system

Abstract

The system according to this embodiment aims to recommend days when employees take paid leave that will have minimal impact on their work. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a recommendation unit. The collection unit collects the work status of employees. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends candidate days that will have the least impact on work, based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for employees to find an appropriate day to minimize the impact on work when taking paid leave.

[0005] The system according to the embodiment aims to recommend days with less impact on work when employees take paid leave.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a recommendation unit. The collection unit collects the work status of employees. The analysis unit analyzes the information collected by the collection unit. The recommendation unit recommends candidate days with less impact on work based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend days when employees take paid leave that will have minimal impact on their work. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The paid leave support system according to an embodiment of the present invention is an internal tool designed to make it easier for employees to take paid leave. When an employee wants to take paid leave but hasn't decided on a specific date, the system analyzes the employee's past work history, intranet activity, chat messages, and email content to recommend candidate dates that will have minimal impact on their work. This allows employees to take paid leave with peace of mind and promotes work-style reform. For example, an employee inputs their desire to take paid leave into the system. Even if a specific date hasn't been decided, the system can handle it. For instance, the employee might input, "I'd like to take paid leave soon, but I haven't decided on a specific date." This information is then entered into the system. Next, the system analyzes the employee's past work history, intranet activity, chat messages, and email content. Based on this information, the system identifies busy and slow periods in the employee's work. For example, it can identify peak work periods from past email sending and receiving history and chat exchanges. Based on the analysis results, the system recommends candidate dates that will have minimal impact on work. For example, the system might provide specific dates such as, "There are no scheduled appointments on October 16, 2024, and there tends to be less work on the middle Wednesday of each month." In this way, employees can take paid leave with peace of mind. This system promotes the use of paid leave by employees. Employees can take paid leave while minimizing the impact on their work. Furthermore, employers can promote work-style reform by encouraging employees to take paid leave. For example, creating an environment where employees can easily take paid leave improves employee satisfaction and work efficiency. Moreover, this system can be offered not only internally but also externally. For example, by introducing it to other companies, work-style reform can be promoted more broadly. In this way, it is possible to create a workplace and society where it is easy to take leave and work is easier. Thus, the paid leave support system can promote the use of paid leave by employees and promote work-style reform.

[0029] The paid leave acquisition support system according to this embodiment comprises a collection unit, an analysis unit, and a recommendation unit. The collection unit collects information on the employee's work status. For example, the collection unit can collect information such as the progress of an employee's tasks, meeting schedules, and project deadlines. The collection unit can also collect information from the intranet, chats, and emails. For example, the collection unit can collect information from posts on bulletin boards on the intranet, chat message history, and email sending and receiving history. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining, statistical analysis, machine learning algorithms, etc. Based on the collected information, the analysis unit identifies peak and off-peak periods for work. For example, the analysis unit analyzes the number of past tasks, project progress, and meeting frequency to identify peak and off-peak periods for work. Based on the information analyzed by the analysis unit, the recommendation unit recommends candidate days that will have minimal impact on work. For example, the recommendation unit presents specific candidate days based on the analysis results. For example, it might suggest a specific date such as, "October 16, 2024 is free, and the middle Wednesday of each month tends to have less work." This allows the paid leave support system, according to the embodiment, to analyze the employee's work situation and recommend candidate days that have minimal impact on work, thereby promoting the use of paid leave.

[0030] The data collection department collects information on employees' work status. Specifically, it collects information such as the progress of tasks assigned to employees, meeting schedules, and project deadlines. This information is automatically retrieved from project management tools and calendar applications used by employees. For example, project management tools record the start date, end date, and progress rate of each task, and the data collection department periodically retrieves this data. Meeting schedules are retrieved from employees' calendar applications, and detailed information such as the date and time of the meeting, participants, and agenda is collected. Furthermore, project deadlines are obtained from project management tools and project pages on the intranet, allowing the department to understand important milestones and deadlines for each project. By centrally managing and updating this information in real time, the data collection department can accurately grasp the status of employees' work. In addition, the data collection department also collects content from the intranet, chats, and emails. By collecting posts on intranet bulletin boards, chat message histories, and email sending and receiving histories, the department can also understand the state of communication and information sharing among employees. For example, project progress reports and important announcements are often posted on intranet bulletin boards, and collecting this information allows for an understanding of project progress and any problems. Chat message history and email sending / receiving history reveal the frequency and content of communication among employees, allowing for the identification of workload and communication bottlenecks. This enables the data collection department to gain a multifaceted understanding of employees' work situations and provide accurate data to the analysis and recommendation departments.

[0031] The analysis department analyzes the information collected by the data collection department. Specifically, it analyzes the collected information from multiple perspectives using data mining, statistical analysis, and machine learning algorithms. Using data mining techniques, it extracts patterns and trends from data such as the number of tasks, project progress, and meeting frequency in the past. For example, if the number of tasks tends to increase sharply at a particular time, it can be identified that this period is a peak time for work. Using statistical analysis, it analyzes the distribution and correlation of each data to quantitatively evaluate peak and off-peak periods for work. For example, it identifies periods when meetings are frequent or when project deadlines are concentrated, and determines that these periods are peak times for work. Using machine learning algorithms, it builds models to predict peak and off-peak periods for work from the collected data. For example, based on past data, it predicts the number of tasks and meeting frequency at a particular time, and identifies peak and off-peak periods for work based on the prediction results. Based on these analysis results, the analysis department can grasp the workload and workload levels of employees in real time and provide accurate information to the recommendation department. Furthermore, the analysis unit can utilize historical data and statistical information to evaluate long-term business trends and risks. For example, based on data from the past few years, it can predict fluctuations in business activity during specific periods and identify future peak and off-peak seasons. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also address long-term business trends and risk management, improving the overall reliability and effectiveness of the system.

[0032] The recommendation team, based on information analyzed by the analytics team, suggests candidate dates that will have the least impact on work. Specifically, it suggests the best dates for employees to take paid leave based on the analysis results. For example, it might suggest specific candidate dates based on the off-peak periods identified by the analytics team. For instance, it might suggest a specific date such as, "October 16, 2024 is free, and the middle Wednesday of each month tends to have less work." The recommendation team can also take into account individual employee circumstances and preferences. For example, if an employee wants to take leave during a specific period, it will recommend the date within that period that will have the least impact on work. Furthermore, the recommendation team can maintain a balance of work across the entire team by considering the employee's past leave history and the leave plans of other employees. For example, to avoid multiple employees in a particular team taking leave simultaneously, it will adjust candidate dates by considering the leave plans of other employees. In addition, the recommendation team can send reminders and notifications to employees to ensure that the recommended candidate dates are actually taken as paid leave. For example, it can send a reminder one week before the recommended candidate date to encourage employees to prepare for their leave. Furthermore, the recommendation department can follow up with employees after they have taken leave, and evaluate the effectiveness and satisfaction levels of the leave. This allows the recommendation department to support employees in taking paid leave efficiently and effectively, minimizing the impact on work.

[0033] The collection unit can collect content from the intranet, chats, and emails. For example, the collection unit can collect the content of posts on an intranet bulletin board. The collection unit can also collect chat message history, for example. The collection unit can also collect email sending and receiving history, for example. By collecting the content of the intranet, chats, and emails, a more accurate understanding of the business situation can be obtained. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the content of posts on an intranet bulletin board into a generating AI and have the generating AI perform analysis of the post content.

[0034] The analysis unit can identify peak and off-peak periods in business operations based on the collected information. For example, the analysis unit can analyze the number of collected tasks to identify peak and off-peak periods. The analysis unit can also analyze the progress of collected projects to identify peak and off-peak periods. The analysis unit can also analyze the frequency of collected meetings to identify peak and off-peak periods. By identifying peak and off-peak periods, it is possible to recommend appropriate candidate dates for paid leave. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the number of collected tasks into a generating AI and have the generating AI identify peak and off-peak periods in business operations.

[0035] The recommendation unit can suggest specific candidate dates based on the analysis results. For example, the recommendation unit can present specific candidate dates in a calendar format based on the analysis results. The recommendation unit can also present specific candidate dates in a list format based on the analysis results. The recommendation unit can also display the reasons for recommending specific candidate dates based on the analysis results. This makes it easier for employees to take paid leave by suggesting specific candidate dates. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the analysis results into a generating AI and have the generating AI perform the task of suggesting specific candidate dates.

[0036] The presentation unit can present candidate dates to the user. For example, the presentation unit can present candidate dates to the user in a calendar format. The presentation unit can also present candidate dates to the user in a list format. The presentation unit can also display the reasons for recommending the candidate dates. By presenting candidate dates to the user, it makes it easier for the user to select paid leave. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input candidate dates into a generation AI and have the generation AI perform the task of presenting candidate dates.

[0037] The notification unit can notify the supervisor after the user has selected candidate dates. For example, the notification unit can notify the supervisor by email after the user has selected candidate dates. The notification unit can also notify the supervisor by pop-up notification after the user has selected candidate dates. The notification unit can also notify the supervisor by push notification after the user has selected candidate dates. This allows for a smoother process for taking paid leave by notifying the supervisor after the user has selected candidate dates. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the candidate dates selected by the user into a generating AI and have the generating AI execute the notification to the supervisor.

[0038] The data collection unit can analyze the user's past work history during data collection and select the optimal collection method. For example, the data collection unit can prioritize collecting information related to tasks the user has frequently performed in the past. For example, the data collection unit can also collect information related to specific projects from the user's past work history. For example, the data collection unit can analyze the user's past work history and collect information related to peak periods of work. This allows the optimal collection method to be selected by analyzing the user's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past work history into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit can prioritize collecting information related to the user's current projects. The data collection unit can also filter and collect information related to the user's areas of interest. For example, the data collection unit can collect necessary information based on the progress of the user's current projects. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. The data collection unit can also collect information related to nearby business based on the user's geographical location. For example, if the user is on a business trip, the data collection unit can prioritize the collection of information related to the business trip destination. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI perform the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect relevant business information based on information shared by the user on social media. The data collection unit can also collect information related to topics of interest from the user's social media activity. For example, the data collection unit can collect relevant business information based on information about accounts the user follows on social media. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0042] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the collected information during the analysis process. For example, the analysis unit can correlate and analyze the contents of collected emails and chats. The analysis unit can also combine and analyze intranet information and work history. For example, the analysis unit can identify peak periods for business by considering the interrelationships of the collected information. This improves the accuracy of the analysis by considering the interrelationships of the collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationship data of the collected information into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0043] The analysis unit can identify peak and off-peak periods in business by referring to the user's work history during analysis. For example, the analysis unit can identify busy and off-peak periods from the user's past work history. The analysis unit can also identify the peak period for a specific project based on the user's work history. For example, the analysis unit can analyze the user's work history to identify annual business fluctuations. This allows the analysis unit to identify peak and off-peak periods in business by referring to the user's work history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's work history data into a generating AI and have the generating AI perform the identification of peak and off-peak periods in business.

[0044] The analysis unit can perform analysis while considering the geographical distribution of the collected information. For example, the analysis unit can identify peak periods for business operations based on the geographical distribution of the collected information. The analysis unit can also analyze the business conditions of a specific region, taking geographical distribution into consideration. For example, the analysis unit can identify busy and slow periods for business operations based on geographical distribution. This allows for more accurate analysis by considering the geographical distribution of the collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of the collected information into a generating AI and have the generating AI perform the analysis.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can identify peak periods for business by referring to relevant literature. The analysis unit can also identify busy and slow periods for business based on relevant literature. The analysis unit can also improve the accuracy of its analysis by referring to relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0046] The recommendation unit can adjust the accuracy of its recommendations based on the level of detail of the analysis results. For example, if the analysis results are detailed, the recommendation unit will recommend specific candidate dates. For example, if the analysis results are general, the recommendation unit may also suggest multiple candidate dates. For example, if the analysis results are unclear, the recommendation unit may also set a lower recommendation accuracy. By adjusting the recommendation accuracy based on the level of detail of the analysis results, more accurate candidate dates can be suggested. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the analysis result data into a generating AI and have the generating AI perform the recommendation accuracy adjustment.

[0047] The recommendation unit can, when making recommendations, refer to the user's work history to suggest the most suitable candidate dates. For example, the recommendation unit can suggest candidate dates that avoid peak seasons based on the user's past work history. For example, the recommendation unit can also suggest candidate dates for taking paid leave during off-peak seasons based on the user's work history. For example, the recommendation unit can suggest the most suitable candidate dates by referring to the user's work history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's work history data into a generating AI and have the generating AI perform the task of suggesting the most suitable candidate dates.

[0048] The recommendation unit can make recommendations while considering the geographical distribution of candidate dates. For example, if the user is in a specific region, the recommendation unit will recommend candidate dates related to that region. The recommendation unit can also recommend candidate dates based on the business situation in a specific region, taking geographical distribution into consideration. For example, the recommendation unit can also recommend candidate dates based on geographical distribution, taking into account busy and slow periods of business. This allows for the recommendation of more appropriate candidate dates by considering the geographical distribution of candidate dates. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input geographical distribution data of candidate dates into a generating AI and have the generating AI execute the recommendations.

[0049] The recommendation unit can improve the accuracy of its recommendations by referring to relevant literature during the recommendation process. For example, the recommendation unit can refer to relevant literature to identify peak periods for business and recommend candidate dates. The recommendation unit can also, for example, identify busy and slow periods for business based on relevant literature and recommend candidate dates. The recommendation unit can also, for example, refer to relevant literature to improve the accuracy of its recommendations. This improves the accuracy of recommendations by referring to relevant literature. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input relevant literature data into a generating AI and have the generating AI perform the recommendation accuracy improvement.

[0050] The presentation unit can select the optimal presentation method by referring to the user's past selection history when making a presentation. For example, the presentation unit can select the optimal presentation method based on candidate dates previously selected by the user. The presentation unit can also prioritize providing a specific presentation method based on the user's past selection history. The presentation unit can also analyze the user's past selection history and select the most effective presentation method. This allows the optimal presentation method to be selected by referring to the user's past selection history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past selection history data into a generating AI and have the generating AI select the optimal presentation method.

[0051] The presentation unit can select the optimal presentation method by considering the user's device information during presentation. For example, if the user is using a smartphone, the presentation unit can provide a presentation method that matches the screen size. For example, if the user is using a tablet, the presentation unit can also provide a presentation method optimized for a larger screen. For example, if the user is using a desktop, the presentation unit can also provide a presentation method that includes detailed information. This allows the optimal presentation method to be selected by considering the user's device information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's device information into a generating AI and have the generating AI select the optimal presentation method.

[0052] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize providing notification methods that the user has preferred to use in the past. The notification unit may also select a specific notification method from the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most effective notification method. This allows the optimal notification method to be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0053] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit provides a push notification. For example, if the user is using a tablet, the notification unit can also provide a notification method adapted to the screen size. For example, if the user is using a desktop, the notification unit can also provide a pop-up notification. This allows the system to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The paid leave support system can also include a project management department. The project management department monitors the progress of each project and provides this information to the analysis department. For example, it can collect data such as project deadlines, progress, and resource usage. This allows the analysis department to recommend optimal paid leave dates, taking the project status into consideration. For instance, if a project deadline is approaching, it can recommend avoiding taking leave. Conversely, if a project is progressing smoothly, it can recommend days when it's easier to take leave. This effectively promotes paid leave based on project progress.

[0056] The paid leave support system can also include a skill matching unit. The skill matching unit matches employees' skill sets with their job descriptions and provides this information to the analysis unit. For example, it can collect data to assign appropriate employees to tasks or projects that require specific skills. This allows the analysis unit to recommend optimal paid leave dates, taking into account the employees' skill sets. For instance, it can recommend leave during periods when there are fewer tasks requiring specific skills. It can also recommend days when employees have overlapping skill sets, making it easier for them to take leave. This effectively promotes paid leave based on employees' skill sets.

[0057] The paid leave support system can also include a learning department. The learning department collects employees' learning history and skill development status and provides it to the analysis department. For example, it can collect data on training courses attended, qualifications obtained, and skills being studied. This allows the analysis department to recommend optimal paid leave dates, taking into account the employee's learning status. For instance, it can recommend leave immediately after training completion, or days before and after qualification exams when leave is more easily taken. This facilitates the use of paid leave based on employees' learning progress.

[0058] The paid leave support system can also include a communications department. This department collects data on employee communication patterns and provides it to the analytics department. For example, it can collect data such as the frequency of emails and chats between employees, and meeting participation. This allows the analytics department to recommend optimal paid leave dates, taking communication patterns into account. For instance, it can recommend avoiding leave during periods of high communication activity, or recommend days when employees are more likely to take leave during periods of low communication activity. This effectively promotes paid leave based on employee communication patterns.

[0059] The paid leave support system can also include a performance evaluation unit. This unit evaluates employees' work performance and provides the data to the analysis unit. For example, it can collect data such as employee work results, evaluation reports, and feedback. This allows the analysis unit to recommend optimal paid leave dates, taking employee performance into account. For instance, if performance is high, it can recommend days when it's easier to take leave. Conversely, if performance is declining, it can recommend taking leave earlier. This effectively promotes paid leave based on employee performance.

[0060] The paid leave support system can also include a career planning department. This department collects employee career plans and provides them to the analysis department. For example, it can collect data such as employees' career goals, desired job roles, and skill development plans. This allows the analysis department to recommend optimal paid leave dates, taking into account the employee's career plan. For instance, it can recommend leave after training for skill development based on the career plan. It can also recommend days when employees are more likely to take leave before or after important projects related to their career goals. This facilitates the use of paid leave based on employees' career plans.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects information on employees' work status. For example, the data collection unit can collect information such as the progress of employees' tasks, meeting schedules, and project deadlines. It can also collect information from the intranet, chats, and emails. For example, it can collect posts on intranet bulletin boards, chat message history, and email sending and receiving history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using methods such as data mining, statistical analysis, and machine learning algorithms. Based on the collected information, the analysis unit identifies peak and off-peak periods in business operations. For example, it analyzes the number of past tasks, project progress, and meeting frequency to identify peak and off-peak periods in business operations. Step 3: The recommendation unit recommends candidate dates that will have minimal impact on work, based on the information analyzed by the analysis unit. For example, the recommendation unit will suggest specific candidate dates based on the analysis results. For example, it might suggest a specific date such as, "October 16, 2024 is free, and Wednesdays in the middle of each month tend to have less work."

[0063] (Example of form 2) The paid leave support system according to an embodiment of the present invention is an internal tool designed to make it easier for employees to take paid leave. When an employee wants to take paid leave but hasn't decided on a specific date, the system analyzes the employee's past work history, intranet activity, chat messages, and email content to recommend candidate dates that will have minimal impact on their work. This allows employees to take paid leave with peace of mind and promotes work-style reform. For example, an employee inputs their desire to take paid leave into the system. Even if a specific date hasn't been decided, the system can handle it. For instance, the employee might input, "I'd like to take paid leave soon, but I haven't decided on a specific date." This information is then entered into the system. Next, the system analyzes the employee's past work history, intranet activity, chat messages, and email content. Based on this information, the system identifies busy and slow periods in the employee's work. For example, it can identify peak work periods from past email sending and receiving history and chat exchanges. Based on the analysis results, the system recommends candidate dates that will have minimal impact on work. For example, the system might provide specific dates such as, "There are no scheduled appointments on October 16, 2024, and there tends to be less work on the middle Wednesday of each month." In this way, employees can take paid leave with peace of mind. This system promotes the use of paid leave by employees. Employees can take paid leave while minimizing the impact on their work. Furthermore, employers can promote work-style reform by encouraging employees to take paid leave. For example, creating an environment where employees can easily take paid leave improves employee satisfaction and work efficiency. Moreover, this system can be offered not only internally but also externally. For example, by introducing it to other companies, work-style reform can be promoted more broadly. In this way, it is possible to create a workplace and society where it is easy to take leave and work is easier. Thus, the paid leave support system can promote the use of paid leave by employees and promote work-style reform.

[0064] The paid leave acquisition support system according to this embodiment comprises a collection unit, an analysis unit, and a recommendation unit. The collection unit collects information on the employee's work status. For example, the collection unit can collect information such as the progress of an employee's tasks, meeting schedules, and project deadlines. The collection unit can also collect information from the intranet, chats, and emails. For example, the collection unit can collect information from posts on bulletin boards on the intranet, chat message history, and email sending and receiving history. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using data mining, statistical analysis, machine learning algorithms, etc. Based on the collected information, the analysis unit identifies peak and off-peak periods for work. For example, the analysis unit analyzes the number of past tasks, project progress, and meeting frequency to identify peak and off-peak periods for work. Based on the information analyzed by the analysis unit, the recommendation unit recommends candidate days that will have minimal impact on work. For example, the recommendation unit presents specific candidate days based on the analysis results. For example, it might suggest a specific date such as, "October 16, 2024 is free, and the middle Wednesday of each month tends to have less work." This allows the paid leave support system, according to the embodiment, to analyze the employee's work situation and recommend candidate days that have minimal impact on work, thereby promoting the use of paid leave.

[0065] The data collection department collects information on employees' work status. Specifically, it collects information such as the progress of tasks assigned to employees, meeting schedules, and project deadlines. This information is automatically retrieved from project management tools and calendar applications used by employees. For example, project management tools record the start date, end date, and progress rate of each task, and the data collection department periodically retrieves this data. Meeting schedules are retrieved from employees' calendar applications, and detailed information such as the date and time of the meeting, participants, and agenda is collected. Furthermore, project deadlines are obtained from project management tools and project pages on the intranet, allowing the department to understand important milestones and deadlines for each project. By centrally managing and updating this information in real time, the data collection department can accurately grasp the status of employees' work. In addition, the data collection department also collects content from the intranet, chats, and emails. By collecting posts on intranet bulletin boards, chat message histories, and email sending and receiving histories, the department can also understand the state of communication and information sharing among employees. For example, project progress reports and important announcements are often posted on intranet bulletin boards, and collecting this information allows for an understanding of project progress and any problems. Chat message history and email sending / receiving history reveal the frequency and content of communication among employees, allowing for the identification of workload and communication bottlenecks. This enables the data collection department to gain a multifaceted understanding of employees' work situations and provide accurate data to the analysis and recommendation departments.

[0066] The analysis department analyzes the information collected by the data collection department. Specifically, it analyzes the collected information from multiple perspectives using data mining, statistical analysis, and machine learning algorithms. Using data mining techniques, it extracts patterns and trends from data such as the number of tasks, project progress, and meeting frequency in the past. For example, if the number of tasks tends to increase sharply at a particular time, it can be identified that this period is a peak time for work. Using statistical analysis, it analyzes the distribution and correlation of each data to quantitatively evaluate peak and off-peak periods for work. For example, it identifies periods when meetings are frequent or when project deadlines are concentrated, and determines that these periods are peak times for work. Using machine learning algorithms, it builds models to predict peak and off-peak periods for work from the collected data. For example, based on past data, it predicts the number of tasks and meeting frequency at a particular time, and identifies peak and off-peak periods for work based on the prediction results. Based on these analysis results, the analysis department can grasp the workload and workload levels of employees in real time and provide accurate information to the recommendation department. Furthermore, the analysis unit can utilize historical data and statistical information to evaluate long-term business trends and risks. For example, based on data from the past few years, it can predict fluctuations in business activity during specific periods and identify future peak and off-peak seasons. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also address long-term business trends and risk management, improving the overall reliability and effectiveness of the system.

[0067] The recommendation team, based on information analyzed by the analytics team, suggests candidate dates that will have the least impact on work. Specifically, it suggests the best dates for employees to take paid leave based on the analysis results. For example, it might suggest specific candidate dates based on the off-peak periods identified by the analytics team. For instance, it might suggest a specific date such as, "October 16, 2024 is free, and the middle Wednesday of each month tends to have less work." The recommendation team can also take into account individual employee circumstances and preferences. For example, if an employee wants to take leave during a specific period, it will recommend the date within that period that will have the least impact on work. Furthermore, the recommendation team can maintain a balance of work across the entire team by considering the employee's past leave history and the leave plans of other employees. For example, to avoid multiple employees in a particular team taking leave simultaneously, it will adjust candidate dates by considering the leave plans of other employees. In addition, the recommendation team can send reminders and notifications to employees to ensure that the recommended candidate dates are actually taken as paid leave. For example, it can send a reminder one week before the recommended candidate date to encourage employees to prepare for their leave. Furthermore, the recommendation department can follow up with employees after they have taken leave, and evaluate the effectiveness and satisfaction levels of the leave. This allows the recommendation department to support employees in taking paid leave efficiently and effectively, minimizing the impact on work.

[0068] The collection unit can collect content from the intranet, chats, and emails. For example, the collection unit can collect the content of posts on an intranet bulletin board. The collection unit can also collect chat message history, for example. The collection unit can also collect email sending and receiving history, for example. By collecting the content of the intranet, chats, and emails, a more accurate understanding of the business situation can be obtained. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the content of posts on an intranet bulletin board into a generating AI and have the generating AI perform analysis of the post content.

[0069] The analysis unit can identify peak and off-peak periods in business operations based on the collected information. For example, the analysis unit can analyze the number of collected tasks to identify peak and off-peak periods. The analysis unit can also analyze the progress of collected projects to identify peak and off-peak periods. The analysis unit can also analyze the frequency of collected meetings to identify peak and off-peak periods. By identifying peak and off-peak periods, it is possible to recommend appropriate candidate dates for paid leave. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the number of collected tasks into a generating AI and have the generating AI identify peak and off-peak periods in business operations.

[0070] The recommendation unit can suggest specific candidate dates based on the analysis results. For example, the recommendation unit can present specific candidate dates in a calendar format based on the analysis results. The recommendation unit can also present specific candidate dates in a list format based on the analysis results. The recommendation unit can also display the reasons for recommending specific candidate dates based on the analysis results. This makes it easier for employees to take paid leave by suggesting specific candidate dates. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the analysis results into a generating AI and have the generating AI perform the task of suggesting specific candidate dates.

[0071] The presentation unit can present candidate dates to the user. For example, the presentation unit can present candidate dates to the user in a calendar format. The presentation unit can also present candidate dates to the user in a list format. The presentation unit can also display the reasons for recommending the candidate dates. By presenting candidate dates to the user, it makes it easier for the user to select paid leave. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input candidate dates into a generation AI and have the generation AI perform the task of presenting candidate dates.

[0072] The notification unit can notify the supervisor after the user has selected candidate dates. For example, the notification unit can notify the supervisor by email after the user has selected candidate dates. The notification unit can also notify the supervisor by pop-up notification after the user has selected candidate dates. The notification unit can also notify the supervisor by push notification after the user has selected candidate dates. This allows for a smoother process for taking paid leave by notifying the supervisor after the user has selected candidate dates. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the candidate dates selected by the user into a generating AI and have the generating AI execute the notification to the supervisor.

[0073] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information related to the stressful task. For example, if the user is relaxed, the data collection unit may prioritize collecting information related to normal tasks. For example, if the user is in a hurry, the data collection unit may prioritize collecting information related to urgent tasks. This allows for the collection of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0074] The data collection unit can analyze the user's past work history during data collection and select the optimal collection method. For example, the data collection unit can prioritize collecting information related to tasks the user has frequently performed in the past. For example, the data collection unit can also collect information related to specific projects from the user's past work history. For example, the data collection unit can analyze the user's past work history and collect information related to peak periods of work. This allows the optimal collection method to be selected by analyzing the user's past work history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past work history into a generating AI and have the generating AI select the optimal collection method.

[0075] The data collection unit can filter data based on the user's current projects and areas of interest during collection. For example, the data collection unit can prioritize collecting information related to the user's current projects. The data collection unit can also filter and collect information related to the user's areas of interest. For example, the data collection unit can collect necessary information based on the progress of the user's current projects. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can quickly collect information that is causing the stress. For example, if the user is relaxed, the data collection unit can also collect information at a normal time. For example, if the user is in a hurry, the data collection unit can quickly collect information that is of high urgency. By adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. The data collection unit can also collect information related to nearby business based on the user's geographical location. For example, if the user is on a business trip, the data collection unit can prioritize the collection of information related to the business trip destination. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI perform the collection of highly relevant information.

[0078] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can collect relevant business information based on information shared by the user on social media. The data collection unit can also collect information related to topics of interest from the user's social media activity. For example, the data collection unit can collect relevant business information based on information about accounts the user follows on social media. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0079] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit will focus on analyzing information that is causing stress. For example, if the user is relaxed, the analysis unit can also analyze information using normal criteria. For example, if the user is in a hurry, the analysis unit can prioritize the analysis of information that is of high urgency. By adjusting the analysis criteria based on the user's emotions, more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis criteria.

[0080] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the collected information during the analysis process. For example, the analysis unit can correlate and analyze the contents of collected emails and chats. The analysis unit can also combine and analyze intranet information and work history. For example, the analysis unit can identify peak periods for business by considering the interrelationships of the collected information. This improves the accuracy of the analysis by considering the interrelationships of the collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationship data of the collected information into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0081] The analysis unit can identify peak and off-peak periods in business by referring to the user's work history during analysis. For example, the analysis unit can identify busy and off-peak periods from the user's past work history. The analysis unit can also identify the peak period for a specific project based on the user's work history. For example, the analysis unit can analyze the user's work history to identify annual business fluctuations. This allows the analysis unit to identify peak and off-peak periods in business by referring to the user's work history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's work history data into a generating AI and have the generating AI perform the identification of peak and off-peak periods in business.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0083] The analysis unit can perform analysis while considering the geographical distribution of the collected information. For example, the analysis unit can identify peak periods for business operations based on the geographical distribution of the collected information. The analysis unit can also analyze the business conditions of a specific region, taking geographical distribution into consideration. For example, the analysis unit can identify busy and slow periods for business operations based on geographical distribution. This allows for more accurate analysis by considering the geographical distribution of the collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of the collected information into a generating AI and have the generating AI perform the analysis.

[0084] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can identify peak periods for business by referring to relevant literature. The analysis unit can also identify busy and slow periods for business based on relevant literature. The analysis unit can also improve the accuracy of its analysis by referring to relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0085] The recommendation unit can estimate the user's emotions and determine the priority of recommended dates based on those emotions. For example, if the user is stressed, the recommendation unit will prioritize earlier dates. If the user is relaxed, the recommendation unit may also recommend normal dates. If the user is in a hurry, the recommendation unit may also prioritize urgent dates. This allows for the recommendation of more appropriate dates by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit may be performed using AI or not. For example, the recommendation unit can input user emotion data into a generative AI and have the generative AI determine the priority of dates.

[0086] The recommendation unit can adjust the accuracy of its recommendations based on the level of detail of the analysis results. For example, if the analysis results are detailed, the recommendation unit will recommend specific candidate dates. For example, if the analysis results are general, the recommendation unit may also suggest multiple candidate dates. For example, if the analysis results are unclear, the recommendation unit may also set a lower recommendation accuracy. By adjusting the recommendation accuracy based on the level of detail of the analysis results, more accurate candidate dates can be suggested. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the analysis result data into a generating AI and have the generating AI perform the recommendation accuracy adjustment.

[0087] The recommendation unit can, when making recommendations, refer to the user's work history to suggest the most suitable candidate dates. For example, the recommendation unit can suggest candidate dates that avoid peak seasons based on the user's past work history. For example, the recommendation unit can also suggest candidate dates for taking paid leave during off-peak seasons based on the user's work history. For example, the recommendation unit can suggest the most suitable candidate dates by referring to the user's work history. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's work history data into a generating AI and have the generating AI perform the task of suggesting the most suitable candidate dates.

[0088] The recommendation section can estimate the user's emotions and adjust how suggested dates are displayed based on those emotions. For example, if the user is stressed, the recommendation section may provide a simple and highly visible display. If the user is relaxed, the recommendation section may also provide a display that includes detailed information. If the user is in a hurry, the recommendation section may also provide a concise display. By adjusting how suggested dates are displayed based on the user's emotions, more appropriate displays can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input user emotion data into a generative AI and have the generative AI adjust how suggested dates are displayed.

[0089] The recommendation unit can make recommendations while considering the geographical distribution of candidate dates. For example, if the user is in a specific region, the recommendation unit will recommend candidate dates related to that region. The recommendation unit can also recommend candidate dates based on the business situation in a specific region, taking geographical distribution into consideration. For example, the recommendation unit can also recommend candidate dates based on geographical distribution, taking into account busy and slow periods of business. This allows for the recommendation of more appropriate candidate dates by considering the geographical distribution of candidate dates. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input geographical distribution data of candidate dates into a generating AI and have the generating AI execute the recommendations.

[0090] The recommendation unit can improve the accuracy of its recommendations by referring to relevant literature during the recommendation process. For example, the recommendation unit can refer to relevant literature to identify peak periods for business and recommend candidate dates. The recommendation unit can also, for example, identify busy and slow periods for business based on relevant literature and recommend candidate dates. The recommendation unit can also, for example, refer to relevant literature to improve the accuracy of its recommendations. This improves the accuracy of recommendations by referring to relevant literature. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input relevant literature data into a generating AI and have the generating AI perform the recommendation accuracy improvement.

[0091] The presentation unit can estimate the user's emotions and adjust the presentation method of candidate dates based on the estimated user emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible presentation method. For example, if the user is relaxed, the presentation unit can also provide a presentation method that includes detailed information. For example, if the user is in a hurry, the presentation unit can also provide a presentation method that gets straight to the point. By adjusting the presentation method of candidate dates based on the user's emotions, more appropriate presentations can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the presentation method of candidate dates.

[0092] The presentation unit can select the optimal presentation method by referring to the user's past selection history when making a presentation. For example, the presentation unit can select the optimal presentation method based on candidate dates previously selected by the user. The presentation unit can also prioritize providing a specific presentation method based on the user's past selection history. The presentation unit can also analyze the user's past selection history and select the most effective presentation method. This allows the optimal presentation method to be selected by referring to the user's past selection history. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past selection history data into a generating AI and have the generating AI select the optimal presentation method.

[0093] The presentation unit can estimate the user's emotions and adjust the order in which candidate dates are presented based on the estimated emotions. For example, if the user is nervous, the presentation unit may present important candidate dates first. If the user is relaxed, the presentation unit may also present detailed candidate dates in a sequential manner. If the user is in a hurry, the presentation unit may also present the most suitable candidate date first. This allows for more appropriate presentation by adjusting the order of candidate dates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the order in which candidate dates are presented.

[0094] The presentation unit can select the optimal presentation method by considering the user's device information during presentation. For example, if the user is using a smartphone, the presentation unit can provide a presentation method that matches the screen size. For example, if the user is using a tablet, the presentation unit can also provide a presentation method optimized for a larger screen. For example, if the user is using a desktop, the presentation unit can also provide a presentation method that includes detailed information. This allows the optimal presentation method to be selected by considering the user's device information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's device information into a generating AI and have the generating AI select the optimal presentation method.

[0095] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can send a notification at a less stressful time. If the user is relaxed, the notification unit can send a notification at a normal time. If the user is in a hurry, the notification unit can send a notification quickly. By adjusting the timing of notifications based on the user's emotions, notifications can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the timing of notifications.

[0096] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize providing notification methods that the user has preferred to use in the past. The notification unit may also select a specific notification method from the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most effective notification method. This allows the optimal notification method to be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the user's past notification history data into a generating AI and have the generating AI select the optimal notification method.

[0097] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. For example, if the user is relaxed, the notification unit can also deliver normal notifications. For example, if the user is in a hurry, the notification unit can also prioritize urgent notifications. This allows for more appropriate notifications by prioritizing notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the notification priorities.

[0098] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit provides a push notification. For example, if the user is using a tablet, the notification unit can also provide a notification method adapted to the screen size. For example, if the user is using a desktop, the notification unit can also provide a pop-up notification. This allows the system to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the user's device information into a generating AI and have the generating AI select the optimal notification method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The paid leave support system can also include a health management department. This department collects employee health data and provides it to the analysis department. For example, it can collect data such as employee sleep patterns, exercise levels, and stress levels. This allows the analysis department to recommend optimal paid leave dates, taking into account the employee's health status. For instance, if an employee is experiencing stress, it can recommend taking leave earlier. Conversely, if an employee is in good health, it can recommend standard leave dates. This effectively promotes paid leave based on employee health conditions.

[0101] The paid leave support system can also include a project management department. The project management department monitors the progress of each project and provides this information to the analysis department. For example, it can collect data such as project deadlines, progress, and resource usage. This allows the analysis department to recommend optimal paid leave dates, taking the project status into consideration. For instance, if a project deadline is approaching, it can recommend avoiding taking leave. Conversely, if a project is progressing smoothly, it can recommend days when it's easier to take leave. This effectively promotes paid leave based on project progress.

[0102] The paid leave support system can also include a skill matching unit. The skill matching unit matches employees' skill sets with their job descriptions and provides this information to the analysis unit. For example, it can collect data to assign appropriate employees to tasks or projects that require specific skills. This allows the analysis unit to recommend optimal paid leave dates, taking into account the employees' skill sets. For instance, it can recommend leave during periods when there are fewer tasks requiring specific skills. It can also recommend days when employees have overlapping skill sets, making it easier for them to take leave. This effectively promotes paid leave based on employees' skill sets.

[0103] The paid leave support system can also include a feedback section. The feedback section collects feedback from employees and provides it to the analysis section. For example, it can collect feedback on employees' impressions after taking leave and the impact on work during their leave. This allows the analysis section to consider the feedback and make more appropriate recommendations for future paid leave dates. For instance, if work was disrupted during an employee's leave, the cause can be identified and reflected in future leave recommendations. Similarly, if an employee enjoyed their leave, that period can be used as a reference for future leave recommendations. This effectively promotes the use of paid leave based on employee feedback.

[0104] The paid leave support system can also include a learning department. The learning department collects employees' learning history and skill development status and provides it to the analysis department. For example, it can collect data on training courses attended, qualifications obtained, and skills being studied. This allows the analysis department to recommend optimal paid leave dates, taking into account the employee's learning status. For instance, it can recommend leave immediately after training completion, or days before and after qualification exams when leave is more easily taken. This facilitates the use of paid leave based on employees' learning progress.

[0105] The paid leave support system can also include an emotion analysis unit. This unit analyzes employees' emotions and provides the data to the analysis unit. For example, it can estimate emotions from the content of employees' emails and chats, and evaluate stress levels and satisfaction levels. This allows the analysis unit to recommend optimal paid leave dates, taking employee emotions into consideration. For instance, if an employee is feeling stressed, it can recommend taking leave earlier. Conversely, if an employee is relaxed, it can recommend regular leave dates. This effectively promotes paid leave based on employee emotions.

[0106] The paid leave support system can also include a communications department. This department collects data on employee communication patterns and provides it to the analytics department. For example, it can collect data such as the frequency of emails and chats between employees, and meeting participation. This allows the analytics department to recommend optimal paid leave dates, taking communication patterns into account. For instance, it can recommend avoiding leave during periods of high communication activity, or recommend days when employees are more likely to take leave during periods of low communication activity. This effectively promotes paid leave based on employee communication patterns.

[0107] The paid leave support system can also include a motivation management department. This department collects employee motivation data and provides it to the analysis department. For example, it can collect data on employee motivation, job satisfaction, and goal achievement. This allows the analysis department to recommend optimal paid leave dates, taking employee motivation into account. For instance, if motivation is low, it can recommend earlier leave. Conversely, if motivation is high, it can recommend standard leave dates. This effectively promotes paid leave based on employee motivation.

[0108] The paid leave support system can also include a performance evaluation unit. This unit evaluates employees' work performance and provides the data to the analysis unit. For example, it can collect data such as employee work results, evaluation reports, and feedback. This allows the analysis unit to recommend optimal paid leave dates, taking employee performance into account. For instance, if performance is high, it can recommend days when it's easier to take leave. Conversely, if performance is declining, it can recommend taking leave earlier. This effectively promotes paid leave based on employee performance.

[0109] The paid leave support system can also include a career planning department. This department collects employee career plans and provides them to the analysis department. For example, it can collect data such as employees' career goals, desired job roles, and skill development plans. This allows the analysis department to recommend optimal paid leave dates, taking into account the employee's career plan. For instance, it can recommend leave after training for skill development based on the career plan. It can also recommend days when employees are more likely to take leave before or after important projects related to their career goals. This facilitates the use of paid leave based on employees' career plans.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects information on employees' work status. For example, the data collection unit can collect information such as the progress of employees' tasks, meeting schedules, and project deadlines. It can also collect information from the intranet, chats, and emails. For example, it can collect posts on intranet bulletin boards, chat message history, and email sending and receiving history. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using methods such as data mining, statistical analysis, and machine learning algorithms. Based on the collected information, the analysis unit identifies peak and off-peak periods in business operations. For example, it analyzes the number of past tasks, project progress, and meeting frequency to identify peak and off-peak periods in business operations. Step 3: The recommendation unit recommends candidate dates that will have minimal impact on work, based on the information analyzed by the analysis unit. For example, the recommendation unit will suggest specific candidate dates based on the analysis results. For example, it might suggest a specific date such as, "October 16, 2024 is free, and Wednesdays in the middle of each month tend to have less work."

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, presentation unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects the work status of employees. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends candidate dates based on the analysis results. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents candidate dates to the user. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the supervisor of the candidate dates selected by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, presentation unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects the work status of employees. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends candidate dates based on the analysis results. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents the candidate dates to the user. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the supervisor of the candidate dates selected by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, presentation unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects the work status of employees. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The recommendation unit is implemented by the identification processing unit 290 of the data processing unit 12 and recommends candidate dates based on the analysis results. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents candidate dates to the user. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the supervisor of the candidate dates selected by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, recommendation unit, presentation unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects the status of employees' work. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The recommendation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and recommends candidate dates based on the analysis results. The presentation unit is implemented by, for example, the control unit 46A of the robot 414 and presents candidate dates to the user. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and notifies the supervisor of the candidate dates selected by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The collection department collects information on the work status of employees, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a recommendation unit that recommends candidate dates that have minimal impact on work based on the information analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect intranet, chat, and email content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected information, we identify peak and off-peak seasons for business operations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation section is, Based on the analysis results, we will present specific candidate dates. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a display section that presents candidate dates to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a notification function that notifies the supervisor after the user has selected a candidate date. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the system analyzes the user's past work history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the interrelationships of the collected information are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the system identifies peak and off-peak periods in business operations by referencing the user's work history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of the collected information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation section is, It estimates the user's sentiment and prioritizes suggested dates based on that sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation section is, When making recommendations, adjust the accuracy of the recommendations based on the level of detail in the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation section is, When making recommendations, the system will refer to the user's work history to suggest the most suitable candidate dates. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation section is, We estimate the user's sentiment and adjust how suggested dates are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation section is, When making recommendations, the geographical distribution of candidate dates should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation section is, When making recommendations, we refer to relevant literature to improve the accuracy of those recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, The system estimates the user's emotions and adjusts the way suggested dates are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting information, the system will refer to the user's past selection history to select the most suitable presentation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, The system estimates the user's emotions and adjusts the order in which suggested dates are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, When presenting information, the optimal presentation method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects information on the work status of employees, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a recommendation unit that recommends candidate dates that have minimal impact on work based on the information analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect intranet, chat, and email content. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected information, we identify peak and off-peak seasons for business operations. The system according to feature 1.

4. The aforementioned recommendation section is, Based on the analysis results, we will present specific candidate dates. The system according to feature 1.

5. It includes a display section that presents candidate dates to the user. The system according to feature 1.

6. It includes a notification function that notifies the supervisor after the user has selected a candidate date. The system according to feature 1.

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

8. The aforementioned collection unit is During data collection, the system analyzes the user's past work history to select the most suitable collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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