system

Abstract

We provide the system. [Solution] A means of collecting employee data, A means of calculating an evaluation score based on that data, A means of automatically determining employee salary increases and promotions based on established criteria, A means of generating the decision results as a list, A means for the administrator to review and modify the generated list, Means of notifying employees of the final decision, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When determining salary increases and promotions for employees, there is a problem that fairness is impaired by the inclusion of personal feelings. Also, since the processes of salary increases and promotions are opaque, many employees may not be convinced. To solve such problems, it is necessary to determine salary increases and promotions based on objective criteria and make the processes transparent.

Means for Solving the Problems

[0005] This invention includes means for collecting employee data and means for calculating evaluation scores based on that data. Furthermore, it includes means for automatically determining employee salary increases and promotions based on set criteria, means for generating the decision results as a list, means for administrators to review and modify the generated list, and means for notifying employees of the final decision. This system eliminates personal feelings and allows for objective and fair decisions regarding salary increases and promotions. In addition, because the process is transparent, it is possible to obtain results that are acceptable to all employees.

[0006] "Employee data" refers to data containing individual employee information, including name, job title, performance score, years of service, project completion rate, working hours, and skill set.

[0007] An "performance score" is an indicator that numerically evaluates an employee's performance and contributions, and refers to the points used as criteria for determining salary increases and promotions.

[0008] "Standards" refer to the rules and conditions set to determine salary increases and promotions, including minimum evaluation points and required years of service.

[0009] "Salary increase" refers to an increase in an employee's salary, which is determined based on performance scores and other criteria.

[0010] "Promotion" refers to an employee's position being changed to a higher one, and is determined based on performance scores and other criteria.

[0011] "List generation" refers to filtering employees who meet certain criteria and compiling them into a list of candidates for salary increases and promotions.

[0012] The "review and correction mechanism" is a function that allows administrators to review the generated list and make adjustments or comments as needed.

[0013] "Notification methods" refer to communication and alert functions used to inform employees of decisions regarding salary increases and promotions.

[0014] An "interface" refers to the screen or means of operation that a user uses to interact with a system, allowing for data entry and list confirmation. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. The following describes the specific program processing and its implementation.

[0037] Data collection

[0038] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0039] Calculation of evaluation score

[0040] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0041] Setting Criteria

[0042] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0043] Automatic decision

[0044] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0045] List generation

[0046] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0047] Confirmation / Correction

[0048] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0049] notification

[0050] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0051] Through the above process, the salary increase and promotion process is conducted transparently and fairly. For example, employee A is added to the promotion list, and after final confirmation by the user (administrator), a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0052] The following describes the processing flow.

[0053] Step 1:

[0054] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. This involves using SQL queries to extract the necessary data.

[0055] Step 2:

[0056] The server temporarily stores the acquired data in memory. This includes information such as employee ID, name, job title, department, and performance score. The data is saved in an appropriate format.

[0057] Step 3:

[0058] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation score of 80 points or higher" or "length of service of 3 years or more" may be entered.

[0059] Step 4:

[0060] The server verifies the entered criteria and saves them to the database. The criteria data is managed with a unique ID. This criterion value is used in subsequent processing.

[0061] Step 5:

[0062] The server begins the scoring process based on the collected employee data. First, it compares each employee's evaluation points and years of service with a standard to determine whether the standard is met.

[0063] Step 6:

[0064] The server calculates each employee's score and adds fields related to the "score" to the database. A flag is also set here to determine whether or not the criteria are met.

[0065] Step 7:

[0066] The server filters out employees whose scores meet the "pass" criteria and adds the filtered employee data to the salary increase / promotion list. This generates a basic salary increase / promotion list.

[0067] Step 8:

[0068] The server displays the generated list to the user (administrator) on the administration screen. The list includes details such as each employee's name, position, performance score, and years of service.

[0069] Step 9:

[0070] The user (administrator) can view the salary increase / promotion list generated through the administration screen. On the administration screen, they can view the employee information displayed in the list and edit or add comments as needed.

[0071] Step 10:

[0072] After the user has finished reviewing the list, they press a button to notify the server of their final decision. Upon receiving this input, the server confirms the final list.

[0073] Step 11:

[0074] The server notifies each employee based on the finalized list of salary increases and promotions. This notification process includes generating an internal notification email and sending it to each employee's device.

[0075] Step 12:

[0076] The device receives an email and displays a notification to the employee. The notification includes details about salary increases or promotions, and information about the next steps (e.g., orientation information regarding the new position).

[0077] By following these steps, employee salary increases and promotions can be conducted fairly and transparently, resulting in outcomes that satisfy everyone.

[0078] (Example 1)

[0079] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0080] Traditional methods for determining salary increases and promotions often relied on subjective judgment, making it difficult to conduct fair and objective evaluations. Furthermore, the manual process of setting criteria for salary increases and promotions, as well as verifying and correcting results, was time-consuming and labor-intensive, and prone to errors and misunderstandings.

[0081] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0082] This invention includes a server that includes means for accessing a database of employees and obtaining performance evaluation data, project completion rates, working hours, skill sets, etc., for all employees; means for calculating an evaluation score for each employee based on the data obtained by the server; means for an administrator to input criteria for salary increases and promotions and for the server to store those criteria; means for the server to automatically determine salary increases and promotions for each employee based on the set criteria; means for the server to generate a list of candidates for salary increases and promotions and provide an interface for administrators to review and modify it; and means for the server to send a notification to the relevant employees after the final decision. This makes it possible to conduct the salary increase and promotion process transparently and fairly, and to prevent errors and inefficiencies caused by manual work.

[0083] A "server" refers to a computer or a system with those functions for performing calculations, managing data, and connecting to a network.

[0084] "Working adults" refers to individuals who work for a company or organization, and includes those who are subject to employee evaluation.

[0085] A "database" refers to a system or software used to systematically store and manage data.

[0086] "Performance evaluation data" refers to data that records the work performance and results of individual employees as numerical values ​​and evaluations.

[0087] "Project completion rate" refers to an indicator that shows the percentage of project goals that were actually achieved.

[0088] "Working hours" refers to data that measures the actual time an employee spends working at their workplace.

[0089] A "skill set" refers to the collection of skills, knowledge, and abilities that a working professional possesses.

[0090] "Evaluation score" refers to a comprehensive numerical evaluation calculated based on collected performance evaluation data and other evaluation factors.

[0091] "Criteria" refers to the conditions or evaluation items set up for use in determining salary increases and promotions.

[0092] An "interface" refers to the user interface or tools that allow a user to interact with a system.

[0093] "Notification" refers to messages or alerts used by a system to transmit information to professionals.

[0094] A "list" refers to a organized display of information about candidates for salary increases and promotions.

[0095] This invention relates to a system for fairly and objectively determining salary increases and promotions for working adults. This system collects data on working adults, calculates evaluation scores, and determines salary increases and promotions based on established criteria. The following describes the specific program processing and its implementation.

[0096] Data collection

[0097] The server accesses a database of working professionals to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. Specific SQL queries are used to extract the data. For example, a query like "SELECT FROM employee_performance WHERE employee_id = 'A123'" might be used.

[0098] Calculation of evaluation score

[0099] Based on the data acquired by the server, an evaluation score is calculated for each employee. During this process, programming languages ​​such as Python and R are used to analyze the data and weight different evaluation criteria. For example, a weighting scheme such as "evaluation points × 0.5 + years of service × 0.3 + project completion rate × 0.2" is used to calculate the overall score.

[0100] Setting Criteria

[0101] The user (administrator) inputs the criteria for salary increases and promotions through an interface, and the server saves these settings. The administrator enters "evaluation points of 80 or higher AND years of service of 3 or more" into a form on a web browser, and the server saves these criteria to the database. A specific SQL query would be something like "INSERT INTO promotion_criteria (point, years) VALUES (80, 3)".

[0102] Automatic decision

[0103] The server automatically determines each employee's salary increase and promotion based on the configured criteria. To filter employees whose evaluation scores meet the criteria, a query such as "SELECT FROM employee_scores WHERE score >= 80 AND years >= 3" is executed.

[0104] List generation

[0105] The server generates a list of candidates for salary increases and promotions. The generated list is displayed on the web interface and can be viewed by administrators in the administration panel. The list includes information such as the candidate's name, performance score, and job title.

[0106] Confirmation / Correction

[0107] The user (administrator) reviews the list generated in the administration screen and adds corrections and comments as needed. The administration screen is built using frameworks such as Django or Flask. For example, the administrator can view the list in a browser and perform operations such as "correcting employee B's evaluation score and adding the comment 'Exceptional Promotion'."

[0108] notification

[0109] After the server makes the final decision regarding salary increases and promotions, it sends a notification email to the employee concerned. The notification is sent using Python's smtplib or Django's email functionality. The notification contains details about the employee's salary increase or promotion. For example, it might include a message like, "Your promotion has been approved. Your new position is XX."

[0110] Examples of prompt statements

[0111] "Please add employees who meet the following conditions to the salary increase list: performance evaluation score of 80 points or higher and length of service of 3 years or more."

[0112] This system ensures that the salary increase and promotion process is transparent and fair, preventing errors and inefficiencies caused by manual processes. For example, employee A is added to the promotion list, and after final confirmation by the user (administrator), a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0113] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0114] Step 1: Data Collection

[0115] The server accesses a database of working employees to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. Specifically, the server executes "SELECT FROM employee_performance" and temporarily stores the retrieved data in memory. The input is a database query, and the output is a list of performance evaluation data for all employees.

[0116] Step 2: Calculation of evaluation score

[0117] Based on the data acquired by the server, an evaluation score is calculated for each employee. Here, a Python script is used to calculate an overall score by summing and weighting evaluation points, years of service, and project completion rate. Specifically, a formula like "evaluation points × 0.5 + years of service × 0.3 + project completion rate × 0.2" is applied. The input is the acquired data for each employee, and the output is each employee's evaluation score.

[0118] Step 3: Setting Criteria

[0119] The user (administrator) inputs the criteria for salary increases and promotions through an interface. For example, the administrator enters "evaluation points of 80 or higher AND years of service of 3 or more" into a web form, and the server saves this setting to the database. Specifically, the query "INSERT INTO promotion_criteria (point, years) VALUES (80, 3)" is executed. The input is the criteria data entered by the administrator, and the output is the saved criteria data.

[0120] Step 4: Automatic decision

[0121] The server automatically determines salary increases and promotions for each employee based on the configured criteria. Here, the system filters employees who meet the criteria based on their evaluation scores and executes the query "SELECT FROM employee_scores WHERE score >= 80 AND years >= 3". The inputs are the criteria data and the evaluation score data, and the output is a list of candidates for salary increases and promotions.

[0122] Step 5: Generating the list

[0123] The server generates a list of candidates for salary increases and promotions. The generated list is displayed in a format that administrators can view on a web interface. Specifically, it dynamically generates HTML pages to display each item in the list (name, performance score, job title, etc.). The input is data on the candidates for salary increases and promotions, and the output is a displayable list.

[0124] Step 6: Review and Correction

[0125] The user (administrator) reviews the list generated in the administration screen and adds corrections and comments as needed. The administrator clicks on a list item in the browser to access a form for entering corrections and comments. Specifically, an "UPDATE" query is executed to reflect the corrected data in the database. The input is the correction data made by the administrator, and the output is the updated list.

[0126] Step 7: Notification

[0127] After the server makes the final decision regarding salary increases and promotions, it sends notification emails to the relevant employees. The notifications are sent using Python's smtplib and Django's email functionality. Specifically, it retrieves each employee's email address and sends an email stating, "Your promotion has been approved. Your new position is XX." The input is the list data after the final decision, and the output is the sent notification email.

[0128] (Application Example 1)

[0129] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0130] Currently, decisions regarding the maintenance and upgrade of factory machinery often rely on the subjective judgment of managers, which can lead to a lack of fairness and efficiency. Furthermore, the collection of machine operation data and the calculation of evaluation scores are performed manually, which is time-consuming, labor-intensive, and prone to errors. Therefore, the present invention aims to provide a system for making fair and efficient decisions regarding the maintenance and upgrade of factory machinery.

[0131] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0132] In this invention, the server includes means for collecting data on factory machinery, means for calculating evaluation scores based on that data, means for automatically deciding on maintenance or upgrades of factory machinery based on set criteria, means for generating the decision results as a list, means for an administrator to review and modify the generated list, and means for notifying the final decision. This makes it possible to fairly and efficiently decide on maintenance and upgrades of factory machinery and manage them through an automated process.

[0133] "Factory machinery" refers to automated equipment and systems used in industrial production.

[0134] "Means of collecting data" refers to devices or systems that acquire and record operational information of machines.

[0135] An "evaluation score" is an index that quantifies the performance and condition of a machine based on collected data.

[0136] "Criteria" refers to the set values ​​that indicate the evaluation points and conditions used when deciding on maintenance or upgrades.

[0137] An "automatic decision-making mechanism" is a system that automatically determines the need for machine maintenance or upgrades based on set criteria.

[0138] "Method for generating as a list" refers to a function that summarizes the decision results in a list format so that administrators can review them.

[0139] A "manager" is a person responsible for the operation of a factory and the maintenance of its machinery.

[0140] The "means of review and correction" refer to a function that allows administrators to review the generated list and make changes as needed.

[0141] "Means of notifying final decisions" refers to a system that informs relevant personnel of confirmed maintenance or upgrade information.

[0142] To implement this invention, a system including the following elements is required: The process from data collection to notification is automated to efficiently and fairly manage the maintenance and upgrades of factory machinery.

[0143] Basic configuration

[0144] This system consists of a sensor system for collecting data from factory machinery, a server, an administrator terminal, and a notification system.

[0145] Data collection

[0146] The server collects operational data from factory machinery using a sensor system placed throughout the factory. This sensor system records data such as operating hours, number of failures, work efficiency, and consumable parts usage rate in real time. The collected data is sent to the server and stored in a database. SQLite is used as the specific database software.

[0147] Calculation of evaluation score

[0148] The server calculates a machine evaluation score based on the collected data. The evaluation score is calculated based on pre-set weights for each element. For example, uptime might be weighted 20%, failure rate -50%, work efficiency 40%, and consumable parts usage rate 30%.

[0149] Setting criteria and filtering

[0150] Administrators can enter criteria and save them on the server. These criteria can include uptime and the number of failures. For example, a criterion might be set as "uptime exceeds 1000 hours and the number of failures is less than 3." Based on these criteria, the server automatically filters machines that require maintenance or upgrades and generates a list of candidates.

[0151] List generation and verification

[0152] The server generates a list of the decision results and displays it on the administrator's terminal for review and modification. The list includes the name of each machine, its evaluation score, and the required maintenance items. Administrators can review this list through the interface and add modifications or comments as needed.

[0153] notification

[0154] After final confirmation by the administrator, the server notifies relevant personnel of any maintenance or upgrade decisions via a notification system. These notifications are sent to administrators and maintenance personnel's terminals via email or alerts, enabling a swift response.

[0155] Specific example

[0156] For example, suppose the data for robot A in a factory is "1500 operating hours, 2 failures, 95% work efficiency, and 80% consumable parts usage." The server calculates an evaluation score based on this data, and the overall evaluation is "90 points." Subsequently, this robot is added to the list of maintenance candidates based on the set criteria. The administrator reviews and modifies the list, and if it is determined that maintenance is necessary, a notification is sent to the maintenance personnel.

[0157] Example of a prompt

[0158] "Develop an automated evaluation and maintenance decision system for factory robots. The collected data will include operating hours, failure count, work efficiency, and consumable parts usage rate. The system will calculate an evaluation score and notify maintenance based on predefined criteria. Use SQLite for the database and SMTPLib for notifications."

[0159] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0160] Step 1:

[0161] The server collects data from sensor systems placed throughout the factory. It receives data such as operating time, failure count, work efficiency, and consumable parts usage rate for each machine as input. This data is collected in real time and stored in a database. The stored data is then used for further processing as output. Specifically, it receives data transmitted from sensors and saves it to a database (e.g., SQLite).

[0162] Step 2:

[0163] The server calculates evaluation scores based on data stored in the database. It uses the operating time, number of failures, work efficiency, and consumable parts usage rate for each machine, collected as input. It applies the set weights to these data items to calculate an integrated evaluation score. The output generates an evaluation score for each machine. Specifically, it calculates the evaluation score using a weighted formula and temporarily stores the result in memory.

[0164] Step 3:

[0165] The user (administrator) sets the criteria. As input, the user enters various criterion values, such as uptime and failure count, through the interface. These set criteria are stored in the database. As output, the set criteria are used in subsequent filtering processes. Specifically, the criteria values ​​entered by the administrator are stored in the database.

[0166] Step 4:

[0167] The server filters the collected data based on configured criteria and automatically determines which machines require maintenance or upgrades. It uses evaluation scores and configured criteria as inputs. This selects machines that meet the criteria as maintenance candidates. A candidate list is generated as output. Specifically, it compares the configured criteria with the evaluation scores and extracts machines that meet the criteria.

[0168] Step 5:

[0169] The server generates a list of the decision results and displays it on the administrator's terminal. It receives filtered machine information as input. The output is displayed to the administrator in list format, allowing them to review and modify the information. Specifically, it generates a list and displays it to the administrator via a web interface or dedicated software.

[0170] Step 6:

[0171] The user (administrator) reviews and modifies the list. The input is the list displayed from the server. Modifications and comments are entered as needed. The output is the final decision regarding maintenance or upgrades. Specific actions include the administrator reviewing the list and making necessary changes.

[0172] Step 7:

[0173] The server notifies the final decision. It receives the list confirmed by the administrator as input. As output, notifications are sent to maintenance personnel and relevant staff. Specifically, this involves sending an email using the SMTP protocol and displaying it as an alert on the staff member's terminal.

[0174] This process makes it possible to automate the maintenance and upgrade of factory machinery fairly and efficiently.

[0175] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0176] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. Furthermore, by incorporating an emotion engine that recognizes user emotions, more nuanced management becomes possible. The following describes the specific program processing and its implementation.

[0177] Data collection

[0178] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0179] Calculation of evaluation score

[0180] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0181] Setting Criteria

[0182] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0183] Automatic decision

[0184] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0185] List generation

[0186] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0187] Using an Emotion Engine

[0188] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0189] List adjustment

[0190] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0191] Final review and corrections

[0192] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0193] notification

[0194] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0195] Through the above process, employee salary increases and promotions are carried out fairly and transparently. As a specific example, employee A is added to the promotion list, and after the user (administrator) makes a final confirmation, the emotion engine detects some doubts and makes additional adjustments, and finally a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0196] The following describes the processing flow.

[0197] Step 1:

[0198] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. SQL queries are used to extract the necessary data. For example, data such as "Employee A: Evaluation score 85 points, years of service 5 years, project completion rate 100%" might be retrieved.

[0199] Step 2:

[0200] The server temporarily stores the acquired data in memory. This includes employee ID, name, job title, department, and performance score. This data will be used in subsequent processes.

[0201] Step 3:

[0202] The user (administrator) inputs criteria for salary increases and promotions through the interface. For example, they can set conditions such as "evaluation score of 80 points or higher" or "length of service of 3 years or more." The server stores these criteria in the database.

[0203] Step 4:

[0204] The server starts the scoring process based on the collected employee data. It compares each employee's evaluation points and years of service against a set standard to determine whether they meet the established criteria.

[0205] Step 5:

[0206] The server calculates each employee's score and adds a field related to the "score" to the database. A flag is also set to indicate whether or not the criteria have been met. For example, employee A is marked as "passed" because they meet the criteria.

[0207] Step 6:

[0208] The server filters employees who have passed the initial screening and generates a list of candidates for salary increases and promotions. The filtered employee data is added to the list and grouped together as candidates for salary increases and promotions.

[0209] Step 7:

[0210] The server displays the generated list to the user (administrator) on the administration screen. The list includes details such as each employee's name, position, performance score, and years of service.

[0211] Step 8:

[0212] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0213] Step 9:

[0214] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0215] Step 10:

[0216] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows the user to perform a human review before making a final decision.

[0217] Step 11:

[0218] After the user has finished reviewing the list, they notify the server of their final decision. This input triggers the server to confirm the final list.

[0219] Step 12:

[0220] The server notifies each employee based on the confirmed salary increase / promotion list. The notification is sent to the employee's device as an email or alert and includes detailed information about the salary increase or promotion.

[0221] Step 13:

[0222] The device receives emails and alerts, displaying notifications to employees. This allows employees to instantly know about decisions regarding their salary increases and promotions.

[0223] The above processing steps ensure that employee raises and promotions are carried out fairly and transparently, and the use of an emotional engine provides further management flexibility. For example, employee A is added to the promotion list, the user (administrator) makes a final review, and if the emotional engine detects any doubts, additional adjustments are made. Finally, a promotion notification is sent to employee A's terminal. In this way, raises and promotions are implemented in a manner that is acceptable to all employees.

[0224] (Example 2)

[0225] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0226] Traditional systems for determining employee salary increases and promotions are based on objective data, but they often leave room for dissatisfaction and doubt because they do not reflect the emotions or intuition of managers. Furthermore, the decision-making process for salary increases and promotions can be time-consuming and lack transparency. It is necessary to address these challenges and implement a fairer and more efficient decision-making process for salary increases and promotions.

[0227] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0228] In this invention, the server includes means for collecting employee data, means for calculating evaluation scores, means for automatically determining employee salary increases and promotions based on set criteria, means for recognizing the manager's emotions and automatically adjusting the generated list based on those emotions, means for the manager to review and modify the generated list, and means for notifying the employee of the final decision. This makes it possible to make fair and transparent salary increase and promotion decisions that reflect not only evaluation data but also the manager's emotions and intuition.

[0229] "Means of collecting employee data" refers to means that include the function of obtaining information such as employee performance evaluation data, project completion rates, working hours, and skill sets from a database.

[0230] "Means for calculating evaluation scores" refers to means that include a function for calculating an overall evaluation score based on collected employee data.

[0231] "Means for automatically determining employee salary increases and promotions based on established criteria" refers to means that include a function to automatically determine employee salary increases and promotions according to evaluation criteria set by the manager.

[0232] "Means for generating decision results as a list" refers to means that include a function to create a list of employees who meet the criteria for salary increases or promotions, and to display or save that list.

[0233] "An emotion engine means that recognizes the emotions of administrators and automatically adjusts the list generated based on those emotions" refers to a means that includes a function to analyze administrator emotion data and adjust the list of salary increases and promotions based on those emotions.

[0234] "Means for administrators to review and modify the generated list" refers to means that administrators can review the generated list of candidates for salary increases and promotions, and add modifications or comments as needed.

[0235] "Means of notifying employees of final decisions" include means of sending employees emails or alerts regarding final decisions on salary increases or promotions.

[0236] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. Furthermore, by incorporating an emotion engine that recognizes user emotions, more nuanced management becomes possible.

[0237] Data collection

[0238] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0239] Calculation of evaluation score

[0240] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0241] Setting Criteria

[0242] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0243] Automatic decision

[0244] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0245] List generation

[0246] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0247] Using an Emotion Engine

[0248] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0249] List adjustment

[0250] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0251] Final review and corrections

[0252] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0253] notification

[0254] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0255] A concrete example of a prompt message is: "Calculate the evaluation scores for the following employee data and generate a promotion list: Employee A: Evaluation 85 points, Years of service 5 years, Project completion rate 100%, Employee B: Evaluation 78 points, Years of service 2 years, Project completion rate 90%."

[0256] This system ensures that employee salary increases and promotions are fair and transparent. For example, if employee A is added to the promotion list and the user (administrator) makes a final confirmation, the emotion engine may detect doubts and make further adjustments before finally sending a promotion notification to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that satisfies all employees.

[0257] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0258] Step 1: Data Collection

[0259] The server accesses the employee database and retrieves data for all employees. The input is a list of employee IDs, and the server retrieves corresponding performance evaluation data, project completion rates, working hours, and skill sets from the database. Specifically, the server uses a database connection driver to execute SQL queries and load the employee data into memory. The output is the collected employee data.

[0260] Step 2: Calculation of evaluation score

[0261] The server calculates each employee's evaluation score based on the data it collects. The input is the collected employee data. Specifically, the server applies a formula that uses evaluation points, years of service, and project completion rate to calculate an overall evaluation score. The output is the overall evaluation score for each employee.

[0262] Step 3: Setting Criteria

[0263] The user (administrator) inputs criteria for salary increases and promotions through an interface. The input consists of criteria set by the administrator. Specifically, the administrator inputs the criteria into the interface, which is then sent to the server and saved to the database. The output is the set criteria saved to the database.

[0264] Step 4: Automatic decision

[0265] The server automatically determines each employee's salary increase and promotion based on the configured criteria. The inputs are the evaluation scores of all employees and the configured criteria. Specifically, the server filters employees who meet the criteria and adds them to a list of salary increase / promotion candidates. The output is a list of candidates.

[0266] Step 5: Generating the list

[0267] The server generates a list of candidates for salary increases and promotions and displays it to the user (administrator) on the administration screen. The input is a list of candidates for salary increases and promotions. Specifically, the server generates a list from the candidate data, generates HTML, and sends it to the browser. The output is the list that will be displayed on the administration screen.

[0268] Step 6: Utilizing the Emotional Engine

[0269] The server activates the emotion engine and recognizes the user's (administrator's) emotions. Inputs include user interface operations, keyboard input, and voice data. Specifically, the server calls the emotion engine's API and analyzes the emotion data. The output is the user's emotional state.

[0270] Step 7: Adjust the list

[0271] The server automatically adjusts the list generated based on emotion data recognized by the emotion engine. The input consists of user emotion state data and a list of candidates. Specifically, the server recalculates the list contents based on the emotion state and filters as needed. The output is the adjusted list.

[0272] Step 8: Final review and corrections

[0273] The user (administrator) reviews the generated list and adds corrections and comments. The input is an adjusted list. Specifically, the user reviews the list through the administration screen and adds corrections and comments. This correction information is sent to the server and stored in the database. The output is the final, corrected list.

[0274] Step 9: Notification

[0275] The server notifies employees of the final decision. The input is the final list. Specifically, the server generates the notification content, refers to the employee's email address and notification settings, and sends the notification via the appropriate medium (email or alert). The output is a confirmation message to the notified employee.

[0276] (Application Example 2)

[0277] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0278] Conventional salary increase and promotion management systems can automatically determine salary increases and promotions based on employee evaluation data, but they have the problem of not being able to make flexible adjustments that take into account the emotions of managers. In particular, in the performance evaluation management of robots working in factories, there is a need for evaluations that take into account not only the robot's operational data but also the emotions of managers. Therefore, this system solves the problem of achieving fairer and more efficient management by recognizing the emotions of managers and adjusting the evaluation list based on them.

[0279] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 2 is realized by the following means.

[0280] In this invention, the server includes means for collecting employee data, means for calculating an evaluation score based on the data, means for automatically determining salary increases and promotions of employees based on set criteria, means for generating the determination results as a list, means for an administrator to check and modify the generated list, means including an emotion recognition engine for recognizing the administrator's emotion and adjusting the list, and means for notifying the employees of the final decision. Thereby, fair and efficient salary increases and promotions reflecting the administrator's emotion become possible.

[0281] "Employee data" is a general term for information including performance evaluation data, project achievement rate, working hours, skill set, etc. of each employee.

[0282] "Evaluation score" is a quantification of the performance and ability of an employee calculated based on the collected employee data.

[0283] "Set criteria" refers to conditions such as evaluation points, years of continuous service, project achievement rate, etc. used as judgment criteria for salary increases and promotions.

[0284] "Means for automatically determining" is a mechanism for automatically judging and determining salary increases and promotions of employees based on set criteria.

[0285] "Means for generating as a list" refers to a function for outputting evaluation results and promotion candidates in list form.

[0286] "Means for checking and modifying" is a function that allows an administrator to check the generated list and make modifications as necessary.

[0287] "Emotion recognition engine" is a system or algorithm for analyzing the administrator's emotion and adjusting the list based on the emotion.

[0288] "Means of notifying employees of the final decision" refers to communication methods such as email or alerts used to inform employees of the final decision regarding salary increases or promotions.

[0289] This invention relates to a system for fairly and objectively determining employee salary increases and promotions, and, as an example of its application, to a work performance evaluation management application for factory robots. This system collects employee data, calculates evaluation scores, and automatically determines salary increases and promotions based on set criteria. Furthermore, by incorporating an emotion recognition engine, management that takes the manager's emotions into account becomes possible.

[0290] System program description

[0291] The server performs data processing and calculations using the following methods.

[0292] 1. Methods for collecting employee data

[0293] The server collects data from the employee database, including employee performance evaluations, project completion rates, working hours, and skill sets. For example, data for employee A might include "85 points in performance evaluation, 5 years of service, and 100% project completion rate."

[0294] 2. Method for calculating evaluation scores

[0295] Based on the collected data, the server calculates each employee's evaluation score. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0296] 3. Means of setting standards

[0297] The manager sets the criteria for salary increases and promotions. The server saves these criteria in the database. For example, criteria such as "evaluation points are 80 or above and years of continuous service are 3 or more" are set.

[0298] 4. Automatic Determination Means for Salary Increases and Promotions

[0299] Based on the set criteria, the server automatically determines the salary increases and promotions for each employee. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increases and promotions. For example, since Employee A meets the criteria, they are added to the list.

[0300] 5. List Generation and Confirmation / Modification Means

[0301] The server generates a list of candidates for salary increases and promotions and displays it on the management screen so that the manager can view it. The manager can check this list and make modifications or add comments as needed.

[0302] 6. List Adjustment Means Using an Emotion Recognition Engine

[0303] An emotion recognition engine is used to recognize the emotions of the manager. The emotion recognition engine analyzes the manager's interface operations and voice data to determine emotions. For example, if the manager shows dissatisfaction, the list is readjusted based on that information.

[0304] 7. Final Decision and Notification Means

[0305] After the server makes the final decision, it sends a notification to the employees whose salary increases and promotions have been determined. The notification is displayed on the employees' terminals as an email or an alert. This allows employees to immediately know that their salary increases and promotions have been approved.

[0306] Hardware and Software to be Used

[0307] Hardware:

[0308] Factory robots

[0309] Administrator's smartphone or PC

[0310] Cameras and microphones for emotion recognition engines

[0311] software:

[0312] Python program

[0313] Emotion recognition engine (e.g., various general-purpose emotion APIs)

[0314] Database management system (e.g., one type of RDBMS)

[0315] Adding specific examples

[0316] For example, in the performance evaluation management of factory robots, suppose robot A has an efficiency of 85%, an error rate of 3%, and 100 hours of operation. The criteria set by the manager are "efficiency of 80% or higher and an error rate of 5% or lower," and since robot A meets these criteria, it is added to the list of robots that do not require maintenance. If the manager expresses dissatisfaction while using the app, the recognition engine will determine that emotion and readjust the list accordingly.

[0317] Examples of prompts to input into a generative AI model:

[0318] "Robot A has an efficiency of 85%, an error rate of 3%, an operating time of 100 hours, and the administrator's emotional state is neutral."

[0319] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0320] Step 1:

[0321] The server accesses the employee database and collects data such as performance evaluations, project completion rates, working hours, and skill sets for all employees. The input is the employee database, and the output is an evaluation dataset for each employee. This data processing clarifies the evaluation elements for each employee.

[0322] Step 2:

[0323] The server calculates each employee's performance score based on the collected data. The input is the employee dataset obtained in Step 1, and the output is each employee's performance score. Data calculations generate an overall score that takes into account each employee's performance evaluation data, project completion rate, working hours, and skill set.

[0324] Step 3:

[0325] Users set the criteria for salary increases and promotions on the server. The input is the criteria set by the user, and the output is the database where those criteria are stored. This setting reflects the criteria for salary increases and promotions in the system.

[0326] Step 4:

[0327] The server automatically determines each employee's salary increase and promotion based on the set criteria. The inputs are the evaluation score obtained in step 2 and the criteria set in step 3, and the output is a list of employees who meet the criteria. In this process, the evaluation scores are filtered to see if they meet the set criteria.

[0328] Step 5:

[0329] The server generates a list of candidates for salary increases and promotions and displays it on the administration screen for user review. The input is a list of employees who meet the criteria obtained in step 4, and the output is a list that can be reviewed by the administrator. This operation allows the administrator to review the candidate list in detail.

[0330] Step 6:

[0331] The user reviews the generated list and adds corrections and comments as needed. The input is the candidate list, and the output is the reviewed and corrected list. In this step, the administrator makes a final confirmation of employee salary increases and promotions.

[0332] Step 7:

[0333] The server activates an emotion recognition engine to recognize the user's emotions. Input is the user's interface actions and voice data, and output is the analyzed emotion data. This analysis identifies the administrator's current emotional state.

[0334] Step 8:

[0335] The server readjusts the list of candidates for salary increases and promotions based on emotional data acquired by the emotion recognition engine. The input is the emotional data and the reviewed / corrected list, while the output is the final adjusted list. Considering emotional data leads to more desirable employee evaluations.

[0336] Step 9:

[0337] After making the final decision, the server sends a notification to the employees who have been approved for a raise or promotion. The input is the list after final adjustments, and the output is the notification sent to the employee's terminal. This notification allows employees to immediately know that their raise or promotion has been confirmed.

[0338] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0339] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0340] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0341] [Second Embodiment]

[0342] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0343] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0344] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0345] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0346] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0347] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0348] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0349] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0350] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0351] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0352] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0353] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0354] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. The following describes the specific program processing and its implementation.

[0355] Data collection

[0356] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0357] Calculation of evaluation score

[0358] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0359] Setting standards

[0360] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0361] Automatic decision

[0362] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0363] List generation

[0364] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0365] Confirmation / Correction

[0366] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0367] notification

[0368] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0369] Through the above process, the salary increase and promotion process is conducted transparently and fairly. For example, employee A is added to the promotion list, and after final confirmation by the user (administrator), a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0370] The following describes the processing flow.

[0371] Step 1:

[0372] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. This involves using SQL queries to extract the necessary data.

[0373] Step 2:

[0374] The server temporarily stores the acquired data in memory. This includes information such as employee ID, name, job title, department, and performance score. The data is saved in an appropriate format.

[0375] Step 3:

[0376] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation score of 80 points or higher" or "length of service of 3 years or more" may be entered.

[0377] Step 4:

[0378] The server verifies the entered criteria and saves them to the database. The criteria data is managed with a unique ID. This criterion value is used in subsequent processing.

[0379] Step 5:

[0380] The server begins the scoring process based on the collected employee data. First, it compares each employee's evaluation points and years of service with a standard to determine whether the standard is met.

[0381] Step 6:

[0382] The server calculates each employee's score and adds fields related to the "score" to the database. A flag is also set here to determine whether or not the criteria are met.

[0383] Step 7:

[0384] The server filters out employees whose scores meet the "pass" criteria and adds the filtered employee data to the salary increase / promotion list. This generates a basic salary increase / promotion list.

[0385] Step 8:

[0386] The server displays the generated list to the user (administrator) on the administration screen. The list includes details such as each employee's name, position, performance score, and years of service.

[0387] Step 9:

[0388] The user (administrator) can view the salary increase / promotion list generated through the administration screen. On the administration screen, they can view the employee information displayed in the list and edit or add comments as needed.

[0389] Step 10:

[0390] After the user has finished reviewing the list, they press a button to notify the server of their final decision. Upon receiving this input, the server confirms the final list.

[0391] Step 11:

[0392] The server notifies each employee based on the finalized list of salary increases and promotions. This notification process includes generating an internal notification email and sending it to each employee's device.

[0393] Step 12:

[0394] The device receives an email and displays a notification to the employee. The notification includes details about salary increases or promotions, and information about the next steps (e.g., orientation information regarding the new position).

[0395] By following these steps, employee salary increases and promotions can be conducted fairly and transparently, resulting in outcomes that satisfy everyone.

[0396] (Example 1)

[0397] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0398] Traditional methods for determining salary increases and promotions often relied on subjective judgment, making it difficult to conduct fair and objective evaluations. Furthermore, the manual process of setting criteria for salary increases and promotions, as well as verifying and correcting results, was time-consuming and labor-intensive, and prone to errors and misunderstandings.

[0399] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0400] This invention includes a server that includes means for accessing a database of employees and obtaining performance evaluation data, project completion rates, working hours, skill sets, etc., for all employees; means for calculating an evaluation score for each employee based on the data obtained by the server; means for an administrator to input criteria for salary increases and promotions and for the server to store those criteria; means for the server to automatically determine salary increases and promotions for each employee based on the set criteria; means for the server to generate a list of candidates for salary increases and promotions and provide an interface for administrators to review and modify it; and means for the server to send a notification to the relevant employees after the final decision. This makes it possible to conduct the salary increase and promotion process transparently and fairly, and to prevent errors and inefficiencies caused by manual work.

[0401] A "server" refers to a computer or a system with those functions for performing calculations, managing data, and connecting to a network.

[0402] "Working adults" refers to individuals who work for a company or organization, and includes those who are subject to employee evaluation.

[0403] A "database" refers to a system or software used to systematically store and manage data.

[0404] "Performance evaluation data" refers to data that records the work performance and results of individual employees as numerical values ​​and evaluations.

[0405] "Project completion rate" refers to an indicator that shows the percentage of project goals that were actually achieved.

[0406] "Working hours" refers to data that measures the actual time an employee spends working at their workplace.

[0407] A "skill set" refers to the collection of skills, knowledge, and abilities that a working professional possesses.

[0408] "Evaluation score" refers to a comprehensive numerical evaluation calculated based on collected performance evaluation data and other evaluation factors.

[0409] "Criteria" refers to the conditions or evaluation items set up for use in determining salary increases and promotions.

[0410] An "interface" refers to the user interface or tools that allow a user to interact with a system.

[0411] "Notification" refers to messages or alerts used by a system to transmit information to professionals.

[0412] A "list" refers to a organized display of information about candidates for salary increases and promotions.

[0413] This invention relates to a system for fairly and objectively determining salary increases and promotions for working adults. This system collects data on working adults, calculates evaluation scores, and determines salary increases and promotions based on established criteria. The following describes the specific program processing and its implementation.

[0414] Data collection

[0415] The server accesses a database of working professionals to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. Specific SQL queries are used to extract the data. For example, a query like "SELECT FROM employee_performance WHERE employee_id = 'A123'" might be used.

[0416] Calculation of evaluation score

[0417] Based on the data acquired by the server, an evaluation score is calculated for each employee. During this process, programming languages ​​such as Python and R are used to analyze the data and weight different evaluation criteria. For example, a weighting scheme such as "evaluation points × 0.5 + years of service × 0.3 + project completion rate × 0.2" is used to calculate the overall score.

[0418] Setting standards

[0419] The user (administrator) inputs the criteria for salary increases and promotions through an interface, and the server saves these settings. The administrator enters "evaluation points of 80 or higher AND years of service of 3 or more" into a form on a web browser, and the server saves these criteria to the database. A specific SQL query would be something like "INSERT INTO promotion_criteria (point, years) VALUES (80, 3)".

[0420] Automatic decision

[0421] The server automatically determines each employee's salary increase and promotion based on the configured criteria. To filter employees whose evaluation scores meet the criteria, a query such as "SELECT FROM employee_scores WHERE score >= 80 AND years >= 3" is executed.

[0422] List generation

[0423] The server generates a list of candidates for salary increases and promotions. The generated list is displayed on the web interface and can be viewed by administrators in the administration panel. The list includes information such as the candidate's name, performance score, and job title.

[0424] Confirmation / Correction

[0425] The user (administrator) reviews the list generated in the administration screen and adds corrections and comments as needed. The administration screen is built using frameworks such as Django or Flask. For example, the administrator can view the list in a browser and perform operations such as "correcting employee B's evaluation score and adding the comment 'Exceptional Promotion'."

[0426] notification

[0427] After the server makes the final decision regarding salary increases and promotions, it sends a notification email to the employee concerned. The notification is sent using Python's smtplib or Django's email functionality. The notification contains details about the employee's salary increase or promotion. For example, it might include a message like, "Your promotion has been approved. Your new position is XX."

[0428] Examples of prompt statements

[0429] "Please add employees who meet the following conditions to the salary increase list: performance evaluation score of 80 points or higher and length of service of 3 years or more."

[0430] This system ensures that the salary increase and promotion process is transparent and fair, preventing errors and inefficiencies caused by manual processes. For example, employee A is added to the promotion list, and after final confirmation by the user (administrator), a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1: Data Collection

[0433] The server accesses a database of working employees to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. Specifically, the server executes "SELECT FROM employee_performance" and temporarily stores the retrieved data in memory. The input is a database query, and the output is a list of performance evaluation data for all employees.

[0434] Step 2: Calculation of evaluation score

[0435] Based on the data acquired by the server, an evaluation score is calculated for each employee. Here, a Python script is used to calculate an overall score by summing and weighting evaluation points, years of service, and project completion rate. Specifically, a formula like "evaluation points × 0.5 + years of service × 0.3 + project completion rate × 0.2" is applied. The input is the acquired data for each employee, and the output is each employee's evaluation score.

[0436] Step 3: Setting Criteria

[0437] The user (administrator) inputs the criteria for salary increases and promotions through an interface. For example, the administrator enters "evaluation points of 80 or higher AND years of service of 3 or more" into a web form, and the server saves this setting to the database. Specifically, the query "INSERT INTO promotion_criteria (point, years) VALUES (80, 3)" is executed. The input is the criteria data entered by the administrator, and the output is the saved criteria data.

[0438] Step 4: Automatic decision

[0439] The server automatically determines salary increases and promotions for each employee based on the configured criteria. Here, the system filters employees who meet the criteria based on their evaluation scores and executes the query "SELECT FROM employee_scores WHERE score >= 80 AND years >= 3". The inputs are the criteria data and the evaluation score data, and the output is a list of candidates for salary increases and promotions.

[0440] Step 5: Generating the list

[0441] The server generates a list of candidates for salary increases and promotions. The generated list is displayed in a format that administrators can view on a web interface. Specifically, it dynamically generates HTML pages to display each item in the list (name, performance score, job title, etc.). The input is data on the candidates for salary increases and promotions, and the output is a displayable list.

[0442] Step 6: Review and Correction

[0443] The user (administrator) reviews the list generated in the administration screen and adds corrections and comments as needed. The administrator clicks on a list item in the browser to access a form for entering corrections and comments. Specifically, an "UPDATE" query is executed to reflect the corrected data in the database. The input is the correction data made by the administrator, and the output is the updated list.

[0444] Step 7: Notification

[0445] After the server makes the final decision regarding salary increases and promotions, it sends notification emails to the relevant employees. The notifications are sent using Python's smtplib and Django's email functionality. Specifically, it retrieves each employee's email address and sends an email stating, "Your promotion has been approved. Your new position is XX." The input is the list data after the final decision, and the output is the sent notification email.

[0446] (Application Example 1)

[0447] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0448] Currently, decisions regarding the maintenance and upgrade of factory machinery often rely on the subjective judgment of managers, which can lead to a lack of fairness and efficiency. Furthermore, the collection of machine operation data and the calculation of evaluation scores are performed manually, which is time-consuming, labor-intensive, and prone to errors. Therefore, the present invention aims to provide a system for making fair and efficient decisions regarding the maintenance and upgrade of factory machinery.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0450] In this invention, the server includes means for collecting data on factory machinery, means for calculating evaluation scores based on that data, means for automatically deciding on maintenance or upgrades of factory machinery based on set criteria, means for generating the decision results as a list, means for an administrator to review and modify the generated list, and means for notifying the final decision. This makes it possible to fairly and efficiently decide on maintenance and upgrades of factory machinery and manage them through an automated process.

[0451] "Factory machinery" refers to automated equipment and systems used in industrial production.

[0452] "Means of collecting data" refers to devices or systems that acquire and record operational information of machines.

[0453] An "evaluation score" is an index that quantifies the performance and condition of a machine based on collected data.

[0454] "Criteria" refers to the set values ​​that indicate the evaluation points and conditions used when deciding on maintenance or upgrades.

[0455] An "automatic decision-making mechanism" is a system that automatically determines the need for machine maintenance or upgrades based on set criteria.

[0456] "Method for generating as a list" refers to a function that summarizes the decision results in a list format so that administrators can review them.

[0457] A "manager" is a person responsible for the operation of a factory and the maintenance of its machinery.

[0458] The "means of review and correction" refer to a function that allows administrators to review the generated list and make changes as needed.

[0459] "Means of notifying final decisions" refers to a system that informs relevant personnel of confirmed maintenance or upgrade information.

[0460] To implement the present invention, a system including the following elements is required: The process from data collection to notification is automated in order to efficiently and fairly manage the maintenance and upgrades of factory machinery.

[0461] Basic configuration

[0462] This system consists of a sensor system for collecting data from factory machinery, a server, an administrator terminal, and a notification system.

[0463] Data collection

[0464] The server collects operational data from factory machinery using a sensor system placed throughout the factory. This sensor system records data such as operating hours, number of failures, work efficiency, and consumable parts usage rate in real time. The collected data is sent to the server and stored in a database. SQLite is used as the specific database software.

[0465] Calculation of evaluation score

[0466] The server calculates a machine evaluation score based on the collected data. The evaluation score is calculated based on pre-set weights for each element. For example, uptime might be weighted 20%, failure rate -50%, work efficiency 40%, and consumable parts usage rate 30%.

[0467] Setting criteria and filtering

[0468] Administrators can enter criteria and save them on the server. These criteria can include uptime and the number of failures. For example, a criterion might be set as "uptime exceeds 1000 hours and the number of failures is less than 3." Based on these criteria, the server automatically filters machines that require maintenance or upgrades and generates a list of candidates.

[0469] List generation and verification

[0470] The server generates a list of the decision results and displays it on the administrator's terminal for review and modification. The list includes the name of each machine, its evaluation score, and the required maintenance items. Administrators can review this list through the interface and add modifications or comments as needed.

[0471] notification

[0472] After final confirmation by the administrator, the server notifies relevant personnel of any maintenance or upgrade decisions via a notification system. These notifications are sent to administrators and maintenance personnel's terminals via email or alerts, enabling a swift response.

[0473] Specific example

[0474] For example, suppose the data for robot A in a factory is "1500 operating hours, 2 failures, 95% work efficiency, and 80% consumable parts usage." The server calculates an evaluation score based on this data, and the overall evaluation is "90 points." Subsequently, this robot is added to the list of maintenance candidates based on the set criteria. The administrator reviews and modifies the list, and if it is determined that maintenance is necessary, a notification is sent to the maintenance personnel.

[0475] Example of a prompt

[0476] "Develop an automated evaluation and maintenance decision system for factory robots. The collected data will include operating hours, failure count, work efficiency, and consumable parts usage rate. The system will calculate an evaluation score and notify maintenance based on predefined criteria. Use SQLite for the database and SMTPLib for notifications."

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] The server collects data from sensor systems placed throughout the factory. It receives data such as operating time, failure count, work efficiency, and consumable parts usage rate for each machine as input. This data is collected in real time and stored in a database. The stored data is then used for further processing as output. Specifically, it receives data transmitted from sensors and saves it to a database (e.g., SQLite).

[0480] Step 2:

[0481] The server calculates evaluation scores based on data stored in the database. It uses the operating time, number of failures, work efficiency, and consumable parts usage rate for each machine, collected as input. It applies the set weights to these data items to calculate an integrated evaluation score. The output generates an evaluation score for each machine. Specifically, it calculates the evaluation score using a weighted formula and temporarily stores the result in memory.

[0482] Step 3:

[0483] The user (administrator) sets the criteria. As input, the user enters various criterion values, such as uptime and failure count, through the interface. These set criteria are stored in the database. As output, the set criteria are used in subsequent filtering processes. Specifically, the criteria values ​​entered by the administrator are stored in the database.

[0484] Step 4:

[0485] The server filters the collected data based on configured criteria and automatically determines which machines require maintenance or upgrades. It uses evaluation scores and configured criteria as inputs. This selects machines that meet the criteria as maintenance candidates. A candidate list is generated as output. Specifically, it compares the configured criteria with the evaluation scores and extracts machines that meet the criteria.

[0486] Step 5:

[0487] The server generates a list of the decision results and displays it on the administrator's terminal. It receives filtered machine information as input. The output is displayed to the administrator in list format, allowing them to review and modify the information. Specifically, it generates a list and displays it to the administrator via a web interface or dedicated software.

[0488] Step 6:

[0489] The user (administrator) reviews and modifies the list. The input is the list displayed from the server. Modifications and comments are entered as needed. The output is the final decision regarding maintenance or upgrades. Specific actions include the administrator reviewing the list and making necessary changes.

[0490] Step 7:

[0491] The server notifies the final decision. It receives the list confirmed by the administrator as input. As output, notifications are sent to maintenance personnel and relevant staff. Specifically, this involves sending an email using the SMTP protocol and displaying it as an alert on the staff member's terminal.

[0492] This process makes it possible to automate the maintenance and upgrade of factory machinery fairly and efficiently.

[0493] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0494] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. Furthermore, by incorporating an emotion engine that recognizes user emotions, more nuanced management becomes possible. The following describes the specific program processing and its implementation.

[0495] Data collection

[0496] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0497] Calculation of evaluation score

[0498] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0499] Setting standards

[0500] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0501] Automatic decision

[0502] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0503] List generation

[0504] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0505] Using an Emotion Engine

[0506] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0507] List adjustment

[0508] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0509] Final review and corrections

[0510] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0511] notification

[0512] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0513] Through the above process, employee salary increases and promotions are carried out fairly and transparently. As a specific example, employee A is added to the promotion list, and after the user (administrator) makes a final confirmation, the emotion engine detects some doubts and makes additional adjustments, and finally a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. SQL queries are used to extract the necessary data. For example, data such as "Employee A: Evaluation score 85 points, years of service 5 years, project completion rate 100%" might be retrieved.

[0517] Step 2:

[0518] The server temporarily stores the acquired data in memory. This includes employee ID, name, job title, department, and performance score. This data will be used in subsequent processes.

[0519] Step 3:

[0520] The user (administrator) inputs criteria for salary increases and promotions through the interface. For example, they can set conditions such as "evaluation score of 80 points or higher" or "length of service of 3 years or more." The server stores these criteria in the database.

[0521] Step 4:

[0522] The server starts the scoring process based on the collected employee data. It compares each employee's evaluation points and years of service against a set standard to determine whether they meet the established criteria.

[0523] Step 5:

[0524] The server calculates each employee's score and adds a field related to the "score" to the database. A flag is also set to indicate whether or not the criteria have been met. For example, employee A is marked as "passed" because they meet the criteria.

[0525] Step 6:

[0526] The server filters employees who have passed the initial screening and generates a list of candidates for salary increases and promotions. The filtered employee data is added to the list and grouped together as candidates for salary increases and promotions.

[0527] Step 7:

[0528] The server displays the generated list to the user (administrator) on the administration screen. The list includes details such as each employee's name, position, performance score, and years of service.

[0529] Step 8:

[0530] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0531] Step 9:

[0532] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0533] Step 10:

[0534] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows the user to perform a human review before making a final decision.

[0535] Step 11:

[0536] After the user has finished reviewing the list, they notify the server of their final decision. This input triggers the server to confirm the final list.

[0537] Step 12:

[0538] The server notifies each employee based on the confirmed salary increase / promotion list. The notification is sent to the employee's device as an email or alert and includes detailed information about the salary increase or promotion.

[0539] Step 13:

[0540] The device receives emails and alerts, displaying notifications to employees. This allows employees to instantly know about decisions regarding their salary increases and promotions.

[0541] The above processing steps ensure that employee raises and promotions are carried out fairly and transparently, and the use of an emotional engine provides further management flexibility. For example, employee A is added to the promotion list, the user (administrator) makes a final review, and if the emotional engine detects any doubts, additional adjustments are made. Finally, a promotion notification is sent to employee A's terminal. In this way, raises and promotions are implemented in a manner that is acceptable to all employees.

[0542] (Example 2)

[0543] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0544] Traditional systems for determining employee salary increases and promotions are based on objective data, but they often leave room for dissatisfaction and doubt because they do not reflect the emotions or intuition of managers. Furthermore, the decision-making process for salary increases and promotions can be time-consuming and lack transparency. It is necessary to address these challenges and implement a fairer and more efficient decision-making process for salary increases and promotions.

[0545] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0546] In this invention, the server includes means for collecting employee data, means for calculating evaluation scores, means for automatically determining employee salary increases and promotions based on set criteria, means for recognizing the manager's emotions and automatically adjusting the generated list based on those emotions, means for the manager to review and modify the generated list, and means for notifying the employee of the final decision. This makes it possible to make fair and transparent salary increase and promotion decisions that reflect not only evaluation data but also the manager's emotions and intuition.

[0547] "Means of collecting employee data" refers to means that include the function of obtaining information such as employee performance evaluation data, project completion rates, working hours, and skill sets from a database.

[0548] "Means for calculating evaluation scores" refers to means that include a function for calculating an overall evaluation score based on collected employee data.

[0549] "Means for automatically determining employee salary increases and promotions based on established criteria" refers to means that include a function to automatically determine employee salary increases and promotions according to evaluation criteria set by the manager.

[0550] "Means for generating decision results as a list" refers to means that include a function to create a list of employees who meet the criteria for salary increases or promotions, and to display or save that list.

[0551] "An emotion engine means that recognizes the emotions of administrators and automatically adjusts the list generated based on those emotions" refers to a means that includes a function to analyze administrator emotion data and adjust the list of salary increases and promotions based on those emotions.

[0552] "Means for administrators to review and modify the generated list" refers to means that administrators can review the generated list of candidates for salary increases and promotions, and add modifications or comments as needed.

[0553] "Means of notifying employees of final decisions" include means of sending employees emails or alerts regarding final decisions on salary increases or promotions.

[0554] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. Furthermore, by incorporating an emotion engine that recognizes user emotions, more nuanced management becomes possible.

[0555] Data collection

[0556] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0557] Calculation of evaluation score

[0558] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0559] Setting standards

[0560] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0561] Automatic decision

[0562] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0563] List generation

[0564] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0565] Using an Emotion Engine

[0566] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0567] List adjustment

[0568] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0569] Final review and corrections

[0570] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0571] notification

[0572] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0573] A concrete example of a prompt message is: "Calculate the evaluation scores for the following employee data and generate a promotion list: Employee A: Evaluation 85 points, Years of service 5 years, Project completion rate 100%, Employee B: Evaluation 78 points, Years of service 2 years, Project completion rate 90%."

[0574] This system ensures that employee salary increases and promotions are fair and transparent. For example, if employee A is added to the promotion list and the user (administrator) makes a final confirmation, the emotion engine may detect doubts and make further adjustments before finally sending a promotion notification to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that satisfies all employees.

[0575] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0576] Step 1: Data Collection

[0577] The server accesses the employee database and retrieves data for all employees. The input is a list of employee IDs, and the server retrieves corresponding performance evaluation data, project completion rates, working hours, and skill sets from the database. Specifically, the server uses a database connection driver to execute SQL queries and load the employee data into memory. The output is the collected employee data.

[0578] Step 2: Calculation of evaluation score

[0579] The server calculates each employee's evaluation score based on the data it collects. The input is the collected employee data. Specifically, the server applies a formula that uses evaluation points, years of service, and project completion rate to calculate an overall evaluation score. The output is the overall evaluation score for each employee.

[0580] Step 3: Setting Criteria

[0581] The user (administrator) inputs criteria for salary increases and promotions through an interface. The input consists of criteria set by the administrator. Specifically, the administrator inputs the criteria into the interface, which is then sent to the server and saved to the database. The output is the set criteria saved to the database.

[0582] Step 4: Automatic decision

[0583] The server automatically determines each employee's salary increase and promotion based on the configured criteria. The inputs are the evaluation scores of all employees and the configured criteria. Specifically, the server filters employees who meet the criteria and adds them to a list of promotion candidates. The output is a list of candidates.

[0584] Step 5: Generating the list

[0585] The server generates a list of candidates for salary increases and promotions and displays it to the user (administrator) on the administration screen. The input is a list of candidates for salary increases and promotions. Specifically, the server generates a list from the candidate data, generates HTML, and sends it to the browser. The output is the list that will be displayed on the administration screen.

[0586] Step 6: Utilizing the Emotional Engine

[0587] The server activates the emotion engine and recognizes the user's (administrator's) emotions. Inputs include user interface operations, keyboard input, and voice data. Specifically, the server calls the emotion engine's API and analyzes the emotion data. The output is the user's emotional state.

[0588] Step 7: Adjust the list

[0589] The server automatically adjusts the list generated based on emotion data recognized by the emotion engine. The input consists of user emotion state data and a list of candidates. Specifically, the server recalculates the list contents based on the emotion state and filters as needed. The output is the adjusted list.

[0590] Step 8: Final review and corrections

[0591] The user (administrator) reviews the generated list and adds corrections and comments. The input is an adjusted list. Specifically, the user reviews the list through the administration screen and adds corrections and comments. This correction information is sent to the server and stored in the database. The output is the final, corrected list.

[0592] Step 9: Notification

[0593] The server notifies employees of the final decision. The input is the final list. Specifically, the server generates the notification content, refers to the employee's email address and notification settings, and sends the notification via the appropriate medium (email or alert). The output is a confirmation message to the notified employee.

[0594] (Application Example 2)

[0595] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0596] Conventional salary increase and promotion management systems can automatically determine salary increases and promotions based on employee evaluation data, but they have the problem of not being able to make flexible adjustments that take into account the emotions of managers. In particular, in the performance evaluation management of robots working in factories, there is a need for evaluations that take into account not only the robot's operational data but also the emotions of managers. Therefore, this system solves the problem of achieving fairer and more efficient management by recognizing the emotions of managers and adjusting the evaluation list based on them.

[0597] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0598] In this invention, the server includes means for collecting employee data, means for calculating evaluation scores based on that data, means for automatically determining employee salary increases and promotions based on set criteria, means for generating the decision results as a list, means for the administrator to review and modify the generated list, means including an emotion recognition engine that recognizes the administrator's emotions and adjusts the list, and means for notifying the employee of the final decision. This enables fair and efficient salary increases and promotions that reflect the administrator's emotions.

[0599] "Employee data" is a general term for information including each employee's performance evaluation data, project completion rate, working hours, skill set, etc.

[0600] An "evaluation score" is a numerical representation of an employee's performance and abilities, calculated based on collected employee data.

[0601] "Established criteria" refers to conditions such as evaluation points, years of service, and project completion rates that are used as criteria for determining salary increases and promotions.

[0602] An "automatic decision-making mechanism" is a mechanism for automatically determining and deciding on employee salary increases and promotions based on established criteria.

[0603] "Means of generating as a list" refers to a function that outputs evaluation results or promotion candidates in list format.

[0604] The "means of review and correction" refer to a function that allows administrators to review the generated list and make corrections as needed.

[0605] An "emotion recognition engine" is a system or algorithm that analyzes the administrator's emotions and adjusts lists based on those emotions.

[0606] "Means of notifying employees of the final decision" refers to communication methods such as email or alerts used to inform employees of the final decision regarding salary increases or promotions.

[0607] This invention relates to a system for fairly and objectively determining employee salary increases and promotions, and, as an example of its application, to a work performance evaluation management application for factory robots. This system collects employee data, calculates evaluation scores, and automatically determines salary increases and promotions based on set criteria. Furthermore, by incorporating an emotion recognition engine, management that takes the manager's emotions into account becomes possible.

[0608] System program description

[0609] The server performs data processing and calculations using the following methods.

[0610] 1. Methods for collecting employee data

[0611] The server collects data from the employee database, including employee performance evaluations, project completion rates, working hours, and skill sets. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0612] 2. Method for calculating evaluation scores

[0613] Based on the collected data, the server calculates an evaluation score for each employee. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0614] 3. Means of setting standards

[0615] The administrator sets the criteria for salary increases and promotions. The server stores these criteria in a database. For example, a criterion such as "performance score of 80 points or higher AND years of service of 3 years or more" might be set.

[0616] 4. Automatic determination methods for salary increases and promotions

[0617] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0618] 5. List generation, verification, and modification methods

[0619] The server generates a list of candidates for salary increases and promotions, which is displayed on the administration screen for administrators to review. Administrators can review this list and make corrections or add comments as needed.

[0620] 6. List adjustment method using an emotion recognition engine

[0621] An emotion recognition engine is used to understand the administrator's emotions. The emotion recognition engine analyzes the administrator's interface interactions and voice data to determine their emotions. For example, if the administrator indicates dissatisfaction, the list is readjusted based on that information.

[0622] 7. Final decision and notification means

[0623] After the server makes the final decision, it sends a notification to employees whose salary increases or promotions have been approved. The notification appears on the employee's device as an email or alert. This allows employees to immediately know that their salary increase or promotion has been approved.

[0624] Hardware and software to be used

[0625] Hardware:

[0626] Factory robots

[0627] Administrator's smartphone or PC

[0628] Cameras and microphones for emotion recognition engines

[0629] software:

[0630] Python program

[0631] Emotion recognition engine (e.g., various general-purpose emotion APIs)

[0632] Database management system (e.g., one type of RDBMS)

[0633] Adding specific examples

[0634] For example, in the performance evaluation management of factory robots, suppose robot A has an efficiency of 85%, an error rate of 3%, and 100 hours of operation. The criteria set by the manager are "efficiency of 80% or higher and an error rate of 5% or lower," and since robot A meets these criteria, it is added to the list of robots that do not require maintenance. If the manager expresses dissatisfaction while using the app, the recognition engine will determine that emotion and readjust the list accordingly.

[0635] Examples of prompts to input into a generative AI model:

[0636] "Robot A has an efficiency of 85%, an error rate of 3%, an operating time of 100 hours, and the administrator's emotional state is neutral."

[0637] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0638] Step 1:

[0639] The server accesses the employee database and collects data such as performance evaluations, project completion rates, working hours, and skill sets for all employees. The input is the employee database, and the output is an evaluation dataset for each employee. This data processing clarifies the evaluation elements for each employee.

[0640] Step 2:

[0641] The server calculates each employee's performance score based on the collected data. The input is the employee dataset obtained in Step 1, and the output is each employee's performance score. Data calculations generate an overall score that takes into account each employee's performance evaluation data, project completion rate, working hours, and skill set.

[0642] Step 3:

[0643] Users set the criteria for salary increases and promotions on the server. The input is the criteria set by the user, and the output is the database where those criteria are stored. This setting reflects the criteria for salary increases and promotions in the system.

[0644] Step 4:

[0645] The server automatically determines each employee's salary increase and promotion based on the set criteria. The inputs are the evaluation score obtained in step 2 and the criteria set in step 3, and the output is a list of employees who meet the criteria. In this process, the evaluation scores are filtered to see if they meet the set criteria.

[0646] Step 5:

[0647] The server generates a list of candidates for salary increases and promotions and displays it on the administration screen for user review. The input is a list of employees who meet the criteria obtained in step 4, and the output is a list that can be reviewed by the administrator. This operation allows the administrator to review the candidate list in detail.

[0648] Step 6:

[0649] The user reviews the generated list and adds corrections and comments as needed. The input is the candidate list, and the output is the reviewed and corrected list. In this step, the administrator makes a final confirmation of employee salary increases and promotions.

[0650] Step 7:

[0651] The server activates an emotion recognition engine to recognize the user's emotions. Input is the user's interface actions and voice data, and output is the analyzed emotion data. This analysis identifies the administrator's current emotional state.

[0652] Step 8:

[0653] The server readjusts the list of candidates for salary increases and promotions based on emotional data acquired by the emotion recognition engine. The input is the emotional data and the reviewed / corrected list, while the output is the final adjusted list. Considering emotional data leads to more desirable employee evaluations.

[0654] Step 9:

[0655] After making the final decision, the server sends a notification to the employees who have been approved for a raise or promotion. The input is the list after final adjustments, and the output is the notification sent to the employee's terminal. This notification allows employees to immediately know that their raise or promotion has been confirmed.

[0656] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0657] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0658] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0659] [Third Embodiment]

[0660] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0661] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0662] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0663] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0664] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0665] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0666] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0667] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0668] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0669] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0670] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0671] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0672] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. The following describes the specific program processing and its implementation.

[0673] Data collection

[0674] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0675] Calculation of evaluation score

[0676] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0677] Setting standards

[0678] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0679] Automatic decision

[0680] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0681] List generation

[0682] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0683] Confirmation / Correction

[0684] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0685] notification

[0686] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0687] Through the above process, the salary increase and promotion process is conducted transparently and fairly. For example, employee A is added to the promotion list, and after final confirmation by the user (administrator), a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. This involves using SQL queries to extract the necessary data.

[0691] Step 2:

[0692] The server temporarily stores the acquired data in memory. This includes information such as employee ID, name, job title, department, and performance score. The data is saved in an appropriate format.

[0693] Step 3:

[0694] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation score of 80 points or higher" or "length of service of 3 years or more" may be entered.

[0695] Step 4:

[0696] The server verifies the entered criteria and saves them to the database. The criteria data is managed with a unique ID. This criterion value is used in subsequent processing.

[0697] Step 5:

[0698] The server begins the scoring process based on the collected employee data. First, it compares each employee's evaluation points and years of service with a standard to determine whether the standard is met.

[0699] Step 6:

[0700] The server calculates each employee's score and adds fields related to the "score" to the database. A flag is also set here to determine whether or not the criteria are met.

[0701] Step 7:

[0702] The server filters out employees whose scores meet the "pass" criteria and adds the filtered employee data to the salary increase / promotion list. This generates a basic salary increase / promotion list.

[0703] Step 8:

[0704] The server displays the generated list to the user (administrator) on the administration screen. The list includes details such as each employee's name, position, performance score, and years of service.

[0705] Step 9:

[0706] The user (administrator) can view the salary increase / promotion list generated through the administration screen. On the administration screen, they can view the employee information displayed in the list and edit or add comments as needed.

[0707] Step 10:

[0708] After the user has finished reviewing the list, they press a button to notify the server of their final decision. Upon receiving this input, the server confirms the final list.

[0709] Step 11:

[0710] The server notifies each employee based on the finalized list of salary increases and promotions. This notification process includes generating an internal notification email and sending it to each employee's device.

[0711] Step 12:

[0712] The device receives an email and displays a notification to the employee. The notification includes details about salary increases or promotions, and information about the next steps (e.g., orientation information regarding the new position).

[0713] By following these steps, employee salary increases and promotions can be conducted fairly and transparently, resulting in outcomes that satisfy everyone.

[0714] (Example 1)

[0715] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0716] Traditional methods for determining salary increases and promotions often relied on subjective judgment, making it difficult to conduct fair and objective evaluations. Furthermore, the manual process of setting criteria for salary increases and promotions, as well as verifying and correcting results, was time-consuming and labor-intensive, and prone to errors and misunderstandings.

[0717] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0718] This invention includes a server that includes means for accessing a database of employees and obtaining performance evaluation data, project completion rates, working hours, skill sets, etc., for all employees; means for calculating an evaluation score for each employee based on the data obtained by the server; means for an administrator to input criteria for salary increases and promotions and for the server to store those criteria; means for the server to automatically determine salary increases and promotions for each employee based on the set criteria; means for the server to generate a list of candidates for salary increases and promotions and provide an interface for administrators to review and modify it; and means for the server to send a notification to the relevant employees after the final decision. This makes it possible to conduct the salary increase and promotion process transparently and fairly, and to prevent errors and inefficiencies caused by manual work.

[0719] A "server" refers to a computer or a system with those functions for performing calculations, managing data, and connecting to a network.

[0720] "Working adults" refers to individuals who work for a company or organization, and includes those who are subject to employee evaluation.

[0721] A "database" refers to a system or software used to systematically store and manage data.

[0722] "Performance evaluation data" refers to data that records the work performance and results of individual employees as numerical values ​​and evaluations.

[0723] "Project completion rate" refers to an indicator that shows the percentage of project goals that were actually achieved.

[0724] "Working hours" refers to data that measures the actual time an employee spends working at their workplace.

[0725] A "skill set" refers to the collection of skills, knowledge, and abilities that a working professional possesses.

[0726] "Evaluation score" refers to a comprehensive numerical evaluation calculated based on collected performance evaluation data and other evaluation factors.

[0727] "Criteria" refers to the conditions or evaluation items set up for use in determining salary increases and promotions.

[0728] An "interface" refers to the user interface or tools that allow a user to interact with a system.

[0729] "Notification" refers to messages or alerts used by a system to transmit information to professionals.

[0730] A "list" refers to a organized display of information about candidates for salary increases and promotions.

[0731] This invention relates to a system for fairly and objectively determining salary increases and promotions for working adults. This system collects data on working adults, calculates evaluation scores, and determines salary increases and promotions based on established criteria. The following describes the specific program processing and its implementation.

[0732] Data collection

[0733] The server accesses a database of working professionals to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. Specific SQL queries are used to extract the data. For example, a query like "SELECT FROM employee_performance WHERE employee_id = 'A123'" might be used.

[0734] Calculation of evaluation score

[0735] Based on the data acquired by the server, an evaluation score is calculated for each employee. During this process, programming languages ​​such as Python and R are used to analyze the data and weight different evaluation criteria. For example, a weighting scheme such as "evaluation points × 0.5 + years of service × 0.3 + project completion rate × 0.2" is used to calculate the overall score.

[0736] Setting standards

[0737] The user (administrator) inputs the criteria for salary increases and promotions through an interface, and the server saves these settings. The administrator enters "evaluation points of 80 or higher AND years of service of 3 or more" into a form on a web browser, and the server saves these criteria to the database. A specific SQL query would be something like "INSERT INTO promotion_criteria (point, years) VALUES (80, 3)".

[0738] Automatic decision

[0739] The server automatically determines each employee's salary increase and promotion based on the configured criteria. To filter employees whose evaluation scores meet the criteria, a query such as "SELECT FROM employee_scores WHERE score >= 80 AND years >= 3" is executed.

[0740] List generation

[0741] The server generates a list of candidates for salary increases and promotions. The generated list is displayed on the web interface and can be viewed by administrators in the administration panel. The list includes information such as the candidate's name, performance score, and job title.

[0742] Confirmation / Correction

[0743] The user (administrator) reviews the list generated in the administration screen and adds corrections and comments as needed. The administration screen is built using frameworks such as Django or Flask. For example, the administrator can view the list in a browser and perform operations such as "correcting employee B's evaluation score and adding the comment 'Exceptional Promotion'."

[0744] notification

[0745] After the server makes the final decision regarding salary increases and promotions, it sends a notification email to the employee concerned. The notification is sent using Python's smtplib or Django's email functionality. The notification contains details about the employee's salary increase or promotion. For example, it might include a message like, "Your promotion has been approved. Your new position is XX."

[0746] Examples of prompt statements

[0747] "Please add employees who meet the following conditions to the salary increase list: performance evaluation score of 80 points or higher and length of service of 3 years or more."

[0748] This system ensures that the salary increase and promotion process is transparent and fair, preventing errors and inefficiencies caused by manual processes. For example, employee A is added to the promotion list, and after final confirmation by the user (administrator), a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0749] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0750] Step 1: Data Collection

[0751] The server accesses a database of working employees to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. Specifically, the server executes "SELECT FROM employee_performance" and temporarily stores the retrieved data in memory. The input is a database query, and the output is a list of performance evaluation data for all employees.

[0752] Step 2: Calculation of evaluation score

[0753] Based on the data acquired by the server, an evaluation score is calculated for each employee. Here, a Python script is used to calculate an overall score by summing and weighting evaluation points, years of service, and project completion rate. Specifically, a formula like "evaluation points × 0.5 + years of service × 0.3 + project completion rate × 0.2" is applied. The input is the acquired data for each employee, and the output is each employee's evaluation score.

[0754] Step 3: Setting Criteria

[0755] The user (administrator) inputs the criteria for salary increases and promotions through an interface. For example, the administrator enters "evaluation points of 80 or higher AND years of service of 3 or more" into a web form, and the server saves this setting to the database. Specifically, the query "INSERT INTO promotion_criteria (point, years) VALUES (80, 3)" is executed. The input is the criteria data entered by the administrator, and the output is the saved criteria data.

[0756] Step 4: Automatic decision

[0757] The server automatically determines salary increases and promotions for each employee based on the configured criteria. Here, the system filters employees who meet the criteria based on their evaluation scores and executes the query "SELECT FROM employee_scores WHERE score >= 80 AND years >= 3". The inputs are the criteria data and the evaluation score data, and the output is a list of candidates for salary increases and promotions.

[0758] Step 5: Generating the list

[0759] The server generates a list of candidates for salary increases and promotions. The generated list is displayed in a format that administrators can view on a web interface. Specifically, it dynamically generates HTML pages to display each item in the list (name, performance score, job title, etc.). The input is data on the candidates for salary increases and promotions, and the output is a displayable list.

[0760] Step 6: Review and Correction

[0761] The user (administrator) reviews the list generated in the administration screen and adds corrections and comments as needed. The administrator clicks on a list item in the browser to access a form for entering corrections and comments. Specifically, an "UPDATE" query is executed to reflect the corrected data in the database. The input is the correction data made by the administrator, and the output is the updated list.

[0762] Step 7: Notification

[0763] After the server makes the final decision regarding salary increases and promotions, it sends notification emails to the relevant employees. The notifications are sent using Python's smtplib and Django's email functionality. Specifically, it retrieves each employee's email address and sends an email stating, "Your promotion has been approved. Your new position is XX." The input is the list data after the final decision, and the output is the sent notification email.

[0764] (Application Example 1)

[0765] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0766] Currently, decisions regarding the maintenance and upgrade of factory machinery often rely on the subjective judgment of managers, which can lead to a lack of fairness and efficiency. Furthermore, the collection of machine operation data and the calculation of evaluation scores are performed manually, which is time-consuming, labor-intensive, and prone to errors. Therefore, the present invention aims to provide a system for making fair and efficient decisions regarding the maintenance and upgrade of factory machinery.

[0767] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0768] In this invention, the server includes means for collecting data on factory machinery, means for calculating evaluation scores based on that data, means for automatically deciding on maintenance or upgrades of factory machinery based on set criteria, means for generating the decision results as a list, means for an administrator to review and modify the generated list, and means for notifying the final decision. This makes it possible to fairly and efficiently decide on maintenance and upgrades of factory machinery and manage them through an automated process.

[0769] "Factory machinery" refers to automated equipment and systems used in industrial production.

[0770] "Means of collecting data" refers to devices or systems that acquire and record operational information of machines.

[0771] An "evaluation score" is an index that quantifies the performance and condition of a machine based on collected data.

[0772] "Criteria" refers to the set values ​​that indicate the evaluation points and conditions used when deciding on maintenance or upgrades.

[0773] An "automatic decision-making mechanism" is a system that automatically determines the need for machine maintenance or upgrades based on set criteria.

[0774] "Method for generating as a list" refers to a function that summarizes the decision results in a list format so that administrators can review them.

[0775] A "manager" is a person responsible for the operation of a factory and the maintenance of its machinery.

[0776] The "means of review and correction" refer to a function that allows administrators to review the generated list and make changes as needed.

[0777] "Means of notifying final decisions" refers to a system that informs relevant personnel of confirmed maintenance or upgrade information.

[0778] To implement the present invention, a system including the following elements is required: The process from data collection to notification is automated in order to efficiently and fairly manage the maintenance and upgrades of factory machinery.

[0779] Basic configuration

[0780] This system consists of a sensor system for collecting data from factory machinery, a server, an administrator terminal, and a notification system.

[0781] Data collection

[0782] The server collects operational data from factory machinery using a sensor system placed throughout the factory. This sensor system records data such as operating hours, number of failures, work efficiency, and consumable parts usage rate in real time. The collected data is sent to the server and stored in a database. SQLite is used as the specific database software.

[0783] Calculation of evaluation score

[0784] The server calculates a machine evaluation score based on the collected data. The evaluation score is calculated based on pre-set weights for each element. For example, uptime might be weighted 20%, failure rate -50%, work efficiency 40%, and consumable parts usage rate 30%.

[0785] Setting criteria and filtering

[0786] Administrators can enter criteria and save them on the server. These criteria can include uptime and the number of failures. For example, a criterion might be set as "uptime exceeds 1000 hours and the number of failures is less than 3." Based on these criteria, the server automatically filters machines that require maintenance or upgrades and generates a list of candidates.

[0787] List generation and verification

[0788] The server generates a list of the decision results and displays it on the administrator's terminal for review and modification. The list includes the name of each machine, its evaluation score, and the required maintenance items. Administrators can review this list through the interface and add modifications or comments as needed.

[0789] notification

[0790] After final confirmation by the administrator, the server notifies relevant personnel of any maintenance or upgrade decisions via a notification system. These notifications are sent to administrators and maintenance personnel's terminals via email or alerts, enabling a swift response.

[0791] Specific example

[0792] For example, suppose the data for robot A in a factory is "1500 operating hours, 2 failures, 95% work efficiency, and 80% consumable parts usage." The server calculates an evaluation score based on this data, and the overall evaluation is "90 points." Subsequently, this robot is added to the list of maintenance candidates based on the set criteria. The administrator reviews and modifies the list, and if it is determined that maintenance is necessary, a notification is sent to the maintenance personnel.

[0793] Example of a prompt

[0794] "Develop an automated evaluation and maintenance decision system for factory robots. The collected data will include operating hours, failure count, work efficiency, and consumable parts usage rate. The system will calculate an evaluation score and notify maintenance based on predefined criteria. Use SQLite for the database and SMTPLib for notifications."

[0795] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0796] Step 1:

[0797] The server collects data from sensor systems placed throughout the factory. It receives data such as operating time, failure count, work efficiency, and consumable parts usage rate for each machine as input. This data is collected in real time and stored in a database. The stored data is then used for further processing as output. Specifically, it receives data transmitted from sensors and saves it to a database (e.g., SQLite).

[0798] Step 2:

[0799] The server calculates evaluation scores based on data stored in the database. It uses the operating time, number of failures, work efficiency, and consumable parts usage rate for each machine, collected as input. It applies the set weights to these data items to calculate an integrated evaluation score. The output generates an evaluation score for each machine. Specifically, it calculates the evaluation score using a weighted formula and temporarily stores the result in memory.

[0800] Step 3:

[0801] The user (administrator) sets the criteria. As input, the user enters various criterion values, such as uptime and failure count, through the interface. These set criteria are stored in the database. As output, the set criteria are used in subsequent filtering processes. Specifically, the criteria values ​​entered by the administrator are stored in the database.

[0802] Step 4:

[0803] The server filters the collected data based on configured criteria and automatically determines which machines require maintenance or upgrades. It uses evaluation scores and configured criteria as inputs. This selects machines that meet the criteria as maintenance candidates. A candidate list is generated as output. Specifically, it compares the configured criteria with the evaluation scores and extracts machines that meet the criteria.

[0804] Step 5:

[0805] The server generates a list of the decision results and displays it on the administrator's terminal. It receives filtered machine information as input. The output is displayed to the administrator in list format, allowing them to review and modify the information. Specifically, it generates a list and displays it to the administrator via a web interface or dedicated software.

[0806] Step 6:

[0807] The user (administrator) reviews and modifies the list. The input is the list displayed from the server. Modifications and comments are entered as needed. The output is the final decision regarding maintenance or upgrades. Specific actions include the administrator reviewing the list and making necessary changes.

[0808] Step 7:

[0809] The server notifies the final decision. It receives the list confirmed by the administrator as input. As output, notifications are sent to maintenance personnel and relevant staff. Specifically, this involves sending an email using the SMTP protocol and displaying it as an alert on the staff member's terminal.

[0810] This process makes it possible to automate the maintenance and upgrade of factory machinery fairly and efficiently.

[0811] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0812] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. Furthermore, by incorporating an emotion engine that recognizes user emotions, more nuanced management becomes possible. The following describes the specific program processing and its implementation.

[0813] Data collection

[0814] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0815] Calculation of evaluation score

[0816] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0817] Setting standards

[0818] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0819] Automatic decision

[0820] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0821] List generation

[0822] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0823] Using an Emotion Engine

[0824] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0825] List adjustment

[0826] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0827] Final review and corrections

[0828] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0829] notification

[0830] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0831] Through the above process, employee salary increases and promotions are carried out fairly and transparently. As a specific example, employee A is added to the promotion list, and after the user (administrator) makes a final confirmation, the emotion engine detects some doubts and makes additional adjustments, and finally a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[0832] The following describes the processing flow.

[0833] Step 1:

[0834] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. SQL queries are used to extract the necessary data. For example, data such as "Employee A: Evaluation score 85 points, years of service 5 years, project completion rate 100%" might be retrieved.

[0835] Step 2:

[0836] The server temporarily stores the acquired data in memory. This includes employee ID, name, job title, department, and performance score. This data will be used in subsequent processes.

[0837] Step 3:

[0838] The user (administrator) inputs criteria for salary increases and promotions through the interface. For example, they can set conditions such as "evaluation score of 80 points or higher" or "length of service of 3 years or more." The server stores these criteria in the database.

[0839] Step 4:

[0840] The server starts the scoring process based on the collected employee data. It compares each employee's evaluation points and years of service against a set standard to determine whether they meet the established criteria.

[0841] Step 5:

[0842] The server calculates each employee's score and adds a field related to the "score" to the database. A flag is also set to indicate whether or not the criteria have been met. For example, employee A is marked as "passed" because they meet the criteria.

[0843] Step 6:

[0844] The server filters employees who have passed the initial screening and generates a list of candidates for salary increases and promotions. The filtered employee data is added to the list and grouped together as candidates for salary increases and promotions.

[0845] Step 7:

[0846] The server displays the generated list to the user (administrator) on the administration screen. The list includes details such as each employee's name, position, performance score, and years of service.

[0847] Step 8:

[0848] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0849] Step 9:

[0850] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0851] Step 10:

[0852] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows the user to perform a human review before making a final decision.

[0853] Step 11:

[0854] After the user has finished reviewing the list, they notify the server of their final decision. This input triggers the server to confirm the final list.

[0855] Step 12:

[0856] The server notifies each employee based on the confirmed salary increase / promotion list. The notification is sent to the employee's device as an email or alert and includes detailed information about the salary increase or promotion.

[0857] Step 13:

[0858] The device receives emails and alerts, displaying notifications to employees. This allows employees to instantly know about decisions regarding their salary increases and promotions.

[0859] The above processing steps ensure that employee raises and promotions are carried out fairly and transparently, and the use of an emotional engine provides further management flexibility. For example, employee A is added to the promotion list, the user (administrator) makes a final review, and if the emotional engine detects any doubts, additional adjustments are made. Finally, a promotion notification is sent to employee A's terminal. In this way, raises and promotions are implemented in a manner that is acceptable to all employees.

[0860] (Example 2)

[0861] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0862] Traditional systems for determining employee salary increases and promotions are based on objective data, but they often leave room for dissatisfaction and doubt because they do not reflect the emotions or intuition of managers. Furthermore, the decision-making process for salary increases and promotions can be time-consuming and lack transparency. It is necessary to address these challenges and implement a fairer and more efficient decision-making process for salary increases and promotions.

[0863] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0864] In this invention, the server includes means for collecting employee data, means for calculating evaluation scores, means for automatically determining employee salary increases and promotions based on set criteria, means for recognizing the manager's emotions and automatically adjusting the generated list based on those emotions, means for the manager to review and modify the generated list, and means for notifying the employee of the final decision. This makes it possible to make fair and transparent salary increase and promotion decisions that reflect not only evaluation data but also the manager's emotions and intuition.

[0865] "Means of collecting employee data" refers to means that include the function of obtaining information such as employee performance evaluation data, project completion rates, working hours, and skill sets from a database.

[0866] "Means for calculating evaluation scores" refers to means that include a function for calculating an overall evaluation score based on collected employee data.

[0867] "Means for automatically determining employee salary increases and promotions based on established criteria" refers to means that include a function to automatically determine employee salary increases and promotions according to evaluation criteria set by the manager.

[0868] "Means for generating decision results as a list" refers to means that include a function to create a list of employees who meet the criteria for salary increases or promotions, and to display or save that list.

[0869] "An emotion engine means that recognizes the emotions of administrators and automatically adjusts the list generated based on those emotions" refers to a means that includes a function to analyze administrator emotion data and adjust the list of salary increases and promotions based on those emotions.

[0870] "Means for administrators to review and modify the generated list" refers to means that administrators can review the generated list of candidates for salary increases and promotions, and add modifications or comments as needed.

[0871] "Means of notifying employees of final decisions" include means of sending employees emails or alerts regarding final decisions on salary increases or promotions.

[0872] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. Furthermore, by incorporating an emotion engine that recognizes user emotions, more nuanced management becomes possible.

[0873] Data collection

[0874] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0875] Calculation of evaluation score

[0876] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0877] Setting standards

[0878] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0879] Automatic decision

[0880] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0881] List generation

[0882] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[0883] Using an Emotion Engine

[0884] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[0885] List adjustment

[0886] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[0887] Final review and corrections

[0888] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[0889] notification

[0890] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[0891] A concrete example of a prompt message is: "Calculate the evaluation scores for the following employee data and generate a promotion list: Employee A: Evaluation 85 points, Years of service 5 years, Project completion rate 100%, Employee B: Evaluation 78 points, Years of service 2 years, Project completion rate 90%."

[0892] This system ensures that employee salary increases and promotions are fair and transparent. For example, if employee A is added to the promotion list and the user (administrator) makes a final confirmation, the emotion engine may detect doubts and make further adjustments before finally sending a promotion notification to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that satisfies all employees.

[0893] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0894] Step 1: Data Collection

[0895] The server accesses the employee database and retrieves data for all employees. The input is a list of employee IDs, and the server retrieves corresponding performance evaluation data, project completion rates, working hours, and skill sets from the database. Specifically, the server uses a database connection driver to execute SQL queries and load the employee data into memory. The output is the collected employee data.

[0896] Step 2: Calculation of evaluation score

[0897] The server calculates each employee's evaluation score based on the data it collects. The input is the collected employee data. Specifically, the server applies a formula that uses evaluation points, years of service, and project completion rate to calculate an overall evaluation score. The output is the overall evaluation score for each employee.

[0898] Step 3: Setting Criteria

[0899] The user (administrator) inputs criteria for salary increases and promotions through an interface. The input consists of criteria set by the administrator. Specifically, the administrator inputs the criteria into the interface, which is then sent to the server and saved to the database. The output is the set criteria saved to the database.

[0900] Step 4: Automatic decision

[0901] The server automatically determines each employee's salary increase and promotion based on the configured criteria. The inputs are the evaluation scores of all employees and the configured criteria. Specifically, the server filters employees who meet the criteria and adds them to a list of promotion candidates. The output is a list of candidates.

[0902] Step 5: Generating the list

[0903] The server generates a list of candidates for salary increases and promotions and displays it to the user (administrator) on the administration screen. The input is a list of candidates for salary increases and promotions. Specifically, the server generates a list from the candidate data, generates HTML, and sends it to the browser. The output is the list that will be displayed on the administration screen.

[0904] Step 6: Utilizing the Emotional Engine

[0905] The server activates the emotion engine and recognizes the user's (administrator's) emotions. Inputs include user interface operations, keyboard input, and voice data. Specifically, the server calls the emotion engine's API and analyzes the emotion data. The output is the user's emotional state.

[0906] Step 7: Adjust the list

[0907] The server automatically adjusts the list generated based on emotion data recognized by the emotion engine. The input consists of user emotion state data and a list of candidates. Specifically, the server recalculates the list contents based on the emotion state and filters as needed. The output is the adjusted list.

[0908] Step 8: Final review and corrections

[0909] The user (administrator) reviews the generated list and adds corrections and comments. The input is an adjusted list. Specifically, the user reviews the list through the administration screen and adds corrections and comments. This correction information is sent to the server and stored in the database. The output is the final, corrected list.

[0910] Step 9: Notification

[0911] The server notifies employees of the final decision. The input is the final list. Specifically, the server generates the notification content, refers to the employee's email address and notification settings, and sends the notification via the appropriate medium (email or alert). The output is a confirmation message to the notified employee.

[0912] (Application Example 2)

[0913] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0914] Conventional salary increase and promotion management systems can automatically determine salary increases and promotions based on employee evaluation data, but they have the problem of not being able to make flexible adjustments that take into account the emotions of managers. In particular, in the performance evaluation management of robots working in factories, there is a need for evaluations that take into account not only the robot's operational data but also the emotions of managers. Therefore, this system solves the problem of achieving fairer and more efficient management by recognizing the emotions of managers and adjusting the evaluation list based on them.

[0915] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0916] In this invention, the server includes means for collecting employee data, means for calculating evaluation scores based on that data, means for automatically determining employee salary increases and promotions based on set criteria, means for generating the decision results as a list, means for the administrator to review and modify the generated list, means including an emotion recognition engine that recognizes the administrator's emotions and adjusts the list, and means for notifying the employee of the final decision. This enables fair and efficient salary increases and promotions that reflect the administrator's emotions.

[0917] "Employee data" is a general term for information including each employee's performance evaluation data, project completion rate, working hours, skill set, etc.

[0918] An "evaluation score" is a numerical representation of an employee's performance and abilities, calculated based on collected employee data.

[0919] "Established criteria" refers to conditions such as evaluation points, years of service, and project completion rates that are used as criteria for determining salary increases and promotions.

[0920] An "automatic decision-making mechanism" is a mechanism for automatically determining and deciding on employee salary increases and promotions based on established criteria.

[0921] "Means of generating as a list" refers to a function that outputs evaluation results or promotion candidates in list format.

[0922] The "means of review and correction" refer to a function that allows administrators to review the generated list and make corrections as needed.

[0923] An "emotion recognition engine" is a system or algorithm that analyzes the administrator's emotions and adjusts lists based on those emotions.

[0924] "Means of notifying employees of the final decision" refers to communication methods such as email or alerts used to inform employees of the final decision regarding salary increases or promotions.

[0925] This invention relates to a system for fairly and objectively determining employee salary increases and promotions, and, as an example of its application, to a work performance evaluation management application for factory robots. This system collects employee data, calculates evaluation scores, and automatically determines salary increases and promotions based on set criteria. Furthermore, by incorporating an emotion recognition engine, management that takes the manager's emotions into account becomes possible.

[0926] System program description

[0927] The server performs data processing and calculations using the following methods.

[0928] 1. Methods for collecting employee data

[0929] The server collects data from the employee database, including employee performance evaluations, project completion rates, working hours, and skill sets. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0930] 2. Method for calculating evaluation scores

[0931] Based on the collected data, the server calculates an evaluation score for each employee. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0932] 3. Means of setting standards

[0933] The administrator sets the criteria for salary increases and promotions. The server stores these criteria in a database. For example, a criterion such as "performance score of 80 points or higher AND years of service of 3 years or more" might be set.

[0934] 4. Automatic determination methods for salary increases and promotions

[0935] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[0936] 5. List generation, verification, and modification methods

[0937] The server generates a list of candidates for salary increases and promotions, which is displayed on the administration screen for administrators to review. Administrators can review this list and make corrections or add comments as needed.

[0938] 6. List adjustment method using an emotion recognition engine

[0939] An emotion recognition engine is used to understand the administrator's emotions. The emotion recognition engine analyzes the administrator's interface interactions and voice data to determine their emotions. For example, if the administrator indicates dissatisfaction, the list is readjusted based on that information.

[0940] 7. Final decision and notification means

[0941] After the server makes the final decision, it sends a notification to employees whose salary increases or promotions have been approved. The notification appears on the employee's device as an email or alert. This allows employees to immediately know that their salary increase or promotion has been approved.

[0942] Hardware and software to be used

[0943] Hardware:

[0944] Factory robots

[0945] Administrator's smartphone or PC

[0946] Cameras and microphones for emotion recognition engines

[0947] software:

[0948] Python program

[0949] Emotion recognition engine (e.g., various general-purpose emotion APIs)

[0950] Database management system (e.g., one type of RDBMS)

[0951] Adding specific examples

[0952] For example, in the performance evaluation management of factory robots, suppose robot A has an efficiency of 85%, an error rate of 3%, and 100 hours of operation. The criteria set by the manager are "efficiency of 80% or higher and an error rate of 5% or lower," and since robot A meets these criteria, it is added to the list of robots that do not require maintenance. If the manager expresses dissatisfaction while using the app, the recognition engine will determine that emotion and readjust the list accordingly.

[0953] Examples of prompts to input into a generative AI model:

[0954] "Robot A has an efficiency of 85%, an error rate of 3%, an operating time of 100 hours, and the administrator's emotional state is neutral."

[0955] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0956] Step 1:

[0957] The server accesses the employee database and collects data such as performance evaluations, project completion rates, working hours, and skill sets for all employees. The input is the employee database, and the output is an evaluation dataset for each employee. This data processing clarifies the evaluation elements for each employee.

[0958] Step 2:

[0959] The server calculates each employee's performance score based on the collected data. The input is the employee dataset obtained in Step 1, and the output is each employee's performance score. Data calculations generate an overall score that takes into account each employee's performance evaluation data, project completion rate, working hours, and skill set.

[0960] Step 3:

[0961] Users set the criteria for salary increases and promotions on the server. The input is the criteria set by the user, and the output is the database where those criteria are stored. This setting reflects the criteria for salary increases and promotions in the system.

[0962] Step 4:

[0963] The server automatically determines each employee's salary increase and promotion based on the set criteria. The inputs are the evaluation score obtained in step 2 and the criteria set in step 3, and the output is a list of employees who meet the criteria. In this process, the evaluation scores are filtered to see if they meet the set criteria.

[0964] Step 5:

[0965] The server generates a list of candidates for salary increases and promotions and displays it on the administration screen for user review. The input is a list of employees who meet the criteria obtained in step 4, and the output is a list that can be reviewed by the administrator. This operation allows the administrator to review the candidate list in detail.

[0966] Step 6:

[0967] The user reviews the generated list and adds corrections and comments as needed. The input is the candidate list, and the output is the reviewed and corrected list. In this step, the administrator makes a final confirmation of employee salary increases and promotions.

[0968] Step 7:

[0969] The server activates an emotion recognition engine to recognize the user's emotions. Input is the user's interface actions and voice data, and output is the analyzed emotion data. This analysis identifies the administrator's current emotional state.

[0970] Step 8:

[0971] The server readjusts the list of candidates for salary increases and promotions based on emotional data acquired by the emotion recognition engine. The input is the emotional data and the reviewed / corrected list, while the output is the final adjusted list. Considering emotional data leads to more desirable employee evaluations.

[0972] Step 9:

[0973] After making the final decision, the server sends a notification to the employees who have been approved for a raise or promotion. The input is the list after final adjustments, and the output is the notification sent to the employee's terminal. This notification allows employees to immediately know that their raise or promotion has been confirmed.

[0974] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0975] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0976] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0977] [Fourth Embodiment]

[0978] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0979] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0980] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0981] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0982] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0983] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0984] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0985] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0986] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0987] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0988] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0989] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0990] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0991] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. The following describes the specific program processing and its implementation.

[0992] Data collection

[0993] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[0994] Calculation of evaluation score

[0995] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[0996] Setting standards

[0997] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[0998] Automatic decision

[0999] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[1000] List generation

[1001] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[1002] Confirmation / Correction

[1003] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[1004] notification

[1005] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[1006] Through the above process, the salary increase and promotion process is conducted transparently and fairly. For example, employee A is added to the promotion list, and after final confirmation by the user (administrator), a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[1007] The following describes the processing flow.

[1008] Step 1:

[1009] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. This involves using SQL queries to extract the necessary data.

[1010] Step 2:

[1011] The server temporarily stores the acquired data in memory. This includes information such as employee ID, name, job title, department, and performance score. The data is saved in an appropriate format.

[1012] Step 3:

[1013] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation score of 80 points or higher" or "length of service of 3 years or more" may be entered.

[1014] Step 4:

[1015] The server verifies the entered criteria and saves them to the database. The criteria data is managed with a unique ID. This criterion value is used in subsequent processing.

[1016] Step 5:

[1017] The server begins the scoring process based on the collected employee data. First, it compares each employee's evaluation points and years of service with a standard to determine whether the standard is met.

[1018] Step 6:

[1019] The server calculates each employee's score and adds fields related to the "score" to the database. A flag is also set here to determine whether or not the criteria are met.

[1020] Step 7:

[1021] The server filters out employees whose scores meet the "pass" criteria and adds the filtered employee data to the salary increase / promotion list. This generates a basic salary increase / promotion list.

[1022] Step 8:

[1023] The server displays the generated list to the user (administrator) on the administration screen. The list includes details such as each employee's name, position, performance score, and years of service.

[1024] Step 9:

[1025] The user (administrator) can view the salary increase / promotion list generated through the administration screen. On the administration screen, they can view the employee information displayed in the list and edit or add comments as needed.

[1026] Step 10:

[1027] After the user has finished reviewing the list, they press a button to notify the server of their final decision. Upon receiving this input, the server confirms the final list.

[1028] Step 11:

[1029] The server notifies each employee based on the finalized list of salary increases and promotions. This notification process includes generating an internal notification email and sending it to each employee's device.

[1030] Step 12:

[1031] The device receives an email and displays a notification to the employee. The notification includes details about salary increases or promotions, and information about the next steps (e.g., orientation information regarding the new position).

[1032] By following these steps, employee salary increases and promotions can be conducted fairly and transparently, resulting in outcomes that satisfy everyone.

[1033] (Example 1)

[1034] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1035] Traditional methods for determining salary increases and promotions often relied on subjective judgment, making it difficult to conduct fair and objective evaluations. Furthermore, the manual process of setting criteria for salary increases and promotions, as well as verifying and correcting results, was time-consuming and labor-intensive, and prone to errors and misunderstandings.

[1036] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1037] This invention includes a server that includes means for accessing a database of employees and obtaining performance evaluation data, project completion rates, working hours, skill sets, etc., for all employees; means for calculating an evaluation score for each employee based on the data obtained by the server; means for an administrator to input criteria for salary increases and promotions and for the server to store those criteria; means for the server to automatically determine salary increases and promotions for each employee based on the set criteria; means for the server to generate a list of candidates for salary increases and promotions and provide an interface for administrators to review and modify it; and means for the server to send a notification to the relevant employees after the final decision. This makes it possible to conduct the salary increase and promotion process transparently and fairly, and to prevent errors and inefficiencies caused by manual work.

[1038] A "server" refers to a computer or a system with those functions for performing calculations, managing data, and connecting to a network.

[1039] "Working adults" refers to individuals who work for a company or organization, and includes those who are subject to employee evaluation.

[1040] A "database" refers to a system or software used to systematically store and manage data.

[1041] "Performance evaluation data" refers to data that records the work performance and results of individual employees as numerical values ​​and evaluations.

[1042] "Project completion rate" refers to an indicator that shows the percentage of project goals that were actually achieved.

[1043] "Working hours" refers to data that measures the actual time an employee spends working at their workplace.

[1044] A "skill set" refers to the collection of skills, knowledge, and abilities that a working professional possesses.

[1045] "Evaluation score" refers to a comprehensive numerical evaluation calculated based on collected performance evaluation data and other evaluation factors.

[1046] "Criteria" refers to the conditions or evaluation items set up for use in determining salary increases and promotions.

[1047] An "interface" refers to the user interface or tools that allow a user to interact with a system.

[1048] "Notification" refers to messages or alerts used by a system to transmit information to professionals.

[1049] A "list" refers to a organized display of information about candidates for salary increases and promotions.

[1050] This invention relates to a system for fairly and objectively determining salary increases and promotions for working adults. This system collects data on working adults, calculates evaluation scores, and determines salary increases and promotions based on established criteria. The following describes the specific program processing and its implementation.

[1051] Data collection

[1052] The server accesses a database of working professionals to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. Specific SQL queries are used to extract the data. For example, a query like "SELECT FROM employee_performance WHERE employee_id = 'A123'" might be used.

[1053] Calculation of evaluation score

[1054] Based on the data acquired by the server, an evaluation score is calculated for each employee. During this process, programming languages ​​such as Python and R are used to analyze the data and weight different evaluation criteria. For example, a weighting scheme such as "evaluation points × 0.5 + years of service × 0.3 + project completion rate × 0.2" is used to calculate the overall score.

[1055] Setting standards

[1056] The user (administrator) inputs the criteria for salary increases and promotions through an interface, and the server saves these settings. The administrator enters "evaluation points of 80 or higher AND years of service of 3 or more" into a form on a web browser, and the server saves these criteria to the database. A specific SQL query would be something like "INSERT INTO promotion_criteria (point, years) VALUES (80, 3)".

[1057] Automatic decision

[1058] The server automatically determines each employee's salary increase and promotion based on the configured criteria. To filter employees whose evaluation scores meet the criteria, a query such as "SELECT FROM employee_scores WHERE score >= 80 AND years >= 3" is executed.

[1059] List generation

[1060] The server generates a list of candidates for salary increases and promotions. The generated list is displayed on the web interface and can be viewed by administrators in the administration panel. The list includes information such as the candidate's name, performance score, and job title.

[1061] Confirmation / Correction

[1062] The user (administrator) reviews the list generated in the administration screen and adds corrections and comments as needed. The administration screen is built using frameworks such as Django or Flask. For example, the administrator can view the list in a browser and perform operations such as "correcting employee B's evaluation score and adding the comment 'Exceptional Promotion'."

[1063] notification

[1064] After the server makes the final decision regarding salary increases and promotions, it sends a notification email to the employee concerned. The notification is sent using Python's smtplib or Django's email functionality. The notification contains details about the employee's salary increase or promotion. For example, it might include a message like, "Your promotion has been approved. Your new position is XX."

[1065] Examples of prompt statements

[1066] "Please add employees who meet the following conditions to the salary increase list: performance evaluation score of 80 points or higher and length of service of 3 years or more."

[1067] This system ensures that the salary increase and promotion process is transparent and fair, preventing errors and inefficiencies caused by manual processes. For example, employee A is added to the promotion list, and after final confirmation by the user (administrator), a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[1068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1069] Step 1: Data Collection

[1070] The server accesses a database of working employees to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. Specifically, the server executes "SELECT FROM employee_performance" and temporarily stores the retrieved data in memory. The input is a database query, and the output is a list of performance evaluation data for all employees.

[1071] Step 2: Calculation of evaluation score

[1072] Based on the data acquired by the server, an evaluation score is calculated for each employee. Here, a Python script is used to calculate an overall score by summing and weighting evaluation points, years of service, and project completion rate. Specifically, a formula like "evaluation points × 0.5 + years of service × 0.3 + project completion rate × 0.2" is applied. The input is the acquired data for each employee, and the output is each employee's evaluation score.

[1073] Step 3: Setting Criteria

[1074] The user (administrator) inputs the criteria for salary increases and promotions through an interface. For example, the administrator enters "evaluation points of 80 or higher AND years of service of 3 or more" into a web form, and the server saves this setting to the database. Specifically, the query "INSERT INTO promotion_criteria (point, years) VALUES (80, 3)" is executed. The input is the criteria data entered by the administrator, and the output is the saved criteria data.

[1075] Step 4: Automatic decision

[1076] The server automatically determines salary increases and promotions for each employee based on the configured criteria. Here, the system filters employees who meet the criteria based on their evaluation scores and executes the query "SELECT FROM employee_scores WHERE score >= 80 AND years >= 3". The inputs are the criteria data and the evaluation score data, and the output is a list of candidates for salary increases and promotions.

[1077] Step 5: Generating the list

[1078] The server generates a list of candidates for salary increases and promotions. The generated list is displayed in a format that administrators can view on a web interface. Specifically, it dynamically generates HTML pages to display each item in the list (name, performance score, job title, etc.). The input is data on the candidates for salary increases and promotions, and the output is a displayable list.

[1079] Step 6: Review and Correction

[1080] The user (administrator) reviews the list generated in the administration screen and adds corrections and comments as needed. The administrator clicks on a list item in the browser to access a form for entering corrections and comments. Specifically, an "UPDATE" query is executed to reflect the corrected data in the database. The input is the correction data made by the administrator, and the output is the updated list.

[1081] Step 7: Notification

[1082] After the server makes the final decision regarding salary increases and promotions, it sends notification emails to the relevant employees. The notifications are sent using Python's smtplib and Django's email functionality. Specifically, it retrieves each employee's email address and sends an email stating, "Your promotion has been approved. Your new position is XX." The input is the list data after the final decision, and the output is the sent notification email.

[1083] (Application Example 1)

[1084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1085] Currently, decisions regarding the maintenance and upgrade of factory machinery often rely on the subjective judgment of managers, which can lead to a lack of fairness and efficiency. Furthermore, the collection of machine operation data and the calculation of evaluation scores are performed manually, which is time-consuming, labor-intensive, and prone to errors. Therefore, the present invention aims to provide a system for making fair and efficient decisions regarding the maintenance and upgrade of factory machinery.

[1086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1087] In this invention, the server includes means for collecting data on factory machinery, means for calculating evaluation scores based on that data, means for automatically deciding on maintenance or upgrades of factory machinery based on set criteria, means for generating the decision results as a list, means for an administrator to review and modify the generated list, and means for notifying the final decision. This makes it possible to fairly and efficiently decide on maintenance and upgrades of factory machinery and manage them through an automated process.

[1088] "Factory machinery" refers to automated equipment and systems used in industrial production.

[1089] "Means of collecting data" refers to devices or systems that acquire and record operational information of machines.

[1090] An "evaluation score" is an index that quantifies the performance and condition of a machine based on collected data.

[1091] "Criteria" refers to the set values ​​that indicate the evaluation points and conditions used when deciding on maintenance or upgrades.

[1092] An "automatic decision-making mechanism" is a system that automatically determines the need for machine maintenance or upgrades based on set criteria.

[1093] "Method for generating as a list" refers to a function that summarizes the decision results in a list format so that administrators can review them.

[1094] A "manager" is a person responsible for the operation of a factory and the maintenance of its machinery.

[1095] The "means of review and correction" refer to a function that allows administrators to review the generated list and make changes as needed.

[1096] "Means of notifying final decisions" refers to a system that informs relevant personnel of confirmed maintenance or upgrade information.

[1097] To implement the present invention, a system including the following elements is required: The process from data collection to notification is automated in order to efficiently and fairly manage the maintenance and upgrades of factory machinery.

[1098] Basic configuration

[1099] This system consists of a sensor system for collecting data from factory machinery, a server, an administrator terminal, and a notification system.

[1100] Data collection

[1101] The server collects operational data from factory machinery using a sensor system placed throughout the factory. This sensor system records data such as operating hours, number of failures, work efficiency, and consumable parts usage rate in real time. The collected data is sent to the server and stored in a database. SQLite is used as the specific database software.

[1102] Calculation of evaluation score

[1103] The server calculates a machine evaluation score based on the collected data. The evaluation score is calculated based on pre-set weights for each element. For example, uptime might be weighted 20%, failure rate -50%, work efficiency 40%, and consumable parts usage rate 30%.

[1104] Setting criteria and filtering

[1105] Administrators can enter criteria and save them on the server. These criteria can include uptime and the number of failures. For example, a criterion might be set as "uptime exceeds 1000 hours and the number of failures is less than 3." Based on these criteria, the server automatically filters machines that require maintenance or upgrades and generates a list of candidates.

[1106] List generation and verification

[1107] The server generates a list of the decision results and displays it on the administrator's terminal for review and modification. The list includes the name of each machine, its evaluation score, and the required maintenance items. Administrators can review this list through the interface and add modifications or comments as needed.

[1108] notification

[1109] After final confirmation by the administrator, the server notifies relevant personnel of any maintenance or upgrade decisions via a notification system. These notifications are sent to administrators and maintenance personnel's terminals via email or alerts, enabling a swift response.

[1110] Specific example

[1111] For example, suppose the data for robot A in a factory is "1500 operating hours, 2 failures, 95% work efficiency, and 80% consumable parts usage." The server calculates an evaluation score based on this data, and the overall evaluation is "90 points." Subsequently, this robot is added to the list of maintenance candidates based on the set criteria. The administrator reviews and modifies the list, and if it is determined that maintenance is necessary, a notification is sent to the maintenance personnel.

[1112] Example of a prompt

[1113] "Develop an automated evaluation and maintenance decision system for factory robots. The collected data will include operating hours, failure count, work efficiency, and consumable parts usage rate. The system will calculate an evaluation score and notify maintenance based on predefined criteria. Use SQLite for the database and SMTPLib for notifications."

[1114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1115] Step 1:

[1116] The server collects data from sensor systems placed throughout the factory. It receives data such as operating time, failure count, work efficiency, and consumable parts usage rate for each machine as input. This data is collected in real time and stored in a database. The stored data is then used for further processing as output. Specifically, it receives data transmitted from sensors and saves it to a database (e.g., SQLite).

[1117] Step 2:

[1118] The server calculates evaluation scores based on data stored in the database. It uses the operating time, number of failures, work efficiency, and consumable parts usage rate for each machine, collected as input. It applies the set weights to these data items to calculate an integrated evaluation score. The output generates an evaluation score for each machine. Specifically, it calculates the evaluation score using a weighted formula and temporarily stores the result in memory.

[1119] Step 3:

[1120] The user (administrator) sets the criteria. As input, the user enters various criterion values, such as uptime and failure count, through the interface. These set criteria are stored in the database. As output, the set criteria are used in subsequent filtering processes. Specifically, the criteria values ​​entered by the administrator are stored in the database.

[1121] Step 4:

[1122] The server filters the collected data based on configured criteria and automatically determines which machines require maintenance or upgrades. It uses evaluation scores and configured criteria as inputs. This selects machines that meet the criteria as maintenance candidates. A candidate list is generated as output. Specifically, it compares the configured criteria with the evaluation scores and extracts machines that meet the criteria.

[1123] Step 5:

[1124] The server generates a list of the decision results and displays it on the administrator's terminal. It receives filtered machine information as input. The output is displayed to the administrator in list format, allowing them to review and modify the information. Specifically, it generates a list and displays it to the administrator via a web interface or dedicated software.

[1125] Step 6:

[1126] The user (administrator) reviews and modifies the list. The input is the list displayed from the server. Modifications and comments are entered as needed. The output is the final decision regarding maintenance or upgrades. Specific actions include the administrator reviewing the list and making necessary changes.

[1127] Step 7:

[1128] The server notifies the final decision. It receives the list confirmed by the administrator as input. As output, notifications are sent to maintenance personnel and relevant staff. Specifically, this involves sending an email using the SMTP protocol and displaying it as an alert on the staff member's terminal.

[1129] This process makes it possible to automate the maintenance and upgrade of factory machinery fairly and efficiently.

[1130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1131] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. Furthermore, by incorporating an emotion engine that recognizes user emotions, more nuanced management becomes possible. The following describes the specific program processing and its implementation.

[1132] Data collection

[1133] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[1134] Calculation of evaluation score

[1135] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[1136] Setting standards

[1137] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[1138] Automatic decision

[1139] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[1140] List generation

[1141] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[1142] Using an Emotion Engine

[1143] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[1144] List adjustment

[1145] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[1146] Final review and corrections

[1147] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[1148] notification

[1149] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[1150] Through the above process, employee salary increases and promotions are carried out fairly and transparently. As a specific example, employee A is added to the promotion list, and after the user (administrator) makes a final confirmation, the emotion engine detects some doubts and makes additional adjustments, and finally a promotion notification is sent to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that is acceptable to all employees.

[1151] The following describes the processing flow.

[1152] Step 1:

[1153] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. SQL queries are used to extract the necessary data. For example, data such as "Employee A: Evaluation score 85 points, years of service 5 years, project completion rate 100%" might be retrieved.

[1154] Step 2:

[1155] The server temporarily stores the acquired data in memory. This includes employee ID, name, job title, department, and performance score. This data will be used in subsequent processes.

[1156] Step 3:

[1157] The user (administrator) inputs criteria for salary increases and promotions through the interface. For example, they can set conditions such as "evaluation score of 80 points or higher" or "length of service of 3 years or more." The server stores these criteria in the database.

[1158] Step 4:

[1159] The server starts the scoring process based on the collected employee data. It compares each employee's evaluation points and years of service against a set standard to determine whether they meet the established criteria.

[1160] Step 5:

[1161] The server calculates each employee's score and adds a field related to the "score" to the database. A flag is also set to indicate whether or not the criteria have been met. For example, employee A is marked as "passed" because they meet the criteria.

[1162] Step 6:

[1163] The server filters employees who have passed the initial screening and generates a list of candidates for salary increases and promotions. The filtered employee data is added to the list and grouped together as candidates for salary increases and promotions.

[1164] Step 7:

[1165] The server displays the generated list to the user (administrator) on the administration screen. The list includes details such as each employee's name, position, performance score, and years of service.

[1166] Step 8:

[1167] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[1168] Step 9:

[1169] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[1170] Step 10:

[1171] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows the user to perform a human review before making a final decision.

[1172] Step 11:

[1173] After the user has finished reviewing the list, they notify the server of their final decision. This input triggers the server to confirm the final list.

[1174] Step 12:

[1175] The server notifies each employee based on the confirmed salary increase / promotion list. The notification is sent to the employee's device as an email or alert and includes detailed information about the salary increase or promotion.

[1176] Step 13:

[1177] The device receives emails and alerts, displaying notifications to employees. This allows employees to instantly know about decisions regarding their salary increases and promotions.

[1178] The above processing steps ensure that employee raises and promotions are carried out fairly and transparently, and the use of an emotional engine provides further management flexibility. For example, employee A is added to the promotion list, the user (administrator) makes a final review, and if the emotional engine detects any doubts, additional adjustments are made. Finally, a promotion notification is sent to employee A's terminal. In this way, raises and promotions are implemented in a manner that is acceptable to all employees.

[1179] (Example 2)

[1180] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1181] Traditional systems for determining employee salary increases and promotions are based on objective data, but they often leave room for dissatisfaction and doubt because they do not reflect the emotions or intuition of managers. Furthermore, the decision-making process for salary increases and promotions can be time-consuming and lack transparency. It is necessary to address these challenges and implement a fairer and more efficient decision-making process for salary increases and promotions.

[1182] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1183] In this invention, the server includes means for collecting employee data, means for calculating evaluation scores, means for automatically determining employee salary increases and promotions based on set criteria, means for recognizing the manager's emotions and automatically adjusting the generated list based on those emotions, means for the manager to review and modify the generated list, and means for notifying the employee of the final decision. This makes it possible to make fair and transparent salary increase and promotion decisions that reflect not only evaluation data but also the manager's emotions and intuition.

[1184] "Means of collecting employee data" refers to means that include the function of obtaining information such as employee performance evaluation data, project completion rates, working hours, and skill sets from a database.

[1185] "Means for calculating evaluation scores" refers to means that include a function for calculating an overall evaluation score based on collected employee data.

[1186] "Means for automatically determining employee salary increases and promotions based on established criteria" refers to means that include a function to automatically determine employee salary increases and promotions according to evaluation criteria set by the manager.

[1187] "Means for generating decision results as a list" refers to means that include a function to create a list of employees who meet the criteria for salary increases or promotions, and to display or save that list.

[1188] "An emotion engine means that recognizes the emotions of administrators and automatically adjusts the list generated based on those emotions" refers to a means that includes a function to analyze administrator emotion data and adjust the list of salary increases and promotions based on those emotions.

[1189] "Means for administrators to review and modify the generated list" refers to means that administrators can review the generated list of candidates for salary increases and promotions, and add modifications or comments as needed.

[1190] "Means of notifying employees of final decisions" include means of sending employees emails or alerts regarding final decisions on salary increases or promotions.

[1191] This invention relates to a system for fairly and objectively determining employee salary increases and promotions. This system collects employee data, calculates evaluation scores, and determines salary increases and promotions based on established criteria. Furthermore, by incorporating an emotion engine that recognizes user emotions, more nuanced management becomes possible.

[1192] Data collection

[1193] The server accesses the employee database to retrieve performance evaluation data, project completion rates, working hours, skill sets, and other information for all employees. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[1194] Calculation of evaluation score

[1195] The server calculates each employee's evaluation score based on the collected data. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[1196] Setting standards

[1197] The user (administrator) enters the criteria for salary increases and promotions through the interface. For example, criteria such as "evaluation points of 80 or higher AND years of service of 3 years or more" can be set. The server saves these criteria in the database.

[1198] Automatic decision

[1199] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[1200] List generation

[1201] The server generates a list of candidates for salary increases and promotions through a list generation method, and displays it on the administration screen for user (administrator) review. The list includes information such as each employee's name, performance score, and job title.

[1202] Using an Emotion Engine

[1203] The server activates the emotion engine and recognizes the user's (administrator's) emotions. The emotion engine analyzes the user's interface operations, keyboard input, voice data, etc., to determine the user's current emotional state.

[1204] List adjustment

[1205] Based on the emotion data recognized by the emotion engine, the server automatically adjusts the generated list. For example, if a user has emotions such as dissatisfaction or doubt, the list will be readjusted based on those emotions.

[1206] Final review and corrections

[1207] The user (administrator) reviews the generated list on the administration screen and adds corrections or comments as needed. This process allows for human review before making a final decision.

[1208] notification

[1209] After the final decision is made, the server sends a notification to the employee whose salary increase or promotion has been approved. The notification appears on the employee's device as an email or alert. This allows the employee to immediately know that their salary increase or promotion has been approved.

[1210] A concrete example of a prompt message is: "Calculate the evaluation scores for the following employee data and generate a promotion list: Employee A: Evaluation 85 points, Years of service 5 years, Project completion rate 100%, Employee B: Evaluation 78 points, Years of service 2 years, Project completion rate 90%."

[1211] This system ensures that employee salary increases and promotions are fair and transparent. For example, if employee A is added to the promotion list and the user (administrator) makes a final confirmation, the emotion engine may detect doubts and make further adjustments before finally sending a promotion notification to employee A's terminal. In this way, salary increases and promotions are implemented in a manner that satisfies all employees.

[1212] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1213] Step 1: Data Collection

[1214] The server accesses the employee database and retrieves data for all employees. The input is a list of employee IDs, and the server retrieves corresponding performance evaluation data, project completion rates, working hours, and skill sets from the database. Specifically, the server uses a database connection driver to execute SQL queries and load the employee data into memory. The output is the collected employee data.

[1215] Step 2: Calculation of evaluation score

[1216] The server calculates each employee's evaluation score based on the data it collects. The input is the collected employee data. Specifically, the server applies a formula that uses evaluation points, years of service, and project completion rate to calculate an overall evaluation score. The output is the overall evaluation score for each employee.

[1217] Step 3: Setting Criteria

[1218] The user (administrator) inputs criteria for salary increases and promotions through an interface. The input consists of criteria set by the administrator. Specifically, the administrator inputs the criteria into the interface, which is then sent to the server and saved to the database. The output is the set criteria saved to the database.

[1219] Step 4: Automatic decision

[1220] The server automatically determines each employee's salary increase and promotion based on the configured criteria. The inputs are the evaluation scores of all employees and the configured criteria. Specifically, the server filters employees who meet the criteria and adds them to a list of promotion candidates. The output is a list of candidates.

[1221] Step 5: Generating the list

[1222] The server generates a list of candidates for salary increases and promotions and displays it to the user (administrator) on the administration screen. The input is a list of candidates for salary increases and promotions. Specifically, the server generates a list from the candidate data, generates HTML, and sends it to the browser. The output is the list that will be displayed on the administration screen.

[1223] Step 6: Utilizing the Emotional Engine

[1224] The server activates the emotion engine and recognizes the user's (administrator's) emotions. Inputs include user interface operations, keyboard input, and voice data. Specifically, the server calls the emotion engine's API and analyzes the emotion data. The output is the user's emotional state.

[1225] Step 7: Adjust the list

[1226] The server automatically adjusts the list generated based on emotion data recognized by the emotion engine. The input consists of user emotion state data and a list of candidates. Specifically, the server recalculates the list contents based on the emotion state and filters as needed. The output is the adjusted list.

[1227] Step 8: Final review and corrections

[1228] The user (administrator) reviews the generated list and adds corrections and comments. The input is an adjusted list. Specifically, the user reviews the list through the administration screen and adds corrections and comments. This correction information is sent to the server and stored in the database. The output is the final, corrected list.

[1229] Step 9: Notification

[1230] The server notifies employees of the final decision. The input is the final list. Specifically, the server generates the notification content, refers to the employee's email address and notification settings, and sends the notification via the appropriate medium (email or alert). The output is a confirmation message to the notified employee.

[1231] (Application Example 2)

[1232] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1233] Conventional salary increase and promotion management systems can automatically determine salary increases and promotions based on employee evaluation data, but they have the problem of not being able to make flexible adjustments that take into account the emotions of managers. In particular, in the performance evaluation management of robots working in factories, there is a need for evaluations that take into account not only the robot's operational data but also the emotions of managers. Therefore, this system solves the problem of achieving fairer and more efficient management by recognizing the emotions of managers and adjusting the evaluation list based on them.

[1234] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1235] In this invention, the server includes means for collecting employee data, means for calculating evaluation scores based on that data, means for automatically determining employee salary increases and promotions based on set criteria, means for generating the decision results as a list, means for the administrator to review and modify the generated list, means including an emotion recognition engine that recognizes the administrator's emotions and adjusts the list, and means for notifying the employee of the final decision. This enables fair and efficient salary increases and promotions that reflect the administrator's emotions.

[1236] "Employee data" is a general term for information including each employee's performance evaluation data, project completion rate, working hours, skill set, etc.

[1237] An "evaluation score" is a numerical representation of an employee's performance and abilities, calculated based on collected employee data.

[1238] "Established criteria" refers to conditions such as evaluation points, years of service, and project completion rates that are used as criteria for determining salary increases and promotions.

[1239] An "automatic decision-making mechanism" is a mechanism for automatically determining and deciding on employee salary increases and promotions based on established criteria.

[1240] "Means of generating as a list" refers to a function that outputs evaluation results or promotion candidates in list format.

[1241] The "means of review and correction" refer to a function that allows administrators to review the generated list and make corrections as needed.

[1242] An "emotion recognition engine" is a system or algorithm that analyzes the administrator's emotions and adjusts lists based on those emotions.

[1243] "Means of notifying employees of the final decision" refers to communication methods such as email or alerts used to inform employees of the final decision regarding salary increases or promotions.

[1244] This invention relates to a system for fairly and objectively determining employee salary increases and promotions, and, as an example of its application, to a work performance evaluation management application for factory robots. This system collects employee data, calculates evaluation scores, and automatically determines salary increases and promotions based on set criteria. Furthermore, by incorporating an emotion recognition engine, management that takes the manager's emotions into account becomes possible.

[1245] System program description

[1246] The server performs data processing and calculations using the following methods.

[1247] 1. Methods for collecting employee data

[1248] The server collects data from the employee database, including employee performance evaluations, project completion rates, working hours, and skill sets. For example, data for employee A might include "85 points in evaluation, 5 years of service, and 100% project completion rate."

[1249] 2. Method for calculating evaluation scores

[1250] Based on the collected data, the server calculates an evaluation score for each employee. The server scores each element, such as evaluation points, years of service, and project completion rate, according to the set criteria. For example, employee A's overall score is calculated to be "85 points in overall evaluation."

[1251] 3. Means of setting standards

[1252] The administrator sets the criteria for salary increases and promotions. The server stores these criteria in a database. For example, a criterion such as "performance score of 80 points or higher AND years of service of 3 years or more" might be set.

[1253] 4. Automatic determination methods for salary increases and promotions

[1254] The server automatically determines each employee's salary increase or promotion based on the set criteria. Employees whose evaluation scores meet the criteria are filtered and added to the list as candidates for salary increase or promotion. For example, employee A meets the criteria and is therefore added to the list.

[1255] 5. List generation, verification, and modification methods

[1256] The server generates a list of candidates for salary increases and promotions, which is displayed on the administration screen for administrators to review. Administrators can review this list and make corrections or add comments as needed.

[1257] 6. List adjustment method using an emotion recognition engine

[1258] An emotion recognition engine is used to understand the administrator's emotions. The emotion recognition engine analyzes the administrator's interface interactions and voice data to determine their emotions. For example, if the administrator indicates dissatisfaction, the list is readjusted based on that information.

[1259] 7. Final decision and notification means

[1260] After the server makes the final decision, it sends a notification to employees whose salary increases or promotions have been approved. The notification appears on the employee's device as an email or alert. This allows employees to immediately know that their salary increase or promotion has been approved.

[1261] Hardware and software to be used

[1262] Hardware:

[1263] Factory robots

[1264] Administrator's smartphone or PC

[1265] Cameras and microphones for emotion recognition engines

[1266] software:

[1267] Python program

[1268] Emotion recognition engine (e.g., various general-purpose emotion APIs)

[1269] Database management system (e.g., one type of RDBMS)

[1270] Adding specific examples

[1271] For example, in the performance evaluation management of factory robots, suppose robot A has an efficiency of 85%, an error rate of 3%, and 100 hours of operation. The criteria set by the manager are "efficiency of 80% or higher and an error rate of 5% or lower," and since robot A meets these criteria, it is added to the list of robots that do not require maintenance. If the manager expresses dissatisfaction while using the app, the recognition engine will determine that emotion and readjust the list accordingly.

[1272] Examples of prompts to input into a generative AI model:

[1273] "Robot A has an efficiency of 85%, an error rate of 3%, an operating time of 100 hours, and the administrator's emotional state is neutral."

[1274] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1275] Step 1:

[1276] The server accesses the employee database and collects data such as performance evaluations, project completion rates, working hours, and skill sets for all employees. The input is the employee database, and the output is an evaluation dataset for each employee. This data processing clarifies the evaluation elements for each employee.

[1277] Step 2:

[1278] The server calculates each employee's performance score based on the collected data. The input is the employee dataset obtained in Step 1, and the output is each employee's performance score. Data calculations generate an overall score that takes into account each employee's performance evaluation data, project completion rate, working hours, and skill set.

[1279] Step 3:

[1280] Users set the criteria for salary increases and promotions on the server. The input is the criteria set by the user, and the output is the database where those criteria are stored. This setting reflects the criteria for salary increases and promotions in the system.

[1281] Step 4:

[1282] The server automatically determines each employee's salary increase and promotion based on the set criteria. The inputs are the evaluation score obtained in step 2 and the criteria set in step 3, and the output is a list of employees who meet the criteria. In this process, the evaluation scores are filtered to see if they meet the set criteria.

[1283] Step 5:

[1284] The server generates a list of candidates for salary increases and promotions and displays it on the administration screen for user review. The input is a list of employees who meet the criteria obtained in step 4, and the output is a list that can be reviewed by the administrator. This operation allows the administrator to review the candidate list in detail.

[1285] Step 6:

[1286] The user reviews the generated list and adds corrections and comments as needed. The input is the candidate list, and the output is the reviewed and corrected list. In this step, the administrator makes a final confirmation of employee salary increases and promotions.

[1287] Step 7:

[1288] The server activates an emotion recognition engine to recognize the user's emotions. Input is the user's interface actions and voice data, and output is the analyzed emotion data. This analysis identifies the administrator's current emotional state.

[1289] Step 8:

[1290] The server readjusts the list of candidates for salary increases and promotions based on emotional data acquired by the emotion recognition engine. The input is the emotional data and the reviewed / corrected list, while the output is the final adjusted list. Considering emotional data leads to more desirable employee evaluations.

[1291] Step 9:

[1292] After making the final decision, the server sends a notification to the employees who have been approved for a raise or promotion. The input is the list after final adjustments, and the output is the notification sent to the employee's terminal. This notification allows employees to immediately know that their raise or promotion has been confirmed.

[1293] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1294] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1295] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1296] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1297] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1298] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1299] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1300] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1301] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1302] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1303] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1304] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1305] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1306] 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.

[1307] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1308] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1309] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1310] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1311] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1312] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1313] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1314] The following is further disclosed regarding the embodiments described above.

[1315] (Claim 1)

[1316] Means of collecting employee data,

[1317] A means of calculating an evaluation score based on that data,

[1318] A means of automatically determining employee salary increases and promotions based on established criteria,

[1319] A means of generating the decision results as a list,

[1320] A means for the administrator to review and modify the generated list,

[1321] Means of notifying employees of the final decision,

[1322] A system that includes this.

[1323] (Claim 2)

[1324] The system according to claim 1, comprising means for an administrator to input and save set criteria.

[1325] (Claim 3)

[1326] The system according to claim 1, comprising means for providing an interface for an administrator to review and modify the generated list.

[1327] "Example 1"

[1328] (Claim 1)

[1329] The server accesses a database of working professionals and retrieves performance evaluation data, project completion rates, working hours, skill sets, etc., for all working professionals.

[1330] A means for calculating an evaluation score for each working person based on data acquired by the server,

[1331] A means by which an administrator inputs criteria for salary increases and promotions, and a server saves those criteria.

[1332] A means for the server to automatically determine each employee's salary increase and promotion based on set criteria,

[1333] A means by which the server generates a list of candidates for salary increases and promotions, and provides an interface for administrators to review and modify it,

[1334] After the server makes a final decision, it will send a notification to the relevant working adult.

[1335] A system that includes this.

[1336] (Claim 2)

[1337] The system according to claim 1, comprising means for an administrator to input criteria for salary increases and promotions through an interface, and for a server to store those settings.

[1338] (Claim 3)

[1339] The system according to claim 1, comprising means for reviewing a list generated by a server and providing an interface for an administrator to modify or add comments as necessary.

[1340] "Application Example 1"

[1341] (Claim 1)

[1342] Means for collecting data on factory machinery,

[1343] A means of calculating an evaluation score based on that data,

[1344] A means of automatically determining maintenance and upgrades for factory machinery based on set criteria,

[1345] A means of generating the decision results as a list,

[1346] A means for the administrator to review and modify the generated list,

[1347] Means of notifying the final decision,

[1348] A system that includes this.

[1349] (Claim 2)

[1350] The system according to claim 1, comprising means for an administrator to input and save set criteria.

[1351] (Claim 3)

[1352] The system according to claim 1, comprising means for providing an interface for an administrator to review and modify the generated list.

[1353] "Example 2 of combining an emotion engine"

[1354] (Claim 1)

[1355] Means of collecting employee data,

[1356] A means of calculating an evaluation score based on that data,

[1357] A means of automatically determining employee salary increases and promotions based on established criteria,

[1358] A means of generating the decision results as a list,

[1359] An emotion engine that recognizes the administrator's emotions and automatically adjusts the list generated based on those emotions,

[1360] A means for the administrator to review and modify the generated list,

[1361] Means of notifying employees of the final decision,

[1362] A system that includes this.

[1363] (Claim 2)

[1364] The system according to claim 1, comprising means for an administrator to input and save set criteria.

[1365] (Claim 3)

[1366] The system according to claim 1, comprising means for providing an interface for an administrator to review and modify the generated list.

[1367] "Application example 2 when combining with an emotional engine"

[1368] (Claim 1)

[1369] Means of collecting employee data,

[1370] A means of calculating an evaluation score based on that data,

[1371] A means of automatically determining employee salary increases and promotions based on established criteria,

[1372] A means of generating the decision results as a list,

[1373] A means for the administrator to review and modify the generated list,

[1374] A means including an emotion recognition engine that recognizes the administrator's emotions and adjusts the list,

[1375] Means of notifying employees of the final decision,

[1376] A system that includes this.

[1377] (Claim 2)

[1378] The system according to claim 1, comprising means for an administrator to input and save set criteria.

[1379] (Claim 3)

[1380] The system according to claim 1, comprising means for providing an interface for an administrator to review and modify the generated list. [Explanation of symbols]

[1381] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting employee data, A means of calculating an evaluation score based on that data, A means of automatically determining employee salary increases and promotions based on set criteria, A means of generating the decision results as a list, A means for the administrator to review and modify the generated list, A means of notifying employees of the final decision, A system that includes this.

2. The system according to claim 1, comprising means for an administrator to input and save set criteria.

3. The system according to claim 1, further comprising means for providing an interface for an administrator to review and modify the generated list.

Images