System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting personnel information for employees; means for storing the collected personnel information in a database; and means for generating job rotation and career improvement suggestions for employees based on the stored personnel information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Reducing employee turnover risk and improving internal engagement are important issues for many companies. However, traditional methods do not provide appropriate job rotation or career advancement suggestions, which can lead to a decline in employee motivation and an increased risk of job change. This also increases the workload of HR personnel, making efficient personnel allocation difficult. Furthermore, there is a lack of ways to effectively utilize accumulated HR data, limiting opportunities for commercialization and service provision to other companies. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a system including a means for collecting employee personnel data and storing it in a database, and a means for generating job rotation and career advancement proposals for employees based on the stored data. Furthermore, the means for generating proposals recommends multiple positions based on an employee's performance score, thereby achieving appropriate personnel placement. In particular, the system supports employees' career growth by recommending managerial positions, mid-level positions, and entry-level positions depending on the range of the employee's performance score. This reduces the risk of employees changing jobs and improves internal engagement.

[0006] An "employee" is an individual who is employed to perform work for a business or organization.

[0007] "Human Resources Data" means data containing information about employees, including names, job titles, departments, and performance scores.

[0008] A "database" is a system for systematically storing and managing information, and is used in particular to store employee personnel data.

[0009] "Job rotation" is a system that aims to broaden skills and grow careers by transferring employees to different positions or departments.

[0010] "Career advancement" refers to an employee's improvement of their job skills through promotion or increased expertise in their career, leading to a higher position or title.

[0011] "Suggestion" refers to a plan or opinion recommending a particular action or choice, and in the present invention means a recommendation for job rotation or career advancement.

[0012] A "performance score" is a numerical value used to evaluate an employee's ability to perform their job, and is calculated based on specific evaluation criteria.

[0013] A "job title" refers to a specific job or position within an organization, indicating the duties and responsibilities that an employee is expected to perform.

[0014] "Managerial position" refers to a high-ranking position within an organization that involves the responsibility of supervising and guiding other employees and subordinates.

[0015] "Mid-level positions" refer to positions that occupy an intermediate position within an organization, require a certain level of specialized knowledge and skills, and play an important role in the execution of work.

[0016] An "entry position" refers to an early-stage job within an organization, typically filled by an employee with little experience.

[0017] A "system" refers to a set of mechanisms or devices in which multiple elements or parts are interrelated and configured to achieve a specific function. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. This system collects employee personnel data, stores it in a database, and generates job rotation and career advancement proposals for employees. Details of an embodiment of the present invention are described below.

[0040] Collection and storage of personnel data

[0041] The server sends requests to the company's API endpoint to collect employee HR data, including each employee's ID, name, job title, department, performance score, etc. The collected data is then parsed appropriately by the server and stored in a local database, which is later used to generate job rotation and career advancement suggestions.

[0042] Job rotation and career advancement proposal generation

[0043] The server evaluates the employee's performance score based on the data stored in the database and generates job rotation and career advancement proposals based on the score. Specific proposals are made as follows:

[0044] Employees with a performance score of 4.5 or above are recommended for managerial positions (e.g., sales manager).

[0045] Employees with a performance score of 3.0 or above but below 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0046] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0047] Providing suggestions

[0048] A human resources manager, who is the user, requests job rotation and career advancement proposals by specifying a specific employee ID. The server searches the database based on the specified employee ID and retrieves the relevant employee information. The server then generates proposals according to the aforementioned policy and provides them to the user. This allows the user to efficiently allocate personnel appropriately.

[0049] Specific examples

[0050] Example 1: A server sends a request to an internal company API endpoint and retrieves employee data such as:

[0051] Employee ID: 12345

[0052] Name: Yamada Taro

[0053] Position: Development Engineer

[0054] Department: Development Department

[0055] Performance score: 4.7

[0056] The server stores this employee data in a local database. Later, when the user requests suggestions for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7.

[0057] Example 2: When the server retrieves employee data in the same way, it gets the following data:

[0058] Employee ID: 67890

[0059] Name: Ichiro Suzuki

[0060] Job Title: Support Engineer

[0061] Department: Support Department

[0062] Performance score: 3.5

[0063] This data is also stored in a local database, and when a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5.

[0064] The present invention is implemented as described above, but it should be understood that other specific examples and application ranges can be modified or adjusted as appropriate.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The server collects employee HR data by sending an HTTP GET request to a company's API endpoint, including authentication information such as an API key in the header.

[0068] Step 2:

[0069] The server receives the response from the API and parses the data in JSON format, which includes the employee's ID, name, job title, department, performance score, etc.

[0070] Step 3:

[0071] The server stores the parsed data in a local database, creating a new one if it does not exist, or updating an existing database as needed.

[0072] Step 4:

[0073] A user (HR professional) requests job rotation and career advancement suggestions by specifying a specific employee ID. This request includes the employee ID.

[0074] Step 5:

[0075] The server receives a request from the user and searches the database based on the specified employee ID to retrieve the corresponding employee information.

[0076] Step 6:

[0077] The server checks the performance score from the acquired employee information and generates appropriate proposals based on that score. For example, if the performance score is 4.5 or higher, it will recommend managerial positions, mid-level positions, entry-level positions, etc.

[0078] Step 7:

[0079] The server returns the generated proposal to the user, who then uses the proposal to implement appropriate personnel placement and career advancement planning.

[0080] Step 8:

[0081] Users then take necessary personnel actions based on the provided suggestions, which may include transferring employees, promoting them, or developing training plans.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] Appropriate job rotation and career advancement proposals are important for reducing employee turnover risk and improving internal engagement. However, it is difficult with current systems to efficiently collect employee performance data and generate accurate proposals based on that data. Furthermore, there is a lack of mechanisms for quickly providing generated proposals to users. This results in delays in placing employees in the right positions, reducing the efficiency of the entire organization.

[0085] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0086] In this invention, the server includes means for collecting employee personnel data, means for storing the collected personnel data in a database, means for generating job rotation and career advancement proposals for employees based on the stored personnel data, and means for providing the content of the proposed job rotation and career advancement to users. This makes it possible to efficiently collect employee data using an API endpoint within the company, analyze the data, and quickly generate job rotation and career advancement proposals suitable for each employee, and provide them to users.

[0087] "Employee personnel data" means data that includes information about an employee, such as ID, name, job title, department, and performance score.

[0088] A "database" is a system for systematically storing collected data and for later searching, retrieving, and processing it.

[0089] "Job rotation" is the practice of providing employees with the opportunity to experience different jobs or roles, with the aim of broadening their skills and knowledge.

[0090] "Career advancement" refers to an employee's promotion to a position of higher rank or responsibility within an organization.

[0091] A "server" is a computer system that processes requests from clients via a network and provides the necessary information.

[0092] "User" refers to the person who uses the system, and is often a human resources professional.

[0093] A "performance score" is a numerical representation of an employee's achievements and contributions.

[0094] "Managerial position" refers to a position that manages and supervises other employees within an organization.

[0095] "Mid-level positions" are positions that involve specialized skills and mid-level positions within an organization.

[0096] An "entry position" is an entry-level position within an organization that allows a person to gain their first work experience.

[0097] "Interface" refers to the screens and applications that allow a user to interact with a system.

[0098] A "memory cache" is a memory area that temporarily stores frequently used data in order to achieve high-speed data access.

[0099] This invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. The system collects and stores employee personnel data, generates job rotation and career advancement proposals based on the data, and provides them to users.

[0100] Collection and storage of personnel data

[0101] The server periodically sends HTTP GET requests to the company's internal API endpoint to collect employee personnel data. Specifically, the server uses a Python script that runs as a scheduled job every day at 2:00 AM. The JSON-formatted data returned by the API endpoint includes employee ID, name, job title, department, performance score, etc.

[0102] The collected data is parsed by the server using a JSON module and then stored in a local database using an ORM such as SQLAlchemy, with transaction management to ensure data integrity.

[0103] Job rotation and career advancement proposal generation

[0104] The server evaluates employees' performance scores based on the personnel data stored in the database and generates job rotation and career advancement proposals based on those scores. Specific proposals are as follows:

[0105] Employees with a performance score of 4.5 or above are recommended for managerial positions (e.g., sales manager).

[0106] Employees with a performance score between 3.0 and 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0107] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0108] The generated suggestions are temporarily stored in a memory cache (e.g. Redis) or a local database.

[0109] Providing suggestions

[0110] A human resources manager, who is a user, requests job rotation and career advancement proposals by specifying a specific employee ID. When the server receives this request, it searches the database and retrieves the relevant employee information. Based on saved proposals and newly generated proposals, the server displays proposals to the user. These proposals allow the user to efficiently allocate personnel appropriately.

[0111] Specific examples

[0112] Example 1

[0113] The server sends a request to the company's API endpoint and retrieves the following employee data:

[0114] Employee ID: 12345

[0115] Name: A. Taro

[0116] Job title: Engineer

[0117] Department: Technology Department

[0118] Performance score: 4.7

[0119] The server stores this data in a local database. At a later date, when the user requests a proposal for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7.

[0120] Example 2

[0121] The server retrieves the employee data in the same way, and gets the following data:

[0122] Employee ID: 67890

[0123] Name: B. Ichiro

[0124] Position: Support Engineer

[0125] Department: Support Department

[0126] Performance score: 3.5

[0127] This data is also stored in a local database, and when a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5.

[0128] Example prompts for generative AI models

[0129] "Generate a job rotation proposal for employee with employee ID 12345."

[0130] In this way, the system can be used to efficiently support employees' career advancement within a company.

[0131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0132] Step 1:

[0133] The server sends an HTTP GET request to the company's internal API endpoint to collect employee personnel data. The input is the endpoint URL and authentication information, and the output is JSON-formatted personnel data. Specifically, the server uses a Python script to execute this process as a scheduled job every day at 2:00 AM. It receives the response returned from the API, verifies that the response code is 200, and logs the data.

[0134] Step 2:

[0135] The server parses the collected JSON-formatted personnel data and saves it in a database. The input is JSON-formatted data, and the output is the parsed data stored in the database. Specifically, the server parses the data using Python's json module and extracts the necessary fields (ID, name, job title, department, performance score). It then connects to the database using an ORM such as SQLAlchemy and executes insert or update SQL queries. It performs transaction management, committing if successful and rolling back if unsuccessful.

[0136] Step 3:

[0137] The server evaluates employee performance scores based on the personnel data stored in the database and generates recommendations. The input is the personnel data in the database, and the output is the generated recommendations. Specifically, the server queries the employee performance scores from the database and applies the following rules to each score:

[0138] If the score is 4.5 or above, a managerial position is recommended.

[0139] If the score is between 3.0 and 4.5, a mid-level position is recommended.

[0140] If the score is less than 3.0, an entry position is recommended.

[0141] The generated suggestions are temporarily stored in a memory cache (e.g. Redis) or a local database.

[0142] Step 4:

[0143] As an HR professional, a user requests a proposal by specifying a specific employee ID. The input is the employee ID, and the output is a proposal for suitable job rotation or career advancement. Specifically, the user enters the employee ID through a web interface or mobile application and sends a request to the server. The server receives the request, retrieves the relevant employee information from the database, and displays it to the user based on saved proposals or newly generated proposals. The browser displays the suitable positions in HTML format, or a notification is sent to the mobile app.

[0144] The above is the specific processing flow of the program of this system.

[0145] (Application example 1)

[0146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] Reducing the risk of employee turnover within a company or organization and improving internal engagement are key challenges. In particular, in environments such as factories where many employees rely on short-term rotations and job changes, efficient personnel placement and career advancement proposals are required. However, traditional systems lack the means to properly generate and quickly notify these proposals, making it difficult to ensure that employees are placed in the right positions.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0149] In this invention, the server includes means for acquiring employee business data, means for storing the acquired business data in data storage, means for generating proposals for transfers and promotions for employees based on the stored business data, and means for notifying the proposals to mobile devices. This enables managers to quickly make appropriate personnel assignments by making appropriate rotations and career advancement proposals in real time based on employees' evaluation scores and performance.

[0150] "Employee Business Data" means job-related information about employees, such as each employee's ID, name, job title, department, and performance evaluation score.

[0151] "Means of acquisition" refers to the functionality for collecting employee business data from a company's internal servers via an API endpoint.

[0152] "Data storage" refers to components including databases and storage devices for storing acquired employee business data.

[0153] "Means of storage" refers to the function of registering collected employee business data in data storage and updating it as necessary.

[0154] "Means for generating transfer or promotion proposals" refers to a function that includes algorithms or logic for analyzing stored employee work data and proposing appropriate new positions or promotions to employees.

[0155] "Means for notifying proposals to mobile devices" refers to a communication function for notifying generated transfer or promotion proposals to mobile devices such as smartphones.

[0156] An "evaluation score" is a numerical indicator of an employee's achievements and performance.

[0157] "Management level positions" refer to senior positions in an organization such as a factory that manage and supervise employees.

[0158] "Mid-level positions" refer to intermediate positions in an organization such as a factory, positioned between management positions and entry-level positions.

[0159] An "entry level position" refers to the first position suitable for a new employee or entry-level employee in an organization such as a factory.

[0160] An embodiment of the present invention will be described in detail below. This system acquires employee work data (hereinafter referred to as personnel data) and makes transfer and promotion proposals to employees based on the data. The system mainly includes a data collection means, a data storage means, a proposal generation means, and a notification means.

[0161] First, the server uses an API endpoint to retrieve employee work data from the company's database, including employee ID, name, job title, department, evaluation score, etc. The server then parses the retrieved data appropriately and stores it in local data storage (e.g., SQLite).

[0162] The server then generates transfer and promotion proposals for employees based on the stored data. The proposals are generated using the employee's evaluation scores. For example, an algorithm is used to suggest managerial positions for employees with an evaluation score of 4.5 or higher, mid-level positions for employees with an evaluation score of 3.0 or higher but less than 4.5, and entry-level positions for employees with an evaluation score less than 3.0.

[0163] Once a proposal is generated, the server notifies the mobile device (e.g., smartphone) of the proposal. The notification means can inform the manager of the proposal in real time, allowing the manager to quickly make appropriate personnel allocation decisions.

[0164] For example, if the server retrieves employee data from a company's API and an employee named Yamada Taro has a performance score of 4.7, the server will suggest a managerial level position for Yamada Taro, and if an employee named Suzuki Ichiro has a performance score of 3.5, the server will suggest a mid-level position.

[0165] Specific examples of prompts for generative AI models are as follows:

[0166] Write Python code to retrieve employee data from the company's API and store it in a SQLite database. Additionally, implement logic to generate job rotation and career advancement suggestions based on the employee's performance score. For example, if the performance score is 4.5 or above, suggest a managerial position; if the performance score is 3.0 or above but less than 4.5, suggest a mid-level position; if the score is less than 3.0, suggest an entry-level position.

[0167] In this way, the present invention provides a specific embodiment of a system for reducing the risk of employees changing jobs and improving internal engagement in companies and organizations.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] The server sends a request to an internal API endpoint to retrieve employee work data. The input is the API endpoint URL, and the output is data such as the employee's ID, name, job title, department, and evaluation score. The server receives this data in JSON format.

[0171] Step 2:

[0172] The server parses the acquired employee business data appropriately and saves it in a database (e.g., SQLite). The input is the JSON data acquired in step 1, and the output is registered in the database in a format that corresponds to each field. During this process, the server reformats the personnel data and updates existing data as necessary.

[0173] Step 3:

[0174] The server analyzes the data stored in the database and generates transfer and promotion proposals based on each employee's evaluation score. The input is each employee's job data stored in the database, and the output is the proposed new position or placement. Based on the evaluation score, the algorithm suggests management-level, mid-level, or entry-level positions.

[0175] Step 4:

[0176] The server notifies the mobile device of the generated proposal. The input is the proposal content generated in step 3, and the output is sent to the administrator's smartphone in the form of a push notification, email, etc. The server selects the notification method during this process and notifies the administrator in the appropriate format.

[0177] Step 5:

[0178] Managers check the notification on their mobile devices and make staffing decisions based on the recommendations. The input is the recommendation notification sent from the server, and the output is instructions and confirmations that the manager can take action on, displayed on the mobile device. Based on this information, managers make decisions to ensure that employees are placed in the right positions.

[0179] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0180] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. This system collects employee personnel data, stores it in a database, and generates job rotation and career advancement proposals for employees. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to make more appropriate proposals. Details of the embodiments of the invention are described below.

[0181] Collection and storage of personnel data

[0182] The server sends requests to the company's API endpoint to collect employee HR data. The collected data includes each employee's ID, name, job title, department, and performance score. The server parses the collected data and stores it in a local database. This database is used to generate job rotation and career advancement recommendations.

[0183] Job rotation and career advancement proposal generation

[0184] The server evaluates the employee's performance score based on the employee data stored in the database and generates job rotation and career advancement proposals based on the score. Specific proposals are as follows:

[0185] Employees with a performance score of 4.5 or above are recommended for management positions (e.g., sales manager).

[0186] Employees with a performance score between 3.0 and 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0187] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0188] Introducing the Emotion Engine

[0189] This system includes an emotion engine that allows the server to recognize the user's emotions. This emotion engine collects and analyzes the user's emotional data and optimizes job rotation and career advancement proposals based on the results.

[0190] For example, if a user is feeling stressed, the emotion engine will analyze that emotion data and recommend positions that will help reduce stress, making it possible to provide flexible suggestions tailored to the user's emotional state.

[0191] Providing suggestions

[0192] The user (human resources officer) requests job rotation and career advancement proposals by specifying a specific employee ID. The server searches the database based on the specified employee ID and retrieves the relevant employee information. Based on the retrieved information, the server also takes into account the analysis results of the emotion engine, generates proposals, and provides them to the user.

[0193] Specific examples

[0194] Example 1: A server sends requests to an internal company API endpoint to collect and store the following employee data:

[0195] Employee ID: 12345

[0196] Name: Yamada Taro

[0197] Position: Development Engineer

[0198] Department: Development Department

[0199] Performance score: 4.7

[0200] When the user requests suggestions for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7. Furthermore, the emotion engine analyzes Taro Yamada's emotional state as "positive," so it places special weight on this suggestion.

[0201] Example 2: The server similarly collects employee data and obtains the following data:

[0202] Employee ID: 67890

[0203] Name: Ichiro Suzuki

[0204] Job Title: Support Engineer

[0205] Department: Support Department

[0206] Performance score: 3.5

[0207] When a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5. Because the emotion engine analyzes Suzuki Ichiro's emotional state as "stressed," it prioritizes suggestions that include job content that can reduce stress.

[0208] The present invention is implemented as described above, but it should be understood that other specific examples and application ranges can be modified or adjusted as appropriate.

[0209] The processing flow will be explained below.

[0210] Step 1:

[0211] The server collects employee HR data by sending an HTTP GET request to a corporate API endpoint, setting a header containing authentication information and retrieving the requested data.

[0212] Step 2:

[0213] The server receives the response from the API and parses it in JSON format, which includes data such as employee ID, name, job title, department, and performance score.

[0214] Step 3:

[0215] The server stores the parsed data in a local database, creating a new one if it does not exist, or updating an existing database if it does.

[0216] Step 4:

[0217] A user (HR professional) requests job rotation and career advancement suggestions by specifying a specific employee ID. The employee ID is included in the request.

[0218] Step 5:

[0219] The server receives a request from the user, searches the database based on the specified employee ID, and retrieves the information of the corresponding employee.

[0220] Step 6:

[0221] The server checks the performance score from the acquired employee information and generates appropriate suggestions based on that score. For example, if the performance score is 4.5 or higher, it recommends a managerial position, if the performance score is 3.0 or higher but less than 4.5, it recommends a mid-level position, and if the performance score is less than 3.0, it recommends an entry-level position.

[0222] Step 7:

[0223] The server uses an emotion engine to analyze the user's emotion data, which is obtained from the user's input and actions.

[0224] Step 8:

[0225] The server optimizes the suggestions based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it will prioritize jobs that will help reduce stress.

[0226] Step 9:

[0227] The server provides the generated optimized proposals to the user, who can use them to plan employee placement and career advancement.

[0228] Step 10:

[0229] Based on the provided suggestions, users can take necessary personnel actions, such as transferring or promoting employees or developing training plans.

[0230] Example 2

[0231] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0232] To effectively manage employee turnover risk and improve internal engagement, it is necessary to provide a system that automatically generates and provides appropriate job rotation and career advancement proposals. It is also necessary to provide a means for making flexible proposals that take into account employees' emotional state.

[0233] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0234] In this invention, the server includes means for sending requests to an API endpoint within the company and collecting personnel data, means for parsing the collected personnel data and storing it in a database, means for evaluating employee performance scores based on the stored personnel data and generating job rotation and career advancement proposals, means for optimizing the proposals using an emotion engine that collects and analyzes employee emotion data, and means for providing the optimized proposals based on user requests. This makes it possible to automatically and efficiently generate and provide appropriate job rotation and career advancement proposals that reduce employee job change risk and improve internal engagement.

[0235] - "Intra-company API endpoint" refers to the access point of an application program interface (API) used within a company, and is an interface used to access various company data.

[0236] "Human Resources Data" refers to various information related to employees, specifically data including employee IDs, names, job titles, departments, and performance scores.

[0237] A "performance score" is an indicator used to evaluate an employee's work efficiency and achievements, and is often expressed numerically.

[0238] "Job rotation" is the process of encouraging employees to periodically take on different tasks or positions.

[0239] "Career advancement" is the process of encouraging employees to advance or expand their position or scope of work.

[0240] An "emotion engine" is a system or software for analyzing employee emotion data and includes a machine learning model for assessing emotional states.

[0241] A "database" is a part of an information system that stores collected data in a structured manner so that it can be later efficiently searched, retrieved, and updated.

[0242] "User" refers to end users, particularly human resources personnel, who use the system to request job rotation and career advancement proposals for employees.

[0243] "Suggestion optimization" is the process of adjusting generated job rotation and career advancement suggestions by taking into account additional data such as the employee's emotional state to provide more appropriate suggestions.

[0244] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. The system is implemented using the following hardware and software.

[0245] Hardware and software used

[0246] Server: The core of the system that collects, stores, analyzes, and generates recommendations. The server includes the following software components:

[0247] API Endpoint Communication Module: Collects HR data from API endpoints within the enterprise via RESTful services.

[0248] Database Management System: Stores and manages data using a relational database system such as MySQL.

[0249] Analytical engine: Evaluates employee performance scores and generates job rotation and career advancement suggestions.

[0250] Emotion Engine: Analyzes employee emotion data using machine learning models (e.g., TensorFlow).

[0251] Suggestion optimization module: Optimizes suggestions based on the results of the sentiment engine.

[0252] Terminal: A device that allows a user (HR professional) to access the system and request and receive proposals. The terminal has the ability to access the system through a browser or a dedicated application.

[0253] System Operation

[0254] The server first sends an HTTP request to the company's API endpoint to collect employee data such as employee ID, name, job title, department, and performance score. This data is returned to the server in JSON format, parsed, and stored in the database. The stored data is then used for subsequent analysis and proposal generation.

[0255] The server then queries employee data from the database and evaluates each employee's performance score. Based on this evaluation, job rotation and career advancement proposals are generated. An emotion engine is used to analyze the employee's emotional data and reflect the results in the proposals. For example, positions that reduce stress may be prioritized for employees with high levels of stress.

[0256] A user requests suggestions by specifying a specific employee ID. In response to the request, the server generates suggestions based on the analyzed employee data and sentiment data and provides them to the user via a web interface or email.

[0257] Specific examples

[0258] Example 1: A server sends a request to an internal API endpoint to collect and store the following data:

[0259] Employee ID: 12345

[0260] Name: Employee A

[0261] Position: Development Engineer

[0262] Department: Development Department

[0263] Performance score: 4.7

[0264] When a user requests suggestions for employee ID 12345, the server recommends the position "Supervisor (Sales Manager)" based on a performance score of 4.7. It also places special weight on this suggestion because the emotion engine has analyzed Employee A's emotional state as "positive."

[0265] Example 2: The server similarly collects employee data and obtains the following data:

[0266] Employee ID: 67890

[0267] Name: Employee B

[0268] Job Title: Support Engineer

[0269] Department: Support Department

[0270] Performance score: 3.5

[0271] When a user requests a proposal for employee ID 67890, the server recommends the position of "Mid-level position (marketing coordinator)" based on a performance score of 3.5. Furthermore, because the emotion engine has analyzed employee B's emotional state as "stressed," the server emphasizes job content that can reduce stress.

[0272] Below is an example of a prompt sentence to input to the generative AI model.

[0273] Prompt statement example 1:

[0274] "Employee ID 12345 has a performance score of 4.7 and a positive emotional state. Please generate optimal job rotation suggestions for this employee."

[0275] Prompt statement example 2:

[0276] "Employee ID 67890 has a performance score of 3.5 and an emotional state of stress. Please generate job rotation suggestions that will reduce stress for this employee."

[0277] As described above, the present invention is a system that automatically and efficiently generates and provides proposals that utilize employee data and emotional data in order to reduce the risk of employees changing jobs and improve internal engagement.

[0278] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0279] Step 1:

[0280] The server sends an HTTP request to an internal API endpoint. The API endpoint provides a RESTful service, and the server authenticates using a dedicated access token. The input is the API endpoint URL and access token, and the output is JSON data of employees. For example, if the input URL is "https: / / company-api.example.com / employees", the corresponding employee data is returned in JSON format. Specifically, the server sends a GET request and processes the JSON data received as a response for use in the next step.

[0281] Step 2:

[0282] The server parses the received JSON-formatted employee data and saves the information in a local database. The input is the JSON data obtained in the previous step, and the output is the parsed employee data saved in the database. Specifically, the server parses the JSON data and executes an INSERT statement to save the data in the "Employee" table. At this time, it performs transaction management to maintain data consistency and integrity.

[0283] Step 3:

[0284] The server queries employee data stored in a database to evaluate each employee's performance score. The input is the employee's ID, and the output is the employee's performance score and related information. Specifically, it executes an SQL query such as "SELECT FROM employee WHERE id = ?" and applies a suggestion generation algorithm based on the retrieved data.

[0285] Step 4:

[0286] The server uses the emotion engine to collect and analyze the user's emotional data. The input is the text data or voice data entered by the user, and the output is the analyzed emotional state (positive, negative, stress, etc.). Specifically, the server sends the text data or voice data to the emotion engine's API, and the obtained analysis results are used for suggestion optimization.

[0287] Step 5:

[0288] The server optimizes job rotation and career advancement proposals by taking into account the analysis results of the emotion engine. The input is the employee data and emotion data obtained in the previous steps, and the output is the optimized proposal. Specifically, the server re-executes the proposal algorithm and selects the optimal proposal.

[0289] Step 6:

[0290] A user requests a proposal by specifying a specific employee ID. The input is the employee ID, and the output is the generated proposal. Specifically, the user enters the employee ID through a web interface or a dedicated application and obtains the generated proposal from the server.

[0291] Step 7:

[0292] The server provides optimized suggestions based on the user's request. The input is the employee ID specified by the user, and the output is the content of the suggestions. Specifically, the server retrieves the employee's data from the database, generates suggestions based on the analysis results, and provides them to the user. The suggestions are delivered to the user via a web interface or email.

[0293] (Application example 2)

[0294] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0295] Traditional employee management systems only suggest job rotations and career advancement based on employee performance data, but do not take into account the emotional state of employees. This can lead to stress and a decline in motivation, resulting in high employee turnover. Furthermore, there is a lack of dedicated applications to effectively utilize these suggestions in physical stores.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0297] In this invention, the server includes: means for collecting employee personnel data; means for storing the collected personnel data in a database; means for generating job rotation and career advancement proposals for employees based on the stored personnel data; means including a smartphone application for providing the proposals; an emotion engine for collecting and analyzing employee emotion data; and means for optimizing the proposals based on the results of the emotion engine. This enables optimal job rotation and career advancement proposals that take into account both employee performance and emotional state, thereby improving engagement and productivity in physical stores.

[0298] "Employee personnel data" is data that includes information such as an employee's ID, name, job title, department, and performance score.

[0299] A "database" is a collection of information that stores collected employee personnel data and is structured in a way that allows it to be searched and analyzed.

[0300] "Job rotation" means periodically changing employees' jobs and positions to allow them to gain diverse experience and skills.

[0301] "Career advancement" refers to promoting an employee in a higher position or role based on their current performance.

[0302] "Proposal" refers to providing employees with optimal job rotation and career advancement strategies.

[0303] A "smartphone application" is a software program that runs on a smartphone and is available to employees and managers.

[0304] "Emotional data" is data that expresses an employee's emotional state in numerical or categorical terms, including stress and motivation levels.

[0305] The "emotion engine" is a software module for collecting and analyzing employees' emotional states.

[0306] "Optimization" is the process of generating the most efficient and effective proposals based on employee performance and sentiment data.

[0307] MODE FOR CARRYING OUT THE INVENTION

[0308] Program Generation

[0309] The program of the system for implementing the present invention has the following main functions.

[0310] 1. Data Collection: The server sends a request to the company's API endpoint to collect employee HR data, including employee ID, name, job title, department, and performance score.

[0311] 2. Database Update: The collected data is parsed and stored in a local database, which is used to generate suggestions.

[0312] 3. Proposal Generation: Based on the stored employee data, job rotation and career advancement proposals are generated based on the employee's performance score. The proposals are provided via a smartphone application.

[0313] 4. Emotion data collection and analysis: The server collects employee emotion data and analyzes it using an emotion engine. Based on the analysis results, the system optimizes the proposals.

[0314] 5. Proposal Provision: A user can request a proposal by specifying a specific employee ID, and the server generates the proposal and provides it through a smartphone application.

[0315] A natural language description of the program's processing

[0316] Data collection step: The server retrieves employee data from the company's API endpoint using an HTTP request, typically using the requests library. The response from the API is in JSON format, which is parsed to extract the required employee information.

[0317] Database update step: The extracted data is stored in a local database. This database is a structured collection of information that can be easily searched and analyzed. Commonly used databases include MySQL, PostgreSQL, and SQLite.

[0318] Proposal generation step: Based on the stored employee data, the employee's performance score and sentiment data are evaluated to generate optimal job rotation and career advancement proposals. The proposals are sent to a smartphone application. The application is often created using, for example, Flutter or React Native.

[0319] Emotion data collection and analysis step: Emotion data is collected from employee speech and behavior and analyzed by an emotion engine, for example, using Microsoft Azure's emotion recognition API or Google Cloud AI.

[0320] Proposal provision step: The user requests a proposal using a specific employee ID through a smartphone application. The server searches the database and obtains the corresponding employee's information. Taking into account the results of the emotion engine, the server generates the optimal proposal and displays it in the application.

[0321] Adding specific examples

[0322] Example 1: High-Performing Employees

[0323] For employee with employee ID 12345, the performance score is 4.7 and the emotional state is positive. In this case, the server generates a suggestion to "recommend a managerial position (e.g., sales manager)."

[0324] Example 2: Stressed mid-level employee

[0325] For an employee with employee ID 67890, the performance score is 3.5 and the emotional state is stress. In this case, the server generates a proposal that "generates proposals for mid-level positions (e.g., marketing coordinator) and job descriptions that can reduce stress."

[0326] Prompt Sentence Examples

[0327] "Employee ID 12345 has a performance score of 4.7. His emotional state is positive. Please generate optimal job rotation and career advancement suggestions for this employee."

[0328] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0329] Step 1:

[0330] The server sends an HTTP request to an API endpoint to retrieve employee personnel data.

[0331] Input: API endpoint URL and authentication information

[0332] Specific operation: Send a GET request using the requests library. The API response contains data in JSON format.

[0333] Output: Employee data in JSON format (employee ID, name, job title, department, performance score, etc.)

[0334] Step 2:

[0335] The server parses the received JSON-formatted employee data and stores it in a local database.

[0336] Input: Employee data in JSON format

[0337] Specific operation: Parse the data using the json library. Insert the parsed data into a database (MySQL, PostgreSQL, etc.).

[0338] Output: Structured employee data stored in the database

[0339] Step 3:

[0340] Users use a smartphone application to request proposals using a specific employee ID.

[0341] Input: Specific Employee ID

[0342] Specific operation: The application sends a request to the backend server. The backend, written in Python, receives the request.

[0343] Output: Request data is sent to the backend server

[0344] Step 4:

[0345] The server searches the database to retrieve the relevant employee information.

[0346] Input: Specific Employee ID

[0347] Specific behavior: Execute a database query to retrieve the relevant employee data (execute an SQL statement)

[0348] Output: Employee information (employee ID, name, job title, department, performance score)

[0349] Step 5:

[0350] The server generates job rotation and career advancement suggestions based on employee performance scores.

[0351] Input: Employee information and performance score

[0352] Specific operations: Performance scores are evaluated and roles are assigned based on the scores. Sentiment data is also analyzed.

[0353] Output: Initial job rotation and career progression suggestions (e.g., management, mid-level, entry-level positions)

[0354] Step 6:

[0355] The server uses an emotion engine to collect and analyze employee emotion data.

[0356] Input: Employee emotional data (utterances and behavioral data)

[0357] Specific operation: Analyze emotion data using emotion recognition APIs (Microsoft Azure, Google Cloud AI, etc.).

[0358] Output: Sentiment analysis result (e.g. positive, stress, etc.)

[0359] Step 7:

[0360] The server optimizes the suggestions based on the results of the emotion engine.

[0361] Input: Initial proposal and sentiment analysis results

[0362] Specific behavior: If emotional state (e.g. stress), adjust job content. If emotional state is positive, maintain normal suggestions.

[0363] Output: Optimized proposal (final proposal for job rotation or career advancement)

[0364] Step 8:

[0365] The server sends the generated optimized proposals to the smartphone application.

[0366] Input: Optimized Proposal

[0367] Specific behavior: Send the generated proposal in JSON format to the smartphone application by reusing the API endpoint and sending a POST request.

[0368] Output: The smartphone application is notified of the suggestion and displayed to the user.

[0369] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0370] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0371] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0372] [Second embodiment]

[0373] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0374] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0375] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0376] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0377] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0378] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0379] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0380] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0381] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0382] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0383] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0384] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0385] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. This system collects employee personnel data, stores it in a database, and generates job rotation and career advancement proposals for employees. Details of an embodiment of the present invention are described below.

[0386] Collection and storage of personnel data

[0387] The server sends requests to the company's API endpoint to collect employee HR data, including each employee's ID, name, job title, department, performance score, etc. The collected data is then parsed appropriately by the server and stored in a local database, which is later used to generate job rotation and career advancement suggestions.

[0388] Job rotation and career advancement proposal generation

[0389] The server evaluates the employee's performance score based on the data stored in the database and generates job rotation and career advancement proposals based on the score. Specific proposals are made as follows:

[0390] Employees with a performance score of 4.5 or above are recommended for managerial positions (e.g., sales manager).

[0391] Employees with a performance score of 3.0 or above but below 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0392] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0393] Providing suggestions

[0394] A human resources manager, who is the user, requests job rotation and career advancement proposals by specifying a specific employee ID. The server searches the database based on the specified employee ID and retrieves the relevant employee information. The server then generates proposals according to the aforementioned policy and provides them to the user. This allows the user to efficiently allocate personnel appropriately.

[0395] Specific examples

[0396] Example 1: A server sends a request to an internal company API endpoint and retrieves employee data such as:

[0397] Employee ID: 12345

[0398] Name: Yamada Taro

[0399] Position: Development Engineer

[0400] Department: Development Department

[0401] Performance score: 4.7

[0402] The server stores this employee data in a local database. Later, when the user requests suggestions for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7.

[0403] Example 2: When the server retrieves employee data in the same way, it gets the following data:

[0404] Employee ID: 67890

[0405] Name: Ichiro Suzuki

[0406] Job Title: Support Engineer

[0407] Department: Support Department

[0408] Performance score: 3.5

[0409] This data is also stored in a local database, and when a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5.

[0410] The present invention is implemented as described above, but it should be understood that other specific examples and application ranges can be modified or adjusted as appropriate.

[0411] The processing flow will be explained below.

[0412] Step 1:

[0413] The server collects employee HR data by sending an HTTP GET request to a company's API endpoint, including authentication information such as an API key in the header.

[0414] Step 2:

[0415] The server receives the response from the API and parses the data in JSON format, which includes the employee's ID, name, job title, department, performance score, etc.

[0416] Step 3:

[0417] The server stores the parsed data in a local database, creating a new one if it does not exist, or updating an existing database as needed.

[0418] Step 4:

[0419] A user (HR professional) requests job rotation and career advancement suggestions by specifying a specific employee ID. This request includes the employee ID.

[0420] Step 5:

[0421] The server receives a request from the user and searches the database based on the specified employee ID to retrieve the corresponding employee information.

[0422] Step 6:

[0423] The server checks the performance score from the acquired employee information and generates appropriate proposals based on that score. For example, if the performance score is 4.5 or higher, it will recommend managerial positions, mid-level positions, entry-level positions, etc.

[0424] Step 7:

[0425] The server returns the generated proposal to the user, who then uses the proposal to implement appropriate personnel placement and career advancement planning.

[0426] Step 8:

[0427] Users then take necessary personnel actions based on the provided suggestions, which may include transferring employees, promoting them, or developing training plans.

[0428] Example 1

[0429] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0430] Appropriate job rotation and career advancement proposals are important for reducing employee turnover risk and improving internal engagement. However, it is difficult with current systems to efficiently collect employee performance data and generate accurate proposals based on that data. Furthermore, there is a lack of mechanisms for quickly providing generated proposals to users. This results in delays in placing employees in the right positions, reducing the efficiency of the entire organization.

[0431] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0432] In this invention, the server includes means for collecting employee personnel data, means for storing the collected personnel data in a database, means for generating job rotation and career advancement proposals for employees based on the stored personnel data, and means for providing the content of the proposed job rotation and career advancement to users. This makes it possible to efficiently collect employee data using an API endpoint within the company, analyze the data, and quickly generate job rotation and career advancement proposals suitable for each employee, and provide them to users.

[0433] "Employee personnel data" means data that includes information about an employee, such as ID, name, job title, department, and performance score.

[0434] A "database" is a system for systematically storing collected data and for later searching, retrieving, and processing it.

[0435] "Job rotation" is the practice of providing employees with the opportunity to experience different jobs or roles, with the aim of broadening their skills and knowledge.

[0436] "Career advancement" refers to an employee's promotion to a position of higher rank or responsibility within an organization.

[0437] A "server" is a computer system that processes requests from clients via a network and provides the necessary information.

[0438] "User" refers to the person who uses the system, and is often a human resources professional.

[0439] A "performance score" is a numerical representation of an employee's achievements and contributions.

[0440] "Managerial position" refers to a position that manages and supervises other employees within an organization.

[0441] "Mid-level positions" are positions that involve specialized skills and mid-level positions within an organization.

[0442] An "entry position" is an entry-level position within an organization that allows a person to gain their first work experience.

[0443] "Interface" refers to the screens and applications that allow a user to interact with a system.

[0444] A "memory cache" is a memory area that temporarily stores frequently used data in order to achieve high-speed data access.

[0445] This invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. The system collects and stores employee personnel data, generates job rotation and career advancement proposals based on the data, and provides them to users.

[0446] Collection and storage of personnel data

[0447] The server periodically sends HTTP GET requests to the company's internal API endpoint to collect employee personnel data. Specifically, the server uses a Python script that runs as a scheduled job every day at 2:00 AM. The JSON-formatted data returned by the API endpoint includes employee ID, name, job title, department, performance score, etc.

[0448] The collected data is parsed by the server using a JSON module and then stored in a local database using an ORM such as SQLAlchemy, with transaction management to ensure data integrity.

[0449] Job rotation and career advancement proposal generation

[0450] The server evaluates employees' performance scores based on the personnel data stored in the database and generates job rotation and career advancement proposals based on those scores. Specific proposals are as follows:

[0451] Employees with a performance score of 4.5 or above are recommended for managerial positions (e.g., sales manager).

[0452] Employees with a performance score between 3.0 and 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0453] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0454] The generated suggestions are temporarily stored in a memory cache (e.g. Redis) or a local database.

[0455] Providing suggestions

[0456] A human resources manager, who is a user, requests job rotation and career advancement proposals by specifying a specific employee ID. When the server receives this request, it searches the database and retrieves the relevant employee information. Based on saved proposals and newly generated proposals, the server displays proposals to the user. These proposals allow the user to efficiently allocate personnel appropriately.

[0457] Specific examples

[0458] Example 1

[0459] The server sends a request to the company's API endpoint and retrieves the following employee data:

[0460] Employee ID: 12345

[0461] Name: A. Taro

[0462] Job title: Engineer

[0463] Department: Technology Department

[0464] Performance score: 4.7

[0465] The server stores this data in a local database. At a later date, when the user requests a proposal for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7.

[0466] Example 2

[0467] The server retrieves the employee data in the same way, and gets the following data:

[0468] Employee ID: 67890

[0469] Name: B. Ichiro

[0470] Position: Support Engineer

[0471] Department: Support Department

[0472] Performance score: 3.5

[0473] This data is also stored in a local database, and when a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5.

[0474] Example prompts for generative AI models

[0475] "Generate a job rotation proposal for employee with employee ID 12345."

[0476] In this way, the system can be used to efficiently support employees' career advancement within a company.

[0477] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0478] Step 1:

[0479] The server sends an HTTP GET request to the company's internal API endpoint to collect employee personnel data. The input is the endpoint URL and authentication information, and the output is JSON-formatted personnel data. Specifically, the server uses a Python script to execute this process as a scheduled job every day at 2:00 AM. It receives the response returned from the API, verifies that the response code is 200, and logs the data.

[0480] Step 2:

[0481] The server parses the collected JSON-formatted personnel data and saves it in a database. The input is JSON-formatted data, and the output is the parsed data stored in the database. Specifically, the server parses the data using Python's json module and extracts the necessary fields (ID, name, job title, department, performance score). It then connects to the database using an ORM such as SQLAlchemy and executes insert or update SQL queries. It performs transaction management, committing if successful and rolling back if unsuccessful.

[0482] Step 3:

[0483] The server evaluates employee performance scores based on the personnel data stored in the database and generates recommendations. The input is the personnel data in the database, and the output is the generated recommendations. Specifically, the server queries the employee performance scores from the database and applies the following rules to each score:

[0484] If the score is 4.5 or above, a managerial position is recommended.

[0485] If the score is between 3.0 and 4.5, a mid-level position is recommended.

[0486] If the score is less than 3.0, an entry position is recommended.

[0487] The generated suggestions are temporarily stored in a memory cache (e.g. Redis) or a local database.

[0488] Step 4:

[0489] As an HR professional, a user requests a proposal by specifying a specific employee ID. The input is the employee ID, and the output is a proposal for suitable job rotation or career advancement. Specifically, the user enters the employee ID through a web interface or mobile application and sends a request to the server. The server receives the request, retrieves the relevant employee information from the database, and displays it to the user based on saved proposals or newly generated proposals. The browser displays the suitable positions in HTML format, or a notification is sent to the mobile app.

[0490] The above is the specific processing flow of the program of this system.

[0491] (Application example 1)

[0492] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0493] Reducing the risk of employee turnover within a company or organization and improving internal engagement are key challenges. In particular, in environments such as factories where many employees rely on short-term rotations and job changes, efficient personnel placement and career advancement proposals are required. However, traditional systems lack the means to properly generate and quickly notify these proposals, making it difficult to ensure that employees are placed in the right positions.

[0494] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0495] In this invention, the server includes means for acquiring employee business data, means for storing the acquired business data in data storage, means for generating proposals for transfers and promotions for employees based on the stored business data, and means for notifying the proposals to mobile devices. This enables managers to quickly make appropriate personnel assignments by making appropriate rotations and career advancement proposals in real time based on employees' evaluation scores and performance.

[0496] "Employee Business Data" means job-related information about employees, such as each employee's ID, name, job title, department, and performance evaluation score.

[0497] "Means of acquisition" refers to the functionality for collecting employee business data from a company's internal servers via an API endpoint.

[0498] "Data storage" refers to components including databases and storage devices for storing acquired employee business data.

[0499] "Means of storage" refers to the function of registering collected employee business data in data storage and updating it as necessary.

[0500] "Means for generating transfer or promotion proposals" refers to a function that includes algorithms or logic for analyzing stored employee work data and proposing appropriate new positions or promotions to employees.

[0501] "Means for notifying proposals to mobile devices" refers to a communication function for notifying generated transfer or promotion proposals to mobile devices such as smartphones.

[0502] An "evaluation score" is a numerical indicator of an employee's achievements and performance.

[0503] "Management level positions" refer to senior positions in an organization such as a factory that manage and supervise employees.

[0504] "Mid-level positions" refer to intermediate positions in an organization such as a factory, positioned between management positions and entry-level positions.

[0505] An "entry level position" refers to the first position suitable for a new employee or entry-level employee in an organization such as a factory.

[0506] An embodiment of the present invention will be described in detail below. This system acquires employee work data (hereinafter referred to as personnel data) and makes transfer and promotion proposals to employees based on the data. The system mainly includes a data collection means, a data storage means, a proposal generation means, and a notification means.

[0507] First, the server uses an API endpoint to retrieve employee work data from the company's database, including employee ID, name, job title, department, evaluation score, etc. The server then parses the retrieved data appropriately and stores it in local data storage (e.g., SQLite).

[0508] The server then generates transfer and promotion proposals for employees based on the stored data. The proposals are generated using the employee's evaluation scores. For example, an algorithm is used to suggest managerial positions for employees with an evaluation score of 4.5 or higher, mid-level positions for employees with an evaluation score of 3.0 or higher but less than 4.5, and entry-level positions for employees with an evaluation score less than 3.0.

[0509] Once a proposal is generated, the server notifies the mobile device (e.g., smartphone) of the proposal. The notification means can inform the manager of the proposal in real time, allowing the manager to quickly make appropriate personnel allocation decisions.

[0510] For example, if the server retrieves employee data from a company's API and an employee named Yamada Taro has a performance score of 4.7, the server will suggest a managerial level position for Yamada Taro, and if an employee named Suzuki Ichiro has a performance score of 3.5, the server will suggest a mid-level position.

[0511] Specific examples of prompts for generative AI models are as follows:

[0512] Write Python code to retrieve employee data from the company's API and store it in a SQLite database. Additionally, implement logic to generate job rotation and career advancement suggestions based on the employee's performance score. For example, if the performance score is 4.5 or above, suggest a managerial position; if the performance score is 3.0 or above but less than 4.5, suggest a mid-level position; if the score is less than 3.0, suggest an entry-level position.

[0513] In this way, the present invention provides a specific embodiment of a system for reducing the risk of employees changing jobs and improving internal engagement in companies and organizations.

[0514] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0515] Step 1:

[0516] The server sends a request to an internal API endpoint to retrieve employee work data. The input is the API endpoint URL, and the output is data such as the employee's ID, name, job title, department, and evaluation score. The server receives this data in JSON format.

[0517] Step 2:

[0518] The server parses the acquired employee business data appropriately and saves it in a database (e.g., SQLite). The input is the JSON data acquired in step 1, and the output is registered in the database in a format that corresponds to each field. During this process, the server reformats the personnel data and updates existing data as necessary.

[0519] Step 3:

[0520] The server analyzes the data stored in the database and generates transfer and promotion proposals based on each employee's evaluation score. The input is each employee's job data stored in the database, and the output is the proposed new position or placement. Based on the evaluation score, the algorithm suggests management-level, mid-level, or entry-level positions.

[0521] Step 4:

[0522] The server notifies the mobile device of the generated proposal. The input is the proposal content generated in step 3, and the output is sent to the administrator's smartphone in the form of a push notification, email, etc. The server selects the notification method during this process and notifies the administrator in the appropriate format.

[0523] Step 5:

[0524] Managers check the notification on their mobile devices and make staffing decisions based on the recommendations. The input is the recommendation notification sent from the server, and the output is instructions and confirmations that the manager can take action on, displayed on the mobile device. Based on this information, managers make decisions to ensure that employees are placed in the right positions.

[0525] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0526] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. This system collects employee personnel data, stores it in a database, and generates job rotation and career advancement proposals for employees. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to make more appropriate proposals. Details of the embodiments of the invention are described below.

[0527] Collection and storage of personnel data

[0528] The server sends requests to the company's API endpoint to collect employee HR data. The collected data includes each employee's ID, name, job title, department, and performance score. The server parses the collected data and stores it in a local database. This database is used to generate job rotation and career advancement recommendations.

[0529] Job rotation and career advancement proposal generation

[0530] The server evaluates the employee's performance score based on the employee data stored in the database and generates job rotation and career advancement proposals based on the score. Specific proposals are as follows:

[0531] Employees with a performance score of 4.5 or above are recommended for management positions (e.g., sales manager).

[0532] Employees with a performance score between 3.0 and 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0533] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0534] Introducing the Emotion Engine

[0535] This system includes an emotion engine that allows the server to recognize the user's emotions. This emotion engine collects and analyzes the user's emotional data and optimizes job rotation and career advancement proposals based on the results.

[0536] For example, if a user is feeling stressed, the emotion engine will analyze that emotion data and recommend positions that will help reduce stress, making it possible to provide flexible suggestions tailored to the user's emotional state.

[0537] Providing suggestions

[0538] The user (human resources officer) requests job rotation and career advancement proposals by specifying a specific employee ID. The server searches the database based on the specified employee ID and retrieves the relevant employee information. Based on the retrieved information, the server also takes into account the analysis results of the emotion engine, generates proposals, and provides them to the user.

[0539] Specific examples

[0540] Example 1: A server sends requests to an internal company API endpoint to collect and store the following employee data:

[0541] Employee ID: 12345

[0542] Name: Yamada Taro

[0543] Position: Development Engineer

[0544] Department: Development Department

[0545] Performance score: 4.7

[0546] When the user requests suggestions for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7. Furthermore, the emotion engine analyzes Taro Yamada's emotional state as "positive," so it places special weight on this suggestion.

[0547] Example 2: The server similarly collects employee data and obtains the following data:

[0548] Employee ID: 67890

[0549] Name: Ichiro Suzuki

[0550] Job Title: Support Engineer

[0551] Department: Support Department

[0552] Performance score: 3.5

[0553] When a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5. Because the emotion engine analyzes Suzuki Ichiro's emotional state as "stressed," it prioritizes suggestions that include job content that can reduce stress.

[0554] The present invention is implemented as described above, but it should be understood that other specific examples and application ranges can be modified or adjusted as appropriate.

[0555] The processing flow will be explained below.

[0556] Step 1:

[0557] The server collects employee HR data by sending an HTTP GET request to a corporate API endpoint, setting a header containing authentication information and retrieving the requested data.

[0558] Step 2:

[0559] The server receives the response from the API and parses it in JSON format, which includes data such as employee ID, name, job title, department, and performance score.

[0560] Step 3:

[0561] The server stores the parsed data in a local database, creating a new one if it does not exist, or updating an existing database if it does.

[0562] Step 4:

[0563] A user (HR professional) requests job rotation and career advancement suggestions by specifying a specific employee ID. The employee ID is included in the request.

[0564] Step 5:

[0565] The server receives a request from the user, searches the database based on the specified employee ID, and retrieves the information of the corresponding employee.

[0566] Step 6:

[0567] The server checks the performance score from the acquired employee information and generates appropriate suggestions based on that score. For example, if the performance score is 4.5 or higher, it recommends a managerial position, if the performance score is 3.0 or higher but less than 4.5, it recommends a mid-level position, and if the performance score is less than 3.0, it recommends an entry-level position.

[0568] Step 7:

[0569] The server uses an emotion engine to analyze the user's emotion data, which is obtained from the user's input and actions.

[0570] Step 8:

[0571] The server optimizes the suggestions based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it will prioritize jobs that will help reduce stress.

[0572] Step 9:

[0573] The server provides the generated optimized proposals to the user, who can use them to plan employee placement and career advancement.

[0574] Step 10:

[0575] Based on the provided suggestions, users can take necessary personnel actions, such as transferring or promoting employees or developing training plans.

[0576] Example 2

[0577] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0578] To effectively manage employee turnover risk and improve internal engagement, it is necessary to provide a system that automatically generates and provides appropriate job rotation and career advancement proposals. It is also necessary to provide a means for making flexible proposals that take into account employees' emotional state.

[0579] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0580] In this invention, the server includes means for sending requests to an API endpoint within the company and collecting personnel data, means for parsing the collected personnel data and storing it in a database, means for evaluating employee performance scores based on the stored personnel data and generating job rotation and career advancement proposals, means for optimizing the proposals using an emotion engine that collects and analyzes employee emotion data, and means for providing the optimized proposals based on user requests. This makes it possible to automatically and efficiently generate and provide appropriate job rotation and career advancement proposals that reduce employee job change risk and improve internal engagement.

[0581] - "Intra-company API endpoint" refers to the access point of an application program interface (API) used within a company, and is an interface used to access various company data.

[0582] "Human Resources Data" refers to various information related to employees, specifically data including employee IDs, names, job titles, departments, and performance scores.

[0583] A "performance score" is an indicator used to evaluate an employee's work efficiency and achievements, and is often expressed numerically.

[0584] "Job rotation" is the process of encouraging employees to periodically take on different tasks or positions.

[0585] "Career advancement" is the process of encouraging employees to advance or expand their position or scope of work.

[0586] An "emotion engine" is a system or software for analyzing employee emotion data and includes a machine learning model for assessing emotional states.

[0587] A "database" is a part of an information system that stores collected data in a structured manner so that it can be later efficiently searched, retrieved, and updated.

[0588] "User" refers to end users, particularly human resources personnel, who use the system to request job rotation and career advancement proposals for employees.

[0589] "Suggestion optimization" is the process of adjusting generated job rotation and career advancement suggestions by taking into account additional data such as the employee's emotional state to provide more appropriate suggestions.

[0590] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. The system is implemented using the following hardware and software.

[0591] Hardware and software used

[0592] Server: The core of the system that collects, stores, analyzes, and generates recommendations. The server includes the following software components:

[0593] API Endpoint Communication Module: Collects HR data from API endpoints within the enterprise via RESTful services.

[0594] Database Management System: Stores and manages data using a relational database system such as MySQL.

[0595] Analytical engine: Evaluates employee performance scores and generates job rotation and career advancement suggestions.

[0596] Emotion Engine: Analyzes employee emotion data using machine learning models (e.g., TensorFlow).

[0597] Suggestion optimization module: Optimizes suggestions based on the results of the sentiment engine.

[0598] Terminal: A device that allows a user (HR professional) to access the system and request and receive proposals. The terminal has the ability to access the system through a browser or a dedicated application.

[0599] System Operation

[0600] The server first sends an HTTP request to the company's API endpoint to collect employee data such as employee ID, name, job title, department, and performance score. This data is returned to the server in JSON format, parsed, and stored in the database. The stored data is then used for subsequent analysis and proposal generation.

[0601] The server then queries employee data from the database and evaluates each employee's performance score. Based on this evaluation, job rotation and career advancement proposals are generated. An emotion engine is used to analyze the employee's emotional data and reflect the results in the proposals. For example, positions that reduce stress may be prioritized for employees with high levels of stress.

[0602] A user requests suggestions by specifying a specific employee ID. In response to the request, the server generates suggestions based on the analyzed employee data and sentiment data and provides them to the user via a web interface or email.

[0603] Specific examples

[0604] Example 1: A server sends a request to an internal API endpoint to collect and store the following data:

[0605] Employee ID: 12345

[0606] Name: Employee A

[0607] Position: Development Engineer

[0608] Department: Development Department

[0609] Performance score: 4.7

[0610] When a user requests suggestions for employee ID 12345, the server recommends the position "Supervisor (Sales Manager)" based on a performance score of 4.7. It also places special weight on this suggestion because the emotion engine has analyzed Employee A's emotional state as "positive."

[0611] Example 2: The server similarly collects employee data and obtains the following data:

[0612] Employee ID: 67890

[0613] Name: Employee B

[0614] Job Title: Support Engineer

[0615] Department: Support Department

[0616] Performance score: 3.5

[0617] When a user requests a proposal for employee ID 67890, the server recommends the position of "Mid-level position (marketing coordinator)" based on a performance score of 3.5. Furthermore, because the emotion engine has analyzed employee B's emotional state as "stressed," the server emphasizes job content that can reduce stress.

[0618] Below is an example of a prompt sentence to input to the generative AI model.

[0619] Prompt statement example 1:

[0620] "Employee ID 12345 has a performance score of 4.7 and a positive emotional state. Please generate optimal job rotation suggestions for this employee."

[0621] Prompt statement example 2:

[0622] "Employee ID 67890 has a performance score of 3.5 and an emotional state of stress. Please generate job rotation suggestions that will reduce stress for this employee."

[0623] As described above, the present invention is a system that automatically and efficiently generates and provides proposals that utilize employee data and emotional data in order to reduce the risk of employees changing jobs and improve internal engagement.

[0624] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0625] Step 1:

[0626] The server sends an HTTP request to an internal API endpoint. The API endpoint provides a RESTful service, and the server authenticates using a dedicated access token. The input is the API endpoint URL and access token, and the output is JSON data of employees. For example, if the input URL is "https: / / company-api.example.com / employees", the corresponding employee data is returned in JSON format. Specifically, the server sends a GET request and processes the JSON data received as a response for use in the next step.

[0627] Step 2:

[0628] The server parses the received JSON-formatted employee data and saves the information in a local database. The input is the JSON data obtained in the previous step, and the output is the parsed employee data saved in the database. Specifically, the server parses the JSON data and executes an INSERT statement to save the data in the "Employee" table. At this time, it performs transaction management to maintain data consistency and integrity.

[0629] Step 3:

[0630] The server queries employee data stored in a database to evaluate each employee's performance score. The input is the employee's ID, and the output is the employee's performance score and related information. Specifically, it executes an SQL query such as "SELECT FROM employee WHERE id = ?" and applies a suggestion generation algorithm based on the retrieved data.

[0631] Step 4:

[0632] The server uses the emotion engine to collect and analyze the user's emotional data. The input is the text data or voice data entered by the user, and the output is the analyzed emotional state (positive, negative, stress, etc.). Specifically, the server sends the text data or voice data to the emotion engine's API, and the obtained analysis results are used for suggestion optimization.

[0633] Step 5:

[0634] The server optimizes job rotation and career advancement proposals by taking into account the analysis results of the emotion engine. The input is the employee data and emotion data obtained in the previous steps, and the output is the optimized proposal. Specifically, the server re-executes the proposal algorithm and selects the optimal proposal.

[0635] Step 6:

[0636] A user requests a proposal by specifying a specific employee ID. The input is the employee ID, and the output is the generated proposal. Specifically, the user enters the employee ID through a web interface or a dedicated application and obtains the generated proposal from the server.

[0637] Step 7:

[0638] The server provides optimized suggestions based on the user's request. The input is the employee ID specified by the user, and the output is the content of the suggestions. Specifically, the server retrieves the employee's data from the database, generates suggestions based on the analysis results, and provides them to the user. The suggestions are delivered to the user via a web interface or email.

[0639] (Application example 2)

[0640] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0641] Traditional employee management systems only suggest job rotations and career advancement based on employee performance data, but do not take into account the emotional state of employees. This can lead to stress and a decline in motivation, resulting in high employee turnover. Furthermore, there is a lack of dedicated applications to effectively utilize these suggestions in physical stores.

[0642] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0643] In this invention, the server includes: means for collecting employee personnel data; means for storing the collected personnel data in a database; means for generating job rotation and career advancement proposals for employees based on the stored personnel data; means including a smartphone application for providing the proposals; an emotion engine for collecting and analyzing employee emotion data; and means for optimizing the proposals based on the results of the emotion engine. This enables optimal job rotation and career advancement proposals that take into account both employee performance and emotional state, thereby improving engagement and productivity in physical stores.

[0644] "Employee personnel data" is data that includes information such as an employee's ID, name, job title, department, and performance score.

[0645] A "database" is a collection of information that stores collected employee personnel data and is structured in a way that allows it to be searched and analyzed.

[0646] "Job rotation" means periodically changing employees' jobs and positions to allow them to gain diverse experience and skills.

[0647] "Career advancement" refers to promoting an employee in a higher position or role based on their current performance.

[0648] "Proposal" refers to providing employees with optimal job rotation and career advancement strategies.

[0649] A "smartphone application" is a software program that runs on a smartphone and is available to employees and managers.

[0650] "Emotional data" is data that expresses an employee's emotional state in numerical or categorical terms, including stress and motivation levels.

[0651] The "emotion engine" is a software module for collecting and analyzing employees' emotional states.

[0652] "Optimization" is the process of generating the most efficient and effective proposals based on employee performance and sentiment data.

[0653] MODE FOR CARRYING OUT THE INVENTION

[0654] Program Generation

[0655] The program of the system for implementing the present invention has the following main functions.

[0656] 1. Data Collection: The server sends a request to the company's API endpoint to collect employee HR data, including employee ID, name, job title, department, and performance score.

[0657] 2. Database Update: The collected data is parsed and stored in a local database, which is used to generate suggestions.

[0658] 3. Proposal Generation: Based on the stored employee data, job rotation and career advancement proposals are generated based on the employee's performance score. The proposals are provided via a smartphone application.

[0659] 4. Emotion data collection and analysis: The server collects employee emotion data and analyzes it using an emotion engine. Based on the analysis results, the system optimizes the proposals.

[0660] 5. Proposal Provision: A user can request a proposal by specifying a specific employee ID, and the server generates the proposal and provides it through a smartphone application.

[0661] A natural language description of the program's processing

[0662] Data collection step: The server retrieves employee data from the company's API endpoint using an HTTP request, typically using the requests library. The response from the API is in JSON format, which is parsed to extract the required employee information.

[0663] Database update step: The extracted data is stored in a local database. This database is a structured collection of information that can be easily searched and analyzed. Commonly used databases include MySQL, PostgreSQL, and SQLite.

[0664] Proposal generation step: Based on the stored employee data, the employee's performance score and sentiment data are evaluated to generate optimal job rotation and career advancement proposals. The proposals are sent to a smartphone application. The application is often created using, for example, Flutter or React Native.

[0665] Emotion data collection and analysis step: Emotion data is collected from employee speech and behavior and analyzed by an emotion engine, for example, using Microsoft Azure's emotion recognition API or Google Cloud AI.

[0666] Proposal provision step: The user requests a proposal using a specific employee ID through a smartphone application. The server searches the database and obtains the corresponding employee's information. Taking into account the results of the emotion engine, the server generates the optimal proposal and displays it in the application.

[0667] Adding specific examples

[0668] Example 1: High-Performing Employees

[0669] For employee with employee ID 12345, the performance score is 4.7 and the emotional state is positive. In this case, the server generates a suggestion to "recommend a managerial position (e.g., sales manager)."

[0670] Example 2: Stressed mid-level employee

[0671] For an employee with employee ID 67890, the performance score is 3.5 and the emotional state is stress. In this case, the server generates a proposal that "generates proposals for mid-level positions (e.g., marketing coordinator) and job descriptions that can reduce stress."

[0672] Prompt Sentence Examples

[0673] "Employee ID 12345 has a performance score of 4.7. His emotional state is positive. Please generate optimal job rotation and career advancement suggestions for this employee."

[0674] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0675] Step 1:

[0676] The server sends an HTTP request to an API endpoint to retrieve employee personnel data.

[0677] Input: API endpoint URL and authentication information

[0678] Specific operation: Send a GET request using the requests library. The API response contains data in JSON format.

[0679] Output: Employee data in JSON format (employee ID, name, job title, department, performance score, etc.)

[0680] Step 2:

[0681] The server parses the received JSON-formatted employee data and stores it in a local database.

[0682] Input: Employee data in JSON format

[0683] Specific operation: Parse the data using the json library. Insert the parsed data into a database (MySQL, PostgreSQL, etc.).

[0684] Output: Structured employee data stored in the database

[0685] Step 3:

[0686] Users use a smartphone application to request proposals using a specific employee ID.

[0687] Input: Specific Employee ID

[0688] Specific operation: The application sends a request to the backend server. The backend, written in Python, receives the request.

[0689] Output: Request data is sent to the backend server

[0690] Step 4:

[0691] The server searches the database to retrieve the relevant employee information.

[0692] Input: Specific Employee ID

[0693] Specific behavior: Execute a database query to retrieve the relevant employee data (execute an SQL statement)

[0694] Output: Employee information (employee ID, name, job title, department, performance score)

[0695] Step 5:

[0696] The server generates job rotation and career advancement suggestions based on employee performance scores.

[0697] Input: Employee information and performance score

[0698] Specific operations: Performance scores are evaluated and roles are assigned based on the scores. Sentiment data is also analyzed.

[0699] Output: Initial job rotation and career progression suggestions (e.g., management, mid-level, entry-level positions)

[0700] Step 6:

[0701] The server uses an emotion engine to collect and analyze employee emotion data.

[0702] Input: Employee emotional data (utterances and behavioral data)

[0703] Specific operation: Analyze emotion data using emotion recognition APIs (Microsoft Azure, Google Cloud AI, etc.).

[0704] Output: Sentiment analysis result (e.g. positive, stress, etc.)

[0705] Step 7:

[0706] The server optimizes the suggestions based on the results of the emotion engine.

[0707] Input: Initial proposal and sentiment analysis results

[0708] Specific behavior: If emotional state (e.g. stress), adjust job content. If emotional state is positive, maintain normal suggestions.

[0709] Output: Optimized proposal (final proposal for job rotation or career advancement)

[0710] Step 8:

[0711] The server sends the generated optimized proposals to the smartphone application.

[0712] Input: Optimized Proposal

[0713] Specific behavior: Send the generated proposal in JSON format to the smartphone application by reusing the API endpoint and sending a POST request.

[0714] Output: The smartphone application is notified of the suggestion and displayed to the user.

[0715] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0716] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0717] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0718] [Third embodiment]

[0719] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0720] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0721] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0722] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0723] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0724] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0725] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0726] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0727] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0728] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0729] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0730] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0731] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. This system collects employee personnel data, stores it in a database, and generates job rotation and career advancement proposals for employees. Details of an embodiment of the present invention are described below.

[0732] Collection and storage of personnel data

[0733] The server sends requests to the company's API endpoint to collect employee HR data, including each employee's ID, name, job title, department, performance score, etc. The collected data is then parsed appropriately by the server and stored in a local database, which is later used to generate job rotation and career advancement suggestions.

[0734] Job rotation and career advancement proposal generation

[0735] The server evaluates the employee's performance score based on the data stored in the database and generates job rotation and career advancement proposals based on the score. Specific proposals are made as follows:

[0736] Employees with a performance score of 4.5 or above are recommended for managerial positions (e.g., sales manager).

[0737] Employees with a performance score of 3.0 or above but below 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0738] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0739] Providing suggestions

[0740] A human resources manager, who is the user, requests job rotation and career advancement proposals by specifying a specific employee ID. The server searches the database based on the specified employee ID and retrieves the relevant employee information. The server then generates proposals according to the aforementioned policy and provides them to the user. This allows the user to efficiently allocate personnel appropriately.

[0741] Specific examples

[0742] Example 1: A server sends a request to an internal company API endpoint and retrieves employee data such as:

[0743] Employee ID: 12345

[0744] Name: Yamada Taro

[0745] Position: Development Engineer

[0746] Department: Development Department

[0747] Performance score: 4.7

[0748] The server stores this employee data in a local database. Later, when the user requests suggestions for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7.

[0749] Example 2: When the server retrieves employee data in the same way, it gets the following data:

[0750] Employee ID: 67890

[0751] Name: Ichiro Suzuki

[0752] Job Title: Support Engineer

[0753] Department: Support Department

[0754] Performance score: 3.5

[0755] This data is also stored in a local database, and when a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5.

[0756] The present invention is implemented as described above, but it should be understood that other specific examples and application ranges can be modified or adjusted as appropriate.

[0757] The processing flow will be explained below.

[0758] Step 1:

[0759] The server collects employee HR data by sending an HTTP GET request to a company's API endpoint, including authentication information such as an API key in the header.

[0760] Step 2:

[0761] The server receives the response from the API and parses the data in JSON format, which includes the employee's ID, name, job title, department, performance score, etc.

[0762] Step 3:

[0763] The server stores the parsed data in a local database, creating a new one if it does not exist, or updating an existing database as needed.

[0764] Step 4:

[0765] A user (HR professional) requests job rotation and career advancement suggestions by specifying a specific employee ID. This request includes the employee ID.

[0766] Step 5:

[0767] The server receives a request from the user and searches the database based on the specified employee ID to retrieve the corresponding employee information.

[0768] Step 6:

[0769] The server checks the performance score from the acquired employee information and generates appropriate proposals based on that score. For example, if the performance score is 4.5 or higher, it will recommend managerial positions, mid-level positions, entry-level positions, etc.

[0770] Step 7:

[0771] The server returns the generated proposal to the user, who then uses the proposal to implement appropriate personnel placement and career advancement planning.

[0772] Step 8:

[0773] Users then take necessary personnel actions based on the provided suggestions, which may include transferring employees, promoting them, or developing training plans.

[0774] Example 1

[0775] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0776] Appropriate job rotation and career advancement proposals are important for reducing employee turnover risk and improving internal engagement. However, it is difficult with current systems to efficiently collect employee performance data and generate accurate proposals based on that data. Furthermore, there is a lack of mechanisms for quickly providing generated proposals to users. This results in delays in placing employees in the right positions, reducing the efficiency of the entire organization.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0778] In this invention, the server includes means for collecting employee personnel data, means for storing the collected personnel data in a database, means for generating job rotation and career advancement proposals for employees based on the stored personnel data, and means for providing the content of the proposed job rotation and career advancement to users. This makes it possible to efficiently collect employee data using an API endpoint within the company, analyze the data, and quickly generate job rotation and career advancement proposals suitable for each employee, and provide them to users.

[0779] "Employee personnel data" means data that includes information about an employee, such as ID, name, job title, department, and performance score.

[0780] A "database" is a system for systematically storing collected data and for later searching, retrieving, and processing it.

[0781] "Job rotation" is the practice of providing employees with the opportunity to experience different jobs or roles, with the aim of broadening their skills and knowledge.

[0782] "Career advancement" refers to an employee's promotion to a position of higher rank or responsibility within an organization.

[0783] A "server" is a computer system that processes requests from clients via a network and provides the necessary information.

[0784] "User" refers to the person who uses the system, and is often a human resources professional.

[0785] A "performance score" is a numerical representation of an employee's achievements and contributions.

[0786] "Managerial position" refers to a position that manages and supervises other employees within an organization.

[0787] "Mid-level positions" are positions that involve specialized skills and mid-level positions within an organization.

[0788] An "entry position" is an entry-level position within an organization that allows a person to gain their first work experience.

[0789] "Interface" refers to the screens and applications that allow a user to interact with a system.

[0790] A "memory cache" is a memory area that temporarily stores frequently used data in order to achieve high-speed data access.

[0791] This invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. The system collects and stores employee personnel data, generates job rotation and career advancement proposals based on the data, and provides them to users.

[0792] Collection and storage of personnel data

[0793] The server periodically sends HTTP GET requests to the company's internal API endpoint to collect employee personnel data. Specifically, the server uses a Python script that runs as a scheduled job every day at 2:00 AM. The JSON-formatted data returned by the API endpoint includes employee ID, name, job title, department, performance score, etc.

[0794] The collected data is parsed by the server using a JSON module and then stored in a local database using an ORM such as SQLAlchemy, with transaction management to ensure data integrity.

[0795] Job rotation and career advancement proposal generation

[0796] The server evaluates employees' performance scores based on the personnel data stored in the database and generates job rotation and career advancement proposals based on those scores. Specific proposals are as follows:

[0797] Employees with a performance score of 4.5 or above are recommended for managerial positions (e.g., sales manager).

[0798] Employees with a performance score between 3.0 and 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0799] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0800] The generated suggestions are temporarily stored in a memory cache (e.g. Redis) or a local database.

[0801] Providing suggestions

[0802] A human resources manager, who is a user, requests job rotation and career advancement proposals by specifying a specific employee ID. When the server receives this request, it searches the database and retrieves the relevant employee information. Based on saved proposals and newly generated proposals, the server displays proposals to the user. These proposals allow the user to efficiently allocate personnel appropriately.

[0803] Specific examples

[0804] Example 1

[0805] The server sends a request to the company's API endpoint and retrieves the following employee data:

[0806] Employee ID: 12345

[0807] Name: A. Taro

[0808] Job title: Engineer

[0809] Department: Technology Department

[0810] Performance score: 4.7

[0811] The server stores this data in a local database. At a later date, when the user requests a proposal for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7.

[0812] Example 2

[0813] The server retrieves the employee data in the same way, and gets the following data:

[0814] Employee ID: 67890

[0815] Name: B. Ichiro

[0816] Position: Support Engineer

[0817] Department: Support Department

[0818] Performance score: 3.5

[0819] This data is also stored in a local database, and when a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5.

[0820] Example prompts for generative AI models

[0821] "Generate a job rotation proposal for employee with employee ID 12345."

[0822] In this way, the system can be used to efficiently support employees' career advancement within a company.

[0823] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0824] Step 1:

[0825] The server sends an HTTP GET request to the company's internal API endpoint to collect employee personnel data. The input is the endpoint URL and authentication information, and the output is JSON-formatted personnel data. Specifically, the server uses a Python script to execute this process as a scheduled job every day at 2:00 AM. It receives the response returned from the API, verifies that the response code is 200, and logs the data.

[0826] Step 2:

[0827] The server parses the collected JSON-formatted personnel data and saves it in a database. The input is JSON-formatted data, and the output is the parsed data stored in the database. Specifically, the server parses the data using Python's json module and extracts the necessary fields (ID, name, job title, department, performance score). It then connects to the database using an ORM such as SQLAlchemy and executes insert or update SQL queries. It performs transaction management, committing if successful and rolling back if unsuccessful.

[0828] Step 3:

[0829] The server evaluates employee performance scores based on the personnel data stored in the database and generates recommendations. The input is the personnel data in the database, and the output is the generated recommendations. Specifically, the server queries the employee performance scores from the database and applies the following rules to each score:

[0830] If the score is 4.5 or above, a managerial position is recommended.

[0831] If the score is between 3.0 and 4.5, a mid-level position is recommended.

[0832] If the score is less than 3.0, an entry position is recommended.

[0833] The generated suggestions are temporarily stored in a memory cache (e.g. Redis) or a local database.

[0834] Step 4:

[0835] As an HR professional, a user requests a proposal by specifying a specific employee ID. The input is the employee ID, and the output is a proposal for suitable job rotation or career advancement. Specifically, the user enters the employee ID through a web interface or mobile application and sends a request to the server. The server receives the request, retrieves the relevant employee information from the database, and displays it to the user based on saved proposals or newly generated proposals. The browser displays the suitable positions in HTML format, or a notification is sent to the mobile app.

[0836] The above is the specific processing flow of the program of this system.

[0837] (Application example 1)

[0838] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0839] Reducing the risk of employee turnover within a company or organization and improving internal engagement are key challenges. In particular, in environments such as factories where many employees rely on short-term rotations and job changes, efficient personnel placement and career advancement proposals are required. However, traditional systems lack the means to properly generate and quickly notify these proposals, making it difficult to ensure that employees are placed in the right positions.

[0840] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0841] In this invention, the server includes means for acquiring employee business data, means for storing the acquired business data in data storage, means for generating proposals for transfers and promotions for employees based on the stored business data, and means for notifying the proposals to mobile devices. This enables managers to quickly make appropriate personnel assignments by making appropriate rotations and career advancement proposals in real time based on employees' evaluation scores and performance.

[0842] "Employee Business Data" means job-related information about employees, such as each employee's ID, name, job title, department, and performance evaluation score.

[0843] "Means of acquisition" refers to the functionality for collecting employee business data from a company's internal servers via an API endpoint.

[0844] "Data storage" refers to components including databases and storage devices for storing acquired employee business data.

[0845] "Means of storage" refers to the function of registering collected employee business data in data storage and updating it as necessary.

[0846] "Means for generating transfer or promotion proposals" refers to a function that includes algorithms or logic for analyzing stored employee work data and proposing appropriate new positions or promotions to employees.

[0847] "Means for notifying proposals to mobile devices" refers to a communication function for notifying generated transfer or promotion proposals to mobile devices such as smartphones.

[0848] An "evaluation score" is a numerical indicator of an employee's achievements and performance.

[0849] "Management level positions" refer to senior positions in an organization such as a factory that manage and supervise employees.

[0850] "Mid-level positions" refer to intermediate positions in an organization such as a factory, positioned between management positions and entry-level positions.

[0851] An "entry level position" refers to the first position suitable for a new employee or entry-level employee in an organization such as a factory.

[0852] An embodiment of the present invention will be described in detail below. This system acquires employee work data (hereinafter referred to as personnel data) and makes transfer and promotion proposals to employees based on the data. The system mainly includes a data collection means, a data storage means, a proposal generation means, and a notification means.

[0853] First, the server uses an API endpoint to retrieve employee work data from the company's database, including employee ID, name, job title, department, evaluation score, etc. The server then parses the retrieved data appropriately and stores it in local data storage (e.g., SQLite).

[0854] The server then generates transfer and promotion proposals for employees based on the stored data. The proposals are generated using the employee's evaluation scores. For example, an algorithm is used to suggest managerial positions for employees with an evaluation score of 4.5 or higher, mid-level positions for employees with an evaluation score of 3.0 or higher but less than 4.5, and entry-level positions for employees with an evaluation score less than 3.0.

[0855] Once a proposal is generated, the server notifies the mobile device (e.g., smartphone) of the proposal. The notification means can inform the manager of the proposal in real time, allowing the manager to quickly make appropriate personnel allocation decisions.

[0856] For example, if the server retrieves employee data from a company's API and an employee named Yamada Taro has a performance score of 4.7, the server will suggest a managerial level position for Yamada Taro, and if an employee named Suzuki Ichiro has a performance score of 3.5, the server will suggest a mid-level position.

[0857] Specific examples of prompts for generative AI models are as follows:

[0858] Write Python code to retrieve employee data from the company's API and store it in a SQLite database. Additionally, implement logic to generate job rotation and career advancement suggestions based on the employee's performance score. For example, if the performance score is 4.5 or above, suggest a managerial position; if the performance score is 3.0 or above but less than 4.5, suggest a mid-level position; if the score is less than 3.0, suggest an entry-level position.

[0859] In this way, the present invention provides a specific embodiment of a system for reducing the risk of employees changing jobs and improving internal engagement in companies and organizations.

[0860] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0861] Step 1:

[0862] The server sends a request to an internal API endpoint to retrieve employee work data. The input is the API endpoint URL, and the output is data such as the employee's ID, name, job title, department, and evaluation score. The server receives this data in JSON format.

[0863] Step 2:

[0864] The server parses the acquired employee business data appropriately and saves it in a database (e.g., SQLite). The input is the JSON data acquired in step 1, and the output is registered in the database in a format that corresponds to each field. During this process, the server reformats the personnel data and updates existing data as necessary.

[0865] Step 3:

[0866] The server analyzes the data stored in the database and generates transfer and promotion proposals based on each employee's evaluation score. The input is each employee's job data stored in the database, and the output is the proposed new position or placement. Based on the evaluation score, the algorithm suggests management-level, mid-level, or entry-level positions.

[0867] Step 4:

[0868] The server notifies the mobile device of the generated proposal. The input is the proposal content generated in step 3, and the output is sent to the administrator's smartphone in the form of a push notification, email, etc. The server selects the notification method during this process and notifies the administrator in the appropriate format.

[0869] Step 5:

[0870] Managers check the notification on their mobile devices and make staffing decisions based on the recommendations. The input is the recommendation notification sent from the server, and the output is instructions and confirmations that the manager can take action on, displayed on the mobile device. Based on this information, managers make decisions to ensure that employees are placed in the right positions.

[0871] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0872] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. This system collects employee personnel data, stores it in a database, and generates job rotation and career advancement proposals for employees. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to make more appropriate proposals. Details of the embodiments of the invention are described below.

[0873] Collection and storage of personnel data

[0874] The server sends requests to the company's API endpoint to collect employee HR data. The collected data includes each employee's ID, name, job title, department, and performance score. The server parses the collected data and stores it in a local database. This database is used to generate job rotation and career advancement recommendations.

[0875] Job rotation and career advancement proposal generation

[0876] The server evaluates the employee's performance score based on the employee data stored in the database and generates job rotation and career advancement proposals based on the score. Specific proposals are as follows:

[0877] Employees with a performance score of 4.5 or above are recommended for management positions (e.g., sales manager).

[0878] Employees with a performance score between 3.0 and 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[0879] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[0880] Introducing the Emotion Engine

[0881] This system includes an emotion engine that allows the server to recognize the user's emotions. This emotion engine collects and analyzes the user's emotional data and optimizes job rotation and career advancement proposals based on the results.

[0882] For example, if a user is feeling stressed, the emotion engine will analyze that emotion data and recommend positions that will help reduce stress, making it possible to provide flexible suggestions tailored to the user's emotional state.

[0883] Providing suggestions

[0884] The user (human resources officer) requests job rotation and career advancement proposals by specifying a specific employee ID. The server searches the database based on the specified employee ID and retrieves the relevant employee information. Based on the retrieved information, the server also takes into account the analysis results of the emotion engine, generates proposals, and provides them to the user.

[0885] Specific examples

[0886] Example 1: A server sends requests to an internal company API endpoint to collect and store the following employee data:

[0887] Employee ID: 12345

[0888] Name: Yamada Taro

[0889] Position: Development Engineer

[0890] Department: Development Department

[0891] Performance score: 4.7

[0892] When the user requests suggestions for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7. Furthermore, the emotion engine analyzes Taro Yamada's emotional state as "positive," so it places special weight on this suggestion.

[0893] Example 2: The server similarly collects employee data and obtains the following data:

[0894] Employee ID: 67890

[0895] Name: Ichiro Suzuki

[0896] Job Title: Support Engineer

[0897] Department: Support Department

[0898] Performance score: 3.5

[0899] When a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5. Because the emotion engine analyzes Suzuki Ichiro's emotional state as "stressed," it prioritizes suggestions that include job content that can reduce stress.

[0900] The present invention is implemented as described above, but it should be understood that other specific examples and application ranges can be modified or adjusted as appropriate.

[0901] The processing flow will be explained below.

[0902] Step 1:

[0903] The server collects employee HR data by sending an HTTP GET request to a corporate API endpoint, setting a header containing authentication information and retrieving the requested data.

[0904] Step 2:

[0905] The server receives the response from the API and parses it in JSON format, which includes data such as employee ID, name, job title, department, and performance score.

[0906] Step 3:

[0907] The server stores the parsed data in a local database, creating a new one if it does not exist, or updating an existing database if it does.

[0908] Step 4:

[0909] A user (HR professional) requests job rotation and career advancement suggestions by specifying a specific employee ID. The employee ID is included in the request.

[0910] Step 5:

[0911] The server receives a request from the user, searches the database based on the specified employee ID, and retrieves the information of the corresponding employee.

[0912] Step 6:

[0913] The server checks the performance score from the acquired employee information and generates appropriate suggestions based on that score. For example, if the performance score is 4.5 or higher, it recommends a managerial position, if the performance score is 3.0 or higher but less than 4.5, it recommends a mid-level position, and if the performance score is less than 3.0, it recommends an entry-level position.

[0914] Step 7:

[0915] The server uses an emotion engine to analyze the user's emotion data, which is obtained from the user's input and actions.

[0916] Step 8:

[0917] The server optimizes the suggestions based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it will prioritize jobs that will help reduce stress.

[0918] Step 9:

[0919] The server provides the generated optimized proposals to the user, who can use them to plan employee placement and career advancement.

[0920] Step 10:

[0921] Based on the provided suggestions, users can take necessary personnel actions, such as transferring or promoting employees or developing training plans.

[0922] Example 2

[0923] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0924] To effectively manage employee turnover risk and improve internal engagement, it is necessary to provide a system that automatically generates and provides appropriate job rotation and career advancement proposals. It is also necessary to provide a means for making flexible proposals that take into account employees' emotional state.

[0925] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0926] In this invention, the server includes means for sending requests to an API endpoint within the company and collecting personnel data, means for parsing the collected personnel data and storing it in a database, means for evaluating employee performance scores based on the stored personnel data and generating job rotation and career advancement proposals, means for optimizing the proposals using an emotion engine that collects and analyzes employee emotion data, and means for providing the optimized proposals based on user requests. This makes it possible to automatically and efficiently generate and provide appropriate job rotation and career advancement proposals that reduce employee job change risk and improve internal engagement.

[0927] - "Intra-company API endpoint" refers to the access point of an application program interface (API) used within a company, and is an interface used to access various company data.

[0928] "Human Resources Data" refers to various information related to employees, specifically data including employee IDs, names, job titles, departments, and performance scores.

[0929] A "performance score" is an indicator used to evaluate an employee's work efficiency and achievements, and is often expressed numerically.

[0930] "Job rotation" is the process of encouraging employees to periodically take on different tasks or positions.

[0931] "Career advancement" is the process of encouraging employees to advance or expand their position or scope of work.

[0932] An "emotion engine" is a system or software for analyzing employee emotion data and includes a machine learning model for assessing emotional states.

[0933] A "database" is a part of an information system that stores collected data in a structured manner so that it can be later efficiently searched, retrieved, and updated.

[0934] "User" refers to end users, particularly human resources personnel, who use the system to request job rotation and career advancement proposals for employees.

[0935] "Suggestion optimization" is the process of adjusting generated job rotation and career advancement suggestions by taking into account additional data such as the employee's emotional state to provide more appropriate suggestions.

[0936] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. The system is implemented using the following hardware and software.

[0937] Hardware and software used

[0938] Server: The core of the system that collects, stores, analyzes, and generates recommendations. The server includes the following software components:

[0939] API Endpoint Communication Module: Collects HR data from API endpoints within the enterprise via RESTful services.

[0940] Database Management System: Stores and manages data using a relational database system such as MySQL.

[0941] Analytical engine: Evaluates employee performance scores and generates job rotation and career advancement suggestions.

[0942] Emotion Engine: Analyzes employee emotion data using machine learning models (e.g., TensorFlow).

[0943] Suggestion optimization module: Optimizes suggestions based on the results of the sentiment engine.

[0944] Terminal: A device that allows a user (HR professional) to access the system and request and receive proposals. The terminal has the ability to access the system through a browser or a dedicated application.

[0945] System Operation

[0946] The server first sends an HTTP request to the company's API endpoint to collect employee data such as employee ID, name, job title, department, and performance score. This data is returned to the server in JSON format, parsed, and stored in the database. The stored data is then used for subsequent analysis and proposal generation.

[0947] The server then queries employee data from the database and evaluates each employee's performance score. Based on this evaluation, job rotation and career advancement proposals are generated. An emotion engine is used to analyze the employee's emotional data and reflect the results in the proposals. For example, positions that reduce stress may be prioritized for employees with high levels of stress.

[0948] A user requests suggestions by specifying a specific employee ID. In response to the request, the server generates suggestions based on the analyzed employee data and sentiment data and provides them to the user via a web interface or email.

[0949] Specific examples

[0950] Example 1: A server sends a request to an internal API endpoint to collect and store the following data:

[0951] Employee ID: 12345

[0952] Name: Employee A

[0953] Position: Development Engineer

[0954] Department: Development Department

[0955] Performance score: 4.7

[0956] When a user requests suggestions for employee ID 12345, the server recommends the position "Supervisor (Sales Manager)" based on a performance score of 4.7. It also places special weight on this suggestion because the emotion engine has analyzed Employee A's emotional state as "positive."

[0957] Example 2: The server similarly collects employee data and obtains the following data:

[0958] Employee ID: 67890

[0959] Name: Employee B

[0960] Job Title: Support Engineer

[0961] Department: Support Department

[0962] Performance score: 3.5

[0963] When a user requests a proposal for employee ID 67890, the server recommends the position of "Mid-level position (marketing coordinator)" based on a performance score of 3.5. Furthermore, because the emotion engine has analyzed employee B's emotional state as "stressed," the server emphasizes job content that can reduce stress.

[0964] Below is an example of a prompt sentence to input to the generative AI model.

[0965] Prompt statement example 1:

[0966] "Employee ID 12345 has a performance score of 4.7 and a positive emotional state. Please generate optimal job rotation suggestions for this employee."

[0967] Prompt statement example 2:

[0968] "Employee ID 67890 has a performance score of 3.5 and an emotional state of stress. Please generate job rotation suggestions that will reduce stress for this employee."

[0969] As described above, the present invention is a system that automatically and efficiently generates and provides proposals that utilize employee data and emotional data in order to reduce the risk of employees changing jobs and improve internal engagement.

[0970] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0971] Step 1:

[0972] The server sends an HTTP request to an internal API endpoint. The API endpoint provides a RESTful service, and the server authenticates using a dedicated access token. The input is the API endpoint URL and access token, and the output is JSON data of employees. For example, if the input URL is "https: / / company-api.example.com / employees", the corresponding employee data is returned in JSON format. Specifically, the server sends a GET request and processes the JSON data received as a response for use in the next step.

[0973] Step 2:

[0974] The server parses the received JSON-formatted employee data and saves the information in a local database. The input is the JSON data obtained in the previous step, and the output is the parsed employee data saved in the database. Specifically, the server parses the JSON data and executes an INSERT statement to save the data in the "Employee" table. At this time, it performs transaction management to maintain data consistency and integrity.

[0975] Step 3:

[0976] The server queries employee data stored in a database to evaluate each employee's performance score. The input is the employee's ID, and the output is the employee's performance score and related information. Specifically, it executes an SQL query such as "SELECT FROM employee WHERE id = ?" and applies a suggestion generation algorithm based on the retrieved data.

[0977] Step 4:

[0978] The server uses the emotion engine to collect and analyze the user's emotional data. The input is the text data or voice data entered by the user, and the output is the analyzed emotional state (positive, negative, stress, etc.). Specifically, the server sends the text data or voice data to the emotion engine's API, and the obtained analysis results are used for suggestion optimization.

[0979] Step 5:

[0980] The server optimizes job rotation and career advancement proposals by taking into account the analysis results of the emotion engine. The input is the employee data and emotion data obtained in the previous steps, and the output is the optimized proposal. Specifically, the server re-executes the proposal algorithm and selects the optimal proposal.

[0981] Step 6:

[0982] A user requests a proposal by specifying a specific employee ID. The input is the employee ID, and the output is the generated proposal. Specifically, the user enters the employee ID through a web interface or a dedicated application and obtains the generated proposal from the server.

[0983] Step 7:

[0984] The server provides optimized suggestions based on the user's request. The input is the employee ID specified by the user, and the output is the content of the suggestions. Specifically, the server retrieves the employee's data from the database, generates suggestions based on the analysis results, and provides them to the user. The suggestions are delivered to the user via a web interface or email.

[0985] (Application example 2)

[0986] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0987] Traditional employee management systems only suggest job rotations and career advancement based on employee performance data, but do not take into account the emotional state of employees. This can lead to stress and a decline in motivation, resulting in high employee turnover. Furthermore, there is a lack of dedicated applications to effectively utilize these suggestions in physical stores.

[0988] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0989] In this invention, the server includes: means for collecting employee personnel data; means for storing the collected personnel data in a database; means for generating job rotation and career advancement proposals for employees based on the stored personnel data; means including a smartphone application for providing the proposals; an emotion engine for collecting and analyzing employee emotion data; and means for optimizing the proposals based on the results of the emotion engine. This enables optimal job rotation and career advancement proposals that take into account both employee performance and emotional state, thereby improving engagement and productivity in physical stores.

[0990] "Employee personnel data" is data that includes information such as an employee's ID, name, job title, department, and performance score.

[0991] A "database" is a collection of information that stores collected employee personnel data and is structured in a way that allows it to be searched and analyzed.

[0992] "Job rotation" means periodically changing employees' jobs and positions to allow them to gain diverse experience and skills.

[0993] "Career advancement" refers to promoting an employee in a higher position or role based on their current performance.

[0994] "Proposal" refers to providing employees with optimal job rotation and career advancement strategies.

[0995] A "smartphone application" is a software program that runs on a smartphone and is available to employees and managers.

[0996] "Emotional data" is data that expresses an employee's emotional state in numerical or categorical terms, including stress and motivation levels.

[0997] The "emotion engine" is a software module for collecting and analyzing employees' emotional states.

[0998] "Optimization" is the process of generating the most efficient and effective proposals based on employee performance and sentiment data.

[0999] MODE FOR CARRYING OUT THE INVENTION

[1000] Program Generation

[1001] The program of the system for implementing the present invention has the following main functions.

[1002] 1. Data Collection: The server sends a request to the company's API endpoint to collect employee HR data, including employee ID, name, job title, department, and performance score.

[1003] 2. Database Update: The collected data is parsed and stored in a local database, which is used to generate suggestions.

[1004] 3. Proposal Generation: Based on the stored employee data, job rotation and career advancement proposals are generated based on the employee's performance score. The proposals are provided via a smartphone application.

[1005] 4. Emotion data collection and analysis: The server collects employee emotion data and analyzes it using an emotion engine. Based on the analysis results, the system optimizes the proposals.

[1006] 5. Proposal Provision: A user can request a proposal by specifying a specific employee ID, and the server generates the proposal and provides it through a smartphone application.

[1007] A natural language description of the program's processing

[1008] Data collection step: The server retrieves employee data from the company's API endpoint using an HTTP request, typically using the requests library. The response from the API is in JSON format, which is parsed to extract the required employee information.

[1009] Database update step: The extracted data is stored in a local database. This database is a structured collection of information that can be easily searched and analyzed. Commonly used databases include MySQL, PostgreSQL, and SQLite.

[1010] Proposal generation step: Based on the stored employee data, the employee's performance score and sentiment data are evaluated to generate optimal job rotation and career advancement proposals. The proposals are sent to a smartphone application. The application is often created using, for example, Flutter or React Native.

[1011] Emotion data collection and analysis step: Emotion data is collected from employee speech and behavior and analyzed by an emotion engine, for example, using Microsoft Azure's emotion recognition API or Google Cloud AI.

[1012] Proposal provision step: The user requests a proposal using a specific employee ID through a smartphone application. The server searches the database and obtains the corresponding employee's information. Taking into account the results of the emotion engine, the server generates the optimal proposal and displays it in the application.

[1013] Adding specific examples

[1014] Example 1: High-Performing Employees

[1015] For employee with employee ID 12345, the performance score is 4.7 and the emotional state is positive. In this case, the server generates a suggestion to "recommend a managerial position (e.g., sales manager)."

[1016] Example 2: Stressed mid-level employee

[1017] For an employee with employee ID 67890, the performance score is 3.5 and the emotional state is stress. In this case, the server generates a proposal that "generates proposals for mid-level positions (e.g., marketing coordinator) and job descriptions that can reduce stress."

[1018] Prompt Sentence Examples

[1019] "Employee ID 12345 has a performance score of 4.7. His emotional state is positive. Please generate optimal job rotation and career advancement suggestions for this employee."

[1020] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1021] Step 1:

[1022] The server sends an HTTP request to an API endpoint to retrieve employee personnel data.

[1023] Input: API endpoint URL and authentication information

[1024] Specific operation: Send a GET request using the requests library. The API response contains data in JSON format.

[1025] Output: Employee data in JSON format (employee ID, name, job title, department, performance score, etc.)

[1026] Step 2:

[1027] The server parses the received JSON-formatted employee data and stores it in a local database.

[1028] Input: Employee data in JSON format

[1029] Specific operation: Parse the data using the json library. Insert the parsed data into a database (MySQL, PostgreSQL, etc.).

[1030] Output: Structured employee data stored in the database

[1031] Step 3:

[1032] Users use a smartphone application to request proposals using a specific employee ID.

[1033] Input: Specific Employee ID

[1034] Specific operation: The application sends a request to the backend server. The backend, written in Python, receives the request.

[1035] Output: Request data is sent to the backend server

[1036] Step 4:

[1037] The server searches the database to retrieve the relevant employee information.

[1038] Input: Specific Employee ID

[1039] Specific behavior: Execute a database query to retrieve the relevant employee data (execute an SQL statement)

[1040] Output: Employee information (employee ID, name, job title, department, performance score)

[1041] Step 5:

[1042] The server generates job rotation and career advancement suggestions based on employee performance scores.

[1043] Input: Employee information and performance score

[1044] Specific operations: Performance scores are evaluated and roles are assigned based on the scores. Sentiment data is also analyzed.

[1045] Output: Initial job rotation and career progression suggestions (e.g., management, mid-level, entry-level positions)

[1046] Step 6:

[1047] The server uses an emotion engine to collect and analyze employee emotion data.

[1048] Input: Employee emotional data (utterances and behavioral data)

[1049] Specific operation: Analyze emotion data using emotion recognition APIs (Microsoft Azure, Google Cloud AI, etc.).

[1050] Output: Sentiment analysis result (e.g. positive, stress, etc.)

[1051] Step 7:

[1052] The server optimizes the suggestions based on the results of the emotion engine.

[1053] Input: Initial proposal and sentiment analysis results

[1054] Specific behavior: If emotional state (e.g. stress), adjust job content. If emotional state is positive, maintain normal suggestions.

[1055] Output: Optimized proposal (final proposal for job rotation or career advancement)

[1056] Step 8:

[1057] The server sends the generated optimized proposals to the smartphone application.

[1058] Input: Optimized Proposal

[1059] Specific behavior: Send the generated proposal in JSON format to the smartphone application by reusing the API endpoint and sending a POST request.

[1060] Output: The smartphone application is notified of the suggestion and displayed to the user.

[1061] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1062] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1063] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1064] [Fourth embodiment]

[1065] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1066] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1067] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1068] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1069] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1070] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1071] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1072] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1073] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1074] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1075] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1076] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1077] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1078] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. This system collects employee personnel data, stores it in a database, and generates job rotation and career advancement proposals for employees. Details of an embodiment of the present invention are described below.

[1079] Collection and storage of personnel data

[1080] The server sends requests to the company's API endpoint to collect employee HR data, including each employee's ID, name, job title, department, performance score, etc. The collected data is then parsed appropriately by the server and stored in a local database, which is later used to generate job rotation and career advancement suggestions.

[1081] Job rotation and career advancement proposal generation

[1082] The server evaluates the employee's performance score based on the data stored in the database and generates job rotation and career advancement proposals based on the score. Specific proposals are made as follows:

[1083] Employees with a performance score of 4.5 or above are recommended for managerial positions (e.g., sales manager).

[1084] Employees with a performance score of 3.0 or above but below 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[1085] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[1086] Providing suggestions

[1087] A human resources manager, who is the user, requests job rotation and career advancement proposals by specifying a specific employee ID. The server searches the database based on the specified employee ID and retrieves the relevant employee information. The server then generates proposals according to the aforementioned policy and provides them to the user. This allows the user to efficiently allocate personnel appropriately.

[1088] Specific examples

[1089] Example 1: A server sends a request to an internal company API endpoint and retrieves employee data such as:

[1090] Employee ID: 12345

[1091] Name: Yamada Taro

[1092] Position: Development Engineer

[1093] Department: Development Department

[1094] Performance score: 4.7

[1095] The server stores this employee data in a local database. Later, when the user requests suggestions for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7.

[1096] Example 2: When the server retrieves employee data in the same way, it gets the following data:

[1097] Employee ID: 67890

[1098] Name: Ichiro Suzuki

[1099] Job Title: Support Engineer

[1100] Department: Support Department

[1101] Performance score: 3.5

[1102] This data is also stored in a local database, and when a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5.

[1103] The present invention is implemented as described above, but it should be understood that other specific examples and application ranges can be modified or adjusted as appropriate.

[1104] The processing flow will be explained below.

[1105] Step 1:

[1106] The server collects employee HR data by sending an HTTP GET request to a company's API endpoint, including authentication information such as an API key in the header.

[1107] Step 2:

[1108] The server receives the response from the API and parses the data in JSON format, which includes the employee's ID, name, job title, department, performance score, etc.

[1109] Step 3:

[1110] The server stores the parsed data in a local database, creating a new one if it does not exist, or updating an existing database as needed.

[1111] Step 4:

[1112] A user (HR professional) requests job rotation and career advancement suggestions by specifying a specific employee ID. This request includes the employee ID.

[1113] Step 5:

[1114] The server receives a request from the user and searches the database based on the specified employee ID to retrieve the corresponding employee information.

[1115] Step 6:

[1116] The server checks the performance score from the acquired employee information and generates appropriate proposals based on that score. For example, if the performance score is 4.5 or higher, it will recommend managerial positions, mid-level positions, entry-level positions, etc.

[1117] Step 7:

[1118] The server returns the generated proposal to the user, who then uses the proposal to implement appropriate personnel placement and career advancement planning.

[1119] Step 8:

[1120] Users then take necessary personnel actions based on the provided suggestions, which may include transferring employees, promoting them, or developing training plans.

[1121] Example 1

[1122] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1123] Appropriate job rotation and career advancement proposals are important for reducing employee turnover risk and improving internal engagement. However, it is difficult with current systems to efficiently collect employee performance data and generate accurate proposals based on that data. Furthermore, there is a lack of mechanisms for quickly providing generated proposals to users. This results in delays in placing employees in the right positions, reducing the efficiency of the entire organization.

[1124] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1125] In this invention, the server includes means for collecting employee personnel data, means for storing the collected personnel data in a database, means for generating job rotation and career advancement proposals for employees based on the stored personnel data, and means for providing the content of the proposed job rotation and career advancement to users. This makes it possible to efficiently collect employee data using an API endpoint within the company, analyze the data, and quickly generate job rotation and career advancement proposals suitable for each employee, and provide them to users.

[1126] "Employee personnel data" means data that includes information about an employee, such as ID, name, job title, department, and performance score.

[1127] A "database" is a system for systematically storing collected data and for later searching, retrieving, and processing it.

[1128] "Job rotation" is the practice of providing employees with the opportunity to experience different jobs or roles, with the aim of broadening their skills and knowledge.

[1129] "Career advancement" refers to an employee's promotion to a position of higher rank or responsibility within an organization.

[1130] A "server" is a computer system that processes requests from clients via a network and provides the necessary information.

[1131] "User" refers to the person who uses the system, and is often a human resources professional.

[1132] A "performance score" is a numerical representation of an employee's achievements and contributions.

[1133] "Managerial position" refers to a position that manages and supervises other employees within an organization.

[1134] "Mid-level positions" are positions that involve specialized skills and mid-level positions within an organization.

[1135] An "entry position" is an entry-level position within an organization that allows a person to gain their first work experience.

[1136] "Interface" refers to the screens and applications that allow a user to interact with a system.

[1137] A "memory cache" is a memory area that temporarily stores frequently used data in order to achieve high-speed data access.

[1138] This invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. The system collects and stores employee personnel data, generates job rotation and career advancement proposals based on the data, and provides them to users.

[1139] Collection and storage of personnel data

[1140] The server periodically sends HTTP GET requests to the company's internal API endpoint to collect employee personnel data. Specifically, the server uses a Python script that runs as a scheduled job every day at 2:00 AM. The JSON-formatted data returned by the API endpoint includes employee ID, name, job title, department, performance score, etc.

[1141] The collected data is parsed by the server using a JSON module and then stored in a local database using an ORM such as SQLAlchemy, with transaction management to ensure data integrity.

[1142] Job rotation and career advancement proposal generation

[1143] The server evaluates employees' performance scores based on the personnel data stored in the database and generates job rotation and career advancement proposals based on those scores. Specific proposals are as follows:

[1144] Employees with a performance score of 4.5 or above are recommended for managerial positions (e.g., sales manager).

[1145] Employees with a performance score between 3.0 and 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[1146] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[1147] The generated suggestions are temporarily stored in a memory cache (e.g. Redis) or a local database.

[1148] Providing suggestions

[1149] A human resources manager, who is a user, requests job rotation and career advancement proposals by specifying a specific employee ID. When the server receives this request, it searches the database and retrieves the relevant employee information. Based on saved proposals and newly generated proposals, the server displays proposals to the user. These proposals allow the user to efficiently allocate personnel appropriately.

[1150] Specific examples

[1151] Example 1

[1152] The server sends a request to the company's API endpoint and retrieves the following employee data:

[1153] Employee ID: 12345

[1154] Name: A. Taro

[1155] Job title: Engineer

[1156] Department: Technology Department

[1157] Performance score: 4.7

[1158] The server stores this data in a local database. At a later date, when the user requests a proposal for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7.

[1159] Example 2

[1160] The server retrieves the employee data in the same way, and gets the following data:

[1161] Employee ID: 67890

[1162] Name: B. Ichiro

[1163] Position: Support Engineer

[1164] Department: Support Department

[1165] Performance score: 3.5

[1166] This data is also stored in a local database, and when a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5.

[1167] Example prompts for generative AI models

[1168] "Generate a job rotation proposal for employee with employee ID 12345."

[1169] In this way, the system can be used to efficiently support employees' career advancement within a company.

[1170] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1171] Step 1:

[1172] The server sends an HTTP GET request to the company's internal API endpoint to collect employee personnel data. The input is the endpoint URL and authentication information, and the output is JSON-formatted personnel data. Specifically, the server uses a Python script to execute this process as a scheduled job every day at 2:00 AM. It receives the response returned from the API, verifies that the response code is 200, and logs the data.

[1173] Step 2:

[1174] The server parses the collected JSON-formatted personnel data and saves it in a database. The input is JSON-formatted data, and the output is the parsed data stored in the database. Specifically, the server parses the data using Python's json module and extracts the necessary fields (ID, name, job title, department, performance score). It then connects to the database using an ORM such as SQLAlchemy and executes insert or update SQL queries. It performs transaction management, committing if successful and rolling back if unsuccessful.

[1175] Step 3:

[1176] The server evaluates employee performance scores based on the personnel data stored in the database and generates recommendations. The input is the personnel data in the database, and the output is the generated recommendations. Specifically, the server queries the employee performance scores from the database and applies the following rules to each score:

[1177] If the score is 4.5 or above, a managerial position is recommended.

[1178] If the score is between 3.0 and 4.5, a mid-level position is recommended.

[1179] If the score is less than 3.0, an entry position is recommended.

[1180] The generated suggestions are temporarily stored in a memory cache (e.g. Redis) or a local database.

[1181] Step 4:

[1182] As an HR professional, a user requests a proposal by specifying a specific employee ID. The input is the employee ID, and the output is a proposal for suitable job rotation or career advancement. Specifically, the user enters the employee ID through a web interface or mobile application and sends a request to the server. The server receives the request, retrieves the relevant employee information from the database, and displays it to the user based on saved proposals or newly generated proposals. The browser displays the suitable positions in HTML format, or a notification is sent to the mobile app.

[1183] The above is the specific processing flow of the program of this system.

[1184] (Application example 1)

[1185] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1186] Reducing the risk of employee turnover within a company or organization and improving internal engagement are key challenges. In particular, in environments such as factories where many employees rely on short-term rotations and job changes, efficient personnel placement and career advancement proposals are required. However, traditional systems lack the means to properly generate and quickly notify these proposals, making it difficult to ensure that employees are placed in the right positions.

[1187] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1188] In this invention, the server includes means for acquiring employee business data, means for storing the acquired business data in data storage, means for generating proposals for transfers and promotions for employees based on the stored business data, and means for notifying the proposals to mobile devices. This enables managers to quickly make appropriate personnel assignments by making appropriate rotations and career advancement proposals in real time based on employees' evaluation scores and performance.

[1189] "Employee Business Data" means job-related information about employees, such as each employee's ID, name, job title, department, and performance evaluation score.

[1190] "Means of acquisition" refers to the functionality for collecting employee business data from a company's internal servers via an API endpoint.

[1191] "Data storage" refers to components including databases and storage devices for storing acquired employee business data.

[1192] "Means of storage" refers to the function of registering collected employee business data in data storage and updating it as necessary.

[1193] "Means for generating transfer or promotion proposals" refers to a function that includes algorithms or logic for analyzing stored employee work data and proposing appropriate new positions or promotions to employees.

[1194] "Means for notifying proposals to mobile devices" refers to a communication function for notifying generated transfer or promotion proposals to mobile devices such as smartphones.

[1195] An "evaluation score" is a numerical indicator of an employee's achievements and performance.

[1196] "Management level positions" refer to senior positions in an organization such as a factory that manage and supervise employees.

[1197] "Mid-level positions" refer to intermediate positions in an organization such as a factory, positioned between management positions and entry-level positions.

[1198] An "entry level position" refers to the first position suitable for a new employee or entry-level employee in an organization such as a factory.

[1199] An embodiment of the present invention will be described in detail below. This system acquires employee work data (hereinafter referred to as personnel data) and makes transfer and promotion proposals to employees based on the data. The system mainly includes a data collection means, a data storage means, a proposal generation means, and a notification means.

[1200] First, the server uses an API endpoint to retrieve employee work data from the company's database, including employee ID, name, job title, department, evaluation score, etc. The server then parses the retrieved data appropriately and stores it in local data storage (e.g., SQLite).

[1201] The server then generates transfer and promotion proposals for employees based on the stored data. The proposals are generated using the employee's evaluation scores. For example, an algorithm is used to suggest managerial positions for employees with an evaluation score of 4.5 or higher, mid-level positions for employees with an evaluation score of 3.0 or higher but less than 4.5, and entry-level positions for employees with an evaluation score less than 3.0.

[1202] Once a proposal is generated, the server notifies the mobile device (e.g., smartphone) of the proposal. The notification means can inform the manager of the proposal in real time, allowing the manager to quickly make appropriate personnel allocation decisions.

[1203] For example, if the server retrieves employee data from a company's API and an employee named Yamada Taro has a performance score of 4.7, the server will suggest a managerial level position for Yamada Taro, and if an employee named Suzuki Ichiro has a performance score of 3.5, the server will suggest a mid-level position.

[1204] Specific examples of prompts for generative AI models are as follows:

[1205] Write Python code to retrieve employee data from the company's API and store it in a SQLite database. Additionally, implement logic to generate job rotation and career advancement suggestions based on the employee's performance score. For example, if the performance score is 4.5 or above, suggest a managerial position; if the performance score is 3.0 or above but less than 4.5, suggest a mid-level position; if the score is less than 3.0, suggest an entry-level position.

[1206] In this way, the present invention provides a specific embodiment of a system for reducing the risk of employees changing jobs and improving internal engagement in companies and organizations.

[1207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1208] Step 1:

[1209] The server sends a request to an internal API endpoint to retrieve employee work data. The input is the API endpoint URL, and the output is data such as the employee's ID, name, job title, department, and evaluation score. The server receives this data in JSON format.

[1210] Step 2:

[1211] The server parses the acquired employee business data appropriately and saves it in a database (e.g., SQLite). The input is the JSON data acquired in step 1, and the output is registered in the database in a format that corresponds to each field. During this process, the server reformats the personnel data and updates existing data as necessary.

[1212] Step 3:

[1213] The server analyzes the data stored in the database and generates transfer and promotion proposals based on each employee's evaluation score. The input is each employee's job data stored in the database, and the output is the proposed new position or placement. Based on the evaluation score, the algorithm suggests management-level, mid-level, or entry-level positions.

[1214] Step 4:

[1215] The server notifies the mobile device of the generated proposal. The input is the proposal content generated in step 3, and the output is sent to the administrator's smartphone in the form of a push notification, email, etc. The server selects the notification method during this process and notifies the administrator in the appropriate format.

[1216] Step 5:

[1217] Managers check the notification on their mobile devices and make staffing decisions based on the recommendations. The input is the recommendation notification sent from the server, and the output is instructions and confirmations that the manager can take action on, displayed on the mobile device. Based on this information, managers make decisions to ensure that employees are placed in the right positions.

[1218] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1219] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. This system collects employee personnel data, stores it in a database, and generates job rotation and career advancement proposals for employees. Furthermore, by combining it with an emotion engine that recognizes user emotions, it becomes possible to make more appropriate proposals. Details of the embodiments of the invention are described below.

[1220] Collection and storage of personnel data

[1221] The server sends requests to the company's API endpoint to collect employee HR data. The collected data includes each employee's ID, name, job title, department, and performance score. The server parses the collected data and stores it in a local database. This database is used to generate job rotation and career advancement recommendations.

[1222] Job rotation and career advancement proposal generation

[1223] The server evaluates the employee's performance score based on the employee data stored in the database and generates job rotation and career advancement proposals based on the score. Specific proposals are as follows:

[1224] Employees with a performance score of 4.5 or above are recommended for management positions (e.g., sales manager).

[1225] Employees with a performance score between 3.0 and 4.5 are recommended for mid-level positions (e.g., marketing coordinator).

[1226] Employees with a performance score below 3.0 are recommended for entry positions (e.g., customer service representative).

[1227] Introducing the Emotion Engine

[1228] This system includes an emotion engine that allows the server to recognize the user's emotions. This emotion engine collects and analyzes the user's emotional data and optimizes job rotation and career advancement proposals based on the results.

[1229] For example, if a user is feeling stressed, the emotion engine will analyze that emotion data and recommend positions that will help reduce stress, making it possible to provide flexible suggestions tailored to the user's emotional state.

[1230] Providing suggestions

[1231] The user (human resources officer) requests job rotation and career advancement proposals by specifying a specific employee ID. The server searches the database based on the specified employee ID and retrieves the relevant employee information. Based on the retrieved information, the server also takes into account the analysis results of the emotion engine, generates proposals, and provides them to the user.

[1232] Specific examples

[1233] Example 1: A server sends requests to an internal company API endpoint to collect and store the following employee data:

[1234] Employee ID: 12345

[1235] Name: Yamada Taro

[1236] Position: Development Engineer

[1237] Department: Development Department

[1238] Performance score: 4.7

[1239] When the user requests suggestions for employee ID 12345, the server recommends the position of "Sales Manager" based on a performance score of 4.7. Furthermore, the emotion engine analyzes Taro Yamada's emotional state as "positive," so it places special weight on this suggestion.

[1240] Example 2: The server similarly collects employee data and obtains the following data:

[1241] Employee ID: 67890

[1242] Name: Ichiro Suzuki

[1243] Job Title: Support Engineer

[1244] Department: Support Department

[1245] Performance score: 3.5

[1246] When a user requests suggestions for employee ID 67890, the server recommends the position of "Marketing Coordinator" based on a performance score of 3.5. Because the emotion engine analyzes Suzuki Ichiro's emotional state as "stressed," it prioritizes suggestions that include job content that can reduce stress.

[1247] The present invention is implemented as described above, but it should be understood that other specific examples and application ranges can be modified or adjusted as appropriate.

[1248] The processing flow will be explained below.

[1249] Step 1:

[1250] The server collects employee HR data by sending an HTTP GET request to a corporate API endpoint, setting a header containing authentication information and retrieving the requested data.

[1251] Step 2:

[1252] The server receives the response from the API and parses it in JSON format, which includes data such as employee ID, name, job title, department, and performance score.

[1253] Step 3:

[1254] The server stores the parsed data in a local database, creating a new one if it does not exist, or updating an existing database if it does.

[1255] Step 4:

[1256] A user (HR professional) requests job rotation and career advancement suggestions by specifying a specific employee ID. The employee ID is included in the request.

[1257] Step 5:

[1258] The server receives a request from the user, searches the database based on the specified employee ID, and retrieves the information of the corresponding employee.

[1259] Step 6:

[1260] The server checks the performance score from the acquired employee information and generates appropriate suggestions based on that score. For example, if the performance score is 4.5 or higher, it recommends a managerial position, if the performance score is 3.0 or higher but less than 4.5, it recommends a mid-level position, and if the performance score is less than 3.0, it recommends an entry-level position.

[1261] Step 7:

[1262] The server uses an emotion engine to analyze the user's emotion data, which is obtained from the user's input and actions.

[1263] Step 8:

[1264] The server optimizes the suggestions based on the analysis results of the emotion engine. For example, if the user is feeling stressed, it will prioritize jobs that will help reduce stress.

[1265] Step 9:

[1266] The server provides the generated optimized proposals to the user, who can use them to plan employee placement and career advancement.

[1267] Step 10:

[1268] Based on the provided suggestions, users can take necessary personnel actions, such as transferring or promoting employees or developing training plans.

[1269] Example 2

[1270] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1271] To effectively manage employee turnover risk and improve internal engagement, it is necessary to provide a system that automatically generates and provides appropriate job rotation and career advancement proposals. It is also necessary to provide a means for making flexible proposals that take into account employees' emotional state.

[1272] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1273] In this invention, the server includes means for sending requests to an API endpoint within the company and collecting personnel data, means for parsing the collected personnel data and storing it in a database, means for evaluating employee performance scores based on the stored personnel data and generating job rotation and career advancement proposals, means for optimizing the proposals using an emotion engine that collects and analyzes employee emotion data, and means for providing the optimized proposals based on user requests. This makes it possible to automatically and efficiently generate and provide appropriate job rotation and career advancement proposals that reduce employee job change risk and improve internal engagement.

[1274] - "Intra-company API endpoint" refers to the access point of an application program interface (API) used within a company, and is an interface used to access various company data.

[1275] "Human Resources Data" refers to various information related to employees, specifically data including employee IDs, names, job titles, departments, and performance scores.

[1276] A "performance score" is an indicator used to evaluate an employee's work efficiency and achievements, and is often expressed numerically.

[1277] "Job rotation" is the process of encouraging employees to periodically take on different tasks or positions.

[1278] "Career advancement" is the process of encouraging employees to advance or expand their position or scope of work.

[1279] An "emotion engine" is a system or software for analyzing employee emotion data and includes a machine learning model for assessing emotional states.

[1280] A "database" is a part of an information system that stores collected data in a structured manner so that it can be later efficiently searched, retrieved, and updated.

[1281] "User" refers to end users, particularly human resources personnel, who use the system to request job rotation and career advancement proposals for employees.

[1282] "Suggestion optimization" is the process of adjusting generated job rotation and career advancement suggestions by taking into account additional data such as the employee's emotional state to provide more appropriate suggestions.

[1283] The present invention relates to a system for reducing the risk of employee turnover within a company or organization and improving internal engagement. The system is implemented using the following hardware and software.

[1284] Hardware and software used

[1285] Server: The core of the system that collects, stores, analyzes, and generates recommendations. The server includes the following software components:

[1286] API Endpoint Communication Module: Collects HR data from API endpoints within the enterprise via RESTful services.

[1287] Database Management System: Stores and manages data using a relational database system such as MySQL.

[1288] Analytical engine: Evaluates employee performance scores and generates job rotation and career advancement suggestions.

[1289] Emotion Engine: Analyzes employee emotion data using machine learning models (e.g., TensorFlow).

[1290] Suggestion optimization module: Optimizes suggestions based on the results of the sentiment engine.

[1291] Terminal: A device that allows a user (HR professional) to access the system and request and receive proposals. The terminal has the ability to access the system through a browser or a dedicated application.

[1292] System Operation

[1293] The server first sends an HTTP request to the company's API endpoint to collect employee data such as employee ID, name, job title, department, and performance score. This data is returned to the server in JSON format, parsed, and stored in the database. The stored data is then used for subsequent analysis and proposal generation.

[1294] The server then queries employee data from the database and evaluates each employee's performance score. Based on this evaluation, job rotation and career advancement proposals are generated. An emotion engine is used to analyze the employee's emotional data and reflect the results in the proposals. For example, positions that reduce stress may be prioritized for employees with high levels of stress.

[1295] A user requests suggestions by specifying a specific employee ID. In response to the request, the server generates suggestions based on the analyzed employee data and sentiment data and provides them to the user via a web interface or email.

[1296] Specific examples

[1297] Example 1: A server sends a request to an internal API endpoint to collect and store the following data:

[1298] Employee ID: 12345

[1299] Name: Employee A

[1300] Position: Development Engineer

[1301] Department: Development Department

[1302] Performance score: 4.7

[1303] When a user requests suggestions for employee ID 12345, the server recommends the position "Supervisor (Sales Manager)" based on a performance score of 4.7. It also places special weight on this suggestion because the emotion engine has analyzed Employee A's emotional state as "positive."

[1304] Example 2: The server similarly collects employee data and obtains the following data:

[1305] Employee ID: 67890

[1306] Name: Employee B

[1307] Job Title: Support Engineer

[1308] Department: Support Department

[1309] Performance score: 3.5

[1310] When a user requests a proposal for employee ID 67890, the server recommends the position of "Mid-level position (marketing coordinator)" based on a performance score of 3.5. Furthermore, because the emotion engine has analyzed employee B's emotional state as "stressed," the server emphasizes job content that can reduce stress.

[1311] Below is an example of a prompt sentence to input to the generative AI model.

[1312] Prompt statement example 1:

[1313] "Employee ID 12345 has a performance score of 4.7 and a positive emotional state. Please generate optimal job rotation suggestions for this employee."

[1314] Prompt statement example 2:

[1315] "Employee ID 67890 has a performance score of 3.5 and an emotional state of stress. Please generate job rotation suggestions that will reduce stress for this employee."

[1316] As described above, the present invention is a system that automatically and efficiently generates and provides proposals that utilize employee data and emotional data in order to reduce the risk of employees changing jobs and improve internal engagement.

[1317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1318] Step 1:

[1319] The server sends an HTTP request to an internal API endpoint. The API endpoint provides a RESTful service, and the server authenticates using a dedicated access token. The input is the API endpoint URL and access token, and the output is JSON data of employees. For example, if the input URL is "https: / / company-api.example.com / employees", the corresponding employee data is returned in JSON format. Specifically, the server sends a GET request and processes the JSON data received as a response for use in the next step.

[1320] Step 2:

[1321] The server parses the received JSON-formatted employee data and saves the information in a local database. The input is the JSON data obtained in the previous step, and the output is the parsed employee data saved in the database. Specifically, the server parses the JSON data and executes an INSERT statement to save the data in the "Employee" table. At this time, it performs transaction management to maintain data consistency and integrity.

[1322] Step 3:

[1323] The server queries employee data stored in a database to evaluate each employee's performance score. The input is the employee's ID, and the output is the employee's performance score and related information. Specifically, it executes an SQL query such as "SELECT FROM employee WHERE id = ?" and applies a suggestion generation algorithm based on the retrieved data.

[1324] Step 4:

[1325] The server uses the emotion engine to collect and analyze the user's emotional data. The input is the text data or voice data entered by the user, and the output is the analyzed emotional state (positive, negative, stress, etc.). Specifically, the server sends the text data or voice data to the emotion engine's API, and the obtained analysis results are used for suggestion optimization.

[1326] Step 5:

[1327] The server optimizes job rotation and career advancement proposals by taking into account the analysis results of the emotion engine. The input is the employee data and emotion data obtained in the previous steps, and the output is the optimized proposal. Specifically, the server re-executes the proposal algorithm and selects the optimal proposal.

[1328] Step 6:

[1329] A user requests a proposal by specifying a specific employee ID. The input is the employee ID, and the output is the generated proposal. Specifically, the user enters the employee ID through a web interface or a dedicated application and obtains the generated proposal from the server.

[1330] Step 7:

[1331] The server provides optimized suggestions based on the user's request. The input is the employee ID specified by the user, and the output is the content of the suggestions. Specifically, the server retrieves the employee's data from the database, generates suggestions based on the analysis results, and provides them to the user. The suggestions are delivered to the user via a web interface or email.

[1332] (Application example 2)

[1333] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1334] Traditional employee management systems only suggest job rotations and career advancement based on employee performance data, but do not take into account the emotional state of employees. This can lead to stress and a decline in motivation, resulting in high employee turnover. Furthermore, there is a lack of dedicated applications to effectively utilize these suggestions in physical stores.

[1335] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1336] In this invention, the server includes: means for collecting employee personnel data; means for storing the collected personnel data in a database; means for generating job rotation and career advancement proposals for employees based on the stored personnel data; means including a smartphone application for providing the proposals; an emotion engine for collecting and analyzing employee emotion data; and means for optimizing the proposals based on the results of the emotion engine. This enables optimal job rotation and career advancement proposals that take into account both employee performance and emotional state, thereby improving engagement and productivity in physical stores.

[1337] "Employee personnel data" is data that includes information such as an employee's ID, name, job title, department, and performance score.

[1338] A "database" is a collection of information that stores collected employee personnel data and is structured in a way that allows it to be searched and analyzed.

[1339] "Job rotation" means periodically changing employees' jobs and positions to allow them to gain diverse experience and skills.

[1340] "Career advancement" refers to promoting an employee in a higher position or role based on their current performance.

[1341] "Proposal" refers to providing employees with optimal job rotation and career advancement strategies.

[1342] A "smartphone application" is a software program that runs on a smartphone and is available to employees and managers.

[1343] "Emotional data" is data that expresses an employee's emotional state in numerical or categorical terms, including stress and motivation levels.

[1344] The "emotion engine" is a software module for collecting and analyzing employees' emotional states.

[1345] "Optimization" is the process of generating the most efficient and effective proposals based on employee performance and sentiment data.

[1346] MODE FOR CARRYING OUT THE INVENTION

[1347] Program Generation

[1348] The program of the system for implementing the present invention has the following main functions.

[1349] 1. Data Collection: The server sends a request to the company's API endpoint to collect employee HR data, including employee ID, name, job title, department, and performance score.

[1350] 2. Database Update: The collected data is parsed and stored in a local database, which is used to generate suggestions.

[1351] 3. Proposal Generation: Based on the stored employee data, job rotation and career advancement proposals are generated based on the employee's performance score. The proposals are provided via a smartphone application.

[1352] 4. Emotion data collection and analysis: The server collects employee emotion data and analyzes it using an emotion engine. Based on the analysis results, the system optimizes the proposals.

[1353] 5. Proposal Provision: A user can request a proposal by specifying a specific employee ID, and the server generates the proposal and provides it through a smartphone application.

[1354] A natural language description of the program's processing

[1355] Data collection step: The server retrieves employee data from the company's API endpoint using an HTTP request, typically using the requests library. The response from the API is in JSON format, which is parsed to extract the required employee information.

[1356] Database update step: The extracted data is stored in a local database. This database is a structured collection of information that can be easily searched and analyzed. Commonly used databases include MySQL, PostgreSQL, and SQLite.

[1357] Proposal generation step: Based on the stored employee data, the employee's performance score and sentiment data are evaluated to generate optimal job rotation and career advancement proposals. The proposals are sent to a smartphone application. The application is often created using, for example, Flutter or React Native.

[1358] Emotion data collection and analysis step: Emotion data is collected from employee speech and behavior and analyzed by an emotion engine, for example, using Microsoft Azure's emotion recognition API or Google Cloud AI.

[1359] Proposal provision step: The user requests a proposal using a specific employee ID through a smartphone application. The server searches the database and obtains the corresponding employee's information. Taking into account the results of the emotion engine, the server generates the optimal proposal and displays it in the application.

[1360] Adding specific examples

[1361] Example 1: High-Performing Employees

[1362] For employee with employee ID 12345, the performance score is 4.7 and the emotional state is positive. In this case, the server generates a suggestion to "recommend a managerial position (e.g., sales manager)."

[1363] Example 2: Stressed mid-level employee

[1364] For an employee with employee ID 67890, the performance score is 3.5 and the emotional state is stress. In this case, the server generates a proposal that "generates proposals for mid-level positions (e.g., marketing coordinator) and job descriptions that can reduce stress."

[1365] Prompt Sentence Examples

[1366] "Employee ID 12345 has a performance score of 4.7. His emotional state is positive. Please generate optimal job rotation and career advancement suggestions for this employee."

[1367] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1368] Step 1:

[1369] The server sends an HTTP request to an API endpoint to retrieve employee personnel data.

[1370] Input: API endpoint URL and authentication information

[1371] Specific operation: Send a GET request using the requests library. The API response contains data in JSON format.

[1372] Output: Employee data in JSON format (employee ID, name, job title, department, performance score, etc.)

[1373] Step 2:

[1374] The server parses the received JSON-formatted employee data and stores it in a local database.

[1375] Input: Employee data in JSON format

[1376] Specific operation: Parse the data using the json library. Insert the parsed data into a database (MySQL, PostgreSQL, etc.).

[1377] Output: Structured employee data stored in the database

[1378] Step 3:

[1379] Users use a smartphone application to request proposals using a specific employee ID.

[1380] Input: Specific Employee ID

[1381] Specific operation: The application sends a request to the backend server. The backend, written in Python, receives the request.

[1382] Output: Request data is sent to the backend server

[1383] Step 4:

[1384] The server searches the database to retrieve the relevant employee information.

[1385] Input: Specific Employee ID

[1386] Specific behavior: Execute a database query to retrieve the relevant employee data (execute an SQL statement)

[1387] Output: Employee information (employee ID, name, job title, department, performance score)

[1388] Step 5:

[1389] The server generates job rotation and career advancement suggestions based on employee performance scores.

[1390] Input: Employee information and performance score

[1391] Specific operations: Performance scores are evaluated and roles are assigned based on the scores. Sentiment data is also analyzed.

[1392] Output: Initial job rotation and career progression suggestions (e.g., management, mid-level, entry-level positions)

[1393] Step 6:

[1394] The server uses an emotion engine to collect and analyze employee emotion data.

[1395] Input: Employee emotional data (utterances and behavioral data)

[1396] Specific operation: Analyze emotion data using emotion recognition APIs (Microsoft Azure, Google Cloud AI, etc.).

[1397] Output: Sentiment analysis result (e.g. positive, stress, etc.)

[1398] Step 7:

[1399] The server optimizes the suggestions based on the results of the emotion engine.

[1400] Input: Initial proposal and sentiment analysis results

[1401] Specific behavior: If emotional state (e.g. stress), adjust job content. If emotional state is positive, maintain normal suggestions.

[1402] Output: Optimized proposal (final proposal for job rotation or career advancement)

[1403] Step 8:

[1404] The server sends the generated optimized proposals to the smartphone application.

[1405] Input: Optimized Proposal

[1406] Specific behavior: Send the generated proposal in JSON format to the smartphone application by reusing the API endpoint and sending a POST request.

[1407] Output: The smartphone application is notified of the suggestion and displayed to the user.

[1408] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1409] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1410] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1411] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1412] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1413] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1414] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1415] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1416] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1417] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1418] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1419] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1420] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1421] 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.

[1422] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1423] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1424] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1425] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1426] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1427] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1428] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1429] The following is further disclosed regarding the above embodiment.

[1430] (Claim 1)

[1431] a means of collecting employee personnel data;

[1432] a means for storing the collected personnel data in a database;

[1433] A means for generating job rotation and career advancement proposals for employees based on the stored personnel data;

[1434] A system including:

[1435] (Claim 2)

[1436] 10. The system of claim 1, wherein the means for generating suggestions includes means for recommending a plurality of positions based on an employee's performance score.

[1437] (Claim 3)

[1438] 3. The system of claim 2, further comprising means for recommending managerial, mid-level, and entry-level positions depending on the range of the employee's performance score.

[1439] "Example 1"

[1440] (Claim 1)

[1441] a means of collecting employee personnel data;

[1442] a means for storing the collected personnel data in a database;

[1443] A means for generating job rotation and career advancement proposals for employees based on the stored personnel data;

[1444] A means to provide users with suggested job rotations and career advancement opportunities;

[1445] A system including:

[1446] (Claim 2)

[1447] 10. The system of claim 1, wherein the means for generating suggestions includes means for recommending a plurality of positions based on an employee's performance score.

[1448] (Claim 3)

[1449] 2. The system of claim 1, wherein the means for providing suggestions includes an interface through which a user requests suggestions by specifying a particular employee ID.

[1450] (Claim 4)

[1451] 3. The system of claim 2, further comprising means for recommending managerial, mid-level, and entry-level positions depending on the range of the employee's performance score.

[1452] (Claim 5)

[1453] 10. The system of claim 1, further comprising means for storing said job rotation and career advancement suggestions in a memory cache or database.

[1454] "Application Example 1"

[1455] (Claim 1)

[1456] a means of obtaining employee business data;

[1457] A means for storing the acquired business data in a data storage;

[1458] means for generating reassignment and promotion proposals for employees based on the stored business data;

[1459] A means of communicating the offer to a mobile device;

[1460] A system including:

[1461] (Claim 2)

[1462] 10. The system of claim 1, further comprising: means for recommending a plurality of positions based on the employee's evaluation score.

[1463] (Claim 3)

[1464] 3. The system of claim 2, further comprising means for recommending management level positions, mid-level positions, and entry level positions according to ranges of the evaluation scores.

[1465] "Example 2: Combining Emotion Engines"

[1466] (Claim 1)

[1467] A means of collecting HR data by sending requests to an internal API endpoint;

[1468] a means for parsing and storing the collected personnel data in a database;

[1469] A means to evaluate employee performance scores and generate job rotation and career advancement suggestions based on stored HR data;

[1470] A means to optimize proposals using an emotion engine that collects and analyzes employee emotion data;

[1471] means for providing optimized suggestions based on user requests;

[1472] A system including:

[1473] (Claim 2)

[1474] 10. The system of claim 1, wherein the means for generating suggestions includes means for recommending a plurality of positions based on an employee's performance score.

[1475] (Claim 3)

[1476] 3. The system of claim 2, further comprising means for recommending managerial, mid-level, and entry-level positions depending on the range of the employee's performance score.

[1477] (Claim 4)

[1478] 10. The system of claim 1, wherein the emotion engine includes means for analyzing a plurality of emotional states, including stress states, negative states, and positive states.

[1479] (Claim 5)

[1480] 10. The system of claim 1, further comprising means for a user to request a proposal by specifying a particular employee ID.

[1481] "Application example 2 when combining emotion engines"

[1482] (Claim 1)

[1483] a means of collecting employee personnel data;

[1484] a means for storing the collected personnel data in a database;

[1485] A means for generating job rotation and career advancement proposals for employees based on the stored personnel data;

[1486] means for providing the suggestions, the means including a smartphone application;

[1487] An emotion engine that collects and analyzes employee emotion data,

[1488] A means of optimizing recommendations based on the results of the emotion engine; and

[1489] A system including:

[1490] (Claim 2)

[1491] 10. The system of claim 1, wherein the means for generating suggestions includes means for recommending a plurality of positions based on an employee's performance score.

[1492] (Claim 3)

[1493] 2. The system of claim 1, further comprising means for recommending stress-reducing job content with particular emphasis on the employee's emotional state. [Explanation of symbols]

[1494] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of collecting employee personnel data; a means for storing the collected personnel data in a database; A means for generating job rotation and career advancement proposals for employees based on the stored personnel data; A system including:

2. The system of claim 1 , wherein the means for generating suggestions includes means for recommending a plurality of positions based on an employee's performance score.

3. The system of claim 2 further comprising means for recommending managerial, mid-level, and entry-level positions according to the range of the employee's performance score.

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