System

Abstract

An object of the system according to the embodiment is to effectively collect complaints and complaints of employees and utilize the collected complaints and complaints for management improvement.SOLUTION: A system according to an embodiment includes a speech recognizer, an analyzer, a summary generator, and a feedback provider. The voice recognition unit converts the dissatisfaction or complaint of the employee from the voice data into text data. The analysis unit analyzes the text data converted by the voice recognition unit. The summary generation unit generates a summary of the management issue based on the data analyzed by the analysis unit. The feedback providing unit provides the summary generated by the summary generation unit to the manager.SELECTED DRAWING: Figure 1

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] Conventional technology has had the problem of not being able to effectively collect employee complaints and grievances and use them to improve management.

[0005] The system according to the embodiment aims to effectively collect employee complaints and grievances and utilize them to improve management. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, an analysis unit, a summary generation unit, and a feedback provision unit. The speech recognition unit converts employee complaints and grievances from speech data into text data. The analysis unit analyzes the text data converted by the speech recognition unit. The summary generation unit creates a summary of the management issue based on the data analyzed by the analysis unit. The feedback provision unit provides the summary created by the summary generation unit to the manager. [Effects of the Invention]

[0007] The system according to the embodiment can effectively collect employee complaints and grievances and use them to improve business management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The employee empowerment system according to an embodiment of the present invention is a system that collects employee complaints and grievances, analyzes them using a generation AI, and provides useful feedback to management. As a result, the employee empowerment system can reflect employee opinions in management improvements and improve the transparency of communication throughout the organization.

[0029] An employee empowerment system according to an embodiment includes a speech recognition unit, an analysis unit, a summary generation unit, and a feedback provision unit. The speech recognition unit converts employee complaints and grievances from speech data into text data. For example, the speech recognition unit converts recorded data into text in real time using speech recognition technology. The speech recognition unit can also analyze speech data input by employees through a dedicated application and convert it into text data. The speech recognition unit removes noise from the speech data to generate highly accurate text data. For example, the speech recognition technology uses an algorithm that analyzes speech characteristics and removes noise. The analysis unit analyzes the text data converted by the speech recognition unit. For example, the analysis unit analyzes the emotional state of employees using emotion analysis technology. The analysis unit can also classify the content of complaints and grievances using keyword extraction technology. The analysis unit identifies management issues based on the content of the text data. For example, the emotion analysis technology analyzes the emotional nuances of the text data and calculates an emotion score. The keyword extraction technology extracts important keywords from the text data and classifies them into categories. The summary generation unit creates a summary of the management issues based on the data analyzed by the analysis unit. For example, the generation AI generates summaries of management issues using text generation AI (e.g., LLM). The generation AI can also create summaries of management issues using multimodal generation AI. Furthermore, the generation AI extracts important information to create summaries. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from management issues and create summaries based on that information. The feedback providing unit provides the summaries created by the summary generation unit to management. For example, the feedback providing unit displays the summaries to management through a dedicated dashboard. The feedback providing unit can also provide the summaries to management in report format. Furthermore, the feedback providing unit notifies management of the summaries in real time. For example, the feedback providing unit visually displays the summaries to management through a dashboard.In the report format, summaries can be provided in PDF or Excel format. In the real-time notification format, summaries are sent to managers via email or push notification. As a result, the employee empowerment system according to the embodiment can reflect employee feedback in management improvements and improve the transparency of communication throughout the organization. For example, managers can grasp the raw feedback from the workplace in real time and quickly adjust management strategies. Employees can see that their feedback is being reflected in management, which gives them a sense of participation and satisfaction. As a whole, communication becomes smoother and an improvement in corporate culture is expected.

[0030] The voice recognition unit allows employees to anonymously input their complaints and grievances, making it possible to collect frank opinions while protecting their privacy. For example, the voice recognition unit provides a platform where employees can anonymously input their complaints and grievances. For example, it builds a system where employees can post opinions anonymously without collecting any personal information. In addition, the voice recognition unit introduces technology to guarantee anonymity to protect privacy. For example, it encrypts the content of posts so that individuals cannot be identified. In addition, in order to promote the anonymous collection of opinions, the voice recognition unit creates an environment where employees can post their opinions with peace of mind. For example, it displays a message emphasizing anonymity and emphasizes the importance of privacy protection. This makes it possible to collect frank opinions while protecting employees' privacy.

[0031] The speech recognition unit can accept input from means other than verbal communication, such as by using text messages or a chatbot, when collecting employee complaints and grievances. The speech recognition unit, for example, uses text messages or a chatbot to build a system that collects employee complaints and grievances. For example, it provides a chatbot that allows employees to input their opinions in text. The speech recognition unit also sets up a text message input form to collect complaints and grievances from means other than verbal communication. For example, it prepares a dedicated web form or application. The speech recognition unit also uses a chatbot to collect employee complaints and grievances in real time. For example, the chatbot collects opinions through dialogue with employees and stores them in a database. This makes it possible to collect complaints and grievances from means other than verbal communication.

[0032] The speech recognition unit can set up dedicated collection channels for different departments or teams, thereby clarifying department-specific issues. For example, the speech recognition unit can set up dedicated collection channels for different departments or teams. For example, it can provide opinion collection forms or chatbots dedicated to each department. The speech recognition unit can also classify and analyze collected data by department to clarify department-specific issues. For example, it can identify trends in dissatisfaction and complaints in each department. The speech recognition unit can also build a system that identifies department-specific issues in real time through dedicated collection channels. For example, it can display data for each department on a dashboard, allowing management to respond immediately. This allows department-specific issues to be clarified, enabling more effective responses.

[0033] The analysis unit compares data with past data to discover new trends and patterns, enabling it to predict management issues. For example, when the generation AI performs an analysis, the analysis unit compares the data with past data to discover new trends and patterns. For example, it compares past dissatisfaction data with current data to identify changes. The analysis unit also builds a system that predicts management issues based on new trends and patterns. For example, it predicts future issues based on changes in trends. The analysis unit also allows the generation AI to comprehensively analyze past and current data to discover new trends and patterns. For example, it extracts patterns using data mining technology. This makes it possible to discover new trends and patterns and predict management issues.

[0034] The summary generation unit can provide more practical feedback by including specific suggestions and solutions from employees. For example, when the generation AI creates a summary, the summary generation unit includes specific suggestions and solutions from employees. For example, it adds content such as "specific measures to address delays in project progress" to the summary. The summary generation unit also collects employee suggestions and solutions and builds a system where the generation AI provides practical feedback based on that. For example, it analyzes the content of the suggestions and reflects them in the summary. The summary generation unit also automatically extracts employee suggestions and solutions and incorporates them into the summary when the generation AI creates a summary. For example, it performs text analysis on the content of the suggestions and adds them to the summary. This allows more practical feedback to be provided by including specific suggestions and solutions.

[0035] The summary generation unit can automatically translate the analyzed data into different languages, making it available to global companies. For example, the summary generation unit builds a system that automatically translates data analyzed by the generation AI and provides it in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The summary generation unit also uses an automatic translation function to make the data analyzed by the generation AI multilingual. For example, it uses a translation API to perform translation in real time. The summary generation unit also automatically translates the data analyzed by the generation AI into different languages, making it available to global companies. For example, it displays the translation results on a dashboard. This allows automatic translation into different languages, making it available to global companies.

[0036] The summary generation unit can convert the analyzed data into visual notes or infographics to make it easier to understand visually. For example, the summary generation unit converts the data analyzed by the generation AI into visual notes and builds a system to display them visually. For example, it shows important points with diagrams or icons. The summary generation unit also uses infographics to make the data analyzed by the generation AI easier to understand visually. For example, it displays the main points of the data in graphs or charts. The summary generation unit also converts the data analyzed by the generation AI into visual notes or infographics to make it easier for managers to understand intuitively. For example, it visually displays a summary of the data. By converting it into a visual note or infographic, it becomes easier to understand visually.

[0037] The feedback providing unit allows managers to refer to past response history and the effects of improvement measures when receiving feedback, thereby promoting continuous improvement. The feedback providing unit, for example, builds a system that allows managers to refer to past response history and the effects of improvement measures when receiving feedback. For example, it displays past feedback and its results on a dashboard. The feedback providing unit also stores the feedback history in a database, allowing managers to easily refer to past response history. For example, it displays the effects of past improvement measures in a graph. The feedback providing unit also develops a system that automatically displays past response history when managers receive feedback, in order to promote continuous improvement. For example, it makes it possible to refer to past success stories and failure stories. This promotes continuous improvement by allowing managers to refer to past response history and the effects of improvement measures.

[0038] The feedback providing unit allows the generation AI to automatically generate proposals so that management can convert the content of the feedback into a specific action plan. For example, the feedback providing unit builds a system in which the generation AI automatically generates proposals based on the content of the feedback that management can convert into a specific action plan. For example, it presents specific improvement measures based on the feedback. The feedback providing unit also analyzes the content of the feedback and the generation AI automatically proposes an action plan. For example, it proposes specific measures to address delays in project progress. The feedback providing unit also develops a system that displays the action plan automatically generated by the generation AI when the management receives feedback. For example, it presents specific steps based on the feedback. In this way, the generation AI automatically generates proposals, allowing management to quickly create specific action plans.

[0039] The feedback providing department can share the content of the feedback not only with management but also with related departments and teams, thereby promoting company-wide improvement. The feedback providing department, for example, builds a system for sharing the content of the feedback not only with management but also with related departments and teams. For example, the feedback content is automatically distributed to each department. The feedback providing department also provides a platform for sharing the content of the feedback to promote company-wide improvement. For example, the feedback content is posted on an internal portal site. The feedback providing department also develops a system for sharing the content of the feedback with related departments and teams in real time, thereby promoting company-wide improvement. For example, the feedback content is automatically notified. In this way, company-wide improvement is promoted by sharing the content of the feedback throughout the company.

[0040] The feedback providing department can automatically generate the content of the feedback as periodic reports, which can be used in management meetings and strategy meetings. The feedback providing department, for example, builds a system that automatically generates the content of the feedback as periodic reports. For example, monthly reports and quarterly reports are automatically generated and used in management meetings. The feedback providing department also uses a generation AI to create periodic reports based on the content of the feedback. For example, the feedback content is summarized and provided in report format. The feedback providing department also develops a system that automatically generates the content of the feedback as periodic reports, for use in management meetings and strategy meetings. For example, the reports are automatically distributed. In this way, automatically generating periodic reports makes it easier to use them in management meetings and strategy meetings.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] When analyzing employee complaints and grievances, the analysis unit can refer to the employee's past comment history and behavioral data to conduct analysis based on the characteristics of each individual employee. For example, it can analyze the frequency of specific keywords from past comment history to identify the employee's concerns and issues. It can also analyze behavioral data (for example, working hours and vacation status) to identify the employee's stress factors. Furthermore, the analysis unit can integrate this data and provide personalized feedback to each employee. This enables more effective feedback based on the employee's characteristics.

[0043] The summary generator can provide more practical feedback by including employee suggestions and solutions when summarizing employee complaints and grievances. For example, the summary can include specific improvement measures proposed by employees. The generator can also propose more specific action plans based on employee suggestions. Furthermore, the summary generator can organize employee suggestions by category, making them easily accessible to managers. This makes it possible to provide practical feedback that utilizes employee suggestions.

[0044] The feedback providing department will enable managers to refer to past response history and the effects of improvement measures when receiving feedback, thereby promoting continuous improvement. For example, it will display past feedback and its results on a dashboard. The feedback providing department will also store the feedback history in a database, allowing managers to easily refer to the past response history. For example, it will display the effects of past improvement measures in a graph. In addition, in order to promote continuous improvement, the feedback providing department will develop a system that automatically displays past response history when managers receive feedback. For example, it will allow managers to refer to past success stories and failure stories. This will promote continuous improvement by allowing them to refer to the past response history and the effects of improvement measures.

[0045] The voice recognition unit allows employees to anonymously input their complaints and grievances, making it possible to collect frank opinions while protecting their privacy. For example, a system is built that allows employees to post opinions anonymously without collecting any personal information. The voice recognition unit also introduces technology to guarantee anonymity to protect privacy. For example, it encrypts the content of posts so that individuals cannot be identified. The voice recognition unit also creates an environment where employees can post their opinions with peace of mind to promote anonymous opinion collection. For example, it displays a message emphasizing anonymity and stressing the importance of privacy protection. This makes it possible to collect frank opinions while protecting employees' privacy.

[0046] When collecting employee complaints and grievances, the speech recognition unit can accept input in ways other than verbal, such as using text messages or a chatbot. For example, a chatbot is provided that allows employees to input their opinions in text. The speech recognition unit also sets up a text message input form to collect complaints and grievances in ways other than verbal. For example, a dedicated web form or application is prepared. The speech recognition unit also uses a chatbot to collect employee complaints and grievances in real time. For example, the chatbot collects opinions through dialogue with employees and stores them in a database. This makes it possible to collect complaints and grievances in ways other than verbal.

[0047] The analysis unit can compare data with past data to discover new trends and patterns and predict management issues. For example, it can compare past dissatisfaction data with current data to identify changes. The analysis unit also builds a system that predicts management issues based on new trends and patterns. For example, it can predict future issues based on changes in trends. The analysis unit also uses generative AI to comprehensively analyze past and current data and discover new trends and patterns. For example, it can extract patterns using data mining technology. This makes it possible to discover new trends and patterns and predict management issues.

[0048] The summary generation unit can automatically translate the analyzed data into different languages, making it usable by global companies. For example, a system can be built in which the data analyzed by the generation AI is automatically translated and provided in different languages. For example, multiple languages ​​such as English, French, and Chinese can be supported. The summary generation unit also uses an automatic translation function to make the data analyzed by the generation AI multilingual. For example, a translation API can be used to perform translation in real time. The summary generation unit also automatically translates the data analyzed by the generation AI into different languages, making it usable by global companies. For example, the translation results can be displayed on a dashboard. This allows automatic translation into different languages, making it usable by global companies.

[0049] The feedback department can share the content of the feedback not only with management but also with related departments and teams, thereby promoting company-wide improvement. For example, a system can be built to share the content of the feedback not only with management but also with related departments and teams. For example, the feedback content can be automatically distributed to each department. The feedback department can also provide a platform for sharing the content of the feedback to promote company-wide improvement. For example, the feedback content can be posted on an internal portal site. The feedback department can also develop a system to share the content of the feedback with related departments and teams in real time, thereby promoting company-wide improvement. For example, the feedback content can be automatically notified. In this way, the feedback content can be shared company-wide, promoting company-wide improvement.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The voice recognition unit converts employee complaints and grievances from voice data into text data. For example, it uses voice recognition technology to convert recorded data into text in real time. It can also analyze voice data entered by employees through a dedicated application and convert it into text data. It also removes noise from the voice data to generate highly accurate text data. Step 2: The analysis unit analyzes the text data converted by the speech recognition unit. For example, it uses emotion analysis technology to analyze the emotional state of employees and keyword extraction technology to classify the content of complaints and grievances. It also identifies management issues based on the content of the text data. Step 3: The summary generation unit creates a summary of the management issue based on the data analyzed by the analysis unit. For example, a generation AI is used to generate a summary of the management issue, extracting important information and creating the summary. Step 4: The feedback provider provides the summary created by the summary generator to the manager. For example, the summary can be displayed on a dedicated dashboard or provided in the form of a report. Furthermore, the feedback provider notifies the manager in real time.

[0052] (Example 2) The employee empowerment system according to an embodiment of the present invention is a system that collects employee complaints and grievances, analyzes them using a generation AI, and provides useful feedback to management. As a result, the employee empowerment system can reflect employee opinions in management improvements and improve the transparency of communication throughout the organization.

[0053] An employee empowerment system according to an embodiment includes a speech recognition unit, an analysis unit, a summary generation unit, and a feedback provision unit. The speech recognition unit converts employee complaints and grievances from speech data into text data. For example, the speech recognition unit converts recorded data into text in real time using speech recognition technology. The speech recognition unit can also analyze speech data input by employees through a dedicated application and convert it into text data. The speech recognition unit removes noise from the speech data to generate highly accurate text data. For example, the speech recognition technology uses an algorithm that analyzes speech characteristics and removes noise. The analysis unit analyzes the text data converted by the speech recognition unit. For example, the analysis unit analyzes the emotional state of employees using emotion analysis technology. The analysis unit can also classify the content of complaints and grievances using keyword extraction technology. The analysis unit identifies management issues based on the content of the text data. For example, the emotion analysis technology analyzes the emotional nuances of the text data and calculates an emotion score. The keyword extraction technology extracts important keywords from the text data and classifies them into categories. The summary generation unit creates a summary of the management issues based on the data analyzed by the analysis unit. For example, the generation AI generates summaries of management issues using text generation AI (e.g., LLM). The generation AI can also create summaries of management issues using multimodal generation AI. Furthermore, the generation AI extracts important information to create summaries. For example, the text generation AI has learned from large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from management issues and create summaries based on that information. The feedback providing unit provides the summaries created by the summary generation unit to management. For example, the feedback providing unit displays the summaries to management through a dedicated dashboard. The feedback providing unit can also provide the summaries to management in report format. Furthermore, the feedback providing unit notifies management of the summaries in real time. For example, the feedback providing unit visually displays the summaries to management through a dashboard.In the report format, summaries can be provided in PDF or Excel format. In the real-time notification format, summaries are sent to managers via email or push notification. As a result, the employee empowerment system according to the embodiment can reflect employee feedback in management improvements and improve the transparency of communication throughout the organization. For example, managers can grasp the raw feedback from the workplace in real time and quickly adjust management strategies. Employees can see that their feedback is being reflected in management, which gives them a sense of participation and satisfaction. As a whole, communication becomes smoother and an improvement in corporate culture is expected.

[0054] The voice recognition unit can analyze the emotional state of employees in real time and set priorities according to the intensity of the emotions. For example, when an employee inputs a complaint or grievance, the voice recognition unit uses an emotion estimation function to analyze the emotional state in real time. For example, it analyzes the tone of the voice and facial expressions to quantify the intensity of the emotion. The voice recognition unit also automatically sets the priority of the collected complaints or grievances according to the intensity of the emotion. For example, if the intensity of the emotion is high, it sets the priority high and responds quickly. The voice recognition unit also uses the emotion estimation function to monitor the emotional state of employees in real time and dynamically adjusts the priority according to changes in emotion. For example, it resets the priority if there is a sudden change in emotion. This enables a quick response by setting priorities based on the emotional state of employees.

[0055] The voice recognition unit can use facial expression recognition technology in addition to voice recognition technology to collect employee complaints and grievances from their facial expressions. The voice recognition unit, for example, combines voice recognition technology and facial expression recognition technology to collect employee complaints and grievances. For example, it simultaneously analyzes voice data and facial expression data to obtain more accurate information. The voice recognition unit also uses facial expression recognition technology to estimate the emotional state of the employee from their facial expression and complement the content of the complaints and grievances. For example, it analyzes smiling and angry facial expressions to evaluate the intensity of the emotion. The voice recognition unit also integrates voice recognition technology and facial expression recognition technology to build a system that analyzes employee complaints and grievances from multiple angles. For example, it combines voice and facial expression data to perform a more detailed analysis. This allows more multifaceted data to be obtained by collecting complaints and grievances from both voice and facial expressions.

[0056] The voice recognition unit allows employees to anonymously input their complaints and grievances, making it possible to collect frank opinions while protecting their privacy. For example, the voice recognition unit provides a platform where employees can anonymously input their complaints and grievances. For example, it builds a system where employees can post opinions anonymously without collecting any personal information. In addition, the voice recognition unit introduces technology to guarantee anonymity to protect privacy. For example, it encrypts the content of posts so that individuals cannot be identified. In addition, in order to promote the anonymous collection of opinions, the voice recognition unit creates an environment where employees can post their opinions with peace of mind. For example, it displays a message emphasizing anonymity and emphasizes the importance of privacy protection. This makes it possible to collect frank opinions while protecting employees' privacy.

[0057] The speech recognition unit can accept input from means other than verbal communication, such as by using text messages or a chatbot, when collecting employee complaints and grievances. The speech recognition unit, for example, uses text messages or a chatbot to build a system that collects employee complaints and grievances. For example, it provides a chatbot that allows employees to input their opinions in text. The speech recognition unit also sets up a text message input form to collect complaints and grievances from means other than verbal communication. For example, it prepares a dedicated web form or application. The speech recognition unit also uses a chatbot to collect employee complaints and grievances in real time. For example, the chatbot collects opinions through dialogue with employees and stores them in a database. This makes it possible to collect complaints and grievances from means other than verbal communication.

[0058] The speech recognition unit can set up dedicated collection channels for different departments or teams, thereby clarifying department-specific issues. For example, the speech recognition unit can set up dedicated collection channels for different departments or teams. For example, it can provide opinion collection forms or chatbots dedicated to each department. The speech recognition unit can also classify and analyze collected data by department to clarify department-specific issues. For example, it can identify trends in dissatisfaction and complaints in each department. The speech recognition unit can also build a system that identifies department-specific issues in real time through dedicated collection channels. For example, it can display data for each department on a dashboard, allowing management to respond immediately. This allows department-specific issues to be clarified, enabling more effective responses.

[0059] The voice recognition unit uses the emotion estimation function to estimate the emotion of an employee when he or she inputs dissatisfaction or complaints in real time, thereby encouraging positive feedback. The voice recognition unit, for example, uses the emotion estimation function to estimate the emotion of an employee when he or she inputs dissatisfaction or complaints in real time. For example, it analyzes voice and facial expressions to evaluate the intensity of the emotion. The voice recognition unit also displays an encouraging message based on the emotion estimation result to encourage positive feedback. For example, if a negative emotion is detected, it displays a positive message. The voice recognition unit also uses the emotion estimation function to monitor the emotional state of employees in real time and build a system that provides positive feedback. For example, it adjusts the content of the feedback according to changes in emotion. This allows for providing positive feedback according to the employee's emotions, thereby improving employee satisfaction.

[0060] The analysis unit compares data with past data to discover new trends and patterns, enabling it to predict management issues. For example, when the generation AI performs an analysis, the analysis unit compares the data with past data to discover new trends and patterns. For example, it compares past dissatisfaction data with current data to identify changes. The analysis unit also builds a system that predicts management issues based on new trends and patterns. For example, it predicts future issues based on changes in trends. The analysis unit also allows the generation AI to comprehensively analyze past and current data to discover new trends and patterns. For example, it extracts patterns using data mining technology. This makes it possible to discover new trends and patterns and predict management issues.

[0061] The summary generation unit can provide more practical feedback by including specific suggestions and solutions from employees. For example, when the generation AI creates a summary, the summary generation unit includes specific suggestions and solutions from employees. For example, it adds content such as "specific measures to address delays in project progress" to the summary. The summary generation unit also collects employee suggestions and solutions and builds a system where the generation AI provides practical feedback based on that. For example, it analyzes the content of the suggestions and reflects them in the summary. The summary generation unit also automatically extracts employee suggestions and solutions and incorporates them into the summary when the generation AI creates a summary. For example, it performs text analysis on the content of the suggestions and adds them to the summary. This allows more practical feedback to be provided by including specific suggestions and solutions.

[0062] The summary generation unit can automatically translate the analyzed data into different languages, making it available to global companies. For example, the summary generation unit builds a system that automatically translates data analyzed by the generation AI and provides it in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The summary generation unit also uses an automatic translation function to make the data analyzed by the generation AI multilingual. For example, it uses a translation API to perform translation in real time. The summary generation unit also automatically translates the data analyzed by the generation AI into different languages, making it available to global companies. For example, it displays the translation results on a dashboard. This allows automatic translation into different languages, making it available to global companies.

[0063] The summary generation unit can convert the analyzed data into visual notes or infographics to make it easier to understand visually. For example, the summary generation unit converts the data analyzed by the generation AI into visual notes and builds a system to display them visually. For example, it shows important points with diagrams or icons. The summary generation unit also uses infographics to make the data analyzed by the generation AI easier to understand visually. For example, it displays the main points of the data in graphs or charts. The summary generation unit also converts the data analyzed by the generation AI into visual notes or infographics to make it easier for managers to understand intuitively. For example, it visually displays a summary of the data. By converting it into a visual note or infographic, it becomes easier to understand visually.

[0064] The summary generation unit can use the emotion estimation function to collect the manager's emotional reactions to the summarized data and improve the quality of feedback. The summary generation unit, for example, uses the emotion estimation function to build a system that collects the manager's emotional reactions to the summarized data. For example, it analyzes the manager's facial expressions and voice and calculates an emotion score. The summary generation unit also improves the quality of feedback based on the manager's emotional reaction data. For example, it prioritizes providing summaries with a high number of positive emotional reactions. The summary generation unit also uses the emotion estimation function to collect the manager's emotional reactions in real time and develop a system that dynamically adjusts the quality of feedback. For example, it adjusts the content of the feedback according to changes in emotion. In this way, the quality of feedback is improved by collecting the manager's emotional reactions.

[0065] The feedback providing unit can use the emotion estimation function to present summaries of management issues created by the generation AI in order of priority according to the manager's emotional state. For example, the feedback providing unit builds a system that uses the emotion estimation function to present summaries of management issues created by the generation AI in order of priority according to the manager's emotional state. For example, if the manager is feeling stressed, it will prioritize issues with a high level of urgency. The feedback providing unit also monitors the manager's emotional state in real time and dynamically adjusts the priority of management issues based on the results. For example, it changes the display order of issues according to changes in emotion. The feedback providing unit also uses the emotion estimation function to develop a system that provides feedback according to the manager's emotional state. For example, it will present important issues when the manager is in a positive emotional state. This enables effective decision-making by presenting management issues in order of priority according to the manager's emotional state.

[0066] The feedback providing unit allows managers to refer to past response history and the effects of improvement measures when receiving feedback, thereby promoting continuous improvement. The feedback providing unit, for example, builds a system that allows managers to refer to past response history and the effects of improvement measures when receiving feedback. For example, it displays past feedback and its results on a dashboard. The feedback providing unit also stores the feedback history in a database, allowing managers to easily refer to past response history. For example, it displays the effects of past improvement measures in a graph. The feedback providing unit also develops a system that automatically displays past response history when managers receive feedback, in order to promote continuous improvement. For example, it makes it possible to refer to past success stories and failure stories. This promotes continuous improvement by allowing managers to refer to past response history and the effects of improvement measures.

[0067] The feedback providing unit allows the generation AI to automatically generate proposals so that management can convert the content of the feedback into a specific action plan. For example, the feedback providing unit builds a system in which the generation AI automatically generates proposals based on the content of the feedback that management can convert into a specific action plan. For example, it presents specific improvement measures based on the feedback. The feedback providing unit also analyzes the content of the feedback and the generation AI automatically proposes an action plan. For example, it proposes specific measures to address delays in project progress. The feedback providing unit also develops a system that displays the action plan automatically generated by the generation AI when the management receives feedback. For example, it presents specific steps based on the feedback. In this way, the generation AI automatically generates proposals, allowing management to quickly create specific action plans.

[0068] The feedback providing department can share the content of the feedback not only with management but also with related departments and teams, thereby promoting company-wide improvement. The feedback providing department, for example, builds a system for sharing the content of the feedback not only with management but also with related departments and teams. For example, the feedback content is automatically distributed to each department. The feedback providing department also provides a platform for sharing the content of the feedback to promote company-wide improvement. For example, the feedback content is posted on an internal portal site. The feedback providing department also develops a system for sharing the content of the feedback with related departments and teams in real time, thereby promoting company-wide improvement. For example, the feedback content is automatically notified. In this way, company-wide improvement is promoted by sharing the content of the feedback throughout the company.

[0069] The feedback providing department can automatically generate the content of the feedback as periodic reports, which can be used in management meetings and strategy meetings. The feedback providing department, for example, builds a system that automatically generates the content of the feedback as periodic reports. For example, monthly reports and quarterly reports are automatically generated and used in management meetings. The feedback providing department also uses a generation AI to create periodic reports based on the content of the feedback. For example, the feedback content is summarized and provided in report format. The feedback providing department also develops a system that automatically generates the content of the feedback as periodic reports, for use in management meetings and strategy meetings. For example, the reports are automatically distributed. In this way, automatically generating periodic reports makes it easier to use them in management meetings and strategy meetings.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] When collecting employee complaints and grievances, the voice recognition unit can collect not only voice data but also biometric data (e.g., heart rate and electrodermal activity) of employees, allowing for more accurate estimation of their emotional state. For example, it can analyze heart rate fluctuations when employees express their dissatisfaction to assess their stress level. It can also measure electrodermal activity to quantify the intensity of their emotions. Furthermore, the voice recognition unit can integrate this biometric data with voice data to perform more detailed emotional analysis. This allows for a multifaceted understanding of employees' emotional state and the provision of more appropriate feedback.

[0072] When analyzing employee complaints and grievances, the analysis unit can refer to the employee's past comment history and behavioral data to conduct analysis based on the characteristics of each individual employee. For example, it can analyze the frequency of specific keywords from past comment history to identify the employee's concerns and issues. It can also analyze behavioral data (for example, working hours and vacation status) to identify the employee's stress factors. Furthermore, the analysis unit can integrate this data and provide personalized feedback to each employee. This enables more effective feedback based on the employee's characteristics.

[0073] The summary generator can provide more practical feedback by including employee suggestions and solutions when summarizing employee complaints and grievances. For example, the summary can include specific improvement measures proposed by employees. The generator can also propose more specific action plans based on employee suggestions. Furthermore, the summary generator can organize employee suggestions by category, making them easily accessible to managers. This makes it possible to provide practical feedback that utilizes employee suggestions.

[0074] The feedback providing unit can use the emotion estimation function to present summaries of management issues created by the generation AI in order of priority according to the manager's emotional state. For example, if the manager is feeling stressed, it will prioritize issues with a high level of urgency. The feedback providing unit also monitors the manager's emotional state in real time and dynamically adjusts the priority of management issues based on the results. For example, it changes the display order of issues according to changes in emotion. The feedback providing unit will also use the emotion estimation function to develop a system that provides feedback according to the manager's emotional state. For example, it will present important issues when the manager is in a positive emotional state. This enables effective decision-making by presenting management issues in order of priority according to the manager's emotional state.

[0075] The feedback providing department will enable managers to refer to past response history and the effects of improvement measures when receiving feedback, thereby promoting continuous improvement. For example, it will display past feedback and its results on a dashboard. The feedback providing department will also store the feedback history in a database, allowing managers to easily refer to the past response history. For example, it will display the effects of past improvement measures in a graph. In addition, in order to promote continuous improvement, the feedback providing department will develop a system that automatically displays past response history when managers receive feedback. For example, it will allow managers to refer to past success stories and failure stories. This will promote continuous improvement by allowing them to refer to the past response history and the effects of improvement measures.

[0076] The voice recognition unit allows employees to anonymously input their complaints and grievances, making it possible to collect frank opinions while protecting their privacy. For example, a system is built that allows employees to post opinions anonymously without collecting any personal information. The voice recognition unit also introduces technology to guarantee anonymity to protect privacy. For example, it encrypts the content of posts so that individuals cannot be identified. The voice recognition unit also creates an environment where employees can post their opinions with peace of mind to promote anonymous opinion collection. For example, it displays a message emphasizing anonymity and stressing the importance of privacy protection. This makes it possible to collect frank opinions while protecting employees' privacy.

[0077] When collecting employee complaints and grievances, the speech recognition unit can accept input in ways other than verbal, such as using text messages or a chatbot. For example, a chatbot is provided that allows employees to input their opinions in text. The speech recognition unit also sets up a text message input form to collect complaints and grievances in ways other than verbal. For example, a dedicated web form or application is prepared. The speech recognition unit also uses a chatbot to collect employee complaints and grievances in real time. For example, the chatbot collects opinions through dialogue with employees and stores them in a database. This makes it possible to collect complaints and grievances in ways other than verbal.

[0078] The analysis unit can compare data with past data to discover new trends and patterns and predict management issues. For example, it can compare past dissatisfaction data with current data to identify changes. The analysis unit also builds a system that predicts management issues based on new trends and patterns. For example, it can predict future issues based on changes in trends. The analysis unit also uses generative AI to comprehensively analyze past and current data and discover new trends and patterns. For example, it can extract patterns using data mining technology. This makes it possible to discover new trends and patterns and predict management issues.

[0079] The summary generation unit can automatically translate the analyzed data into different languages, making it usable by global companies. For example, a system can be built in which the data analyzed by the generation AI is automatically translated and provided in different languages. For example, multiple languages ​​such as English, French, and Chinese can be supported. The summary generation unit also uses an automatic translation function to make the data analyzed by the generation AI multilingual. For example, a translation API can be used to perform translation in real time. The summary generation unit also automatically translates the data analyzed by the generation AI into different languages, making it usable by global companies. For example, the translation results can be displayed on a dashboard. This allows automatic translation into different languages, making it usable by global companies.

[0080] The feedback department can share the content of the feedback not only with management but also with related departments and teams, thereby promoting company-wide improvement. For example, a system can be built to share the content of the feedback not only with management but also with related departments and teams. For example, the feedback content can be automatically distributed to each department. The feedback department can also provide a platform for sharing the content of the feedback to promote company-wide improvement. For example, the feedback content can be posted on an internal portal site. The feedback department can also develop a system to share the content of the feedback with related departments and teams in real time, thereby promoting company-wide improvement. For example, the feedback content can be automatically notified. In this way, the feedback content can be shared company-wide, promoting company-wide improvement.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The voice recognition unit converts employee complaints and grievances from voice data into text data. For example, it uses voice recognition technology to convert recorded data into text in real time. It can also analyze voice data entered by employees through a dedicated application and convert it into text data. It also removes noise from the voice data to generate highly accurate text data. Step 2: The analysis unit analyzes the text data converted by the speech recognition unit. For example, it uses emotion analysis technology to analyze the emotional state of employees and keyword extraction technology to classify the content of complaints and grievances. It also identifies management issues based on the content of the text data. Step 3: The summary generation unit creates a summary of the management issue based on the data analyzed by the analysis unit. For example, a generation AI is used to generate a summary of the management issue, extracting important information and creating the summary. Step 4: The feedback provider provides the summary created by the summary generator to the manager. For example, the summary can be displayed on a dedicated dashboard or provided in the form of a report. Furthermore, the feedback provider notifies the manager in real time.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0127] In the robot 414, 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 robot 414 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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. [Explanation of symbols]

[0150] 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 voice recognition unit that converts employee complaints and grievances from voice data into text data; an analysis unit that analyzes the text data converted by the speech recognition unit; a summary generation unit that generates a summary of the management issues based on the data analyzed by the analysis unit; a feedback providing unit that provides the summary created by the summary generating unit to the manager. A system characterized by:

2. The voice recognition unit Analyze the employee's emotional state in real time and set priorities based on the intensity of their emotions 2. The system of claim 1.

3. The voice recognition unit In addition to voice recognition technology, facial expression recognition technology is used to collect the dissatisfaction and complaints from the facial expressions of the employees.

2. The system of claim 1.

4. The voice recognition unit To allow employees to anonymously input their complaints and grievances, and to collect honest opinions while protecting their privacy.

2. The system of claim 1.

5. The voice recognition unit When collecting the employee's complaints and grievances, the company accepts input in ways other than verbal communication using text messages or chatbots.

2. The system of claim 1.

6. The voice recognition unit Create dedicated collection channels for different departments and teams to clarify the specific issues for said departments 2. The system of claim 1.

7. The voice recognition unit The emotions of the employees when they input their dissatisfaction or complaints are estimated in real time, and positive feedback is encouraged.

2. The system of claim 1.

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