system

Abstract

The system according to the embodiment aims to efficiently create shifts by taking into consideration employees' work preferences, skills, and past work history. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a shift generation unit, and an adjustment unit. The collection unit collects data on employees' work preferences, skills, and past work history. The analysis unit analyzes the data collected by the collection unit. The shift generation unit generates shifts based on the data analyzed by the analysis unit. The adjustment unit regenerates shifts in real time when changes or adjustments to shifts are necessary.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating shifts that take into account employees' work preferences, skills, and past work history was time-consuming and inefficient.

[0005] The system according to the embodiment aims to efficiently create shifts by taking into consideration employees' work preferences, skills, and past work history. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a shift generation unit, and an adjustment unit. The collection unit collects data on employees' work preferences, skills, and past work history. The analysis unit analyzes the data collected by the collection unit. The shift generation unit generates shifts based on the data analyzed by the analysis unit. The adjustment unit regenerates shifts in real time when changes or adjustments to shifts are necessary. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create shifts by taking into account employees' work preferences, skills, and past work history. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A shift scheduling system according to an embodiment of the present invention automatically collects and analyzes data such as employee work preferences, skills, and past work history to generate optimal shifts and, if necessary, regenerate them in real time. The shift scheduling system collects data such as employee work preferences, skills, and past work history, analyzes it using AI, and generates optimal shifts. The generated shifts take employee preferences and skills into full consideration, improving work efficiency. Furthermore, if shift changes or adjustments are necessary, the AI ​​automatically responds and regenerates optimal shifts in real time. For example, the shift scheduling system collects detailed data such as each employee's work preferences, skills, and past work history. For example, information such as the employee's desired working hours, vacation preferences, and whether or not they possess specific skills or qualifications is collected. Next, the AI ​​analyzes the collected data. Based on data such as the employee's work preferences, skills, and past work history, the AI ​​provides basic data for generating optimal shifts. For example, it can assign appropriate shifts to tasks requiring employees with specific skills. Furthermore, a shift generation unit generates optimal shifts based on the analyzed data. The shift generation unit takes employee preferences and skills into full consideration to create shifts that improve work efficiency. For example, appropriate shifts can be assigned to tasks that require employees with specific skills. Finally, if shift changes or adjustments are necessary, the coordination department automatically responds. The coordination department regenerates shifts in real time and provides the optimal shift based on employee preferences and work conditions. For example, in the event of sudden vacation or sick leave, the coordination department automatically regenerates shifts to ensure no disruption to work. This allows the shift creation system to significantly reduce the effort required for shift creation and improve employee satisfaction. As a result, the shift creation system generates optimal shifts based on data such as employee work preferences, skills, and past work history, and can also respond to shift changes and adjustments in real time. For example, by collecting information such as employees' desired working hours, vacation preferences, and whether they have specific skills or qualifications, and analyzing it with AI, it is possible to create shifts that take employee preferences and skills into maximum consideration.In addition, even if an employee suddenly takes a vacation or sick leave, the coordination department can automatically regenerate the shifts to prevent any disruption to business operations. This significantly reduces the effort required for creating shifts and improves employee satisfaction.

[0029] The shift creation system according to the embodiment includes a collection unit, an analysis unit, a shift generation unit, and an adjustment unit. The collection unit collects data on employees' work preferences, skills, and past work histories. Employees' work preferences include, but are not limited to, desired working hours, work days, and work locations. The collection unit collects information on, for example, employees' desired working hours, desired vacation times, and whether or not they possess specific skills or qualifications. The collection unit can also collect employees' past work histories. For example, the collection unit collects information on the number of working days, working hours, and work responsibilities of employees. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit provides basic data for generating optimal shifts based on data such as employees' work preferences, skills, and past work histories. The shift generation unit generates shifts based on the data analyzed by the analysis unit. The shifts include, for example, but are not limited to, information on working hours, break times, and work locations. For example, the shift generation unit creates shifts that take employee preferences and skills into maximum consideration to improve work efficiency. The adjustment unit regenerates shifts in real time when shift changes or adjustments are necessary. Adjustments are made, for example, in the event of sudden vacation or sick leave, but are not limited to such examples. For example, the adjustment unit automatically regenerates shifts in the event of sudden vacation or sick leave, preventing business interruptions. As a result, the shift creation system according to the embodiment generates optimal shifts based on data such as employees' work preferences, skills, and past work history, and can also handle shift changes and adjustments in real time. For example, by collecting information such as employees' desired working hours, vacation requests, and whether they possess specific skills or qualifications, and analyzing it using AI, it is possible to generate shifts that take employee preferences and skills into maximum consideration. Furthermore, even in the event of sudden vacation or sick leave, the adjustment unit automatically regenerates shifts to prevent business interruptions. This significantly reduces the effort required for shift creation and improves employee satisfaction.

[0030] The collection unit can analyze the employee's past work history and select the optimal data collection method. The collection unit analyzes the employee's past work history and selects the optimal data collection method. Optimal data collection methods include, but are not limited to, questionnaires, interviews, sensor data, etc. For example, the collection unit preferentially suggests input methods (voice, text, etc.) that the employee has frequently used in the past. The collection unit can also obtain the employee's work preferences for specific time periods from the employee's past work history. The collection unit can also select the optimal data collection method based on the employee's past work history. This allows for efficient data collection by selecting the optimal data collection method based on the employee's past work history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's past work history data into the generation AI and have the generation AI select the optimal data collection method.

[0031] When collecting work preferences, the collection unit may filter them based on the employee's current project or area of ​​interest. When collecting work preferences, the collection unit may filter them based on the employee's current project or area of ​​interest. Examples of current projects include, but are not limited to, the project's name, purpose, and progress. Examples of areas of interest include, but are not limited to, technical fields, work fields, and hobbies. For example, the collection unit may preferentially collect work preferences related to projects currently being undertaken by the employee. The collection unit may also filter related work preferences based on the employee's area of ​​interest. The collection unit may also collect work preferences taking into account the progress of the employee's current project. In this way, highly relevant data can be collected by filtering based on the employee's current project or area of ​​interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input the employee's project data and area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0032] The collection unit can select the optimal collection means depending on the employee's input method when collecting work preferences. The collection unit selects the optimal collection means depending on the employee's input method when collecting work preferences. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the employee prefers voice input, the collection unit can prioritize voice input. Furthermore, if the employee prefers text input, the collection unit can prioritize text input. Furthermore, if the employee prefers image input, the collection unit can prioritize image input. This allows efficient data collection by selecting the optimal collection means depending on the employee's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's input data into a generation AI and have the generation AI select the optimal collection means.

[0033] When collecting work preferences, the collection unit can prioritize collecting highly relevant data by taking into account the employee's geographical location information. When collecting work preferences, the collection unit prioritizes collecting highly relevant data by taking into account the employee's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location sensors, etc. For example, if an employee is in a specific area, the collection unit prioritizes collecting work preferences related to that area. The collection unit can also suggest optimal work preferences based on the employee's geographical location information. The collection unit can also collect relevant data based on the employee's current location. This allows for efficient data collection by prioritizing the collection of highly relevant data by taking into account the employee's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's geographical location data into the generation AI and cause the generation AI to collect highly relevant data.

[0034] The collection unit may analyze the employee's social media activity and collect related data when collecting the work preferences. The collection unit may analyze the employee's social media activity and collect related data when collecting the work preferences. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit may collect work preferences related to locations where the employee checked in on social media. The collection unit may also analyze the content of the employee's social media posts to collect related work preferences. The collection unit may also collect related work preferences based on the activities of the employee's friends on social media. In this way, highly relevant data can be collected by analyzing the employee's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the employee's social media data into the generation AI and cause the generation AI to collect related data.

[0035] The collection unit can customize the collection method by reflecting employees' past feedback when collecting work preferences. The collection unit customizes the collection method by reflecting employees' past feedback when collecting work preferences. Past feedback includes, but is not limited to, questionnaire results, interview records, and evaluation comments. For example, the collection unit proposes an optimal collection method based on feedback provided by employees in the past. The collection unit can also customize the collection method from employees' past feedback. The collection unit can also improve the collection method by reflecting employees' past feedback. In this way, the optimal collection method can be proposed by reflecting employees' past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employees' past feedback data into a generation AI and cause the generation AI to customize the collection method.

[0036] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of data includes, but is not limited to, for example, the impact on business operations and the reliability of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms depending on the data category during analysis. The analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, text data, numerical data, and image data. Analysis algorithms include, but are not limited to, regression analysis, clustering, and deep learning. For example, the analysis unit applies a specific analysis algorithm to work history data. The analysis unit can also apply a different analysis algorithm to skill data. The analysis unit can also select an optimal analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. During analysis, the analysis unit improves the accuracy of the analysis by referring to the employee's past analysis results. Past analysis results include, but are not limited to, past reports, analysis logs, and evaluation results. For example, the analysis unit improves the accuracy of the analysis based on the employee's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also improve the accuracy of the analysis by reflecting the employee's past analysis results. In this way, the accuracy of the analysis is improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit can determine the analysis priority based on the time of data submission during analysis. The data submission time includes, but is not limited to, for example, a submission deadline, a submission frequency, and a submission timing. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also analyze older data later. The analysis unit can also determine the analysis priority based on the time of data submission. This allows for efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. Data relevance includes, but is not limited to, correlation, causal relationship, and co-occurrence relationship, for example. For example, the analysis unit prioritizes analysis of highly related data. The analysis unit can also postpone analysis of less related data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] The analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise during the analysis. The analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise during the analysis. Examples of technical terminology include, but are not limited to, qualifications, years of experience, and test results. Examples of technical terminology include, but are not limited to, technical terms, industry jargon, and academic terms. For example, the analysis unit uses a lot of technical terminology for employees with high levels of expertise. The analysis unit can also avoid technical terminology for employees with low levels of expertise. The analysis unit can also adjust the use of technical terminology in the analysis according to the employee's level of expertise. By adjusting the use of technical terminology in the analysis according to the employee's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] The shift generation unit can analyze an employee's past work history to select an optimal shift generation method when generating shifts. The shift generation unit analyzes an employee's past work history to select an optimal shift generation method when generating shifts. Past work history includes, but is not limited to, the number of working days, working hours, and assigned tasks. Optimal shift generation methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, the shift generation unit selects an optimal shift generation method based on an employee's past work history. The shift generation unit can also reflect work preferences for specific time periods based on the past work history. The shift generation unit can also analyze an employee's past work history to select an optimal shift generation method. This allows for efficient shift generation by selecting an optimal shift generation method based on the employee's past work history. Some or all of the above-described processing in the shift generation unit may be performed using, or without, AI. For example, the shift generation unit can input an employee's past work history data into a generation AI and have the generation AI select an optimal shift generation method.

[0043] The shift generation unit can customize the shift generation means based on the employee's current living situation when generating shifts. The shift generation unit customizes the shift generation means based on the employee's current living situation when generating shifts. Current living situations include, but are not limited to, for example, home environment, health status, and commute time. Shift generation means include, but are not limited to, algorithms, rule-based methods, heuristics, and the like. For example, the shift generation unit customizes the shift generation means taking into account the employee's current living situation. The shift generation unit can also propose an optimal shift generation method based on the employee's living situation. The shift generation unit can also customize the shift generation means to reflect the employee's current living situation. This allows for more appropriate shifts to be generated by customizing the shift generation means based on the employee's current living situation. Some or all of the above-described processing in the shift generation unit may be performed using, or without, AI. For example, the shift generation unit can input employee living situation data into the generation AI and have the generation AI customize the shift generation means.

[0044] The shift generation unit can improve the shift generation method by reflecting employee feedback when generating shifts. The shift generation unit improves the shift generation method by reflecting employee feedback when generating shifts. Feedback includes, but is not limited to, survey results, interview records, and evaluation comments. Shift generation methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, the shift generation unit improves the shift generation method based on employee feedback. The shift generation unit can also improve the shift generation means by reflecting employee feedback. The shift generation unit can also customize the shift generation method by reflecting employee feedback. In this way, the shift generation method can be improved by reflecting employee feedback. Some or all of the above-described processing in the shift generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the shift generation unit can input employee feedback data into the generation AI and cause the generation AI to improve the shift generation method.

[0045] The shift generation unit can select the optimal shift generation method by taking into account the geographic location information of employees when generating shifts. The shift generation unit selects the optimal shift generation method by taking into account the geographic location information of employees when generating shifts. Examples of geographic location information include, but are not limited to, GPS data, address information, and location sensors. Examples of optimal shift generation methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, if an employee is in a specific area, the shift generation unit prioritizes generating shifts related to that area. The shift generation unit can also suggest the optimal shift generation method based on the geographic location information of employees. The shift generation unit can also generate related shifts based on the employee's current location. This allows the optimal shift generation method to be selected by taking into account the geographic location information of employees. Some or all of the above-described processing in the shift generation unit may be performed using, for example, AI, or without AI. For example, the shift generation unit can input the employee's geographic location data into the generation AI and have the generation AI select the optimal shift generation method.

[0046] The shift generation unit can analyze employees' social media activities to suggest shift generation methods when generating shifts. The shift generation unit can analyze employees' social media activities to suggest shift generation methods when generating shifts. Social media activities include, but are not limited to, examples of posts, number of likes, and number of followers. Shift generation methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, the shift generation unit generates shifts related to locations where employees have checked in on social media. The shift generation unit can also generate related shifts by analyzing the content of employees' social media posts. The shift generation unit can also generate related shifts based on the activities of the employees' friends on social media. In this way, related shifts can be generated by analyzing employees' social media activities. Some or all of the above-described processing in the shift generation unit can be performed using, for example, AI, or without AI. For example, the shift generation unit can input employees' social media data into a generation AI and have the generation AI suggest shift generation methods.

[0047] The shift generation unit can customize the shift generation method by reflecting past employee feedback when generating shifts. The shift generation unit customizes the shift generation method by reflecting past employee feedback when generating shifts. Past feedback includes, but is not limited to, examples of questionnaire results, interview records, and evaluation comments. Shift generation methods include, but are not limited to, examples of algorithms, rule-based methods, and heuristic methods. For example, the shift generation unit customizes the shift generation method based on past employee feedback. The shift generation unit can also improve the shift generation means by reflecting past employee feedback. The shift generation unit can also customize the shift generation method by reflecting past employee feedback. In this way, the shift generation method can be customized by reflecting past employee feedback. Some or all of the above-described processing in the shift generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the shift generation unit can input past employee feedback data into the generation AI and cause the generation AI to customize the shift generation method.

[0048] The adjustment unit can analyze an employee's past work history to select an optimal shift adjustment method when adjusting shifts. The adjustment unit analyzes an employee's past work history to select an optimal shift adjustment method when adjusting shifts. Past work history includes, but is not limited to, the number of working days, working hours, and assigned tasks. Optimal shift adjustment methods include, but are not limited to, algorithms, rule-based methods, heuristic methods, and the like. For example, the adjustment unit selects an optimal shift adjustment method based on an employee's past work history. The adjustment unit can also reflect work preferences for specific time periods based on the past work history. The adjustment unit can also analyze an employee's past work history to select an optimal shift adjustment method. This allows for efficient shift adjustment by selecting an optimal shift adjustment method based on the employee's past work history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input an employee's past work history data into a generation AI and cause the generation AI to select an optimal shift adjustment method.

[0049] The adjustment unit can customize the shift adjustment method based on the employee's current living situation when adjusting shifts. The adjustment unit customizes the shift adjustment method based on the employee's current living situation when adjusting shifts. The current living situation includes, but is not limited to, for example, home environment, health status, and commute time. The shift adjustment method includes, but is not limited to, algorithms, rule-based methods, heuristics, and the like. For example, the adjustment unit customizes the shift adjustment method taking into account the employee's current living situation. The adjustment unit can also propose an optimal shift adjustment method based on the employee's living situation. The adjustment unit can also customize the shift adjustment method to reflect the employee's current living situation. This enables more appropriate shift adjustment by customizing the shift adjustment method based on the employee's current living situation. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the employee's living situation data into the generation AI and cause the generation AI to customize the shift adjustment method.

[0050] The adjustment unit can improve the shift adjustment method by reflecting employee feedback during shift adjustment. The adjustment unit improves the shift adjustment method by reflecting employee feedback during shift adjustment. Examples of feedback include, but are not limited to, survey results, interview records, and evaluation comments. Examples of shift adjustment methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, the adjustment unit improves the shift adjustment method based on employee feedback. The adjustment unit can also improve the shift adjustment means by reflecting employee feedback. The adjustment unit can also customize the shift adjustment method by reflecting employee feedback. In this way, the shift adjustment method can be improved by reflecting employee feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input employee feedback data into a generation AI and cause the generation AI to improve the shift adjustment method.

[0051] The adjustment unit can select an optimal shift adjustment method by taking into account the employee's geographical location information when adjusting shifts. The adjustment unit selects an optimal shift adjustment method by taking into account the employee's geographical location information when adjusting shifts. Examples of geographical location information include, but are not limited to, GPS data, address information, and location sensors. Examples of optimal shift adjustment methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, if an employee is in a specific area, the adjustment unit prioritizes adjusting shifts related to that area. The adjustment unit can also propose an optimal shift adjustment method based on the employee's geographical location information. The adjustment unit can also adjust related shifts based on the employee's current location. This allows the optimal shift adjustment method to be selected by taking into account the employee's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the employee's geographical location data into a generation AI and cause the generation AI to select an optimal shift adjustment method.

[0052] The adjustment unit can analyze the employee's social media activity to suggest a shift adjustment method when adjusting shifts. The adjustment unit can analyze the employee's social media activity to suggest a shift adjustment method when adjusting shifts. Social media activity includes, but is not limited to, examples of posts, number of likes, number of followers, etc. Shift adjustment methods include, but are not limited to, examples of algorithms, rule-based methods, heuristic methods, etc. For example, the adjustment unit adjusts shifts related to locations where the employee checked in on social media. The adjustment unit can also analyze the employee's social media posts to adjust the related shifts. The adjustment unit can also adjust the related shifts based on the activities of the employee's friends on social media. In this way, the related shifts can be adjusted by analyzing the employee's social media activity. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input the employee's social media data into a generation AI and cause the generation AI to suggest a shift adjustment method.

[0053] The adjustment unit can customize the shift adjustment method by reflecting past employee feedback during shift adjustment. The adjustment unit customizes the shift adjustment method by reflecting past employee feedback during shift adjustment. Past feedback includes, but is not limited to, examples of questionnaire results, interview records, and evaluation comments. Shift adjustment methods include, but are not limited to, examples of algorithms, rule-based methods, and heuristic methods. For example, the adjustment unit customizes the shift adjustment method based on past employee feedback. The adjustment unit can also improve shift adjustment means by reflecting past employee feedback. The adjustment unit can also customize the shift adjustment method by reflecting past employee feedback. In this way, the shift adjustment method can be customized by reflecting past employee feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past employee feedback data into the generation AI and cause the generation AI to customize the shift adjustment method.

[0054] The adjustment unit can automatically regenerate a shift when a sudden vacation or sick leave occurs. The adjustment unit automatically regenerates a shift when a sudden vacation or sick leave occurs. Sudden vacation or sick leave includes, for example, but is not limited to, the type of vacation, the method of proving sick leave, and response procedures. Shift regeneration includes, for example, but is not limited to, the timing of regeneration, the algorithm to be used, and the adjustment method. For example, the adjustment unit automatically regenerates a shift when a sudden vacation or sick leave occurs, thereby preventing business operations from being disrupted. This allows for automatic regeneration of a shift when a sudden vacation or sick leave occurs, thereby preventing business operations from being disrupted. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without AI. For example, the adjustment unit can input data about sudden vacation or sick leave into the generation AI and have the generation AI regenerate the shift.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The shift creation system can also collect employee health data and adjust shifts based on their health status. For example, the collection unit collects employee health data (e.g., heart rate, sleep time, stress level, etc.), and the analysis unit analyzes this data. The shift generation unit can generate shifts that take employees' health status into consideration based on the analysis results. For example, employees with high stress levels can be assigned shifts that include more break time. Also, sleep-deprived employees can be offered shifts that avoid night shifts. In this way, generating shifts that take employees' health status into consideration can improve employee health management and work efficiency.

[0057] The shift creation system can also generate shifts taking into account employees' hobbies and interests. For example, the collection unit collects data on employees' hobbies and interests, and the analysis unit analyzes this data. The shift generation unit can generate shifts that assign related tasks based on employees' hobbies and interests. For example, employees who are interested in technology can be provided with shifts that include many technology-related tasks. Also, employees who enjoy creative activities can be provided with shifts that include design and planning tasks. In this way, generating shifts that take employees' hobbies and interests into consideration can improve employee motivation and work efficiency.

[0058] The shift creation system can also generate shifts taking into account employees' commute times. For example, the collection unit collects data on employees' commute times, and the analysis unit analyzes this data. The shift generation unit can provide shifts that shorten employees' commute times by taking into account their commute times. For example, employees with long commute times can be provided with shifts that allow remote work to shorten their commute times. Employees with short commute times can also be provided with regular shifts. In this way, generating shifts that take employees' commute times into account can reduce the burden on employees and improve work efficiency.

[0059] The shift creation system can also collect training data for employee skill development and reflect it in shifts. For example, the collection unit collects data related to employee skill development (e.g., training attended, qualifications obtained, etc.), and the analysis unit analyzes this data. The shift generation unit can provide shifts that take into account employee skill development. For example, an employee who has acquired a new skill can be provided with a shift that includes work that can utilize that skill. Also, an employee who has acquired a specific qualification can be provided with a shift that includes work that can utilize that qualification. In this way, by generating shifts that take into account employee skill development, it is possible to improve employee growth and work efficiency.

[0060] The shift creation system can also generate shifts taking into account the family structure of employees. For example, the collection unit collects data on the employee's family structure (e.g., whether they have children, whether they need care, etc.), and the analysis unit analyzes this. The shift generation unit can provide shifts that take into account the employee's family structure. For example, employees with children can be provided with shifts that fit their children's school or nursery school schedules. In addition, employees with family members who require care can be provided with shifts that allow them to take time to care for them. In this way, generating shifts that take into account the employee's family structure can improve the balance between their work and personal life.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection department collects data on employees' work preferences, skills, and past work history. Employees' work preferences include desired working hours, work days, and work location. The collection department collects information such as employees' desired working hours, desired vacation time, and whether they have specific skills or qualifications. The department also collects employees' past work history. For example, it collects information such as the number of days an employee works, working hours, and responsibilities. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit provides basic data for generating optimal shifts based on data such as employees' work preferences, skills, and past work history. Step 3: The shift generation unit generates shifts based on the data analyzed by the analysis unit. Shifts include information such as working hours, break times, and work locations. The shift generation unit takes into maximum consideration the wishes and skills of employees to create shifts that improve work efficiency. Step 4: The reconciliation department regenerates shifts in real time when changes or adjustments are required. Adjustments are made in the event of unexpected vacation or sick leave. The reconciliation department automatically regenerates shifts in the event of unexpected vacation or sick leave, ensuring that work is not disrupted.

[0063] (Example 2) A shift scheduling system according to an embodiment of the present invention automatically collects and analyzes data such as employee work preferences, skills, and past work history to generate optimal shifts and, if necessary, regenerate them in real time. The shift scheduling system collects data such as employee work preferences, skills, and past work history, analyzes it using AI, and generates optimal shifts. The generated shifts take employee preferences and skills into full consideration, improving work efficiency. Furthermore, if shift changes or adjustments are necessary, the AI ​​automatically responds and regenerates optimal shifts in real time. For example, the shift scheduling system collects detailed data such as each employee's work preferences, skills, and past work history. For example, information such as the employee's desired working hours, vacation preferences, and whether or not they possess specific skills or qualifications is collected. Next, the AI ​​analyzes the collected data. Based on data such as the employee's work preferences, skills, and past work history, the AI ​​provides basic data for generating optimal shifts. For example, it can assign appropriate shifts to tasks requiring employees with specific skills. Furthermore, a shift generation unit generates optimal shifts based on the analyzed data. The shift generation unit takes employee preferences and skills into full consideration to create shifts that improve work efficiency. For example, appropriate shifts can be assigned to tasks that require employees with specific skills. Finally, if shift changes or adjustments are necessary, the coordination department automatically responds. The coordination department regenerates shifts in real time and provides the optimal shift based on employee preferences and work conditions. For example, in the event of sudden vacation or sick leave, the coordination department automatically regenerates shifts to ensure no disruption to work. This allows the shift creation system to significantly reduce the effort required for shift creation and improve employee satisfaction. As a result, the shift creation system generates optimal shifts based on data such as employee work preferences, skills, and past work history, and can also respond to shift changes and adjustments in real time. For example, by collecting information such as employees' desired working hours, vacation preferences, and whether they have specific skills or qualifications, and analyzing it with AI, it is possible to create shifts that take employee preferences and skills into maximum consideration.In addition, even if an employee suddenly takes a vacation or sick leave, the coordination department can automatically regenerate the shifts to prevent any disruption to business operations. This significantly reduces the effort required for creating shifts and improves employee satisfaction.

[0064] The shift creation system according to the embodiment includes a collection unit, an analysis unit, a shift generation unit, and an adjustment unit. The collection unit collects data on employees' work preferences, skills, and past work histories. Employees' work preferences include, but are not limited to, desired working hours, work days, and work locations. The collection unit collects information on, for example, employees' desired working hours, desired vacation times, and whether or not they possess specific skills or qualifications. The collection unit can also collect employees' past work histories. For example, the collection unit collects information on the number of working days, working hours, and work responsibilities of employees. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit provides basic data for generating optimal shifts based on data such as employees' work preferences, skills, and past work histories. The shift generation unit generates shifts based on the data analyzed by the analysis unit. The shifts include, for example, but are not limited to, information on working hours, break times, and work locations. For example, the shift generation unit creates shifts that take employee preferences and skills into maximum consideration to improve work efficiency. The adjustment unit regenerates shifts in real time when shift changes or adjustments are necessary. Adjustments are made, for example, in the event of sudden vacation or sick leave, but are not limited to such examples. For example, the adjustment unit automatically regenerates shifts in the event of sudden vacation or sick leave, preventing business interruptions. As a result, the shift creation system according to the embodiment generates optimal shifts based on data such as employees' work preferences, skills, and past work history, and can also handle shift changes and adjustments in real time. For example, by collecting information such as employees' desired working hours, vacation requests, and whether they possess specific skills or qualifications, and analyzing it using AI, it is possible to generate shifts that take employee preferences and skills into maximum consideration. Furthermore, even in the event of sudden vacation or sick leave, the adjustment unit automatically regenerates shifts to prevent business interruptions. This significantly reduces the effort required for shift creation and improves employee satisfaction.

[0065] The shift scheduling system includes a collection unit that estimates employee emotions and adjusts the timing of work preference acquisition based on the estimated employee emotions. The collection unit estimates employee emotions and adjusts the timing of work preference acquisition based on the estimated employee emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if an employee is stressed, the collection unit may acquire the work preference during a relaxed time. Alternatively, if the employee is relaxed, the collection unit may acquire the work preference immediately. Alternatively, if the employee is busy, the collection unit may postpone acquisition of the work preference during a quieter time. This allows data to be collected at a more appropriate time by adjusting the timing of work preference acquisition based on the employee emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input employee emotion data into the generation AI and have the generation AI perform emotion estimation.

[0066] The collection unit can analyze the employee's past work history and select the optimal data collection method. The collection unit analyzes the employee's past work history and selects the optimal data collection method. Optimal data collection methods include, but are not limited to, questionnaires, interviews, sensor data, etc. For example, the collection unit preferentially suggests input methods (voice, text, etc.) that the employee has frequently used in the past. The collection unit can also obtain the employee's work preferences for specific time periods from the employee's past work history. The collection unit can also select the optimal data collection method based on the employee's past work history. This allows for efficient data collection by selecting the optimal data collection method based on the employee's past work history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's past work history data into the generation AI and have the generation AI select the optimal data collection method.

[0067] When collecting work preferences, the collection unit may filter them based on the employee's current project or area of ​​interest. When collecting work preferences, the collection unit may filter them based on the employee's current project or area of ​​interest. Examples of current projects include, but are not limited to, the project's name, purpose, and progress. Examples of areas of interest include, but are not limited to, technical fields, work fields, and hobbies. For example, the collection unit may preferentially collect work preferences related to projects currently being undertaken by the employee. The collection unit may also filter related work preferences based on the employee's area of ​​interest. The collection unit may also collect work preferences taking into account the progress of the employee's current project. In this way, highly relevant data can be collected by filtering based on the employee's current project or area of ​​interest. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input the employee's project data and area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0068] The collection unit can select the optimal collection means depending on the employee's input method when collecting work preferences. The collection unit selects the optimal collection means depending on the employee's input method when collecting work preferences. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the employee prefers voice input, the collection unit can prioritize voice input. Furthermore, if the employee prefers text input, the collection unit can prioritize text input. Furthermore, if the employee prefers image input, the collection unit can prioritize image input. This allows efficient data collection by selecting the optimal collection means depending on the employee's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's input data into a generation AI and have the generation AI select the optimal collection means.

[0069] The collection unit can estimate the employee's emotions and prioritize the data to be collected based on the estimated employee emotions. The collection unit can estimate the employee's emotions and prioritize the data to be collected based on the estimated employee emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the employee is stressed, the collection unit can prioritize collecting important data. Also, if the employee is relaxed, the collection unit can collect detailed data. Also, if the employee is busy, the collection unit can collect the minimum necessary data. In this way, by prioritizing the data to be collected based on the employee's emotions, important data can be prioritized. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the employee's emotion data into the generation AI and have the generation AI determine the data prioritization.

[0070] When collecting work preferences, the collection unit can prioritize collecting highly relevant data by taking into account the employee's geographical location information. When collecting work preferences, the collection unit prioritizes collecting highly relevant data by taking into account the employee's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, location sensors, etc. For example, if an employee is in a specific area, the collection unit prioritizes collecting work preferences related to that area. The collection unit can also suggest optimal work preferences based on the employee's geographical location information. The collection unit can also collect relevant data based on the employee's current location. This allows for efficient data collection by prioritizing the collection of highly relevant data by taking into account the employee's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's geographical location data into the generation AI and cause the generation AI to collect highly relevant data.

[0071] The collection unit may analyze the employee's social media activity and collect related data when collecting the work preferences. The collection unit may analyze the employee's social media activity and collect related data when collecting the work preferences. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the collection unit may collect work preferences related to locations where the employee checked in on social media. The collection unit may also analyze the content of the employee's social media posts to collect related work preferences. The collection unit may also collect related work preferences based on the activities of the employee's friends on social media. In this way, highly relevant data can be collected by analyzing the employee's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the employee's social media data into the generation AI and cause the generation AI to collect related data.

[0072] The collection unit can customize the collection method by reflecting employees' past feedback when collecting work preferences. The collection unit customizes the collection method by reflecting employees' past feedback when collecting work preferences. Past feedback includes, but is not limited to, questionnaire results, interview records, and evaluation comments. For example, the collection unit proposes an optimal collection method based on feedback provided by employees in the past. The collection unit can also customize the collection method from employees' past feedback. The collection unit can also improve the collection method by reflecting employees' past feedback. In this way, the optimal collection method can be proposed by reflecting employees' past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input employees' past feedback data into a generation AI and cause the generation AI to customize the collection method.

[0073] The analysis unit can estimate the employee's emotions and adjust the analysis presentation method based on the estimated employee emotions. The analysis unit can estimate the employee's emotions and adjust the analysis presentation method based on the estimated employee emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the employee is nervous, the analysis unit can provide a simple, highly visible analysis result. If the employee is relaxed, the analysis unit can provide a detailed analysis result. If the employee is in a hurry, the analysis unit can provide a concise analysis result. This allows for more appropriate analysis results to be provided by adjusting the analysis presentation method according to the employee's emotions. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's emotion data into the generative AI and have the generative AI adjust the analysis presentation method.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of data includes, but is not limited to, for example, the impact on business operations and the reliability of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. The analysis unit applies different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, text data, numerical data, and image data. Analysis algorithms include, but are not limited to, regression analysis, clustering, and deep learning. For example, the analysis unit applies a specific analysis algorithm to work history data. The analysis unit can also apply a different analysis algorithm to skill data. The analysis unit can also select an optimal analysis algorithm depending on the data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the optimal analysis algorithm.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. During analysis, the analysis unit improves the accuracy of the analysis by referring to the employee's past analysis results. Past analysis results include, but are not limited to, past reports, analysis logs, and evaluation results. For example, the analysis unit improves the accuracy of the analysis based on the employee's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also improve the accuracy of the analysis by reflecting the employee's past analysis results. In this way, the accuracy of the analysis is improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0077] The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated employee emotions. The analysis unit can estimate the employee's emotions and adjust the length of the analysis based on the estimated employee emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the employee is in a hurry, the analysis unit can provide a short and concise analysis result. If the employee is relaxed, the analysis unit can provide a detailed analysis result. If the employee is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows for adjusting the length of the analysis according to the employee's emotions to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input employee emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0078] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit can determine the analysis priority based on the time of data submission during analysis. The data submission time includes, but is not limited to, for example, a submission deadline, a submission frequency, and a submission timing. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also analyze older data later. The analysis unit can also determine the analysis priority based on the time of data submission. This allows for efficient analysis by determining the analysis priority based on the time of data submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit adjusts the order of analysis based on the relevance of the data during analysis. Data relevance includes, but is not limited to, correlation, causal relationship, and co-occurrence relationship, for example. For example, the analysis unit prioritizes analysis of highly related data. The analysis unit can also postpone analysis of less related data. The analysis unit can also adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0080] The analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise during the analysis. The analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise during the analysis. Examples of technical terminology include, but are not limited to, qualifications, years of experience, and test results. Examples of technical terminology include, but are not limited to, technical terms, industry jargon, and academic terms. For example, the analysis unit uses a lot of technical terminology for employees with high levels of expertise. The analysis unit can also avoid technical terminology for employees with low levels of expertise. The analysis unit can also adjust the use of technical terminology in the analysis according to the employee's level of expertise. By adjusting the use of technical terminology in the analysis according to the employee's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0081] The shift generation unit can estimate employee emotions and adjust the shift generation method based on the estimated employee emotions. The shift generation unit can estimate employee emotions and adjust the shift generation method based on the estimated employee emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if an employee is stressed, the shift generation unit can simplify the shift generation method. Alternatively, if an employee is relaxed, the shift generation unit can provide a detailed shift generation method. Alternatively, if an employee is in a hurry, the shift generation unit can quickly generate shifts. This allows for more appropriate shifts to be generated by adjusting the shift generation method according to the employee emotions. Some or all of the above-described processing in the shift generation unit can be performed using AI, for example, or without AI. For example, the shift generation unit can input employee emotion data into the generation AI and have the generation AI adjust the shift generation method.

[0082] The shift generation unit can analyze an employee's past work history to select an optimal shift generation method when generating shifts. The shift generation unit analyzes an employee's past work history to select an optimal shift generation method when generating shifts. Past work history includes, but is not limited to, the number of working days, working hours, and assigned tasks. Optimal shift generation methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, the shift generation unit selects an optimal shift generation method based on an employee's past work history. The shift generation unit can also reflect work preferences for specific time periods based on the past work history. The shift generation unit can also analyze an employee's past work history to select an optimal shift generation method. This allows for efficient shift generation by selecting an optimal shift generation method based on the employee's past work history. Some or all of the above-described processing in the shift generation unit may be performed using, or without, AI. For example, the shift generation unit can input an employee's past work history data into a generation AI and have the generation AI select an optimal shift generation method.

[0083] The shift generation unit can customize the shift generation means based on the employee's current living situation when generating shifts. The shift generation unit customizes the shift generation means based on the employee's current living situation when generating shifts. Current living situations include, but are not limited to, for example, home environment, health status, and commute time. Shift generation means include, but are not limited to, algorithms, rule-based methods, heuristics, and the like. For example, the shift generation unit customizes the shift generation means taking into account the employee's current living situation. The shift generation unit can also propose an optimal shift generation method based on the employee's living situation. The shift generation unit can also customize the shift generation means to reflect the employee's current living situation. This allows for more appropriate shifts to be generated by customizing the shift generation means based on the employee's current living situation. Some or all of the above-described processing in the shift generation unit may be performed using, or without, AI. For example, the shift generation unit can input employee living situation data into the generation AI and have the generation AI customize the shift generation means.

[0084] The shift generation unit can improve the shift generation method by reflecting employee feedback when generating shifts. The shift generation unit improves the shift generation method by reflecting employee feedback when generating shifts. Feedback includes, but is not limited to, survey results, interview records, and evaluation comments. Shift generation methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, the shift generation unit improves the shift generation method based on employee feedback. The shift generation unit can also improve the shift generation means by reflecting employee feedback. The shift generation unit can also customize the shift generation method by reflecting employee feedback. In this way, the shift generation method can be improved by reflecting employee feedback. Some or all of the above-described processing in the shift generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the shift generation unit can input employee feedback data into the generation AI and cause the generation AI to improve the shift generation method.

[0085] The shift generation unit can estimate employee emotions and determine the priority of shift generation based on the estimated employee emotions. The shift generation unit can estimate employee emotions and determine the priority of shift generation based on the estimated employee emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if an employee is stressed, the shift generation unit can prioritize generating important shifts. Also, if an employee is relaxed, the shift generation unit can generate detailed shifts. Also, if an employee is in a hurry, the shift generation unit can quickly generate shifts. In this way, by determining the priority of shift generation based on employee emotions, important shifts can be prioritized. Some or all of the above-described processing in the shift generation unit may be performed using AI, for example, or without AI. For example, the shift generation unit can input employee emotion data into the generation AI and have the generation AI determine the priority of shift generation.

[0086] The shift generation unit can select the optimal shift generation method by taking into account the geographic location information of employees when generating shifts. The shift generation unit selects the optimal shift generation method by taking into account the geographic location information of employees when generating shifts. Examples of geographic location information include, but are not limited to, GPS data, address information, and location sensors. Examples of optimal shift generation methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, if an employee is in a specific area, the shift generation unit prioritizes generating shifts related to that area. The shift generation unit can also suggest the optimal shift generation method based on the geographic location information of employees. The shift generation unit can also generate related shifts based on the employee's current location. This allows the optimal shift generation method to be selected by taking into account the geographic location information of employees. Some or all of the above-described processing in the shift generation unit may be performed using, for example, AI, or without AI. For example, the shift generation unit can input the employee's geographic location data into the generation AI and have the generation AI select the optimal shift generation method.

[0087] The shift generation unit can analyze employees' social media activities to suggest shift generation methods when generating shifts. The shift generation unit can analyze employees' social media activities to suggest shift generation methods when generating shifts. Social media activities include, but are not limited to, examples of posts, number of likes, and number of followers. Shift generation methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, the shift generation unit generates shifts related to locations where employees have checked in on social media. The shift generation unit can also generate related shifts by analyzing the content of employees' social media posts. The shift generation unit can also generate related shifts based on the activities of the employees' friends on social media. In this way, related shifts can be generated by analyzing employees' social media activities. Some or all of the above-described processing in the shift generation unit can be performed using, for example, AI, or without AI. For example, the shift generation unit can input employees' social media data into a generation AI and have the generation AI suggest shift generation methods.

[0088] The shift generation unit can customize the shift generation method by reflecting past employee feedback when generating shifts. The shift generation unit customizes the shift generation method by reflecting past employee feedback when generating shifts. Past feedback includes, but is not limited to, examples of questionnaire results, interview records, and evaluation comments. Shift generation methods include, but are not limited to, examples of algorithms, rule-based methods, and heuristic methods. For example, the shift generation unit customizes the shift generation method based on past employee feedback. The shift generation unit can also improve the shift generation means by reflecting past employee feedback. The shift generation unit can also customize the shift generation method by reflecting past employee feedback. In this way, the shift generation method can be customized by reflecting past employee feedback. Some or all of the above-described processing in the shift generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the shift generation unit can input past employee feedback data into the generation AI and cause the generation AI to customize the shift generation method.

[0089] The adjustment unit can estimate the employee's emotions and adjust the shift adjustment method based on the estimated employee emotions. The adjustment unit can estimate the employee's emotions and adjust the shift adjustment method based on the estimated employee emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if an employee is stressed, the adjustment unit can simplify the shift adjustment method. Alternatively, if an employee is relaxed, the adjustment unit can provide a detailed shift adjustment method. Alternatively, if an employee is in a hurry, the adjustment unit can quickly adjust the shift. This enables more appropriate shift adjustment by adjusting the shift adjustment method according to the employee's emotions. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input employee emotion data into the generative AI and have the generative AI adjust the shift adjustment method.

[0090] The adjustment unit can analyze an employee's past work history to select an optimal shift adjustment method when adjusting shifts. The adjustment unit analyzes an employee's past work history to select an optimal shift adjustment method when adjusting shifts. Past work history includes, but is not limited to, the number of working days, working hours, and assigned tasks. Optimal shift adjustment methods include, but are not limited to, algorithms, rule-based methods, heuristic methods, and the like. For example, the adjustment unit selects an optimal shift adjustment method based on an employee's past work history. The adjustment unit can also reflect work preferences for specific time periods based on the past work history. The adjustment unit can also analyze an employee's past work history to select an optimal shift adjustment method. This allows for efficient shift adjustment by selecting an optimal shift adjustment method based on the employee's past work history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input an employee's past work history data into a generation AI and cause the generation AI to select an optimal shift adjustment method.

[0091] The adjustment unit can customize the shift adjustment method based on the employee's current living situation when adjusting shifts. The adjustment unit customizes the shift adjustment method based on the employee's current living situation when adjusting shifts. The current living situation includes, but is not limited to, for example, home environment, health status, and commute time. The shift adjustment method includes, but is not limited to, algorithms, rule-based methods, heuristics, and the like. For example, the adjustment unit customizes the shift adjustment method taking into account the employee's current living situation. The adjustment unit can also propose an optimal shift adjustment method based on the employee's living situation. The adjustment unit can also customize the shift adjustment method to reflect the employee's current living situation. This enables more appropriate shift adjustment by customizing the shift adjustment method based on the employee's current living situation. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the employee's living situation data into the generation AI and cause the generation AI to customize the shift adjustment method.

[0092] The adjustment unit can improve the shift adjustment method by reflecting employee feedback during shift adjustment. The adjustment unit improves the shift adjustment method by reflecting employee feedback during shift adjustment. Examples of feedback include, but are not limited to, survey results, interview records, and evaluation comments. Examples of shift adjustment methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, the adjustment unit improves the shift adjustment method based on employee feedback. The adjustment unit can also improve the shift adjustment means by reflecting employee feedback. The adjustment unit can also customize the shift adjustment method by reflecting employee feedback. In this way, the shift adjustment method can be improved by reflecting employee feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input employee feedback data into a generation AI and cause the generation AI to improve the shift adjustment method.

[0093] The adjustment unit can estimate employee emotions and determine shift adjustment priorities based on the estimated employee emotions. The adjustment unit can estimate employee emotions and determine shift adjustment priorities based on the estimated employee emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if an employee is stressed, the adjustment unit can prioritize important shifts. Also, if an employee is relaxed, the adjustment unit can adjust detailed shifts. Also, if an employee is in a hurry, the adjustment unit can quickly adjust shifts. In this way, by determining the shift adjustment priorities based on the employee emotions, important shifts can be prioritized. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input employee emotion data into the generative AI and have the generative AI determine the shift adjustment priorities.

[0094] The adjustment unit can select an optimal shift adjustment method by taking into account the employee's geographical location information when adjusting shifts. The adjustment unit selects an optimal shift adjustment method by taking into account the employee's geographical location information when adjusting shifts. Examples of geographical location information include, but are not limited to, GPS data, address information, and location sensors. Examples of optimal shift adjustment methods include, but are not limited to, algorithms, rule-based methods, and heuristic methods. For example, if an employee is in a specific area, the adjustment unit prioritizes adjusting shifts related to that area. The adjustment unit can also propose an optimal shift adjustment method based on the employee's geographical location information. The adjustment unit can also adjust related shifts based on the employee's current location. This allows the optimal shift adjustment method to be selected by taking into account the employee's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the employee's geographical location data into a generation AI and cause the generation AI to select an optimal shift adjustment method.

[0095] The adjustment unit can analyze the employee's social media activity to suggest a shift adjustment method when adjusting shifts. The adjustment unit can analyze the employee's social media activity to suggest a shift adjustment method when adjusting shifts. Social media activity includes, but is not limited to, examples of posts, number of likes, number of followers, etc. Shift adjustment methods include, but are not limited to, examples of algorithms, rule-based methods, heuristic methods, etc. For example, the adjustment unit adjusts shifts related to locations where the employee checked in on social media. The adjustment unit can also analyze the employee's social media posts to adjust the related shifts. The adjustment unit can also adjust the related shifts based on the activities of the employee's friends on social media. In this way, the related shifts can be adjusted by analyzing the employee's social media activity. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or without AI. For example, the adjustment unit can input the employee's social media data into a generation AI and cause the generation AI to suggest a shift adjustment method.

[0096] The adjustment unit can customize the shift adjustment method by reflecting past employee feedback during shift adjustment. The adjustment unit customizes the shift adjustment method by reflecting past employee feedback during shift adjustment. Past feedback includes, but is not limited to, examples of questionnaire results, interview records, and evaluation comments. Shift adjustment methods include, but are not limited to, examples of algorithms, rule-based methods, and heuristic methods. For example, the adjustment unit customizes the shift adjustment method based on past employee feedback. The adjustment unit can also improve shift adjustment means by reflecting past employee feedback. The adjustment unit can also customize the shift adjustment method by reflecting past employee feedback. In this way, the shift adjustment method can be customized by reflecting past employee feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past employee feedback data into the generation AI and cause the generation AI to customize the shift adjustment method.

[0097] The adjustment unit can automatically regenerate a shift when a sudden vacation or sick leave occurs. The adjustment unit automatically regenerates a shift when a sudden vacation or sick leave occurs. Sudden vacation or sick leave includes, for example, but is not limited to, the type of vacation, the method of proving sick leave, and response procedures. Shift regeneration includes, for example, but is not limited to, the timing of regeneration, the algorithm to be used, and the adjustment method. For example, the adjustment unit automatically regenerates a shift when a sudden vacation or sick leave occurs, thereby preventing business operations from being disrupted. This allows for automatic regeneration of a shift when a sudden vacation or sick leave occurs, thereby preventing business operations from being disrupted. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without AI. For example, the adjustment unit can input data about sudden vacation or sick leave into the generation AI and have the generation AI regenerate the shift. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, shift generation unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects employee work preferences, skills, and past work history using the camera 42 and microphone 38B of the smart device 14, and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The shift generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal shift based on the analyzed data. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and regenerates shifts in real time when shift changes or adjustments are necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, shift generation unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects employee work preferences, skills, and past work history using the camera 42 and microphone 238 of the smart glasses 214, and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The shift generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal shift based on the analyzed data. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and regenerates shifts in real time when shift changes or adjustments are necessary. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, shift generation unit, and adjustment unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects employee work preferences, skills, and past work history using the camera 42 and microphone 238 of the headset terminal 314, and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The shift generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal shift based on the analyzed data. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and regenerates shifts in real time when shift changes or adjustments are necessary. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, shift generation unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects employee work preferences, skills, and past work history using the camera 42 and microphone 238 of the robot 414, and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The shift generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an optimal shift based on the analyzed data. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and regenerates shifts in real time when shift changes or adjustments are necessary.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The shift creation system can also collect employee health data and adjust shifts based on their health status. For example, the collection unit collects employee health data (e.g., heart rate, sleep time, stress level, etc.), and the analysis unit analyzes this data. The shift generation unit can generate shifts that take employees' health status into consideration based on the analysis results. For example, employees with high stress levels can be assigned shifts that include more break time. Also, sleep-deprived employees can be offered shifts that avoid night shifts. In this way, generating shifts that take employees' health status into consideration can improve employee health management and work efficiency.

[0100] The shift creation system can also estimate employee emotions and adjust shift flexibility based on the estimated emotions. For example, the collection unit collects employee emotion data, and the analysis unit analyzes it. The shift generation unit can provide flexible shifts if an employee is feeling stressed. For example, it can provide shifts that reduce working hours or allow remote work to employees who are feeling stressed. It can also provide regular shifts to employees who are relaxed. In this way, by providing flexible shifts according to employees' emotions, it is possible to improve employee satisfaction and work efficiency.

[0101] The shift creation system can also generate shifts taking into account employees' hobbies and interests. For example, the collection unit collects data on employees' hobbies and interests, and the analysis unit analyzes this data. The shift generation unit can generate shifts that assign related tasks based on employees' hobbies and interests. For example, employees who are interested in technology can be provided with shifts that include many technology-related tasks. Also, employees who enjoy creative activities can be provided with shifts that include design and planning tasks. In this way, generating shifts that take employees' hobbies and interests into consideration can improve employee motivation and work efficiency.

[0102] The shift creation system can also estimate employee emotions and adjust shift communication methods based on the estimated emotions. For example, the collection unit collects employee emotion data, and the analysis unit analyzes it. The shift creation unit can flexibly communicate about shift changes or adjustments if an employee is feeling stressed. For example, an employee who is feeling stressed can be notified of the shift change via email or chat. Alternatively, an employee who is relaxed can be notified of the shift change through direct conversation. This makes it possible to reduce employee stress and improve work efficiency by providing communication methods that correspond to the employee's emotions.

[0103] The shift creation system can also generate shifts taking into account employees' commute times. For example, the collection unit collects data on employees' commute times, and the analysis unit analyzes this data. The shift generation unit can provide shifts that shorten employees' commute times by taking into account their commute times. For example, employees with long commute times can be provided with shifts that allow remote work to shorten their commute times. Employees with short commute times can also be provided with regular shifts. In this way, generating shifts that take employees' commute times into account can reduce the burden on employees and improve work efficiency.

[0104] The shift creation system can also estimate employee emotions and adjust the shift feedback method based on the estimated emotions. For example, the collection unit collects employee emotion data, and the analysis unit analyzes it. The shift creation unit can flexibly provide feedback when an employee is feeling stressed. For example, it can prioritize positive feedback for employees who are feeling stressed. It can also provide detailed feedback to employees who are relaxed. In this way, by providing feedback methods that correspond to employees' emotions, it is possible to improve employee satisfaction and work efficiency.

[0105] The shift creation system can also collect training data for employee skill development and reflect it in shifts. For example, the collection unit collects data related to employee skill development (e.g., training attended, qualifications obtained, etc.), and the analysis unit analyzes this data. The shift generation unit can provide shifts that take into account employee skill development. For example, an employee who has acquired a new skill can be provided with a shift that includes work that can utilize that skill. Also, an employee who has acquired a specific qualification can be provided with a shift that includes work that can utilize that qualification. In this way, by generating shifts that take into account employee skill development, it is possible to improve employee growth and work efficiency.

[0106] The shift creation system can also estimate employee emotions and adjust the shift evaluation method based on the estimated emotions. For example, the collection unit collects employee emotion data, and the analysis unit analyzes it. The shift creation unit can flexibly adjust the evaluation method if an employee is feeling stressed. For example, the evaluation criteria can be relaxed for employees who are feeling stressed. Also, the normal evaluation criteria can be applied to employees who are relaxed. In this way, by providing an evaluation method that corresponds to the employee's emotions, it is possible to reduce employee stress and improve work efficiency.

[0107] The shift creation system can also generate shifts taking into account the family structure of employees. For example, the collection unit collects data on the employee's family structure (e.g., whether they have children, whether they need care, etc.), and the analysis unit analyzes this. The shift generation unit can provide shifts that take into account the employee's family structure. For example, employees with children can be provided with shifts that fit their children's school or nursery school schedules. In addition, employees with family members who require care can be provided with shifts that allow them to take time to care for them. In this way, generating shifts that take into account the employee's family structure can improve the balance between their work and personal life.

[0108] The shift creation system can also estimate employee emotions and adjust the shift notification method based on the estimated emotions. For example, the collection unit collects employee emotion data, and the analysis unit analyzes it. The shift generation unit can flexibly adjust the shift notification method if an employee is feeling stressed. For example, for employees who are feeling stressed, shift notifications can be sent via email or chat. In addition, for employees who are relaxed, shift notifications can be sent through direct conversation. In this way, by providing shift notification methods that correspond to employees' emotions, it is possible to reduce employee stress and improve work efficiency.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The collection department collects data on employees' work preferences, skills, and past work history. Employees' work preferences include desired working hours, work days, and work location. The collection department collects information such as employees' desired working hours, desired vacation time, and whether they have specific skills or qualifications. The department also collects employees' past work history. For example, it collects information such as the number of days an employee works, working hours, and responsibilities. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit provides basic data for generating optimal shifts based on data such as employees' work preferences, skills, and past work history. Step 3: The shift generation unit generates shifts based on the data analyzed by the analysis unit. Shifts include information such as working hours, break times, and work locations. The shift generation unit takes into maximum consideration the wishes and skills of employees to create shifts that improve work efficiency. Step 4: The reconciliation department regenerates shifts in real time when changes or adjustments are required. Adjustments are made in the event of unexpected vacation or sick leave. The reconciliation department automatically regenerates shifts in the event of unexpected vacation or sick leave, ensuring that work is not disrupted.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects data on employees' work preferences, skills, and past work history; an analysis unit that analyzes the data collected by the collection unit; a shift generation unit that generates a shift based on the data analyzed by the analysis unit; and an adjustment unit that regenerates shifts in real time when a shift change or adjustment is required. A system characterized by:

2. The collecting unit Estimate employee sentiment and adjust the timing of work preference acquisition based on the estimated employee sentiment 2. The system of claim 1.

3. The collecting unit Analyze employees' past work history and select the most appropriate data collection method 2. The system of claim 1.

4. The collecting unit When collecting work preferences, filter them based on employees' current projects and areas of interest 2. The system of claim 1.

5. The collecting unit When collecting work preferences, select the most appropriate collection method depending on the employee's input method 2. The system of claim 1.

6. The collecting unit Estimate employee sentiment and prioritize data collection based on estimated employee sentiment 2. The system of claim 1.

7. The collecting unit When collecting work preferences, consider employees' geographic location to prioritize the collection of relevant data 2. The system of claim 1.

8. The collecting unit Analyze employees' social media activity and collect relevant data when collecting job applications.

2. The system of claim 1.

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