system

Abstract

The system according to the embodiment aims to improve work efficiency and employee satisfaction through the optimization of shift creation and paid leave acquisition. [Solution] A system according to an embodiment includes a collection unit, a learning unit, and a suggestion unit. The collection unit collects past data related to shift creation. The learning unit learns the data collected by the collection unit. The suggestion unit suggests shifts based on the data learned by the learning unit. The suggestion unit suggests suitable dates for taking paid leave based on the shifts suggested by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately optimize shift creation and paid leave acquisition, leaving room for improvement in operational efficiency and employee satisfaction.

[0005] The system according to the embodiment aims to improve work efficiency and employee satisfaction through the optimization of shift creation and paid leave acquisition. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, and a suggestion unit. The collection unit collects past data related to shift creation. The learning unit learns the data collected by the collection unit. The suggestion unit suggests shifts based on the data learned by the learning unit. The suggestion unit suggests suitable dates for taking paid leave based on the shifts suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve work efficiency and employee satisfaction through the optimization of shift creation and paid leave acquisition. [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 system according to an embodiment of the present invention streamlines shift scheduling in a retail organization. This system collects past data related to shift scheduling, trains AI, and proposes next month's shifts. Furthermore, the system considers the previous year's performance and revenue productivity data to suggest optimal dates for taking paid leave, helping to create a comfortable working environment for employees. For example, the system collects each employee's working hours, role, past shift patterns, skills and qualifications, individual performance data, etc. Next, the AI ​​proposes next month's shifts based on the learned data. The AI ​​generates optimal shifts by taking into account each employee's working hours, role, and past shift patterns. For example, if a specific employee works more on a particular day of the week, the system will consider that tendency when proposing shifts. Furthermore, the AI ​​also considers the previous year's performance and revenue productivity data. For example, it learns sales and productivity data for the same month of the previous year and optimizes shifts based on that data. This enables more productive shift scheduling. Finally, the AI ​​also suggests optimal dates for taking paid leave. For example, it identifies days with less work during a particular period from past data and proposes those days as potential days for taking paid leave. This allows employees to take paid leave efficiently, creating a comfortable working environment. This system makes shift creation work more efficient and improves employee satisfaction. For example, it reduces the time it takes to create shifts, reducing the burden on managers. In addition, employees can take paid leave efficiently, creating a comfortable working environment. This system makes shift creation work more efficient and improves employee satisfaction.

[0029] The shift scheduling system according to the embodiment includes a collection unit, a learning unit, and a proposal unit. The collection unit collects past data related to shift scheduling. The collection unit collects, for example, each employee's working hours, role, past shift patterns, skills and qualifications, individual performance data, and the like. For example, the collection unit collects shift data from the past year and trains an AI on the data. The learning unit learns from the data collected by the collection unit. The learning unit learns from the collected data, for example, using AI. For example, the learning unit learns from the collected data using deep learning or machine learning algorithms. The proposal unit proposes shifts based on the data learned by the learning unit. The proposal unit generates optimal shifts, for example, by taking into account each employee's working hours, role, and past shift patterns. For example, if a specific employee works more on a specific day of the week, the proposal unit takes that tendency into account. The proposal unit proposes shifts by taking into account the previous year's performance and revenue productivity data. For example, the proposal unit learns sales data and productivity data from the same month of the previous year and optimizes shifts based on that data. This enables more productive shift scheduling. The suggestion unit suggests the optimal dates for taking paid leave, taking into account busy and slow periods. For example, the suggestion unit identifies days with less work during a specific period from past data and suggests those days as candidate dates for taking paid leave. This allows employees to take paid leave efficiently and creates a comfortable working environment. The suggestion unit displays the shift suggestions through a user interface. The suggestion unit displays the shift suggestions through, for example, a web interface or a mobile app. This allows users to easily check the shifts. As a result, the shift creation system according to the embodiment can streamline shift creation operations and improve employee satisfaction.

[0030] The collection unit can collect each employee's working hours, role, past shift patterns, skills or qualifications, and individual performance data. The collection unit, for example, collects each employee's working hours. For example, the collection unit collects working hours such as full-time, part-time, and shift work. The collection unit can also collect each employee's role. For example, the collection unit collects roles such as manager, staff member, and assistant. The collection unit can also collect past shift patterns. For example, the collection unit collects past shift patterns such as weekly shifts and monthly shifts. The collection unit can also collect each employee's skills and qualifications. For example, the collection unit collects technical skills and professional qualifications. The collection unit can also collect each employee's individual performance data. For example, the collection unit collects performance data such as performance evaluations and work efficiency. By collecting detailed employee data, more accurate shift suggestions can be made. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection department can input each employee's working hours and role, past shift patterns, skills and qualifications, and individual performance data into the AI, and have the AI ​​collect the data.

[0031] The learning unit can learn the collected data using AI. The learning unit, for example, learns the collected data using AI. For example, the learning unit learns the collected data using deep learning or a machine learning algorithm. For example, the learning unit can learn the collected data using deep learning. The learning unit can also learn the collected data using a machine learning algorithm. For example, the learning unit can input the collected data into AI and have the AI ​​learn the data. This improves the accuracy of shift suggestions through learning by AI. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the collected data into AI and have the AI ​​learn the data.

[0032] The proposal unit can propose shifts based on the previous year's performance and revenue productivity data. The proposal unit, for example, proposes shifts based on the previous year's performance and revenue productivity data. For example, the proposal unit learns sales data and productivity data for the same month of the previous year and optimizes shifts based on that data. For example, the proposal unit can propose shifts based on the previous year's sales data. The proposal unit can also propose shifts based on the previous year's productivity data. For example, the proposal unit can input the previous year's sales data and productivity data into an AI and have the AI ​​execute a shift proposal. This enables more productive shift configurations by taking the previous year's performance and revenue productivity data into account. Some or all of the above-described processing in the proposal unit may be performed using, or without, an AI. For example, the proposal unit can input the previous year's performance and revenue productivity data into an AI and have the AI ​​execute a shift proposal.

[0033] The suggestion unit can suggest suitable dates for taking paid leave based on busy and slow periods. The suggestion unit suggests suitable dates for taking paid leave based on, for example, busy and slow periods. For example, the suggestion unit identifies days with less work during a specific period from past data and suggests those days as candidate dates for taking paid leave. For example, the suggestion unit can suggest suitable dates for taking paid leave, taking into account busy and slow periods. This allows employees to take paid leave efficiently. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on busy and slow periods into AI and have the AI ​​suggest suitable dates for taking paid leave.

[0034] The suggestion unit can display the shift proposal through a user interface. For example, the suggestion unit displays the shift proposal through a user interface. For example, the suggestion unit displays the shift proposal through a web interface or a mobile app. For example, the suggestion unit can display the shift proposal through a web interface. The suggestion unit can also display the shift proposal through a mobile app. This allows the user to easily confirm the shift. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the shift proposal into AI and cause the AI ​​to execute processing to display it through a user interface.

[0035] The collection unit can monitor the health status and stress level of each employee and reflect the results in the collected data. For example, the collection unit can periodically check the health status of each employee and suspend data collection if an abnormality is detected. The collection unit can also prioritize the collection of data on employees with high stress levels and take appropriate measures. The collection unit can also adjust the shifts of other employees based on the data of employees in good health. For example, the collection unit can monitor the health status and stress level of employees using methods such as wearable devices or self-reporting. This allows for more appropriate shift proposals by taking into account the employee's health status and stress level. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the employee's health status and stress level into AI and have the AI ​​collect the data.

[0036] The collection unit can filter data based on the employee's individual goals and career plans when collecting the data. For example, the collection unit filters data taking into account the employee's individual goals and career plans when collecting the data. For example, the collection unit prioritizes collecting data on employees with specific skills based on their career plans. The collection unit can also collect only necessary data to match individual goals, eliminating waste. The collection unit can also collect data in line with career plans and use it to propose future shifts. For example, the collection unit collects employee goals and career plans using methods such as personal interviews and questionnaire surveys. This allows the data to be used to propose future shifts by taking into account the employee's goals and career plans. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on employee goals and career plans into AI and have the AI ​​perform data filtering.

[0037] The collection unit can collect data based on the employee's home environment and lifestyle habits at the time of collection. For example, the collection unit collects data taking into account the employee's home environment and lifestyle habits at the time of collection. For example, the collection unit collects data at a specific time period based on the home environment. The collection unit can also adjust the frequency of data collection to suit the lifestyle habits. The collection unit can also collect data according to the home environment and lifestyle habits and reflect it in shift suggestions. For example, the collection unit collects information about the employee's home environment and lifestyle habits using methods such as questionnaire surveys and interviews. This enables more appropriate shift suggestions by taking the employee's home environment and lifestyle habits into consideration. Some or all of the above-mentioned 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 data about the employee's home environment and lifestyle habits into AI and have the AI ​​collect the data.

[0038] The collection unit may analyze employees' social media activities and collect related data at the time of collection. For example, the collection unit may analyze employees' social media activities and collect related data at the time of collection. For example, the collection unit may analyze the content of social media posts to understand employees' stress levels. The collection unit may also adjust the timing of data collection based on the time spent on social media. The collection unit may also determine the priority of data collection based on the number of social media followers and engagement. For example, the collection unit may analyze employees' social media activities using methods such as analyzing the content of social media posts and the number of followers. This enables more appropriate shift proposals by taking employees' social media activities into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data on employees' social media activities into AI and have the AI ​​collect the data.

[0039] The learning unit can optimize the learning algorithm by referring to the employee's past performance data during learning. For example, the learning unit optimizes the learning algorithm by referring to the employee's past performance data during learning. For example, the learning unit adjusts the parameters of the learning algorithm based on the past performance data. The learning unit can also prioritize learning data of high-performing employees and optimize the algorithm. The learning unit can also analyze data of low-performing employees and reflect improvements in the learning algorithm. For example, the learning unit adjusts the parameters of the learning algorithm based on the past performance data. In this way, by referring to the employee's past performance data, the accuracy of the learning algorithm is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the employee's past performance data into AI and cause the AI ​​to optimize the learning algorithm.

[0040] The learning unit can perform learning based on the employee's skill improvement and qualification acquisition history during learning. For example, the learning unit performs learning while taking into account the employee's skill improvement and qualification acquisition history during learning. For example, the learning unit selects learning data based on the employee's skill improvement and qualification acquisition history. The learning unit can also learn performance data after qualification acquisition and reflect it in the algorithm. The learning unit can also consider the skill improvement history and use it to propose future shifts. For example, the learning unit collects the employee's skill improvement and qualification acquisition history using methods such as training records and qualification certificates. This allows the employee's skill improvement and qualification acquisition history to be taken into account and used to propose future shifts. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the employee's skill improvement and qualification acquisition history into AI and have the AI ​​perform learning.

[0041] The learning unit can weight the learning data based on the employee's geographic location information during learning. For example, the learning unit weights the learning data taking into account the employee's geographic location information during learning. For example, the learning unit prioritizes learning data from a specific region based on the geographic location information. The learning unit can also adjust the weighting of the data according to the geographic location information and reflect this in the learning algorithm. The learning unit can also take the geographic location information into account and use it to propose shifts for each region. For example, the learning unit can collect employee geographic location information using methods such as GPS data or address information. This can be used to propose shifts for each region by taking the employee's geographic location information into account. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input employee geographic location information into AI and have the AI ​​weight the learning data.

[0042] The learning unit can analyze employees' social media activities during learning and reflect related data in the learning. For example, the learning unit can analyze employees' social media activities during learning and reflect related data in the learning. For example, the learning unit can analyze the content of social media posts and reflect the employee's stress level in the learning data. The learning unit can also adjust the weighting of the learning data based on the amount of time spent on social media. The learning unit can also select learning data based on the number of social media followers and engagement. For example, the learning unit can analyze employees' social media activities using methods such as analyzing the content of social media posts and the number of followers. This enables more appropriate shift proposals by taking employees' social media activities into consideration. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input data on employees' social media activities into AI and reflect the data in the learning.

[0043] The proposal unit can optimize shifts by taking into account individual employee performance data when making proposals. The proposal unit, for example, optimizes shifts by taking into account individual employee performance data when making proposals. For example, the proposal unit prioritizes high-performing employees in shifts. The proposal unit can also adjust the shifts of low-performing employees to reflect areas for improvement. The proposal unit can also propose an optimal shift balance based on performance data. For example, the proposal unit collects performance data such as performance evaluations and work efficiency, and optimizes shifts based on that data. This makes it possible to propose optimal shifts by taking employee performance data into consideration. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input employee performance data into AI and have the AI ​​perform shift optimization.

[0044] The proposal unit can customize shifts based on employee skills or qualifications when proposing them. The proposal unit, for example, customizes shifts based on employee skills and qualifications when proposing them. For example, the proposal unit prioritizes employees with specific skills in shifts. The proposal unit can also adjust shifts of qualified employees to achieve optimal allocation. The proposal unit can also adjust the balance of shifts based on skills and qualifications. For example, the proposal unit collects data such as technical skills and professional qualifications and customizes shifts based on that data. This makes it possible to propose optimal shifts by taking employee skills and qualifications into consideration. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on employee skills and qualifications into AI and have the AI ​​customize the shifts.

[0045] The suggestion unit can propose shifts based on the geographic location information of employees when proposing them. For example, the suggestion unit proposes shifts taking into account the geographic location information of employees when proposing them. For example, the suggestion unit prioritizes employees in specific areas into shifts based on the geographic location information. The suggestion unit can also adjust the balance of shifts according to the geographic location information. The suggestion unit can also propose shifts for each area by taking into account the geographic location information. For example, the suggestion unit collects the geographic location information of employees using methods such as GPS data or address information. This makes it possible to propose shifts for each area by taking into account the geographic location information of employees. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the geographic location information of employees into AI and have the AI ​​execute the shift proposal.

[0046] The suggestion unit can analyze employees' social media activities and propose relevant shifts when making suggestions. For example, the suggestion unit can analyze employees' social media activities and propose relevant shifts when making suggestions. For example, the suggestion unit can analyze social media posts and reflect employees' stress levels in shift suggestions. The suggestion unit can also adjust shift balance based on social media activity time. The suggestion unit can also make shift suggestions based on the number of social media followers and engagement. For example, the suggestion unit can analyze employees' social media activities using methods such as analyzing social media posts and the number of followers. This enables more appropriate shift suggestions by taking employees' social media activities into consideration. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input data on employees' social media activities into AI and have the AI ​​execute shift suggestions.

[0047] When making a proposal, the suggestion unit can suggest the optimal date by referring to the employee's past paid leave history. When making a proposal, the suggestion unit, for example, can suggest the optimal date by referring to the employee's past paid leave history. For example, the suggestion unit suggests the optimal date based on the employee's past paid leave history. The suggestion unit can also analyze the frequency and timing of paid leave use and suggest the optimal date. The suggestion unit can also suggest a date that will have the least impact on work by referring to the past paid leave history. For example, the suggestion unit collects past paid leave history and suggests the optimal date based on that. In this way, the optimal paid leave date can be suggested by referring to the employee's past paid leave history. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the employee's past paid leave history into AI and have the AI ​​suggest the optimal date.

[0048] The suggestion unit can suggest paid leave dates based on the employee's individual living situation and home environment when making a suggestion. For example, the suggestion unit can suggest paid leave dates taking into account the employee's individual living situation and home environment when making a suggestion. For example, the suggestion unit can suggest paid leave dates that coincide with specific events or occasions based on the home environment. The suggestion unit can also suggest paid leave dates when employees need to refresh themselves, depending on their living situation. The suggestion unit can also suggest paid leave dates that have minimal impact on work, taking into account the home environment and living situation. For example, the suggestion unit can collect information about employees' living situations and home environments using methods such as questionnaire surveys and interviews. This allows the suggestion unit to suggest more appropriate paid leave dates by taking into account the employee's living situation and home environment. Some or all of the above-described processing by the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input data about the employee's living situation and home environment into AI and have the AI ​​execute the suggested paid leave dates.

[0049] The suggestion unit can propose paid leave dates taking into account the employee's geographic location information when making a proposal. For example, the suggestion unit proposes paid leave dates taking into account the employee's geographic location information when making a proposal. For example, the suggestion unit proposes paid leave dates that coincide with events or occasions in a specific region based on the geographic location information. The suggestion unit can also propose paid leave dates when a refresh is needed, depending on the geographic location information. The suggestion unit can also propose paid leave dates that will have minimal impact on work, taking into account the geographic location information. For example, the suggestion unit collects the employee's geographic location information using methods such as GPS data or address information. This makes it possible to propose paid leave dates for each region by taking into account the employee's geographic location information. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the employee's geographic location information into AI and have the AI ​​execute the proposed paid leave dates.

[0050] The suggestion unit may analyze the employee's social media activity and suggest relevant paid leave dates when making a suggestion. For example, the suggestion unit may analyze the employee's social media activity and suggest relevant paid leave dates when making a suggestion. For example, the suggestion unit may analyze the content of social media posts and reflect the employee's stress level in the paid leave suggestion. The suggestion unit may also adjust the balance of paid leave days based on the amount of time spent on social media. The suggestion unit may also suggest paid leave dates based on the number of social media followers and engagement. For example, the suggestion unit may analyze the employee's social media activity using methods such as analyzing the content of social media posts and the number of followers. This allows for more appropriate paid leave suggestions by taking the employee's social media activity into consideration. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input data on the employee's social media activity into AI and have the AI ​​execute the paid leave suggestion.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The suggestion department can also suggest shifts based on employees' hobbies and interests. For example, if an employee has plans to participate in a particular sporting event, the suggestion department can adjust shifts to take that day into account. Also, if an employee wants to devote time to a particular hobby, the suggestion department can adjust shifts to ensure that time is available. Furthermore, if an employee has plans to attend an event or seminar that interests them, the suggestion department can prioritize shifts on that day. This makes it possible to suggest shifts that take into account the individual hobbies and interests of employees, improving employee satisfaction.

[0053] The data collection department can also collect data taking into account employees' commute times and means of transportation. For example, if an employee has a long commute, the department can adjust shifts to reduce that burden. If an employee uses public transportation, the department can adjust shifts to match that schedule. Furthermore, if an employee uses their own car, the department can adjust shifts to avoid heavy traffic hours. This reduces the commute burden on employees and provides a comfortable working environment.

[0054] The learning department can also customize learning data based on an employee's learning style and learning speed. For example, if an employee has a visual learning style, it can prioritize learning visual data. Alternatively, if an employee has an auditory learning style, it can prioritize learning audio data. Furthermore, it can adjust the amount and difficulty of learning data according to the employee's learning speed. This allows for optimal learning according to the employee's learning style and learning speed.

[0055] The suggestion department can also suggest shifts based on an employee's health condition and fitness level. For example, if an employee has a specific health problem identified during a health check, the shift can be adjusted to reduce the employee's burden. Also, if an employee is participating in a fitness program, the shift can be adjusted to fit that schedule. Furthermore, it can ensure appropriate break times based on the employee's health condition and fitness level. This makes it possible to suggest shifts that take employee health into consideration.

[0056] The Suggestion Department can also suggest shifts based on an employee's career goals and growth plans. For example, if an employee plans to attend training to acquire a specific skill, the shift can be adjusted to take that day into consideration. Also, if an employee is aiming to advance their career, the shift can be adjusted to match that goal. Furthermore, based on the employee's growth plan, it can suggest shifts that will allow them to gain appropriate work experience. This makes it possible to suggest shifts that support an employee's career goals and growth plans.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection department collects past data related to shift scheduling, such as each employee's working hours, role, past shift patterns, skills and qualifications, and individual performance data. The collection department collects shift data from the past year and trains the AI. Step 2: The learning unit learns the data collected by the collection unit. For example, the learning unit uses AI to learn the collected data using deep learning or machine learning algorithms. Step 3: The suggestion unit proposes shifts based on the data learned by the learning unit. For example, it generates optimal shifts by taking into account each employee's working hours, role, and past shift patterns. It proposes shifts by taking into account the previous year's performance and revenue productivity data, and proposes optimal dates for taking paid leave by taking into account busy and slow periods. The suggestion unit identifies days with less work during specific periods from past data and proposes those days as candidate dates for taking paid leave. The suggestion unit displays the shift proposals through a user interface. For example, it displays the shift proposals through a web interface or mobile app.

[0059] (Example 2) A system according to an embodiment of the present invention streamlines shift scheduling in a retail organization. This system collects past data related to shift scheduling, trains AI, and proposes next month's shifts. Furthermore, the system considers the previous year's performance and revenue productivity data to suggest optimal dates for taking paid leave, helping to create a comfortable working environment for employees. For example, the system collects each employee's working hours, role, past shift patterns, skills and qualifications, individual performance data, etc. Next, the AI ​​proposes next month's shifts based on the learned data. The AI ​​generates optimal shifts by taking into account each employee's working hours, role, and past shift patterns. For example, if a specific employee works more on a particular day of the week, the system will consider that tendency when proposing shifts. Furthermore, the AI ​​also considers the previous year's performance and revenue productivity data. For example, it learns sales and productivity data for the same month of the previous year and optimizes shifts based on that data. This enables more productive shift scheduling. Finally, the AI ​​also suggests optimal dates for taking paid leave. For example, it identifies days with less work during a particular period from past data and proposes those days as potential days for taking paid leave. This allows employees to take paid leave efficiently, creating a comfortable working environment. This system makes shift creation work more efficient and improves employee satisfaction. For example, it reduces the time it takes to create shifts, reducing the burden on managers. In addition, employees can take paid leave efficiently, creating a comfortable working environment. This system makes shift creation work more efficient and improves employee satisfaction.

[0060] The shift scheduling system according to the embodiment includes a collection unit, a learning unit, and a proposal unit. The collection unit collects past data related to shift scheduling. The collection unit collects, for example, each employee's working hours, role, past shift patterns, skills and qualifications, individual performance data, and the like. For example, the collection unit collects shift data from the past year and trains an AI on the data. The learning unit learns from the data collected by the collection unit. The learning unit learns from the collected data, for example, using AI. For example, the learning unit learns from the collected data using deep learning or machine learning algorithms. The proposal unit proposes shifts based on the data learned by the learning unit. The proposal unit generates optimal shifts, for example, by taking into account each employee's working hours, role, and past shift patterns. For example, if a specific employee works more on a specific day of the week, the proposal unit takes that tendency into account. The proposal unit proposes shifts by taking into account the previous year's performance and revenue productivity data. For example, the proposal unit learns sales data and productivity data from the same month of the previous year and optimizes shifts based on that data. This enables more productive shift scheduling. The suggestion unit suggests the optimal dates for taking paid leave, taking into account busy and slow periods. For example, the suggestion unit identifies days with less work during a specific period from past data and suggests those days as candidate dates for taking paid leave. This allows employees to take paid leave efficiently and creates a comfortable working environment. The suggestion unit displays the shift suggestions through a user interface. The suggestion unit displays the shift suggestions through, for example, a web interface or a mobile app. This allows users to easily check the shifts. As a result, the shift creation system according to the embodiment can streamline shift creation operations and improve employee satisfaction.

[0061] The collection unit can collect each employee's working hours, role, past shift patterns, skills or qualifications, and individual performance data. The collection unit, for example, collects each employee's working hours. For example, the collection unit collects working hours such as full-time, part-time, and shift work. The collection unit can also collect each employee's role. For example, the collection unit collects roles such as manager, staff member, and assistant. The collection unit can also collect past shift patterns. For example, the collection unit collects past shift patterns such as weekly shifts and monthly shifts. The collection unit can also collect each employee's skills and qualifications. For example, the collection unit collects technical skills and professional qualifications. The collection unit can also collect each employee's individual performance data. For example, the collection unit collects performance data such as performance evaluations and work efficiency. By collecting detailed employee data, more accurate shift suggestions can be made. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection department can input each employee's working hours and role, past shift patterns, skills and qualifications, and individual performance data into the AI, and have the AI ​​collect the data.

[0062] The learning unit can learn the collected data using AI. The learning unit, for example, learns the collected data using AI. For example, the learning unit learns the collected data using deep learning or a machine learning algorithm. For example, the learning unit can learn the collected data using deep learning. The learning unit can also learn the collected data using a machine learning algorithm. For example, the learning unit can input the collected data into AI and have the AI ​​learn the data. This improves the accuracy of shift suggestions through learning by AI. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the collected data into AI and have the AI ​​learn the data.

[0063] The proposal unit can propose shifts based on the previous year's performance and revenue productivity data. The proposal unit, for example, proposes shifts based on the previous year's performance and revenue productivity data. For example, the proposal unit learns sales data and productivity data for the same month of the previous year and optimizes shifts based on that data. For example, the proposal unit can propose shifts based on the previous year's sales data. The proposal unit can also propose shifts based on the previous year's productivity data. For example, the proposal unit can input the previous year's sales data and productivity data into an AI and have the AI ​​execute a shift proposal. This enables more productive shift configurations by taking the previous year's performance and revenue productivity data into account. Some or all of the above-described processing in the proposal unit may be performed using, or without, an AI. For example, the proposal unit can input the previous year's performance and revenue productivity data into an AI and have the AI ​​execute a shift proposal.

[0064] The suggestion unit can suggest suitable dates for taking paid leave based on busy and slow periods. The suggestion unit suggests suitable dates for taking paid leave based on, for example, busy and slow periods. For example, the suggestion unit identifies days with less work during a specific period from past data and suggests those days as candidate dates for taking paid leave. For example, the suggestion unit can suggest suitable dates for taking paid leave, taking into account busy and slow periods. This allows employees to take paid leave efficiently. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on busy and slow periods into AI and have the AI ​​suggest suitable dates for taking paid leave.

[0065] The suggestion unit can display the shift proposal through a user interface. For example, the suggestion unit displays the shift proposal through a user interface. For example, the suggestion unit displays the shift proposal through a web interface or a mobile app. For example, the suggestion unit can display the shift proposal through a web interface. The suggestion unit can also display the shift proposal through a mobile app. This allows the user to easily confirm the shift. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the shift proposal into AI and cause the AI ​​to execute processing to display it through a user interface.

[0066] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. Furthermore, if the user is relaxed, the collection unit can collect detailed data to increase accuracy. Furthermore, if the user is in a hurry, the collection unit can collect the minimum amount of data necessary to quickly proceed with processing. For example, the collection unit estimates the user's emotions using technologies such as facial expression recognition or voice analysis and adjusts the timing of data collection based on the results. This adjusts the timing of data collection according to the user's emotions, thereby reducing the burden on the user. 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. 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 can input user emotional data into the AI ​​and have the AI ​​adjust the timing of data collection.

[0067] The collection unit can monitor the health status and stress level of each employee and reflect the results in the collected data. For example, the collection unit can periodically check the health status of each employee and suspend data collection if an abnormality is detected. The collection unit can also prioritize the collection of data on employees with high stress levels and take appropriate measures. The collection unit can also adjust the shifts of other employees based on the data of employees in good health. For example, the collection unit can monitor the health status and stress level of employees using methods such as wearable devices or self-reporting. This allows for more appropriate shift proposals by taking into account the employee's health status and stress level. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the employee's health status and stress level into AI and have the AI ​​collect the data.

[0068] The collection unit can filter data based on the employee's individual goals and career plans when collecting the data. For example, the collection unit filters data taking into account the employee's individual goals and career plans when collecting the data. For example, the collection unit prioritizes collecting data on employees with specific skills based on their career plans. The collection unit can also collect only necessary data to match individual goals, eliminating waste. The collection unit can also collect data in line with career plans and use it to propose future shifts. For example, the collection unit collects employee goals and career plans using methods such as personal interviews and questionnaire surveys. This allows the data to be used to propose future shifts by taking into account the employee's goals and career plans. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on employee goals and career plans into AI and have the AI ​​perform data filtering.

[0069] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting important data. Furthermore, when the user is relaxed, the collection unit can collect detailed data to improve the accuracy of analysis. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the minimum amount of data necessary. For example, the collection unit can estimate the user's emotions using technologies such as facial expression recognition or voice analysis and determine the priority of data to be collected based on the results. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input user emotional data into the AI ​​and have the AI ​​determine the priority of the data.

[0070] The collection unit can collect data based on the employee's home environment and lifestyle habits at the time of collection. For example, the collection unit collects data taking into account the employee's home environment and lifestyle habits at the time of collection. For example, the collection unit collects data at a specific time period based on the home environment. The collection unit can also adjust the frequency of data collection to suit the lifestyle habits. The collection unit can also collect data according to the home environment and lifestyle habits and reflect it in shift suggestions. For example, the collection unit collects information about the employee's home environment and lifestyle habits using methods such as questionnaire surveys and interviews. This enables more appropriate shift suggestions by taking the employee's home environment and lifestyle habits into consideration. Some or all of the above-mentioned 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 data about the employee's home environment and lifestyle habits into AI and have the AI ​​collect the data.

[0071] The collection unit may analyze employees' social media activities and collect related data at the time of collection. For example, the collection unit may analyze employees' social media activities and collect related data at the time of collection. For example, the collection unit may analyze the content of social media posts to understand employees' stress levels. The collection unit may also adjust the timing of data collection based on the time spent on social media. The collection unit may also determine the priority of data collection based on the number of social media followers and engagement. For example, the collection unit may analyze employees' social media activities using methods such as analyzing the content of social media posts and the number of followers. This enables more appropriate shift proposals by taking employees' social media activities into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit may input data on employees' social media activities into AI and have the AI ​​collect the data.

[0072] The learning unit can estimate a user's emotions and select training data based on the estimated user emotions. For example, the learning unit estimates a user's emotions and selects training data based on the estimated user emotions. For example, if the user is stressed, the learning unit may use a simple data set for training. Also, if the user is relaxed, the learning unit may use a detailed data set for training. Also, if the user is in a hurry, the learning unit may use a minimal data set for training. For example, the learning unit may estimate a user's emotions using techniques such as facial expression recognition or voice analysis and select training data based on the results. This enables efficient training by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or without AI. For example, the learning unit can input user emotional data into the AI ​​and have the AI ​​select the learning data.

[0073] The learning unit can optimize the learning algorithm by referring to the employee's past performance data during learning. For example, the learning unit optimizes the learning algorithm by referring to the employee's past performance data during learning. For example, the learning unit adjusts the parameters of the learning algorithm based on the past performance data. The learning unit can also prioritize learning data of high-performing employees and optimize the algorithm. The learning unit can also analyze data of low-performing employees and reflect improvements in the learning algorithm. For example, the learning unit adjusts the parameters of the learning algorithm based on the past performance data. In this way, by referring to the employee's past performance data, the accuracy of the learning algorithm is improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the employee's past performance data into AI and cause the AI ​​to optimize the learning algorithm.

[0074] The learning unit can perform learning based on the employee's skill improvement and qualification acquisition history during learning. For example, the learning unit performs learning while taking into account the employee's skill improvement and qualification acquisition history during learning. For example, the learning unit selects learning data based on the employee's skill improvement and qualification acquisition history. The learning unit can also learn performance data after qualification acquisition and reflect it in the algorithm. The learning unit can also consider the skill improvement history and use it to propose future shifts. For example, the learning unit collects the employee's skill improvement and qualification acquisition history using methods such as training records and qualification certificates. This allows the employee's skill improvement and qualification acquisition history to be taken into account and used to propose future shifts. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the employee's skill improvement and qualification acquisition history into AI and have the AI ​​perform learning.

[0075] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and adjusts the frequency of learning based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit reduces the frequency of learning to reduce the burden. Furthermore, if the user is relaxed, the learning unit can increase the frequency of learning to improve accuracy. Furthermore, if the user is in a hurry, the learning unit can perform learning at the minimum necessary frequency. For example, the learning unit estimates the user's emotions using technologies such as facial expression recognition or voice analysis and adjusts the frequency of learning based on the results. This enables efficient learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the frequency of learning.

[0076] The learning unit can weight the learning data based on the employee's geographic location information during learning. For example, the learning unit weights the learning data taking into account the employee's geographic location information during learning. For example, the learning unit prioritizes learning data from a specific region based on the geographic location information. The learning unit can also adjust the weighting of the data according to the geographic location information and reflect this in the learning algorithm. The learning unit can also take the geographic location information into account and use it to propose shifts for each region. For example, the learning unit can collect employee geographic location information using methods such as GPS data or address information. This can be used to propose shifts for each region by taking the employee's geographic location information into account. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input employee geographic location information into AI and have the AI ​​weight the learning data.

[0077] The learning unit can analyze employees' social media activities during learning and reflect related data in the learning. For example, the learning unit can analyze employees' social media activities during learning and reflect related data in the learning. For example, the learning unit can analyze the content of social media posts and reflect the employee's stress level in the learning data. The learning unit can also adjust the weighting of the learning data based on the amount of time spent on social media. The learning unit can also select learning data based on the number of social media followers and engagement. For example, the learning unit can analyze employees' social media activities using methods such as analyzing the content of social media posts and the number of followers. This enables more appropriate shift proposals by taking employees' social media activities into consideration. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input data on employees' social media activities into AI and reflect the data in the learning.

[0078] The suggestion unit can estimate the user's emotions and adjust the way the shift suggestions are presented based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the way the shift suggestions are presented based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, highly visible shift suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide shift suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide shift suggestions that focus on the main points. For example, the suggestion unit can estimate the user's emotions using technologies such as facial expression recognition or voice analysis and adjust the way the shift suggestions are presented based on the results. This reduces the burden on the user by adjusting the way the shift suggestions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the way the shift suggestion is expressed.

[0079] The proposal unit can optimize shifts by taking into account individual employee performance data when making proposals. The proposal unit, for example, optimizes shifts by taking into account individual employee performance data when making proposals. For example, the proposal unit prioritizes high-performing employees in shifts. The proposal unit can also adjust the shifts of low-performing employees to reflect areas for improvement. The proposal unit can also propose an optimal shift balance based on performance data. For example, the proposal unit collects performance data such as performance evaluations and work efficiency, and optimizes shifts based on that data. This makes it possible to propose optimal shifts by taking employee performance data into consideration. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input employee performance data into AI and have the AI ​​perform shift optimization.

[0080] The proposal unit can customize shifts based on employee skills or qualifications when proposing them. The proposal unit, for example, customizes shifts based on employee skills and qualifications when proposing them. For example, the proposal unit prioritizes employees with specific skills in shifts. The proposal unit can also adjust shifts of qualified employees to achieve optimal allocation. The proposal unit can also adjust the balance of shifts based on skills and qualifications. For example, the proposal unit collects data such as technical skills and professional qualifications and customizes shifts based on that data. This makes it possible to propose optimal shifts by taking employee skills and qualifications into consideration. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, AI. For example, the proposal unit can input data on employee skills and qualifications into AI and have the AI ​​customize the shifts.

[0081] The suggestion unit can estimate the user's emotions and prioritize the shift proposals based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and prioritizes the shift proposals based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit prioritizes shift proposals with higher importance. Furthermore, if the user is relaxed, the suggestion unit can also provide detailed shift proposals. Furthermore, if the user is in a hurry, the suggestion unit can prioritize the minimum necessary shift proposals. For example, the suggestion unit can estimate the user's emotions using technologies such as facial expression recognition or voice analysis and prioritize the shift proposals based on the results. This enables efficient shift proposals by prioritizing the shift proposals according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input user emotional data into the AI ​​and have the AI ​​determine the priority of shift suggestions.

[0082] The suggestion unit can propose shifts based on the geographic location information of employees when proposing them. For example, the suggestion unit proposes shifts taking into account the geographic location information of employees when proposing them. For example, the suggestion unit prioritizes employees in specific areas into shifts based on the geographic location information. The suggestion unit can also adjust the balance of shifts according to the geographic location information. The suggestion unit can also propose shifts for each area by taking into account the geographic location information. For example, the suggestion unit collects the geographic location information of employees using methods such as GPS data or address information. This makes it possible to propose shifts for each area by taking into account the geographic location information of employees. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the geographic location information of employees into AI and have the AI ​​execute the shift proposal.

[0083] The suggestion unit can analyze employees' social media activities and propose relevant shifts when making suggestions. For example, the suggestion unit can analyze employees' social media activities and propose relevant shifts when making suggestions. For example, the suggestion unit can analyze social media posts and reflect employees' stress levels in shift suggestions. The suggestion unit can also adjust shift balance based on social media activity time. The suggestion unit can also make shift suggestions based on the number of social media followers and engagement. For example, the suggestion unit can analyze employees' social media activities using methods such as analyzing social media posts and the number of followers. This enables more appropriate shift suggestions by taking employees' social media activities into consideration. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input data on employees' social media activities into AI and have the AI ​​execute shift suggestions.

[0084] The suggestion unit can estimate the user's emotions and adjust the method of suggesting paid leave based on the estimated user emotions. For example, the suggestion unit can estimate the user's emotions and adjust the method of suggesting paid leave based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make a simple, highly visible paid leave suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a paid leave suggestion that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make a paid leave suggestion that focuses on the main points. For example, the suggestion unit can estimate the user's emotions using technologies such as facial expression recognition or voice analysis and adjust the method of suggesting paid leave based on the results. This reduces the burden on the user by adjusting the method of suggesting paid leave based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​adjust the proposed method for taking paid leave.

[0085] When making a proposal, the suggestion unit can suggest the optimal date by referring to the employee's past paid leave history. When making a proposal, the suggestion unit, for example, can suggest the optimal date by referring to the employee's past paid leave history. For example, the suggestion unit suggests the optimal date based on the employee's past paid leave history. The suggestion unit can also analyze the frequency and timing of paid leave use and suggest the optimal date. The suggestion unit can also suggest a date that will have the least impact on work by referring to the past paid leave history. For example, the suggestion unit collects past paid leave history and suggests the optimal date based on that. In this way, the optimal paid leave date can be suggested by referring to the employee's past paid leave history. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the employee's past paid leave history into AI and have the AI ​​suggest the optimal date.

[0086] The suggestion unit can suggest paid leave dates based on the employee's individual living situation and home environment when making a suggestion. For example, the suggestion unit can suggest paid leave dates taking into account the employee's individual living situation and home environment when making a suggestion. For example, the suggestion unit can suggest paid leave dates that coincide with specific events or occasions based on the home environment. The suggestion unit can also suggest paid leave dates when employees need to refresh themselves, depending on their living situation. The suggestion unit can also suggest paid leave dates that have minimal impact on work, taking into account the home environment and living situation. For example, the suggestion unit can collect information about employees' living situations and home environments using methods such as questionnaire surveys and interviews. This allows the suggestion unit to suggest more appropriate paid leave dates by taking into account the employee's living situation and home environment. Some or all of the above-described processing by the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input data about the employee's living situation and home environment into AI and have the AI ​​execute the suggested paid leave dates.

[0087] The suggestion unit can estimate the user's emotions and determine the priority of paid leave taking based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and determines the priority of paid leave taking based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit may suggest taking paid leave immediately. Furthermore, if the user is relaxed, the suggestion unit may also provide detailed paid leave taking suggestions. Furthermore, if the user is in a hurry, the suggestion unit may prioritize the minimum amount of paid leave taking suggestions. For example, the suggestion unit may estimate the user's emotions using technologies such as facial expression recognition or voice analysis and determine the priority of paid leave taking based on the results. This enables efficient paid leave taking suggestions by determining the priority of paid leave taking based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the AI ​​and have the AI ​​decide the priorities for taking paid leave.

[0088] The suggestion unit can propose paid leave dates taking into account the employee's geographic location information when making a proposal. For example, the suggestion unit proposes paid leave dates taking into account the employee's geographic location information when making a proposal. For example, the suggestion unit proposes paid leave dates that coincide with events or occasions in a specific region based on the geographic location information. The suggestion unit can also propose paid leave dates when a refresh is needed, depending on the geographic location information. The suggestion unit can also propose paid leave dates that will have minimal impact on work, taking into account the geographic location information. For example, the suggestion unit collects the employee's geographic location information using methods such as GPS data or address information. This makes it possible to propose paid leave dates for each region by taking into account the employee's geographic location information. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the employee's geographic location information into AI and have the AI ​​execute the proposed paid leave dates.

[0089] The suggestion unit may analyze the employee's social media activity and suggest relevant paid leave dates when making a suggestion. For example, the suggestion unit may analyze the employee's social media activity and suggest relevant paid leave dates when making a suggestion. For example, the suggestion unit may analyze the content of social media posts and reflect the employee's stress level in the paid leave suggestion. The suggestion unit may also adjust the balance of paid leave days based on the amount of time spent on social media. The suggestion unit may also suggest paid leave dates based on the number of social media followers and engagement. For example, the suggestion unit may analyze the employee's social media activity using methods such as analyzing the content of social media posts and the number of followers. This allows for more appropriate paid leave suggestions by taking the employee's social media activity into consideration. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit may input data on the employee's social media activity into AI and have the AI ​​execute the paid leave suggestion. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, learning unit, suggestion unit, and display 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 is realized by the control unit 46A of the smart device 14 and collects data such as each employee's working hours, role, and past shift patterns. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns from the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal shifts based on the learned data. The display unit is realized, for example, by the output device 40 of the smart device 14 and displays the shift suggestions through a user interface. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, learning unit, suggestion unit, and display 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 is realized by the control unit 46A of the smart glasses 214 and collects data such as each employee's working hours, role, and past shift patterns. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal shifts based on the learned data. The display unit is realized, for example, by the output device 40 of the smart glasses 214 and displays the shift suggestions through a user interface. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, learning unit, suggestion unit, and display 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 is realized by the control unit 46A of the headset terminal 314 and collects data such as each employee's working hours, role, and past shift patterns. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns from the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal shifts based on the learned data. The display unit is realized, for example, by the output device 40 of the headset terminal 314 and displays the shift suggestions through a user interface. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, learning unit, suggestion unit, and display 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 is realized by the control unit 46A of the robot 414 and collects data such as the working hours and roles of each employee, past shift patterns, etc. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns from the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests optimal shifts based on the learned data. The display unit is realized, for example, by the output device 40 of the robot 414 and displays the shift suggestions through a user interface.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The suggestion department can also suggest shifts based on employees' hobbies and interests. For example, if an employee has plans to participate in a particular sporting event, the suggestion department can adjust shifts to take that day into account. Also, if an employee wants to devote time to a particular hobby, the suggestion department can adjust shifts to ensure that time is available. Furthermore, if an employee has plans to attend an event or seminar that interests them, the suggestion department can prioritize shifts on that day. This makes it possible to suggest shifts that take into account the individual hobbies and interests of employees, improving employee satisfaction.

[0092] The data collection department can also collect data taking into account employees' commute times and means of transportation. For example, if an employee has a long commute, the department can adjust shifts to reduce that burden. If an employee uses public transportation, the department can adjust shifts to match that schedule. Furthermore, if an employee uses their own car, the department can adjust shifts to avoid heavy traffic hours. This reduces the commute burden on employees and provides a comfortable working environment.

[0093] The learning department can also customize learning data based on an employee's learning style and learning speed. For example, if an employee has a visual learning style, it can prioritize learning visual data. Alternatively, if an employee has an auditory learning style, it can prioritize learning audio data. Furthermore, it can adjust the amount and difficulty of learning data according to the employee's learning speed. This allows for optimal learning according to the employee's learning style and learning speed.

[0094] The suggestion department can also suggest shifts based on an employee's health condition and fitness level. For example, if an employee has a specific health problem identified during a health check, the shift can be adjusted to reduce the employee's burden. Also, if an employee is participating in a fitness program, the shift can be adjusted to fit that schedule. Furthermore, it can ensure appropriate break times based on the employee's health condition and fitness level. This makes it possible to suggest shifts that take employee health into consideration.

[0095] The Suggestion Department can also suggest shifts based on an employee's career goals and growth plans. For example, if an employee plans to attend training to acquire a specific skill, the shift can be adjusted to take that day into consideration. Also, if an employee is aiming to advance their career, the shift can be adjusted to match that goal. Furthermore, based on the employee's growth plan, it can suggest shifts that will allow them to gain appropriate work experience. This makes it possible to suggest shifts that support an employee's career goals and growth plans.

[0096] The suggestion unit can estimate the user's emotions and adjust the timing of shift suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the timing of the shift suggestions can be delayed and suggested when the user is relaxed. Also, if the user is in a hurry, the suggestion unit can make quick shift suggestions. Furthermore, if the user is relaxed, the suggestion unit can make detailed shift suggestions. This allows shift suggestions to be made at the optimal timing according to the user's emotions.

[0097] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is feeling stressed, data can be collected in the form of simple questions. Alternatively, if the user is relaxed, data can be collected in the form of a detailed questionnaire. Furthermore, if the user is in a hurry, a data collection method that can be completed in a short time can be adopted. This allows data collection to be performed in the optimal way according to the user's emotions.

[0098] The learning unit can estimate the user's emotions and determine the priority of learning data based on the estimated user emotions. For example, if the user is feeling stressed, data of high importance is given priority in learning. Also, if the user is relaxed, detailed data can be learned to improve accuracy. Furthermore, if the user is in a hurry, the minimum necessary data can be given priority in learning. This makes it possible to determine the optimal priority of learning data according to the user's emotions.

[0099] The suggestion unit can estimate the user's emotions and adjust the timing of the suggestion to take paid leave based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest taking paid leave immediately. If the user is relaxed, the suggestion unit can make a detailed suggestion to take paid leave. Furthermore, if the user is in a hurry, the suggestion unit can make a concise suggestion to take paid leave. This allows the suggestion unit to suggest taking paid leave at the optimal timing according to the user's emotions.

[0100] The suggestion unit can estimate the user's emotions and adjust the content of the shift suggestions based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible shift suggestion can be made. If the user is relaxed, a shift suggestion including detailed information can be made. Furthermore, if the user is in a hurry, a shift suggestion that focuses on the main points can be made. This makes it possible to provide the most appropriate shift suggestion content according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection department collects past data related to shift scheduling, such as each employee's working hours, role, past shift patterns, skills and qualifications, and individual performance data. The collection department collects shift data from the past year and trains the AI. Step 2: The learning unit learns the data collected by the collection unit. For example, the learning unit uses AI to learn the collected data using deep learning or machine learning algorithms. Step 3: The suggestion unit proposes shifts based on the data learned by the learning unit. For example, it generates optimal shifts by taking into account each employee's working hours, role, and past shift patterns. It proposes shifts by taking into account the previous year's performance and revenue productivity data, and proposes optimal dates for taking paid leave by taking into account busy and slow periods. The suggestion unit identifies days with less work during specific periods from past data and proposes those days as candidate dates for taking paid leave. The suggestion unit displays the shift proposals through a user interface. For example, it displays the shift proposals through a web interface or mobile app.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 unit that collects past data related to shift creation; a learning unit that learns the data collected by the collection unit; a suggestion unit that suggests shifts based on the data learned by the learning unit; a proposal unit that proposes suitable dates for taking paid leave based on the shifts proposed by the proposal unit; Equipped with A system characterized by:

2. The collecting unit Collect each employee's hours worked, role, past shift patterns, skills or qualifications, and individual performance data 2. The system of claim 1.

3. The learning unit Learn from collected data using AI 2. The system of claim 1.

4. The proposal unit Recommend shifts based on previous year's performance and revenue productivity data 2. The system of claim 1.

5. The proposal unit Suggest suitable dates for taking paid leave based on peak and off-peak periods 2. The system of claim 1.

6. The proposal unit Displaying shift proposals through the user interface 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Monitor the health and stress levels of each employee and reflect this in the data collected.

2. The system of claim 1.

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