system

Abstract

The system according to this embodiment aims to effectively utilize employee satisfaction survey data to identify issues and propose improvement measures. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, and a proposal unit. The collection unit collects employee satisfaction survey data. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies issues based on the data analyzed by the analysis unit. The proposal unit proposes improvement measures based on the issues identified by the identification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it cannot be said that the employee satisfaction survey data is effectively utilized to identify problems and propose improvement measures, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize the employee satisfaction survey data to identify problems and propose improvement measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an identification unit, and a proposal unit. The data collection unit collects employee satisfaction survey data. The analysis unit analyzes the data collected by the data collection unit. The identification unit identifies issues based on the data analyzed by the analysis unit. The proposal unit proposes improvement measures based on the issues identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively utilize employee satisfaction survey data to identify issues and propose improvement measures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The employee satisfaction improvement agent system according to an embodiment of the present invention is an agent system for improving employee satisfaction. This system has two versions: one for managers and one for general employees. First, the agent system for managers will be described. This system collects data from monthly and annual employee satisfaction surveys, and AI analyzes this data. Based on the analyzed data, it proactively acquires the challenges of the organization to which it belongs and proposes improvement measures. For example, based on employee feedback and survey results, it identifies organizational problems and presents specific improvement measures. This system allows managers to take appropriate measures to improve employee satisfaction, regardless of their experience or skills. Next, the agent system for general employees will be described. This system collects data from employee satisfaction surveys entered by general employees themselves, and AI analyzes this data. Based on the analyzed data, it proposes countermeasures to the employees themselves. For example, when an employee enters their stress or dissatisfaction, the AI ​​analyzes the information and provides specific countermeasures and advice. This system allows general employees to acquire the means to solve problems themselves and reduces the burden on managers. Thus, the employee satisfaction improvement agent offers two versions, one for managers and one for general employees, allowing it to address the sensitive and ever-changing needs of employee satisfaction on a monthly basis. This makes it possible to improve employee satisfaction across the entire organization. As a result, the employee satisfaction improvement agent system can implement appropriate measures to improve employee satisfaction.

[0029] The employee satisfaction improvement agent system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a proposal unit. The collection unit collects employee satisfaction survey data. The collection unit collects employee satisfaction survey data, for example, using online questionnaires. The collection unit can also collect employee satisfaction survey data through interviews. Furthermore, the collection unit can extract employee satisfaction survey data from a database. For example, the collection unit conducts online questionnaires and collects feedback from employees. It can also collect employees' opinions and impressions through interviews. It can also extract past employee satisfaction survey data from a database and use it for analysis. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes employee satisfaction survey data, for example, using statistical analysis. Furthermore, the analysis unit can also analyze employee satisfaction survey data using text mining. Furthermore, the analysis unit can also analyze employee satisfaction survey data using machine learning algorithms. For example, the analysis unit uses statistical analysis to understand changes in employee satisfaction. It can also extract important keywords from employee feedback using text mining. It can also predict trends in employee satisfaction using machine learning algorithms. The Specialization Department identifies issues based on data analyzed by the Analysis Department. For example, the Specialization Department identifies frequently occurring problems. It can also identify factors contributing to low ratings. Furthermore, the Specialization Department can identify the causes of declining employee satisfaction. For example, the Specialization Department can list frequently occurring problems and identify areas requiring improvement. It can also analyze factors contributing to low ratings and propose specific improvement measures. It can identify the causes of declining employee satisfaction and implement countermeasures. The Proposal Department proposes improvement measures based on the issues identified by the Specialization Department. For example, the Proposal Department proposes the introduction of best practices. It can also propose the implementation of training programs. Furthermore, the Proposal Department can propose process reviews. For example, the Proposal Department can improve employee satisfaction by introducing best practices. It can also improve employee skills by implementing training programs. It can also improve operational efficiency by reviewing processes.As a result, the employee satisfaction improvement agent system according to the embodiment can collect, analyze, identify issues with, and propose improvement measures based on employee satisfaction survey data.

[0030] The data collection department collects employee satisfaction survey data. For example, the department collects employee satisfaction survey data using online questionnaires. Specifically, the online questionnaires are conducted in a format that allows employees to respond anonymously, and the questions include topics such as job satisfaction, work environment, relationships with superiors and colleagues, and opportunities for career growth. This makes it easier for employees to provide candid opinions, improving the reliability of the collected data. The data collection department can also collect employee satisfaction survey data through interviews. Interviews are conducted individually or in groups, allowing for a deeper exploration of employees' specific opinions and impressions. The interviews are recorded and later transcribed for analysis. Furthermore, the data collection department can extract employee satisfaction survey data from databases. These databases store past survey results and interview records, and by extracting this data and comparing it with current survey results, long-term trends and changes can be identified. For example, the data collection department conducts online questionnaires to collect feedback from employees. It can also collect employee opinions and impressions through interviews. Past employee satisfaction survey data can be extracted from databases and used for analysis. This allows the data collection unit to gather data on employee satisfaction in a variety of ways and provide comprehensive information.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes employee satisfaction survey data using statistical analysis. Specifically, statistical analysis calculates basic statistics such as the mean, median, and standard deviation to grasp the overall trend of employee satisfaction. Cross-tabulation can also be performed to reveal differences in satisfaction levels by department and job position. Furthermore, the analysis department can also analyze employee satisfaction survey data using text mining. Text mining extracts important keywords and phrases from employees' free-response texts to identify frequently occurring themes and problems. For example, if many words indicating negative emotions appear, it is necessary to investigate the cause in detail. Furthermore, the analysis department can also analyze employee satisfaction survey data using machine learning algorithms. Machine learning algorithms learn from large amounts of data and identify factors that influence satisfaction. For example, decision trees and random forests can be used to compare the characteristics of employees with high and low satisfaction levels and derive specific measures to improve satisfaction. In this way, the analysis department can analyze the collected data from multiple perspectives and clarify the current state and challenges of employee satisfaction.

[0032] The specific department identifies issues based on data analyzed by the analysis department. For example, the specific department identifies frequently occurring problems. Specifically, it lists problems that are frequently pointed out in the analysis results and prioritizes those with the greatest impact. The specific department can also identify factors contributing to low ratings. Possible factors contributing to low ratings include, for example, dissatisfaction with the work environment, insufficient communication with superiors, and lack of opportunities for career growth. These factors are analyzed in detail, and specific improvement measures are considered. Furthermore, the specific department can identify the causes of declining employee satisfaction. Possible causes of declining employee satisfaction include, for example, increased workload, dissatisfaction with compensation and benefits, and problems with workplace relationships. By identifying these causes and taking countermeasures, employee satisfaction can be improved. For example, the specific department can list frequently occurring problems and identify areas that need improvement. It can also analyze the factors contributing to low ratings and propose specific improvement measures. It can also identify the causes of declining employee satisfaction and take countermeasures. In this way, the specific department can clarify specific issues for improving employee satisfaction and derive effective improvement measures.

[0033] The proposal department proposes improvement measures based on the issues identified by specific departments. For example, the proposal department proposes the introduction of best practices. Specifically, it references successful measures from other companies or departments and customizes them to suit the company's situation. The proposal department can also propose the implementation of training programs. These training programs support employee skill development and career growth, and include, for example, leadership training and workshops to improve communication skills. Furthermore, the proposal department can also propose process reviews. Business process reviews aim to improve work efficiency and reduce the burden on employees, and may include, for example, simplifying work flows or introducing automation tools. For example, the proposal department can improve employee satisfaction by introducing best practices. It can also improve employee skills by implementing training programs. It can also improve work efficiency by reviewing processes. In this way, the proposal department can propose and implement specific improvement measures to improve employee satisfaction. Furthermore, the proposal department can monitor the effectiveness of the proposed improvement measures and make revisions or additional suggestions as needed. In this way, the proposal department can continuously support the improvement of employee satisfaction.

[0034] The data collection unit can collect employee satisfaction survey data that changes monthly and annually. For example, the data collection unit can collect data from employee satisfaction surveys conducted on a monthly basis. It can also collect data from employee satisfaction surveys conducted annually. Furthermore, the data collection unit can compare and collect data that changes monthly and annually. For example, the data collection unit can collect the results of monthly surveys to understand changes in employee satisfaction. It can also collect the results of annual surveys to analyze long-term trends. By comparing data that changes monthly and annually, it is possible to understand seasonal fluctuations and year-to-year trends. This allows for understanding changes in employee satisfaction by collecting data that changes monthly and annually.

[0035] The analysis unit can analyze the collected data and understand changes in employee satisfaction. For example, the analysis unit can statistically analyze the collected data to understand changes in employee satisfaction. It can also text-min the collected data to understand changes in employee satisfaction. Furthermore, the analysis unit can analyze the collected data using machine learning algorithms to understand changes in employee satisfaction. For example, the analysis unit can use statistical analysis to understand increases and decreases in employee satisfaction scores. It can also use text mining to extract factors influencing satisfaction fluctuations from employee feedback. It can also use machine learning algorithms to predict trends in employee satisfaction. In this way, by analyzing the collected data, changes in employee satisfaction can be understood.

[0036] A specific department can identify organizational-wide issues based on the analyzed data. For example, the department can identify frequently occurring problems from the analyzed data. Furthermore, the department can identify factors contributing to low performance from the analyzed data. In addition, the department can identify organizational-wide issues from the analyzed data. For example, the department can list frequently occurring problems from the analyzed data and identify organizational-wide issues. They can also analyze factors contributing to low performance and identify organizational-wide issues. They can identify organizational-wide issues from the analyzed data and identify areas requiring improvement. This allows for the identification of organizational-wide issues based on the analyzed data.

[0037] The specific unit can identify individual challenges based on the analyzed data. For example, the specific unit can identify individual skill deficiencies from the analyzed data. Furthermore, the specific unit can identify decreased motivation from the analyzed data. In addition, the specific unit can identify individual challenges from the analyzed data. For example, the specific unit can list individual skill deficiencies from the analyzed data and identify individual challenges. It can also analyze decreased motivation and identify individual challenges. It can identify individual challenges from the analyzed data and identify areas that need improvement. This allows for the identification of individual challenges based on the analyzed data.

[0038] The proposal department can propose improvement measures based on identified organization-wide issues. For example, the proposal department can propose the introduction of best practices based on identified organization-wide issues. It can also propose the implementation of training programs based on identified organization-wide issues. Furthermore, the proposal department can propose process revisions based on identified organization-wide issues. For example, the proposal department can improve employee satisfaction by introducing best practices based on identified organization-wide issues. It can also improve employee skills by implementing training programs. It can also improve operational efficiency by revising processes. This allows the proposal department to suggest improvement measures based on identified organization-wide issues.

[0039] The proposal department can propose solutions based on the identified individual's challenges. For example, the proposal department can propose individualized feedback based on the identified individual's challenges. Furthermore, the proposal department can propose career counseling based on the identified individual's challenges. In addition, the proposal department can propose training programs based on the identified individual's challenges. For example, the proposal department can improve employees' skills by providing individualized feedback based on the identified individual's challenges. They can also conduct career counseling to clarify employees' career paths. They can also propose training programs to improve employees' skills. This allows them to propose solutions based on the identified individual's challenges.

[0040] The data collection department can analyze past employee satisfaction survey data and select the optimal data collection method. For example, the data collection department can select the method with the highest response rate from past data. It can also select the method that yielded the most accurate information from past data. Furthermore, it can select the method that allowed for the fastest data collection from past data. This allows the optimal data collection method to be selected by analyzing past data.

[0041] The data collection unit can filter employee satisfaction survey data based on employees' current projects and work content. For example, the unit can prioritize collecting questions related to ongoing projects. It can also filter and collect questions that are highly relevant based on work content. Furthermore, the unit can collect data at appropriate times based on project progress. This allows for the collection of highly relevant data by filtering based on current projects and work content.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting employee satisfaction survey data. For example, the data collection unit can prioritize the collection of data from employees who are geographically close. Furthermore, the data collection unit can compare and collect data from geographically different regions. In addition, the data collection unit can prioritize the collection of data from specific regions based on geographical location information. For example, the data collection unit can prioritize the collection of data from employees who are geographically close. It can also compare and collect data from geographically different regions. It can also prioritize the collection of data from specific regions based on geographical location information. This allows for the priority collection of highly relevant data by considering geographical location information.

[0043] The data collection unit can analyze employees' social media activity and collect relevant data when collecting employee satisfaction survey data. For example, the data collection unit can collect satisfaction-related data from employees' social media activity. It can also analyze the content of social media posts and collect satisfaction-related data. Furthermore, the data collection unit can collect satisfaction-related data based on the frequency of social media activity. In this way, relevant data can be collected by analyzing social media activity.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of employee satisfaction. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. Furthermore, the analysis unit can determine the priority of the analysis according to importance. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. It can also determine the priority of the analysis according to importance. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of employee satisfaction.

[0045] The analysis unit can apply different analysis algorithms depending on the employee satisfaction category during analysis. For example, the analysis unit can apply a stress analysis algorithm to stress-related data. It can also apply a motivation analysis algorithm to motivation-related data. Furthermore, it can apply a communication analysis algorithm to communication-related data. By applying different analysis algorithms depending on the employee satisfaction category, more accurate analysis results can be obtained.

[0046] The analysis unit can determine the priority of analysis based on the submission date of employee satisfaction survey data. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can determine the order of analysis based on the submission date. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. It can also determine the order of analysis based on the submission date. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the submission date.

[0047] The analysis unit can adjust the order of analysis based on the relevance of employee satisfaction survey data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. It can also determine the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on relevance.

[0048] A specific department can improve the accuracy of its problem identification by considering the interrelationships of employee satisfaction. For example, it can identify problems by considering the interrelationship between stress and motivation. It can also identify problems by considering the interrelationship between communication and teamwork. Furthermore, it can identify problems by considering the interrelationship between job content and satisfaction. For example, a specific department can identify problems by considering the interrelationship between stress and motivation. It can also identify problems by considering the interrelationship between communication and teamwork. It can also identify problems by considering the interrelationship between job content and satisfaction. By considering the interrelationships of employee satisfaction, the accuracy of problem identification can be improved.

[0049] The task force can identify issues by considering employee attribute information. For example, the task force can identify issues based on age and gender. It can also identify issues based on job title and position. Furthermore, it can identify issues based on length of service. For example, the task force can identify issues based on age and gender. It can also identify issues based on job title and position. It can also identify issues based on length of service. By considering employee attribute information, it becomes possible to identify issues with higher accuracy.

[0050] The task force can identify issues by considering the geographical distribution of employee satisfaction. For example, the task force can prioritize identifying issues affecting employees who are geographically close. It can also compare and identify issues in geographically different regions. Furthermore, it can prioritize identifying issues in specific regions based on geographical location information. This allows for improved accuracy in identification by considering geographical distribution.

[0051] The identification unit can improve the accuracy of its identification by referring to relevant literature on employee satisfaction survey data when identifying issues. For example, the identification unit can improve the accuracy of its identification by referring to relevant literature. Furthermore, the identification unit can also improve the accuracy of its identification based on past research results. In addition, the identification unit can improve the accuracy of its identification by referring to the latest research findings. For example, the identification unit can improve the accuracy of its identification by referring to relevant literature. It can also improve the accuracy of its identification based on past research results. It can also improve the accuracy of its identification by referring to the latest research findings. Thus, by referring to relevant literature, the accuracy of its identification can be improved.

[0052] The proposal department can adjust the level of detail in its proposals based on the importance of the issues. For example, it can propose detailed solutions for high-priority issues, and simplified solutions for low-priority issues. Furthermore, the proposal department can prioritize proposals according to their importance. This allows for the proposal of detailed solutions for important issues by adjusting the level of detail based on the importance of the issues.

[0053] The proposal department can apply different proposal algorithms depending on the category of the problem when proposing improvement measures. For example, for a problem related to stress, the proposal department can apply an algorithm that proposes stress reduction measures. It can also apply an algorithm that proposes motivation improvement measures for a problem related to motivation. Furthermore, it can apply an algorithm that proposes communication improvement measures for a problem related to communication. By applying different proposal algorithms depending on the category of the problem, more appropriate improvement measures can be proposed.

[0054] The proposal department can prioritize proposals based on when the issues were submitted. For example, the proposal department can prioritize proposals for the most recent issues. It can also postpone older issues. Furthermore, the proposal department can determine the order of proposals based on the submission date. For example, the proposal department can prioritize proposals for the most recent issues, postpone older issues, and determine the order of proposals based on the submission date. This allows for prioritizing proposals based on the submission date, ensuring that the most recent issues receive priority in proposals.

[0055] The proposal department can adjust the order of proposals based on the relevance of the issues when proposing improvement measures. For example, the proposal department can prioritize proposing improvement measures for highly relevant issues. It can also postpone issues with low relevance. Furthermore, the proposal department can determine the order of proposals based on the relevance of the issues. For example, the proposal department can prioritize proposing improvement measures for highly relevant issues. It can also postpone issues with low relevance. It can also determine the order of proposals based on the relevance of the issues. This allows for prioritizing the proposal of improvement measures for highly relevant issues by adjusting the order of proposals based on their relevance.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit collects employee working hours and vacation data, and the analysis unit can use this data to evaluate employees' work-life balance. For example, the data collection unit collects data on the length of employees' working hours and vacation usage. The analysis unit can analyze this data to identify imbalances in employees' work-life balance. The identification unit identifies employees with work-life balance imbalances, and the proposal unit can propose appropriate vacation arrangements and adjustments to working hours. This can improve employees' work-life balance and increase their satisfaction.

[0058] The analysis department can analyze employee health data and understand changes in their health status. For example, the data collection department collects employee health checkup results and fitness data. The analysis department can analyze this data and identify changes in employees' health status. The identification department can identify employees whose health is deteriorating, and the proposal department can propose advice and programs for health improvement. This can improve employee health and increase their satisfaction.

[0059] The specific department can analyze data related to employees' career paths and identify challenges in their career advancement. For example, the data collection department collects employees' promotion history and skill development status. The analysis department can analyze this data and identify challenges in their career advancement. The specific department can identify employees whose career advancement is stagnant, and the proposal department can propose career counseling or training programs for skill development. This can support employees' career paths and improve their satisfaction.

[0060] The proposal department can suggest activities for employees to refresh themselves based on their hobbies and interests. For example, the data collection department collects data on employees' hobbies and interests. The analysis department can analyze this data and identify activities that employees can use to refresh themselves. The proposal department can then suggest activities for refreshing themselves based on employees' hobbies and interests. This can reduce employee stress and improve job satisfaction.

[0061] The data collection department collects employee communication patterns, and the analysis department can use this data to identify areas for communication improvement. For example, the data collection department collects employee email and chat histories. The analysis department can analyze this data to evaluate the frequency and quality of communication. The identification department can identify employees with communication problems, and the proposal department can propose training and workshops to improve communication skills. This can improve communication among employees and increase employee satisfaction.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects employee satisfaction survey data. For example, employee satisfaction survey data can be collected using online questionnaires. Alternatively, employee satisfaction survey data can be collected through interviews. Furthermore, employee satisfaction survey data can be extracted from a database. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze employee satisfaction survey data using statistical analysis. It can also analyze employee satisfaction survey data using text mining. Furthermore, it can analyze employee satisfaction survey data using machine learning algorithms. Step 3: The Identification Unit identifies issues based on the data analyzed by the Analysis Unit. For example, it can identify frequently occurring problems. It can also identify factors contributing to low ratings. Furthermore, it can identify the causes of declining employee satisfaction. Step 4: The proposing department proposes improvement measures based on the issues identified by the specific department. For example, they may propose the implementation of best practices, the implementation of a training program, or a review of the process.

[0064] (Example of form 2) The employee satisfaction improvement agent system according to an embodiment of the present invention is an agent system for improving employee satisfaction. This system has two versions: one for managers and one for general employees. First, the agent system for managers will be described. This system collects data from monthly and annual employee satisfaction surveys, and AI analyzes this data. Based on the analyzed data, it proactively acquires the challenges of the organization to which it belongs and proposes improvement measures. For example, based on employee feedback and survey results, it identifies organizational problems and presents specific improvement measures. This system allows managers to take appropriate measures to improve employee satisfaction, regardless of their experience or skills. Next, the agent system for general employees will be described. This system collects data from employee satisfaction surveys entered by general employees themselves, and AI analyzes this data. Based on the analyzed data, it proposes countermeasures to the employees themselves. For example, when an employee enters their stress or dissatisfaction, the AI ​​analyzes the information and provides specific countermeasures and advice. This system allows general employees to acquire the means to solve problems themselves and reduces the burden on managers. Thus, the employee satisfaction improvement agent offers two versions, one for managers and one for general employees, allowing it to address the sensitive and ever-changing needs of employee satisfaction on a monthly basis. This makes it possible to improve employee satisfaction across the entire organization. As a result, the employee satisfaction improvement agent system can implement appropriate measures to improve employee satisfaction.

[0065] The employee satisfaction improvement agent system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a proposal unit. The collection unit collects employee satisfaction survey data. The collection unit collects employee satisfaction survey data, for example, using online questionnaires. The collection unit can also collect employee satisfaction survey data through interviews. Furthermore, the collection unit can extract employee satisfaction survey data from a database. For example, the collection unit conducts online questionnaires and collects feedback from employees. It can also collect employees' opinions and impressions through interviews. It can also extract past employee satisfaction survey data from a database and use it for analysis. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes employee satisfaction survey data, for example, using statistical analysis. Furthermore, the analysis unit can also analyze employee satisfaction survey data using text mining. Furthermore, the analysis unit can also analyze employee satisfaction survey data using machine learning algorithms. For example, the analysis unit uses statistical analysis to understand changes in employee satisfaction. It can also extract important keywords from employee feedback using text mining. It can also predict trends in employee satisfaction using machine learning algorithms. The Specialization Department identifies issues based on data analyzed by the Analysis Department. For example, the Specialization Department identifies frequently occurring problems. It can also identify factors contributing to low ratings. Furthermore, the Specialization Department can identify the causes of declining employee satisfaction. For example, the Specialization Department can list frequently occurring problems and identify areas requiring improvement. It can also analyze factors contributing to low ratings and propose specific improvement measures. It can identify the causes of declining employee satisfaction and implement countermeasures. The Proposal Department proposes improvement measures based on the issues identified by the Specialization Department. For example, the Proposal Department proposes the introduction of best practices. It can also propose the implementation of training programs. Furthermore, the Proposal Department can propose process reviews. For example, the Proposal Department can improve employee satisfaction by introducing best practices. It can also improve employee skills by implementing training programs. It can also improve operational efficiency by reviewing processes.As a result, the employee satisfaction improvement agent system according to the embodiment can collect, analyze, identify issues with, and propose improvement measures based on employee satisfaction survey data.

[0066] The data collection department collects employee satisfaction survey data. For example, the department collects employee satisfaction survey data using online questionnaires. Specifically, the online questionnaires are conducted in a format that allows employees to respond anonymously, and the questions include topics such as job satisfaction, work environment, relationships with superiors and colleagues, and opportunities for career growth. This makes it easier for employees to provide candid opinions, improving the reliability of the collected data. The data collection department can also collect employee satisfaction survey data through interviews. Interviews are conducted individually or in groups, allowing for a deeper exploration of employees' specific opinions and impressions. The interviews are recorded and later transcribed for analysis. Furthermore, the data collection department can extract employee satisfaction survey data from databases. These databases store past survey results and interview records, and by extracting this data and comparing it with current survey results, long-term trends and changes can be identified. For example, the data collection department conducts online questionnaires to collect feedback from employees. It can also collect employee opinions and impressions through interviews. Past employee satisfaction survey data can be extracted from databases and used for analysis. This allows the data collection unit to gather data on employee satisfaction in a variety of ways and provide comprehensive information.

[0067] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes employee satisfaction survey data using statistical analysis. Specifically, statistical analysis calculates basic statistics such as the mean, median, and standard deviation to grasp the overall trend of employee satisfaction. Cross-tabulation can also be performed to reveal differences in satisfaction levels by department and job position. Furthermore, the analysis department can also analyze employee satisfaction survey data using text mining. Text mining extracts important keywords and phrases from employees' free-response texts to identify frequently occurring themes and problems. For example, if many words indicating negative emotions appear, it is necessary to investigate the cause in detail. Furthermore, the analysis department can also analyze employee satisfaction survey data using machine learning algorithms. Machine learning algorithms learn from large amounts of data and identify factors that influence satisfaction. For example, decision trees and random forests can be used to compare the characteristics of employees with high and low satisfaction levels and derive specific measures to improve satisfaction. In this way, the analysis department can analyze the collected data from multiple perspectives and clarify the current state and challenges of employee satisfaction.

[0068] The specific department identifies issues based on data analyzed by the analysis department. For example, the specific department identifies frequently occurring problems. Specifically, it lists problems that are frequently pointed out in the analysis results and prioritizes those with the greatest impact. The specific department can also identify factors contributing to low ratings. Possible factors contributing to low ratings include, for example, dissatisfaction with the work environment, insufficient communication with superiors, and lack of opportunities for career growth. These factors are analyzed in detail, and specific improvement measures are considered. Furthermore, the specific department can identify the causes of declining employee satisfaction. Possible causes of declining employee satisfaction include, for example, increased workload, dissatisfaction with compensation and benefits, and problems with workplace relationships. By identifying these causes and taking countermeasures, employee satisfaction can be improved. For example, the specific department can list frequently occurring problems and identify areas that need improvement. It can also analyze the factors contributing to low ratings and propose specific improvement measures. It can also identify the causes of declining employee satisfaction and take countermeasures. In this way, the specific department can clarify specific issues for improving employee satisfaction and derive effective improvement measures.

[0069] The proposal department proposes improvement measures based on the issues identified by specific departments. For example, the proposal department proposes the introduction of best practices. Specifically, it references successful measures from other companies or departments and customizes them to suit the company's situation. The proposal department can also propose the implementation of training programs. These training programs support employee skill development and career growth, and include, for example, leadership training and workshops to improve communication skills. Furthermore, the proposal department can also propose process reviews. Business process reviews aim to improve work efficiency and reduce the burden on employees, and may include, for example, simplifying work flows or introducing automation tools. For example, the proposal department can improve employee satisfaction by introducing best practices. It can also improve employee skills by implementing training programs. It can also improve work efficiency by reviewing processes. In this way, the proposal department can propose and implement specific improvement measures to improve employee satisfaction. Furthermore, the proposal department can monitor the effectiveness of the proposed improvement measures and make revisions or additional suggestions as needed. In this way, the proposal department can continuously support the improvement of employee satisfaction.

[0070] The data collection unit can collect employee satisfaction survey data that changes monthly and annually. For example, the data collection unit can collect data from employee satisfaction surveys conducted on a monthly basis. It can also collect data from employee satisfaction surveys conducted annually. Furthermore, the data collection unit can compare and collect data that changes monthly and annually. For example, the data collection unit can collect the results of monthly surveys to understand changes in employee satisfaction. It can also collect the results of annual surveys to analyze long-term trends. By comparing data that changes monthly and annually, it is possible to understand seasonal fluctuations and year-to-year trends. This allows for understanding changes in employee satisfaction by collecting data that changes monthly and annually.

[0071] The analysis unit can analyze the collected data and understand changes in employee satisfaction. For example, the analysis unit can statistically analyze the collected data to understand changes in employee satisfaction. It can also text-min the collected data to understand changes in employee satisfaction. Furthermore, the analysis unit can analyze the collected data using machine learning algorithms to understand changes in employee satisfaction. For example, the analysis unit can use statistical analysis to understand increases and decreases in employee satisfaction scores. It can also use text mining to extract factors influencing satisfaction fluctuations from employee feedback. It can also use machine learning algorithms to predict trends in employee satisfaction. In this way, by analyzing the collected data, changes in employee satisfaction can be understood.

[0072] A specific department can identify organizational-wide issues based on the analyzed data. For example, the department can identify frequently occurring problems from the analyzed data. Furthermore, the department can identify factors contributing to low performance from the analyzed data. In addition, the department can identify organizational-wide issues from the analyzed data. For example, the department can list frequently occurring problems from the analyzed data and identify organizational-wide issues. They can also analyze factors contributing to low performance and identify organizational-wide issues. They can identify organizational-wide issues from the analyzed data and identify areas requiring improvement. This allows for the identification of organizational-wide issues based on the analyzed data.

[0073] The specific unit can identify individual challenges based on the analyzed data. For example, the specific unit can identify individual skill deficiencies from the analyzed data. Furthermore, the specific unit can identify decreased motivation from the analyzed data. In addition, the specific unit can identify individual challenges from the analyzed data. For example, the specific unit can list individual skill deficiencies from the analyzed data and identify individual challenges. It can also analyze decreased motivation and identify individual challenges. It can identify individual challenges from the analyzed data and identify areas that need improvement. This allows for the identification of individual challenges based on the analyzed data.

[0074] The proposal department can propose improvement measures based on identified organization-wide issues. For example, the proposal department can propose the introduction of best practices based on identified organization-wide issues. It can also propose the implementation of training programs based on identified organization-wide issues. Furthermore, the proposal department can propose process revisions based on identified organization-wide issues. For example, the proposal department can improve employee satisfaction by introducing best practices based on identified organization-wide issues. It can also improve employee skills by implementing training programs. It can also improve operational efficiency by revising processes. This allows the proposal department to suggest improvement measures based on identified organization-wide issues.

[0075] The proposal department can propose solutions based on the identified individual's challenges. For example, the proposal department can propose individualized feedback based on the identified individual's challenges. Furthermore, the proposal department can propose career counseling based on the identified individual's challenges. In addition, the proposal department can propose training programs based on the identified individual's challenges. For example, the proposal department can improve employees' skills by providing individualized feedback based on the identified individual's challenges. They can also conduct career counseling to clarify employees' career paths. They can also propose training programs to improve employees' skills. This allows them to propose solutions based on the identified individual's challenges.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection for employee satisfaction surveys based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. Alternatively, if the user is relaxed, the data collection unit can collect data immediately to obtain accurate information. Furthermore, if the user is busy, the data collection unit can adjust the collection timing to collect data during breaks in their work. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. If the user is relaxed, the data collection unit can collect data immediately to obtain accurate information. If the user is busy, the data collection timing can be adjusted to collect data during breaks in their work. This allows for the collection of accurate data by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The data collection department can analyze past employee satisfaction survey data and select the optimal data collection method. For example, the data collection department can select the method with the highest response rate from past data. It can also select the method that yielded the most accurate information from past data. Furthermore, it can select the method that allowed for the fastest data collection from past data. This allows the optimal data collection method to be selected by analyzing past data.

[0078] The data collection unit can filter employee satisfaction survey data based on employees' current projects and work content. For example, the unit can prioritize collecting questions related to ongoing projects. It can also filter and collect questions that are highly relevant based on work content. Furthermore, the unit can collect data at appropriate times based on project progress. This allows for the collection of highly relevant data by filtering based on current projects and work content.

[0079] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. It can also prioritize collecting data related to overall satisfaction if the user is relaxed. Furthermore, if the user is busy, the data collection unit can prioritize collecting important data. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of employees when collecting employee satisfaction survey data. For example, the data collection unit can prioritize the collection of data from employees who are geographically close. Furthermore, the data collection unit can compare and collect data from geographically different regions. In addition, the data collection unit can prioritize the collection of data from specific regions based on geographical location information. For example, the data collection unit can prioritize the collection of data from employees who are geographically close. It can also compare and collect data from geographically different regions. It can also prioritize the collection of data from specific regions based on geographical location information. This allows for the priority collection of highly relevant data by considering geographical location information.

[0081] The data collection unit can analyze employees' social media activity and collect relevant data when collecting employee satisfaction survey data. For example, the data collection unit can collect satisfaction-related data from employees' social media activity. It can also analyze the content of social media posts and collect satisfaction-related data. Furthermore, the data collection unit can collect satisfaction-related data based on the frequency of social media activity. In this way, relevant data can be collected by analyzing social media activity.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the data analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can represent the data using simple graphs or charts. If the user is relaxed, the analysis unit can also provide detailed data analysis results. Furthermore, if the user is busy, the analysis unit can provide concise data analysis results. For example, if the user is stressed, the analysis unit can represent the data using simple graphs or charts. If the user is relaxed, it can also provide detailed data analysis results. If the user is busy, it can also provide concise data analysis results. By adjusting the presentation of the data analysis based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of employee satisfaction. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. Furthermore, the analysis unit can determine the priority of the analysis according to importance. For example, the analysis unit can perform a detailed analysis on high-importance data. It can also perform a simplified analysis on low-importance data. It can also determine the priority of the analysis according to importance. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of employee satisfaction.

[0084] The analysis unit can apply different analysis algorithms depending on the employee satisfaction category during analysis. For example, the analysis unit can apply a stress analysis algorithm to stress-related data. It can also apply a motivation analysis algorithm to motivation-related data. Furthermore, it can apply a communication analysis algorithm to communication-related data. By applying different analysis algorithms depending on the employee satisfaction category, more accurate analysis results can be obtained.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-read display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, it can provide a concise display method if the user is busy. By adjusting the display method of the analysis results based on the user's emotions, a highly visual display method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The analysis unit can determine the priority of analysis based on the submission date of employee satisfaction survey data. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can determine the order of analysis based on the submission date. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. It can also determine the order of analysis based on the submission date. This allows the analysis to prioritize the analysis of the most recent data by determining the priority of analysis based on the submission date.

[0087] The analysis unit can adjust the order of analysis based on the relevance of employee satisfaction survey data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can determine the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. It can also determine the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on relevance.

[0088] The identification unit can estimate the user's emotions and adjust the criteria for identifying issues based on the estimated emotions. For example, if the user is stressed, the identification unit will prioritize identifying stress-related issues. If the user is relaxed, the identification unit can also prioritize identifying issues related to overall satisfaction. Furthermore, if the user is busy, the identification unit can prioritize identifying important issues. For example, if the user is stressed, the identification unit will prioritize identifying stress-related issues. If the user is relaxed, it can also prioritize identifying issues related to overall satisfaction. If the user is busy, it can also prioritize identifying important issues. This allows for the identification of more appropriate issues by adjusting the criteria for identifying issues based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] A specific department can improve the accuracy of its problem identification by considering the interrelationships of employee satisfaction. For example, it can identify problems by considering the interrelationship between stress and motivation. It can also identify problems by considering the interrelationship between communication and teamwork. Furthermore, it can identify problems by considering the interrelationship between job content and satisfaction. For example, a specific department can identify problems by considering the interrelationship between stress and motivation. It can also identify problems by considering the interrelationship between communication and teamwork. It can also identify problems by considering the interrelationship between job content and satisfaction. By considering the interrelationships of employee satisfaction, the accuracy of problem identification can be improved.

[0090] The task force can identify issues by considering employee attribute information. For example, the task force can identify issues based on age and gender. It can also identify issues based on job title and position. Furthermore, it can identify issues based on length of service. For example, the task force can identify issues based on age and gender. It can also identify issues based on job title and position. It can also identify issues based on length of service. By considering employee attribute information, it becomes possible to identify issues with higher accuracy.

[0091] The system can estimate the user's emotions and adjust the display order of identified tasks based on the estimated emotions. For example, if the user is stressed, the system will prioritize displaying stress-related tasks. It can also prioritize displaying tasks related to overall satisfaction if the user is relaxed. Furthermore, if the user is busy, the system can prioritize displaying important tasks. This allows for prioritizing the display of important tasks by adjusting the task order based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The task force can identify issues by considering the geographical distribution of employee satisfaction. For example, the task force can prioritize identifying issues affecting employees who are geographically close. It can also compare and identify issues in geographically different regions. Furthermore, it can prioritize identifying issues in specific regions based on geographical location information. This allows for improved accuracy in identification by considering geographical distribution.

[0093] The identification unit can improve the accuracy of its identification by referring to relevant literature on employee satisfaction survey data when identifying issues. For example, the identification unit can improve the accuracy of its identification by referring to relevant literature. Furthermore, the identification unit can also improve the accuracy of its identification based on past research results. In addition, the identification unit can improve the accuracy of its identification by referring to the latest research findings. For example, the identification unit can improve the accuracy of its identification by referring to relevant literature. It can also improve the accuracy of its identification based on past research results. It can also improve the accuracy of its identification by referring to the latest research findings. Thus, by referring to relevant literature, the accuracy of its identification can be improved.

[0094] The suggestion function can estimate the user's emotions and adjust the way improvement measures are presented based on those emotions. For example, if the user is stressed, the suggestion function will provide a simple and easy-to-understand presentation. If the user is relaxed, the suggestion function can also provide detailed improvement measures. Furthermore, if the user is busy, the suggestion function can provide concise improvement measures. By adjusting the presentation of improvement measures based on the user's emotions, it is possible to provide improvement measures that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The proposal department can adjust the level of detail in its proposals based on the importance of the issues. For example, it can propose detailed solutions for high-priority issues, and simplified solutions for low-priority issues. Furthermore, the proposal department can prioritize proposals according to their importance. This allows for the proposal of detailed solutions for important issues by adjusting the level of detail based on the importance of the issues.

[0096] The proposal department can apply different proposal algorithms depending on the category of the problem when proposing improvement measures. For example, for a problem related to stress, the proposal department can apply an algorithm that proposes stress reduction measures. It can also apply an algorithm that proposes motivation improvement measures for a problem related to motivation. Furthermore, it can apply an algorithm that proposes communication improvement measures for a problem related to communication. By applying different proposal algorithms depending on the category of the problem, more appropriate improvement measures can be proposed.

[0097] The suggestion function can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion function will prioritize suggesting stress reduction measures. If the user is relaxed, the suggestion function can also suggest overall improvement measures. Furthermore, if the user is busy, the suggestion function can prioritize suggesting important improvements. For example, if the user is stressed, the suggestion function will prioritize suggesting stress reduction measures. If the user is relaxed, it can also suggest overall improvement measures. If the user is busy, it can also prioritize suggesting important improvements. This allows for prioritizing suggestions based on the user's emotions, thereby prioritizing important improvements. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The proposal department can prioritize proposals based on when the issues were submitted. For example, the proposal department can prioritize proposals for the most recent issues. It can also postpone older issues. Furthermore, the proposal department can determine the order of proposals based on the submission date. For example, the proposal department can prioritize proposals for the most recent issues, postpone older issues, and determine the order of proposals based on the submission date. This allows for prioritizing proposals based on the submission date, ensuring that the most recent issues receive priority in proposals.

[0099] The proposal department can adjust the order of proposals based on the relevance of the issues when proposing improvement measures. For example, the proposal department can prioritize proposing improvement measures for highly relevant issues. It can also postpone issues with low relevance. Furthermore, the proposal department can determine the order of proposals based on the relevance of the issues. For example, the proposal department can prioritize proposing improvement measures for highly relevant issues. It can also postpone issues with low relevance. It can also determine the order of proposals based on the relevance of the issues. This allows for prioritizing the proposal of improvement measures for highly relevant issues by adjusting the order of proposals based on their relevance.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit collects employee working hours and vacation data, and the analysis unit can use this data to evaluate employees' work-life balance. For example, the data collection unit collects data on the length of employees' working hours and vacation usage. The analysis unit can analyze this data to identify imbalances in employees' work-life balance. The identification unit identifies employees with work-life balance imbalances, and the proposal unit can propose appropriate vacation arrangements and adjustments to working hours. This can improve employees' work-life balance and increase their satisfaction.

[0102] The analysis department can analyze employee health data and understand changes in their health status. For example, the data collection department collects employee health checkup results and fitness data. The analysis department can analyze this data and identify changes in employees' health status. The identification department can identify employees whose health is deteriorating, and the proposal department can propose advice and programs for health improvement. This can improve employee health and increase their satisfaction.

[0103] The specific department can analyze data related to employees' career paths and identify challenges in their career advancement. For example, the data collection department collects employees' promotion history and skill development status. The analysis department can analyze this data and identify challenges in their career advancement. The specific department can identify employees whose career advancement is stagnant, and the proposal department can propose career counseling or training programs for skill development. This can support employees' career paths and improve their satisfaction.

[0104] The proposal department can suggest activities for employees to refresh themselves based on their hobbies and interests. For example, the data collection department collects data on employees' hobbies and interests. The analysis department can analyze this data and identify activities that employees can use to refresh themselves. The proposal department can then suggest activities for refreshing themselves based on employees' hobbies and interests. This can reduce employee stress and improve job satisfaction.

[0105] The data collection department collects employee communication patterns, and the analysis department can use this data to identify areas for communication improvement. For example, the data collection department collects employee email and chat histories. The analysis department can analyze this data to evaluate the frequency and quality of communication. The identification department can identify employees with communication problems, and the proposal department can propose training and workshops to improve communication skills. This can improve communication among employees and increase employee satisfaction.

[0106] The analysis unit can estimate user emotions and, based on these estimated emotions, identify factors that influence fluctuations in employee satisfaction. For example, the data collection unit collects employee feedback and survey results. The analysis unit analyzes this data and can estimate user emotions. The identification unit identifies the factors that cause fluctuations in emotions, and the proposal unit can propose measures to mitigate these fluctuations. This allows for reduced fluctuations in employee emotions and improved satisfaction.

[0107] The identification unit can estimate user emotions and, based on the estimated emotions, identify employee stressors. For example, the collection unit collects employee feedback and survey results. The analysis unit analyzes this data and can estimate user emotions. The identification unit identifies stressors, and the proposal unit can propose measures to reduce stress. This can reduce employee stress and improve satisfaction.

[0108] The proposal department can estimate user emotions and, based on those estimated emotions, propose measures to improve employee motivation. For example, the data collection department collects employee feedback and survey results. The analysis department analyzes this data and can estimate user emotions. The proposal department can then propose specific measures to improve motivation. This can lead to increased employee motivation and satisfaction.

[0109] The data collection unit can estimate user emotions and adjust the questions in the employee satisfaction survey based on those estimated emotions. For example, the data collection unit collects employee feedback and survey results. The analysis unit analyzes this data and can estimate user emotions. Based on these emotions, the data collection unit can adjust the questions and collect more accurate data. This allows for the collection of accurate data and improved satisfaction by adjusting questions based on employee emotions.

[0110] The analysis unit can estimate user emotions and adjust the timing of data analysis based on the estimated user emotions. For example, the data collection unit collects employee feedback and survey results. The analysis unit can analyze this data and estimate user emotions. Based on these emotions, the analysis unit can adjust the timing of data analysis to obtain more accurate analysis results. This means that by adjusting the timing of data analysis based on employee emotions, accurate analysis results can be obtained.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit collects employee satisfaction survey data. For example, employee satisfaction survey data can be collected using online questionnaires. Alternatively, employee satisfaction survey data can be collected through interviews. Furthermore, employee satisfaction survey data can be extracted from a database. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze employee satisfaction survey data using statistical analysis. It can also analyze employee satisfaction survey data using text mining. Furthermore, it can analyze employee satisfaction survey data using machine learning algorithms. Step 3: The Identification Unit identifies issues based on the data analyzed by the Analysis Unit. For example, it can identify frequently occurring problems. It can also identify factors contributing to low ratings. Furthermore, it can identify the causes of declining employee satisfaction. Step 4: The proposing department proposes improvement measures based on the issues identified by the specific department. For example, they may propose the implementation of best practices, the implementation of a training program, or a review of the process.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee satisfaction survey data using the camera 42 and microphone 38B of the smart device 14 and manages the data with the control unit 46A. The analysis unit is implemented in the identification unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented in the identification unit 290 of the data processing unit 12 and identifies problems based on the analyzed data. The proposal unit is implemented in the identification unit 290 of the data processing unit 12 and proposes improvement measures based on the identified problems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, identification unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects employee satisfaction survey data using the camera 42 and microphone 238 of the smart glasses 214 and manages the data with the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and identifies problems based on the analyzed data. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and proposes improvement measures based on the identified problems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects employee satisfaction survey data using the camera 42 and microphone 238 of the headset terminal 314 and manages the data with the control unit 46A. The analysis unit is implemented in the identification unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented in the identification unit 290 of the data processing unit 12 and identifies problems based on the analyzed data. The proposal unit is implemented in the identification unit 290 of the data processing unit 12 and proposes improvement measures based on the identified problems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and proposal unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects employee satisfaction survey data using the camera 42 and microphone 238 of the robot 414 and manages the data with the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and identifies problems based on the analyzed data. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and proposes improvement measures based on the identified problems. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The data collection department collects employee satisfaction survey data, An analysis unit analyzes the data collected by the aforementioned collection unit, An identification unit identifies a problem based on the data analyzed by the aforementioned analysis unit, A proposal unit that proposes improvement measures based on the issues identified by the aforementioned specific unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect employee satisfaction survey data that changes monthly and annually. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We will analyze the collected data to understand changes in employee satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, Based on the analyzed data, identify challenges across the entire organization. The system described in Appendix 1, characterized by the features described herein. (Note 5) The specified part is, Identify individual challenges based on analyzed data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the identified organization-wide challenges, we propose improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Based on the identified individual challenges, we propose solutions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate user emotions and adjust the timing of employee satisfaction survey data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We will analyze past employee satisfaction survey data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting employee satisfaction survey data, filtering is performed based on employees' current projects and work responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting employee satisfaction survey data, the system prioritizes the collection of highly relevant data by considering employees' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting employee satisfaction survey data, analyze employees' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the representation of the data analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of employee satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the employee satisfaction category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the employee satisfaction survey data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of employee satisfaction survey data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, We estimate user emotions and adjust the criteria for identifying issues based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, When identifying issues, consider the interrelationship with employee satisfaction to improve the accuracy of the identification process. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, When identifying issues, consider employee attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, It estimates the user's emotions and adjusts the display order of identified issues based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, When identifying issues, consider the geographical distribution of employee satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 25) The specified part is, When identifying issues, refer to relevant literature on employee satisfaction survey data to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, We estimate the user's emotions and adjust the way improvement measures are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When proposing improvement measures, adjust the level of detail in the proposal based on the importance of the issue. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When proposing improvement measures, different proposal algorithms are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When proposing improvement measures, prioritize the proposals based on the submission deadline for the issues. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When proposing improvement measures, adjust the order of the proposals based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects employee satisfaction survey data, An analysis unit analyzes the data collected by the aforementioned collection unit, An identification unit identifies a problem based on the data analyzed by the aforementioned analysis unit, A proposal unit that proposes improvement measures based on the issues identified by the aforementioned specific unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect employee satisfaction survey data that changes monthly and annually. The system according to feature 1.

3. The aforementioned analysis unit, We will analyze the collected data to understand changes in employee satisfaction. The system according to feature 1.

4. The specified part is, Based on the analyzed data, identify challenges across the entire organization. The system according to feature 1.

5. The specified part is, Identify individual challenges based on analyzed data. The system according to feature 1.

6. The aforementioned proposal section is, Based on the identified organization-wide challenges, we propose improvement measures. The system according to feature 1.

7. The aforementioned proposal section is, Based on the identified individual challenges, we propose solutions. The system according to feature 1.

8. The aforementioned collection unit is We estimate user emotions and adjust the timing of employee satisfaction survey data collection based on the estimated user emotions. The system according to feature 1.

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