system

Abstract

The system according to this embodiment aims to efficiently manage employee shifts and monitor their performance. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a recognition unit, and a monitoring unit. The collection unit collects employee attendance data, sales data, and weather and event information. The analysis unit analyzes the data collected by the collection unit. The generation unit generates optimal shifts based on the analysis results obtained by the analysis unit. The recognition unit uses conversational AI to understand the home environment of employees. The monitoring unit monitors staff performance through voice and image recognition.

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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 prior art, the shift management and performance monitoring of employees are not efficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently perform the shift management and performance monitoring of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a data recognition unit, and a monitoring unit. The data collection unit collects employee attendance data, sales data, and weather and event information. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates optimal shifts based on the analysis results obtained by the analysis unit. The data recognition unit uses conversational AI to understand the home environment of employees. The monitoring unit monitors staff performance through voice and image recognition. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage employee shifts and monitor their performance. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages 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 staffing AI agent according to an embodiment of the present invention is a system that analyzes employee attendance data, sales data, weather and event information in real time to ensure the optimal workforce and skill set. This staffing AI agent uses conversational AI to understand the home environment of part-time employees and generates shifts while consulting with them individually. Furthermore, it monitors staff performance through voice and image recognition, and works in conjunction with IoT devices to provide an optimal work environment and improve operational efficiency. First, the staffing AI agent collects employee attendance data, sales data, weather and event information. This data is analyzed in real time by AI and used as information to ensure the optimal workforce and skill set. For example, if sales surge during a specific time period, employees with the appropriate skills can be assigned to that time period. Next, the staffing AI agent uses conversational AI to understand the home environment of part-time employees. This enables flexible shift management that takes into account the individual circumstances of employees. For example, if an employee cannot work during a specific time period due to family reasons, their shift can be adjusted to another time period. Furthermore, the staffing AI agent monitors staff performance through voice and image recognition. This allows for real-time monitoring of employee performance and immediate feedback as needed. For example, if there are problems with customer service or work efficiency, improvement instructions can be issued immediately. The staffing AI agent also works in conjunction with IoT devices to provide an optimal work environment. For instance, it monitors environmental factors such as temperature, humidity, and lighting, and maintains them in optimal conditions to maximize employee performance. In this way, the staffing AI agent is a system that improves operational efficiency by analyzing employee attendance data, sales data, weather and event information in real time, generating shifts through individual consultations using conversational AI, monitoring staff performance through voice and image recognition, and providing an optimal work environment in conjunction with IoT devices.This allows the staffing AI agent to analyze employee attendance data, sales data, weather and event information in real time, enabling it to secure the optimal workforce and skill set.

[0029] The staffing AI agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a recognition unit, and a monitoring unit. The collection unit collects employee attendance data, sales data, and weather and event information. For example, the collection unit can collect attendance data such as employee arrival time, departure time, and break time. The collection unit can also collect sales data such as sales amount, sales quantity, and product category. Furthermore, the collection unit can collect weather and event information such as temperature, precipitation, event type, and date and time. For example, the collection unit records employee arrival times in real time and saves them as attendance data. The collection unit obtains sales data from a POS system and records sales amount and sales quantity. The collection unit obtains weather information from a weather database and records temperature and precipitation. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data in real time using AI. Based on the collected data, the analysis unit generates information to ensure the optimal workforce and skill set. For example, the analysis unit generates information to assign employees with the appropriate skills to a specific time slot if sales surge during that time. The analysis unit uses AI to analyze the collected data and provide information to generate the optimal shift. The generation unit generates the optimal shift based on the analysis results obtained by the analysis unit. The generation unit can, for example, use AI to automatically generate the optimal shift. The generation unit generates the optimal shift considering the balance between employees' skills and working hours. For example, if sales surge during a specific time slot, the generation unit generates a shift that assigns employees with the appropriate skills to that time slot. The generation unit uses AI to generate a shift that considers the balance between employees' skills and working hours. The information gathering unit uses conversational AI to understand employees' family circumstances. For example, the information gathering unit can converse with employees using conversational AI to understand their family circumstances. The information gathering unit performs flexible shift management considering employees' family circumstances. For example, the information gathering unit adjusts shifts for employees who cannot work during a specific time slot due to family reasons. The information gathering unit uses conversational AI to understand employees' family circumstances and performs flexible shift management.The monitoring unit monitors staff performance through voice and image recognition. For example, the monitoring unit can monitor staff performance using voice recognition technology. The monitoring unit grasps the status of staff work performance in real time and provides immediate feedback as needed. For example, if there are problems with customer service attitude or work efficiency, the monitoring unit will immediately issue instructions for improvement. The monitoring unit monitors staff performance using voice recognition technology and provides immediate feedback as needed. As a result, the staffing AI agent according to the embodiment can analyze employee attendance data, sales data, weather and event information in real time to ensure the optimal workforce and skill set.

[0030] The data collection unit collects employee attendance data, sales data, and weather and event information. Specifically, it can collect attendance data such as employee clock-in times, clock-out times, and break times. This data is automatically recorded when employees punch their time cards and stored in a cloud-based database. The data collection unit can also collect sales data such as sales amount, sales quantity, and product category. Sales data is obtained in real time from the POS system and used to understand the sales status and sales trends of each product. Furthermore, the data collection unit can collect weather and event information such as temperature, precipitation, event type, and date and time. Weather information is obtained from a weather database via API, and event information is collected from local event calendars and publicly available information on social media. For example, the data collection unit records employee clock-in times in real time and stores them as attendance data. This allows for accurate understanding of employee work status and is useful for shift management. The data collection unit obtains sales data from the POS system and records sales amount and sales quantity. This allows for real-time understanding of sales status and is useful for inventory management and sales strategy planning. The data collection unit retrieves weather information from a meteorological database and records temperature and precipitation. This enables flexible shift management and adjustment of sales strategies in response to weather changes. The data collection unit centrally manages this data and can link with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze the collected data in real time. Specifically, it integrates employee attendance data, sales data, weather and event information to generate information for ensuring optimal workforce and skill sets. For example, if sales surge during a specific time period, the analysis unit generates information for assigning employees with the appropriate skills to that time slot. The analysis unit uses AI to analyze the collected data and provide information for generating optimal shifts. The AI ​​uses machine learning algorithms to learn patterns from past data and predict future demand. For example, if sales tend to surge on specific days or times, it generates information for assigning employees with the appropriate skills to that time slot. Based on the collected data, the analysis unit provides information for generating optimal shifts that consider the balance between employee skills and working hours. This allows the analysis unit to quickly and accurately analyze the collected data and provide information for achieving optimal workforce allocation. Furthermore, the analysis unit can also utilize historical data and statistical information to forecast long-term labor demand and perform trend analysis. For example, based on past sales data, it can predict fluctuations in labor demand during specific seasons or event periods and formulate future shift plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term labor management and anomaly detection, improving the overall reliability and efficiency of the system.

[0032] The generation unit generates the optimal shift based on the analysis results obtained by the analysis unit. The generation unit can automatically generate the optimal shift, for example, using AI. Specifically, it generates the optimal shift considering the balance of employee skills and working hours. For example, if sales surge during a specific time period, it generates a shift that places employees with the appropriate skills during that time. The generation unit uses AI to generate shifts that consider the balance of employee skills and working hours. The AI ​​calculates the optimal shift based on the employee's past work history and skill set. For example, if a particular employee performs well in a specific task, it generates a shift that places that employee in that task. Furthermore, the generation unit can generate flexible shifts that consider employee preferences and constraints. For example, if a particular employee cannot work during a specific time period due to family reasons, it adjusts the shift to accommodate that employee's wishes. The generation unit uses AI to generate shifts that consider the balance of employee skills and working hours. This allows the generation unit to efficiently and effectively generate optimal shifts, improving employee satisfaction and operational efficiency. In addition, the generation unit can update the generated shifts in real time to respond to the latest situations. For example, shifts can be instantly regenerated to respond to sudden absences or changes in workload. This allows the generation unit to always provide the optimal shifts, enabling improved operational efficiency and flexible responses.

[0033] The employee information unit uses conversational AI to understand employees' family circumstances. Specifically, it can converse with employees using conversational AI to understand their family circumstances. The conversational AI uses natural language processing technology to understand employees' family circumstances and individual situations through dialogue. For example, if an employee is unable to work during a specific time due to family circumstances, the unit understands the situation and adjusts the shift accordingly. The employee information unit performs flexible shift management, taking employees' family circumstances into consideration. For example, it adjusts the shift of an employee who cannot work during a specific time due to family circumstances to another time slot. The employee information unit uses conversational AI to understand employees' family circumstances and perform flexible shift management. This enables shift management that takes individual employee circumstances into account, improving employee satisfaction and work efficiency. Furthermore, the employee information unit can understand employees' motivation and stress levels through dialogue and provide appropriate support. For example, if an employee is experiencing stress, the unit can identify the cause and provide appropriate support. This helps maintain employee mental health and improve work efficiency. The employee information unit uses conversational AI to understand employees' family circumstances and individual situations, providing flexible shift management and appropriate support. This can improve employee satisfaction and work efficiency.

[0034] The monitoring department monitors staff performance through voice and image recognition. Specifically, it can monitor staff performance using voice recognition technology. Voice recognition technology analyzes the content and tone of staff conversations to evaluate customer service attitude and communication skills. For example, it analyzes the content of conversations when staff interact with customers to evaluate whether appropriate responses are being made. The monitoring department grasps the status of staff work performance in real time and provides immediate feedback as needed. For example, if there are problems with customer service attitude or work efficiency, it immediately issues instructions for improvement. The monitoring department uses voice recognition technology to monitor staff performance and provides immediate feedback as needed. This can improve staff performance. Furthermore, the monitoring department can use image recognition technology to monitor staff movements and work status. Image recognition technology analyzes camera footage to evaluate staff movements and work status. For example, it monitors whether staff are performing tasks in the correct procedure and immediately points out any problems. This can improve staff work efficiency and quality. The monitoring department combines voice recognition technology and image recognition technology to comprehensively evaluate staff performance and provide immediate feedback as needed. This can improve staff performance and enhance operational efficiency and customer satisfaction. Furthermore, the monitoring department can accumulate staff performance data and use it for long-term evaluation and training planning. This enables improvements in staff skills and operational efficiency.

[0035] The service provider works in conjunction with IoT devices to provide an optimal work environment. For example, the service provider maximizes employee performance by monitoring environmental factors such as temperature, humidity, and lighting, and maintaining them in optimal conditions. The service provider uses IoT devices to monitor various parameters of the work environment in real time and adjust them as needed. For example, the service provider uses a temperature sensor to monitor the temperature of the work environment and maintain it at an optimal temperature. The service provider uses a humidity sensor to monitor the humidity of the work environment and maintain it at an optimal humidity level. The service provider uses a lighting sensor to monitor the lighting of the work environment and maintain it at an optimal lighting level. In this way, the service provider can maximize employee performance by providing an optimal work environment in conjunction with IoT devices. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data acquired from IoT devices into a generating AI and have the generating AI perform the optimization of the work environment.

[0036] The Feedback Department provides immediate feedback based on employee performance. For example, the Feedback Department monitors employees' work performance in real time and provides immediate feedback as needed. The Feedback Department evaluates employee performance and identifies areas for improvement. For example, if there are problems with customer service attitude or work efficiency, the Feedback Department immediately issues instructions for improvement. The Feedback Department evaluates employee performance and provides immediate feedback. For example, the Feedback Department monitors employees' work performance in real time and provides immediate feedback as needed. This allows the Feedback Department to quickly improve operations by providing immediate feedback based on employee performance. Some or all of the above processes in the Feedback Department may be performed using AI, or not. For example, the Feedback Department can input employee performance data into a generating AI and have the generating AI generate the feedback content.

[0037] The Management Department implements flexible shift management that takes into account the individual circumstances of each employee. For example, the Management Department implements flexible shift management that takes into account the employee's family environment and health condition. The Management Department adjusts shifts according to the individual circumstances of each employee. For example, the Management Department adjusts shifts for employees who cannot work during certain hours due to family reasons. The Management Department adjusts shifts that take into account the employee's health condition. For example, the Management Department provides less strenuous shifts for employees who are not in good health. In this way, the Management Department can improve the ease of working for employees by implementing flexible shift management that takes into account the individual circumstances of each employee. Some or all of the above processes in the Management Department may be performed using AI, for example, or not using AI. For example, the Management Department can input employee family environment data into a generating AI and have the generating AI perform the shift adjustments.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history. The data collection unit can analyze past data collection history and identify areas for improvement in the collection method. The data collection unit can customize the collection method based on past data collection history. In this way, the data collection unit can select the optimal collection method by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can change the type of data it collects depending on specific events or seasons. For example, the data collection unit can prioritize collecting relevant data during specific events. The data collection unit can change the type of data required depending on the season. The data collection unit can adjust the frequency of data collection depending on events or seasons. In this way, the data collection unit can efficiently collect the necessary data by changing the type of data collected depending on specific events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input event and season-related data into a generating AI and have the generating AI perform the change in the type of data to be collected.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on geographical location information. The data collection unit optimizes the scope of data collection by considering geographical location information. The data collection unit selects the types of data to collect based on geographical location information. As a result, the data collection unit can efficiently collect data by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the selection of highly relevant data.

[0041] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit analyzes social media activity and collects relevant data. Based on social media activity, the data collection unit determines the priority of data collection. The data collection unit selects the types of data to collect, taking social media activity into consideration. This allows the data collection unit to efficiently collect the necessary data by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. 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 data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit performs a simplified analysis on data with low importance. The analysis unit optimizes the resources used for the analysis according to the importance of the data. This allows the analysis unit to perform analysis efficiently by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit selects the optimal analysis algorithm according to the data category. The analysis unit applies different analysis methods for each data category. The analysis unit adjusts the level of detail of the analysis based on the data category. In this way, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms according to the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0044] The analysis unit can adjust the order of analysis based on the data collection timing during analysis. For example, the analysis unit optimizes the order of analysis based on the data collection timing. The analysis unit determines the priority of analysis considering the data collection timing. The analysis unit adjusts the level of detail of the analysis according to the data collection timing. In this way, the analysis unit can perform analysis efficiently by adjusting the order of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing to a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The analysis unit can improve the accuracy of the analysis by referring to relevant external data during the analysis. For example, the analysis unit improves the accuracy of the analysis by referring to relevant external data. The analysis unit supplements the analysis results based on the external data. The analysis unit adjusts the level of detail of the analysis by utilizing the external data. In this way, the analysis unit can obtain more accurate analysis results by improving the accuracy of the analysis by referring to relevant external data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input external data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0046] The generation unit can adjust the level of detail of shifts based on the employee's skill set when generating shifts. For example, the generation unit generates the optimal shifts based on the employee's skill set. The generation unit adjusts the level of detail of shifts, taking the skill set into consideration. The generation unit determines the priority of shifts according to the skill set. In this way, the generation unit can generate the optimal shifts by adjusting the level of detail of shifts based on the employee's skill set. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the employee's skill set into a generation AI and have the generation AI perform the adjustment of the level of detail of shifts.

[0047] The generation unit can generate the optimal shifts by referring to the employee's past shift history when generating shifts. For example, the generation unit generates the optimal shifts based on past shift history. The generation unit adjusts the level of detail of the shifts by referring to the shift history. The generation unit determines the priority of the shifts by considering the shift history. In this way, the generation unit can generate the optimal shifts by referring to the employee's past shift history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past shift history into a generation AI and have the generation AI perform the generation of the optimal shifts.

[0048] The generation unit can generate optimal shifts by considering the geographical location information of employees during shift generation. For example, the generation unit generates optimal shifts based on geographical location information. The generation unit adjusts the level of detail of the shifts considering geographical location information. The generation unit determines the priority of the shifts according to the geographical location information. As a result, the generation unit enables efficient shift management by generating optimal shifts while considering the geographical location information of employees. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI perform the generation of optimal shifts.

[0049] The generation unit can analyze employees' social media activity and adjust shifts when generating them. For example, the generation unit analyzes social media activity to generate the optimal shifts. The generation unit adjusts the level of detail of the shifts based on social media activity. The generation unit determines the priority of shifts, taking social media activity into consideration. This allows the generation unit to analyze employees' social media activity and adjust shifts accordingly, enabling flexible shift management tailored to each employee's situation. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input social media activity data into a generation AI and have the generation AI perform the shift adjustments.

[0050] The information gathering unit can select the optimal method of gathering information by referring to the employee's past family circumstances when gathering information about their family environment. For example, the information gathering unit selects the optimal method of gathering information based on past family circumstances. The information gathering unit adjusts the level of detail of the gathering method by referring to the family circumstances. The information gathering unit determines the priority of the gathering method by considering the family circumstances. In this way, the information gathering unit can select the optimal method of gathering information by referring to the employee's past family circumstances. Some or all of the above processes in the information gathering unit may be performed using AI, for example, or without using AI. For example, the information gathering unit can input past family circumstances data into a generating AI and have the generating AI perform the selection of the optimal gathering method.

[0051] The information gathering unit can customize the means of gathering information based on the employee's living situation when gathering information about their home environment. For example, the information gathering unit selects the optimal means of gathering information based on the employee's living situation. The information gathering unit adjusts the level of detail of the means of gathering information, taking into account the living situation. The information gathering unit determines the priority of the means of gathering information according to the living situation. In this way, the information gathering unit can customize the means of gathering information based on the employee's living situation, enabling flexible gathering of information according to the employee's situation. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without using AI. For example, the information gathering unit can input employee living situation data into a generating AI and have the generating AI perform the customization of the means of gathering information.

[0052] The information gathering unit can select the optimal method for gathering information about an employee's home environment, taking into account the employee's geographical location. For example, the information gathering unit selects the optimal method based on geographical location information. The information gathering unit adjusts the level of detail of the information gathering method, taking geographical location information into consideration. The information gathering unit determines the priority of the information gathering methods according to the geographical location information. In this way, the information gathering unit can efficiently gather information about an employee's home environment by selecting the optimal method, taking into account the employee's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input geographical location information into a generating AI and have the generating AI select the optimal method for gathering information.

[0053] The information gathering unit can understand an employee's home environment by analyzing their social media activity. For example, the information gathering unit analyzes social media activity to understand the home environment. Based on the social media activity, the information gathering unit adjusts the level of detail in the home environment analysis. The information gathering unit determines the priority of the home environment analysis by considering social media activity. This allows the information gathering unit to analyze an employee's social media activity to understand their home environment, enabling flexible analysis tailored to the employee's situation. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input social media activity data into a generating AI and have the generating AI perform the home environment analysis.

[0054] The monitoring unit can select the optimal monitoring method by referring to the employee's past performance data during performance monitoring. For example, the monitoring unit selects the optimal monitoring method based on past performance data. The monitoring unit adjusts the level of detail of the monitoring method by referring to the performance data. The monitoring unit determines the priority of the monitoring method by considering the performance data. In this way, the monitoring unit can select the optimal monitoring method by referring to the employee's past performance data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input past performance data into a generating AI and have the generating AI perform the selection of the optimal monitoring method.

[0055] The monitoring unit can adjust the level of detail of monitoring based on the employee's skill set when monitoring performance. For example, the monitoring unit selects the optimal monitoring method based on the skill set. The monitoring unit adjusts the level of detail of monitoring, taking the skill set into consideration. The monitoring unit determines the monitoring priority according to the skill set. This allows the monitoring unit to efficiently monitor performance by adjusting the level of detail of monitoring based on the employee's skill set. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the employee's skill set into a generating AI and have the generating AI perform the adjustment of the level of detail of monitoring.

[0056] The monitoring unit can select the optimal monitoring method when monitoring performance, taking into account the geographical location information of employees. For example, the monitoring unit selects the optimal monitoring method based on geographical location information. The monitoring unit adjusts the level of detail of the monitoring method, taking geographical location information into consideration. The monitoring unit determines the priority of the monitoring method according to the geographical location information. This allows the monitoring unit to efficiently monitor performance by selecting the optimal monitoring method, taking into account the geographical location information of employees. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical location information into a generating AI and have the generating AI select the optimal monitoring method.

[0057] The monitoring unit can analyze employees' social media activity to monitor performance. For example, the monitoring unit analyzes social media activity to monitor performance. The monitoring unit adjusts the level of detail of monitoring based on social media activity. The monitoring unit determines monitoring priorities considering social media activity. This allows the monitoring unit to perform flexible monitoring tailored to the employee's situation by analyzing employees' social media activity to monitor performance. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input social media activity data into a generating AI and have the generating AI perform performance monitoring.

[0058] The service provider can select the optimal service provision method by referring to the employee's past work environment data when providing the work environment. For example, the service provider selects the optimal service provision method based on past work environment data. The service provider adjusts the level of detail of the service provision method by referring to the work environment data. The service provider determines the priority of the service provision method by considering the work environment data. In this way, the service provider can select the optimal service provision method by referring to the employee's past work environment data. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past work environment data into a generating AI and have the generating AI perform the selection of the optimal service provision method.

[0059] The service provider can select the optimal service provision method when providing the work environment, taking into account the geographical location information of the employees. For example, the service provider selects the optimal service provision method based on geographical location information. The service provider adjusts the level of detail of the service provision method, taking geographical location information into consideration. The service provider determines the priority of the service provision method according to the geographical location information. In this way, the service provider can efficiently provide the work environment by selecting the optimal service provision method, taking into account the geographical location information of the employees. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal service provision method.

[0060] The feedback unit can select the optimal feedback method by referring to the employee's past feedback history when providing feedback. For example, the feedback unit selects the optimal feedback method based on the past feedback history. The feedback unit adjusts the level of detail of the feedback method by referring to the feedback history. The feedback unit determines the priority of the feedback method by considering the feedback history. In this way, the feedback unit can select the optimal feedback method by referring to the employee's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input past feedback history into a generating AI and have the generating AI perform the selection of the optimal feedback method.

[0061] The feedback unit can select the optimal feedback method when providing feedback, taking into account the employee's geographical location information. For example, the feedback unit selects the optimal feedback method based on geographical location information. The feedback unit adjusts the level of detail of the feedback method, taking geographical location information into consideration. The feedback unit determines the priority of the feedback method according to the geographical location information. This allows the feedback unit to efficiently provide feedback by selecting the optimal feedback method, taking into account the employee's geographical location information. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0062] The management department can select the optimal management method by referring to an employee's past shift history when managing shifts. For example, the management department selects the optimal management method based on past shift history. The management department adjusts the level of detail of the management method by referring to the shift history. The management department determines the priority of the management method by considering the shift history. In this way, the management department can select the optimal management method by referring to an employee's past shift history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past shift history into a generating AI and have the generating AI perform the selection of the optimal management method.

[0063] The management department can select the optimal management method when managing shifts, taking into account the geographical location information of employees. For example, the management department selects the optimal management method based on geographical location information. The management department adjusts the level of detail of the management method, taking geographical location information into consideration. The management department determines the priority of the management method according to the geographical location information. In this way, the management department can efficiently manage shifts by selecting the optimal management method, taking into account the geographical location information of employees. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal management method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The analytics unit can collect employee health data and adjust shifts based on their health status. For example, it can collect employee sleep data and assign less demanding shifts to employees who are sleep-deprived. It can also collect employee exercise data and provide shifts with adequate rest to employees who are not physically active. Furthermore, it can collect employee dietary data and adjust shifts to provide healthier meals to employees with unbalanced diets. In this way, the analytics unit can maintain employee health and improve performance by adjusting shifts based on employees' health status.

[0066] The management department can adjust shifts to take employees' career paths into consideration. For example, the management department can provide shifts to help employees develop their skills based on their career goals. It can also provide shifts to allow employees to gain experience in different tasks based on their career goals. Furthermore, it can provide shifts to help employees develop their leadership skills based on their career goals. By adjusting shifts to consider employees' career paths, the management department can support their career growth and improve their motivation.

[0067] The recruitment department can understand employees' hobbies and interests and use this information to adjust shifts. For example, it can assign tasks related to employees' hobbies based on their interests. It can also offer tasks that are interesting to employees based on their interests. Furthermore, it can plan team-building activities based on employees' hobbies and interests. In this way, the recruitment department can improve employee motivation by understanding employees' hobbies and interests and using this information to adjust shifts.

[0068] The generation unit can generate shifts for skill development, taking into account employees' skill sets. For example, it can assign skill development tasks based on an employee's current skill level. It can also provide skill development training based on an employee's skill set. Furthermore, it can assign skill development projects based on an employee's skill set. This allows the generation unit to support employee growth and improve motivation by generating skill development shifts that take employee skill sets into account.

[0069] The monitoring department can analyze employee performance data and provide advice for performance improvement. For example, it can analyze employee work efficiency and provide specific advice to improve it. It can also analyze employee customer service attitudes and provide specific advice to improve customer service skills. Furthermore, it can analyze employee work errors and provide specific advice to reduce errors. In this way, the monitoring department can improve employee performance by analyzing employee performance data and providing advice for performance improvement.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects employee attendance data, sales data, and weather and event information. For example, it collects attendance data such as employee arrival time, departure time, and break time; sales data such as sales amount, sales quantity, and product category; and weather and event information such as temperature, precipitation, and the type and date of the event. The data collection unit records and stores this data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the collected data in real time and generate information to ensure the optimal workforce and skill sets. If sales surge during a specific time period, the analysis unit generates information to allocate employees with the appropriate skills during that time. Step 3: The generation unit generates the optimal shift based on the analysis results obtained by the analysis unit. For example, it uses AI to automatically generate the optimal shift that takes into account the balance of employee skills and working hours. If sales surge during a specific time period, the generation unit generates a shift that places employees with the appropriate skills during that time period. Step 4: The understanding unit uses conversational AI to understand the employees' home circumstances. For example, by interacting with employees, it understands their home circumstances and adjusts the shifts of employees who cannot work during certain times due to family reasons. Step 5: The monitoring unit monitors staff performance through voice and image recognition. For example, it uses voice recognition technology to understand staff work status in real time and provides immediate feedback as needed. If there are problems with customer service attitude or work efficiency, it immediately issues instructions for improvement.

[0072] (Example of form 2) The staffing AI agent according to an embodiment of the present invention is a system that analyzes employee attendance data, sales data, weather and event information in real time to ensure the optimal workforce and skill set. This staffing AI agent uses conversational AI to understand the home environment of part-time employees and generates shifts while consulting with them individually. Furthermore, it monitors staff performance through voice and image recognition, and works in conjunction with IoT devices to provide an optimal work environment and improve operational efficiency. First, the staffing AI agent collects employee attendance data, sales data, weather and event information. This data is analyzed in real time by AI and used as information to ensure the optimal workforce and skill set. For example, if sales surge during a specific time period, employees with the appropriate skills can be assigned to that time period. Next, the staffing AI agent uses conversational AI to understand the home environment of part-time employees. This enables flexible shift management that takes into account the individual circumstances of employees. For example, if an employee cannot work during a specific time period due to family reasons, their shift can be adjusted to another time period. Furthermore, the staffing AI agent monitors staff performance through voice and image recognition. This allows for real-time monitoring of employee performance and immediate feedback as needed. For example, if there are problems with customer service or work efficiency, improvement instructions can be issued immediately. The staffing AI agent also works in conjunction with IoT devices to provide an optimal work environment. For instance, it monitors environmental factors such as temperature, humidity, and lighting, and maintains them in optimal conditions to maximize employee performance. In this way, the staffing AI agent is a system that improves operational efficiency by analyzing employee attendance data, sales data, weather and event information in real time, generating shifts through individual consultations using conversational AI, monitoring staff performance through voice and image recognition, and providing an optimal work environment in conjunction with IoT devices.This allows the staffing AI agent to analyze employee attendance data, sales data, weather and event information in real time, enabling it to secure the optimal workforce and skill set.

[0073] The staffing AI agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a recognition unit, and a monitoring unit. The collection unit collects employee attendance data, sales data, and weather and event information. For example, the collection unit can collect attendance data such as employee arrival time, departure time, and break time. The collection unit can also collect sales data such as sales amount, sales quantity, and product category. Furthermore, the collection unit can collect weather and event information such as temperature, precipitation, event type, and date and time. For example, the collection unit records employee arrival times in real time and saves them as attendance data. The collection unit obtains sales data from a POS system and records sales amount and sales quantity. The collection unit obtains weather information from a weather database and records temperature and precipitation. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data in real time using AI. Based on the collected data, the analysis unit generates information to ensure the optimal workforce and skill set. For example, the analysis unit generates information to assign employees with the appropriate skills to a specific time slot if sales surge during that time. The analysis unit uses AI to analyze the collected data and provide information to generate the optimal shift. The generation unit generates the optimal shift based on the analysis results obtained by the analysis unit. The generation unit can, for example, use AI to automatically generate the optimal shift. The generation unit generates the optimal shift considering the balance between employees' skills and working hours. For example, if sales surge during a specific time slot, the generation unit generates a shift that assigns employees with the appropriate skills to that time slot. The generation unit uses AI to generate a shift that considers the balance between employees' skills and working hours. The information gathering unit uses conversational AI to understand employees' family circumstances. For example, the information gathering unit can converse with employees using conversational AI to understand their family circumstances. The information gathering unit performs flexible shift management considering employees' family circumstances. For example, the information gathering unit adjusts shifts for employees who cannot work during a specific time slot due to family reasons. The information gathering unit uses conversational AI to understand employees' family circumstances and performs flexible shift management.The monitoring unit monitors staff performance through voice and image recognition. For example, the monitoring unit can monitor staff performance using voice recognition technology. The monitoring unit grasps the status of staff work performance in real time and provides immediate feedback as needed. For example, if there are problems with customer service attitude or work efficiency, the monitoring unit will immediately issue instructions for improvement. The monitoring unit monitors staff performance using voice recognition technology and provides immediate feedback as needed. As a result, the staffing AI agent according to the embodiment can analyze employee attendance data, sales data, weather and event information in real time to ensure the optimal workforce and skill set.

[0074] The data collection unit collects employee attendance data, sales data, and weather and event information. Specifically, it can collect attendance data such as employee clock-in times, clock-out times, and break times. This data is automatically recorded when employees punch their time cards and stored in a cloud-based database. The data collection unit can also collect sales data such as sales amount, sales quantity, and product category. Sales data is obtained in real time from the POS system and used to understand the sales status and sales trends of each product. Furthermore, the data collection unit can collect weather and event information such as temperature, precipitation, event type, and date and time. Weather information is obtained from a weather database via API, and event information is collected from local event calendars and publicly available information on social media. For example, the data collection unit records employee clock-in times in real time and stores them as attendance data. This allows for accurate understanding of employee work status and is useful for shift management. The data collection unit obtains sales data from the POS system and records sales amount and sales quantity. This allows for real-time understanding of sales status and is useful for inventory management and sales strategy planning. The data collection unit retrieves weather information from a meteorological database and records temperature and precipitation. This enables flexible shift management and adjustment of sales strategies in response to weather changes. The data collection unit centrally manages this data and can link with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0075] The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze the collected data in real time. Specifically, it integrates employee attendance data, sales data, weather and event information to generate information for ensuring optimal workforce and skill sets. For example, if sales surge during a specific time period, the analysis unit generates information for assigning employees with the appropriate skills to that time slot. The analysis unit uses AI to analyze the collected data and provide information for generating optimal shifts. The AI ​​uses machine learning algorithms to learn patterns from past data and predict future demand. For example, if sales tend to surge on specific days or times, it generates information for assigning employees with the appropriate skills to that time slot. Based on the collected data, the analysis unit provides information for generating optimal shifts that consider the balance between employee skills and working hours. This allows the analysis unit to quickly and accurately analyze the collected data and provide information for achieving optimal workforce allocation. Furthermore, the analysis unit can also utilize historical data and statistical information to forecast long-term labor demand and perform trend analysis. For example, based on past sales data, it can predict fluctuations in labor demand during specific seasons or event periods and formulate future shift plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term labor management and anomaly detection, improving the overall reliability and efficiency of the system.

[0076] The generation unit generates the optimal shift based on the analysis results obtained by the analysis unit. The generation unit can automatically generate the optimal shift, for example, using AI. Specifically, it generates the optimal shift considering the balance of employee skills and working hours. For example, if sales surge during a specific time period, it generates a shift that places employees with the appropriate skills during that time. The generation unit uses AI to generate shifts that consider the balance of employee skills and working hours. The AI ​​calculates the optimal shift based on the employee's past work history and skill set. For example, if a particular employee performs well in a specific task, it generates a shift that places that employee in that task. Furthermore, the generation unit can generate flexible shifts that consider employee preferences and constraints. For example, if a particular employee cannot work during a specific time period due to family reasons, it adjusts the shift to accommodate that employee's wishes. The generation unit uses AI to generate shifts that consider the balance of employee skills and working hours. This allows the generation unit to efficiently and effectively generate optimal shifts, improving employee satisfaction and operational efficiency. In addition, the generation unit can update the generated shifts in real time to respond to the latest situations. For example, shifts can be instantly regenerated to respond to sudden absences or changes in workload. This allows the generation unit to always provide the optimal shifts, enabling improved operational efficiency and flexible responses.

[0077] The employee information unit uses conversational AI to understand employees' family circumstances. Specifically, it can converse with employees using conversational AI to understand their family circumstances. The conversational AI uses natural language processing technology to understand employees' family circumstances and individual situations through dialogue. For example, if an employee is unable to work during a specific time due to family circumstances, the unit understands the situation and adjusts the shift accordingly. The employee information unit performs flexible shift management, taking employees' family circumstances into consideration. For example, it adjusts the shift of an employee who cannot work during a specific time due to family circumstances to another time slot. The employee information unit uses conversational AI to understand employees' family circumstances and perform flexible shift management. This enables shift management that takes individual employee circumstances into account, improving employee satisfaction and work efficiency. Furthermore, the employee information unit can understand employees' motivation and stress levels through dialogue and provide appropriate support. For example, if an employee is experiencing stress, the unit can identify the cause and provide appropriate support. This helps maintain employee mental health and improve work efficiency. The employee information unit uses conversational AI to understand employees' family circumstances and individual situations, providing flexible shift management and appropriate support. This can improve employee satisfaction and work efficiency.

[0078] The monitoring department monitors staff performance through voice and image recognition. Specifically, it can monitor staff performance using voice recognition technology. Voice recognition technology analyzes the content and tone of staff conversations to evaluate customer service attitude and communication skills. For example, it analyzes the content of conversations when staff interact with customers to evaluate whether appropriate responses are being made. The monitoring department grasps the status of staff work performance in real time and provides immediate feedback as needed. For example, if there are problems with customer service attitude or work efficiency, it immediately issues instructions for improvement. The monitoring department uses voice recognition technology to monitor staff performance and provides immediate feedback as needed. This can improve staff performance. Furthermore, the monitoring department can use image recognition technology to monitor staff movements and work status. Image recognition technology analyzes camera footage to evaluate staff movements and work status. For example, it monitors whether staff are performing tasks in the correct procedure and immediately points out any problems. This can improve staff work efficiency and quality. The monitoring department combines voice recognition technology and image recognition technology to comprehensively evaluate staff performance and provide immediate feedback as needed. This can improve staff performance and enhance operational efficiency and customer satisfaction. Furthermore, the monitoring department can accumulate staff performance data and use it for long-term evaluation and training planning. This enables improvements in staff skills and operational efficiency.

[0079] The service provider works in conjunction with IoT devices to provide an optimal work environment. For example, the service provider maximizes employee performance by monitoring environmental factors such as temperature, humidity, and lighting, and maintaining them in optimal conditions. The service provider uses IoT devices to monitor various parameters of the work environment in real time and adjust them as needed. For example, the service provider uses a temperature sensor to monitor the temperature of the work environment and maintain it at an optimal temperature. The service provider uses a humidity sensor to monitor the humidity of the work environment and maintain it at an optimal humidity level. The service provider uses a lighting sensor to monitor the lighting of the work environment and maintain it at an optimal lighting level. In this way, the service provider can maximize employee performance by providing an optimal work environment in conjunction with IoT devices. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data acquired from IoT devices into a generating AI and have the generating AI perform the optimization of the work environment.

[0080] The Feedback Department provides immediate feedback based on employee performance. For example, the Feedback Department monitors employees' work performance in real time and provides immediate feedback as needed. The Feedback Department evaluates employee performance and identifies areas for improvement. For example, if there are problems with customer service attitude or work efficiency, the Feedback Department immediately issues instructions for improvement. The Feedback Department evaluates employee performance and provides immediate feedback. For example, the Feedback Department monitors employees' work performance in real time and provides immediate feedback as needed. This allows the Feedback Department to quickly improve operations by providing immediate feedback based on employee performance. Some or all of the above processes in the Feedback Department may be performed using AI, or not. For example, the Feedback Department can input employee performance data into a generating AI and have the generating AI generate the feedback content.

[0081] The Management Department implements flexible shift management that takes into account the individual circumstances of each employee. For example, the Management Department implements flexible shift management that takes into account the employee's family environment and health condition. The Management Department adjusts shifts according to the individual circumstances of each employee. For example, the Management Department adjusts shifts for employees who cannot work during certain hours due to family reasons. The Management Department adjusts shifts that take into account the employee's health condition. For example, the Management Department provides less strenuous shifts for employees who are not in good health. In this way, the Management Department can improve the ease of working for employees by implementing flexible shift management that takes into account the individual circumstances of each employee. Some or all of the above processes in the Management Department may be performed using AI, for example, or not using AI. For example, the Management Department can input employee family environment data into a generating AI and have the generating AI perform the shift adjustments.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. If the user is relaxed, the data collection unit can accelerate the collection timing to collect data efficiently. If the user is in a hurry, the data collection unit can optimize the collection timing to collect data quickly. In this way, the data collection unit can reduce the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0083] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can select the most efficient collection method from past data collection history. The data collection unit can analyze past data collection history and identify areas for improvement in the collection method. The data collection unit can customize the collection method based on past data collection history. In this way, the data collection unit can select the optimal collection method by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0084] The data collection unit can change the type of data it collects depending on specific events or seasons. For example, the data collection unit can prioritize collecting relevant data during specific events. The data collection unit can change the type of data required depending on the season. The data collection unit can adjust the frequency of data collection depending on events or seasons. In this way, the data collection unit can efficiently collect the necessary data by changing the type of data collected depending on specific events or seasons. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input event and season-related data into a generating AI and have the generating AI perform the change in the type of data to be collected.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. In this way, the data collection unit can prioritize collecting important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on geographical location information. The data collection unit optimizes the scope of data collection by considering geographical location information. The data collection unit selects the types of data to collect based on geographical location information. As a result, the data collection unit can efficiently collect data by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the selection of highly relevant data.

[0087] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit analyzes social media activity and collects relevant data. Based on social media activity, the data collection unit determines the priority of data collection. The data collection unit selects the types of data to collect, taking social media activity into consideration. This allows the data collection unit to efficiently collect the necessary data by analyzing social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. 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 data.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit will select a simple analysis method. If the user is relaxed, the analysis unit will select a detailed analysis method. If the user is in a hurry, the analysis unit will select a method that allows for rapid analysis. In this way, the analysis unit can reduce the burden on the user by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit performs a simplified analysis on data with low importance. The analysis unit optimizes the resources used for the analysis according to the importance of the data. This allows the analysis unit to perform analysis efficiently by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit selects the optimal analysis algorithm according to the data category. The analysis unit applies different analysis methods for each data category. The analysis unit adjusts the level of detail of the analysis based on the data category. In this way, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms according to the data category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the optimal analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing high-priority data. If the user is relaxed, the analysis unit will prioritize analyzing detailed data. If the user is in a hurry, the analysis unit will prioritize analyzing data that can be analyzed quickly. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the data collection timing during analysis. For example, the analysis unit optimizes the order of analysis based on the data collection timing. The analysis unit determines the priority of analysis considering the data collection timing. The analysis unit adjusts the level of detail of the analysis according to the data collection timing. In this way, the analysis unit can perform analysis efficiently by adjusting the order of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing to a generating AI and have the generating AI perform the adjustment of the analysis order.

[0093] The analysis unit can improve the accuracy of the analysis by referring to relevant external data during the analysis. For example, the analysis unit improves the accuracy of the analysis by referring to relevant external data. The analysis unit supplements the analysis results based on the external data. The analysis unit adjusts the level of detail of the analysis by utilizing the external data. In this way, the analysis unit can obtain more accurate analysis results by improving the accuracy of the analysis by referring to relevant external data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input external data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0094] The generation unit can estimate the user's emotions and adjust the shift generation method based on the estimated user emotions. For example, if the user is stressed, the generation unit will select a simple shift generation method. If the user is relaxed, the generation unit will select a detailed shift generation method. If the user is in a hurry, the generation unit will select a method that can generate shifts quickly. In this way, the generation unit can reduce the user's burden by adjusting the shift generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the shift generation method.

[0095] The generation unit can adjust the level of detail of shifts based on the employee's skill set when generating shifts. For example, the generation unit generates the optimal shifts based on the employee's skill set. The generation unit adjusts the level of detail of shifts, taking the skill set into consideration. The generation unit determines the priority of shifts according to the skill set. In this way, the generation unit can generate the optimal shifts by adjusting the level of detail of shifts based on the employee's skill set. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the employee's skill set into a generation AI and have the generation AI perform the adjustment of the level of detail of shifts.

[0096] The generation unit can generate the optimal shifts by referring to the employee's past shift history when generating shifts. For example, the generation unit generates the optimal shifts based on past shift history. The generation unit adjusts the level of detail of the shifts by referring to the shift history. The generation unit determines the priority of the shifts by considering the shift history. In this way, the generation unit can generate the optimal shifts by referring to the employee's past shift history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past shift history into a generation AI and have the generation AI perform the generation of the optimal shifts.

[0097] The generation unit can estimate the user's emotions and determine shift priorities based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating high-priority shifts. If the user is relaxed, the generation unit will prioritize generating detailed shifts. If the user is in a hurry, the generation unit will prioritize generating shifts that can be generated quickly. In this way, the generation unit can prioritize generating important shifts by determining shift priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine shift priorities.

[0098] The generation unit can generate optimal shifts by considering the geographical location information of employees during shift generation. For example, the generation unit generates optimal shifts based on geographical location information. The generation unit adjusts the level of detail of the shifts considering geographical location information. The generation unit determines the priority of the shifts according to the geographical location information. As a result, the generation unit enables efficient shift management by generating optimal shifts while considering the geographical location information of employees. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input geographical location information into a generation AI and have the generation AI perform the generation of optimal shifts.

[0099] The generation unit can analyze employees' social media activity and adjust shifts when generating them. For example, the generation unit analyzes social media activity to generate the optimal shifts. The generation unit adjusts the level of detail of the shifts based on social media activity. The generation unit determines the priority of shifts, taking social media activity into consideration. This allows the generation unit to analyze employees' social media activity and adjust shifts accordingly, enabling flexible shift management tailored to each employee's situation. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input social media activity data into a generation AI and have the generation AI perform the shift adjustments.

[0100] The sensing unit can estimate the user's emotions and adjust the method of sensing the home environment based on the estimated emotions. For example, if the user is stressed, the sensing unit will select a simple sensing method. If the user is relaxed, the sensing unit will select a detailed sensing method. If the user is in a hurry, the sensing unit will select a method that allows for quick sensing. In this way, the sensing unit can reduce the user's burden by adjusting the method of sensing the home environment according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's emotion data into a generative AI and have the generative AI adjust the method of sensing the home environment.

[0101] The information gathering unit can select the optimal method of gathering information by referring to the employee's past family circumstances when gathering information about their family environment. For example, the information gathering unit selects the optimal method of gathering information based on past family circumstances. The information gathering unit adjusts the level of detail of the gathering method by referring to the family circumstances. The information gathering unit determines the priority of the gathering method by considering the family circumstances. In this way, the information gathering unit can select the optimal method of gathering information by referring to the employee's past family circumstances. Some or all of the above processes in the information gathering unit may be performed using AI, for example, or without using AI. For example, the information gathering unit can input past family circumstances data into a generating AI and have the generating AI perform the selection of the optimal gathering method.

[0102] The information gathering unit can customize the means of gathering information based on the employee's living situation when gathering information about their home environment. For example, the information gathering unit selects the optimal means of gathering information based on the employee's living situation. The information gathering unit adjusts the level of detail of the means of gathering information, taking into account the living situation. The information gathering unit determines the priority of the means of gathering information according to the living situation. In this way, the information gathering unit can customize the means of gathering information based on the employee's living situation, enabling flexible gathering of information according to the employee's situation. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without using AI. For example, the information gathering unit can input employee living situation data into a generating AI and have the generating AI perform the customization of the means of gathering information.

[0103] The sensing unit can estimate the user's emotions and determine the priority of the home environment based on the estimated emotions. For example, if the user is stressed, the sensing unit will prioritize understanding the home environment of high importance. If the user is relaxed, the sensing unit will prioritize understanding the home environment of detail. If the user is in a hurry, the sensing unit will prioritize understanding the home environment that can be quickly understood. In this way, the sensing unit can prioritize understanding important home environments by determining the priority of the home environment according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the user's emotion data into a generative AI and have the generative AI perform the determination of the priority of the home environment.

[0104] The information gathering unit can select the optimal method for gathering information about an employee's home environment, taking into account the employee's geographical location. For example, the information gathering unit selects the optimal method based on geographical location information. The information gathering unit adjusts the level of detail of the information gathering method, taking geographical location information into consideration. The information gathering unit determines the priority of the information gathering methods according to the geographical location information. In this way, the information gathering unit can efficiently gather information about an employee's home environment by selecting the optimal method, taking into account the employee's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input geographical location information into a generating AI and have the generating AI select the optimal method for gathering information.

[0105] The information gathering unit can understand an employee's home environment by analyzing their social media activity. For example, the information gathering unit analyzes social media activity to understand the home environment. Based on the social media activity, the information gathering unit adjusts the level of detail in the home environment analysis. The information gathering unit determines the priority of the home environment analysis by considering social media activity. This allows the information gathering unit to analyze an employee's social media activity to understand their home environment, enabling flexible analysis tailored to the employee's situation. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input social media activity data into a generating AI and have the generating AI perform the home environment analysis.

[0106] The monitoring unit can estimate the user's emotions and adjust the performance monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit will select a simple monitoring method. If the user is relaxed, the monitoring unit will select a detailed monitoring method. If the user is in a hurry, the monitoring unit will select a method that allows for rapid monitoring. In this way, the monitoring unit can reduce the burden on the user by adjusting the performance monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the performance monitoring method.

[0107] The monitoring unit can select the optimal monitoring method by referring to the employee's past performance data during performance monitoring. For example, the monitoring unit selects the optimal monitoring method based on past performance data. The monitoring unit adjusts the level of detail of the monitoring method by referring to the performance data. The monitoring unit determines the priority of the monitoring method by considering the performance data. In this way, the monitoring unit can select the optimal monitoring method by referring to the employee's past performance data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input past performance data into a generating AI and have the generating AI perform the selection of the optimal monitoring method.

[0108] The monitoring unit can adjust the level of detail of monitoring based on the employee's skill set when monitoring performance. For example, the monitoring unit selects the optimal monitoring method based on the skill set. The monitoring unit adjusts the level of detail of monitoring, taking the skill set into consideration. The monitoring unit determines the monitoring priority according to the skill set. This allows the monitoring unit to efficiently monitor performance by adjusting the level of detail of monitoring based on the employee's skill set. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the employee's skill set into a generating AI and have the generating AI perform the adjustment of the level of detail of monitoring.

[0109] The monitoring unit can estimate the user's emotions and determine the priority of performance monitoring based on the estimated user emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring high-priority performance. If the user is relaxed, the monitoring unit will prioritize monitoring detailed performance. If the user is in a hurry, the monitoring unit will prioritize monitoring performance that can be monitored quickly. In this way, the monitoring unit can prioritize monitoring important performance by determining the priority of performance monitoring according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform the determination of performance monitoring priorities.

[0110] The monitoring unit can select the optimal monitoring method when monitoring performance, taking into account the geographical location information of employees. For example, the monitoring unit selects the optimal monitoring method based on geographical location information. The monitoring unit adjusts the level of detail of the monitoring method, taking geographical location information into consideration. The monitoring unit determines the priority of the monitoring method according to the geographical location information. This allows the monitoring unit to efficiently monitor performance by selecting the optimal monitoring method, taking into account the geographical location information of employees. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical location information into a generating AI and have the generating AI select the optimal monitoring method.

[0111] The monitoring unit can analyze employees' social media activity to monitor performance. For example, the monitoring unit analyzes social media activity to monitor performance. The monitoring unit adjusts the level of detail of monitoring based on social media activity. The monitoring unit determines monitoring priorities considering social media activity. This allows the monitoring unit to perform flexible monitoring tailored to the employee's situation by analyzing employees' social media activity to monitor performance. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input social media activity data into a generating AI and have the generating AI perform performance monitoring.

[0112] The service provider can estimate the user's emotions and adjust the way the work environment is provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide a relaxing work environment. If the user is relaxed, the service provider can provide a work environment that allows them to work efficiently. If the user is in a hurry, the service provider can provide a work environment that allows them to work quickly. In this way, the service provider can reduce the burden on the user by adjusting the way the work environment is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the way the work environment is provided.

[0113] The service provider can select the optimal service provision method by referring to the employee's past work environment data when providing the work environment. For example, the service provider selects the optimal service provision method based on past work environment data. The service provider adjusts the level of detail of the service provision method by referring to the work environment data. The service provider determines the priority of the service provision method by considering the work environment data. In this way, the service provider can select the optimal service provision method by referring to the employee's past work environment data. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past work environment data into a generating AI and have the generating AI perform the selection of the optimal service provision method.

[0114] The service provider can estimate the user's emotions and prioritize work environments based on those emotions. For example, if the user is stressed, the service provider will prioritize providing high-priority work environments. If the user is relaxed, the service provider will prioritize providing detailed work environments. If the user is in a hurry, the service provider will prioritize providing work environments that can be provided quickly. In this way, the service provider can prioritize important work environments by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the determination of work environment priorities.

[0115] The service provider can select the optimal service provision method when providing the work environment, taking into account the geographical location information of the employees. For example, the service provider selects the optimal service provision method based on geographical location information. The service provider adjusts the level of detail of the service provision method, taking geographical location information into consideration. The service provider determines the priority of the service provision method according to the geographical location information. In this way, the service provider can efficiently provide the work environment by selecting the optimal service provision method, taking into account the geographical location information of the employees. Some or all of the above processes in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal service provision method.

[0116] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is stressed, the feedback unit will select a simple feedback method. If the user is relaxed, the feedback unit will select a detailed feedback method. If the user is in a hurry, the feedback unit will select a method that allows for quick feedback. In this way, the feedback unit can reduce the burden on the user by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the feedback method.

[0117] The feedback unit can select the optimal feedback method by referring to the employee's past feedback history when providing feedback. For example, the feedback unit selects the optimal feedback method based on the past feedback history. The feedback unit adjusts the level of detail of the feedback method by referring to the feedback history. The feedback unit determines the priority of the feedback method by considering the feedback history. In this way, the feedback unit can select the optimal feedback method by referring to the employee's past feedback history. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input past feedback history into a generating AI and have the generating AI perform the selection of the optimal feedback method.

[0118] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize providing high-importance feedback. If the user is relaxed, the feedback unit will prioritize providing detailed feedback. If the user is in a hurry, the feedback unit will prioritize providing feedback that can be delivered quickly. In this way, the feedback unit can prioritize important feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0119] The feedback unit can select the optimal feedback method when providing feedback, taking into account the employee's geographical location information. For example, the feedback unit selects the optimal feedback method based on geographical location information. The feedback unit adjusts the level of detail of the feedback method, taking geographical location information into consideration. The feedback unit determines the priority of the feedback method according to the geographical location information. This allows the feedback unit to efficiently provide feedback by selecting the optimal feedback method, taking into account the employee's geographical location information. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0120] The management department can estimate the user's emotions and adjust the shift management method based on the estimated emotions. For example, if the user is stressed, the management department will select a simple shift management method. If the user is relaxed, the management department will select a detailed shift management method. If the user is in a hurry, the management department will select a method that allows for quick shift management. In this way, the management department can reduce the burden on the user by adjusting the shift management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user emotion data into a generative AI and have the generative AI perform the adjustment of the shift management method.

[0121] The management department can select the optimal management method by referring to an employee's past shift history when managing shifts. For example, the management department selects the optimal management method based on past shift history. The management department adjusts the level of detail of the management method by referring to the shift history. The management department determines the priority of the management method by considering the shift history. In this way, the management department can select the optimal management method by referring to an employee's past shift history. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input past shift history into a generating AI and have the generating AI perform the selection of the optimal management method.

[0122] The management department can estimate the user's emotions and determine shift management priorities based on the estimated emotions. For example, if the user is stressed, the management department will prioritize high-priority shifts. If the user is relaxed, the management department will prioritize detailed shifts. If the user is in a hurry, the management department will prioritize shifts that can be managed quickly. In this way, the management department can prioritize important shifts by determining shift management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input user emotion data into a generative AI and have the generative AI perform the determination of shift management priorities.

[0123] The management department can select the optimal management method when managing shifts, taking into account the geographical location information of employees. For example, the management department selects the optimal management method based on geographical location information. The management department adjusts the level of detail of the management method, taking geographical location information into consideration. The management department determines the priority of the management method according to the geographical location information. In this way, the management department can efficiently manage shifts by selecting the optimal management method, taking into account the geographical location information of employees. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input geographical location information into a generating AI and have the generating AI perform the selection of the optimal management method.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The analytics unit can collect employee health data and adjust shifts based on their health status. For example, it can collect employee sleep data and assign less demanding shifts to employees who are sleep-deprived. It can also collect employee exercise data and provide shifts with adequate rest to employees who are not physically active. Furthermore, it can collect employee dietary data and adjust shifts to provide healthier meals to employees with unbalanced diets. In this way, the analytics unit can maintain employee health and improve performance by adjusting shifts based on employees' health status.

[0126] The support department can monitor employees' stress levels and provide an environment that reduces stress. For example, it can play relaxing music for employees with high stress levels. It can also provide relaxing scents for employees with high stress levels. Furthermore, it can provide relaxing lighting for employees with high stress levels. In this way, the support department can reduce employee stress and improve performance by monitoring employees' stress levels and providing an environment that reduces stress.

[0127] The feedback department can provide feedback to improve employee motivation. For example, the feedback department can offer specific words of praise for goals achieved by employees. It can also offer special rewards to acknowledge employees' efforts. Furthermore, the feedback department can point out specific areas for improvement and set new goals to encourage employee growth. In this way, the feedback department can improve employee performance by providing feedback that enhances employee motivation.

[0128] The management department can adjust shifts to take employees' career paths into consideration. For example, the management department can provide shifts to help employees develop their skills based on their career goals. It can also provide shifts to allow employees to gain experience in different tasks based on their career goals. Furthermore, it can provide shifts to help employees develop their leadership skills based on their career goals. By adjusting shifts to consider employees' career paths, the management department can support their career growth and improve their motivation.

[0129] The recruitment department can understand employees' hobbies and interests and use this information to adjust shifts. For example, it can assign tasks related to employees' hobbies based on their interests. It can also offer tasks that are interesting to employees based on their interests. Furthermore, it can plan team-building activities based on employees' hobbies and interests. In this way, the recruitment department can improve employee motivation by understanding employees' hobbies and interests and using this information to adjust shifts.

[0130] The analytics unit can estimate an employee's emotions and adjust the content of feedback based on those estimates. For example, if an employee is stressed, the analytics unit can offer words of encouragement. If an employee is relaxed, the analytics unit can point out specific areas for improvement. Furthermore, if an employee is in a hurry, the analytics unit can provide quick feedback. In this way, by adjusting the content of feedback based on an employee's emotions, the analytics unit can reduce employee burden and improve performance.

[0131] The generation unit can generate shifts for skill development, taking into account employees' skill sets. For example, it can assign skill development tasks based on an employee's current skill level. It can also provide skill development training based on an employee's skill set. Furthermore, it can assign skill development projects based on an employee's skill set. This allows the generation unit to support employee growth and improve motivation by generating skill development shifts that take employee skill sets into account.

[0132] The service provider can estimate employees' emotions and adjust the work environment based on those estimates. For example, if an employee is feeling stressed, the service provider can provide a relaxing environment. If an employee is relaxed, the service provider can provide an environment conducive to efficient work. Furthermore, if an employee is in a hurry, the service provider can provide an environment that allows them to work quickly. In this way, by adjusting the work environment based on employees' emotions, the service provider can reduce employee burden and improve performance.

[0133] The monitoring department can analyze employee performance data and provide advice for performance improvement. For example, it can analyze employee work efficiency and provide specific advice to improve it. It can also analyze employee customer service attitudes and provide specific advice to improve customer service skills. Furthermore, it can analyze employee work errors and provide specific advice to reduce errors. In this way, the monitoring department can improve employee performance by analyzing employee performance data and providing advice for performance improvement.

[0134] The management department can estimate employees' emotions and prioritize shifts based on those estimates. For example, if an employee is stressed, the management department can prioritize high-priority shifts. Similarly, if an employee is relaxed, the management department can prioritize detailed shifts. Furthermore, if an employee is in a hurry, the management department can prioritize shifts that can be managed quickly. This allows the management department to prioritize important shifts by determining shift priorities based on employee emotions.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The data collection unit collects employee attendance data, sales data, and weather and event information. For example, it collects attendance data such as employee arrival time, departure time, and break time; sales data such as sales amount, sales quantity, and product category; and weather and event information such as temperature, precipitation, and the type and date of the event. The data collection unit records and stores this data in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the collected data in real time and generate information to ensure the optimal workforce and skill sets. If sales surge during a specific time period, the analysis unit generates information to allocate employees with the appropriate skills during that time. Step 3: The generation unit generates the optimal shift based on the analysis results obtained by the analysis unit. For example, it uses AI to automatically generate the optimal shift that takes into account the balance of employee skills and working hours. If sales surge during a specific time period, the generation unit generates a shift that places employees with the appropriate skills during that time period. Step 4: The understanding unit uses conversational AI to understand the employees' home circumstances. For example, by interacting with employees, it understands their home circumstances and adjusts the shifts of employees who cannot work during certain times due to family reasons. Step 5: The monitoring unit monitors staff performance through voice and image recognition. For example, it uses voice recognition technology to understand staff work status in real time and provides immediate feedback as needed. If there are problems with customer service attitude or work efficiency, it immediately issues instructions for improvement.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, understanding unit, monitoring unit, provision unit, feedback unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects employee attendance data and sales data via the communication I / F 44 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates information to ensure the optimal workforce and skill set based on the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal shift based on the analysis results. The understanding unit is implemented by the control unit 46A of the smart device 14 and uses conversational AI to understand the home environment of employees. The monitoring unit monitors staff performance using the camera 42 and microphone 38B of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The provisioning unit interacts with IoT devices via the communication interface 44 of the smart device 14 to provide an optimal work environment. The feedback unit is implemented by the specific processing unit 290 of the data processing device 12 and provides immediate feedback based on employee performance. The management unit is implemented by the control unit 46A of the smart device 14 and performs flexible shift management that takes into account the individual circumstances of employees. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, understanding unit, monitoring unit, provision unit, feedback unit, and management unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects employee attendance data and sales data via the communication I / F 44 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates information to ensure the optimal workforce and skill set based on the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal shift based on the analysis results. The understanding unit is implemented by the control unit 46A of the smart glasses 214 and uses conversational AI to understand the home environment of employees. The monitoring unit monitors staff performance using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The provisioning unit interacts with IoT devices via the communication I / F 44 of the smart glasses 214 to provide an optimal work environment. The feedback unit is implemented by the specific processing unit 290 of the data processing device 12 and provides immediate feedback based on employee performance. The management unit is implemented by the control unit 46A of the smart glasses 214 and performs flexible shift management that takes into account the individual circumstances of employees. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, understanding unit, monitoring unit, provision unit, feedback unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects employee attendance data and sales data via the communication I / F 44 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates information to ensure the optimal workforce and skill set based on the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal shift based on the analysis results. The understanding unit is implemented by the control unit 46A of the headset terminal 314 and uses conversational AI to understand the home environment of employees. The monitoring unit monitors staff performance using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The provisioning unit interacts with IoT devices via the communication interface 44 of the headset terminal 314 to provide an optimal work environment. The feedback unit is implemented by the specific processing unit 290 of the data processing device 12 and provides immediate feedback based on employee performance. The management unit is implemented by the control unit 46A of the headset terminal 314 and performs flexible shift management that takes into account the individual circumstances of employees. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, grasping unit, monitoring unit, provision unit, feedback unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects employee attendance data and sales data via the robot 414's communication I / F 44 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates information to ensure the optimal workforce and skill set based on the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal shift based on the analysis results. The grasping unit is implemented by the control unit 46A of the robot 414 and uses conversational AI to understand the employees' home environment. The monitoring unit monitors staff performance using the robot 414's camera 42 and microphone 238 and analyzes it using the specific processing unit 290 of the data processing unit 12. The supply unit interacts with IoT devices via the robot 414's communication interface 44 to provide an optimal work environment. The feedback unit is implemented by the specific processing unit 290 of the data processing device 12 and provides immediate feedback based on employee performance. The management unit is implemented by the control unit 46A of the robot 414 and performs flexible shift management that takes into account the individual circumstances of employees. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) The data collection department collects employee attendance data, sales data, weather and event information, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an optimal shift based on the analysis results obtained by the analysis unit, A unit that uses conversational AI to understand the home environment of employees, The monitoring department monitors staff performance through voice and image recognition, Equipped with A system characterized by the following features. (Note 2) It features a service unit that provides an optimal work environment in conjunction with IoT devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a feedback unit that provides immediate feedback based on employee performance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The company has a management department that provides flexible shift management that takes into account the individual circumstances of each employee. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, change the type of data collected depending on specific events or seasons. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we refer to relevant external data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is The system estimates the user's emotions and adjusts the shift generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating shifts, adjust the level of detail based on the employee's skill set. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating shifts, the system references employees' past shift history to create the optimal shifts. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and determines shift priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating shifts, the system takes into account the geographical location of employees to create the optimal shift schedule. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When creating work schedules, we analyze employees' social media activity to adjust the schedules accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 23) The gripping part is, The system estimates the user's emotions and adjusts the method of understanding the home environment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The gripping part is, When assessing an employee's family background, the most appropriate assessment method is selected by referring to the employee's past family circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 25) The gripping part is, When assessing an employee's home environment, customize the assessment methods based on their living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The gripping part is, It estimates the user's emotions and determines the priority of the home environment based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The gripping part is, When assessing an employee's home environment, the most appropriate assessment method should be selected, taking into account the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The gripping part is, When assessing an employee's home environment, analyze their social media activity to understand their family situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, We estimate user sentiment and adjust performance monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, When monitoring performance, the optimal monitoring method is selected by referring to the employee's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned monitoring unit, When monitoring performance, adjust the level of detail based on the employee's skill set. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned monitoring unit, It estimates user sentiment and prioritizes performance monitoring based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned monitoring unit, When monitoring performance, select the optimal monitoring method by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned monitoring unit, During performance monitoring, analyze employees' social media activity to monitor performance. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, The system estimates user emotions and adjusts the way the work environment is provided based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing a work environment, the optimal method of provision is selected by referring to employees' past work environment data. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned supply unit is, It estimates user emotions and determines priorities for the work environment based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When providing a work environment, the optimal method of provision will be selected considering the geographical location information of employees. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned feedback unit is When providing feedback, refer to the employee's past feedback history to select the most appropriate feedback method. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned feedback unit is When providing feedback, select the most appropriate feedback method by considering the employee's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned management department, The system estimates user emotions and adjusts shift management methods based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned management department, When managing shifts, refer to the employee's past shift history to select the most suitable management method. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned management department, The system estimates user emotions and determines shift management priorities based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned management department, When managing shifts, select the optimal management method by considering the geographical location of employees. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects employee attendance data, sales data, weather and event information, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an optimal shift based on the analysis results obtained by the analysis unit, A unit that uses conversational AI to understand the home environment of employees, The monitoring department monitors staff performance through voice and image recognition, Equipped with A system characterized by the following features.

2. It features a service unit that provides an optimal work environment in conjunction with IoT devices. The system according to feature 1.

3. It includes a feedback unit that provides immediate feedback based on employee performance. The system according to feature 1.

4. The company has a management department that provides flexible shift management that takes into account the individual circumstances of each employee. The system according to feature 1.

5. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

7. The aforementioned collection unit is During data collection, change the type of data collected depending on specific events or seasons. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

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