficiency. Management can understand employee dissatisfaction based on the summary report and take appropriate measures. For example, implementing improvement measures in departments with high levels of employee dissatisfaction can facilitate smooth communication. This system is particularly effective in companies with significant communication barriers, providing objective analysis and anonymity. Employees can anonymously express their complaints and dissatisfaction, allowing them to express their opinions with peace of mind. Furthermore, AI objectively analyzes the data, resulting in unbiased analysis results. Thus, the present invention is a system that uses AI to collect and analyze employee complaints and dissatisfaction and provide a summary report to management, thereby eliminating communication gaps within a company and improving management efficiency. This system can collect and analyze employee complaints and dissatisfaction, generate a summary report, and provide it to management.

[0029] A communication gap elimination system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects employees' verbal complaints and dissatisfaction. The employee's verbal complaints and dissatisfaction may be, for example, a conversational format or a specific topic, but is not limited to these examples. The collection unit collects employees' verbal complaints and dissatisfaction using, for example, a recording device or a text input form. Examples of recording devices include smartphones and dedicated recorders. Examples of text input forms include web forms and in-app forms. The analysis unit analyzes the collected text data and extracts frequently occurring keywords and phrases. Methods for extracting frequently occurring keywords and phrases include, for example, frequency thresholds and co-occurrence network analysis. The analysis unit analyzes the collected text data using, for example, natural language processing technology, and extracts frequently occurring keywords and phrases. The summarization unit generates a summary report including the main causes of employees' complaints and dissatisfaction and areas for improvement. The summary report includes, for example, the main causes of employee complaints and dissatisfaction and areas for improvement. The summarizing unit, for example, analyzes the collected text data, extracts the main causes of employee complaints and dissatisfaction and areas for improvement, and generates a summary report based on the extracted data. The providing unit provides the summary report to management, providing information for the management to take appropriate measures. The providing unit, for example, sends the summary report to the management by email. The providing unit can also provide the summary report to the management via a web application or a mobile application. In this way, the communication gap elimination system according to the embodiment can collect and analyze employee complaints and dissatisfaction, generate a summary report, and provide it to the management. Some or all of the above-described processes in the collecting unit, analyzing unit, summarizing unit, and providing unit may be performed using, for example, AI, or may be performed without AI. For example, the collecting unit collects employees' verbal complaints and dissatisfaction using a recording device and inputs the data into AI, and the analyzing unit analyzes the collected text data using AI to extract frequently occurring keywords and phrases. The summary section uses AI to extract the main causes of employee complaints and dissatisfaction and areas for improvement, and then generates a summary report based on that. The provision section uses AI to provide the summary report to management.

[0030] The collection unit can collect employees' verbal complaints or dissatisfaction using a recording device or a text input form. The collection unit, for example, collects employees' verbal complaints or dissatisfaction using a recording device. Examples of recording devices include smartphones and dedicated recorders. The collection unit can also collect employees' verbal complaints or dissatisfaction using a text input form. Examples of text input forms include web forms and in-app forms. This allows the collection unit to efficiently collect employees' verbal complaints or dissatisfaction. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data collected by a recording device into AI and convert it into text data.

[0031] The analysis unit can analyze the collected text data and extract frequently occurring keywords and phrases. The analysis unit can, for example, analyze the collected text data using natural language processing technology and extract frequently occurring keywords and phrases. Methods for extracting frequently occurring keywords and phrases include, for example, an appearance frequency threshold and co-occurrence network analysis. This allows the analysis unit to grasp trends in employee complaints and dissatisfaction. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected text data into AI and extract frequently occurring keywords and phrases.

[0032] The summarizing unit can generate a summary report including the main causes or improvements of employee complaints or dissatisfaction. The summarizing unit, for example, analyzes the collected text data, extracts the main causes and improvements of employee complaints or dissatisfaction, and generates a summary report based on the extracted data. The summary report includes, for example, the main causes and improvements of employee complaints or dissatisfaction. This allows the summarizing unit to generate a summary report that concisely summarizes the content of employee complaints or dissatisfaction. Some or all of the above-described processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input the collected text data into AI, extract the main causes and improvements of employee complaints or dissatisfaction, and generate a summary report based on the extracted data.

[0033] The provision unit can provide the summary report to the management and provide information for the management to take appropriate measures. For example, the provision unit can send the summary report to the management by email. The provision unit can also provide the summary report to the management through a web application or a mobile application. This allows the provision unit to provide information for the management to understand employee dissatisfaction and take appropriate measures. Some or all of the above-mentioned processing in the provision unit can be performed using, for example, AI, or can be performed without using AI. For example, the provision unit can input the summary report into AI and select the optimal method for providing it to the management.

[0034] The collection unit can analyze the employee's past speech history and select the optimal collection method. For example, the collection unit analyzes the time periods in which the employee frequently complained or expressed dissatisfaction in the past and collects during those time periods. The collection unit can also preferentially suggest collection methods (such as audio recording or text input) that the employee has used in the past. Furthermore, the collection unit can preferentially collect complaints or dissatisfaction on specific topics from the employee's past speech history. This allows the collection unit to select the optimal collection method based on the employee's past speech history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's past speech history into AI and select the optimal collection method.

[0035] When collecting complaints and grievances, the collection unit can filter them based on the employee's current project or area of ​​interest. For example, the collection unit can prioritize collecting complaints and grievances related to the project the employee is currently working on. The collection unit can also filter and collect related complaints and grievances based on the employee's area of ​​interest. Furthermore, the collection unit can prioritize collecting complaints that the employee has about a specific project. This allows the collection unit to collect related complaints and grievances based on the employee's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the employee's current project or area of ​​interest into AI, and then filter and collect related complaints and grievances.

[0036] When collecting complaints and grievances, the collection unit can prioritize collecting highly relevant content by taking into account the employees' geographical location information. For example, the collection unit prioritizes collecting complaints and grievances expressed by employees in the office. The collection unit can also prioritize collecting complaints and grievances expressed by employees while on a business trip. Furthermore, the collection unit can prioritize collecting complaints and grievances expressed by employees while working remotely. This allows the collection unit to collect highly relevant complaints and grievances based on the employees' geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employees' geographical location information into AI and prioritize collecting highly relevant complaints and grievances.

[0037] When collecting complaints and grievances, the collection unit can analyze employees' social media activities and collect related content. For example, the collection unit collects complaints and grievances posted by employees on social media. The collection unit can also collect related complaints and grievances based on articles and posts shared by employees on social media. Furthermore, the collection unit can collect related complaints and grievances based on accounts followed by employees on social media. This allows the collection unit to collect related complaints and grievances based on employees' social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employees' social media activity data into AI to collect related complaints and grievances.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the complaints or dissatisfaction. For example, the analysis unit performs a detailed analysis on complaints or dissatisfaction with high importance. The analysis unit can also perform a simplified analysis on complaints or dissatisfaction with low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on complaints or dissatisfaction with medium importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the complaints or dissatisfaction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of complaints or dissatisfaction into AI and adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of complaints or dissatisfaction. For example, the analysis unit can apply a specific analysis algorithm to complaints or dissatisfaction related to the work environment. The analysis unit can also apply a different analysis algorithm to complaints or dissatisfaction related to human relationships. Furthermore, the analysis unit can apply yet another analysis algorithm to complaints or dissatisfaction related to work content. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of complaints or dissatisfaction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of complaints or dissatisfaction into AI and apply the optimal analysis algorithm.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the complaints or dissatisfaction were submitted. For example, the analysis unit prioritizes analysis of recently submitted complaints or dissatisfaction. The analysis unit can also postpone analysis of complaints or dissatisfaction submitted in the past. Furthermore, the analysis unit can also analyze complaints or dissatisfaction submitted at a moderate time with a moderate priority. This allows the analysis unit to determine the priority of analysis based on the time when the complaints or dissatisfaction were submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the complaints or dissatisfaction were submitted into AI to determine the priority of analysis.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the complaints and dissatisfaction. For example, the analysis unit prioritizes analysis of highly relevant complaints and dissatisfaction. The analysis unit can also postpone analysis of less relevant complaints and dissatisfaction. Furthermore, the analysis unit can analyze complaints and dissatisfaction with a moderate relevance in an appropriate order. This allows the analysis unit to adjust the order of analysis based on the relevance of the complaints and dissatisfaction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of complaints and dissatisfaction into AI and adjust the order of analysis.

[0042] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the complaint or dissatisfaction. For example, the summarization unit generates a detailed summary for a complaint or dissatisfaction of high importance. The summarization unit can also generate a simplified summary for a complaint or dissatisfaction of low importance. Furthermore, the summarization unit can also generate a summary with an appropriate level of detail for a complaint or dissatisfaction of medium importance. This allows the summarization unit to adjust the level of detail of the summary based on the importance of the complaint or dissatisfaction. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input importance data of the complaint or dissatisfaction into AI and adjust the level of detail of the summary.

[0043] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the complaint or dissatisfaction. For example, the summarization unit can apply a specific summarization algorithm to complaints or dissatisfaction about the work environment. The summarization unit can also apply a different summarization algorithm to complaints or dissatisfaction about interpersonal relationships. Furthermore, the summarization unit can apply yet another summarization algorithm to complaints or dissatisfaction about work content. This allows the summarization unit to apply the optimal summarization algorithm depending on the category of the complaint or dissatisfaction. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input category data of complaints or dissatisfaction into AI and apply the optimal summarization algorithm.

[0044] When generating summaries, the summarizing unit can determine the priority of summaries based on the time of submission of the complaints or grievances. For example, the summarizing unit prioritizes summarizing recently submitted complaints or grievances. The summarizing unit can also summarize previously submitted complaints or grievances later. Furthermore, the summarizing unit can summarize complaints or grievances that were submitted at a moderate time with a moderate priority. This allows the summarizing unit to determine the priority of summaries based on the time of submission of the complaints or grievances. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input data on the time of submission of complaints or grievances into AI to determine the priority of summaries.

[0045] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the complaints and complaints. For example, the summarization unit prioritizes summarization of highly relevant complaints and complaints. The summarization unit can also postpone summarization of less relevant complaints and complaints. Furthermore, the summarization unit can summarize complaints and complaints with medium relevance in an appropriate order. This allows the summarization unit to adjust the order of summaries based on the relevance of the complaints and complaints. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input relevance data of complaints and complaints into AI and adjust the order of summaries.

[0046] At the time of provision, the provision department can select the optimal provision method based on the manager's past feedback. For example, the provision department can preferentially use a reporting format that the manager has previously preferred. The provision department can also select a reporting format that reflects improvements that the manager has previously pointed out. Furthermore, the provision department can analyze the manager's past feedback and select the most effective reporting format. This allows the provision department to select the optimal provision method based on the manager's past feedback. Some or all of the above-mentioned processing in the provision department may be performed using, for example, AI, or may be performed without using AI. For example, the provision department can input the manager's past feedback data into AI to select the optimal provision method.

[0047] The providing unit can apply different providing algorithms depending on the category of the summary report when providing the summary report. For example, the providing unit can apply a specific providing algorithm to a summary report regarding the work environment. The providing unit can also apply a different providing algorithm to a summary report regarding human relationships. Furthermore, the providing unit can apply yet another providing algorithm to a summary report regarding work content. This allows the providing unit to apply the optimal providing algorithm depending on the category of the summary report. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of the summary report into AI and apply the optimal providing algorithm.

[0048] The providing unit can determine the priority of provision based on the submission time of the summary report at the time of provision. For example, the providing unit can provide a recently submitted summary report with priority. The providing unit can also provide a summary report submitted in the past later. Furthermore, the providing unit can provide a summary report submitted in the medium future with moderate priority. This allows the providing unit to determine the priority of provision based on the submission time of the summary report. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the submission time of the summary report into AI to determine the priority of provision.

[0049] The providing unit can adjust the order of providing the summary reports based on the relevance of the summary reports when providing them. For example, the providing unit can provide highly relevant summary reports with priority. The providing unit can also provide less relevant summary reports later. Furthermore, the providing unit can provide moderately relevant summary reports in an appropriate order. This allows the providing unit to adjust the order of providing the summary reports based on the relevance of the summary reports. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the summary reports into AI and adjust the order of providing.

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

[0051] When analyzing the collected text data, the analysis unit can refer to the employee's past comment history and analyze the consistency and changes in the comments. For example, it can compare the employee's past complaints with their current complaints to see if the same problem is recurring. It can also analyze how the employee's comments change over time to understand the progress of the problem. Furthermore, it can check the employee's comment history to see if complaints about a particular topic are increasing. This allows the analysis unit to perform more detailed analysis based on the employee's past comment history.

[0052] When providing a summary report to management, the reporting department can analyze the management's past feedback and select the optimal reporting method. For example, the reporting department can prioritize the use of a reporting format that the management has preferred in the past. It can also select a reporting format that reflects areas for improvement that the management has pointed out in the past. Furthermore, it can analyze the management's past feedback and select the most effective reporting format. This allows the reporting department to select the optimal reporting method based on the management's past feedback.

[0053] When analyzing collected text data, the analysis unit can determine the analysis priorities by taking into account the employees' geographic location information. For example, it can prioritize analysis of complaints expressed in the office. It can also prioritize analysis of complaints expressed while on a business trip. It can also prioritize analysis of complaints expressed while working remotely. This allows the analysis unit to determine optimal analysis priorities based on the employees' geographic location information.

[0054] When providing a summary report to a manager, the providing unit can apply different providing algorithms depending on the category of the summary report. For example, a specific providing algorithm can be applied to a summary report regarding the work environment. A different providing algorithm can be applied to a summary report regarding human relationships. Furthermore, a still different providing algorithm can be applied to a summary report regarding work content. This allows the providing unit to apply the optimal providing algorithm depending on the category of the summary report.

[0055] When providing summary reports to the manager, the provision department can determine the priority of provision based on the time of submission of the summary reports. For example, a summary report submitted recently can be provided with priority. Alternatively, a summary report submitted in the past can be provided later. Furthermore, a summary report submitted in the middle of the deadline can be provided with moderate priority. In this way, the provision department can determine the priority of provision based on the time of submission of the summary reports.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection department collects employee verbal complaints and grievances. Collection methods include recording devices and text entry forms. Recording devices include smartphones and dedicated recorders, and text entry forms include web forms and in-app forms. Step 2: The analysis unit analyzes the collected text data and extracts frequently occurring keywords and phrases. Analysis methods include frequency thresholds and co-occurrence network analysis. The analysis unit does this using natural language processing techniques. Step 3: The summarizing section generates a summary report including the main causes of employee complaints and dissatisfaction and areas for improvement. The summary report includes the main causes of employee complaints and dissatisfaction and areas for improvement. Step 4: The Delivery Department delivers the summary report to management via email, web application, or mobile application.

[0058] (Example 2) A communication gap elimination system according to an embodiment of the present invention collects employees' verbal complaints and dissatisfaction, analyzes them using AI, generates a summary report, and provides it to management. This system contributes to strengthening internal communication and improving management efficiency. It is particularly effective for companies with significant communication barriers, providing objective analysis and anonymity. For example, employees' verbal complaints and dissatisfaction are collected by recording what employees say freely and collecting it as text data. For example, employees' speech during meetings or breaks is recorded and the data is collected. Next, the collected data is analyzed by AI. The AI ​​analyzes the collected text data and summarizes the content of employees' complaints and dissatisfaction. For example, the AI ​​analyzes the text data and extracts frequently occurring keywords and phrases to identify trends in employee dissatisfaction. Based on the analyzed data, the AI ​​generates a summary report. This summary report concisely summarizes the content of employees' complaints and dissatisfaction and is provided to management. For example, a summary report generated by AI may include the main causes of employee dissatisfaction and areas for improvement. This summary report is provided to management, contributing to strengthening internal communication and improving management efficiency. Management can understand employee dissatisfaction based on the summary report and take appropriate measures. For example, implementing improvement measures in departments with high levels of employee dissatisfaction can facilitate smooth communication. This system is particularly effective in companies with significant communication barriers, providing objective analysis and anonymity. Employees can anonymously express their complaints and dissatisfaction, allowing them to express their opinions with peace of mind. Furthermore, AI objectively analyzes the data, resulting in unbiased analysis results. Thus, the present invention is a system that uses AI to collect and analyze employee complaints and dissatisfaction and provide a summary report to management, thereby eliminating communication gaps within a company and improving management efficiency. This system can collect and analyze employee complaints and dissatisfaction, generate a summary report, and provide it to management.

[0059] A communication gap elimination system according to an embodiment includes a collection unit, an analysis unit, a summarization unit, and a provision unit. The collection unit collects employees' verbal complaints and dissatisfaction. The employee's verbal complaints and dissatisfaction may be, for example, a conversational format or a specific topic, but is not limited to these examples. The collection unit collects employees' verbal complaints and dissatisfaction using, for example, a recording device or a text input form. Examples of recording devices include smartphones and dedicated recorders. Examples of text input forms include web forms and in-app forms. The analysis unit analyzes the collected text data and extracts frequently occurring keywords and phrases. Methods for extracting frequently occurring keywords and phrases include, for example, frequency thresholds and co-occurrence network analysis. The analysis unit analyzes the collected text data using, for example, natural language processing technology, and extracts frequently occurring keywords and phrases. The summarization unit generates a summary report including the main causes of employees' complaints and dissatisfaction and areas for improvement. The summary report includes, for example, the main causes of employee complaints and dissatisfaction and areas for improvement. The summarizing unit, for example, analyzes the collected text data, extracts the main causes of employee complaints and dissatisfaction and areas for improvement, and generates a summary report based on the extracted data. The providing unit provides the summary report to management, providing information for the management to take appropriate measures. The providing unit, for example, sends the summary report to the management by email. The providing unit can also provide the summary report to the management via a web application or a mobile application. In this way, the communication gap elimination system according to the embodiment can collect and analyze employee complaints and dissatisfaction, generate a summary report, and provide it to the management. Some or all of the above-described processes in the collecting unit, analyzing unit, summarizing unit, and providing unit may be performed using, for example, AI, or may be performed without AI. For example, the collecting unit collects employees' verbal complaints and dissatisfaction using a recording device and inputs the data into AI, and the analyzing unit analyzes the collected text data using AI to extract frequently occurring keywords and phrases. The summary section uses AI to extract the main causes of employee complaints and dissatisfaction and areas for improvement, and then generates a summary report based on that. The provision section uses AI to provide the summary report to management.

[0060] The collection unit can collect employees' verbal complaints or dissatisfaction using a recording device or a text input form. The collection unit, for example, collects employees' verbal complaints or dissatisfaction using a recording device. Examples of recording devices include smartphones and dedicated recorders. The collection unit can also collect employees' verbal complaints or dissatisfaction using a text input form. Examples of text input forms include web forms and in-app forms. This allows the collection unit to efficiently collect employees' verbal complaints or dissatisfaction. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data collected by a recording device into AI and convert it into text data.

[0061] The analysis unit can analyze the collected text data and extract frequently occurring keywords and phrases. The analysis unit can, for example, analyze the collected text data using natural language processing technology and extract frequently occurring keywords and phrases. Methods for extracting frequently occurring keywords and phrases include, for example, an appearance frequency threshold and co-occurrence network analysis. This allows the analysis unit to grasp trends in employee complaints and dissatisfaction. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected text data into AI and extract frequently occurring keywords and phrases.

[0062] The summarizing unit can generate a summary report including the main causes or improvements of employee complaints or dissatisfaction. The summarizing unit, for example, analyzes the collected text data, extracts the main causes and improvements of employee complaints or dissatisfaction, and generates a summary report based on the extracted data. The summary report includes, for example, the main causes and improvements of employee complaints or dissatisfaction. This allows the summarizing unit to generate a summary report that concisely summarizes the content of employee complaints or dissatisfaction. Some or all of the above-described processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input the collected text data into AI, extract the main causes and improvements of employee complaints or dissatisfaction, and generate a summary report based on the extracted data.

[0063] The provision unit can provide the summary report to the management and provide information for the management to take appropriate measures. For example, the provision unit can send the summary report to the management by email. The provision unit can also provide the summary report to the management through a web application or a mobile application. This allows the provision unit to provide information for the management to understand employee dissatisfaction and take appropriate measures. Some or all of the above-mentioned processing in the provision unit can be performed using, for example, AI, or can be performed without using AI. For example, the provision unit can input the summary report into AI and select the optimal method for providing it to the management.

[0064] The collection unit can estimate the employee's emotions and adjust the timing of collecting complaints and dissatisfaction based on the estimated employee emotions. For example, if an employee is feeling stressed, the collection unit uses an AI to estimate the employee's emotions and collects complaints and dissatisfaction when the employee's stress peaks. The collection unit can also estimate the employee's emotions when the employee is relaxed and collect complaints and dissatisfaction when the employee is relaxed. Furthermore, if an employee is tired, the collection unit can estimate the employee's emotions and collect complaints and dissatisfaction when the employee is tired. This allows the collection unit to collect complaints and dissatisfaction at the optimal timing depending on the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using an AI, or may be performed without an AI. For example, the collection unit can input employee emotion data into an AI and determine the optimal collection timing.

[0065] The collection unit can analyze the employee's past speech history and select the optimal collection method. For example, the collection unit analyzes the time periods in which the employee frequently complained or expressed dissatisfaction in the past and collects during those time periods. The collection unit can also preferentially suggest collection methods (such as audio recording or text input) that the employee has used in the past. Furthermore, the collection unit can preferentially collect complaints or dissatisfaction on specific topics from the employee's past speech history. This allows the collection unit to select the optimal collection method based on the employee's past speech history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's past speech history into AI and select the optimal collection method.

[0066] When collecting complaints and grievances, the collection unit can filter them based on the employee's current project or area of ​​interest. For example, the collection unit can prioritize collecting complaints and grievances related to the project the employee is currently working on. The collection unit can also filter and collect related complaints and grievances based on the employee's area of ​​interest. Furthermore, the collection unit can prioritize collecting complaints that the employee has about a specific project. This allows the collection unit to collect related complaints and grievances based on the employee's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the employee's current project or area of ​​interest into AI, and then filter and collect related complaints and grievances.

[0067] The collection unit can estimate the employee's emotions and determine the priority of the complaints and dissatisfaction to be collected based on the estimated employee emotions. For example, if the employee is feeling strongly dissatisfied, the collection unit can prioritize collecting the complaints. Furthermore, if the employee is only complaining lightly, the collection unit can also collect the complaints later. Furthermore, if the employee is feeling moderately dissatisfied, the collection unit can collect the dissatisfaction with a moderate priority. This allows the collection unit to determine the priority of the complaints and dissatisfaction based on the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the employee's emotion data into an AI and determine the priority of the complaints and dissatisfaction to be collected.

[0068] When collecting complaints and grievances, the collection unit can prioritize collecting highly relevant content by taking into account the employees' geographical location information. For example, the collection unit prioritizes collecting complaints and grievances expressed by employees in the office. The collection unit can also prioritize collecting complaints and grievances expressed by employees while on a business trip. Furthermore, the collection unit can prioritize collecting complaints and grievances expressed by employees while working remotely. This allows the collection unit to collect highly relevant complaints and grievances based on the employees' geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employees' geographical location information into AI and prioritize collecting highly relevant complaints and grievances.

[0069] When collecting complaints and grievances, the collection unit can analyze employees' social media activities and collect related content. For example, the collection unit collects complaints and grievances posted by employees on social media. The collection unit can also collect related complaints and grievances based on articles and posts shared by employees on social media. Furthermore, the collection unit can collect related complaints and grievances based on accounts followed by employees on social media. This allows the collection unit to collect related complaints and grievances based on employees' social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employees' social media activity data into AI to collect related complaints and grievances.

[0070] The analysis unit can estimate the employee's emotions and adjust the presentation method of the analysis based on the estimated employee's emotions. For example, if the employee is feeling strongly dissatisfied, the analysis unit can emphasize that dissatisfaction and reflect it in the analysis results. Furthermore, if the employee is expressing mild complaints, the analysis unit can downplay the complaint and reflect it in the analysis results. Furthermore, if the employee is feeling moderately dissatisfied, the analysis unit can appropriately reflect that dissatisfaction and include it in the analysis results. This allows the analysis unit to adjust the presentation method of the analysis according to the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's emotion data into AI and adjust the presentation method of the analysis.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the complaints or dissatisfaction. For example, the analysis unit performs a detailed analysis on complaints or dissatisfaction with high importance. The analysis unit can also perform a simplified analysis on complaints or dissatisfaction with low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on complaints or dissatisfaction with medium importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the complaints or dissatisfaction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of complaints or dissatisfaction into AI and adjust the level of detail of the analysis.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of complaints or dissatisfaction. For example, the analysis unit can apply a specific analysis algorithm to complaints or dissatisfaction related to the work environment. The analysis unit can also apply a different analysis algorithm to complaints or dissatisfaction related to human relationships. Furthermore, the analysis unit can apply yet another analysis algorithm to complaints or dissatisfaction related to work content. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of complaints or dissatisfaction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of complaints or dissatisfaction into AI and apply the optimal analysis algorithm.

[0073] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated employee emotions. For example, if the employee is feeling strongly dissatisfied, the analysis unit can perform a longer analysis of the dissatisfaction. Furthermore, if the employee is only mildly complaining, the analysis unit can also perform a shorter analysis of the complaint. Furthermore, if the employee is feeling moderately dissatisfied, the analysis unit can also perform an appropriate length of analysis of the dissatisfaction. This allows the analysis unit to adjust the length of the analysis based on the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's emotion data into AI and adjust the length of the analysis.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time when the complaints or dissatisfaction were submitted. For example, the analysis unit prioritizes analysis of recently submitted complaints or dissatisfaction. The analysis unit can also postpone analysis of complaints or dissatisfaction submitted in the past. Furthermore, the analysis unit can also analyze complaints or dissatisfaction submitted at a moderate time with a moderate priority. This allows the analysis unit to determine the priority of analysis based on the time when the complaints or dissatisfaction were submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the complaints or dissatisfaction were submitted into AI to determine the priority of analysis.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the complaints and dissatisfaction. For example, the analysis unit prioritizes analysis of highly relevant complaints and dissatisfaction. The analysis unit can also postpone analysis of less relevant complaints and dissatisfaction. Furthermore, the analysis unit can analyze complaints and dissatisfaction with a moderate relevance in an appropriate order. This allows the analysis unit to adjust the order of analysis based on the relevance of the complaints and dissatisfaction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of complaints and dissatisfaction into AI and adjust the order of analysis.

[0076] The summarization unit can estimate the employee's emotions and adjust the summary expression method based on the estimated employee emotions. For example, if the employee is feeling strongly dissatisfied, the summarization unit can emphasize the dissatisfaction and reflect it in the summary. Furthermore, if the employee is expressing a mild complaint, the summarization unit can downplay the complaint and reflect it in the summary. Furthermore, if the employee is feeling moderately dissatisfied, the summarization unit can appropriately reflect the dissatisfaction and include it in the summary. This allows the summarization unit to adjust the summary expression method according to the employee's emotions. The employee's emotions are estimated using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the summarization unit can be performed using, for example, AI, or without AI. For example, the summarization unit can input the employee's emotion data into AI and adjust the summary expression method.

[0077] When generating a summary, the summarization unit can adjust the level of detail of the summary based on the importance of the complaint or dissatisfaction. For example, the summarization unit generates a detailed summary for a complaint or dissatisfaction of high importance. The summarization unit can also generate a simplified summary for a complaint or dissatisfaction of low importance. Furthermore, the summarization unit can also generate a summary with an appropriate level of detail for a complaint or dissatisfaction of medium importance. This allows the summarization unit to adjust the level of detail of the summary based on the importance of the complaint or dissatisfaction. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input importance data of the complaint or dissatisfaction into AI and adjust the level of detail of the summary.

[0078] When generating a summary, the summarization unit can apply different summarization algorithms depending on the category of the complaint or dissatisfaction. For example, the summarization unit can apply a specific summarization algorithm to complaints or dissatisfaction about the work environment. The summarization unit can also apply a different summarization algorithm to complaints or dissatisfaction about interpersonal relationships. Furthermore, the summarization unit can apply yet another summarization algorithm to complaints or dissatisfaction about work content. This allows the summarization unit to apply the optimal summarization algorithm depending on the category of the complaint or dissatisfaction. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input category data of complaints or dissatisfaction into AI and apply the optimal summarization algorithm.

[0079] The summarization unit can estimate the employee's emotions and adjust the length of the summary based on the estimated employee emotions. For example, if an employee is very dissatisfied, the summarization unit can generate a longer summary for that dissatisfaction. Furthermore, if an employee is only slightly complaining, the summarization unit can generate a shorter summary for that complaint. Furthermore, if an employee is moderately dissatisfied, the summarization unit can generate a summary of an appropriate length for that dissatisfaction. This allows the summarization unit to adjust the length of the summary based on the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the summarization unit can be performed using, for example, AI, or without AI. For example, the summarization unit can input the employee's emotion data into AI and adjust the length of the summary.

[0080] When generating summaries, the summarizing unit can determine the priority of summaries based on the time of submission of the complaints or grievances. For example, the summarizing unit prioritizes summarizing recently submitted complaints or grievances. The summarizing unit can also summarize previously submitted complaints or grievances later. Furthermore, the summarizing unit can summarize complaints or grievances that were submitted at a moderate time with a moderate priority. This allows the summarizing unit to determine the priority of summaries based on the time of submission of the complaints or grievances. Some or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can input data on the time of submission of complaints or grievances into AI to determine the priority of summaries.

[0081] When generating summaries, the summarization unit can adjust the order of summaries based on the relevance of the complaints and complaints. For example, the summarization unit prioritizes summarization of highly relevant complaints and complaints. The summarization unit can also postpone summarization of less relevant complaints and complaints. Furthermore, the summarization unit can summarize complaints and complaints with medium relevance in an appropriate order. This allows the summarization unit to adjust the order of summaries based on the relevance of the complaints and complaints. Some or all of the above-mentioned processing in the summarization unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarization unit can input relevance data of complaints and complaints into AI and adjust the order of summaries.

[0082] The providing unit can estimate the employee's emotions and adjust the presentation style of the summary report to be provided based on the estimated employee's emotions. For example, if the employee is feeling strongly dissatisfied, the providing unit can emphasize the dissatisfaction and reflect it in the summary report. Furthermore, if the employee is expressing mild complaints, the providing unit can downplay the complaint and reflect it in the summary report. Furthermore, if the employee is feeling moderately dissatisfied, the providing unit can appropriately reflect the dissatisfaction and include it in the summary report. This allows the providing unit to adjust the presentation style of the summary report based on the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the employee's emotion data into AI and adjust the presentation style of the summary report.

[0083] At the time of provision, the provision department can select the optimal provision method based on the manager's past feedback. For example, the provision department can preferentially use a reporting format that the manager has previously preferred. The provision department can also select a reporting format that reflects improvements that the manager has previously pointed out. Furthermore, the provision department can analyze the manager's past feedback and select the most effective reporting format. This allows the provision department to select the optimal provision method based on the manager's past feedback. Some or all of the above-mentioned processing in the provision department may be performed using, for example, AI, or may be performed without using AI. For example, the provision department can input the manager's past feedback data into AI to select the optimal provision method.

[0084] The providing unit can apply different providing algorithms depending on the category of the summary report when providing the summary report. For example, the providing unit can apply a specific providing algorithm to a summary report regarding the work environment. The providing unit can also apply a different providing algorithm to a summary report regarding human relationships. Furthermore, the providing unit can apply yet another providing algorithm to a summary report regarding work content. This allows the providing unit to apply the optimal providing algorithm depending on the category of the summary report. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of the summary report into AI and apply the optimal providing algorithm.

[0085] The providing unit can estimate the employee's emotions and determine the priority of summary reports to be provided based on the estimated employee emotions. For example, if the employee is feeling strongly dissatisfied, the providing unit can prioritize providing a summary report related to that dissatisfaction. Furthermore, if the employee is expressing mild complaints, the providing unit can postpone providing a summary report related to that complaint. Furthermore, if the employee is feeling moderately dissatisfied, the providing unit can provide a summary report related to that dissatisfaction with moderate priority. This allows the providing unit to determine the priority of summary reports based on the employee's emotions. The estimation of the employee's emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input employee emotion data into AI to determine the priority of summary reports.

[0086] The providing unit can determine the priority of provision based on the submission time of the summary report at the time of provision. For example, the providing unit can provide a recently submitted summary report with priority. The providing unit can also provide a summary report submitted in the past later. Furthermore, the providing unit can provide a summary report submitted in the medium future with moderate priority. This allows the providing unit to determine the priority of provision based on the submission time of the summary report. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the submission time of the summary report into AI to determine the priority of provision.

[0087] The providing unit can adjust the order of providing the summary reports based on the relevance of the summary reports when providing them. For example, the providing unit can provide highly relevant summary reports with priority. The providing unit can also provide less relevant summary reports later. Furthermore, the providing unit can provide moderately relevant summary reports in an appropriate order. This allows the providing unit to adjust the order of providing the summary reports based on the relevance of the summary reports. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the summary reports into AI and adjust the order of providing. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, summarization unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects employees' verbal complaints and dissatisfaction using a recording device or text input form of the smart device 14. The analysis unit analyzes the collected text data by, for example, the specific processing unit 290 of the data processing device 12 and extracts frequently occurring keywords and phrases. The summarization unit extracts, for example, the main causes and areas for improvement of the employees' complaints and dissatisfaction using the specific processing unit 290 of the data processing device 12, and generates a summary report based on the extracted information. The provision unit provides the summary report to management by, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, summarization unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects employees' verbal complaints and dissatisfaction using a recording device or a text input form of the smart glasses 214. The analysis unit, for example, analyzes the collected text data by the specific processing unit 290 of the data processing device 12 and extracts frequently occurring keywords and phrases. For example, the summarization unit, for example, extracts the main causes and areas for improvement of the employees' complaints and dissatisfaction by the specific processing unit 290 of the data processing device 12, and generates a summary report based on the extracted information. The provision unit, for example, provides the summary report to management by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, summarization unit, and provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects employees' verbal complaints and dissatisfaction using a recording device or text input form of the headset terminal 314. The analysis unit analyzes the collected text data by, for example, the specific processing unit 290 of the data processing device 12 and extracts frequently occurring keywords and phrases. The summarization unit extracts, for example, the main causes of employees' complaints and dissatisfaction and areas for improvement by, for example, the specific processing unit 290 of the data processing device 12, and generates a summary report based on the extracted information. The provision unit provides the summary report to management by, for example, the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, summarization unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects employees' verbal complaints and dissatisfaction using a recording device or a text input form of the robot 414. The analysis unit analyzes the collected text data by, for example, the specific processing unit 290 of the data processing device 12 and extracts frequently occurring keywords and phrases. The summarization unit extracts, for example, the main causes and areas for improvement of employees' complaints and dissatisfaction by, for example, the specific processing unit 290 of the data processing device 12, and generates a summary report based on the extracted information. The provision unit provides the summary report to management by, for example, the control unit 46A of the robot 414.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] When collecting employee complaints and dissatisfaction, the collection department can analyze the employee's tone of voice and speaking patterns to estimate the strength of their emotions. For example, if an employee speaks in a high-pitched voice, it can be assumed that they are feeling strong emotions, and that content can be collected as a priority. Also, if an employee speaks slowly, it can be assumed that they are calm, and that content can be put off for later. Furthermore, if an employee speaks quickly, it can be assumed that there is a high level of urgency, and that content can be collected immediately. This allows the collection department to select the optimal collection method based on the employee's tone of voice and speaking patterns.

[0090] When analyzing the collected text data, the analysis unit can refer to the employee's past comment history and analyze the consistency and changes in the comments. For example, it can compare the employee's past complaints with their current complaints to see if the same problem is recurring. It can also analyze how the employee's comments change over time to understand the progress of the problem. Furthermore, it can check the employee's comment history to see if complaints about a particular topic are increasing. This allows the analysis unit to perform more detailed analysis based on the employee's past comment history.

[0091] When generating a summary report, the summarizing unit can estimate the employee's emotions and adjust the level of detail in the summary according to the strength of the emotions. For example, if an employee is feeling strongly dissatisfied, the summary of the dissatisfaction can be written in detail. Also, if an employee is expressing a mild complaint, the summary of the complaint can be written in a simplified manner. Furthermore, if an employee is feeling moderately dissatisfied, the summary of the dissatisfaction can be written in moderate detail. In this way, the summarizing unit can adjust the level of detail in the summary report based on the employee's emotions.

[0092] When providing a summary report to management, the reporting department can analyze the management's past feedback and select the optimal reporting method. For example, the reporting department can prioritize the use of a reporting format that the management has preferred in the past. It can also select a reporting format that reflects areas for improvement that the management has pointed out in the past. Furthermore, it can analyze the management's past feedback and select the most effective reporting format. This allows the reporting department to select the optimal reporting method based on the management's past feedback.

[0093] The collection unit can estimate the employee's emotions and determine the priority of complaints and dissatisfaction to be collected based on the estimated emotions. For example, if an employee is feeling strong dissatisfaction, the collection unit can collect the complaints as a priority. Also, if an employee is expressing a minor complaint, the collection unit can collect the complaints at a later date. Furthermore, if an employee is feeling a moderate level of dissatisfaction, the collection unit can collect the complaints with a moderate priority. In this way, the collection unit can determine the priority of complaints and dissatisfaction to be collected based on the employee's emotions.

[0094] When analyzing collected text data, the analysis unit can determine the analysis priorities by taking into account the employees' geographic location information. For example, it can prioritize analysis of complaints expressed in the office. It can also prioritize analysis of complaints expressed while on a business trip. It can also prioritize analysis of complaints expressed while working remotely. This allows the analysis unit to determine optimal analysis priorities based on the employees' geographic location information.

[0095] When generating a summary report, the summarization unit can estimate the employee's emotions and adjust the way the summary is expressed depending on the strength of the emotions. For example, if an employee is feeling strongly dissatisfied, the summarization unit can emphasize the dissatisfaction and reflect it in the summary. Also, if an employee is expressing a mild complaint, the summarization unit can downplay the complaint and reflect it in the summary. Furthermore, if an employee is feeling a moderate level of dissatisfaction, the summarization unit can reflect the dissatisfaction moderately and include it in the summary. In this way, the summarization unit can adjust the way the summary is expressed depending on the employee's emotions.

[0096] When providing a summary report to a manager, the providing unit can apply different providing algorithms depending on the category of the summary report. For example, a specific providing algorithm can be applied to a summary report regarding the work environment. A different providing algorithm can be applied to a summary report regarding human relationships. Furthermore, a still different providing algorithm can be applied to a summary report regarding work content. This allows the providing unit to apply the optimal providing algorithm depending on the category of the summary report.

[0097] The collection unit can estimate the employee's emotions and determine the priority of complaints and dissatisfaction to be collected based on the estimated emotions. For example, if an employee is feeling strong dissatisfaction, the collection unit can collect the complaints as a priority. Also, if an employee is expressing a minor complaint, the collection unit can collect the complaints at a later date. Furthermore, if an employee is feeling a moderate level of dissatisfaction, the collection unit can collect the complaints with a moderate priority. In this way, the collection unit can determine the priority of complaints and dissatisfaction to be collected based on the employee's emotions.

[0098] When providing summary reports to the manager, the provision department can determine the priority of provision based on the time of submission of the summary reports. For example, a summary report submitted recently can be provided with priority. Alternatively, a summary report submitted in the past can be provided later. Furthermore, a summary report submitted in the middle of the deadline can be provided with moderate priority. In this way, the provision department can determine the priority of provision based on the time of submission of the summary reports.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection department collects employee verbal complaints and grievances. Collection methods include recording devices and text entry forms. Recording devices include smartphones and dedicated recorders, and text entry forms include web forms and in-app forms. Step 2: The analysis unit analyzes the collected text data and extracts frequently occurring keywords and phrases. Analysis methods include frequency thresholds and co-occurrence network analysis. The analysis unit does this using natural language processing techniques. Step 3: The summarizing section generates a summary report including the main causes of employee complaints and dissatisfaction and areas for improvement. The summary report includes the main causes of employee complaints and dissatisfaction and areas for improvement. Step 4: The Delivery Department delivers the summary report to management via email, web application, or mobile application.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department for collecting verbal complaints or grievances from employees; an analysis unit that analyzes the data collected by the collection unit; a summarization unit that generates a summary report based on the data analyzed by the analysis unit; a providing unit that provides a summary report generated by the summarizing unit to a manager; Equipped with A system characterized by:

2. The collecting unit Collecting employee verbal complaints or grievances using recording devices or text entry forms The system of claim 1 .

3. The analysis unit Analyze the collected text data and extract frequently occurring keywords and phrases The system of claim 1 .

4. The summary section Generate a summary report containing the main causes of employee complaints or dissatisfaction or areas for improvement The system of claim 1 .

5. The providing unit Providing summary reports to management and providing information for management to take appropriate action The system of claim 1 .

6. The collecting unit Estimate employee emotions and adjust the timing of collecting complaints and grievances based on the estimated employee emotions. The system of claim 1 .

7. The collecting unit Analyze employees' past statements and select the most appropriate collection method The system of claim 1 .

8. The collecting unit When collecting complaints or grievances, filter them based on employees' current projects or areas of interest The system of claim 1 .

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