system

Abstract

The system according to this embodiment aims to analyze employee behavior, identify unnecessary movements, and provide improvement suggestions. [Solution] The system according to the embodiment comprises an analysis unit, an identification unit, a proposal unit, and an alert unit. The analysis unit analyzes the behavior of employees. The identification unit identifies unnecessary movements based on the behavioral data analyzed by the analysis unit. The proposal unit makes improvement suggestions based on the unnecessary movements identified by the identification unit. The alert unit issues an alert in real time when a problem occurs.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the actions of employees have not been sufficiently analyzed in real time to identify wasteful movements and make improvement proposals, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the actions of employees, identify wasteful movements, and make improvement proposals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an identification unit, a proposal unit, and an alert unit. The analysis unit analyzes the behavior of employees. The identification unit identifies unnecessary movements based on the behavioral data analyzed by the analysis unit. The proposal unit makes improvement suggestions based on the unnecessary movements identified by the identification unit. The alert unit issues alerts in real time when a problem occurs. [Effects of the Invention]

[0007] The system according to this embodiment can analyze employee behavior, identify unnecessary movements, and make improvement suggestions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The efficiency agent system according to an embodiment of the present invention is a system that analyzes employee behavior in real time, identifies unnecessary movements, and makes specific improvement suggestions. This efficiency agent system uses an AI camera to monitor employee movements in real time, and the AI ​​analyzes the data to identify unnecessary movements. For example, if an employee repeatedly goes back and forth to the same place, the system will determine that the movement is unnecessary and make improvement suggestions. In addition, it provides immediate notification when a problem occurs through a real-time alert function, prompting a quick response. For example, if an employee makes a dangerous movement, an alert is issued in real time and notified to the manager. This enables a quick response and can prevent accidents. Furthermore, the efficiency agent system automatically generates sustainable improvement suggestions. The AI ​​learns what improvements are effective based on past data and makes specific improvement suggestions. For example, it can show specific numerical effects such as a reduction in work time or a reduction in the error rate. In this way, the efficiency agent system utilizes AI technology to optimize employee behavior and improve work efficiency. This reduces the workload on employees and contributes to improved employee satisfaction. It also realizes time and cost reductions in all industries where optimization and efficiency of the workforce are required. This allows the efficiency agent system to efficiently analyze employee behavior, identify unnecessary movements, suggest improvements, and issue alerts in real time.

[0029] The efficiency agent system according to this embodiment comprises an analysis unit, an identification unit, a proposal unit, and an alert unit. The analysis unit analyzes employee behavior. The analysis unit monitors employee movements in real time using, for example, an AI camera and analyzes the data. The analysis unit uses AI to analyze patterns of employee movement and identify unnecessary movements. The identification unit identifies unnecessary movements based on the behavioral data analyzed by the analysis unit. The identification unit determines, for example, that an employee repeatedly goes back and forth to the same place, and that such movement is unnecessary. The identification unit uses AI to execute an algorithm for identifying unnecessary movements. The proposal unit makes improvement suggestions based on the unnecessary movements identified by the identification unit. The proposal unit makes, for example, specific suggestions to improve the efficiency of employee movements. The proposal unit uses AI to automatically generate improvement suggestions. The alert unit issues alerts in real time when a problem occurs. The alert unit issues an alert in real time and notifies the manager, for example, if an employee makes a dangerous movement. The alert unit uses AI to detect the occurrence of a problem and issue an alert. As a result, the efficiency agent system according to the embodiment can efficiently analyze employee behavior, identify unnecessary movements, suggest improvements, and issue alerts in real time.

[0030] The analysis department analyzes employee behavior. For example, it uses AI cameras to monitor employee movements in real time and analyzes the data. Specifically, the AI ​​cameras capture high-resolution video and record employee movements in detail. This video data is transmitted to the analysis department and analyzed by AI algorithms. The AI ​​uses image recognition technology to track employee movements and extract movement patterns. For example, it analyzes what paths employees take and how much time they spend on each task. Furthermore, the AI ​​can identify abnormal or inefficient movements by comparing them with past data. This allows the analysis department to gain a detailed understanding of employee behavior and provide foundational data for improving efficiency. Because the analysis department processes this data in real time and outputs results immediately, rapid response is possible.

[0031] The identification unit identifies unnecessary movements based on behavioral data analyzed by the analysis unit. For example, if an employee repeatedly moves back and forth to the same location, the identification unit will determine that movement is unnecessary. The identification unit uses AI to execute algorithms for identifying unnecessary movements. Specifically, the identification unit analyzes employee movement patterns and uses rule-based algorithms and machine learning models to detect inefficient movements. For example, it identifies unnecessary movements when an employee repeatedly performs the same task or moves longer distances than necessary. To identify these unnecessary movements, the identification unit refers to past data and standard operational patterns to detect abnormal movements. Furthermore, the identification unit can analyze the causes of unnecessary movements and identify specific points for improvement. This allows the identification unit to efficiently identify inefficiencies in employee behavior and provide foundational data for improvement.

[0032] The proposal department makes improvement suggestions based on wasteful movements identified by specific departments. For example, the proposal department makes specific suggestions to improve the efficiency of employee movements. The proposal department uses AI to automatically generate improvement suggestions. Specifically, the proposal department generates specific action plans for efficiency improvements based on wasteful movement data provided by specific departments. For example, it may propose a new layout to optimize employee movement routes or provide specific steps to improve efficiency by reviewing work procedures. The proposal department can also propose appropriate training programs based on employees' skills and experience. The AI ​​runs an algorithm that references historical data and industry best practices to generate optimal improvement suggestions. This allows the proposal department to provide specific and actionable suggestions to improve the efficiency of employee actions. Furthermore, the proposal department can evaluate the effectiveness of the suggestions and modify them as needed. This allows the proposal department to support continuous improvement and improve the efficiency of the entire system.

[0033] The alert unit issues real-time alerts when problems occur. For example, if an employee makes a dangerous move, the alert unit will issue a real-time alert and notify the administrator. The alert unit uses AI to detect the occurrence of problems and issue alerts. Specifically, the alert unit monitors employee movements in real time and executes algorithms to detect dangerous movements and abnormal behavior. For example, if an employee approaches a dangerous area or makes an unusual move, an alert will be issued immediately. Alerts are communicated to administrators through multiple means, such as voice notifications, visual alerts, and email notifications. This allows administrators to respond quickly and ensure employee safety. Furthermore, the alert unit can analyze past alert data and build a feedback loop to improve the accuracy of alerts. This allows the alert unit to support early detection and rapid response to problems, improving the overall safety and reliability of the system.

[0034] The data collection unit can collect employee behavior data. For example, the data collection unit can use an AI camera to monitor employee movements in real time and collect the data. The data collection unit uses AI to execute algorithms for collecting employee movement data. The data collection unit can also use sensors to detect employee movements and collect the data. The data collection unit uses AI to analyze the data from the sensors and collect employee movement data. This improves the accuracy of the analysis by collecting employee behavior data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input video data acquired by an AI camera into a generating AI and have the generating AI generate behavior data from the video data.

[0035] The proposal unit may include a generation unit that automatically generates sustainable improvement proposals. The generation unit, for example, uses AI to analyze employee movement data and automatically generates sustainable improvement proposals. The generation unit uses AI to learn from past data and learn what kinds of improvements are effective. The generation unit automatically generates improvement proposals that show specific numerical effects, such as reducing work time or lowering the error rate. This improves employee work efficiency by automatically generating sustainable improvement proposals. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past data into a generation AI and have the generation AI perform the generation of improvement proposals.

[0036] The generation unit may include a learning unit that learns from past data. The learning unit, for example, uses AI to analyze past data and learn what improvements are effective. The learning unit uses AI to execute algorithms based on past data to improve the accuracy of improvement suggestions. The learning unit learns from data from the past year or data from a specific period to improve the accuracy of improvement suggestions. In this way, the accuracy of improvement suggestions is improved by learning from past data. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input past data into a generation AI and have the generation AI perform data learning.

[0037] The alert unit can immediately notify when a problem occurs. For example, if an employee makes a dangerous move, the alert unit will issue a real-time alert and notify the administrator. The alert unit uses AI to detect the occurrence of a problem and execute an algorithm to issue an alert. The alert unit can issue a notification within a few seconds, for example. This allows for a quick response by notifying immediately when a problem occurs. Some or all of the above-described processes in the alert unit may be performed using AI or not. For example, the alert unit can input data detecting the occurrence of a problem into a generating AI and have the generating AI execute the alert.

[0038] The analysis unit can optimize its analysis algorithm by referring to employees' past behavior patterns during analysis. For example, the analysis unit optimizes an algorithm to identify unnecessary movements based on employees' past behavior data. The analysis unit analyzes employees' past behavior patterns and predicts unnecessary movements at specific times or situations. The analysis unit applies the most suitable analysis algorithm to each individual employee by referring to employees' past behavior data. This improves the accuracy of identifying unnecessary movements by optimizing the analysis algorithm through the reference of employees' past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past behavior data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0039] The analysis unit can apply different analysis methods depending on the employee's role and job duties during the analysis. For example, the analysis unit can apply different analysis methods depending on the employee's role to identify unnecessary movements. The analysis unit analyzes specific actions as unnecessary movements based on the employee's job duties. The analysis unit customizes the analysis methods according to the employee's role and job duties to improve accuracy. As a result, the accuracy of identifying unnecessary movements is improved by applying analysis methods according to the employee's role and job duties. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the employee's role and job duties into a generating AI and have the generating AI execute the application of analysis methods.

[0040] The analysis unit can perform analysis while considering the geographical location information of employees. For example, the analysis unit can analyze unnecessary movements in a specific location based on the geographical location information of employees. The analysis unit analyzes the employee's movement path and identifies unnecessary movements. The analysis unit analyzes behavioral patterns in a specific area, taking into account the geographical location information of employees. In this way, unnecessary movements in a specific location can be analyzed by considering the geographical location information of employees. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical location information into a generating AI and have the generating AI perform analysis based on the location information.

[0041] The analysis unit can analyze employees' social media activities during the analysis process and reflect the findings in the behavioral analysis. For example, the analysis unit can analyze employees' social media activities and analyze changes in their behavioral patterns. The analysis unit can estimate employees' emotional states based on the content of their social media posts and reflect these findings in the behavioral analysis. The analysis unit can analyze employees' social media activities and identify unnecessary actions in specific situations. In this way, by analyzing employees' social media activities, changes in their behavioral patterns can be reflected in the analysis. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input social media activity data into a generating AI and have the generating AI perform the reflection of the data in the behavioral analysis.

[0042] The identification unit can optimize the identification algorithm by referring to the employee's past data on wasted movements at a given time. For example, the identification unit optimizes the identification algorithm based on the employee's past data on wasted movements. The identification unit analyzes the employee's past data on wasted movements and predicts wasted movements at specific times and situations. The identification unit applies the most suitable identification algorithm to each individual employee by referring to the employee's past data on wasted movements. This improves the accuracy of identifying wasted movements by optimizing the identification algorithm by referring to the employee's past data on wasted movements. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input past data on wasted movements into a generating AI and have the generating AI perform the optimization of the identification algorithm.

[0043] The identification unit can apply different identification methods depending on the employee's role and job duties at the time of identification. For example, the identification unit applies different identification methods depending on the employee's role to identify unnecessary movements. The identification unit identifies specific actions as unnecessary movements based on the employee's job duties. The identification unit customizes the identification methods according to the employee's role and job duties to improve accuracy. This improves the accuracy of identifying unnecessary movements by applying identification methods according to the employee's role and job duties. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input data on the employee's role and job duties into a generating AI and have the generating AI execute the application of identification methods.

[0044] The identification unit can perform identification while considering the geographical location information of employees. For example, the identification unit can identify unnecessary movements in a specific location based on the geographical location information of employees. The identification unit identifies the employee's movement route and identifies unnecessary movements. The identification unit identifies behavioral patterns in a specific area, taking into account the geographical location information of employees. In this way, unnecessary movements in a specific location can be identified by considering the geographical location information of employees. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input geographical location information into a generating AI and have the generating AI perform identification based on the location information.

[0045] The identification unit can analyze employees' social media activities at the time of identification and reflect the results in identifying unnecessary actions. For example, the identification unit can analyze employees' social media activities and identify changes in behavioral patterns. The identification unit can estimate emotional states based on the content of employees' social media posts and reflect the results in identifying unnecessary actions. The identification unit identifies employees' social media activities and identifies unnecessary actions in specific situations. In this way, by analyzing employees' social media activities, changes in behavioral patterns can be reflected in the identification. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input social media activity data into a generating AI and have the generating AI perform the process of reflecting the results in identifying unnecessary actions.

[0046] The proposal unit can adjust the level of detail of its proposals based on the importance of the unnecessary movements. For example, the proposal unit can provide detailed improvement suggestions for highly important unnecessary movements, and concise improvement suggestions for less important unnecessary movements. The proposal unit adjusts the level of detail of its proposals according to the importance of the unnecessary movements to provide the most optimal improvement suggestions. This allows the proposal unit to provide the most optimal improvement suggestions by adjusting the level of detail according to the importance of the unnecessary movements. Some or all of the above processing in the proposal unit may be performed using AI, or it may be performed without AI. For example, the proposal unit can input data on the importance of unnecessary movements into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the category of wasted movement when making a proposal. For example, the proposal unit applies different proposal algorithms depending on the category of wasted movement to make the optimal improvement proposal. The proposal unit makes specific improvement proposals based on the category of wasted movement. The proposal unit customizes the proposal algorithm according to the category of wasted movement to improve accuracy. As a result, by applying the proposal algorithm according to the category of wasted movement, the optimal improvement proposal can be made. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the category data of wasted movement into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0048] The proposal unit can determine the priority of proposals based on when unnecessary movements occur. For example, the proposal unit can determine the priority of proposals based on the time of day when unnecessary movements occur most frequently. If unnecessary movements occur in a specific event or situation, the proposal unit can determine the priority of proposals based on that timing. The proposal unit adjusts the priority of proposals according to when unnecessary movements occur and makes the most optimal improvement proposals. In this way, by determining the priority of proposals based on when unnecessary movements occur, the proposal unit can make the most optimal improvement proposals. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input data on when unnecessary movements occur into a generating AI and have the generating AI perform the determination of proposal priorities.

[0049] The proposal unit can adjust the order of proposals based on the relationships between unnecessary movements during the proposal process. For example, the proposal unit adjusts the order of proposals based on the relationships between unnecessary movements to provide the optimal improvement proposal. When unnecessary movements are related, the proposal unit determines the order of proposals based on that relationship. The proposal unit customizes the order of proposals according to the relationships between unnecessary movements to improve accuracy. This allows the proposal unit to provide the optimal improvement proposal by adjusting the order of proposals based on the relationships between unnecessary movements. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input data on the relationships between unnecessary movements into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0050] The alert unit can adjust the level of detail of an alert based on the severity of the problem when an alert is issued. For example, the alert unit issues a detailed alert for a high-severity problem. The alert unit issues a concise alert for a low-severity problem. The alert unit adjusts the level of detail of the alert according to the severity of the problem to issue the most appropriate alert. This allows the alert unit to issue the most appropriate alert by adjusting the level of detail according to the severity of the problem. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input problem severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the alert.

[0051] The alert unit can apply different alert algorithms depending on the problem category when an alert is issued. For example, the alert unit applies different alert algorithms depending on the problem category to issue the most appropriate alert. The alert unit issues a specific alert based on the problem category. The alert unit customizes the alert algorithm depending on the problem category to improve accuracy. This allows the system to issue the most appropriate alert by applying the alert algorithm according to the problem category. Some or all of the above-described processes in the alert unit may be performed using AI or not. For example, the alert unit can input problem category data into a generating AI and have the generating AI execute the application of the alert algorithm.

[0052] The alert unit can determine the priority of alerts based on when the problem occurs. For example, the alert unit can determine the priority of alerts based on the time of day when the problem frequently occurs. If the problem occurs in a specific event or situation, the alert unit can determine the priority of alerts based on that timing. The alert unit adjusts the priority of alerts according to when the problem occurs and issues the most appropriate alert. In this way, the alert unit can issue the most appropriate alert by determining the priority of alerts based on when the problem occurs. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input data on when the problem occurred into a generating AI and have the generating AI perform the determination of the alert priority.

[0053] The alert unit can adjust the order of alerts based on the relevance of the issues when an alert is issued. For example, the alert unit adjusts the order of alerts based on the relevance of the issues to issue the most appropriate alert. When issues are related, the alert unit determines the order of alerts based on that relevance. The alert unit customizes the order of alerts according to the relevance of the issues to improve accuracy. This allows the alert unit to issue the most appropriate alert by adjusting the order of alerts based on the relevance of the issues. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input issue relevance data into a generating AI and have the generating AI perform the adjustment of the order of alerts.

[0054] The data collection unit can optimize its collection algorithm by referring to employees' past behavioral data during collection. For example, the data collection unit optimizes its collection algorithm based on employees' past behavioral data. The data collection unit analyzes employees' past behavioral data and collects behavioral data for specific time periods and situations. The data collection unit applies the most suitable collection algorithm to each individual employee by referring to employees' past behavioral data. This optimizes the collection algorithm by referring to employees' past behavioral data, improving the accuracy of the collected data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral data into a generating AI and have the generating AI perform the optimization of the collection algorithm.

[0055] The data collection unit can apply different collection methods depending on the employee's role and job duties during collection. For example, the data collection unit applies different collection methods depending on the employee's role to collect behavioral data. The data collection unit collects specific behavioral data based on the employee's job duties. The data collection unit customizes the collection methods to improve accuracy depending on the employee's role and job duties. This improves the accuracy of the collected data by applying collection methods according to the employee's role and job duties. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the employee's role and job duties into a generating AI and have the generating AI execute the application of collection methods.

[0056] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during the collection process. For example, the data collection unit can prioritize the collection of behavioral data at specific locations based on the geographical location information of employees. The data collection unit can collect highly relevant behavioral data by considering the employee's travel route. The data collection unit can prioritize the collection of behavioral data in specific areas by considering the geographical location information of employees. In this way, by considering the geographical location information of employees, highly relevant data can be prioritized for collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0057] The data collection unit can analyze employees' social media activity and collect relevant behavioral data during the collection process. For example, the data collection unit can analyze employees' social media activity and collect changes in behavioral patterns. The data collection unit can estimate emotional states based on the content of employees' social media posts and collect relevant behavioral data. The data collection unit collects employees' social media activity and collects behavioral data in specific situations. This allows for the collection of relevant behavioral data by analyzing employees' social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant behavioral data.

[0058] The generation unit can optimize its generation algorithm by referring to past improvement suggestion data during generation. For example, the generation unit optimizes its generation algorithm based on past improvement suggestion data. The generation unit analyzes past improvement suggestion data and generates improvement suggestions for specific situations. The generation unit refers to past improvement suggestion data to generate the most suitable improvement suggestions for each individual employee. This optimizes the generation algorithm by referring to past improvement suggestion data, improving the accuracy of the improvement suggestions. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input past improvement suggestion data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0059] The generation unit can apply different generation methods depending on the category of wasted movement during generation. For example, the generation unit applies different generation methods depending on the category of wasted movement to generate the optimal improvement suggestion. The generation unit generates specific improvement suggestions based on the category of wasted movement. The generation unit customizes the generation method depending on the category of wasted movement to improve accuracy. As a result, by applying the generation method according to the category of wasted movement, the optimal improvement suggestion can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the category data of wasted movement into a generation AI and have the generation AI execute the application of the generation method.

[0060] The generation unit can determine the priority of suggestions to be generated based on the timing of the occurrence of unnecessary movements during the generation process. For example, the generation unit can determine the priority of suggestions based on the time periods in which unnecessary movements frequently occur. If unnecessary movements occur in a specific event or situation, the generation unit can determine the priority of suggestions based on that timing. The generation unit adjusts the priority of suggestions according to the timing of the occurrence of unnecessary movements to generate the optimal improvement suggestion. In this way, the optimal improvement suggestion can be generated by determining the priority of suggestions based on the timing of the occurrence of unnecessary movements. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input data on the timing of the occurrence of unnecessary movements into a generation AI and have the generation AI perform the determination of the suggestion priority.

[0061] The generation unit can adjust the order of suggestions generated based on the relationships between unnecessary movements during generation. For example, the generation unit adjusts the order of suggestions based on the relationships between unnecessary movements to generate the optimal improvement suggestions. When unnecessary movements are related, the generation unit determines the order of suggestions based on that relationship. The generation unit customizes the order of suggestions according to the relationships between unnecessary movements to improve accuracy. In this way, the optimal improvement suggestions can be generated by adjusting the order of suggestions based on the relationships between unnecessary movements. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input data on the relationships between unnecessary movements into a generation AI and have the generation AI perform the adjustment of the order of suggestions.

[0062] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit optimizes the learning algorithm based on past learning data. The learning unit analyzes past learning data and optimizes the learning algorithm for specific situations. The learning unit applies the optimal learning algorithm to each individual employee by referring to past learning data. This optimizes the learning algorithm by referring to past learning data, improving the accuracy of learning. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0063] The learning unit can apply different learning methods depending on the category of wasted movement during learning. For example, the learning unit applies different learning methods depending on the category of wasted movement to perform optimal learning. The learning unit applies a specific learning method based on the category of wasted movement. The learning unit customizes the learning method according to the category of wasted movement to improve accuracy. This allows for optimal learning by applying a learning method according to the category of wasted movement. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the category data of wasted movement into a generating AI and have the generating AI perform the application of the learning method.

[0064] The learning unit can weight the training data based on the timing of unnecessary movements during training. For example, the learning unit weights the training data based on the time periods in which unnecessary movements frequently occur. If unnecessary movements occur in specific events or situations, the learning unit weights the training data based on the timing of those events. The learning unit adjusts the weighting of the training data according to the timing of the unnecessary movements to perform optimal training. This allows for optimal training by weighting the training data based on the timing of the unnecessary movements. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input data on the timing of unnecessary movements into a generating AI and have the generating AI perform the weighting of the training data.

[0065] The learning unit can adjust the order of training data based on the relationships between unnecessary movements during training. For example, the learning unit adjusts the order of training data based on the relationships between unnecessary movements to perform optimal training. When unnecessary movements are related, the learning unit determines the order of training data based on those relationships. The learning unit customizes the order of training data according to the relationships between unnecessary movements to improve accuracy. In this way, optimal training can be performed by adjusting the order of training data based on the relationships between unnecessary movements. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input data on the relationships between unnecessary movements into a generating AI and have the generating AI perform the adjustment of the order of training data.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The analysis unit can consider the employee's health status when analyzing employee behavioral data. For example, if an employee has a specific health risk based on the results of a health checkup, the analysis unit will take that risk into account when analyzing the behavioral data. The analysis unit can adjust the algorithm for identifying unnecessary movements based on the employee's health status. This allows for more accurate behavioral analysis by considering the employee's health status. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0068] The proposal department can make suggestions to improve the overall efficiency of the team based on employee behavioral data. For example, if a particular team member is working more efficiently than others, the department can use that member's actions as a reference to make improvement suggestions to other members. The proposal department can also analyze the overall team's actions and make suggestions to strengthen team collaboration. This can improve the overall efficiency of the team. Some or all of the above processes in the proposal department may be performed using AI, or they may not.

[0069] The alert unit can predict problems that may occur during specific time periods based on employee behavior data and issue alerts in advance. For example, if past data shows that accidents frequently occur during a particular time period, the alert unit can issue an alert in advance during that time period to draw attention. The alert unit can execute algorithms to predict the occurrence of problems and issue alerts in advance. This makes it possible to prevent problems from occurring. Some or all of the above-described processes in the alert unit may be performed using AI or not.

[0070] The data collection unit can consider the employee's work environment when collecting employee behavioral data. For example, if the work environment is hot or cold, it can adjust the method of collecting behavioral data accordingly. The data collection unit can adjust the behavioral data collection algorithm based on the work environment. This allows for the collection of more accurate behavioral data by considering the work environment. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0071] The analysis unit can consider the skill level of employees when analyzing employee behavior data. For example, it can compare the movements of high-skilled and low-skilled employees to identify unnecessary movements according to their skill level. The analysis unit can adjust the behavior analysis algorithm based on the employee's skill level. This allows for more accurate behavior analysis by considering skill levels. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The analysis unit analyzes employee behavior. The analysis unit monitors employee movements in real time, for example, using AI cameras, and analyzes the data. The analysis unit uses AI to analyze patterns in employee movements and identify unnecessary movements. Step 2: The identification unit identifies unnecessary movements based on the behavioral data analyzed by the analysis unit. For example, if an employee repeatedly goes back and forth to the same place, the identification unit will determine that movement is unnecessary. The identification unit uses AI to execute an algorithm to identify unnecessary movements. Step 3: The proposal department makes improvement suggestions based on the wasteful movements identified by the specific department. For example, the proposal department makes specific suggestions to improve the efficiency of employee movements. The proposal department uses AI to automatically generate improvement suggestions. Step 4: The alert unit issues real-time alerts when a problem occurs. For example, if an employee makes a dangerous move, the alert unit will issue a real-time alert and notify the administrator. The alert unit uses AI to detect the occurrence of a problem and issue an alert.

[0074] (Example of form 2) The efficiency agent system according to an embodiment of the present invention is a system that analyzes employee behavior in real time, identifies unnecessary movements, and makes specific improvement suggestions. This efficiency agent system uses an AI camera to monitor employee movements in real time, and the AI ​​analyzes the data to identify unnecessary movements. For example, if an employee repeatedly goes back and forth to the same place, the system will determine that the movement is unnecessary and make improvement suggestions. In addition, it provides immediate notification when a problem occurs through a real-time alert function, prompting a quick response. For example, if an employee makes a dangerous movement, an alert is issued in real time and notified to the manager. This enables a quick response and can prevent accidents. Furthermore, the efficiency agent system automatically generates sustainable improvement suggestions. The AI ​​learns what improvements are effective based on past data and makes specific improvement suggestions. For example, it can show specific numerical effects such as a reduction in work time or a reduction in the error rate. In this way, the efficiency agent system utilizes AI technology to optimize employee behavior and improve work efficiency. This reduces the workload on employees and contributes to improved employee satisfaction. It also realizes time and cost reductions in all industries where optimization and efficiency of the workforce are required. This allows the efficiency agent system to efficiently analyze employee behavior, identify unnecessary movements, suggest improvements, and issue alerts in real time.

[0075] The efficiency agent system according to this embodiment comprises an analysis unit, an identification unit, a proposal unit, and an alert unit. The analysis unit analyzes employee behavior. The analysis unit monitors employee movements in real time using, for example, an AI camera and analyzes the data. The analysis unit uses AI to analyze patterns of employee movement and identify unnecessary movements. The identification unit identifies unnecessary movements based on the behavioral data analyzed by the analysis unit. The identification unit determines, for example, that an employee repeatedly goes back and forth to the same place, and that such movement is unnecessary. The identification unit uses AI to execute an algorithm for identifying unnecessary movements. The proposal unit makes improvement suggestions based on the unnecessary movements identified by the identification unit. The proposal unit makes, for example, specific suggestions to improve the efficiency of employee movements. The proposal unit uses AI to automatically generate improvement suggestions. The alert unit issues alerts in real time when a problem occurs. The alert unit issues an alert in real time and notifies the manager, for example, if an employee makes a dangerous movement. The alert unit uses AI to detect the occurrence of a problem and issue an alert. As a result, the efficiency agent system according to the embodiment can efficiently analyze employee behavior, identify unnecessary movements, suggest improvements, and issue alerts in real time.

[0076] The analysis department analyzes employee behavior. For example, it uses AI cameras to monitor employee movements in real time and analyzes the data. Specifically, the AI ​​cameras capture high-resolution video and record employee movements in detail. This video data is transmitted to the analysis department and analyzed by AI algorithms. The AI ​​uses image recognition technology to track employee movements and extract movement patterns. For example, it analyzes what paths employees take and how much time they spend on each task. Furthermore, the AI ​​can identify abnormal or inefficient movements by comparing them with past data. This allows the analysis department to gain a detailed understanding of employee behavior and provide foundational data for improving efficiency. Because the analysis department processes this data in real time and outputs results immediately, rapid response is possible.

[0077] The identification unit identifies unnecessary movements based on behavioral data analyzed by the analysis unit. For example, if an employee repeatedly moves back and forth to the same location, the identification unit will determine that movement is unnecessary. The identification unit uses AI to execute algorithms for identifying unnecessary movements. Specifically, the identification unit analyzes employee movement patterns and uses rule-based algorithms and machine learning models to detect inefficient movements. For example, it identifies unnecessary movements when an employee repeatedly performs the same task or moves longer distances than necessary. To identify these unnecessary movements, the identification unit refers to past data and standard operational patterns to detect abnormal movements. Furthermore, the identification unit can analyze the causes of unnecessary movements and identify specific points for improvement. This allows the identification unit to efficiently identify inefficiencies in employee behavior and provide foundational data for improvement.

[0078] The proposal department makes improvement suggestions based on wasteful movements identified by specific departments. For example, the proposal department makes specific suggestions to improve the efficiency of employee movements. The proposal department uses AI to automatically generate improvement suggestions. Specifically, the proposal department generates specific action plans for efficiency improvements based on wasteful movement data provided by specific departments. For example, it may propose a new layout to optimize employee movement routes or provide specific steps to improve efficiency by reviewing work procedures. The proposal department can also propose appropriate training programs based on employees' skills and experience. The AI ​​runs an algorithm that references historical data and industry best practices to generate optimal improvement suggestions. This allows the proposal department to provide specific and actionable suggestions to improve the efficiency of employee actions. Furthermore, the proposal department can evaluate the effectiveness of the suggestions and modify them as needed. This allows the proposal department to support continuous improvement and improve the efficiency of the entire system.

[0079] The alert unit issues real-time alerts when problems occur. For example, if an employee makes a dangerous move, the alert unit will issue a real-time alert and notify the administrator. The alert unit uses AI to detect the occurrence of problems and issue alerts. Specifically, the alert unit monitors employee movements in real time and executes algorithms to detect dangerous movements and abnormal behavior. For example, if an employee approaches a dangerous area or makes an unusual move, an alert will be issued immediately. Alerts are communicated to administrators through multiple means, such as voice notifications, visual alerts, and email notifications. This allows administrators to respond quickly and ensure employee safety. Furthermore, the alert unit can analyze past alert data and build a feedback loop to improve the accuracy of alerts. This allows the alert unit to support early detection and rapid response to problems, improving the overall safety and reliability of the system.

[0080] The data collection unit can collect employee behavior data. For example, the data collection unit can use an AI camera to monitor employee movements in real time and collect the data. The data collection unit uses AI to execute algorithms for collecting employee movement data. The data collection unit can also use sensors to detect employee movements and collect the data. The data collection unit uses AI to analyze the data from the sensors and collect employee movement data. This improves the accuracy of the analysis by collecting employee behavior data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input video data acquired by an AI camera into a generating AI and have the generating AI generate behavior data from the video data.

[0081] The proposal unit may include a generation unit that automatically generates sustainable improvement proposals. The generation unit, for example, uses AI to analyze employee movement data and automatically generates sustainable improvement proposals. The generation unit uses AI to learn from past data and learn what kinds of improvements are effective. The generation unit automatically generates improvement proposals that show specific numerical effects, such as reducing work time or lowering the error rate. This improves employee work efficiency by automatically generating sustainable improvement proposals. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past data into a generation AI and have the generation AI perform the generation of improvement proposals.

[0082] The generation unit may include a learning unit that learns from past data. The learning unit, for example, uses AI to analyze past data and learn what improvements are effective. The learning unit uses AI to execute algorithms based on past data to improve the accuracy of improvement suggestions. The learning unit learns from data from the past year or data from a specific period to improve the accuracy of improvement suggestions. In this way, the accuracy of improvement suggestions is improved by learning from past data. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input past data into a generation AI and have the generation AI perform data learning.

[0083] The alert unit can immediately notify when a problem occurs. For example, if an employee makes a dangerous move, the alert unit will issue a real-time alert and notify the administrator. The alert unit uses AI to detect the occurrence of a problem and execute an algorithm to issue an alert. The alert unit can issue a notification within a few seconds, for example. This allows for a quick response by notifying immediately when a problem occurs. Some or all of the above-described processes in the alert unit may be performed using AI or not. For example, the alert unit can input data detecting the occurrence of a problem into a generating AI and have the generating AI execute the alert.

[0084] The analysis unit can estimate the employee's emotions and adjust the accuracy of the behavioral analysis based on the estimated emotions. For example, if an employee is stressed, the analysis unit can improve the accuracy of the behavioral analysis to more accurately identify unnecessary movements. If an employee is relaxed, the analysis unit can adjust the accuracy of the behavioral analysis to distinguish between normal and unnecessary movements. If an employee is tired, the analysis unit can adjust the accuracy of the behavioral analysis to take into account changes in movement due to fatigue. This allows for more accurate identification of unnecessary movements by adjusting the accuracy of the behavioral analysis based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI perform an adjustment of the accuracy of the behavioral analysis based on emotions.

[0085] The analysis unit can optimize its analysis algorithm by referring to employees' past behavior patterns during analysis. For example, the analysis unit optimizes an algorithm to identify unnecessary movements based on employees' past behavior data. The analysis unit analyzes employees' past behavior patterns and predicts unnecessary movements at specific times or situations. The analysis unit applies the most suitable analysis algorithm to each individual employee by referring to employees' past behavior data. This improves the accuracy of identifying unnecessary movements by optimizing the analysis algorithm through the reference of employees' past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past behavior data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0086] The analysis unit can apply different analysis methods depending on the employee's role and job duties during the analysis. For example, the analysis unit can apply different analysis methods depending on the employee's role to identify unnecessary movements. The analysis unit analyzes specific actions as unnecessary movements based on the employee's job duties. The analysis unit customizes the analysis methods according to the employee's role and job duties to improve accuracy. As a result, the accuracy of identifying unnecessary movements is improved by applying analysis methods according to the employee's role and job duties. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the employee's role and job duties into a generating AI and have the generating AI execute the application of analysis methods.

[0087] The analysis unit can estimate employees' emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if an employee is stressed, the analysis unit will display the analysis results simply and in an easy-to-understand manner. If an employee is relaxed, the analysis unit will display detailed analysis results to clearly identify areas for improvement. If an employee is tired, the analysis unit will display the analysis results in a visually easy-to-understand manner to reduce their burden. In this way, adjusting how the analysis results are displayed based on the employee's emotions makes the results easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust how the analysis results are displayed based on the emotions.

[0088] The analysis unit can perform analysis while considering the geographical location information of employees. For example, the analysis unit can analyze unnecessary movements in a specific location based on the geographical location information of employees. The analysis unit analyzes the employee's movement path and identifies unnecessary movements. The analysis unit analyzes behavioral patterns in a specific area, taking into account the geographical location information of employees. In this way, unnecessary movements in a specific location can be analyzed by considering the geographical location information of employees. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical location information into a generating AI and have the generating AI perform analysis based on the location information.

[0089] The analysis unit can analyze employees' social media activities during the analysis process and reflect the findings in the behavioral analysis. For example, the analysis unit can analyze employees' social media activities and analyze changes in their behavioral patterns. The analysis unit can estimate employees' emotional states based on the content of their social media posts and reflect these findings in the behavioral analysis. The analysis unit can analyze employees' social media activities and identify unnecessary actions in specific situations. In this way, by analyzing employees' social media activities, changes in their behavioral patterns can be reflected in the analysis. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input social media activity data into a generating AI and have the generating AI perform the reflection of the data in the behavioral analysis.

[0090] The identification unit can estimate an employee's emotions and adjust the criteria for identifying unnecessary movements based on the estimated emotions. For example, if an employee is stressed, the identification unit will tighten the criteria for identifying unnecessary movements. If an employee is relaxed, the identification unit will loosen the criteria for identifying unnecessary movements. If an employee is tired, the identification unit will adjust the criteria for identifying unnecessary movements to account for changes in movement due to fatigue. This allows for more accurate identification of unnecessary movements by adjusting the criteria for identifying unnecessary movements based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the identification criteria based on emotions.

[0091] The identification unit can optimize the identification algorithm by referring to the employee's past data on wasted movements at a given time. For example, the identification unit optimizes the identification algorithm based on the employee's past data on wasted movements. The identification unit analyzes the employee's past data on wasted movements and predicts wasted movements at specific times and situations. The identification unit applies the most suitable identification algorithm to each individual employee by referring to the employee's past data on wasted movements. This improves the accuracy of identifying wasted movements by optimizing the identification algorithm by referring to the employee's past data on wasted movements. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input past data on wasted movements into a generating AI and have the generating AI perform the optimization of the identification algorithm.

[0092] The identification unit can apply different identification methods depending on the employee's role and job duties at the time of identification. For example, the identification unit applies different identification methods depending on the employee's role to identify unnecessary movements. The identification unit identifies specific actions as unnecessary movements based on the employee's job duties. The identification unit customizes the identification methods according to the employee's role and job duties to improve accuracy. This improves the accuracy of identifying unnecessary movements by applying identification methods according to the employee's role and job duties. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input data on the employee's role and job duties into a generating AI and have the generating AI execute the application of identification methods.

[0093] The identification unit can estimate an employee's emotions and adjust how the identification results are displayed based on the estimated emotions. For example, if an employee is stressed, the identification unit may display the identification results simply and in an easy-to-understand manner. If an employee is relaxed, the identification unit may display detailed identification results to clearly indicate areas for improvement. If an employee is tired, the identification unit may display the identification results in a visually easy-to-understand manner to reduce their burden. In this way, the identification results become easier to understand by adjusting how they are displayed based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit may input employee emotion data into a generative AI and have the generative AI adjust how the identification results are displayed based on emotions.

[0094] The identification unit can perform identification while considering the geographical location information of employees. For example, the identification unit can identify unnecessary movements in a specific location based on the geographical location information of employees. The identification unit identifies the employee's movement route and identifies unnecessary movements. The identification unit identifies behavioral patterns in a specific area, taking into account the geographical location information of employees. In this way, unnecessary movements in a specific location can be identified by considering the geographical location information of employees. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input geographical location information into a generating AI and have the generating AI perform identification based on the location information.

[0095] The identification unit can analyze employees' social media activities at the time of identification and reflect the results in identifying unnecessary actions. For example, the identification unit can analyze employees' social media activities and identify changes in behavioral patterns. The identification unit can estimate emotional states based on the content of employees' social media posts and reflect the results in identifying unnecessary actions. The identification unit identifies employees' social media activities and identifies unnecessary actions in specific situations. In this way, by analyzing employees' social media activities, changes in behavioral patterns can be reflected in the identification. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input social media activity data into a generating AI and have the generating AI perform the process of reflecting the results in identifying unnecessary actions.

[0096] The suggestion department can estimate an employee's emotions and adjust the way improvement suggestions are presented based on those emotions. For example, if an employee is stressed, the suggestion department will provide simple and easy-to-understand improvement suggestions. If an employee is relaxed, the suggestion department will provide detailed suggestions, indicating specific areas for improvement. If an employee is tired, the suggestion department will provide visually easy-to-understand suggestions to reduce their burden. By adjusting the presentation of improvement suggestions based on the employee's emotions, the suggestions become easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the presentation of improvement suggestions based on those emotions.

[0097] The proposal unit can adjust the level of detail of its proposals based on the importance of the unnecessary movements. For example, the proposal unit can provide detailed improvement suggestions for highly important unnecessary movements, and concise improvement suggestions for less important unnecessary movements. The proposal unit adjusts the level of detail of its proposals according to the importance of the unnecessary movements to provide the most optimal improvement suggestions. This allows the proposal unit to provide the most optimal improvement suggestions by adjusting the level of detail according to the importance of the unnecessary movements. Some or all of the above processing in the proposal unit may be performed using AI, or it may be performed without AI. For example, the proposal unit can input data on the importance of unnecessary movements into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0098] The proposal unit can apply different proposal algorithms depending on the category of wasted movement when making a proposal. For example, the proposal unit applies different proposal algorithms depending on the category of wasted movement to make the optimal improvement proposal. The proposal unit makes specific improvement proposals based on the category of wasted movement. The proposal unit customizes the proposal algorithm according to the category of wasted movement to improve accuracy. As a result, by applying the proposal algorithm according to the category of wasted movement, the optimal improvement proposal can be made. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the category data of wasted movement into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0099] The suggestion department can estimate an employee's emotions and adjust the length of suggestions based on those emotions. For example, if an employee is stressed, the suggestion department will make short, concise suggestions. If an employee is relaxed, the suggestion department will make detailed suggestions and provide specific areas for improvement. If an employee is tired, the suggestion department will make visually easy-to-understand suggestions to reduce their burden. By adjusting the length of suggestions based on employee emotions, suggestions become easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI adjust the length of suggestions based on those emotions.

[0100] The proposal unit can determine the priority of proposals based on when unnecessary movements occur. For example, the proposal unit can determine the priority of proposals based on the time of day when unnecessary movements occur most frequently. If unnecessary movements occur in a specific event or situation, the proposal unit can determine the priority of proposals based on that timing. The proposal unit adjusts the priority of proposals according to when unnecessary movements occur and makes the most optimal improvement proposals. In this way, by determining the priority of proposals based on when unnecessary movements occur, the proposal unit can make the most optimal improvement proposals. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input data on when unnecessary movements occur into a generating AI and have the generating AI perform the determination of proposal priorities.

[0101] The proposal unit can adjust the order of proposals based on the relationships between unnecessary movements during the proposal process. For example, the proposal unit adjusts the order of proposals based on the relationships between unnecessary movements to provide the optimal improvement proposal. When unnecessary movements are related, the proposal unit determines the order of proposals based on that relationship. The proposal unit customizes the order of proposals according to the relationships between unnecessary movements to improve accuracy. This allows the proposal unit to provide the optimal improvement proposal by adjusting the order of proposals based on the relationships between unnecessary movements. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input data on the relationships between unnecessary movements into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0102] The alert unit can estimate an employee's emotions and adjust how it issues alerts based on those emotions. For example, if an employee is stressed, the alert unit will issue an alert in a calm tone. If an employee is relaxed, the alert unit will issue an alert in a normal tone. If an employee is in a hurry, the alert unit will issue an alert quickly. By adjusting how alerts are issued based on an employee's emotions, the alerts become easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input employee emotion data into a generative AI and have the generative AI adjust how alerts are issued based on those emotions.

[0103] The alert unit can adjust the level of detail of an alert based on the severity of the problem when an alert is issued. For example, the alert unit issues a detailed alert for a high-severity problem. The alert unit issues a concise alert for a low-severity problem. The alert unit adjusts the level of detail of the alert according to the severity of the problem to issue the most appropriate alert. This allows the alert unit to issue the most appropriate alert by adjusting the level of detail according to the severity of the problem. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input problem severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the alert.

[0104] The alert unit can apply different alert algorithms depending on the problem category when an alert is issued. For example, the alert unit applies different alert algorithms depending on the problem category to issue the most appropriate alert. The alert unit issues a specific alert based on the problem category. The alert unit customizes the alert algorithm depending on the problem category to improve accuracy. This allows the system to issue the most appropriate alert by applying the alert algorithm according to the problem category. Some or all of the above-described processes in the alert unit may be performed using AI or not. For example, the alert unit can input problem category data into a generating AI and have the generating AI execute the application of the alert algorithm.

[0105] The alert unit can estimate an employee's emotions and adjust how alerts are displayed based on the estimated emotions. For example, if an employee is stressed, the alert unit will display a simple and easy-to-understand alert. If an employee is relaxed, the alert unit will display a detailed alert indicating specific areas for improvement. If an employee is tired, the alert unit will display an alert in a visually clear and easy-to-understand manner to reduce their burden. In this way, adjusting how alerts are displayed based on an employee's emotions makes them easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input employee emotion data into a generative AI and have the generative AI adjust how alerts are displayed based on emotions.

[0106] The alert unit can determine the priority of alerts based on when the problem occurs. For example, the alert unit can determine the priority of alerts based on the time of day when the problem frequently occurs. If the problem occurs in a specific event or situation, the alert unit can determine the priority of alerts based on that timing. The alert unit adjusts the priority of alerts according to when the problem occurs and issues the most appropriate alert. In this way, the alert unit can issue the most appropriate alert by determining the priority of alerts based on when the problem occurs. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input data on when the problem occurred into a generating AI and have the generating AI perform the determination of the alert priority.

[0107] The alert unit can adjust the order of alerts based on the relevance of the issues when an alert is issued. For example, the alert unit adjusts the order of alerts based on the relevance of the issues to issue the most appropriate alert. When issues are related, the alert unit determines the order of alerts based on that relevance. The alert unit customizes the order of alerts according to the relevance of the issues to improve accuracy. This allows the alert unit to issue the most appropriate alert by adjusting the order of alerts based on the relevance of the issues. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input issue relevance data into a generating AI and have the generating AI perform the adjustment of the order of alerts.

[0108] The data collection unit can estimate an employee's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if an employee is stressed, the data collection unit will collect behavioral data more frequently. If an employee is relaxed, the data collection unit will collect behavioral data at a normal pace. If an employee is tired, the data collection unit will adjust the timing of behavioral data collection to account for changes in movement due to fatigue. This improves the accuracy of the collected data by adjusting the timing of behavioral data collection based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of collection timing based on emotions.

[0109] The data collection unit can optimize its collection algorithm by referring to employees' past behavioral data during collection. For example, the data collection unit optimizes its collection algorithm based on employees' past behavioral data. The data collection unit analyzes employees' past behavioral data and collects behavioral data for specific time periods and situations. The data collection unit applies the most suitable collection algorithm to each individual employee by referring to employees' past behavioral data. This optimizes the collection algorithm by referring to employees' past behavioral data, improving the accuracy of the collected data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavioral data into a generating AI and have the generating AI perform the optimization of the collection algorithm.

[0110] The data collection unit can apply different collection methods depending on the employee's role and job duties during collection. For example, the data collection unit applies different collection methods depending on the employee's role to collect behavioral data. The data collection unit collects specific behavioral data based on the employee's job duties. The data collection unit customizes the collection methods to improve accuracy depending on the employee's role and job duties. This improves the accuracy of the collected data by applying collection methods according to the employee's role and job duties. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on the employee's role and job duties into a generating AI and have the generating AI execute the application of collection methods.

[0111] The data collection unit can estimate an employee's emotions and prioritize the behavioral data to collect based on the estimated emotions. For example, if an employee is stressed, the data collection unit prioritizes collecting important behavioral data. If an employee is relaxed, the data collection unit collects normal behavioral data. If an employee is tired, the data collection unit adjusts the priority of the behavioral data to collect to account for changes in movement due to fatigue. This allows for the priority collection of important data by prioritizing the behavioral data to be collected based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input employee emotion data into a generative AI and have the generative AI perform the determination of priority of behavioral data based on emotions.

[0112] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of employees during the collection process. For example, the data collection unit can prioritize the collection of behavioral data at specific locations based on the geographical location information of employees. The data collection unit can collect highly relevant behavioral data by considering the employee's travel route. The data collection unit can prioritize the collection of behavioral data in specific areas by considering the geographical location information of employees. In this way, by considering the geographical location information of employees, highly relevant data can be prioritized for collection. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0113] The data collection unit can analyze employees' social media activity and collect relevant behavioral data during the collection process. For example, the data collection unit can analyze employees' social media activity and collect changes in behavioral patterns. The data collection unit can estimate emotional states based on the content of employees' social media posts and collect relevant behavioral data. The data collection unit collects employees' social media activity and collects behavioral data in specific situations. This allows for the collection of relevant behavioral data by analyzing employees' social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the collection of relevant behavioral data.

[0114] The generation unit can estimate an employee's emotions and adjust the method of generating improvement suggestions based on the estimated emotions. For example, if an employee is stressed, the generation unit generates simple and easy-to-understand improvement suggestions. If an employee is relaxed, the generation unit generates detailed improvement suggestions, indicating specific areas for improvement. If an employee is tired, the generation unit generates visually easy-to-understand improvement suggestions to reduce their burden. In this way, by adjusting the method of generating improvement suggestions based on the employee's emotions, the suggestions become easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into the generation AI and have the generation AI adjust the method of generating improvement suggestions based on emotions.

[0115] The generation unit can optimize its generation algorithm by referring to past improvement suggestion data during generation. For example, the generation unit optimizes its generation algorithm based on past improvement suggestion data. The generation unit analyzes past improvement suggestion data and generates improvement suggestions for specific situations. The generation unit refers to past improvement suggestion data to generate the most suitable improvement suggestions for each individual employee. This optimizes the generation algorithm by referring to past improvement suggestion data, improving the accuracy of the improvement suggestions. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input past improvement suggestion data into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0116] The generation unit can apply different generation methods depending on the category of wasted movement during generation. For example, the generation unit applies different generation methods depending on the category of wasted movement to generate the optimal improvement suggestion. The generation unit generates specific improvement suggestions based on the category of wasted movement. The generation unit customizes the generation method depending on the category of wasted movement to improve accuracy. As a result, by applying the generation method according to the category of wasted movement, the optimal improvement suggestion can be generated. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the category data of wasted movement into a generation AI and have the generation AI execute the application of the generation method.

[0117] The generation unit can estimate an employee's emotions and determine the priority of improvement suggestions to generate based on the estimated emotions. For example, if an employee is stressed, the generation unit will prioritize generating important improvement suggestions. If an employee is relaxed, the generation unit will generate normal improvement suggestions. If an employee is tired, the generation unit will adjust the priority of the improvement suggestions to be generated to account for changes in behavior due to fatigue. This allows for the prioritization of important suggestions by determining the priority of improvement suggestions based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input employee emotion data into a generation AI and have the generation AI perform the determination of priority of improvement suggestions based on emotions.

[0118] The generation unit can determine the priority of suggestions to be generated based on the timing of the occurrence of unnecessary movements during the generation process. For example, the generation unit can determine the priority of suggestions based on the time periods in which unnecessary movements frequently occur. If unnecessary movements occur in a specific event or situation, the generation unit can determine the priority of suggestions based on that timing. The generation unit adjusts the priority of suggestions according to the timing of the occurrence of unnecessary movements to generate the optimal improvement suggestion. In this way, the optimal improvement suggestion can be generated by determining the priority of suggestions based on the timing of the occurrence of unnecessary movements. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input data on the timing of the occurrence of unnecessary movements into a generation AI and have the generation AI perform the determination of the suggestion priority.

[0119] The generation unit can adjust the order of suggestions generated based on the relationships between unnecessary movements during generation. For example, the generation unit adjusts the order of suggestions based on the relationships between unnecessary movements to generate the optimal improvement suggestions. When unnecessary movements are related, the generation unit determines the order of suggestions based on that relationship. The generation unit customizes the order of suggestions according to the relationships between unnecessary movements to improve accuracy. In this way, the optimal improvement suggestions can be generated by adjusting the order of suggestions based on the relationships between unnecessary movements. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input data on the relationships between unnecessary movements into a generation AI and have the generation AI perform the adjustment of the order of suggestions.

[0120] The learning unit can estimate employees' emotions and select training data based on the estimated emotions. For example, if an employee is stressed, the learning unit will select training data that helps reduce stress. If an employee is relaxed, the learning unit will select normal training data. If an employee is tired, the learning unit will select training data that helps reduce fatigue. This improves the effectiveness of learning by selecting training data based on employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input employee emotion data into a generative AI and have the generative AI perform the selection of training data based on emotions.

[0121] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit optimizes the learning algorithm based on past learning data. The learning unit analyzes past learning data and optimizes the learning algorithm for specific situations. The learning unit applies the optimal learning algorithm to each individual employee by referring to past learning data. This optimizes the learning algorithm by referring to past learning data, improving the accuracy of learning. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0122] The learning unit can apply different learning methods depending on the category of wasted movement during learning. For example, the learning unit applies different learning methods depending on the category of wasted movement to perform optimal learning. The learning unit applies a specific learning method based on the category of wasted movement. The learning unit customizes the learning method according to the category of wasted movement to improve accuracy. This allows for optimal learning by applying a learning method according to the category of wasted movement. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the category data of wasted movement into a generating AI and have the generating AI perform the application of the learning method.

[0123] The learning unit can estimate an employee's emotions and adjust the learning frequency based on the estimated emotions. For example, if an employee is stressed, the learning unit increases the learning frequency. If an employee is relaxed, the learning unit returns the learning frequency to normal. If an employee is tired, the learning unit adjusts the learning frequency to account for changes in movement due to fatigue. This improves the effectiveness of learning by adjusting the learning frequency based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input employee emotion data into a generative AI and have the generative AI perform emotion-based adjustments to the learning frequency.

[0124] The learning unit can weight the training data based on the timing of unnecessary movements during training. For example, the learning unit weights the training data based on the time periods in which unnecessary movements frequently occur. If unnecessary movements occur in specific events or situations, the learning unit weights the training data based on the timing of those events. The learning unit adjusts the weighting of the training data according to the timing of the unnecessary movements to perform optimal training. This allows for optimal training by weighting the training data based on the timing of the unnecessary movements. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input data on the timing of unnecessary movements into a generating AI and have the generating AI perform the weighting of the training data.

[0125] The learning unit can adjust the order of training data based on the relationships between unnecessary movements during training. For example, the learning unit adjusts the order of training data based on the relationships between unnecessary movements to perform optimal training. When unnecessary movements are related, the learning unit determines the order of training data based on those relationships. The learning unit customizes the order of training data according to the relationships between unnecessary movements to improve accuracy. In this way, optimal training can be performed by adjusting the order of training data based on the relationships between unnecessary movements. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input data on the relationships between unnecessary movements into a generating AI and have the generating AI perform the adjustment of the order of training data.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The analysis unit can consider the employee's health status when analyzing employee behavioral data. For example, if an employee has a specific health risk based on the results of a health checkup, the analysis unit will take that risk into account when analyzing the behavioral data. The analysis unit can adjust the algorithm for identifying unnecessary movements based on the employee's health status. This allows for more accurate behavioral analysis by considering the employee's health status. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0128] The proposal department can make suggestions to improve the overall efficiency of the team based on employee behavioral data. For example, if a particular team member is working more efficiently than others, the department can use that member's actions as a reference to make improvement suggestions to other members. The proposal department can also analyze the overall team's actions and make suggestions to strengthen team collaboration. This can improve the overall efficiency of the team. Some or all of the above processes in the proposal department may be performed using AI, or they may not.

[0129] The alert unit can predict problems that may occur during specific time periods based on employee behavior data and issue alerts in advance. For example, if past data shows that accidents frequently occur during a particular time period, the alert unit can issue an alert in advance during that time period to draw attention. The alert unit can execute algorithms to predict the occurrence of problems and issue alerts in advance. This makes it possible to prevent problems from occurring. Some or all of the above-described processes in the alert unit may be performed using AI or not.

[0130] The data collection unit can consider the employee's work environment when collecting employee behavioral data. For example, if the work environment is hot or cold, it can adjust the method of collecting behavioral data accordingly. The data collection unit can adjust the behavioral data collection algorithm based on the work environment. This allows for the collection of more accurate behavioral data by considering the work environment. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0131] The analysis unit can consider the skill level of employees when analyzing employee behavior data. For example, it can compare the movements of high-skilled and low-skilled employees to identify unnecessary movements according to their skill level. The analysis unit can adjust the behavior analysis algorithm based on the employee's skill level. This allows for more accurate behavior analysis by considering skill levels. Some or all of the above processing in the analysis unit may be performed using AI or not.

[0132] The suggestion unit can estimate the employee's emotions and adjust the timing of the suggestion based on the estimated emotions. For example, if an employee is feeling stressed, the suggestion can be delayed to allow them to relax before making the suggestion. The suggestion unit can execute an algorithm to adjust the timing of the suggestion based on the employee's emotions. This enables suggestions that take the employee's emotions into consideration. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the suggestion unit may be performed using AI or not.

[0133] The alert unit can estimate an employee's emotions and adjust the content of the alert based on the estimated emotions. For example, if an employee is stressed, the alert content can be made simpler and easier to understand. The alert unit can execute an algorithm to adjust the content of the alert based on the employee's emotions. This enables alerts that take employee emotions into consideration. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the alert unit may be performed using AI or not.

[0134] The data collection unit can estimate employees' emotions and adjust the types of data collected based on the estimated emotions. For example, if an employee is stressed, it will prioritize collecting stress-related data. The data collection unit can execute algorithms that adjust the types of data collected based on employees' emotions. This enables data collection that takes employees' emotions into consideration. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0135] The analysis unit can estimate the employee's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if an employee is feeling stressed, the analysis results will be notified in a calm tone. The analysis unit can execute an algorithm that adjusts the notification method of the analysis results based on the employee's emotions. This makes it possible to notify employees of analysis results that take their emotions into consideration. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0136] The suggestion department can estimate an employee's emotions and adjust the content of the suggestion based on the estimated emotions. For example, if an employee is feeling stressed, the suggestion may be made more concise and easier to understand. The suggestion department can execute an algorithm to adjust the content of the suggestion based on the employee's emotions. This makes it possible to make suggestions that take employees' emotions into consideration. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the suggestion department may be performed using AI or not.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The analysis unit analyzes employee behavior. The analysis unit monitors employee movements in real time, for example, using AI cameras, and analyzes the data. The analysis unit uses AI to analyze patterns in employee movements and identify unnecessary movements. Step 2: The identification unit identifies unnecessary movements based on the behavioral data analyzed by the analysis unit. For example, if an employee repeatedly goes back and forth to the same place, the identification unit will determine that movement is unnecessary. The identification unit uses AI to execute an algorithm to identify unnecessary movements. Step 3: The proposal department makes improvement suggestions based on the wasteful movements identified by the specific department. For example, the proposal department makes specific suggestions to improve the efficiency of employee movements. The proposal department uses AI to automatically generate improvement suggestions. Step 4: The alert unit issues real-time alerts when a problem occurs. For example, if an employee makes a dangerous move, the alert unit will issue a real-time alert and notify the administrator. The alert unit uses AI to detect the occurrence of a problem and issue an alert.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0141] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the analysis unit, identification unit, proposal unit, alert unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit monitors employee movements in real time using the camera 42 and microphone 38B of the smart device 14 and analyzes the data using the control unit 46A. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies unnecessary movements based on the analyzed behavioral data. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and makes improvement suggestions based on the identified unnecessary movements. The alert unit is implemented in the control unit 46A of the smart device 14 and issues a real-time alert when a problem occurs, notifying the administrator. The collection unit monitors employee movements in real time using the camera 42 and sensors of the smart device 14 and collects the data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the analysis unit, identification unit, proposal unit, alert unit, and collection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit monitors employee movements in real time using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the data using the control unit 46A. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and identifies unnecessary movements based on the analyzed behavioral data. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and makes improvement suggestions based on the identified unnecessary movements. The alert unit is implemented, for example, in the control unit 46A of the smart glasses 214 and issues an alert in real time when a problem occurs, notifying the administrator. The collection unit monitors employee movements in real time using the camera 42 and sensors of the smart glasses 214 and collects the data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the analysis unit, identification unit, proposal unit, alert unit, and collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit monitors employee movements in real time using the camera 42 and microphone 238 of the headset terminal 314 and analyzes the data using the control unit 46A. The identification unit is implemented in the identification processing unit 290 of the data processing unit 12 and identifies unnecessary movements based on the analyzed behavioral data. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and makes improvement suggestions based on the identified unnecessary movements. The alert unit is implemented in the control unit 46A of the headset terminal 314 and issues a real-time alert when a problem occurs, notifying the administrator. The collection unit monitors employee movements in real time using the camera 42 and sensors of the headset terminal 314 and collects the data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0177] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0178] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0179] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0181] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0182] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0183] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0184] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0185] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0186] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0187] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0188] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0189] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0190] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0191] Each of the multiple elements described above, including the analysis unit, identification unit, proposal unit, alert unit, and collection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit monitors employee movements in real time using the camera 42 and microphone 238 of the robot 414 and analyzes the data using the control unit 46A. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies unnecessary movements based on the analyzed behavioral data. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and makes improvement suggestions based on the identified unnecessary movements. The alert unit is implemented, for example, by the control unit 46A of the robot 414 and issues an alert in real time when a problem occurs, notifying the administrator. The collection unit monitors employee movements in real time using the camera 42 and sensors of the robot 414 and collects the data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0192] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0193] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0194] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0195] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0196] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0197] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0199] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0200] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0201] 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.

[0202] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0203] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0204] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0205] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0206] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0207] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0208] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0209] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0210] (Note 1) The analysis department analyzes employee behavior, An identification unit identifies unnecessary movements based on the behavioral data analyzed by the aforementioned analysis unit, A proposal unit that makes improvement suggestions based on the unnecessary movements identified by the aforementioned specific unit, It includes an alert unit that issues alerts in real time when a problem occurs. A system characterized by the following features. (Note 2) It includes a data collection unit for collecting employee behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, It includes a generation unit that automatically generates sustainable improvement suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is It has a learning unit that learns from past data. The system described in Appendix 3, characterized by the features described herein. (Note 5) The alert unit is, Notify immediately when a problem occurs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We estimate employee emotions and adjust the accuracy of behavioral analysis based on the estimated employee emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the employee's past behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During the analysis, different analytical methods are applied depending on the employee's role and job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates employee emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During the analysis, the geographical location information of employees will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, we will analyze employees' social media activity and incorporate it into the behavioral analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The specified part is, We estimate employee emotions and adjust the criteria for identifying wasteful movements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The specified part is, At specific times, the system optimizes a particular algorithm by referencing past data on employees' inefficient movements. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, At specific times, different identification methods are applied depending on the employee's role and job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, The system estimates employee sentiment and adjusts how specific results are displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, When identifying an employee, their geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The specified part is, At specific times, analyze employees' social media activity to identify wasteful actions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate employees' emotions and adjust the way improvement suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of any unnecessary actions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of unnecessary movements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the employee's feelings and adjust the length of the suggestion based on those feelings. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making proposals, prioritize them based on when unnecessary actions occur. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of unnecessary movements. The system described in Appendix 1, characterized by the features described herein. (Note 24) The alert unit is, The system estimates employee sentiment and adjusts how alerts are issued based on that estimation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The alert unit is, When an alert is issued, the level of detail in the alert is reduced based on the severity of the issue. The system described in Appendix 1, characterized by the features described herein. (Note 26) The alert unit is, When an alert is issued, different alert algorithms are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 27) The alert unit is, It estimates employee sentiment and adjusts how alerts are displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The alert unit is, When an alert is issued, the priority of the alert is determined based on when the problem occurred. The system described in Appendix 1, characterized by the features described herein. (Note 29) The alert unit is, When an alert is issued, the order of the alerts will be adjusted based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned collection unit is We estimate employees' emotions and adjust the timing of behavioral data collection based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned collection unit is During data collection, the collection algorithm is optimized by referring to the employee's past behavioral data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned collection unit is When collecting data, different collection methods are applied depending on the employee's role and job responsibilities. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned collection unit is The system estimates employee sentiment and prioritizes the behavioral data to collect based on the estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the geographical location of employees. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned collection unit is During data collection, we analyze employees' social media activity and collect relevant behavioral data. The system described in Appendix 2, characterized by the features described herein. (Note 36) The generating unit is We estimate employee sentiment and adjust the method of generating improvement suggestions based on the estimated employee sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 37) The generating unit is During generation, the generation algorithm is optimized by referring to past improvement suggestion data. The system described in Appendix 3, characterized by the features described herein. (Note 38) The generating unit is During generation, different generation methods are applied depending on the category of unnecessary movements. The system described in Appendix 3, characterized by the features described herein. (Note 39) The generating unit is Estimate employee sentiment and prioritize improvement suggestions based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 40) The generating unit is During generation, the priority of the proposals to be generated is determined based on when unnecessary movements occur. The system described in Appendix 3, characterized by the features described herein. (Note 41) The generating unit is During generation, adjust the order of the generated suggestions based on the relevance of unnecessary movements. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned learning unit, The system estimates the emotions of employees and selects training data based on the estimated emotions of the employees. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned learning unit, During training, different learning methods are applied depending on the category of unnecessary movements. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned learning unit, It estimates employee emotions and adjusts the frequency of learning based on the estimated employee emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned learning unit, During training, the training data is weighted based on when unnecessary movements occur. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned learning unit, During training, the order of training data is adjusted based on the relationships between unnecessary movements. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes employee behavior, An identification unit identifies unnecessary movements based on the behavioral data analyzed by the aforementioned analysis unit, A proposal unit that makes improvement suggestions based on the unnecessary movements identified by the aforementioned specific unit, It includes an alert unit that issues alerts in real time when a problem occurs. A system characterized by the following features.

2. It includes a data collection unit for collecting employee behavioral data. The system according to feature 1.

3. The aforementioned proposal section is, It includes a generation unit that automatically generates sustainable improvement suggestions. The system according to feature 1.

4. The generating unit is It has a learning unit that learns from past data. The system according to claim 3.

5. The alert unit is, Notify immediately when a problem occurs. The system according to feature 1.

6. The aforementioned analysis unit, We estimate employee emotions and adjust the accuracy of behavioral analysis based on the estimated employee emotions. The system according to feature 1.

7. The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the employee's past behavioral patterns. The system according to feature 1.

8. The aforementioned analysis unit, During the analysis, different analytical methods are applied depending on the employee's role and job responsibilities. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates employee emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, During the analysis, the geographical location information of employees will be taken into consideration. The system according to feature 1.

11. The aforementioned analysis unit, During the analysis, we will analyze employees' social media activity and incorporate it into the behavioral analysis. The system according to feature 1.

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