System

Abstract

An object of a system according to an embodiment is to analyze behavior data of an employee and score a degree of dissatisfaction.SOLUTION: A system according to an embodiment includes a behavior data collector, a data preprocessor, a behavior pattern analyzer, a dissatisfaction scorer, and a feedback unit. The behavior data collection unit collects daily behavior data of the employee. The data preprocessing unit preprocesses the action data collected by the action data collection unit. The behavior pattern analysis unit analyzes the behavior data preprocessed by the data preprocessing unit. The dissatisfaction scorer scores the employee's dissatisfaction based on the data analyzed by the behavior pattern analyzer. The feedback unit feeds back the dissatisfaction scored by the dissatisfaction scoring unit to the manager.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires employees to conduct surveys to understand their level of dissatisfaction, which is time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to analyze employee behavioral data and score the level of dissatisfaction. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavioral data collection unit, a data pre-processing unit, a behavioral pattern analysis unit, a dissatisfaction scoring unit, and a feedback unit. The behavioral data collection unit collects daily behavioral data of employees. The data pre-processing unit pre-processes the behavioral data collected by the behavioral data collection unit. The behavioral pattern analysis unit analyzes the behavioral data pre-processed by the data pre-processing unit. The dissatisfaction scoring unit scores the dissatisfaction level of employees based on the data analyzed by the behavioral pattern analysis unit. The feedback unit feeds back the dissatisfaction level scored by the dissatisfaction scoring unit to a manager. [Effects of the Invention]

[0007] The system according to the embodiment can analyze employee behavioral data and score the level of dissatisfaction. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The dissatisfaction scoring system according to an embodiment of the present invention collects data on employees' daily behavior, analyzes it using a generation AI, and scores the level of dissatisfaction with the company. This allows the dissatisfaction scoring system to understand employees' true feelings and take appropriate measures.

[0029] A dissatisfaction scoring system according to an embodiment includes a behavioral data collection unit, a data preprocessing unit, a behavioral pattern analysis unit, a dissatisfaction scoring unit, and a feedback unit. The behavioral data collection unit collects data on employees' daily behavior. For example, the data may include arrival and departure times, PC usage during work, frequency of meeting participation, and email transmissions and receptions. The data preprocessing unit preprocesses the collected behavioral data. For example, the data may be cleaned and normalized to convert it into a format suitable for analysis. The behavioral pattern analysis unit uses a generation AI to analyze the preprocessed behavioral data. For example, the generation AI may analyze frequent lateness or early departures, long periods of internet browsing during work, and reduced frequency of meeting participation. The dissatisfaction scoring unit scores the employee's dissatisfaction level based on the analyzed data. For example, a score may be set from 0 to 100, with a higher score indicating a higher level of dissatisfaction. The feedback unit provides feedback on the scored dissatisfaction level to a manager. For example, highly dissatisfied employees may be interviewed and specific improvement measures may be proposed. As a result, the dissatisfaction scoring system of the embodiment can collect employees' daily behavioral data, analyze it using generation AI, score the dissatisfaction level, and provide feedback to managers.

[0030] The behavioral data collection unit also collects data from employees' smartphones or wearable devices, allowing for a more detailed understanding of behavioral patterns. For example, the behavioral data collection unit collects GPS data from employees' smartphones and analyzes their commute routes and travel times. This allows for an evaluation of the impact of commuting stress and length of travel time on dissatisfaction levels. The behavioral data collection unit also collects heart rate and step count data from wearable devices to analyze employees' health conditions. This allows for an evaluation of the impact of health conditions on dissatisfaction levels. Furthermore, the behavioral data collection unit collects smartphone app usage data and analyzes concentration levels during work. This allows for an evaluation of the impact of concentration levels on dissatisfaction levels. In this way, by collecting data from employees' smartphones and wearable devices, more detailed behavioral patterns can be understood.

[0031] The behavioral data collection unit can analyze employees' usage of internal chat or SNS to evaluate the quality and frequency of communication. The behavioral data collection unit, for example, analyzes the number of messages and reply speed in internal chat to evaluate the activeness of communication. This evaluates the impact of lack of communication on dissatisfaction. The behavioral data collection unit also analyzes the content of SNS posts and the number of responses to evaluate employees' sociability. This evaluates the impact of sociability on dissatisfaction. Furthermore, the behavioral data collection unit analyzes the amount of time spent on internal chat or SNS to evaluate the quality of communication during work. This evaluates the impact of communication quality on dissatisfaction. In this way, by analyzing employees' usage of internal chat or SNS, the quality and frequency of communication can be evaluated.

[0032] The behavioral data collection unit collects behavioral data of employees when they work remotely from home, and can evaluate their working style outside the office. The behavioral data collection unit, for example, collects computer usage status during remote work and analyzes working hours and break times. This evaluates the impact of the remote work environment on dissatisfaction levels. The behavioral data collection unit also collects participation status in online meetings and evaluates the quality of communication. This evaluates the impact of the quality of online meetings on dissatisfaction levels. Furthermore, the behavioral data collection unit collects information about the work environment during remote work and evaluates work efficiency. This evaluates the impact of the work environment on dissatisfaction levels. In this way, by collecting behavioral data of employees when they work remotely from home, it is possible to evaluate their working style outside the office.

[0033] The behavioral data collection unit collects employee data from different industries or occupations, and can identify industry-specific causes of dissatisfaction. The behavioral data collection unit, for example, collects employee data from different industries and analyzes behavioral patterns for each industry. This identifies industry-specific causes of dissatisfaction. The behavioral data collection unit also collects employee data from different occupations and analyzes behavioral patterns for each occupation. This identifies job-specific causes of dissatisfaction. Furthermore, the behavioral data collection unit compares employee data from different industries or occupations and identifies common causes of dissatisfaction. This identifies common causes of dissatisfaction. By collecting employee data from different industries and occupations, it is possible to identify industry-specific causes of dissatisfaction.

[0034] The data preprocessing unit can develop algorithms that automate the detection and correction of outliers. For example, the data preprocessing unit develops an outlier detection algorithm to automatically detect outliers from collected data, thereby improving data quality. The data preprocessing unit also develops an outlier correction algorithm to automatically correct detected outliers, thereby maintaining data consistency. Furthermore, the data preprocessing unit develops an algorithm that detects and corrects outliers in real time, thereby improving data quality in real time. By developing an algorithm that automates the detection and correction of outliers, data quality is improved.

[0035] The data preprocessing unit can introduce anonymization technology to protect the privacy of employees. For example, the data preprocessing unit introduces technology to anonymize the personal information of employees in the data preprocessing stage, thereby protecting privacy. The data preprocessing unit also introduces data masking technology to conceal the personal information, thereby ensuring data security. Furthermore, the data preprocessing unit introduces pseudo-anonymization technology to protect the personal information, thereby protecting data privacy. Thus, by introducing anonymization technology to protect the privacy of employees, data security is ensured.

[0036] The data preprocessing unit can convert the preprocessed data into a standardized format for comparison with other companies or industries. For example, the data preprocessing unit converts the preprocessed data into a standardized format to enable comparison with other companies or industries. This ensures data compatibility. The data preprocessing unit also converts the data based on the standardized format to make it comparable. This makes it easier to compare the data. Furthermore, the data preprocessing unit organizes the data according to the standardized format to provide a standard for comparison with other companies or industries. This allows accurate data comparison. This ensures data compatibility by converting the preprocessed data into a standardized format for comparison with other companies or industries.

[0037] The behavior pattern analysis unit uses generative AI to analyze employee behavior patterns in real time and provide instantaneous feedback. The behavior pattern analysis unit, for example, uses generative AI to analyze employee behavior patterns in real time and build a system that provides instantaneous feedback. This enables rapid response. The behavior pattern analysis unit also develops an algorithm that analyzes behavior patterns in real time and provides instantaneous feedback. This improves the accuracy of the feedback. Furthermore, the behavior pattern analysis unit operates a system that uses generative AI to analyze behavior patterns and provide instantaneous feedback. This maximizes the effectiveness of the feedback. This enables rapid response by using generative AI to analyze employee behavior patterns in real time and provide instantaneous feedback.

[0038] The behavior pattern analysis unit can also take into account the employee's career path and past evaluation data when analyzing behavior patterns. For example, the behavior pattern analysis unit takes into account the employee's career path data when analyzing behavior patterns. This evaluates the impact of career progress on behavior patterns. The behavior pattern analysis unit also takes into account past evaluation data and analyzes changes in behavior patterns. This evaluates the impact of evaluation data on behavior patterns. Furthermore, the behavior pattern analysis unit analyzes behavior patterns based on the career path and evaluation data to evaluate the employee's growth. This evaluates the impact of growth on behavior patterns. By taking into account the employee's career path and past evaluation data when analyzing behavior patterns, more accurate analysis is possible.

[0039] The behavior pattern analysis unit can compare the behavior patterns of the employees of different job types or positions and identify the causes of dissatisfaction that are specific to the job type. The behavior pattern analysis unit, for example, compares the behavior patterns of employees of different job types and identifies the causes of dissatisfaction for each job type. This allows for the implementation of measures that are specific to the job type. The behavior pattern analysis unit also compares the behavior patterns of employees of different positions and identifies the causes of dissatisfaction for each position. This allows for the implementation of measures that are specific to the position. Furthermore, the behavior pattern analysis unit compares the behavior patterns of each job type or position and identifies common causes of dissatisfaction. This allows for the implementation of common measures. In this way, by comparing the behavior patterns of employees of different job types or positions, it is possible to identify causes of dissatisfaction that are specific to the job type.

[0040] The behavior pattern analysis unit can compare the analysis results of the behavior patterns with other companies or the industry and set the benchmark. The behavior pattern analysis unit, for example, compares the analysis results of the behavior patterns with other companies and sets a benchmark. This allows the company's performance to be evaluated. The behavior pattern analysis unit also sets a benchmark for each industry and evaluates the analysis results of the behavior patterns. This allows the company's position within the industry to be understood. Furthermore, the behavior pattern analysis unit compares with past benchmarks and evaluates changes in the behavior patterns. This allows the progress of improvement to be evaluated. This allows the company's performance to be evaluated by comparing the analysis results of the behavior patterns with other companies or industries and setting a benchmark.

[0041] The dissatisfaction scoring unit can take into account the employee's characteristics and the background information when calculating the dissatisfaction score. The dissatisfaction scoring unit, for example, takes into account individual characteristics such as the employee's age and gender when calculating the dissatisfaction score. This evaluates the impact of individual characteristics on the dissatisfaction level. The dissatisfaction scoring unit also calculates the dissatisfaction score by taking into account background information such as the employee's work history and educational background. This evaluates the impact of the background information on the dissatisfaction level. Furthermore, the dissatisfaction scoring unit adjusts the dissatisfaction score based on the employee's characteristics and background information, providing a more accurate score. This makes it possible to provide a more accurate score by taking into account the employee's individual characteristics and background information when calculating the dissatisfaction score.

[0042] The dissatisfaction scoring unit can improve the calculation algorithm of the score and provide the dissatisfaction score with higher accuracy. The dissatisfaction scoring unit, for example, improves the score calculation algorithm and builds a system that provides a more accurate dissatisfaction score. This improves the reliability of the score. Furthermore, the dissatisfaction scoring unit introduces a machine learning algorithm to improve the calculation accuracy of the score. This increases the accuracy of the score. Furthermore, the dissatisfaction scoring unit improves the score calculation algorithm using statistical methods and provides a more accurate score. This improves the reliability of the score. This improves the score calculation algorithm and makes it possible to provide a more accurate dissatisfaction score.

[0043] The dissatisfaction scoring unit can compare the dissatisfaction scores for different departments or teams, and identify the dissatisfaction factors specific to the department. The dissatisfaction scoring unit, for example, compares the dissatisfaction scores for different departments and identifies the dissatisfaction factors specific to the department. This allows measures to be taken for each department. The dissatisfaction scoring unit also compares the dissatisfaction scores for different teams and identifies the dissatisfaction factors specific to the team. This allows measures to be taken for each team. Furthermore, the dissatisfaction scoring unit compares the dissatisfaction scores for different departments or teams, and identifies common dissatisfaction factors. This allows common measures to be taken. This allows dissatisfaction factors specific to the department to be identified by comparing the dissatisfaction scores for different departments or teams.

[0044] The dissatisfaction scoring unit can compare the calculation result of the score with other companies or the industry and set the benchmark. The dissatisfaction scoring unit, for example, compares the calculation result of the dissatisfaction score with other companies and sets a benchmark. This allows the company's performance to be evaluated. The dissatisfaction scoring unit also sets a benchmark for each industry and evaluates the calculation result of the dissatisfaction score. This allows the company's position within the industry to be understood. Furthermore, the dissatisfaction scoring unit compares the calculation result of the dissatisfaction score with past benchmarks and evaluates changes in the dissatisfaction score. This allows the progress of improvement to be evaluated. This allows the company's performance to be evaluated by comparing the calculation result of the score with other companies or industries and setting a benchmark.

[0045] The feedback unit can provide the feedback for each employee individually in the score feedback and suggest specific improvement measures. The feedback unit, for example, builds a system for providing feedback for each employee individually in the score feedback. This suggests specific improvement measures. The feedback unit also analyzes the behavioral patterns of employees and develops an algorithm for providing individual feedback. This improves the accuracy of the feedback. Furthermore, the feedback unit operates a system for providing feedback for each employee individually and suggesting specific improvement measures. This maximizes the effectiveness of the feedback. This makes it possible to promote improvement in behavioral patterns by providing feedback for each employee individually in the score feedback and suggesting specific improvement measures.

[0046] The feedback department can take into account the employee's past feedback history when providing the feedback, thereby promoting continuous improvement. For example, the feedback department builds a system that takes into account the employee's past feedback history when providing feedback. This promotes continuous improvement. The feedback department also analyzes the past feedback history and develops an algorithm that promotes continuous improvement. This improves the accuracy of feedback. Furthermore, the feedback department operates a system that takes into account the employee's past feedback history and promotes continuous improvement. This maximizes the effectiveness of feedback. This makes it possible to promote continuous improvement by taking into account the employee's past feedback history when providing feedback.

[0047] The feedback unit can provide the feedback of the scores for each of the different departments or teams, and propose the improvement measures specific to the department. The feedback unit, for example, builds a system that provides feedback on the scores for each department and proposes improvement measures specific to the department. This allows measures to be taken for each department. The feedback unit also develops an algorithm that provides feedback for each team and proposes improvement measures specific to the team. This improves the accuracy of the feedback. Furthermore, the feedback unit operates a system that provides feedback for each department or team and proposes improvement measures specific to the team. This maximizes the effect of the feedback. This allows measures to be taken for each department by providing feedback on the scores for each of the different departments or teams and proposing improvement measures specific to the department.

[0048] The feedback department can compare the results of the feedback with other companies or the industry and set the benchmark. The feedback department, for example, builds a system that compares the results of the feedback with other companies and sets benchmarks. This allows the company's performance to be evaluated. The feedback department also sets benchmarks for each industry and evaluates the feedback results. This allows the company to understand its position within the industry. Furthermore, the feedback department compares the results of the feedback with past benchmarks and evaluates them. This allows the company to evaluate its progress in improvement. This allows the company's performance to be evaluated by comparing the results of the feedback with other companies or the industry and setting benchmarks.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The behavioral data collection unit collects environmental data around the employee's desk, allowing it to evaluate the impact of the work environment on the level of dissatisfaction. For example, it collects environmental data such as the organization and tidiness of the desk, the brightness of the lighting, the temperature, and humidity. This allows it to evaluate the impact of the work environment on the level of dissatisfaction. The behavioral data collection unit also measures the noise level around the employee's desk, allowing it to evaluate the impact of noise on the level of dissatisfaction. Furthermore, the behavioral data collection unit records the presence and placement of plants around the desk, allowing it to evaluate the impact of the natural environment on the level of dissatisfaction. In this way, by collecting environmental data around the employee's desk, it is possible to evaluate the impact of the work environment on the level of dissatisfaction.

[0051] The behavioral data collection unit collects employee meal and break patterns to evaluate the impact of health management on dissatisfaction levels. For example, it records the content and time of employee meals to evaluate the impact of nutritional balance on dissatisfaction levels. The behavioral data collection unit also records the length and frequency of break times to evaluate the impact of break-taking on dissatisfaction levels. Furthermore, the behavioral data collection unit records the amount of water intake by employees to evaluate the impact of hydration on dissatisfaction levels. In this way, by collecting employee meal and break patterns, it is possible to evaluate the impact of health management on dissatisfaction levels.

[0052] The behavioral data collection unit collects data on employees' hobbies and leisure activities, and can evaluate the impact of activities outside of work on dissatisfaction levels. For example, the type and frequency of employees' hobbies is recorded, and the impact of hobby activities on dissatisfaction levels is evaluated. The behavioral data collection unit also records the content and duration of employees' leisure activities, and evaluates the impact of how employees spend their leisure time on dissatisfaction levels. Furthermore, the behavioral data collection unit records how employees spend time with their families, and evaluates the impact of family relationships on dissatisfaction levels. In this way, by collecting data on employees' hobbies and leisure activities, it is possible to evaluate the impact of activities outside of work on dissatisfaction levels.

[0053] The behavioral data collection unit collects employees' sleep patterns and can evaluate the impact of sleep quality on dissatisfaction. For example, the behavioral data collection unit records employees' sleep duration and sleep depth to evaluate the impact of sleep quality on dissatisfaction. The behavioral data collection unit also records employees' sleep environments (e.g., bedroom temperature and noise level) to evaluate the impact of the sleep environment on dissatisfaction. Furthermore, the behavioral data collection unit records employees' pre-sleep behaviors (e.g., smartphone use and caffeine intake) to evaluate the impact of these behaviors on sleep quality. In this way, by collecting employees' sleep patterns, it is possible to evaluate the impact of sleep quality on dissatisfaction.

[0054] The behavioral data collection unit collects employees' commuting patterns and can evaluate the impact of commuting stress on dissatisfaction. For example, the behavioral data collection unit records employees' commuting times and commuting means to evaluate the impact of commuting stress on dissatisfaction. The behavioral data collection unit also records congestion and traffic conditions during the commute to evaluate the impact of these factors on dissatisfaction. Furthermore, the behavioral data collection unit records mood and physical condition during the commute to evaluate the impact of these factors on dissatisfaction. In this way, by collecting employees' commuting patterns, the impact of commuting stress on dissatisfaction can be evaluated.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The behavioral data collection department collects data on employees' daily behavior, such as arrival and departure times, computer usage during work, frequency of participation in meetings, and the content of emails sent and received. Step 2: The data preprocessing section preprocesses the collected behavioral data, for example by cleaning and normalizing the data and converting it into a format suitable for analysis. Step 3: The behavioral pattern analysis unit uses the generated AI to analyze the preprocessed behavioral data, such as frequent lateness or early departure, long periods of internet browsing during work hours, and reduced attendance at meetings. Step 4: The dissatisfaction scoring unit scores the employee's dissatisfaction level based on the analyzed data. For example, the score is set in the range of 0 to 100, with a higher score indicating a higher level of dissatisfaction. Step 5: The feedback department provides the manager with the scored level of dissatisfaction. For example, if an employee has a high level of dissatisfaction, they may hold an interview and propose specific measures for improvement.

[0057] (Example 2) The dissatisfaction scoring system according to an embodiment of the present invention collects data on employees' daily behavior, analyzes it using a generation AI, and scores the level of dissatisfaction with the company. This allows the dissatisfaction scoring system to understand employees' true feelings and take appropriate measures.

[0058] A dissatisfaction scoring system according to an embodiment includes a behavioral data collection unit, a data preprocessing unit, a behavioral pattern analysis unit, a dissatisfaction scoring unit, and a feedback unit. The behavioral data collection unit collects data on employees' daily behavior. For example, the data may include arrival and departure times, PC usage during work, frequency of meeting participation, and email transmissions and receptions. The data preprocessing unit preprocesses the collected behavioral data. For example, the data may be cleaned and normalized to convert it into a format suitable for analysis. The behavioral pattern analysis unit uses a generation AI to analyze the preprocessed behavioral data. For example, the generation AI may analyze frequent lateness or early departures, long periods of internet browsing during work, and reduced frequency of meeting participation. The dissatisfaction scoring unit scores the employee's dissatisfaction level based on the analyzed data. For example, a score may be set from 0 to 100, with a higher score indicating a higher level of dissatisfaction. The feedback unit provides feedback on the scored dissatisfaction level to a manager. For example, highly dissatisfied employees may be interviewed and specific improvement measures may be proposed. As a result, the dissatisfaction scoring system of the embodiment can collect employees' daily behavioral data, analyze it using generation AI, score the dissatisfaction level, and provide feedback to managers.

[0059] The behavioral data collection unit also collects data from employees' smartphones or wearable devices, allowing for a more detailed understanding of behavioral patterns. For example, the behavioral data collection unit collects GPS data from employees' smartphones and analyzes their commute routes and travel times. This allows for an evaluation of the impact of commuting stress and length of travel time on dissatisfaction levels. The behavioral data collection unit also collects heart rate and step count data from wearable devices to analyze employees' health conditions. This allows for an evaluation of the impact of health conditions on dissatisfaction levels. Furthermore, the behavioral data collection unit collects smartphone app usage data and analyzes concentration levels during work. This allows for an evaluation of the impact of concentration levels on dissatisfaction levels. In this way, by collecting data from employees' smartphones and wearable devices, more detailed behavioral patterns can be understood.

[0060] The behavioral data collection unit can analyze employees' usage of internal chat or SNS to evaluate the quality and frequency of communication. The behavioral data collection unit, for example, analyzes the number of messages and reply speed in internal chat to evaluate the activeness of communication. This evaluates the impact of lack of communication on dissatisfaction. The behavioral data collection unit also analyzes the content of SNS posts and the number of responses to evaluate employees' sociability. This evaluates the impact of sociability on dissatisfaction. Furthermore, the behavioral data collection unit analyzes the amount of time spent on internal chat or SNS to evaluate the quality of communication during work. This evaluates the impact of communication quality on dissatisfaction. In this way, by analyzing employees' usage of internal chat or SNS, the quality and frequency of communication can be evaluated.

[0061] The behavioral data collection unit can use the emotion estimation function to analyze the employee's facial expression or tone of voice and add the emotional state to the data. For example, the behavioral data collection unit captures the employee's facial expression with a camera and analyzes the emotional state using an emotion estimation algorithm. This evaluates the impact of emotional changes during work on the dissatisfaction level. The behavioral data collection unit also records the employee's tone of voice and analyzes the emotional state using voice analysis technology. This evaluates the impact of the tone of voice on the dissatisfaction level. Furthermore, the behavioral data collection unit collects the employee's biometric data (heart rate and electrodermal activity) with a sensor and analyzes the emotional state using an emotion estimation algorithm. This evaluates the impact of the biometric data on the dissatisfaction level. This evaluates the impact of the biometric data on the dissatisfaction level. The emotion estimation function can analyze the employee's facial expression and tone of voice and add the emotional state to the data.

[0062] The behavioral data collection unit collects behavioral data of employees when they work remotely from home, and can evaluate their working style outside the office. The behavioral data collection unit, for example, collects computer usage status during remote work and analyzes working hours and break times. This evaluates the impact of the remote work environment on dissatisfaction levels. The behavioral data collection unit also collects participation status in online meetings and evaluates the quality of communication. This evaluates the impact of the quality of online meetings on dissatisfaction levels. Furthermore, the behavioral data collection unit collects information about the work environment during remote work and evaluates work efficiency. This evaluates the impact of the work environment on dissatisfaction levels. In this way, by collecting behavioral data of employees when they work remotely from home, it is possible to evaluate their working style outside the office.

[0063] The behavioral data collection unit collects employee data from different industries or occupations, and can identify industry-specific causes of dissatisfaction. The behavioral data collection unit, for example, collects employee data from different industries and analyzes behavioral patterns for each industry. This identifies industry-specific causes of dissatisfaction. The behavioral data collection unit also collects employee data from different occupations and analyzes behavioral patterns for each occupation. This identifies job-specific causes of dissatisfaction. Furthermore, the behavioral data collection unit compares employee data from different industries or occupations and identifies common causes of dissatisfaction. This identifies common causes of dissatisfaction. By collecting employee data from different industries and occupations, it is possible to identify industry-specific causes of dissatisfaction.

[0064] The behavioral data collection unit uses the emotion estimation function to monitor emotional changes in real time when employees perform specific tasks, thereby identifying stress factors. The behavioral data collection unit, for example, uses the emotion estimation function to monitor emotional changes in real time when employees perform specific tasks. This identifies stress factors for each task. The behavioral data collection unit also analyzes employees' facial expressions and tone of voice to monitor emotional changes in real time. This evaluates the impact of emotional changes on stress factors. Furthermore, the behavioral data collection unit collects employees' biometric data and monitors emotional changes in real time. This evaluates the impact of biometric data on stress factors. This makes it possible to monitor emotional changes in real time when employees perform specific tasks using the emotion estimation function, thereby identifying stress factors.

[0065] The data preprocessing unit can develop algorithms that automate the detection and correction of outliers. For example, the data preprocessing unit develops an outlier detection algorithm to automatically detect outliers from collected data, thereby improving data quality. The data preprocessing unit also develops an outlier correction algorithm to automatically correct detected outliers, thereby maintaining data consistency. Furthermore, the data preprocessing unit develops an algorithm that detects and corrects outliers in real time, thereby improving data quality in real time. By developing an algorithm that automates the detection and correction of outliers, data quality is improved.

[0066] The data preprocessing unit can introduce anonymization technology to protect the privacy of employees. For example, the data preprocessing unit introduces technology to anonymize the personal information of employees in the data preprocessing stage, thereby protecting privacy. The data preprocessing unit also introduces data masking technology to conceal the personal information, thereby ensuring data security. Furthermore, the data preprocessing unit introduces pseudo-anonymization technology to protect the personal information, thereby protecting data privacy. Thus, by introducing anonymization technology to protect the privacy of employees, data security is ensured.

[0067] The data preprocessing unit uses the emotion estimation function to assign emotion tags to the preprocessed data, thereby allowing emotional elements to be reflected in the analysis. The data preprocessing unit, for example, uses the emotion estimation function to assign emotion tags to the preprocessed data, thereby allowing emotional elements to be reflected in the analysis. The data preprocessing unit also classifies the data based on the emotion tags and allows emotional elements to be reflected in the analysis, thereby evaluating the impact of the emotional elements on the analysis. The data preprocessing unit also filters the data using the emotion tags and allows emotional elements to be reflected in the analysis, thereby evaluating the impact of the emotional elements on the analysis. The data preprocessing unit uses the emotion estimation function to assign emotion tags to the preprocessed data, thereby allowing emotional elements to be reflected in the analysis.

[0068] The data preprocessing unit can convert the preprocessed data into a standardized format for comparison with other companies or industries. For example, the data preprocessing unit converts the preprocessed data into a standardized format to enable comparison with other companies or industries. This ensures data compatibility. The data preprocessing unit also converts the data based on the standardized format to make it comparable. This makes it easier to compare the data. Furthermore, the data preprocessing unit organizes the data according to the standardized format to provide a standard for comparison with other companies or industries. This allows accurate data comparison. This ensures data compatibility by converting the preprocessed data into a standardized format for comparison with other companies or industries.

[0069] The data preprocessing unit can use the emotion estimation function to detect and correct emotional bias in the preprocessed data. For example, the data preprocessing unit uses the emotion estimation function to detect emotional bias in the preprocessed data, thereby maintaining the neutrality of the data. The data preprocessing unit also develops an algorithm to correct emotional bias and improve the neutrality of the data, thereby ensuring the reliability of the data. Furthermore, the data preprocessing unit builds a system to detect and correct emotional bias in real time, thereby maintaining the neutrality of the data in real time. As a result, the emotional bias in the preprocessed data using the emotion estimation function can be detected and corrected, thereby maintaining the neutrality of the data.

[0070] The behavior pattern analysis unit uses generative AI to analyze employee behavior patterns in real time and provide instantaneous feedback. The behavior pattern analysis unit, for example, uses generative AI to analyze employee behavior patterns in real time and build a system that provides instantaneous feedback. This enables rapid response. The behavior pattern analysis unit also develops an algorithm that analyzes behavior patterns in real time and provides instantaneous feedback. This improves the accuracy of the feedback. Furthermore, the behavior pattern analysis unit operates a system that uses generative AI to analyze behavior patterns and provide instantaneous feedback. This maximizes the effectiveness of the feedback. This enables rapid response by using generative AI to analyze employee behavior patterns in real time and provide instantaneous feedback.

[0071] The behavior pattern analysis unit can also take into account the employee's career path and past evaluation data when analyzing behavior patterns. For example, the behavior pattern analysis unit takes into account the employee's career path data when analyzing behavior patterns. This evaluates the impact of career progress on behavior patterns. The behavior pattern analysis unit also takes into account past evaluation data and analyzes changes in behavior patterns. This evaluates the impact of evaluation data on behavior patterns. Furthermore, the behavior pattern analysis unit analyzes behavior patterns based on the career path and evaluation data to evaluate the employee's growth. This evaluates the impact of growth on behavior patterns. By taking into account the employee's career path and past evaluation data when analyzing behavior patterns, more accurate analysis is possible.

[0072] The behavioral pattern analysis unit uses the emotion estimation function to analyze the correlation between an employee's behavioral patterns and emotional states, thereby identifying emotional factors. The behavioral pattern analysis unit, for example, uses the emotion estimation function to build a system that analyzes the correlation between an employee's behavioral patterns and emotional states. This identifies emotional factors. The behavioral pattern analysis unit also develops an algorithm that analyzes emotional states and evaluates the correlation with behavioral patterns. This evaluates the impact of emotional states on behavioral patterns. Furthermore, the behavioral pattern analysis unit operates a system that uses the emotion estimation function to analyze the correlation between behavioral patterns and emotional states in real time. This identifies emotional factors in real time. This makes it possible to analyze the correlation between an employee's behavioral patterns and emotional states using the emotion estimation function, thereby identifying emotional factors.

[0073] The behavior pattern analysis unit can compare the behavior patterns of the employees of different job types or positions and identify the causes of dissatisfaction that are specific to the job type. The behavior pattern analysis unit, for example, compares the behavior patterns of employees of different job types and identifies the causes of dissatisfaction for each job type. This allows for the implementation of measures that are specific to the job type. The behavior pattern analysis unit also compares the behavior patterns of employees of different positions and identifies the causes of dissatisfaction for each position. This allows for the implementation of measures that are specific to the position. Furthermore, the behavior pattern analysis unit compares the behavior patterns of each job type or position and identifies common causes of dissatisfaction. This allows for the implementation of common measures. In this way, by comparing the behavior patterns of employees of different job types or positions, it is possible to identify causes of dissatisfaction that are specific to the job type.

[0074] The behavior pattern analysis unit can compare the analysis results of the behavior patterns with other companies or the industry and set the benchmark. The behavior pattern analysis unit, for example, compares the analysis results of the behavior patterns with other companies and sets a benchmark. This allows the company's performance to be evaluated. The behavior pattern analysis unit also sets a benchmark for each industry and evaluates the analysis results of the behavior patterns. This allows the company's position within the industry to be understood. Furthermore, the behavior pattern analysis unit compares with past benchmarks and evaluates changes in the behavior patterns. This allows the progress of improvement to be evaluated. This allows the company's performance to be evaluated by comparing the analysis results of the behavior patterns with other companies or industries and setting a benchmark.

[0075] The behavior pattern analysis unit can use the emotion estimation function to monitor the employee's emotional reactions to the behavior patterns in real time and identify the stress factors. The behavior pattern analysis unit, for example, uses the emotion estimation function to build a system that monitors the employee's emotional reactions to the behavior patterns in real time. This identifies the stress factors. The behavior pattern analysis unit also develops an algorithm that analyzes the emotional reactions and evaluates the correlation with the behavior patterns. This evaluates the impact of the emotional reactions on the behavior patterns. The behavior pattern analysis unit also uses the emotion estimation function to operate a system that monitors the emotional reactions to the behavior patterns in real time and identifies the stress factors. This identifies the stress factors in real time. This makes it possible to monitor the employee's emotional reactions to the behavior patterns in real time and identify the stress factors using the emotion estimation function.

[0076] The dissatisfaction scoring unit can take into account the employee's characteristics and the background information when calculating the dissatisfaction score. The dissatisfaction scoring unit, for example, takes into account individual characteristics such as the employee's age and gender when calculating the dissatisfaction score. This evaluates the impact of individual characteristics on the dissatisfaction level. The dissatisfaction scoring unit also calculates the dissatisfaction score by taking into account background information such as the employee's work history and educational background. This evaluates the impact of the background information on the dissatisfaction level. Furthermore, the dissatisfaction scoring unit adjusts the dissatisfaction score based on the employee's characteristics and background information, providing a more accurate score. This makes it possible to provide a more accurate score by taking into account the employee's individual characteristics and background information when calculating the dissatisfaction score.

[0077] The dissatisfaction scoring unit can improve the calculation algorithm of the score and provide the dissatisfaction score with higher accuracy. The dissatisfaction scoring unit, for example, improves the score calculation algorithm and builds a system that provides a more accurate dissatisfaction score. This improves the reliability of the score. Furthermore, the dissatisfaction scoring unit introduces a machine learning algorithm to improve the calculation accuracy of the score. This increases the accuracy of the score. Furthermore, the dissatisfaction scoring unit improves the score calculation algorithm using statistical methods and provides a more accurate score. This improves the reliability of the score. This improves the score calculation algorithm and makes it possible to provide a more accurate dissatisfaction score.

[0078] The dissatisfaction scoring unit can compare the dissatisfaction scores for different departments or teams, and identify the dissatisfaction factors specific to the department. The dissatisfaction scoring unit, for example, compares the dissatisfaction scores for different departments and identifies the dissatisfaction factors specific to the department. This allows measures to be taken for each department. The dissatisfaction scoring unit also compares the dissatisfaction scores for different teams and identifies the dissatisfaction factors specific to the team. This allows measures to be taken for each team. Furthermore, the dissatisfaction scoring unit compares the dissatisfaction scores for different departments or teams, and identifies common dissatisfaction factors. This allows common measures to be taken. This allows dissatisfaction factors specific to the department to be identified by comparing the dissatisfaction scores for different departments or teams.

[0079] The dissatisfaction scoring unit can compare the calculation result of the score with other companies or the industry and set the benchmark. The dissatisfaction scoring unit, for example, compares the calculation result of the dissatisfaction score with other companies and sets a benchmark. This allows the company's performance to be evaluated. The dissatisfaction scoring unit also sets a benchmark for each industry and evaluates the calculation result of the dissatisfaction score. This allows the company's position within the industry to be understood. Furthermore, the dissatisfaction scoring unit compares the calculation result of the dissatisfaction score with past benchmarks and evaluates changes in the dissatisfaction score. This allows the progress of improvement to be evaluated. This allows the company's performance to be evaluated by comparing the calculation result of the score with other companies or industries and setting a benchmark.

[0080] The dissatisfaction level scoring unit can use the emotion estimation function to detect and correct the emotional bias in the score calculation process. The dissatisfaction level scoring unit, for example, uses the emotion estimation function to detect emotional bias in the score calculation process. This maintains the neutrality of the score. The dissatisfaction level scoring unit also develops an algorithm to correct the emotional bias and improves the neutrality of the score. This ensures the reliability of the score. Furthermore, the dissatisfaction level scoring unit builds a system that detects and corrects emotional bias in real time. This maintains the neutrality of the score in real time. This makes it possible to maintain the neutrality of the score by using the emotion estimation function to detect and correct emotional bias in the score calculation process.

[0081] The feedback unit can provide the feedback for each employee individually in the score feedback and suggest specific improvement measures. The feedback unit, for example, builds a system for providing feedback for each employee individually in the score feedback. This suggests specific improvement measures. The feedback unit also analyzes the behavioral patterns of employees and develops an algorithm for providing individual feedback. This improves the accuracy of the feedback. Furthermore, the feedback unit operates a system for providing feedback for each employee individually and suggesting specific improvement measures. This maximizes the effectiveness of the feedback. This makes it possible to promote improvement in behavioral patterns by providing feedback for each employee individually in the score feedback and suggesting specific improvement measures.

[0082] The feedback department can take into account the employee's past feedback history when providing the feedback, thereby promoting continuous improvement. For example, the feedback department builds a system that takes into account the employee's past feedback history when providing feedback. This promotes continuous improvement. The feedback department also analyzes the past feedback history and develops an algorithm that promotes continuous improvement. This improves the accuracy of feedback. Furthermore, the feedback department operates a system that takes into account the employee's past feedback history and promotes continuous improvement. This maximizes the effectiveness of feedback. This makes it possible to promote continuous improvement by taking into account the employee's past feedback history when providing feedback.

[0083] The feedback unit can use the emotion estimation function to monitor the employee's emotional reaction when providing feedback and take the appropriate action. The feedback unit, for example, uses the emotion estimation function to build a system that monitors the employee's emotional reaction when providing feedback. This allows for an appropriate response. The feedback unit also develops an algorithm that analyzes the emotional reaction and takes an appropriate response. This improves the accuracy of the feedback. Furthermore, the feedback unit uses the emotion estimation function to monitor the emotional reaction in real time and operates a system that takes an appropriate response. This maximizes the effectiveness of the feedback. This allows for the effectiveness of feedback to be improved by using the emotion estimation function to monitor the employee's emotional reaction when providing feedback and taking an appropriate response.

[0084] The feedback unit can provide the feedback of the scores for each of the different departments or teams, and propose the improvement measures specific to the department. The feedback unit, for example, builds a system that provides feedback on the scores for each department and proposes improvement measures specific to the department. This allows measures to be taken for each department. The feedback unit also develops an algorithm that provides feedback for each team and proposes improvement measures specific to the team. This improves the accuracy of the feedback. Furthermore, the feedback unit operates a system that provides feedback for each department or team and proposes improvement measures specific to the team. This maximizes the effect of the feedback. This allows measures to be taken for each department by providing feedback on the scores for each of the different departments or teams and proposing improvement measures specific to the department.

[0085] The feedback department can compare the results of the feedback with other companies or the industry and set the benchmark. The feedback department, for example, builds a system that compares the results of the feedback with other companies and sets benchmarks. This allows the company's performance to be evaluated. The feedback department also sets benchmarks for each industry and evaluates the feedback results. This allows the company to understand its position within the industry. Furthermore, the feedback department compares the results of the feedback with past benchmarks and evaluates them. This allows the company to evaluate its progress in improvement. This allows the company's performance to be evaluated by comparing the results of the feedback with other companies or the industry and setting benchmarks.

[0086] The feedback unit can use the emotion estimation function to monitor the employee's emotional reaction when providing feedback in real time and take the appropriate action. The feedback unit, for example, uses the emotion estimation function to build a system that monitors the employee's emotional reaction when providing feedback in real time. This allows for an appropriate response. The feedback unit also develops an algorithm that analyzes the emotional reaction and takes an appropriate response. This improves the accuracy of the feedback. Furthermore, the feedback unit uses the emotion estimation function to operate a system that monitors the emotional reaction in real time and takes an appropriate response. This improves the quality of feedback. This allows for the emotion estimation function to monitor the employee's emotional reaction when providing feedback in real time and take an appropriate response, thereby improving the quality of feedback.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The behavioral data collection unit collects environmental data around the employee's desk, allowing it to evaluate the impact of the work environment on the level of dissatisfaction. For example, it collects environmental data such as the organization and tidiness of the desk, the brightness of the lighting, the temperature, and humidity. This allows it to evaluate the impact of the work environment on the level of dissatisfaction. The behavioral data collection unit also measures the noise level around the employee's desk, allowing it to evaluate the impact of noise on the level of dissatisfaction. Furthermore, the behavioral data collection unit records the presence and placement of plants around the desk, allowing it to evaluate the impact of the natural environment on the level of dissatisfaction. In this way, by collecting environmental data around the employee's desk, it is possible to evaluate the impact of the work environment on the level of dissatisfaction.

[0089] The behavioral data collection unit collects employee meal and break patterns to evaluate the impact of health management on dissatisfaction levels. For example, it records the content and time of employee meals to evaluate the impact of nutritional balance on dissatisfaction levels. The behavioral data collection unit also records the length and frequency of break times to evaluate the impact of break-taking on dissatisfaction levels. Furthermore, the behavioral data collection unit records the amount of water intake by employees to evaluate the impact of hydration on dissatisfaction levels. In this way, by collecting employee meal and break patterns, it is possible to evaluate the impact of health management on dissatisfaction levels.

[0090] The behavioral data collection unit collects data on employees' hobbies and leisure activities, and can evaluate the impact of activities outside of work on dissatisfaction levels. For example, the type and frequency of employees' hobbies is recorded, and the impact of hobby activities on dissatisfaction levels is evaluated. The behavioral data collection unit also records the content and duration of employees' leisure activities, and evaluates the impact of how employees spend their leisure time on dissatisfaction levels. Furthermore, the behavioral data collection unit records how employees spend time with their families, and evaluates the impact of family relationships on dissatisfaction levels. In this way, by collecting data on employees' hobbies and leisure activities, it is possible to evaluate the impact of activities outside of work on dissatisfaction levels.

[0091] The behavioral data collection unit collects employees' sleep patterns and can evaluate the impact of sleep quality on dissatisfaction. For example, the behavioral data collection unit records employees' sleep duration and sleep depth to evaluate the impact of sleep quality on dissatisfaction. The behavioral data collection unit also records employees' sleep environments (e.g., bedroom temperature and noise level) to evaluate the impact of the sleep environment on dissatisfaction. Furthermore, the behavioral data collection unit records employees' pre-sleep behaviors (e.g., smartphone use and caffeine intake) to evaluate the impact of these behaviors on sleep quality. In this way, by collecting employees' sleep patterns, it is possible to evaluate the impact of sleep quality on dissatisfaction.

[0092] The behavioral data collection unit collects employees' commuting patterns and can evaluate the impact of commuting stress on dissatisfaction. For example, the behavioral data collection unit records employees' commuting times and commuting means to evaluate the impact of commuting stress on dissatisfaction. The behavioral data collection unit also records congestion and traffic conditions during the commute to evaluate the impact of these factors on dissatisfaction. Furthermore, the behavioral data collection unit records mood and physical condition during the commute to evaluate the impact of these factors on dissatisfaction. In this way, by collecting employees' commuting patterns, the impact of commuting stress on dissatisfaction can be evaluated.

[0093] The behavioral pattern analysis unit can use the employee emotion estimation function to analyze the correlation between an employee's emotional state and work performance. For example, it can analyze an employee's emotional state and evaluate the impact of the emotional state on work performance. The behavioral pattern analysis unit can also use the emotion estimation function to monitor changes in an employee's emotional state and work performance in real time. Furthermore, the behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and work performance over the long term. This allows the impact of emotional factors on work performance to be evaluated by analyzing the correlation between an employee's emotional state and work performance.

[0094] The behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and team dynamics. For example, it can analyze an employee's emotional state and evaluate the impact of the emotional state on team cooperation and communication. The behavioral pattern analysis unit can also use the emotion estimation function to monitor changes in an employee's emotional state and team performance in real time. Furthermore, the behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and team dynamics over the long term. This makes it possible to evaluate the impact of emotional factors on team performance by analyzing the correlation between an employee's emotional state and team dynamics.

[0095] The behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and stress level. For example, it can analyze an employee's emotional state and evaluate the impact of the emotional state on stress level. The behavioral pattern analysis unit can also use the emotion estimation function to monitor changes in an employee's emotional state and stress level in real time. Furthermore, the behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and stress level over the long term. This allows the impact of emotional factors on stress level to be evaluated by analyzing the correlation between an employee's emotional state and stress level.

[0096] The behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and motivation. For example, it can analyze an employee's emotional state and evaluate the impact of the emotional state on motivation. The behavioral pattern analysis unit can also use the emotion estimation function to monitor changes in an employee's emotional state and motivation in real time. Furthermore, the behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and motivation over the long term. This allows the impact of emotional factors on motivation to be evaluated by analyzing the correlation between an employee's emotional state and motivation.

[0097] The behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and creativity. For example, it can analyze an employee's emotional state and evaluate the impact of the emotional state on creativity. The behavioral pattern analysis unit can also use the emotion estimation function to monitor changes in an employee's emotional state and creativity in real time. Furthermore, the behavioral pattern analysis unit can use the emotion estimation function to analyze the correlation between an employee's emotional state and creativity over the long term. This makes it possible to evaluate the impact of emotional factors on creativity by analyzing the correlation between an employee's emotional state and creativity.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The behavioral data collection department collects data on employees' daily behavior, such as arrival and departure times, computer usage during work, frequency of participation in meetings, and the content of emails sent and received. Step 2: The data preprocessing section preprocesses the collected behavioral data, for example by cleaning and normalizing the data and converting it into a format suitable for analysis. Step 3: The behavioral pattern analysis unit uses the generated AI to analyze the preprocessed behavioral data, such as frequent lateness or early departure, long periods of internet browsing during work hours, and reduced attendance at meetings. Step 4: The dissatisfaction scoring unit scores the employee's dissatisfaction level based on the analyzed data. For example, the score is set in the range of 0 to 100, with a higher score indicating a higher level of dissatisfaction. Step 5: The feedback department provides the manager with the scored level of dissatisfaction. For example, if an employee has a high level of dissatisfaction, they may hold an interview and propose specific measures for improvement.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0104] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

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

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A department that collects data on employees' daily behavior, a data preprocessing unit that preprocesses the behavioral data collected by the behavioral data collecting unit; a behavior pattern analysis unit that analyzes the behavior data preprocessed by the data preprocessing unit; a dissatisfaction level scoring unit that scores the dissatisfaction level of the employee based on the data analyzed by the behavior pattern analysis unit; a feedback unit that feeds back the dissatisfaction level scored by the dissatisfaction level scoring unit to a manager. A system characterized by:

2. The behavioral data collection unit Data from the employee's smartphone or wearable device will also be collected to understand more detailed behavioral patterns.

2. The system of claim 1.

3. The behavioral data collection unit Collect data on the employee's behavior while working remotely from home and evaluate how they work outside the office 2. The system of claim 1.

4. The data preprocessing unit Develop algorithms to automate outlier detection and correction 2. The system of claim 1.

5. The behavior pattern analysis unit Using generative AI, the behavioral patterns of the employee are analyzed in real time and the feedback is provided immediately.

2. The system of claim 1.

6. The dissatisfaction degree scoring unit Consider the employee's characteristics and background information when calculating the dissatisfaction score 2. The system of claim 1.

7. The feedback unit In the above-mentioned feedback of the score, we provide individual feedback to employees and suggest specific improvement measures.

2. The system of claim 1.

8. The behavioral data collection unit Emotion estimation is used to analyze the employee's facial expression or tone of voice and add their emotional state to the data.

2. The system of claim 1.

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