system

Abstract

We provide the system. [Solution] Means for obtaining employee attendance and departure information, A means for analyzing workers' job information and leave history, A method using an artificial intelligence algorithm to calculate the optimal vacation days, A means of collecting feedback on vacation proposals presented to workers, A system that includes this.

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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problems faced by workers when obtaining paid leave are that they cannot grasp the optimal timing to obtain leave and tend not to take paid leave due to concerns about the impact on work. In particular, paid leave is often intuitively determined without considering the business situation within the company or the individual job load, resulting in irregular leave acquisition or a decrease in work efficiency. It is required to improve such a situation and provide an environment in which workers can obtain paid leave with confidence and efficiently.

Means for Solving the Problems

[0005] This invention provides a system that acquires employee attendance and departure information and analyzes the data based on job information and leave history. Furthermore, it uses an artificial intelligence algorithm to calculate the optimal leave days for each employee and proposes them to the employee. This system collects feedback from employees on the proposed leave days and includes means to evaluate leave acquisition trends and impact on work, thereby improving the accuracy of the suggestions. It also refers to the company's work calendar and supports flexible leave planning that takes into account busy periods and holidays. As a result, employees can take paid leave with minimal impact on work, thereby improving overall business efficiency and employee satisfaction.

[0006] A "worker" is an individual who is employed by a company or organization and performs specific tasks.

[0007] "Attendance and departure information" refers to records of the time an employee starts and ends their work.

[0008] "Job information" refers to detailed information related to an employee's job, such as the duties they are responsible for, their position, and the team structure they are part of.

[0009] "Leave history" refers to a record of the dates and duration of leave taken by an employee in the past.

[0010] An "artificial intelligence algorithm" is a method or program used to analyze data and automatically make certain judgments or predictions.

[0011] An "optimal vacation day" is a date and time that is deemed to have minimal impact on work and allow employees to take vacation efficiently.

[0012] "Feedback" refers to the reactions, such as opinions and evaluations, given to employees regarding proposed vacation days.

[0013] A "business calendar" is a schedule that shows a company's work schedule, official holidays, busy periods, and other important information.

[0014] "Peak period" refers to a specific period in a company or organization when the volume of business is higher than usual.

[0015] "Accuracy of the proposal" refers to an indicator showing how well the proposed vacation days suit the actual situation of the workers and the company.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. 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).

[0023] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] The 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.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention provides a system for optimizing the acquisition of paid leave by workers, and its embodiments are described in detail below.

[0038] This system is primarily composed of three elements: servers, terminals, and users.

[0039] Server Role

[0040] The server is the core of the system, responsible for collecting, processing, and analyzing critical data. It automatically collects employee attendance and departure information, as well as job information, vacation history, and company work calendar information. Based on this information, it runs an artificial intelligence algorithm to calculate the optimal vacation days for each employee. The server continuously learns from past data and employee feedback to improve the accuracy of the algorithm's suggestions.

[0041] Terminal role

[0042] The terminal controls interactions with the user and, through the user interface, presents the user with the optimal vacation dates received from the server. The terminal receives feedback from the user and sends it to the server to further refine the next suggestion.

[0043] User roles

[0044] The user is the worker themselves, and receives vacation suggestions based on their work data. The user reviews the suggested vacation days and sends their opinions and feedback to the server via their terminal as needed. This feedback contributes to the improvement of the entire system.

[0045] Specific example

[0046] For example, if an employee requests a vacation in the near future, they access the system via their terminal, and the server collects data to process that request. Taking into account that the user is nearing a project deadline, the server suggests a day when the vacation can be taken most efficiently with minimal impact on work. If Wednesday is suggested, the reasons given might include that it is a good time to balance the schedules and workloads of other team members. The user can accept this or provide feedback. In this way, the present invention enables workers to effectively utilize vacations while minimizing disruption to their work.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects employee attendance and departure information from the company's internal attendance management system and stores it in a database. Regular synchronization is performed to ensure that the most up-to-date information is always reflected.

[0050] Step 2:

[0051] The server retrieves employee job information and leave history from the HR system. This allows the system to understand each employee's work content and past leave history, and use this information to plan their leave.

[0052] Step 3:

[0053] The server references the company calendar to collect information on peak seasons and special dates such as holidays. This information is an important factor in suggesting vacation dates.

[0054] Step 4:

[0055] The server uses the data collected above to run an artificial intelligence algorithm and calculate the optimal vacation days for each worker. This takes into account workload and the balance of work within the team.

[0056] Step 5:

[0057] The server compiles the calculated optimal vacation days into a list of suggestions for each worker and sends it to their terminal. This list is displayed on the terminal as a suggestion document.

[0058] Step 6:

[0059] The device presents the user with vacation date suggestions via its user interface. The user can then use these suggestions to plan their vacation schedule.

[0060] Step 7:

[0061] Users can input their opinions and questions about the proposed vacation days via their device and send them to the server as feedback.

[0062] Step 8:

[0063] The server stores user feedback in a database and uses it to improve the accuracy and refinement of future suggestions. This feedback serves as training data for the AI ​​model.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] In today's work environment, it is crucial for employees to take their paid leave appropriately in order to maintain work efficiency while improving their quality of life. However, in many companies, employees decide the timing of their leave on their own, which can lead to situations where they take leave without considering the impact on work, or conversely, where it becomes difficult to take leave. This can result in decreased work efficiency and problems where employees are unable to get enough rest.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for collecting employee work information, means for analyzing data based on the employee's work history and leave history, and means for calculating optimal leave days using a generative AI model. This makes it possible to minimize the impact on work when employees take leave and to take appropriate leave according to their individual needs.

[0069] "Worker work information" refers to various pieces of information that detail an employee's work status, such as their arrival and departure times, number of working days, and number of days of leave taken.

[0070] "Work history" refers to historical information that shows the types of work an employee has performed and the changes in their job responsibilities within a company in the past.

[0071] "Leave history" refers to historical information about leave taken by an employee in the past, such as the number of days, timing, and reasons for leave.

[0072] A "generative AI model" is an algorithm or system designed to analyze data using artificial intelligence technology and perform predictions and optimizations.

[0073] A "user terminal" refers to a device such as a computer or smartphone that a worker directly interacts with, enabling the display and input of information.

[0074] "Means of collecting feedback" refers to mechanisms and methods for inputting and recording opinions and evaluations from workers into a system.

[0075] "Means of improving algorithms" refer to methods and techniques for improving the performance and accuracy of algorithms based on collected feedback and new data.

[0076] An "organizational activity plan" is a plan that includes the schedule and tasks of operations set by a company or organization, and is fundamental information for managing operational efficiency and productivity.

[0077] This invention is a system that optimizes the acquisition of paid leave by workers, and is mainly composed of a server, terminals, and users.

[0078] The server is the core of this system. The server collects employee work information, including clock-in and clock-out times, work history, and leave history. This data is automatically retrieved from the company's human resources management and time management systems and securely stored in cloud storage. The server uses data analysis libraries such as Python's Pandas and NumPy to analyze the collected information. Furthermore, the server utilizes generative AI models to calculate the optimal leave days for each individual employee. This model employs machine learning frameworks such as TENSORFLOW® and scikit-learn.

[0079] The terminal provides a user interface and displays vacation suggestions sent from the server to the user. The terminal is implemented as a desktop or mobile application, allowing workers to input their work schedules and review vacation suggestions. This enables the system to directly collect user feedback, which can then be used to improve future suggestions.

[0080] Users review vacation suggestions provided through their devices and submit feedback as needed. This allows the system to continuously improve the accuracy of its suggestions. For users, the process of reviewing suggested vacation dates is simple, enabling them to minimize the impact of taking desired vacations on their work.

[0081] As a concrete example, when a user uses this system to plan a vacation in the near future, the server calculates the optimal vacation dates based on the user's work data and displays the results on the terminal. For example, by using a prompt such as, "Please suggest the optimal vacation dates based on the worker's attendance history and work calendar," practical vacation dates will be suggested.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server collects employee work information. This process connects to the company's human resources management and time management systems, retrieving data such as employee clock-in and clock-out times, work history, and leave history via APIs. Inputs come from various databases, and output is employee information stored in an integrated database on the server. This makes the collected data available for use in subsequent processing stages.

[0085] Step 2:

[0086] The server analyzes the collected data. Here, it uses Python's Pandas and NumPy to clean and format the data. The input is the collected worker data, and the output is the formatted dataset. Specifically, it performs tasks such as imputing missing data, handling outliers, and calculating necessary variables. This prepares the data for subsequent algorithmic processing.

[0087] Step 3:

[0088] The server uses a generative AI model to calculate the optimal vacation days. This process involves inputting the formatted data from the previous step into the model to calculate suitable vacation days for each worker. Libraries such as TensorFlow and scikit-learn are used in the model. The input is the formatted dataset, and the output is a list of optimal vacation day candidates. The server then prepares to provide the calculation results to the user.

[0089] Step 4:

[0090] The terminal presents the user with vacation suggestions sent from the server. The suggested dates are displayed in an intuitive and easy-to-understand format through the user interface. Input is the suggestion data from the server, and output is a visual presentation on the user's terminal. Specifically, it displays date suggestions in a calendar format, allowing the user to select or review their preferred dates.

[0091] Step 5:

[0092] Users review suggested vacation dates via their devices and send their opinions as feedback to the server. Feedback is provided through a dedicated form or comment section. Input is the user's thoughts and evaluations, and output is the feedback data sent to the server. This allows the server to obtain valuable data to improve future suggestions.

[0093] Step 6:

[0094] The server analyzes the collected feedback and improves the generating AI model by incorporating the feedback data. The input is user feedback data, and the output is the updated algorithm. Specifically, the feedback data is added to the model's training dataset and retrained to improve the accuracy of the suggestions. This allows the entire system to more effectively optimize worker vacation time.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] While employees taking vacation time is crucial for their individual work-life balance, uneven vacation schedules can negatively impact the overall efficiency of an organization. Furthermore, traffic congestion in urban areas also affects work efficiency; therefore, there is a need to simultaneously achieve both employee vacation time and optimized traffic conditions.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for acquiring workers' work information, means for analyzing workers' job-related information and vacation history, means for using a machine learning model to derive the overall optimal vacation days, and means for evaluating traffic data and optimizing the overall traffic efficiency of the city. This makes it possible to optimize vacation time according to the individual needs of workers while also alleviating traffic congestion throughout the city.

[0100] A "worker" is an individual who belongs to an organization and acts based on work information and job-related information.

[0101] "Work information" refers to data about an employee's daily work schedule, such as their start date, end time, and work location.

[0102] "Job-related information" refers to information about the work an employee is engaged in, the projects they are assigned to, their job title, etc.

[0103] "Leave history" refers to a record of the dates and reasons for paid leave taken by an employee in the past.

[0104] A "machine learning model" is one of the artificial intelligence techniques used to learn patterns from data and perform predictions and classifications.

[0105] An "optimal holiday day" is a date selected to allow employees to take leave effectively while minimizing the impact on business operations.

[0106] "Traffic data" refers to information about road congestion within cities, the use of public transport, and traffic volume.

[0107] "Transportation efficiency" is a concept that refers to the smoothness of traffic flow within a city and the effective utilization of available transportation resources.

[0108] This system aims to optimize worker vacation time and transportation efficiency, and is based on the use of advanced machine learning models that analyze worker work information and transportation data.

[0109] The server aggregates employee work information from a company's HR system via APIs and collects traffic data using the Google Maps API. The collected data is processed using a machine learning algorithm based on TensorFlow to calculate the optimal vacation days for each employee. This allows for the presentation of highly efficient vacation plans that avoid peak traffic times while minimizing the impact on individual employees' work.

[0110] The terminal serves to suggest vacation days to workers via a smartphone app and allow them to review the suggestions through a highly intuitive user interface. The app is developed using React Native and also allows users to provide feedback on the suggested vacation days.

[0111] Users review suggested vacations using the app and determine if they meet their needs. The system continuously learns from the feedback, improving the accuracy of its algorithms. This mechanism is expected to have a positive impact on commuting behavior across cities.

[0112] For example, if an employee wants to take their vacation most efficiently during the week, the system will recommend taking it on Wednesday to avoid the busy Monday and Friday schedules. Also, if an employee has plans to attend a popular event that weekend, the system will take that information into account and make adjustments accordingly.

[0113] Examples of prompt statements for a generative AI model are as follows:

[0114] "Please suggest the optimal day off based on attendance data and traffic information. The user wants to take Friday off, but please recalculate the best day to avoid peak traffic."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server retrieves employee work information from the company's HR system via an API. This information includes attendance dates, departure times, and job title. First, it receives attendance information in JSON format from the HR system as input and stores it in the database. The data is preprocessed for analysis, and unnecessary information is filtered out.

[0118] Step 2:

[0119] The server collects traffic data from the Google Maps API. This involves obtaining data such as road congestion information, public transport usage, and traffic volume within cities. Real-time traffic information is obtained from the API as input, and analysis is performed to identify peak congestion. Here, historical traffic trend data is also utilized to improve the accuracy of congestion predictions.

[0120] Step 3:

[0121] The server executes a machine learning algorithm using TensorFlow to calculate the optimal vacation days for each worker. This step combines the work information obtained in step 1 with the traffic data obtained in step 2. Using individual worker information and city traffic data as input data, it performs complex calculations to derive the most efficient vacation days.

[0122] Step 4:

[0123] The terminal displays the calculation results to the worker via a smartphone app. The user interface uses React Native, allowing the worker to view the suggested vacation days. The input here is the vacation day information calculated in step 3, and the output is a visual presentation via the user interface.

[0124] Step 5:

[0125] The user provides feedback on the suggested vacation dates from their device. This feedback is sent to the server via the device to evaluate whether the vacation dates are appropriate. Based on the feedback, the server adjusts the parameters of the AI ​​algorithm to improve the accuracy of future suggestions. The input is the user's feedback, and the output is the model's learning effect.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention relates to a system that proposes the optimal timing and content for employees taking paid leave, and in particular, provides leave suggestions that take into account the user's emotional state.

[0128] This system includes servers, terminals, and a user interface, and further incorporates an emotion engine to achieve advanced user support.

[0129] Server Role

[0130] The server collects and stores employee attendance and departure information, job information, vacation history, and company calendar data in a database. Based on this information, it analyzes typical work patterns and uses an AI algorithm to calculate optimal vacation days. In addition, an emotion engine is used to analyze employee input and feedback as real-time emotion data. This emotion information is used to adjust the content and timing of vacation suggestions.

[0131] Terminal role

[0132] The terminal is responsible for presenting suggestions to the user and receiving feedback. It displays vacation suggestions received from the server to the worker via the UI. Furthermore, based on interpretation from the emotion engine, it can understand in real time how the user is reacting to each suggestion and flexibly change the displayed content accordingly.

[0133] User roles

[0134] Users can review vacation suggestions from the system and provide feedback via their device. The input feedback is further analyzed by an emotion engine that analyzes the user's verbal and nonverbal emotional expressions. Feedback reflecting the user's emotions is also used to improve future suggestions.

[0135] Specific example

[0136] For example, if the system determines that a user is currently experiencing high levels of work-related stress, the server will suggest a refreshing vacation via the terminal to help alleviate that stress. For instance, it might suggest taking Friday afternoon of the following week as vacation. This suggestion takes into account the user's recently reported fatigue and stress levels, and is adjusted according to the psychological state inferred by the emotion engine from the user's specified feedback. In this way, the present invention enables users to receive better vacation suggestions tailored to their emotional state, thereby promoting mental and physical well-being.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server collects employee attendance and departure information, job information, and leave history from the company's attendance management system and HR system, and stores it in a database. This allows for an understanding of the latest work status and past leave trends.

[0140] Step 2:

[0141] The server references the company's business calendar to retrieve important date information, such as peak seasons and holidays. This information is used to adjust the timing and duration of vacation suggestions.

[0142] Step 3:

[0143] The server collects worker behavior and feedback data and uses an emotion engine to analyze the user's emotional state. It calculates emotional indicators such as stress and satisfaction from text analysis and behavioral history.

[0144] Step 4:

[0145] The server uses AI algorithms to comprehensively analyze the data collected so far and calculate the optimal vacation days for each worker. The results of the emotion engine analysis are also taken into consideration, allowing for suggestions of earlier vacations, especially for workers experiencing high levels of stress.

[0146] Step 5:

[0147] The server generates optimized vacation suggestions and sends them to the terminal. These suggestions include recommended vacation days, along with the reasons for their selection and the expected benefits of rest.

[0148] Step 6:

[0149] The device presents vacation suggestions to the user through its user interface. It also uses an emotion engine to diagnose the user's initial response and adjust the suggestions in real time as needed.

[0150] Step 7:

[0151] Users receive suggestions via their devices and provide feedback on their content. They can include emotionally charged comments and additional requests.

[0152] Step 8:

[0153] The device collects user feedback and sends it to the server. This feedback is further analyzed by an emotion engine and used to improve future vacation suggestions.

[0154] Step 9:

[0155] The server stores the collected feedback data in a database and fine-tunes it to improve the quality of future suggestions. Through the accumulation of emotional and operational data, the system continuously learns, enabling more personalized vacation suggestions.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] In the current work environment, it is difficult to take leave at the optimal time, and since emotional stress is not taken into consideration, there is a risk that the physical and mental health of workers will be compromised. Furthermore, if leave cannot be taken in consideration of the company calendar and busy periods, it will lead to decreased work efficiency and lower employee satisfaction.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes means for acquiring employee work information and departure information, means for analyzing employee work information and leave history, and means for using an artificial intelligence algorithm to calculate the optimal leave days. This makes it possible to adjust leave suggestions based on employee emotional information and propose leave that suits the individual's emotional state. Furthermore, it enables the optimization of leave considering the company calendar and busy periods, leading to improved work efficiency and the maintenance of employee health.

[0161] "Worker work information" refers to data about individual workers' arrival and departure times, including working hours and job duties.

[0162] "Job information" refers to data related to the duties and tasks that an employee is responsible for in the workplace.

[0163] "Leave history" refers to a record of leave taken by an employee in the past, including information such as the date and type of leave.

[0164] An "artificial intelligence algorithm" refers to an automated calculation method used in computer programs to analyze worker data and determine the optimal vacation days.

[0165] "Emotional information" refers to data about the emotions expressed by workers and their current psychological state, and is collected from both verbal and nonverbal elements.

[0166] A "corporate calendar" refers to schedule information that shows the work schedule, holidays, and busy periods within a company.

[0167] A "peak season" refers to a specific period in a company or business where the workload increases significantly.

[0168] This invention provides a system that enables workers to take vacations effectively, and is implemented using a server, terminals, and a user interface. This allows for personalized vacation suggestions that take emotional information into account.

[0169] The server is connected to the company's HR system and other databases, and uses APIs to collect employee work information, job information, vacation history, and company calendar data. This data is stored in the database, and AI algorithms are used to analyze work status and generate optimal vacation day suggestions.

[0170] In addition, the server is equipped with an emotion engine that analyzes user feedback using natural language processing technology. This allows it to understand the user's stress level and emotional state and reflect this in vacation suggestions.

[0171] The terminal presents the user with vacation suggestions sent from the server via a user interface. The interface is intuitive, allowing workers to easily review the suggestions and provide feedback.

[0172] Users review the displayed vacation suggestions using their devices and provide feedback on their experiences. This feedback is sent to the server and used to improve the accuracy of future suggestions.

[0173] For example, if a user provides feedback such as, "I would like to be offered a refreshing day off next Friday," the system could then suggest a more appropriate timing based on that feedback. In this way, it becomes possible to offer day off tailored to the individual needs of each worker, supporting their physical and mental well-being.

[0174] Examples of prompt statements for a generative AI model are as follows:

[0175] "Based on worker A's emotional state and work data, generate suggestions for the optimal vacation days."

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The server collects employee work information, job information, and leave history from a company's HR system via an API. The input is information from the company's database, and the output is stored in the database in a structured data format. This prepares an initial dataset for a comprehensive understanding of employee work performance.

[0179] Step 2:

[0180] The server inputs work and job information into an AI algorithm to analyze the worker's typical work patterns and workload. The input data includes arrival and departure times, job duties, etc., and the output is the individual worker's work pattern. This analysis helps determine the worker's workload and the most efficient timing for taking leave.

[0181] Step 3:

[0182] The server inputs user feedback into an emotion engine to analyze the worker's emotional state. Inputs include text-based feedback and selectable responses from a user interface, and the emotion engine uses natural language processing techniques for analysis. The output is quantitative data on the worker's stress level and emotional state. This result is used to tailor vacation suggestions to each worker's psychological needs.

[0183] Step 4:

[0184] The server uses an AI algorithm to calculate the optimal vacation days based on collected work information, job information, and emotional state. The input is the analysis results obtained in the previous step, and the output is the recommended vacation period from the start date to the end date. This allows the server to suggest the best timing for employees to take vacation.

[0185] Step 5:

[0186] The terminal displays vacation suggestions sent from the server to the user. The input is the recommended vacation period from the server, and the output is information visually presented on the user interface. Specifically, the terminal informs the worker of the suggestions through notifications and pop-up screens.

[0187] Step 6:

[0188] Users provide feedback on presented vacation suggestions via a terminal. Input includes user text comments and selectable responses, while output is the transmission of feedback to the server. This feedback serves as important data for improving future suggestions.

[0189] Step 7:

[0190] The server uses user feedback to make adjustments that improve the accuracy of future vacation suggestions. The input is user feedback data, and the output is the adjusted suggestion algorithm and rule set. Through this process, the system becomes capable of making more sophisticated vacation suggestions over time.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] The physical and mental health of workers is a crucial factor that directly impacts productivity and job satisfaction. However, currently, vacation proposals do not take into account the emotional state of individual workers, making it difficult to select the optimal timing for vacation. As a result, health problems due to excessive stress and inefficiencies in vacation taking have become apparent issues. This invention aims to solve these problems.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] In this invention, the server includes means for acquiring employee attendance and departure information, means for analyzing employee job information and leave history, means for using an artificial intelligence algorithm to calculate optimal leave days, means for collecting feedback on leave suggestions presented to employees, means for using an emotion analysis engine to analyze employees' emotional states, and means for making leave suggestions based on emotional states. This makes it possible to make optimal leave suggestions that take into account employees' emotional states.

[0196] A "worker" is a person who performs a specific job within a company or organization.

[0197] "Attendance and departure information" refers to data on the time an employee arrives at the workplace and begins work, and the time they finish work and leave the workplace.

[0198] "Job information" refers to data about the work content, roles, and responsibilities of an employee.

[0199] "Leave history" refers to a record of leave taken by an employee in the past, including dates and durations.

[0200] An "artificial intelligence algorithm" is a computational method that uses machine learning and other AI technologies to analyze and make decisions based on data.

[0201] "Feedback" refers to the reactions or opinions that workers give in response to a proposal or result.

[0202] An "emotion analysis engine" is a software component that analyzes the emotions and mental state of workers based on data they input.

[0203] A "vacation suggestion" is a proposal that offers workers recommendations on when they should take their vacation.

[0204] The system implementing this invention provides optimal vacation suggestions that take into account the emotional state of workers, and consists of three components: a server, a terminal, and a user. The server functions as a core system for acquiring and analyzing workers' attendance and departure information, job information, and vacation history. Furthermore, an artificial intelligence algorithm calculates the optimal vacation days using the collected data.

[0205] The server uses an emotion analysis engine to analyze the emotional state of workers based on their input and feedback. Based on this analysis, it generates vacation suggestions for workers in real time. These suggestions are presented to the user through a terminal provided to them. The terminal displays the received suggestions to the worker through a user interface and simultaneously records the human interaction.

[0206] When a worker uses a terminal to review a proposal and provides feedback, that information is sent to a server. The feedback includes verbal and nonverbal sentiments, which are further analyzed by a sentiment analysis engine. This feedback is then used to improve future vacation proposals.

[0207] As a concrete example, imagine a scenario where a worker inputs "This week's stress level is 8" using their smartphone or smartwatch. The server then suggests a refreshing vacation for the following weekend. By utilizing generative AI models, the accuracy of analyzing such emotional data can be improved.

[0208] An example of a prompt message might be, "Please record your stress level today on a scale of 1 to 10." This prompt allows workers to easily record their emotional state.

[0209] This system utilizes an emotion analysis engine and artificial intelligence algorithms to improve users' health and job satisfaction.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The server retrieves employee attendance and departure information, job information, and leave history. This input data is collected through an information aggregation system and stored in a database. The collected data serves as basic information for later analysis.

[0213] Step 2:

[0214] The server uses an artificial intelligence algorithm to calculate the optimal vacation days based on the collected data. This process involves data calculations that take into account employee work patterns and workloads, and outputs vacation suggestions.

[0215] Step 3:

[0216] The server uses an emotion analysis engine to analyze feedback and emotional data entered by workers through their terminals. It receives data related to emotional states as input and evaluates the workers' psychological state using text analysis and pattern recognition technologies. This results in an evaluation based on emotions.

[0217] Step 4:

[0218] The server adjusts vacation suggestions based on the sentiment analysis results and presents them to employees at the optimal time. By utilizing a generative AI model, vacation suggestions that integrate sentiment data and work data are output.

[0219] Step 5:

[0220] The terminal displays vacation suggestions received from the server to the worker. The suggestions are presented through a user interface, offering the worker options in an interactive format.

[0221] Step 6:

[0222] Users review the proposals via their devices and provide feedback. This feedback includes recording their satisfaction level and opinions on the proposals, and entering responses. This data is then sent back to the server.

[0223] Step 7:

[0224] The server receives feedback from users and uses it to improve future vacation suggestions. The feedback data is analyzed, and improvements are made to enhance the overall accuracy of the system.

[0225] 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.

[0226] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0227] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0232] 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.

[0233] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0234] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0235] 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.

[0236] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0237] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0238] The 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.

[0239] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0240] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0241] This invention provides a system for optimizing the acquisition of paid leave by workers, and its embodiments are described in detail below.

[0242] This system is primarily composed of three elements: servers, terminals, and users.

[0243] Server Role

[0244] The server is the core of the system, responsible for collecting, processing, and analyzing critical data. It automatically collects employee attendance and departure information, as well as job information, vacation history, and company work calendar information. Based on this information, it runs an artificial intelligence algorithm to calculate the optimal vacation days for each employee. The server continuously learns from past data and employee feedback to improve the accuracy of the algorithm's suggestions.

[0245] Terminal role

[0246] The terminal controls interactions with the user and, through the user interface, presents the user with the optimal vacation dates received from the server. The terminal receives feedback from the user and sends it to the server to further refine the next suggestion.

[0247] User roles

[0248] The user is the worker themselves, and receives vacation suggestions based on their work data. The user reviews the suggested vacation days and sends their opinions and feedback to the server via their terminal as needed. This feedback contributes to the improvement of the entire system.

[0249] Specific example

[0250] For example, if an employee requests a vacation in the near future, they access the system via their terminal, and the server collects data to process that request. Taking into account that the user is nearing a project deadline, the server suggests a day when the vacation can be taken most efficiently with minimal impact on work. If Wednesday is suggested, the reasons given might include that it is a good time to balance the schedules and workloads of other team members. The user can accept this or provide feedback. In this way, the present invention enables workers to effectively utilize vacations while minimizing disruption to their work.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server collects employee attendance and departure information from the company's internal attendance management system and stores it in a database. Regular synchronization is performed to ensure that the most up-to-date information is always reflected.

[0254] Step 2:

[0255] The server retrieves employee job information and leave history from the HR system. This allows the system to understand each employee's work content and past leave history, and use this information to plan their leave.

[0256] Step 3:

[0257] The server references the company calendar to collect information on peak seasons and special dates such as holidays. This information is an important factor in suggesting vacation dates.

[0258] Step 4:

[0259] The server uses the data collected above to run an artificial intelligence algorithm and calculate the optimal vacation days for each worker. This takes into account workload and the balance of work within the team.

[0260] Step 5:

[0261] The server compiles the calculated optimal vacation days into a list of suggestions for each worker and sends it to their terminal. This list is displayed on the terminal as a suggestion document.

[0262] Step 6:

[0263] The device presents the user with vacation date suggestions via its user interface. The user can then use these suggestions to plan their vacation schedule.

[0264] Step 7:

[0265] Users can input their opinions and questions about the proposed vacation days via their device and send them to the server as feedback.

[0266] Step 8:

[0267] The server stores user feedback in a database and uses it to improve the accuracy and refinement of future suggestions. This feedback serves as training data for the AI ​​model.

[0268] (Example 1)

[0269] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] In today's work environment, it is crucial for employees to take their paid leave appropriately in order to maintain work efficiency while improving their quality of life. However, in many companies, employees decide the timing of their leave on their own, which can lead to situations where they take leave without considering the impact on work, or conversely, where it becomes difficult to take leave. This can result in decreased work efficiency and problems where employees are unable to get enough rest.

[0271] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0272] In this invention, the server includes means for collecting employee work information, means for analyzing data based on the employee's work history and leave history, and means for calculating optimal leave days using a generative AI model. This makes it possible to minimize the impact on work when employees take leave and to take appropriate leave according to their individual needs.

[0273] "Worker work information" refers to various pieces of information that detail an employee's work status, such as their arrival and departure times, number of working days, and number of days of leave taken.

[0274] "Work history" refers to historical information that shows the types of work an employee has performed and the changes in their job responsibilities within a company in the past.

[0275] "Leave history" refers to historical information about leave taken by an employee in the past, such as the number of days, timing, and reasons for leave.

[0276] A "generative AI model" is an algorithm or system designed to analyze data using artificial intelligence technology and perform predictions and optimizations.

[0277] A "user terminal" refers to a device such as a computer or smartphone that a worker directly interacts with, enabling the display and input of information.

[0278] "Means of collecting feedback" refers to mechanisms and methods for inputting and recording opinions and evaluations from workers into a system.

[0279] "Means of improving algorithms" refer to methods and techniques for improving the performance and accuracy of algorithms based on collected feedback and new data.

[0280] An "organizational activity plan" is a plan that includes the schedule and tasks of operations set by a company or organization, and is fundamental information for managing operational efficiency and productivity.

[0281] This invention is a system that optimizes the acquisition of paid leave by workers, and is mainly composed of a server, terminals, and users.

[0282] The server is at the core of this system. The server collects the work information of workers, which includes working hours, duty history, vacation history, etc. These data are automatically obtained from the company's human resources management system and time management system and are safely stored in cloud storage. The server uses data analysis libraries such as Python's Pandas and NumPy to analyze the collected information. Furthermore, the server utilizes a generative AI model to calculate the optimal vacation days for each worker. Machine learning frameworks such as TensorFlow and scikit-learn are used in this model.

[0283] The terminal provides a user interface and displays the vacation proposals sent from the server to the user. The terminal is implemented as a desktop or mobile application, and workers can input their work schedules or check vacation proposals. This enables the system to directly collect feedback from users, and this feedback can be used to improve future proposals.

[0284] The user checks the vacation proposals provided through their terminal and sends feedback if necessary. This allows the system to continuously improve the accuracy of the proposals. For the user, the process of checking the proposed vacation days is simple and enables minimizing the impact of the targeted vacation on work.

[0285] As a specific example, when a user uses this system to plan a vacation in the near future, the server calculates the optimal vacation days based on the user's work data and presents the results to the terminal. For example, by using a prompt such as "Please propose the optimal vacation days based on the worker's attendance and departure history and work calendar.", practical vacation days are presented.

[0286] The flow of the specific process in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The server collects employee work information. This process connects to the company's human resources management and time management systems, retrieving data such as employee clock-in and clock-out times, work history, and leave history via APIs. Inputs come from various databases, and output is employee information stored in an integrated database on the server. This makes the collected data available for use in subsequent processing stages.

[0289] Step 2:

[0290] The server analyzes the collected data. Here, it uses Python's Pandas and NumPy to clean and format the data. The input is the collected worker data, and the output is the formatted dataset. Specifically, it performs tasks such as imputing missing data, handling outliers, and calculating necessary variables. This prepares the data for subsequent algorithmic processing.

[0291] Step 3:

[0292] The server uses a generative AI model to calculate the optimal vacation days. This process involves inputting the formatted data from the previous step into the model to calculate suitable vacation days for each worker. Libraries such as TensorFlow and scikit-learn are used in the model. The input is the formatted dataset, and the output is a list of optimal vacation day candidates. The server then prepares to provide the calculation results to the user.

[0293] Step 4:

[0294] The terminal presents the user with vacation suggestions sent from the server. The suggested dates are displayed in an intuitive and easy-to-understand format through the user interface. Input is the suggestion data from the server, and output is a visual presentation on the user's terminal. Specifically, it displays date suggestions in a calendar format, allowing the user to select or review their preferred dates.

[0295] Step 5:

[0296] Users review suggested vacation dates via their devices and send their opinions as feedback to the server. Feedback is provided through a dedicated form or comment section. Input is the user's thoughts and evaluations, and output is the feedback data sent to the server. This allows the server to obtain valuable data to improve future suggestions.

[0297] Step 6:

[0298] The server analyzes the collected feedback and improves the generating AI model by incorporating the feedback data. The input is user feedback data, and the output is the updated algorithm. Specifically, the feedback data is added to the model's training dataset and retrained to improve the accuracy of the suggestions. This allows the entire system to more effectively optimize worker vacation time.

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] While employees taking vacation time is crucial for their individual work-life balance, uneven vacation schedules can negatively impact the overall efficiency of an organization. Furthermore, traffic congestion in urban areas also affects work efficiency; therefore, there is a need to simultaneously achieve both employee vacation time and optimized traffic conditions.

[0302] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0303] In this invention, the server includes means for acquiring the work information of workers, means for analyzing the job-related information and vacation history of workers, means for using a machine learning model to derive comprehensive optimal vacation days, and means for evaluating traffic data and optimizing the traffic efficiency of the entire city. As a result, it becomes possible to optimize the acquisition of vacations according to the individual needs of workers while also alleviating traffic congestion throughout the city.

[0304] A "worker" refers to an individual who belongs to an organization and conducts activities based on work information and job-related information.

[0305] "Work information" refers to data regarding daily work patterns such as the attendance date, leaving time, and workplace of workers.

[0306] "Job-related information" refers to information regarding the work content, assigned projects, job positions, etc. that workers are engaged in.

[0307] "Vacation history" refers to records regarding the dates and reasons for paid vacations that workers have taken in the past.

[0308] A "machine learning model" is one of the artificial intelligence techniques for learning patterns from data and performing predictions and classifications.

[0309] "Optimal vacation days" are dates selected so that workers can effectively take vacations while minimizing the impact on work.

[0310] "Traffic data" refers to information regarding road congestion conditions, public transportation usage conditions, traffic volume, etc. within a city.

[0311] "Traffic efficiency" is a concept that refers to the smoothness of traffic flow within a city and the effective utilization of the availability of traffic resources.

[0312] This system aims to optimize the vacation acquisition of workers and traffic efficiency, and is based on the utilization of an advanced machine learning model that analyzes the work information of workers and traffic data.

[0313] The server aggregates employee work information from a company's HR system via APIs and collects traffic data using the Google Maps API. The collected data is processed using a machine learning algorithm based on TensorFlow to calculate the optimal vacation days for each employee. This allows for the presentation of highly efficient vacation plans that avoid peak traffic times while minimizing the impact on individual employees' work.

[0314] The terminal serves to suggest vacation days to workers via a smartphone app and allow them to review the suggestions through a highly intuitive user interface. The app is developed using React Native and also allows users to provide feedback on the suggested vacation days.

[0315] Users review suggested vacations using the app and determine if they meet their needs. The system continuously learns from the feedback, improving the accuracy of its algorithms. This mechanism is expected to have a positive impact on commuting behavior across cities.

[0316] For example, if an employee wants to take their vacation most efficiently during the week, the system will recommend taking it on Wednesday to avoid the busy Monday and Friday schedules. Also, if an employee has plans to attend a popular event that weekend, the system will take that information into account and make adjustments accordingly.

[0317] Examples of prompt statements for a generative AI model are as follows:

[0318] "Please suggest the optimal day off based on attendance data and traffic information. The user wants to take Friday off, but please recalculate the best day to avoid peak traffic."

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The server retrieves employee work information from the company's HR system via an API. This information includes attendance dates, departure times, and job title. First, it receives attendance information in JSON format from the HR system as input and stores it in the database. The data is preprocessed for analysis, and unnecessary information is filtered out.

[0322] Step 2:

[0323] The server collects traffic data from the Google Maps API. This involves obtaining data such as road congestion information, public transport usage, and traffic volume within cities. Real-time traffic information is obtained from the API as input, and analysis is performed to identify peak congestion. Here, historical traffic trend data is also utilized to improve the accuracy of congestion predictions.

[0324] Step 3:

[0325] The server executes a machine learning algorithm using TensorFlow to calculate the optimal vacation days for each worker. This step combines the work information obtained in step 1 with the traffic data obtained in step 2. Using individual worker information and city traffic data as input data, it performs complex calculations to derive the most efficient vacation days.

[0326] Step 4:

[0327] The terminal displays the calculation results to the worker via a smartphone app. The user interface uses React Native, allowing the worker to view the suggested vacation days. The input here is the vacation day information calculated in step 3, and the output is a visual presentation via the user interface.

[0328] Step 5:

[0329] The user provides feedback on the suggested vacation dates from their device. This feedback is sent to the server via the device to evaluate whether the vacation dates are appropriate. Based on the feedback, the server adjusts the parameters of the AI ​​algorithm to improve the accuracy of future suggestions. The input is the user's feedback, and the output is the model's learning effect.

[0330] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0331] This invention relates to a system that proposes the optimal timing and content for employees taking paid leave, and in particular, provides leave suggestions that take into account the user's emotional state.

[0332] This system includes servers, terminals, and a user interface, and further incorporates an emotion engine to achieve advanced user support.

[0333] Server Role

[0334] The server collects and stores employee attendance and departure information, job information, vacation history, and company calendar data in a database. Based on this information, it analyzes typical work patterns and uses an AI algorithm to calculate optimal vacation days. In addition, an emotion engine is used to analyze employee input and feedback as real-time emotion data. This emotion information is used to adjust the content and timing of vacation suggestions.

[0335] Terminal role

[0336] The terminal is responsible for presenting suggestions to the user and receiving feedback. It displays vacation suggestions received from the server to the worker via the UI. Furthermore, based on interpretation from the emotion engine, it can understand in real time how the user is reacting to each suggestion and flexibly change the displayed content accordingly.

[0337] User roles

[0338] Users can review vacation suggestions from the system and provide feedback via their device. The input feedback is further analyzed by an emotion engine that analyzes the user's verbal and nonverbal emotional expressions. Feedback reflecting the user's emotions is also used to improve future suggestions.

[0339] Specific example

[0340] For example, if the system determines that a user is currently experiencing high levels of work-related stress, the server will suggest a refreshing vacation via the terminal to help alleviate that stress. For instance, it might suggest taking Friday afternoon of the following week as vacation. This suggestion takes into account the user's recently reported fatigue and stress levels, and is adjusted according to the psychological state inferred by the emotion engine from the user's specified feedback. In this way, the present invention enables users to receive better vacation suggestions tailored to their emotional state, thereby promoting mental and physical well-being.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The server collects employee attendance and departure information, job information, and leave history from the company's attendance management system and HR system, and stores it in a database. This allows for an understanding of the latest work status and past leave trends.

[0344] Step 2:

[0345] The server references the company's business calendar to retrieve important date information, such as peak seasons and holidays. This information is used to adjust the timing and duration of vacation suggestions.

[0346] Step 3:

[0347] The server collects worker behavior and feedback data and uses an emotion engine to analyze the user's emotional state. It calculates emotional indicators such as stress and satisfaction from text analysis and behavioral history.

[0348] Step 4:

[0349] The server uses AI algorithms to comprehensively analyze the data collected so far and calculate the optimal vacation days for each worker. The results of the emotion engine analysis are also taken into consideration, allowing for suggestions of earlier vacations, especially for workers experiencing high levels of stress.

[0350] Step 5:

[0351] The server generates optimized vacation suggestions and sends them to the terminal. These suggestions include recommended vacation days, along with the reasons for their selection and the expected benefits of rest.

[0352] Step 6:

[0353] The device presents vacation suggestions to the user through its user interface. It also uses an emotion engine to diagnose the user's initial response and adjust the suggestions in real time as needed.

[0354] Step 7:

[0355] Users receive suggestions via their devices and provide feedback on their content. They can include emotionally charged comments and additional requests.

[0356] Step 8:

[0357] The device collects user feedback and sends it to the server. This feedback is further analyzed by an emotion engine and used to improve future vacation suggestions.

[0358] Step 9:

[0359] The server stores the collected feedback data in a database and fine-tunes it to improve the quality of future suggestions. Through the accumulation of emotional and operational data, the system continuously learns, enabling more personalized vacation suggestions.

[0360] (Example 2)

[0361] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0362] In the current work environment, it is difficult to take leave at the optimal time, and since emotional stress is not taken into consideration, there is a risk that the physical and mental health of workers will be compromised. Furthermore, if leave cannot be taken in consideration of the company calendar and busy periods, it will lead to decreased work efficiency and lower employee satisfaction.

[0363] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0364] In this invention, the server includes means for acquiring employee work information and departure information, means for analyzing employee work information and leave history, and means for using an artificial intelligence algorithm to calculate the optimal leave days. This makes it possible to adjust leave suggestions based on employee emotional information and propose leave that suits the individual's emotional state. Furthermore, it enables the optimization of leave considering the company calendar and busy periods, leading to improved work efficiency and the maintenance of employee health.

[0365] "Worker work information" refers to data about individual workers' arrival and departure times, including working hours and job duties.

[0366] "Job information" refers to data related to the duties and tasks that an employee is responsible for in the workplace.

[0367] "Leave history" refers to a record of leave taken by an employee in the past, including information such as the date and type of leave.

[0368] An "artificial intelligence algorithm" refers to an automated calculation method used in computer programs to analyze worker data and determine the optimal vacation days.

[0369] "Emotional information" refers to data about the emotions expressed by workers and their current psychological state, and is collected from both verbal and nonverbal elements.

[0370] A "corporate calendar" refers to schedule information that shows the work schedule, holidays, and busy periods within a company.

[0371] A "peak season" refers to a specific period in a company or business where the workload increases significantly.

[0372] This invention provides a system that enables workers to take vacations effectively, and is implemented using a server, terminals, and a user interface. This allows for personalized vacation suggestions that take emotional information into account.

[0373] The server is connected to the company's HR system and other databases, and uses APIs to collect employee work information, job information, vacation history, and company calendar data. This data is stored in the database, and AI algorithms are used to analyze work status and generate optimal vacation day suggestions.

[0374] In addition, the server is equipped with an emotion engine that analyzes user feedback using natural language processing technology. This allows it to understand the user's stress level and emotional state and reflect this in vacation suggestions.

[0375] The terminal presents the user with vacation suggestions sent from the server via a user interface. The interface is intuitive, allowing workers to easily review the suggestions and provide feedback.

[0376] Users review the displayed vacation suggestions using their devices and provide feedback on their experiences. This feedback is sent to the server and used to improve the accuracy of future suggestions.

[0377] For example, if a user provides feedback such as, "I would like to be offered a refreshing day off next Friday," the system could then suggest a more appropriate timing based on that feedback. In this way, it becomes possible to offer day off tailored to the individual needs of each worker, supporting their physical and mental well-being.

[0378] Examples of prompt statements for a generative AI model are as follows:

[0379] "Based on worker A's emotional state and work data, generate suggestions for the optimal vacation days."

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The server collects employee work information, job information, and leave history from a company's HR system via an API. The input is information from the company's database, and the output is stored in the database in a structured data format. This prepares an initial dataset for a comprehensive understanding of employee work performance.

[0383] Step 2:

[0384] The server inputs work and job information into an AI algorithm to analyze the worker's typical work patterns and workload. The input data includes arrival and departure times, job duties, etc., and the output is the individual worker's work pattern. This analysis helps determine the worker's workload and the most efficient timing for taking leave.

[0385] Step 3:

[0386] The server inputs user feedback into an emotion engine to analyze the worker's emotional state. Inputs include text-based feedback and selectable responses from a user interface, and the emotion engine uses natural language processing techniques for analysis. The output is quantitative data on the worker's stress level and emotional state. This result is used to tailor vacation suggestions to each worker's psychological needs.

[0387] Step 4:

[0388] The server uses an AI algorithm to calculate the optimal vacation days based on collected work information, job information, and emotional state. The input is the analysis results obtained in the previous step, and the output is the recommended vacation period from the start date to the end date. This allows the server to suggest the best timing for employees to take vacation.

[0389] Step 5:

[0390] The terminal displays vacation suggestions sent from the server to the user. The input is the recommended vacation period from the server, and the output is information visually presented on the user interface. Specifically, the terminal informs the worker of the suggestions through notifications and pop-up screens.

[0391] Step 6:

[0392] Users provide feedback on presented vacation suggestions via a terminal. Input includes user text comments and selectable responses, while output is the transmission of feedback to the server. This feedback serves as important data for improving future suggestions.

[0393] Step 7:

[0394] The server uses user feedback to make adjustments that improve the accuracy of future vacation suggestions. The input is user feedback data, and the output is the adjusted suggestion algorithm and rule set. Through this process, the system becomes capable of making more sophisticated vacation suggestions over time.

[0395] (Application Example 2)

[0396] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0397] The physical and mental health of workers is a crucial factor that directly impacts productivity and job satisfaction. However, currently, vacation proposals do not take into account the emotional state of individual workers, making it difficult to select the optimal timing for vacation. As a result, health problems due to excessive stress and inefficiencies in vacation taking have become apparent issues. This invention aims to solve these problems.

[0398] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0399] In this invention, the server includes means for acquiring employee attendance and departure information, means for analyzing employee job information and leave history, means for using an artificial intelligence algorithm to calculate optimal leave days, means for collecting feedback on leave suggestions presented to employees, means for using an emotion analysis engine to analyze employees' emotional states, and means for making leave suggestions based on emotional states. This makes it possible to make optimal leave suggestions that take into account employees' emotional states.

[0400] A "worker" is a person who performs a specific job within a company or organization.

[0401] "Attendance and departure information" refers to data on the time an employee arrives at the workplace and begins work, and the time they finish work and leave the workplace.

[0402] "Job information" refers to data about the work content, roles, and responsibilities of an employee.

[0403] "Leave history" refers to a record of leave taken by an employee in the past, including dates and durations.

[0404] An "artificial intelligence algorithm" is a computational method that uses machine learning and other AI technologies to analyze and make decisions based on data.

[0405] "Feedback" refers to the reactions or opinions that workers give in response to a proposal or result.

[0406] An "emotion analysis engine" is a software component that analyzes the emotions and mental state of workers based on data they input.

[0407] A "vacation suggestion" is a proposal that offers workers recommendations on when they should take their vacation.

[0408] The system implementing this invention provides optimal vacation suggestions that take into account the emotional state of workers, and consists of three components: a server, a terminal, and a user. The server functions as a core system for acquiring and analyzing workers' attendance and departure information, job information, and vacation history. Furthermore, an artificial intelligence algorithm calculates the optimal vacation days using the collected data.

[0409] The server uses an emotion analysis engine to analyze the emotional state of workers based on their input and feedback. Based on this analysis, it generates vacation suggestions for workers in real time. These suggestions are presented to the user through a terminal provided to them. The terminal displays the received suggestions to the worker through a user interface and simultaneously records the human interaction.

[0410] When a worker uses a terminal to review a proposal and provides feedback, that information is sent to a server. The feedback includes verbal and nonverbal sentiments, which are further analyzed by a sentiment analysis engine. This feedback is then used to improve future vacation proposals.

[0411] As a concrete example, imagine a scenario where a worker inputs "This week's stress level is 8" using their smartphone or smartwatch. The server then suggests a refreshing vacation for the following weekend. By utilizing generative AI models, the accuracy of analyzing such emotional data can be improved.

[0412] An example of a prompt message might be, "Please record your stress level today on a scale of 1 to 10." This prompt allows workers to easily record their emotional state.

[0413] This system utilizes an emotion analysis engine and artificial intelligence algorithms to improve users' health and job satisfaction.

[0414] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0415] Step 1:

[0416] The server retrieves employee attendance and departure information, job information, and leave history. This input data is collected through an information aggregation system and stored in a database. The collected data serves as basic information for later analysis.

[0417] Step 2:

[0418] The server uses an artificial intelligence algorithm to calculate the optimal vacation days based on the collected data. This process involves data calculations that take into account employee work patterns and workloads, and outputs vacation suggestions.

[0419] Step 3:

[0420] The server uses an emotion analysis engine to analyze feedback and emotional data entered by workers through their terminals. It receives data related to emotional states as input and evaluates the workers' psychological state using text analysis and pattern recognition technologies. This results in an evaluation based on emotions.

[0421] Step 4:

[0422] The server adjusts vacation suggestions based on the sentiment analysis results and presents them to employees at the optimal time. By utilizing a generative AI model, vacation suggestions that integrate sentiment data and work data are output.

[0423] Step 5:

[0424] The terminal displays vacation suggestions received from the server to the worker. The suggestions are presented through a user interface, offering the worker options in an interactive format.

[0425] Step 6:

[0426] Users review the proposals via their devices and provide feedback. This feedback includes recording their satisfaction level and opinions on the proposals, and entering responses. This data is then sent back to the server.

[0427] Step 7:

[0428] The server receives feedback from users and uses it to improve future vacation suggestions. The feedback data is analyzed, and improvements are made to enhance the overall accuracy of the system.

[0429] 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.

[0430] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0431] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0432] [Third Embodiment]

[0433] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0434] 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.

[0435] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0436] 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.

[0437] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0438] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0439] 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.

[0440] 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.

[0441] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0442] The 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.

[0443] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0444] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0445] This invention provides a system for optimizing the acquisition of paid leave by workers, and its embodiments are described in detail below.

[0446] This system is primarily composed of three elements: servers, terminals, and users.

[0447] Server Role

[0448] The server is the core of the system, responsible for collecting, processing, and analyzing critical data. It automatically collects employee attendance and departure information, as well as job information, vacation history, and company work calendar information. Based on this information, it runs an artificial intelligence algorithm to calculate the optimal vacation days for each employee. The server continuously learns from past data and employee feedback to improve the accuracy of the algorithm's suggestions.

[0449] Terminal role

[0450] The terminal controls interactions with the user and, through the user interface, presents the user with the optimal vacation dates received from the server. The terminal receives feedback from the user and sends it to the server to further refine the next suggestion.

[0451] User roles

[0452] The user is the worker themselves, and receives vacation suggestions based on their work data. The user reviews the suggested vacation days and sends their opinions and feedback to the server via their terminal as needed. This feedback contributes to the improvement of the entire system.

[0453] Specific example

[0454] For example, if an employee requests a vacation in the near future, they access the system via their terminal, and the server collects data to process that request. Taking into account that the user is nearing a project deadline, the server suggests a day when the vacation can be taken most efficiently with minimal impact on work. If Wednesday is suggested, the reasons given might include that it is a good time to balance the schedules and workloads of other team members. The user can accept this or provide feedback. In this way, the present invention enables workers to effectively utilize vacations while minimizing disruption to their work.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The server collects employee attendance and departure information from the company's internal attendance management system and stores it in a database. Regular synchronization is performed to ensure that the most up-to-date information is always reflected.

[0458] Step 2:

[0459] The server retrieves employee job information and leave history from the HR system. This allows the system to understand each employee's work content and past leave history, and use this information to plan their leave.

[0460] Step 3:

[0461] The server references the company calendar to collect information on peak seasons and special dates such as holidays. This information is an important factor in suggesting vacation dates.

[0462] Step 4:

[0463] The server uses the data collected above to run an artificial intelligence algorithm and calculate the optimal vacation days for each worker. This takes into account workload and the balance of work within the team.

[0464] Step 5:

[0465] The server compiles the calculated optimal vacation days into a list of suggestions for each worker and sends it to their terminal. This list is displayed on the terminal as a suggestion document.

[0466] Step 6:

[0467] The device presents the user with vacation date suggestions via its user interface. The user can then use these suggestions to plan their vacation schedule.

[0468] Step 7:

[0469] Users can input their opinions and questions about the proposed vacation days via their device and send them to the server as feedback.

[0470] Step 8:

[0471] The server stores user feedback in a database and uses it to improve the accuracy and refinement of future suggestions. This feedback serves as training data for the AI ​​model.

[0472] (Example 1)

[0473] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] In today's work environment, it is crucial for employees to take their paid leave appropriately in order to maintain work efficiency while improving their quality of life. However, in many companies, employees decide the timing of their leave on their own, which can lead to situations where they take leave without considering the impact on work, or conversely, where it becomes difficult to take leave. This can result in decreased work efficiency and problems where employees are unable to get enough rest.

[0475] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0476] In this invention, the server includes means for collecting employee work information, means for analyzing data based on the employee's work history and leave history, and means for calculating optimal leave days using a generative AI model. This makes it possible to minimize the impact on work when employees take leave and to take appropriate leave according to their individual needs.

[0477] "Worker work information" refers to various pieces of information that detail an employee's work status, such as their arrival and departure times, number of working days, and number of days of leave taken.

[0478] "Work history" refers to historical information that shows the types of work an employee has performed and the changes in their job responsibilities within a company in the past.

[0479] "Leave history" refers to historical information about leave taken by an employee in the past, such as the number of days, timing, and reasons for leave.

[0480] A "generative AI model" is an algorithm or system designed to analyze data using artificial intelligence technology and perform predictions and optimizations.

[0481] A "user terminal" refers to a device such as a computer or smartphone that a worker directly interacts with, enabling the display and input of information.

[0482] "Means of collecting feedback" refers to mechanisms and methods for inputting and recording opinions and evaluations from workers into a system.

[0483] "Means of improving algorithms" refer to methods and techniques for improving the performance and accuracy of algorithms based on collected feedback and new data.

[0484] An "organizational activity plan" is a plan that includes the schedule and tasks of operations set by a company or organization, and is fundamental information for managing operational efficiency and productivity.

[0485] This invention is a system that optimizes the acquisition of paid leave by workers, and is mainly composed of a server, terminals, and users.

[0486] The server is the core of this system. The server collects employee work information, including clock-in and clock-out times, work history, and leave history. This data is automatically retrieved from the company's human resources management and time management systems and securely stored in cloud storage. The server uses data analysis libraries such as Python's Pandas and NumPy to analyze the collected information. Furthermore, the server utilizes generative AI models to calculate the optimal leave days for each individual employee. Machine learning frameworks such as TensorFlow and scikit-learn are used in these models.

[0487] The terminal provides a user interface and displays vacation suggestions sent from the server to the user. The terminal is implemented as a desktop or mobile application, allowing workers to input their work schedules and review vacation suggestions. This enables the system to directly collect user feedback, which can then be used to improve future suggestions.

[0488] Users review vacation suggestions provided through their devices and submit feedback as needed. This allows the system to continuously improve the accuracy of its suggestions. For users, the process of reviewing suggested vacation dates is simple, enabling them to minimize the impact of taking desired vacations on their work.

[0489] As a concrete example, when a user uses this system to plan a vacation in the near future, the server calculates the optimal vacation dates based on the user's work data and displays the results on the terminal. For example, by using a prompt such as, "Please suggest the optimal vacation dates based on the worker's attendance history and work calendar," practical vacation dates will be suggested.

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The server collects employee work information. This process connects to the company's human resources management and time management systems, retrieving data such as employee clock-in and clock-out times, work history, and leave history via APIs. Inputs come from various databases, and output is employee information stored in an integrated database on the server. This makes the collected data available for use in subsequent processing stages.

[0493] Step 2:

[0494] The server analyzes the collected data. Here, it uses Python's Pandas and NumPy to clean and format the data. The input is the collected worker data, and the output is the formatted dataset. Specifically, it performs tasks such as imputing missing data, handling outliers, and calculating necessary variables. This prepares the data for subsequent algorithmic processing.

[0495] Step 3:

[0496] The server uses a generative AI model to calculate the optimal vacation days. This process involves inputting the formatted data from the previous step into the model to calculate suitable vacation days for each worker. Libraries such as TensorFlow and scikit-learn are used in the model. The input is the formatted dataset, and the output is a list of optimal vacation day candidates. The server then prepares to provide the calculation results to the user.

[0497] Step 4:

[0498] The terminal presents the user with vacation suggestions sent from the server. The suggested dates are displayed in an intuitive and easy-to-understand format through the user interface. Input is the suggestion data from the server, and output is a visual presentation on the user's terminal. Specifically, it displays date suggestions in a calendar format, allowing the user to select or review their preferred dates.

[0499] Step 5:

[0500] Users review suggested vacation dates via their devices and send their opinions as feedback to the server. Feedback is provided through a dedicated form or comment section. Input is the user's thoughts and evaluations, and output is the feedback data sent to the server. This allows the server to obtain valuable data to improve future suggestions.

[0501] Step 6:

[0502] The server analyzes the collected feedback and improves the generating AI model by incorporating the feedback data. The input is user feedback data, and the output is the updated algorithm. Specifically, the feedback data is added to the model's training dataset and retrained to improve the accuracy of the suggestions. This allows the entire system to more effectively optimize worker vacation time.

[0503] (Application Example 1)

[0504] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0505] While employees taking vacation time is crucial for their individual work-life balance, uneven vacation schedules can negatively impact the overall efficiency of an organization. Furthermore, traffic congestion in urban areas also affects work efficiency; therefore, there is a need to simultaneously achieve both employee vacation time and optimized traffic conditions.

[0506] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0507] In this invention, the server includes means for acquiring workers' work information, means for analyzing workers' job-related information and vacation history, means for using a machine learning model to derive the overall optimal vacation days, and means for evaluating traffic data and optimizing the overall traffic efficiency of the city. This makes it possible to optimize vacation time according to the individual needs of workers while also alleviating traffic congestion throughout the city.

[0508] A "worker" is an individual who belongs to an organization and acts based on work information and job-related information.

[0509] "Work information" refers to data about an employee's daily work schedule, such as their start date, end time, and work location.

[0510] "Job-related information" refers to information about the work an employee is engaged in, the projects they are assigned to, their job title, etc.

[0511] "Leave history" refers to a record of the dates and reasons for paid leave taken by an employee in the past.

[0512] A "machine learning model" is one of the artificial intelligence techniques used to learn patterns from data and perform predictions and classifications.

[0513] An "optimal holiday day" is a date selected to allow employees to take leave effectively while minimizing the impact on business operations.

[0514] "Traffic data" refers to information about road congestion within cities, the use of public transport, and traffic volume.

[0515] "Transportation efficiency" is a concept that refers to the smoothness of traffic flow within a city and the effective utilization of available transportation resources.

[0516] This system aims to optimize worker vacation time and transportation efficiency, and is based on the use of advanced machine learning models that analyze worker work information and transportation data.

[0517] The server aggregates employee work information from a company's HR system via APIs and collects traffic data using the Google Maps API. The collected data is processed using a machine learning algorithm based on TensorFlow to calculate the optimal vacation days for each employee. This allows for the presentation of highly efficient vacation plans that avoid peak traffic times while minimizing the impact on individual employees' work.

[0518] The terminal serves to suggest vacation days to workers via a smartphone app and allow them to review the suggestions through a highly intuitive user interface. The app is developed using React Native and also allows users to provide feedback on the suggested vacation days.

[0519] Users review suggested vacations using the app and determine if they meet their needs. The system continuously learns from the feedback, improving the accuracy of its algorithms. This mechanism is expected to have a positive impact on commuting behavior across cities.

[0520] For example, if an employee wants to take their vacation most efficiently during the week, the system will recommend taking it on Wednesday to avoid the busy Monday and Friday schedules. Also, if an employee has plans to attend a popular event that weekend, the system will take that information into account and make adjustments accordingly.

[0521] Examples of prompt statements for a generative AI model are as follows:

[0522] "Please suggest the optimal day off based on attendance data and traffic information. The user wants to take Friday off, but please recalculate the best day to avoid peak traffic."

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The server retrieves employee work information from the company's HR system via an API. This information includes attendance dates, departure times, and job title. First, it receives attendance information in JSON format from the HR system as input and stores it in the database. The data is preprocessed for analysis, and unnecessary information is filtered out.

[0526] Step 2:

[0527] The server collects traffic data from the Google Maps API. This involves obtaining data such as road congestion information, public transport usage, and traffic volume within cities. Real-time traffic information is obtained from the API as input, and analysis is performed to identify peak congestion. Here, historical traffic trend data is also utilized to improve the accuracy of congestion predictions.

[0528] Step 3:

[0529] The server executes a machine learning algorithm using TensorFlow to calculate the optimal vacation days for each worker. This step combines the work information obtained in step 1 with the traffic data obtained in step 2. Using individual worker information and city traffic data as input data, it performs complex calculations to derive the most efficient vacation days.

[0530] Step 4:

[0531] The terminal displays the calculation results to the worker via a smartphone app. The user interface uses React Native, allowing the worker to view the suggested vacation days. The input here is the vacation day information calculated in step 3, and the output is a visual presentation via the user interface.

[0532] Step 5:

[0533] The user provides feedback on the suggested vacation dates from their device. This feedback is sent to the server via the device to evaluate whether the vacation dates are appropriate. Based on the feedback, the server adjusts the parameters of the AI ​​algorithm to improve the accuracy of future suggestions. The input is the user's feedback, and the output is the model's learning effect.

[0534] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0535] This invention relates to a system that proposes the optimal timing and content for employees taking paid leave, and in particular, provides leave suggestions that take into account the user's emotional state.

[0536] This system includes servers, terminals, and a user interface, and further incorporates an emotion engine to achieve advanced user support.

[0537] Server Role

[0538] The server collects and stores employee attendance and departure information, job information, vacation history, and company calendar data in a database. Based on this information, it analyzes typical work patterns and uses an AI algorithm to calculate optimal vacation days. In addition, an emotion engine is used to analyze employee input and feedback as real-time emotion data. This emotion information is used to adjust the content and timing of vacation suggestions.

[0539] Terminal role

[0540] The terminal is responsible for presenting suggestions to the user and receiving feedback. It displays vacation suggestions received from the server to the worker via the UI. Furthermore, based on interpretation from the emotion engine, it can understand in real time how the user is reacting to each suggestion and flexibly change the displayed content accordingly.

[0541] User roles

[0542] Users can review vacation suggestions from the system and provide feedback via their device. The input feedback is further analyzed by an emotion engine that analyzes the user's verbal and nonverbal emotional expressions. Feedback reflecting the user's emotions is also used to improve future suggestions.

[0543] Specific example

[0544] For example, if the system determines that a user is currently experiencing high levels of work-related stress, the server will suggest a refreshing vacation via the terminal to help alleviate that stress. For instance, it might suggest taking Friday afternoon of the following week as vacation. This suggestion takes into account the user's recently reported fatigue and stress levels, and is adjusted according to the psychological state inferred by the emotion engine from the user's specified feedback. In this way, the present invention enables users to receive better vacation suggestions tailored to their emotional state, thereby promoting mental and physical well-being.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The server collects employee attendance and departure information, job information, and leave history from the company's attendance management system and HR system, and stores it in a database. This allows for an understanding of the latest work status and past leave trends.

[0548] Step 2:

[0549] The server references the company's business calendar to retrieve important date information, such as peak seasons and holidays. This information is used to adjust the timing and duration of vacation suggestions.

[0550] Step 3:

[0551] The server collects worker behavior and feedback data and uses an emotion engine to analyze the user's emotional state. It calculates emotional indicators such as stress and satisfaction from text analysis and behavioral history.

[0552] Step 4:

[0553] The server uses AI algorithms to comprehensively analyze the data collected so far and calculate the optimal vacation days for each worker. The results of the emotion engine analysis are also taken into consideration, allowing for suggestions of earlier vacations, especially for workers experiencing high levels of stress.

[0554] Step 5:

[0555] The server generates optimized vacation suggestions and sends them to the terminal. These suggestions include recommended vacation days, along with the reasons for their selection and the expected benefits of rest.

[0556] Step 6:

[0557] The device presents vacation suggestions to the user through its user interface. It also uses an emotion engine to diagnose the user's initial response and adjust the suggestions in real time as needed.

[0558] Step 7:

[0559] Users receive suggestions via their devices and provide feedback on their content. They can include emotionally charged comments and additional requests.

[0560] Step 8:

[0561] The device collects user feedback and sends it to the server. This feedback is further analyzed by an emotion engine and used to improve future vacation suggestions.

[0562] Step 9:

[0563] The server stores the collected feedback data in a database and fine-tunes it to improve the quality of future suggestions. Through the accumulation of emotional and operational data, the system continuously learns, enabling more personalized vacation suggestions.

[0564] (Example 2)

[0565] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0566] In the current work environment, it is difficult to take leave at the optimal time, and since emotional stress is not taken into consideration, there is a risk that the physical and mental health of workers will be compromised. Furthermore, if leave cannot be taken in consideration of the company calendar and busy periods, it will lead to decreased work efficiency and lower employee satisfaction.

[0567] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0568] In this invention, the server includes means for acquiring employee work information and departure information, means for analyzing employee work information and leave history, and means for using an artificial intelligence algorithm to calculate the optimal leave days. This makes it possible to adjust leave suggestions based on employee emotional information and propose leave that suits the individual's emotional state. Furthermore, it enables the optimization of leave considering the company calendar and busy periods, leading to improved work efficiency and the maintenance of employee health.

[0569] "Worker work information" refers to data about individual workers' arrival and departure times, including working hours and job duties.

[0570] "Job information" refers to data related to the duties and tasks that an employee is responsible for in the workplace.

[0571] "Leave history" refers to a record of leave taken by an employee in the past, including information such as the date and type of leave.

[0572] An "artificial intelligence algorithm" refers to an automated calculation method used in computer programs to analyze worker data and determine the optimal vacation days.

[0573] "Emotional information" refers to data about the emotions expressed by workers and their current psychological state, and is collected from both verbal and nonverbal elements.

[0574] A "corporate calendar" refers to schedule information that shows the work schedule, holidays, and busy periods within a company.

[0575] A "peak season" refers to a specific period in a company or business where the workload increases significantly.

[0576] This invention provides a system that enables workers to take vacations effectively, and is implemented using a server, terminals, and a user interface. This allows for personalized vacation suggestions that take emotional information into account.

[0577] The server is connected to the company's HR system and other databases, and uses APIs to collect employee work information, job information, vacation history, and company calendar data. This data is stored in the database, and AI algorithms are used to analyze work status and generate optimal vacation day suggestions.

[0578] In addition, the server is equipped with an emotion engine that analyzes user feedback using natural language processing technology. This allows it to understand the user's stress level and emotional state and reflect this in vacation suggestions.

[0579] The terminal presents the user with vacation suggestions sent from the server via a user interface. The interface is intuitive, allowing workers to easily review the suggestions and provide feedback.

[0580] Users review the displayed vacation suggestions using their devices and provide feedback on their experiences. This feedback is sent to the server and used to improve the accuracy of future suggestions.

[0581] For example, if a user provides feedback such as, "I would like to be offered a refreshing day off next Friday," the system could then suggest a more appropriate timing based on that feedback. In this way, it becomes possible to offer day off tailored to the individual needs of each worker, supporting their physical and mental well-being.

[0582] Examples of prompt statements for a generative AI model are as follows:

[0583] "Based on worker A's emotional state and work data, generate suggestions for the optimal vacation days."

[0584] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0585] Step 1:

[0586] The server collects employee work information, job information, and leave history from a company's HR system via an API. The input is information from the company's database, and the output is stored in the database in a structured data format. This prepares an initial dataset for a comprehensive understanding of employee work performance.

[0587] Step 2:

[0588] The server inputs work and job information into an AI algorithm to analyze the worker's typical work patterns and workload. The input data includes arrival and departure times, job duties, etc., and the output is the individual worker's work pattern. This analysis helps determine the worker's workload and the most efficient timing for taking leave.

[0589] Step 3:

[0590] The server inputs user feedback into an emotion engine to analyze the worker's emotional state. Inputs include text-based feedback and selectable responses from a user interface, and the emotion engine uses natural language processing techniques for analysis. The output is quantitative data on the worker's stress level and emotional state. This result is used to tailor vacation suggestions to each worker's psychological needs.

[0591] Step 4:

[0592] The server uses an AI algorithm to calculate the optimal vacation days based on collected work information, job information, and emotional state. The input is the analysis results obtained in the previous step, and the output is the recommended vacation period from the start date to the end date. This allows the server to suggest the best timing for employees to take vacation.

[0593] Step 5:

[0594] The terminal displays vacation suggestions sent from the server to the user. The input is the recommended vacation period from the server, and the output is information visually presented on the user interface. Specifically, the terminal informs the worker of the suggestions through notifications and pop-up screens.

[0595] Step 6:

[0596] Users provide feedback on presented vacation suggestions via a terminal. Input includes user text comments and selectable responses, while output is the transmission of feedback to the server. This feedback serves as important data for improving future suggestions.

[0597] Step 7:

[0598] The server uses user feedback to make adjustments that improve the accuracy of future vacation suggestions. The input is user feedback data, and the output is the adjusted suggestion algorithm and rule set. Through this process, the system becomes capable of making more sophisticated vacation suggestions over time.

[0599] (Application Example 2)

[0600] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0601] The physical and mental health of workers is a crucial factor that directly impacts productivity and job satisfaction. However, currently, vacation proposals do not take into account the emotional state of individual workers, making it difficult to select the optimal timing for vacation. As a result, health problems due to excessive stress and inefficiencies in vacation taking have become apparent issues. This invention aims to solve these problems.

[0602] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0603] In this invention, the server includes means for acquiring employee attendance and departure information, means for analyzing employee job information and leave history, means for using an artificial intelligence algorithm to calculate optimal leave days, means for collecting feedback on leave suggestions presented to employees, means for using an emotion analysis engine to analyze employees' emotional states, and means for making leave suggestions based on emotional states. This makes it possible to make optimal leave suggestions that take into account employees' emotional states.

[0604] A "worker" is a person who performs a specific job within a company or organization.

[0605] "Attendance and departure information" refers to data on the time an employee arrives at the workplace and begins work, and the time they finish work and leave the workplace.

[0606] "Job information" refers to data about the work content, roles, and responsibilities of an employee.

[0607] "Leave history" refers to a record of leave taken by an employee in the past, including dates and durations.

[0608] An "artificial intelligence algorithm" is a computational method that uses machine learning and other AI technologies to analyze and make decisions based on data.

[0609] "Feedback" refers to the reactions or opinions that workers give in response to a proposal or result.

[0610] An "emotion analysis engine" is a software component that analyzes the emotions and mental state of workers based on data they input.

[0611] A "vacation suggestion" is a proposal that offers workers recommendations on when they should take their vacation.

[0612] The system implementing this invention provides optimal vacation suggestions that take into account the emotional state of workers, and consists of three components: a server, a terminal, and a user. The server functions as a core system for acquiring and analyzing workers' attendance and departure information, job information, and vacation history. Furthermore, an artificial intelligence algorithm calculates the optimal vacation days using the collected data.

[0613] The server uses an emotion analysis engine to analyze the emotional state of workers based on their input and feedback. Based on this analysis, it generates vacation suggestions for workers in real time. These suggestions are presented to the user through a terminal provided to them. The terminal displays the received suggestions to the worker through a user interface and simultaneously records the human interaction.

[0614] When a worker uses a terminal to review a proposal and provides feedback, that information is sent to a server. The feedback includes verbal and nonverbal sentiments, which are further analyzed by a sentiment analysis engine. This feedback is then used to improve future vacation proposals.

[0615] As a concrete example, imagine a scenario where a worker inputs "This week's stress level is 8" using their smartphone or smartwatch. The server then suggests a refreshing vacation for the following weekend. By utilizing generative AI models, the accuracy of analyzing such emotional data can be improved.

[0616] An example of a prompt message might be, "Please record your stress level today on a scale of 1 to 10." This prompt allows workers to easily record their emotional state.

[0617] This system utilizes an emotion analysis engine and artificial intelligence algorithms to improve users' health and job satisfaction.

[0618] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0619] Step 1:

[0620] The server retrieves employee attendance and departure information, job information, and leave history. This input data is collected through an information aggregation system and stored in a database. The collected data serves as basic information for later analysis.

[0621] Step 2:

[0622] The server uses an artificial intelligence algorithm to calculate the optimal vacation days based on the collected data. This process involves data calculations that take into account employee work patterns and workloads, and outputs vacation suggestions.

[0623] Step 3:

[0624] The server uses an emotion analysis engine to analyze feedback and emotional data entered by workers through their terminals. It receives data related to emotional states as input and evaluates the workers' psychological state using text analysis and pattern recognition technologies. This results in an evaluation based on emotions.

[0625] Step 4:

[0626] The server adjusts vacation suggestions based on the sentiment analysis results and presents them to employees at the optimal time. By utilizing a generative AI model, vacation suggestions that integrate sentiment data and work data are output.

[0627] Step 5:

[0628] The terminal displays vacation suggestions received from the server to the worker. The suggestions are presented through a user interface, offering the worker options in an interactive format.

[0629] Step 6:

[0630] Users review the proposals via their devices and provide feedback. This feedback includes recording their satisfaction level and opinions on the proposals, and entering responses. This data is then sent back to the server.

[0631] Step 7:

[0632] The server receives feedback from users and uses it to improve future vacation suggestions. The feedback data is analyzed, and improvements are made to enhance the overall accuracy of the system.

[0633] 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.

[0634] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0635] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0636] [Fourth Embodiment]

[0637] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0638] 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.

[0639] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0640] 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.

[0641] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0642] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0643] 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.

[0644] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0645] 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.

[0646] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0647] The 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.

[0648] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0649] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0650] This invention provides a system for optimizing the acquisition of paid leave by workers, and its embodiments are described in detail below.

[0651] This system is primarily composed of three elements: servers, terminals, and users.

[0652] Server Role

[0653] The server is the core of the system, responsible for collecting, processing, and analyzing critical data. It automatically collects employee attendance and departure information, as well as job information, vacation history, and company work calendar information. Based on this information, it runs an artificial intelligence algorithm to calculate the optimal vacation days for each employee. The server continuously learns from past data and employee feedback to improve the accuracy of the algorithm's suggestions.

[0654] Terminal role

[0655] The terminal controls interactions with the user and, through the user interface, presents the user with the optimal vacation dates received from the server. The terminal receives feedback from the user and sends it to the server to further refine the next suggestion.

[0656] User roles

[0657] The user is the worker themselves, and receives vacation suggestions based on their work data. The user reviews the suggested vacation days and sends their opinions and feedback to the server via their terminal as needed. This feedback contributes to the improvement of the entire system.

[0658] Specific example

[0659] For example, if an employee requests a vacation in the near future, they access the system via their terminal, and the server collects data to process that request. Taking into account that the user is nearing a project deadline, the server suggests a day when the vacation can be taken most efficiently with minimal impact on work. If Wednesday is suggested, the reasons given might include that it is a good time to balance the schedules and workloads of other team members. The user can accept this or provide feedback. In this way, the present invention enables workers to effectively utilize vacations while minimizing disruption to their work.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The server collects employee attendance and departure information from the company's internal attendance management system and stores it in a database. Regular synchronization is performed to ensure that the most up-to-date information is always reflected.

[0663] Step 2:

[0664] The server retrieves employee job information and leave history from the HR system. This allows the system to understand each employee's work content and past leave history, and use this information to plan their leave.

[0665] Step 3:

[0666] The server references the company calendar to collect information on peak seasons and special dates such as holidays. This information is an important factor in suggesting vacation dates.

[0667] Step 4:

[0668] The server uses the data collected above to run an artificial intelligence algorithm and calculate the optimal vacation days for each worker. This takes into account workload and the balance of work within the team.

[0669] Step 5:

[0670] The server compiles the calculated optimal vacation days into a list of suggestions for each worker and sends it to their terminal. This list is displayed on the terminal as a suggestion document.

[0671] Step 6:

[0672] The device presents the user with vacation date suggestions via its user interface. The user can then use these suggestions to plan their vacation schedule.

[0673] Step 7:

[0674] Users can input their opinions and questions about the proposed vacation days via their device and send them to the server as feedback.

[0675] Step 8:

[0676] The server stores user feedback in a database and uses it to improve the accuracy and refinement of future suggestions. This feedback serves as training data for the AI ​​model.

[0677] (Example 1)

[0678] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] In today's work environment, it is crucial for employees to take their paid leave appropriately in order to maintain work efficiency while improving their quality of life. However, in many companies, employees decide the timing of their leave on their own, which can lead to situations where they take leave without considering the impact on work, or conversely, where it becomes difficult to take leave. This can result in decreased work efficiency and problems where employees are unable to get enough rest.

[0680] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0681] In this invention, the server includes means for collecting employee work information, means for analyzing data based on the employee's work history and leave history, and means for calculating optimal leave days using a generative AI model. This makes it possible to minimize the impact on work when employees take leave and to take appropriate leave according to their individual needs.

[0682] "Worker work information" refers to various pieces of information that detail an employee's work status, such as their arrival and departure times, number of working days, and number of days of leave taken.

[0683] "Work history" refers to historical information that shows the types of work an employee has performed and the changes in their job responsibilities within a company in the past.

[0684] "Leave history" refers to historical information about leave taken by an employee in the past, such as the number of days, timing, and reasons for leave.

[0685] A "generative AI model" is an algorithm or system designed to analyze data using artificial intelligence technology and perform predictions and optimizations.

[0686] A "user terminal" refers to a device such as a computer or smartphone that a worker directly interacts with, enabling the display and input of information.

[0687] "Means of collecting feedback" refers to mechanisms and methods for inputting and recording opinions and evaluations from workers into a system.

[0688] "Means of improving algorithms" refer to methods and techniques for improving the performance and accuracy of algorithms based on collected feedback and new data.

[0689] An "organizational activity plan" is a plan that includes the schedule and tasks of operations set by a company or organization, and is fundamental information for managing operational efficiency and productivity.

[0690] This invention is a system that optimizes the acquisition of paid leave by workers, and is mainly composed of a server, terminals, and users.

[0691] The server is the core of this system. The server collects employee work information, including clock-in and clock-out times, work history, and leave history. This data is automatically retrieved from the company's human resources management and time management systems and securely stored in cloud storage. The server uses data analysis libraries such as Python's Pandas and NumPy to analyze the collected information. Furthermore, the server utilizes generative AI models to calculate the optimal leave days for each individual employee. Machine learning frameworks such as TensorFlow and scikit-learn are used in these models.

[0692] The terminal provides a user interface and displays vacation suggestions sent from the server to the user. The terminal is implemented as a desktop or mobile application, allowing workers to input their work schedules and review vacation suggestions. This enables the system to directly collect user feedback, which can then be used to improve future suggestions.

[0693] Users review vacation suggestions provided through their devices and submit feedback as needed. This allows the system to continuously improve the accuracy of its suggestions. For users, the process of reviewing suggested vacation dates is simple, enabling them to minimize the impact of taking desired vacations on their work.

[0694] As a concrete example, when a user uses this system to plan a vacation in the near future, the server calculates the optimal vacation dates based on the user's work data and displays the results on the terminal. For example, by using a prompt such as, "Please suggest the optimal vacation dates based on the worker's attendance history and work calendar," practical vacation dates will be suggested.

[0695] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0696] Step 1:

[0697] The server collects employee work information. This process connects to the company's human resources management and time management systems, retrieving data such as employee clock-in and clock-out times, work history, and leave history via APIs. Inputs come from various databases, and output is employee information stored in an integrated database on the server. This makes the collected data available for use in subsequent processing stages.

[0698] Step 2:

[0699] The server analyzes the collected data. Here, it uses Python's Pandas and NumPy to clean and format the data. The input is the collected worker data, and the output is the formatted dataset. Specifically, it performs tasks such as imputing missing data, handling outliers, and calculating necessary variables. This prepares the data for subsequent algorithmic processing.

[0700] Step 3:

[0701] The server uses a generative AI model to calculate the optimal vacation days. This process involves inputting the formatted data from the previous step into the model to calculate suitable vacation days for each worker. Libraries such as TensorFlow and scikit-learn are used in the model. The input is the formatted dataset, and the output is a list of optimal vacation day candidates. The server then prepares to provide the calculation results to the user.

[0702] Step 4:

[0703] The terminal presents the user with vacation suggestions sent from the server. The suggested dates are displayed in an intuitive and easy-to-understand format through the user interface. Input is the suggestion data from the server, and output is a visual presentation on the user's terminal. Specifically, it displays date suggestions in a calendar format, allowing the user to select or review their preferred dates.

[0704] Step 5:

[0705] Users review suggested vacation dates via their devices and send their opinions as feedback to the server. Feedback is provided through a dedicated form or comment section. Input is the user's thoughts and evaluations, and output is the feedback data sent to the server. This allows the server to obtain valuable data to improve future suggestions.

[0706] Step 6:

[0707] The server analyzes the collected feedback and improves the generating AI model by incorporating the feedback data. The input is user feedback data, and the output is the updated algorithm. Specifically, the feedback data is added to the model's training dataset and retrained to improve the accuracy of the suggestions. This allows the entire system to more effectively optimize worker vacation time.

[0708] (Application Example 1)

[0709] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0710] While employees taking vacation time is crucial for their individual work-life balance, uneven vacation schedules can negatively impact the overall efficiency of an organization. Furthermore, traffic congestion in urban areas also affects work efficiency; therefore, there is a need to simultaneously achieve both employee vacation time and optimized traffic conditions.

[0711] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0712] In this invention, the server includes means for acquiring workers' work information, means for analyzing workers' job-related information and vacation history, means for using a machine learning model to derive the overall optimal vacation days, and means for evaluating traffic data and optimizing the overall traffic efficiency of the city. This makes it possible to optimize vacation time according to the individual needs of workers while also alleviating traffic congestion throughout the city.

[0713] A "worker" is an individual who belongs to an organization and acts based on work information and job-related information.

[0714] "Work information" refers to data about an employee's daily work schedule, such as their start date, end time, and work location.

[0715] "Job-related information" refers to information about the work an employee is engaged in, the projects they are assigned to, their job title, etc.

[0716] "Leave history" refers to a record of the dates and reasons for paid leave taken by an employee in the past.

[0717] A "machine learning model" is one of the artificial intelligence techniques used to learn patterns from data and perform predictions and classifications.

[0718] An "optimal holiday day" is a date selected to allow employees to take leave effectively while minimizing the impact on business operations.

[0719] "Traffic data" refers to information about road congestion within cities, the use of public transport, and traffic volume.

[0720] "Transportation efficiency" is a concept that refers to the smoothness of traffic flow within a city and the effective utilization of available transportation resources.

[0721] This system aims to optimize worker vacation time and transportation efficiency, and is based on the use of advanced machine learning models that analyze worker work information and transportation data.

[0722] The server aggregates employee work information from a company's HR system via APIs and collects traffic data using the Google Maps API. The collected data is processed using a machine learning algorithm based on TensorFlow to calculate the optimal vacation days for each employee. This allows for the presentation of highly efficient vacation plans that avoid peak traffic times while minimizing the impact on individual employees' work.

[0723] The terminal serves to suggest vacation days to workers via a smartphone app and allow them to review the suggestions through a highly intuitive user interface. The app is developed using React Native and also allows users to provide feedback on the suggested vacation days.

[0724] Users review suggested vacations using the app and determine if they meet their needs. The system continuously learns from the feedback, improving the accuracy of its algorithms. This mechanism is expected to have a positive impact on commuting behavior across cities.

[0725] For example, if an employee wants to take their vacation most efficiently during the week, the system will recommend taking it on Wednesday to avoid the busy Monday and Friday schedules. Also, if an employee has plans to attend a popular event that weekend, the system will take that information into account and make adjustments accordingly.

[0726] Examples of prompt statements for a generative AI model are as follows:

[0727] "Please suggest the optimal day off based on attendance data and traffic information. The user wants to take Friday off, but please recalculate the best day to avoid peak traffic."

[0728] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0729] Step 1:

[0730] The server retrieves employee work information from the company's HR system via an API. This information includes attendance dates, departure times, and job title. First, it receives attendance information in JSON format from the HR system as input and stores it in the database. The data is preprocessed for analysis, and unnecessary information is filtered out.

[0731] Step 2:

[0732] The server collects traffic data from the Google Maps API. This involves obtaining data such as road congestion information, public transport usage, and traffic volume within cities. Real-time traffic information is obtained from the API as input, and analysis is performed to identify peak congestion. Here, historical traffic trend data is also utilized to improve the accuracy of congestion predictions.

[0733] Step 3:

[0734] The server executes a machine learning algorithm using TensorFlow to calculate the optimal vacation days for each worker. This step combines the work information obtained in step 1 with the traffic data obtained in step 2. Using individual worker information and city traffic data as input data, it performs complex calculations to derive the most efficient vacation days.

[0735] Step 4:

[0736] The terminal displays the calculation results to the worker via a smartphone app. The user interface uses React Native, allowing the worker to view the suggested vacation days. The input here is the vacation day information calculated in step 3, and the output is a visual presentation via the user interface.

[0737] Step 5:

[0738] The user provides feedback on the suggested vacation dates from their device. This feedback is sent to the server via the device to evaluate whether the vacation dates are appropriate. Based on the feedback, the server adjusts the parameters of the AI ​​algorithm to improve the accuracy of future suggestions. The input is the user's feedback, and the output is the model's learning effect.

[0739] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0740] This invention relates to a system that proposes the optimal timing and content for employees taking paid leave, and in particular, provides leave suggestions that take into account the user's emotional state.

[0741] This system includes servers, terminals, and a user interface, and further incorporates an emotion engine to achieve advanced user support.

[0742] Server Role

[0743] The server collects and stores employee attendance and departure information, job information, vacation history, and company calendar data in a database. Based on this information, it analyzes typical work patterns and uses an AI algorithm to calculate optimal vacation days. In addition, an emotion engine is used to analyze employee input and feedback as real-time emotion data. This emotion information is used to adjust the content and timing of vacation suggestions.

[0744] Terminal role

[0745] The terminal is responsible for presenting suggestions to the user and receiving feedback. It displays vacation suggestions received from the server to the worker via the UI. Furthermore, based on interpretation from the emotion engine, it can understand in real time how the user is reacting to each suggestion and flexibly change the displayed content accordingly.

[0746] User roles

[0747] Users can review vacation suggestions from the system and provide feedback via their device. The input feedback is further analyzed by an emotion engine that analyzes the user's verbal and nonverbal emotional expressions. Feedback reflecting the user's emotions is also used to improve future suggestions.

[0748] Specific example

[0749] For example, if the system determines that a user is currently experiencing high levels of work-related stress, the server will suggest a refreshing vacation via the terminal to help alleviate that stress. For instance, it might suggest taking Friday afternoon of the following week as vacation. This suggestion takes into account the user's recently reported fatigue and stress levels, and is adjusted according to the psychological state inferred by the emotion engine from the user's specified feedback. In this way, the present invention enables users to receive better vacation suggestions tailored to their emotional state, thereby promoting mental and physical well-being.

[0750] The following describes the processing flow.

[0751] Step 1:

[0752] The server collects employee attendance and departure information, job information, and leave history from the company's attendance management system and HR system, and stores it in a database. This allows for an understanding of the latest work status and past leave trends.

[0753] Step 2:

[0754] The server references the company's business calendar to retrieve important date information, such as peak seasons and holidays. This information is used to adjust the timing and duration of vacation suggestions.

[0755] Step 3:

[0756] The server collects worker behavior and feedback data and uses an emotion engine to analyze the user's emotional state. It calculates emotional indicators such as stress and satisfaction from text analysis and behavioral history.

[0757] Step 4:

[0758] The server uses AI algorithms to comprehensively analyze the data collected so far and calculate the optimal vacation days for each worker. The results of the emotion engine analysis are also taken into consideration, allowing for suggestions of earlier vacations, especially for workers experiencing high levels of stress.

[0759] Step 5:

[0760] The server generates optimized vacation suggestions and sends them to the terminal. These suggestions include recommended vacation days, along with the reasons for their selection and the expected benefits of rest.

[0761] Step 6:

[0762] The device presents vacation suggestions to the user through its user interface. It also uses an emotion engine to diagnose the user's initial response and adjust the suggestions in real time as needed.

[0763] Step 7:

[0764] Users receive suggestions via their devices and provide feedback on their content. They can include emotionally charged comments and additional requests.

[0765] Step 8:

[0766] The device collects user feedback and sends it to the server. This feedback is further analyzed by an emotion engine and used to improve future vacation suggestions.

[0767] Step 9:

[0768] The server stores the collected feedback data in a database and fine-tunes it to improve the quality of future suggestions. Through the accumulation of emotional and operational data, the system continuously learns, enabling more personalized vacation suggestions.

[0769] (Example 2)

[0770] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0771] In the current work environment, it is difficult to take leave at the optimal time, and since emotional stress is not taken into consideration, there is a risk that the physical and mental health of workers will be compromised. Furthermore, if leave cannot be taken in consideration of the company calendar and busy periods, it will lead to decreased work efficiency and lower employee satisfaction.

[0772] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0773] In this invention, the server includes means for acquiring employee work information and departure information, means for analyzing employee work information and leave history, and means for using an artificial intelligence algorithm to calculate the optimal leave days. This makes it possible to adjust leave suggestions based on employee emotional information and propose leave that suits the individual's emotional state. Furthermore, it enables the optimization of leave considering the company calendar and busy periods, leading to improved work efficiency and the maintenance of employee health.

[0774] "Worker work information" refers to data about individual workers' arrival and departure times, including working hours and job duties.

[0775] "Job information" refers to data related to the duties and tasks that an employee is responsible for in the workplace.

[0776] "Leave history" refers to a record of leave taken by an employee in the past, including information such as the date and type of leave.

[0777] An "artificial intelligence algorithm" refers to an automated calculation method used in computer programs to analyze worker data and determine the optimal vacation days.

[0778] "Emotional information" refers to data about the emotions expressed by workers and their current psychological state, and is collected from both verbal and nonverbal elements.

[0779] A "corporate calendar" refers to schedule information that shows the work schedule, holidays, and busy periods within a company.

[0780] A "peak season" refers to a specific period in a company or business where the workload increases significantly.

[0781] This invention provides a system that enables workers to take vacations effectively, and is implemented using a server, terminals, and a user interface. This allows for personalized vacation suggestions that take emotional information into account.

[0782] The server is connected to the company's HR system and other databases, and uses APIs to collect employee work information, job information, vacation history, and company calendar data. This data is stored in the database, and AI algorithms are used to analyze work status and generate optimal vacation day suggestions.

[0783] In addition, the server is equipped with an emotion engine that analyzes user feedback using natural language processing technology. This allows it to understand the user's stress level and emotional state and reflect this in vacation suggestions.

[0784] The terminal presents the user with vacation suggestions sent from the server via a user interface. The interface is intuitive, allowing workers to easily review the suggestions and provide feedback.

[0785] Users review the displayed vacation suggestions using their devices and provide feedback on their experiences. This feedback is sent to the server and used to improve the accuracy of future suggestions.

[0786] For example, if a user provides feedback such as, "I would like to be offered a refreshing day off next Friday," the system could then suggest a more appropriate timing based on that feedback. In this way, it becomes possible to offer day off tailored to the individual needs of each worker, supporting their physical and mental well-being.

[0787] Examples of prompt statements for a generative AI model are as follows:

[0788] "Based on worker A's emotional state and work data, generate suggestions for the optimal vacation days."

[0789] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0790] Step 1:

[0791] The server collects employee work information, job information, and leave history from a company's HR system via an API. The input is information from the company's database, and the output is stored in the database in a structured data format. This prepares an initial dataset for a comprehensive understanding of employee work performance.

[0792] Step 2:

[0793] The server inputs work and job information into an AI algorithm to analyze the worker's typical work patterns and workload. The input data includes arrival and departure times, job duties, etc., and the output is the individual worker's work pattern. This analysis helps determine the worker's workload and the most efficient timing for taking leave.

[0794] Step 3:

[0795] The server inputs user feedback into an emotion engine to analyze the worker's emotional state. Inputs include text-based feedback and selectable responses from a user interface, and the emotion engine uses natural language processing techniques for analysis. The output is quantitative data on the worker's stress level and emotional state. This result is used to tailor vacation suggestions to each worker's psychological needs.

[0796] Step 4:

[0797] The server uses an AI algorithm to calculate the optimal vacation days based on collected work information, job information, and emotional state. The input is the analysis results obtained in the previous step, and the output is the recommended vacation period from the start date to the end date. This allows the server to suggest the best timing for employees to take vacation.

[0798] Step 5:

[0799] The terminal displays vacation suggestions sent from the server to the user. The input is the recommended vacation period from the server, and the output is information visually presented on the user interface. Specifically, the terminal informs the worker of the suggestions through notifications and pop-up screens.

[0800] Step 6:

[0801] Users provide feedback on presented vacation suggestions via a terminal. Input includes user text comments and selectable responses, while output is the transmission of feedback to the server. This feedback serves as important data for improving future suggestions.

[0802] Step 7:

[0803] The server uses user feedback to make adjustments that improve the accuracy of future vacation suggestions. The input is user feedback data, and the output is the adjusted suggestion algorithm and rule set. Through this process, the system becomes capable of making more sophisticated vacation suggestions over time.

[0804] (Application Example 2)

[0805] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0806] The physical and mental health of workers is a crucial factor that directly impacts productivity and job satisfaction. However, currently, vacation proposals do not take into account the emotional state of individual workers, making it difficult to select the optimal timing for vacation. As a result, health problems due to excessive stress and inefficiencies in vacation taking have become apparent issues. This invention aims to solve these problems.

[0807] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0808] In this invention, the server includes means for acquiring employee attendance and departure information, means for analyzing employee job information and leave history, means for using an artificial intelligence algorithm to calculate optimal leave days, means for collecting feedback on leave suggestions presented to employees, means for using an emotion analysis engine to analyze employees' emotional states, and means for making leave suggestions based on emotional states. This makes it possible to make optimal leave suggestions that take into account employees' emotional states.

[0809] A "worker" is a person who performs a specific job within a company or organization.

[0810] "Attendance and departure information" refers to data on the time an employee arrives at the workplace and begins work, and the time they finish work and leave the workplace.

[0811] "Job information" refers to data about the work content, roles, and responsibilities of an employee.

[0812] "Leave history" refers to a record of leave taken by an employee in the past, including dates and durations.

[0813] An "artificial intelligence algorithm" is a computational method that uses machine learning and other AI technologies to analyze and make decisions based on data.

[0814] "Feedback" refers to the reactions or opinions that workers give in response to a proposal or result.

[0815] An "emotion analysis engine" is a software component that analyzes the emotions and mental state of workers based on data they input.

[0816] A "vacation suggestion" is a proposal that offers workers recommendations on when they should take their vacation.

[0817] The system implementing this invention provides optimal vacation suggestions that take into account the emotional state of workers, and consists of three components: a server, a terminal, and a user. The server functions as a core system for acquiring and analyzing workers' attendance and departure information, job information, and vacation history. Furthermore, an artificial intelligence algorithm calculates the optimal vacation days using the collected data.

[0818] The server uses an emotion analysis engine to analyze the emotional state of workers based on their input and feedback. Based on this analysis, it generates vacation suggestions for workers in real time. These suggestions are presented to the user through a terminal provided to them. The terminal displays the received suggestions to the worker through a user interface and simultaneously records the human interaction.

[0819] When a worker uses a terminal to review a proposal and provides feedback, that information is sent to a server. The feedback includes verbal and nonverbal sentiments, which are further analyzed by a sentiment analysis engine. This feedback is then used to improve future vacation proposals.

[0820] As a concrete example, imagine a scenario where a worker inputs "This week's stress level is 8" using their smartphone or smartwatch. The server then suggests a refreshing vacation for the following weekend. By utilizing generative AI models, the accuracy of analyzing such emotional data can be improved.

[0821] An example of a prompt message might be, "Please record your stress level today on a scale of 1 to 10." This prompt allows workers to easily record their emotional state.

[0822] This system utilizes an emotion analysis engine and artificial intelligence algorithms to improve users' health and job satisfaction.

[0823] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0824] Step 1:

[0825] The server retrieves employee attendance and departure information, job information, and leave history. This input data is collected through an information aggregation system and stored in a database. The collected data serves as basic information for later analysis.

[0826] Step 2:

[0827] The server uses an artificial intelligence algorithm to calculate the optimal vacation days based on the collected data. This process involves data calculations that take into account employee work patterns and workloads, and outputs vacation suggestions.

[0828] Step 3:

[0829] The server uses an emotion analysis engine to analyze feedback and emotional data entered by workers through their terminals. It receives data related to emotional states as input and evaluates the workers' psychological state using text analysis and pattern recognition technologies. This results in an evaluation based on emotions.

[0830] Step 4:

[0831] The server adjusts vacation suggestions based on the sentiment analysis results and presents them to employees at the optimal time. By utilizing a generative AI model, vacation suggestions that integrate sentiment data and work data are output.

[0832] Step 5:

[0833] The terminal displays vacation suggestions received from the server to the worker. The suggestions are presented through a user interface, offering the worker options in an interactive format.

[0834] Step 6:

[0835] Users review the proposals via their devices and provide feedback. This feedback includes recording their satisfaction level and opinions on the proposals, and entering responses. This data is then sent back to the server.

[0836] Step 7:

[0837] The server receives feedback from users and uses it to improve future vacation suggestions. The feedback data is analyzed, and improvements are made to enhance the overall accuracy of the system.

[0838] 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.

[0839] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0840] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0841] 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.

[0842] Figure 9 shows an 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.

[0843] 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.

[0844] 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.

[0845] 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, motorcycles, etc., 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, for example, based 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.

[0846] 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."

[0847] 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.

[0848] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0849] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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 the like 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.

[0858] 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.

[0859] The following is further disclosed regarding the embodiments described above.

[0860] (Claim 1)

[0861] Means for obtaining employee attendance and departure information,

[0862] A means for analyzing workers' job information and leave history,

[0863] A method using an artificial intelligence algorithm to calculate the optimal vacation days,

[0864] A means of collecting feedback on vacation proposals presented to workers,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising means for analyzing the vacation taking trends of each worker and evaluating the impact on business operations.

[0868] (Claim 3)

[0869] The system according to claim 1, comprising means for referencing busy periods and holidays from a company's business calendar.

[0870] "Example 1"

[0871] (Claim 1)

[0872] Means for collecting workers' work information,

[0873] A means of analyzing data based on workers' work history and leave history,

[0874] A method for calculating the optimal vacation days using a generative AI model,

[0875] A means of notifying workers of proposed leave information via user terminals,

[0876] A means of collecting feedback from workers and storing it in a database,

[0877] A means of continuously learning from feedback and improving the algorithm,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, comprising means for analyzing the work trends of each worker in data collection and minimizing the impact on operations.

[0881] (Claim 3)

[0882] The system according to claim 1, comprising means for referencing important periods and holidays from the organization's activity plan.

[0883] "Application Example 1"

[0884] (Claim 1)

[0885] Means of obtaining worker work information,

[0886] A means for analyzing workers' job-related information and leave history,

[0887] A method using a machine learning model to derive the overall optimal vacation days,

[0888] A means of collecting feedback on vacation proposals notified to workers,

[0889] A means to evaluate traffic data and optimize the overall traffic efficiency of a city,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, comprising means for analyzing the vacation acquisition patterns of each worker and evaluating the degree of impact on business operations.

[0893] (Claim 3)

[0894] The system according to claim 1, comprising means for referencing peak seasons and holidays from the organization's work schedule.

[0895] "Example 2 of combining an emotion engine"

[0896] (Claim 1)

[0897] Means for obtaining employee work information and departure information,

[0898] A means for analyzing workers' work information and leave history,

[0899] A method using an artificial intelligence algorithm to calculate the optimal vacation days,

[0900] A means of collecting feedback on vacation proposals presented to workers,

[0901] A means of analyzing the emotional state of workers,

[0902] A means of adjusting vacation proposals based on workers' sentiment information,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, comprising means for analyzing the vacation taking trends of each worker and evaluating the impact on business operations.

[0906] (Claim 3)

[0907] The system according to claim 1, comprising means for referencing peak seasons and holidays from a company's business schedule.

[0908] "Application example 2 when combining with an emotional engine"

[0909] (Claim 1)

[0910] Means for obtaining employee attendance and departure information,

[0911] A means for analyzing workers' job information and leave history,

[0912] A method using an artificial intelligence algorithm to calculate the optimal vacation days,

[0913] A means of collecting feedback on vacation proposals presented to workers,

[0914] A means of using an emotion analysis engine to analyze the emotional state of workers,

[0915] A means of making vacation suggestions based on emotional state,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, comprising means for analyzing the vacation taking trends of each worker and evaluating the impact on business operations.

[0919] (Claim 3)

[0920] The system according to claim 1, comprising means for referencing busy periods and holidays from a company's business calendar. [Explanation of symbols]

[0921] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for obtaining employee attendance and departure information, A means for analyzing workers' job information and leave history, A method using an artificial intelligence algorithm to calculate the optimal vacation days, A means of collecting feedback on vacation proposals presented to workers, A system that includes this.

2. The system according to claim 1, comprising means for analyzing the vacation taking trends of each worker and evaluating the impact on business operations.

3. The system according to claim 1, comprising means for referencing busy periods and holidays from a company's business calendar.

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