System

Abstract

A system is provided.SOLUTION: A system comprising: artificial intelligence means for automatically setting an appropriate goal for an employee using historical assessment data and performance data; user interface means for the employee to confirm the set goal and input a modification request if necessary; artificial intelligence means for accepting the modification request and performing a re-assessment; database means for collecting employee progress and storing data; and assessment means for automatically calculating intermediate and final assessments and generating feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Employee evaluation systems play an important role in companies, but traditional evaluation methods often lack a sense of fairness and satisfaction, leading to a decline in employee motivation. Furthermore, the evaluation process places a heavy burden on evaluators, making efficient operation a key requirement. Furthermore, when employee job descriptions and goal setting are unclear, evaluation standards vary, making fair evaluations difficult. The present invention aims to solve these problems, realize an efficient and fair evaluation system, and improve employee motivation. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system that includes artificial intelligence means for automatically setting appropriate goals for employees using past evaluation data and performance data, user interface means for employees to check the set goals and input correction requests as necessary, artificial intelligence means for accepting correction requests and performing reevaluations, database means for collecting and storing data on employee progress, and evaluation means for automatically calculating interim and final evaluations and generating feedback. Furthermore, the system includes a user interface means for employees to check the evaluation results and receive feedback, thereby improving the transparency of the evaluation results. Furthermore, the system includes data storage and analysis means for saving the final evaluation results and reflecting them in setting the next goal, thereby achieving continuous improvement of the evaluation system.

[0006] "Artificial intelligence means" refers to technology that has the ability to automatically set appropriate goals for employees using past evaluation data and performance data.

[0007] "User interface means" refers to technology that provides an operation screen or input form for employees to check the set goals and input correction requests as necessary.

[0008] The "database means" is a system for collecting and storing evaluation data such as employee progress.

[0009] An "evaluation tool" is a technology that has the functionality to automatically calculate interim and final evaluations and generate feedback.

[0010] "Data storage and analysis means" refers to the technology for storing the final evaluation results and analyzing the data to reflect them in setting goals for the next period. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2]1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0012] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0015] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0016] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0017] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0019] [First embodiment]

[0020] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0021] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0024] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0027] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0031] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0032] As an embodiment for realizing the present invention, a specific system configuration and program processing will be described below.

[0033] System configuration overview

[0034] This system is a reputation management system in which the server, terminals, and users work together. The entire system is composed of the following main elements:

[0035] 1. Server

[0036] 2. Terminal

[0037] 3. Users (Employees and Raters)

[0038] Program processing overview

[0039] Initial data collection and storage

[0040] server

[0041] The administrator uses the input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores it in the database. This allows all initial data to be managed within the system.

[0042] AI-driven goal setting

[0043] server

[0044] The system uses AI algorithms based on past evaluation data and employee performance data to automatically set appropriate goals for each employee. For example, it generates goals such as "acquire five new customers" or "increase sales by 20% year-on-year" by taking into account past goal achievement data.

[0045] Review goals and request revisions

[0046] User (Employee)

[0047] Check the set goal on your device and enter a request for correction if necessary. For example, enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[0048] server

[0049] The AI ​​will accept requests for revisions and review the targets again. For example, it may "revise the new customer target from five companies to three companies" taking into account the overall situation of the sales department.

[0050] Progress monitoring and data collection

[0051] Terminal

[0052] Employees periodically enter progress data, such as "two new clients acquired" as a sales activity report.

[0053] server

[0054] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[0055] Interim evaluation input and feedback

[0056] User (Employee)

[0057] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[0058] server

[0059] The interim evaluations are compiled and feedback is generated. For example, feedback such as "The acquisition of three new clients is going well. Sales targets are on track" is sent to employees.

[0060] Final rating calculation

[0061] server

[0062] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For example, the evaluation is calculated as "6 new customers acquired, sales increased 25% compared to the previous year."

[0063] Notification and confirmation of final evaluation results

[0064] User (Employee)

[0065] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[0066] Saving data and reflecting it in the next period

[0067] server

[0068] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[0069] The above process will improve the sense of satisfaction and fairness of the traditional evaluation method, as well as improve the efficiency of the entire evaluation system. It will also increase employee motivation.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] server

[0073] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores the data in a database. Specifically, the administrator enters values ​​for each item via a web interface and registers it.

[0074] Step 2:

[0075] server

[0076] The AI ​​algorithm is run based on past evaluation data and each employee's performance data to automatically set appropriate goals for each employee. For example, past goal achievement data can be input into a machine learning model to generate goals for a specific employee, such as "acquire five new customers" or "increase sales by 20% compared to the previous year."

[0077] Step 3:

[0078] User (Employee)

[0079] Check the goals set on your device and enter correction requests as necessary. For example, you can enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[0080] Step 4:

[0081] server

[0082] An AI algorithm is run to accept and reevaluate requests for revisions. For example, the system may consider the overall situation of the sales department and make a change such as "revise the new customer target from five companies to three companies."

[0083] Step 5:

[0084] Terminal

[0085] Employees periodically enter progress data, such as the progress of a sales activity, such as "acquiring two new clients."

[0086] Step 6:

[0087] server

[0088] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[0089] Step 7:

[0090] User (Employee)

[0091] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[0092] Step 8:

[0093] server

[0094] The interim evaluation data is compiled and feedback is generated. The feedback is then communicated to the employee. For example, the feedback might be something like, "The acquisition of three new clients is going smoothly. Sales targets are on track."

[0095] Step 9:

[0096] server

[0097] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For a specific employee, the evaluation result is calculated as "Acquired six new clients and increased sales by 25% compared to the previous year."

[0098] Step 10:

[0099] User (Employee)

[0100] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[0101] Step 11:

[0102] server

[0103] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] In conventional employee evaluation systems, evaluation settings and progress management are done manually, which creates issues with the fairness and efficiency of evaluations. It is also difficult to set appropriate goals and monitor progress in real time, which leads to a lack of satisfaction with evaluations and improved employee motivation. Furthermore, it is difficult to reflect evaluation results in setting goals for the next period.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] In this invention, the server includes an artificial intelligence unit that automatically sets appropriate goals for employees using past evaluation data and performance data, a user interface unit that allows employees to check the set goals and input revision requests as necessary, and an artificial intelligence unit that accepts revision requests and performs reevaluation. This improves the fairness and efficiency of evaluations and enables appropriate goal setting and real-time monitoring of progress. It also makes it easier to reflect evaluation results in setting next-period goals, contributing to improved employee motivation.

[0109] "Artificial intelligence tools" are algorithms or programs that automatically set appropriate goals for employees based on past evaluation data and performance data, and then reevaluate them.

[0110] "User interface means" refers to an interface through which employees can access the system, check the set goals, input correction requests as necessary, and check the evaluation results.

[0111] The "database means" is a database system for storing collected progress data and evaluation data, and for saving and managing necessary information.

[0112] An "assessment tool" is an algorithm or program that automatically calculates interim and final assessments and generates feedback.

[0113] A "final rating tool" is an algorithm or program that automatically calculates a final rating based on the data collected at the end of the rating period.

[0114] A "generative AI model" is an artificial intelligence model used to analyze employee performance data in real time and monitor progress.

[0115] "Monitoring Measures" means systems or tools for analyzing collected progress data in real time and monitoring employee progress.

[0116] "Input means" refers to a form or interface that allows employees to enter progress information and send the data to the server.

[0117] "Data storage and analysis means" refers to a system for storing the final evaluation results and analyzing the data to reflect them in setting goals for the next period.

[0118] The evaluation management system of the present invention functions in cooperation with the server, terminals, and users to efficiently and fairly evaluate employee performance. It is realized by using "artificial intelligence means," "user interface means," "database means," "evaluation means," etc. The specific configuration and processing flow of the system are explained below.

[0119] System Configuration

[0120] server

[0121] The server is the core of the system and is responsible for:

[0122] Artificial intelligence measures: Using generative AI models (e.g., random forests or neural networks) to automatically set appropriate goals for employees based on past appraisal and performance data.

[0123] Database means: Collected data is stored using a database system such as MySQL or PostgreSQL.

[0124] Assessment instruments: Automatically calculate mid-term and final grades and generate feedback.

[0125] Monitoring measures: Analyze employee performance data in real time to monitor progress.

[0126] Terminal

[0127] The devices are used by employees and assessors and perform the following functions:

[0128] User interface means: Provide an interface for employees to check the set goals, input correction requests, and check the evaluation results.

[0129] Input methods: Provide employees with a form to periodically enter their progress.

[0130] User

[0131] Users (employees and raters) interact with the system through the following means:

[0132] Employees: Enter their own performance and progress, and check the set goals and evaluation results.

[0133] Evaluator: Review employee evaluation data and provide feedback as needed.

[0134] Examples and prompts

[0135] A specific example of use would be when a sales department employee is set a goal of "acquiring five new customers" and enters their progress into the system.

[0136] Examples:

[0137] Employee A reports that he has acquired two new clients over the past three months, and an interim evaluation is generated based on that data. Based on this interim evaluation, the server provides feedback such as, "You're making good progress, but you need to put in a little more effort to achieve your goals."

[0138] Example prompt sentence:

[0139] Goal: Acquire 5 new customers

[0140] Progress: Acquired 2 companies in 3 months

[0141] Generate mid-term evaluation feedback.

[0142] By inputting this prompt into a generative AI model, specific feedback is generated.

[0143] As described above, the evaluation management system of the present invention consistently automates the entire process from employee goal setting to progress management and evaluation, achieving fair and efficient evaluation. This system is expected to improve employee motivation and overall corporate performance.

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Step 1: Collect and store initial data

[0146] The server provides a web form exclusively for administrators, and collects data such as company information, organizational goals, and each employee's grade, rank, job type, and job description. The required information is entered into the input form and stored in a database.

[0147] Input: Company information, organizational goals, employee data

[0148] Output: Initial data stored in the database

[0149] How it works: An administrator accesses a web form and enters the required information in real time. The entered data is immediately validated and any incomplete fields are flagged with a warning. After validation is complete, the data is saved to the database.

[0150] Step 2: AI-driven goal setting

[0151] The server uses a generative AI model to set appropriate goals for each employee, based on past evaluation and performance data, and performs analysis using Python-based random forests and neural networks.

[0152] Input: Past evaluation data, performance data

[0153] Output: Goals for each employee

[0154] How it works: The server retrieves past performance data from the database, runs the generative AI model, and generates appropriate goals for each employee. The generated goals are then stored in the database.

[0155] Step 3: Review your goals and request revisions

[0156] Users (employees) log in to the system from their own terminals, check the set goals, and input correction requests as necessary.

[0157] Input: Employee Goals

[0158] Output: Correction request

[0159] Specific operation: An employee logs in to the system and checks the presented goals. If any corrections are needed, they fill out a form with the desired corrections and submit it to the server.

[0160] The server runs the AI ​​again based on the correction request and corrects the goal if necessary.

[0161] Input: Correction request

[0162] Output: revised target

[0163] Specific operation: The server verifies the received correction request and re-evaluates it using the generative AI model. The re-evaluated goal is generated and saved in the database.

[0164] Step 4: Monitor progress and collect data

[0165] The terminal provides a form for employees to periodically enter their progress, which is used as evaluation material.

[0166] Input: Progress data (e.g., acquisition of two new customers)

[0167] Output: Save progress data to database

[0168] Specific operation: Employees periodically access their terminals and enter their progress information into a form. The entered data is sent to the server and stored in a database.

[0169] The server collects and monitors progress data in real time.

[0170] Input: Progress data

[0171] Output: Real-time progress monitoring

[0172] Specific operation: Stores progress data in a database and displays progress in real time on a dashboard.

[0173] Step 5: Input of mid-term evaluation and feedback

[0174] Users (employees) enter interim evaluations and report progress.

[0175] Input: Interim evaluation data (e.g., 3 new customers acquired)

[0176] Output: Interim evaluation input data

[0177] Specific operation: Employees enter their progress into the interim evaluation form from their terminal and send it to the server.

[0178] The server aggregates the intermediate evaluations and generates feedback using an AI model.

[0179] Input: Interim evaluation data

[0180] Output: Feedback

[0181] Specific operation: The server collects intermediate evaluation data, runs the generative AI model to perform evaluation, and generates specific feedback that is sent to the employee.

[0182] Step 6: Calculate the final rating

[0183] At the end of the evaluation period, the server calculates a final evaluation based on all collected data.

[0184] Input: Collected data (progress data, interim evaluation data)

[0185] Output: Final evaluation result

[0186] Specific operation: The server processes all collected data in a batch and generates a final quantitative evaluation, which is then stored in a database.

[0187] Step 7: Notification and confirmation of final evaluation results

[0188] The user (employee) checks the final evaluation results on their own device.

[0189] Input: Final evaluation result

[0190] Output: Display of evaluation results

[0191] Specific operation: An employee logs in to the system and checks the evaluation results. For example, the evaluation shows, "You have achieved results that far exceed the targets, so you are a candidate for promotion."

[0192] Step 8: Save the data and reflect it in the next period

[0193] The server stores the final evaluation results and performs data analysis to reflect them in setting goals for the next period.

[0194] Input: Final evaluation result

[0195] Output: Analysis data for setting next goals

[0196] Specific operation: The server stores the final evaluation results in a database and analyzes the data using an analytical engine. Based on the analysis results, data for setting the next goal is provided to the generative AI model.

[0197] (Application example 1)

[0198] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0199] Managing and optimizing the productivity of work equipment (such as robots) in factories is important in modern manufacturing, but traditional methods have the drawback of requiring a great deal of effort to manually set goals and manage progress, and are prone to subjective judgment. Another issue is that the inability to grasp goal achievement status and evaluation results in real time makes it difficult to adapt to situations that require rapid response.

[0200] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0201] In this invention, the server includes artificial intelligence means for automatically setting appropriate targets for work equipment using past evaluation data and performance data, user interface means for the work equipment manager to check the set targets and input correction requests as necessary, artificial intelligence means for accepting correction requests and performing re-evaluation, database means for collecting progress status of work equipment and storing the data, and evaluation means for automatically calculating interim and final evaluations and generating feedback. This makes it possible to automate production target setting and improve the efficiency of progress management.

[0202] "Past evaluation data" is data based on targets and performance that the work equipment has achieved in the past.

[0203] "Performance data" is specific numerical data relating to the productivity and efficiency of work equipment.

[0204] "Appropriate targets" are reasonable and achievable production targets set based on the performance of work equipment and past performance.

[0205] "Artificial intelligence means" is a system that uses artificial intelligence algorithms to analyze data and set and reassess goals.

[0206] The "user interface means" refers to an interface means through which the administrator interacts with the system, and is used to confirm goals and input correction requests.

[0207] A "modification request" is input information that allows an administrator to request a change to a set goal.

[0208] "Database means" refers to a database system for storing and managing progress data, etc.

[0209] "Mid-term evaluation" is the process of evaluating the progress of work equipment midway through the evaluation period.

[0210] "Final evaluation" is the process of evaluating the final performance of the work equipment at the end of the evaluation period.

[0211] The "evaluation means" is a means for automatically calculating intermediate and final evaluations of the work equipment based on the data and generating feedback.

[0212] "Feedback" is information that provides comments and suggestions regarding the productivity and efficiency of work equipment based on the evaluation results.

[0213] The system for realizing the present invention is a rating management system in which a server, terminals, and users work together. This system is composed of the following elements:

[0214] System configuration overview

[0215] 1. Server: The server includes an artificial intelligence means for setting appropriate targets for work equipment using past evaluation data and performance data, a database means for collecting and storing data, and an evaluation means for automatically calculating intermediate and final evaluations and generating feedback.

[0216] 2. Terminal: The terminal has a user interface for the user (administrator) to check the set goals and input correction requests as necessary. It also includes an interface for checking the evaluation results and receiving feedback.

[0217] 3. User: As an administrator, the user interacts with the system using a terminal, setting goals, checking evaluation results, and receiving feedback.

[0218] Program processing overview

[0219] Data Processing Description

[0220] 1. Initial data collection and storage

[0221] Server: The manager uses a smartphone to input information about the work equipment (model number, functions, capacity, etc.) and production targets (production volume of part A, etc.), and stores the data on the server. The collected data is saved in a database.

[0222] 2. AI-driven goal setting

[0223] Server: The server uses past production and performance data from the work equipment to set optimal production targets using artificial intelligence means. For example, an AI model is used to generate a target such as "produce 50 units of part A per hour."

[0224] 3. Review goals and request revisions

[0225] User: The administrator uses a smartphone to check the set goals and input correction requests if necessary. The correction requests are sent to the server for re-evaluation.

[0226] 4. Progress monitoring and data collection

[0227] Work equipment: Work equipment periodically sends production data to the server, which collects the progress data and stores it in a database, allowing real-time progress monitoring.

[0228] 5. Calculation of Interim and Final Grades

[0229] Server: The server automatically calculates interim and final evaluations based on the collected data and generates feedback. For example, the interim evaluation generates feedback such as "Production of part A is proceeding as planned."

[0230] 6. Notification and confirmation of evaluation results

[0231] User: The manager uses a smartphone to check the final evaluation results and feedback. The evaluation result may be, for example, "Part A was produced beyond the target, so the evaluation is A."

[0232] Examples of concrete examples and prompts

[0233] Examples:

[0234] For example, if a factory manager uses a smartphone to enter a prompt such as, "To achieve this year's goal, please set the target production volume for part A on this factory robot production line," the AI ​​model will generate a goal such as, "The target production volume for part A is 50 units per hour."

[0235] In this way, an AI-based production activity evaluation and management system can improve production efficiency within a factory and reduce management efforts. The specific technologies used include smartphones, work equipment, and servers as hardware, and databases (MySQL, etc.), AI models (TensorFlow), and smartphone applications (compatible with iOS and Android) as software.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Step 1:

[0238] Initial data collection and storage

[0239] The server provides an input form for the manager to use a smartphone to enter information about the work equipment (model number, functions, capacity, etc.) and production targets (production volume of part A, etc.). The entered data is sent to the server and stored in a database. The input at this time is the basic data and target data for the work equipment, and the output is the initial data for each work equipment stored in the database.

[0240] Step 2:

[0241] AI-driven goal setting

[0242] The server inputs past production data and performance data of the work equipment into an AI model (such as TensorFlow). The AI ​​analyzes the data and sets optimal production targets for each piece of work equipment. For example, based on the input data, it generates a target such as "produce 50 units of part A per hour." The input at this time is past evaluation data and performance data, and the output is the newly set target data.

[0243] Step 3:

[0244] Review goals and request revisions

[0245] The user uses a smartphone to check the target sent from the server. If necessary, the user inputs a correction request and sends it to the server. For example, the user inputs a request such as "I would like to change the target for part A from 50 units per hour to 45 units." The input in this case is the target data and correction request confirmed by the user, and the output is the correction request sent to the server.

[0246] Step 4:

[0247] Processing correction requests

[0248] The server re-inputs the received correction request into the AI ​​model and re-evaluates the goal. As a result of the re-evaluation, new goal data is generated and notified to the user. The input at this time is the correction request, and the output is the re-evaluated new goal data.

[0249] Step 5:

[0250] Progress monitoring and data collection

[0251] The work equipment sends production data (e.g., "Produce 48 units of part A in 1 hour") to the server at specified intervals. The server stores the sent data in a database and monitors the progress in real time. The input at this time is the progress data from the work equipment, and the output is the progress data stored in the database.

[0252] Step 6:

[0253] Mid-term assessment calculation and feedback

[0254] The server calculates an interim evaluation based on the collected progress data and generates feedback. For example, it generates feedback such as "Currently, production of part A is proceeding as planned" and notifies the user. The input at this time is the progress data, and the output is the interim evaluation and feedback data.

[0255] Step 7:

[0256] Final rating calculation

[0257] At the end of the evaluation period, the server calculates the final evaluation based on the collected data. For example, it generates a final evaluation such as "Part A was produced beyond the target, so the evaluation is A." The input at this time is all the progress data during the evaluation period, and the output is the final evaluation data.

[0258] Step 8:

[0259] Notification and confirmation of evaluation results

[0260] The user uses a smartphone to check the final evaluation results and feedback sent from the server. The input is the final evaluation data and feedback data, and the output is the evaluation results checked by the user.

[0261] Step 9:

[0262] Saving evaluation results and reflecting them in the next period

[0263] The server saves the final evaluation results in a database and performs data analysis to reflect them in setting the next target. For example, it analyzes the "appropriateness of the production target for part A" and uses the data as reference data for setting the next target. The input at this time is the final evaluation data, and the output is the data analysis results and the saved evaluation data.

[0264] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0265] As an embodiment for realizing the present invention, a specific system configuration and program processing will be described below.

[0266] System configuration overview

[0267] This system is an evaluation management system that operates in cooperation with the server, terminals, and users, and by combining it with an emotion engine, the emotional state of employees is reflected in the evaluation process. The entire system consists of the following main elements:

[0268] 1. Server

[0269] 2. Terminal

[0270] 3. Users (Employees and Raters)

[0271] 4. Emotion Engine

[0272] Program processing overview

[0273] Initial data collection and storage

[0274] server

[0275] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores the data in a database. The administrator enters this information via a web interface.

[0276] AI-driven goal setting

[0277] server

[0278] The system uses AI algorithms based on past evaluation data and performance data to automatically set appropriate goals for each employee. For example, it uses past goal achievement data to generate goals for a specific employee, such as "acquire five new customers" or "increase sales by 20% year-on-year."

[0279] Collecting and reflecting emotional data

[0280] server

[0281] An emotion engine is run to collect employee emotional data and reflect it in the evaluation process. The emotion engine recognizes emotions from employees' facial expressions and voices, and stores the data in a database.

[0282] User (Employee)

[0283] The system also has a function to input emotional states on the employee's own device. For example, when receiving feedback, the system prompts the employee to "enter their current emotional state," and the employee can input their emotional state.

[0284] Review goals and request revisions

[0285] User (Employee)

[0286] Check the set goals on your device and enter correction requests as necessary. For example, enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[0287] server

[0288] It accepts revision requests and runs AI algorithms to re-evaluate them, taking into account sentiment data collected by the sentiment engine.

[0289] Progress monitoring and data collection

[0290] Terminal

[0291] Employees periodically enter progress data, such as the progress of a sales activity, such as "acquiring two new clients."

[0292] server

[0293] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[0294] Interim evaluation input and feedback

[0295] User (Employee)

[0296] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[0297] server

[0298] Intermediate evaluation data is compiled and feedback is generated. For example, "The acquisition of three new customers is going smoothly. Sales targets are as expected." The content of the feedback is adjusted based on the emotional data.

[0299] Final rating calculation

[0300] server

[0301] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For a specific employee, the evaluation result is calculated as "Acquired six new clients and increased sales by 25% compared to the previous year."

[0302] Notification and confirmation of evaluation results

[0303] User (Employee)

[0304] The employee checks the final evaluation results on their own device. Feedback is displayed along with the evaluation results. For example, the evaluation may say, "You have significantly exceeded your targets and are a candidate for promotion." The emotion engine adjusts the timing and method of notification of the evaluation results based on the employee's emotional state.

[0305] Saving data and reflecting it in the next period

[0306] server

[0307] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[0308] The above process can improve the sense of satisfaction and fairness of traditional evaluation methods, as well as improve the efficiency of the entire evaluation system. Furthermore, by taking into account the emotional state of employees, motivation can be further increased.

[0309] The processing flow will be explained below.

[0310] Step 1:

[0311] server

[0312] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores it in the database. Specifically, the administrator enters and registers the information through a web interface.

[0313] Step 2:

[0314] server

[0315] The system uses AI algorithms based on past evaluation data and performance data to automatically set appropriate goals for each employee. Specifically, it analyzes past goal achievement data and generates goals such as "acquire five new customers" or "increase sales by 20% year-on-year."

[0316] Step 3:

[0317] server

[0318] An emotion engine is used to collect employee emotion data, including data obtained through facial expression recognition and voice analysis of employees.

[0319] Step 4:

[0320] User (Employee)

[0321] Check the set goal on your device and enter a request for correction if necessary. For example, you can enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[0322] Step 5:

[0323] server

[0324] Along with the sentiment data collected by the sentiment engine, an AI algorithm is run to reevaluate the modification request, which could result in a change such as "modify new customer goal from 5 to 3."

[0325] Step 6:

[0326] Terminal

[0327] Employees periodically enter progress data, such as sales activity reports like "two new clients acquired."

[0328] Step 7:

[0329] server

[0330] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[0331] Step 8:

[0332] User (Employee)

[0333] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[0334] Step 9:

[0335] server

[0336] Intermediate evaluation data is compiled and feedback is generated. Emotional data is referenced and the feedback content is adapted to the employee's emotional state. For example, the generated feedback might be, "The acquisition of three new clients is going smoothly. Sales targets are as expected."

[0337] Step 10:

[0338] server

[0339] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data, and the employee's evaluation result is "Acquired six new clients and increased sales by 25% compared to the previous year."

[0340] Step 11:

[0341] User (Employee)

[0342] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[0343] Step 12:

[0344] server

[0345] The final evaluation results are stored in a database and analyzed to reflect them in setting goals for the next period. Emotional data is also included in the analysis, and for example, the "appropriateness of the target for the number of new customers acquired" is evaluated and used as a reference for setting future goals.

[0346] Step 13:

[0347] server

[0348] The timing and method of notifying the employee of the evaluation results can be adjusted based on the employee's emotional state as recognized by the emotion engine. For example, if the emotional data indicates that the employee is feeling stressed, the notification of the evaluation results can be adjusted to be more gentle.

[0349] Example 2

[0350] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0351] In conventional goal setting and evaluation systems, goals are set and evaluations are performed without taking into account the emotional state of employees, resulting in problems with employee motivation and stress management. Furthermore, the goal setting and revision process is inefficient, making it difficult to increase employees' sense of satisfaction and fairness. Furthermore, feedback is uniform and not adjusted to suit each employee's individual state, resulting in the inability to provide effective feedback.

[0352] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0353] In this invention, the server includes: an artificial intelligence unit that automatically sets appropriate goals for employees using past evaluation data and performance data; a user interface unit that allows employees to input their emotional state and collect that data; an emotion analysis unit that reflects the collected emotional data in the evaluation process; a user interface unit that allows employees to check their set goals and input correction requests as necessary; an artificial intelligence unit that accepts correction requests and performs reevaluations; a database unit that collects and stores data on employee progress; an evaluation unit that automatically calculates interim and final evaluations and generates feedback; and a unit that adjusts the content of the feedback based on the emotional data. This enables goal setting and evaluation to take employees' emotional states into consideration, contributing to improved employee motivation and stress management. Furthermore, an efficient and convincing goal setting and correction process is realized, allowing for effective feedback tailored to each individual employee.

[0354] "Artificial intelligence means" refers to functions that analyze past evaluation data and performance data, automatically set appropriate goals for employees, and re-evaluate them after receiving requests for corrections.

[0355] "User interface means" refers to devices or software that provide an interface through which employees can input their emotional state and goal modification requests and view evaluation results and feedback.

[0356] "Emotion analysis means" refers to the function of analyzing emotional data collected from employees and reflecting it in the evaluation process and feedback.

[0357] "Database means" refers to a system that stores information such as employee progress and evaluation data, and allows for efficient management, retrieval, and use of that information.

[0358] "Assessment tool" refers to a system or algorithm that includes functionality for automatically calculating interim and final assessments and generating feedback.

[0359] "Emotional Data" refers to emotional states entered by employees and emotional data collected and analyzed by emotion analysis tools.

[0360] "Goal setting" refers to the process of defining specific work goals for employees to achieve.

[0361] "Modification request" refers to a request entered by an employee when they wish to modify a set goal.

[0362] "Feedback" refers to comments and evaluations provided based on an employee's progress toward achieving goals and evaluation results.

[0363] The present invention is a system that utilizes past evaluation data and performance data to set appropriate goals and evaluate employees, taking into account their emotional state. This system operates using multiple means, with a server, terminals, and users working together.

[0364] Server processing

[0365] server

[0366] As an initial setup, managers enter company information, employee job descriptions, goals, past performance data, etc. through a web interface and store them in a database. The AI ​​tool analyzes this data and automatically sets appropriate goals for each employee.

[0367] As a specific example, the server analyzes the performance data of employee A for the past three years and sends the goal "acquire five new clients" as a prompt to the generative AI model. The prompt sentence of the generative AI model is "Please set the next goal for employee A."

[0368] User Action

[0369] User (Employee)

[0370] Employees can review their goals on their own devices and input their emotional data. By inputting their emotional state, data is collected that is reflected in the evaluation process. Employees can also input requests to modify their goals.

[0371] For example, after confirming the target, employee B inputs into the terminal, "Five new customers is too many, so I would like to change it to three." The server receives this request and uses AI tools to reevaluate.

[0372] Processing by the terminal

[0373] Terminal

[0374] The terminals where employees input their daily work progress send the collected data to a server in real time. This data is stored in a database and used to monitor and evaluate progress. The terminals also have a user interface means for displaying the evaluation results and feedback.

[0375] For example, employee C enters "two new clients acquired" into the terminal. This information is sent to the server and stored in the database.

[0376] Feedback generation and notification

[0377] server

[0378] The evaluation method automatically calculates interim and final evaluations based on the collected data and generates feedback. During this process, the collected emotional data is reflected in the feedback content. The timing and method of notification of the feedback are also adjusted based on the emotional data.

[0379] As a specific example, the server generates feedback such as "Goals are being achieved smoothly, and sales are as expected" based on progress data such as "Three new customers have been acquired."

[0380] Saving data and reflecting it in the next period

[0381] server

[0382] The final evaluation results are stored in a database and analyzed as reference data for setting the next goal, which will result in appropriate goal setting for the next period.

[0383] As a concrete example, the server analyzes the "target appropriateness of the number of new customers acquired" and uses it as the basic data for goal setting to be applied in the next evaluation period. It sends a prompt to the generative AI model saying, "Please analyze the appropriateness of the number of new customers acquired in order to set the next goal."

[0384] In this way, the present invention can realize a more convincing and fair evaluation system that takes into account the emotional state of employees, which is more effective than conventional evaluation methods in that it contributes to improving employee motivation and stress management.

[0385] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0386] Step 1:

[0387] Initial data collection and storage

[0388] The server receives company and employee information entered by the administrator through a web interface and stores the data in a database in real time. Specific inputs include organizational structure, organizational goals, employee names, job titles, and job descriptions. The server converts this input data into JSON format and stores it in the database.

[0389] Specific behavior:

[0390] When an administrator opens the administration screen in a browser, enters data into each field, and clicks the "Submit" button, the entered data is sent to the server, which receives the data and stores it in a database.

[0391] Step 2:

[0392] AI-driven goal setting

[0393] The server uses a generative AI model to analyze past evaluation and performance data stored in the database. Specifically, it analyzes each employee's past goal achievement rate and performance to generate appropriate goals for the next period.

[0394] Input: Past evaluation data, performance data

[0395] Data processing: A prompt sentence is generated based on the input data for the generative AI model and sent to the AI ​​model.

[0396] Output: Appropriate goal setting data

[0397] Specific behavior:

[0398] The server generates a prompt saying, "Please generate an appropriate goal based on employee A's past evaluation data," and sends it to the generative AI model. The AI ​​model generates a goal, "Acquire five new customers," and sends it back to the server.

[0399] Step 3:

[0400] Collecting and reflecting emotional data

[0401] Users (employees) input their emotional state during the evaluation process or when receiving feedback. The data is sent to the server in real time and stored in a database. The server then reflects the emotional data in the evaluation process.

[0402] Input: Emotional state input data

[0403] Data processing: Analyze sentiment data and incorporate it into the evaluation process.

[0404] Output: Emotion data reflected in the evaluation

[0405] Specific behavior:

[0406] When an employee sees a prompt on the terminal asking, "Please enter your current emotional state," they enter "I feel stressed," and click the "Submit" button. The entered emotional data is sent to the server and stored in a database.

[0407] Step 4:

[0408] Check your goals and enter correction requests

[0409] The user (employee) checks the set goals on the terminal and inputs correction requests as necessary. The server receives this request and uses the AI ​​model to reassess.

[0410] Input: Goal setting data, correction request data

[0411] Data processing: Reevaluation process using generative AI models

[0412] Output: Reassessed goal setting data

[0413] Specific behavior:

[0414] The employee opens the goal confirmation screen, checks the goal "Acquire 5 new customers," enters "Five new customers is too many, so I would like to change it to three," and submits it. The server receives this request and sends a reevaluation prompt to the generative AI model: "Please reevaluate Employee A's new goal setting." The AI ​​model generates a new appropriate goal and notifies the server.

[0415] Step 5:

[0416] Progress monitoring and data collection

[0417] The terminal provides a screen where employees can input their daily work progress. The progress data entered by employees is sent to the server in real time and stored in a database. The server uses this data to monitor the progress.

[0418] Input: Progress input data

[0419] Data processing: progress data collection and real-time monitoring

[0420] Output: Progress data

[0421] Specific behavior:

[0422] An employee types "Acquired two new clients" into a terminal and clicks the send button. The entered data is sent to the server and saved in a database. The server uses the data to monitor progress in real time.

[0423] Step 6:

[0424] Enter mid-term evaluations and generate feedback

[0425] The server calculates intermediate evaluations based on the collected progress data and generates feedback, which is adjusted based on the emotional data.

[0426] Input: progress data, emotional state data

[0427] Data processing: Calculating intermediate assessments and generating feedback

[0428] Output: Regulated feedback data

[0429] Specific behavior:

[0430] The server performs an interim evaluation based on progress data such as "three new clients acquired," and generates feedback such as "goals are being achieved smoothly." The content of the feedback and the timing of notifications are adjusted based on the emotional data.

[0431] Step 7:

[0432] Calculation and notification of final grade

[0433] At the end of the evaluation period, the server automatically calculates the final evaluation based on all collected data and notifies the user along with feedback. The timing and method of notification are also adjusted based on the emotional data.

[0434] Input: progress data, emotional state data

[0435] Data processing: Calculating final grades and generating feedback

[0436] Output: Final evaluation result data and feedback data

[0437] Specific behavior:

[0438] At the end of the period, the server consolidates all the aggregated data and sends a prompt to the generative AI model saying, "Please generate the final evaluation result for employee F." The final evaluation and feedback are generated and notified to the employee's device.

[0439] Step 8:

[0440] Confirmation of evaluation results and reflection on the next period

[0441] The user (employee) checks the evaluation results and feedback on the terminal, and the server stores the results in a database and reflects them in setting goals for the next period.

[0442] Input: Final evaluation result data

[0443] Data processing: Data storage and analysis for setting next goals

[0444] Output: Saved final evaluation results

[0445] Specific behavior:

[0446] The employee opens the evaluation results screen and checks the evaluation result: "Acquired six new customers, increased sales by 25% compared to the previous year." The server saves this data in a database and analyzes it for use in setting goals for the next period. The server sends a prompt to the AI ​​model saying, "Please analyze the appropriateness of the number of new customers acquired in order to set goals for the next period," and sets goals based on the results.

[0447] (Application example 2)

[0448] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0449] Conventional employee evaluation systems have problems in that the appropriateness of set goals and the quality of feedback have a significant impact on employee motivation and performance. Furthermore, because they were unable to take into account employees' emotions and stress levels, it was difficult to appropriately adjust workloads and maintain motivation. This led to issues such as a lack of fairness and a sense of satisfaction in evaluations, which ultimately led to a decline in work efficiency.

[0450] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: artificial intelligence means for automatically setting appropriate goals for employees using past evaluation data and performance data; user interface means for employees to check the set goals and input correction requests as necessary; artificial intelligence means for accepting correction requests and performing reevaluations; emotion recognition means for collecting employee emotion data and adjusting work instructions and feedback based on the data; database means for collecting employee progress and storing the data; and evaluation means for automatically calculating interim and final evaluations and generating feedback. This enables appropriate goal setting and workload adjustment taking into account employees' emotions and stress levels, thereby improving the sense of satisfaction and fairness of evaluations and optimizing work efficiency and maintaining employee motivation.

[0451] "Artificial intelligence means" refers to technology that analyzes past evaluation data and performance data and automatically sets appropriate goals for employees.

[0452] The "user interface means" is an interface through which employees can interact with the system, and is a function that allows employees to confirm set goals and input correction requests.

[0453] "Emotion recognition means" is a technology that collects emotional data from employees' facial expressions and voices, and adjusts work instructions and feedback based on that data.

[0454] "Database means" is a system for efficiently collecting and storing employee progress and emotional data, and has the function of centrally managing data.

[0455] The "evaluation tool" is a technology that automatically generates feedback and evaluates employees based on collected mid-term and final evaluation data.

[0456] "Emotional data" refers to data obtained from an employee's facial expressions, voice, and other emotional expressions, and is information that quantifies or qualitatively evaluates the employee's emotional state.

[0457] The present invention relates to a system for improving the sense of fairness and satisfaction of evaluations by reflecting emotional data in the performance evaluations of employees. Specific embodiments for carrying out the present invention will be described in detail below.

[0458] System Configuration

[0459] This system is an evaluation management system that operates in cooperation with a server, terminals, and users (employees and evaluators), and by combining it with an emotion engine, reflects the employee's emotional state in the evaluation process. The entire system includes the following elements:

[0460] Server: The server is the core of the system and includes artificial intelligence means, database means, and evaluation means. Specifically, the server analyzes past evaluation data and performance data to set appropriate goals for employees, accepts correction requests, and performs reevaluations. It also has the function of collecting employee emotional data using emotion recognition means and adjusting work instructions and feedback based on that data.

[0461] Terminal: A device used by the employee and evaluator that contains a user interface through which the employee can view set goals, input correction requests, report progress, and view feedback.

[0462] Users (employees and evaluators): Users access the system and input their own work status and emotional data. Employees report their progress and emotional state, and evaluators provide appropriate feedback based on the collected data.

[0463] Hardware and software used

[0464] Hardware

[0465] Camera: Used to capture employee facial expressions.

[0466] Computer terminal: Used to operate the user interface.

[0467] Server machine: Used to process and store data centrally.

[0468] software

[0469] OpenCV: Used to acquire and process camera images.

[0470] EmotionRecognitionEngine: Functions as an emotion recognition engine, analyzing employees' emotional states from facial expressions and voice data. For example, "Face++" or "Microsoft Azure Emotion API."

[0471] RobotController: Uses a robot control library such as ROS to transmit work instructions and feedback to the robot.

[0472] DatabaseClient: Use a database client such as "SQLite" to efficiently store emotion data and rating data.

[0473] Artificial intelligence model: A generative AI model that analyzes past evaluation data and performance data to generate appropriate goals.

[0474] Explanation of program processing

[0475] Server: The server first uses the administrator's input form to collect company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores them in a database. Next, it uses an AI algorithm to set appropriate goals based on past evaluation data and performance data. It also runs an emotion recognition engine to collect employee emotional data and reflect it in the evaluation process. Finally, it automatically calculates interim and final evaluations and generates feedback.

[0476] Terminal: Employees use the terminal to check the set goals and input correction requests as necessary. Progress data and emotional state are also entered through this terminal. Employees also use this terminal to check evaluation results and feedback.

[0477] Users: Employees and evaluators access the system through a user interface to input and confirm the necessary data. In particular, real-time feedback and work adjustments based on emotional data are possible.

[0478] Specific examples

[0479] As a specific use case, the following prompts can be input to the generative AI model:

[0480] Please provide an example of a system that uses emotion recognition data from employees to enable factory robots to provide appropriate work instructions and feedback. Please also explain how workloads are adjusted when stress levels are high, and how positive feedback is provided when happiness levels are low.

[0481] This prompt can be fed into a generative AI model to generate additional ideas for similar implementations and improvements.

[0482] As described above, the present invention provides an evaluation management system based on employee emotional data, and is expected to improve employee motivation and performance.

[0483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0484] Step 1:

[0485] The server uses the administrator's input form to collect data such as company information, organizational goals, each employee's grade, rank, job type, and job description, and stores it in a database. This prepares the initial data necessary for evaluation. The input data is the information entered by the administrator, and the output is the prepared data stored in the database.

[0486] Step 2:

[0487] The server runs an artificial intelligence model based on past evaluation data and performance data, automatically setting appropriate goals for each employee. For example, by analyzing past goal achievement data, goals such as "acquire five new clients" or "increase sales by 20% compared to the previous year" are generated for a specific employee. The input data are past evaluation data and performance data, and the output is the appropriate goal generated for each employee.

[0488] Step 3:

[0489] Using a terminal, an employee checks the set targets and inputs correction requests as necessary. For example, they can input a comment such as, "Five new customers is too many, so I would like to reduce it to three." The input data is the displayed targets and correction requests, and the output shows the state after the correction requests have been input.

[0490] Step 4:

[0491] The server accepts the correction request and re-runs the AI ​​model to re-evaluate it. The input data is the correction request entered by the employee, and the output is the re-evaluated goal. This re-evaluation also takes into account the employee's emotional data.

[0492] Step 5:

[0493] The server uses a camera and an emotion recognition engine to collect emotional data from employees' facial expressions and voices. This emotional data is stored in a database. The input data is camera footage and audio data, and the output is analyzed emotional data.

[0494] Step 6:

[0495] The server adjusts work instructions and feedback based on the employee's emotional data. For example, if the stress level is high, it reduces the workload, and if the happiness level is low, it provides positive feedback. The input data is emotional data, and the output is adjusted work instructions and feedback.

[0496] Step 7:

[0497] Employees periodically input progress data using terminals. For example, the progress of sales activities such as "acquiring two new clients" is entered. The input data is specific information indicating the progress, and the output is progress data stored in a database.

[0498] Step 8:

[0499] The server collects the entered progress data and stores it in a database, allowing employees' progress toward their goals to be monitored in real time. The input data is the progress data entered by the employees, and the output is the progress data stored in the database.

[0500] Step 9:

[0501] Employees use terminals to input interim evaluations and report the progress of each goal. For example, they input progress data such as "three new clients acquired." The input data is the progress data as interim evaluations, and the output is the interim evaluation data sent to the server.

[0502] Step 10:

[0503] The server aggregates the interim evaluation data and generates feedback. For example, the feedback might be something like, "The acquisition of three new customers is going smoothly. Sales targets are as expected." The content of the feedback is adjusted based on the emotional data. The input data is the interim evaluation data, and the output is the generated feedback.

[0504] Step 11:

[0505] At the end of the evaluation period, the server automatically calculates the final evaluation based on the collected data. For example, the evaluation result calculated is "6 new customers acquired, sales increased 25% compared to the previous year." The input data is all the collected evaluation data, and the output is the final evaluation result.

[0506] Step 12:

[0507] The employee uses a terminal to check the final evaluation results. Feedback is displayed along with the evaluation results. For example, an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion" may be displayed. The input data is the final evaluation results sent from the server, and the output is the state of the evaluation results as viewed by the employee.

[0508] Step 13:

[0509] The server saves the final evaluation results in a database and performs data analysis to reflect them in setting goals for the next period. For example, it analyzes the "target appropriateness of the number of new customers acquired" and uses it as reference data for setting goals for the next period. The input data is the final evaluation results, and the output is the analyzed reference data.

[0510] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0511] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0512] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0513] [Second embodiment]

[0514] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0515] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0516] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0517] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0518] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0519] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0520] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0521] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0522] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0523] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0524] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0525] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0526] As an embodiment for realizing the present invention, a specific system configuration and program processing will be described below.

[0527] System configuration overview

[0528] This system is a reputation management system in which the server, terminals, and users work together. The entire system is composed of the following main elements:

[0529] 1. Server

[0530] 2. Terminal

[0531] 3. Users (Employees and Raters)

[0532] Program processing overview

[0533] Initial data collection and storage

[0534] server

[0535] The administrator uses the input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores it in the database. This allows all initial data to be managed within the system.

[0536] AI-driven goal setting

[0537] server

[0538] The system uses AI algorithms based on past evaluation data and employee performance data to automatically set appropriate goals for each employee. For example, it generates goals such as "acquire five new customers" or "increase sales by 20% year-on-year" by taking into account past goal achievement data.

[0539] Review goals and request revisions

[0540] User (Employee)

[0541] Check the set goal on your device and enter a request for correction if necessary. For example, enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[0542] server

[0543] The AI ​​will accept requests for revisions and review the targets again. For example, it may "revise the new customer target from five companies to three companies" taking into account the overall situation of the sales department.

[0544] Progress monitoring and data collection

[0545] Terminal

[0546] Employees periodically enter progress data, such as "two new clients acquired" as a sales activity report.

[0547] server

[0548] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[0549] Interim evaluation input and feedback

[0550] User (Employee)

[0551] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[0552] server

[0553] The interim evaluations are compiled and feedback is generated. For example, feedback such as "The acquisition of three new clients is going well. Sales targets are on track" is sent to employees.

[0554] Final rating calculation

[0555] server

[0556] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For example, the evaluation is calculated as "6 new customers acquired, sales increased 25% compared to the previous year."

[0557] Notification and confirmation of final evaluation results

[0558] User (Employee)

[0559] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[0560] Saving data and reflecting it in the next period

[0561] server

[0562] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[0563] The above process will improve the sense of satisfaction and fairness of the traditional evaluation method, as well as improve the efficiency of the entire evaluation system. It will also increase employee motivation.

[0564] The processing flow will be explained below.

[0565] Step 1:

[0566] server

[0567] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores the data in a database. Specifically, the administrator enters values ​​for each item via a web interface and registers it.

[0568] Step 2:

[0569] server

[0570] The AI ​​algorithm is run based on past evaluation data and each employee's performance data to automatically set appropriate goals for each employee. For example, past goal achievement data can be input into a machine learning model to generate goals for a specific employee, such as "acquire five new customers" or "increase sales by 20% compared to the previous year."

[0571] Step 3:

[0572] User (Employee)

[0573] Check the goals set on your device and enter correction requests as necessary. For example, you can enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[0574] Step 4:

[0575] server

[0576] An AI algorithm is run to accept and reevaluate requests for revisions. For example, the system may consider the overall situation of the sales department and make a change such as "revise the new customer target from five companies to three companies."

[0577] Step 5:

[0578] Terminal

[0579] Employees periodically enter progress data, such as the progress of a sales activity, such as "acquiring two new clients."

[0580] Step 6:

[0581] server

[0582] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[0583] Step 7:

[0584] User (Employee)

[0585] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[0586] Step 8:

[0587] server

[0588] The interim evaluation data is compiled and feedback is generated. The feedback is then communicated to the employee. For example, the feedback might be something like, "The acquisition of three new clients is going smoothly. Sales targets are on track."

[0589] Step 9:

[0590] server

[0591] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For a specific employee, the evaluation result is calculated as "Acquired six new clients and increased sales by 25% compared to the previous year."

[0592] Step 10:

[0593] User (Employee)

[0594] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[0595] Step 11:

[0596] server

[0597] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[0598] Example 1

[0599] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0600] In conventional employee evaluation systems, evaluation settings and progress management are done manually, which creates issues with the fairness and efficiency of evaluations. It is also difficult to set appropriate goals and monitor progress in real time, which leads to a lack of satisfaction with evaluations and improved employee motivation. Furthermore, it is difficult to reflect evaluation results in setting goals for the next period.

[0601] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0602] In this invention, the server includes an artificial intelligence unit that automatically sets appropriate goals for employees using past evaluation data and performance data, a user interface unit that allows employees to check the set goals and input revision requests as necessary, and an artificial intelligence unit that accepts revision requests and performs reevaluation. This improves the fairness and efficiency of evaluations and enables appropriate goal setting and real-time monitoring of progress. It also makes it easier to reflect evaluation results in setting next-period goals, contributing to improved employee motivation.

[0603] "Artificial intelligence tools" are algorithms or programs that automatically set appropriate goals for employees based on past evaluation data and performance data, and then reevaluate them.

[0604] "User interface means" refers to an interface through which employees can access the system, check the set goals, input correction requests as necessary, and check the evaluation results.

[0605] The "database means" is a database system for storing collected progress data and evaluation data, and for saving and managing necessary information.

[0606] An "assessment tool" is an algorithm or program that automatically calculates interim and final assessments and generates feedback.

[0607] A "final rating tool" is an algorithm or program that automatically calculates a final rating based on the data collected at the end of the rating period.

[0608] A "generative AI model" is an artificial intelligence model used to analyze employee performance data in real time and monitor progress.

[0609] "Monitoring Measures" means systems or tools for analyzing collected progress data in real time and monitoring employee progress.

[0610] "Input means" refers to a form or interface that allows employees to enter progress information and send the data to the server.

[0611] "Data storage and analysis means" refers to a system for storing the final evaluation results and analyzing the data to reflect them in setting goals for the next period.

[0612] The evaluation management system of the present invention functions in cooperation with the server, terminals, and users to efficiently and fairly evaluate employee performance. It is realized by using "artificial intelligence means," "user interface means," "database means," "evaluation means," etc. The specific configuration and processing flow of the system are explained below.

[0613] System Configuration

[0614] server

[0615] The server is the core of the system and is responsible for:

[0616] Artificial intelligence measures: Using generative AI models (e.g., random forests or neural networks) to automatically set appropriate goals for employees based on past appraisal and performance data.

[0617] Database means: Collected data is stored using a database system such as MySQL or PostgreSQL.

[0618] Assessment instruments: Automatically calculate mid-term and final grades and generate feedback.

[0619] Monitoring measures: Analyze employee performance data in real time to monitor progress.

[0620] Terminal

[0621] The devices are used by employees and assessors and perform the following functions:

[0622] User interface means: Provide an interface for employees to check the set goals, input correction requests, and check the evaluation results.

[0623] Input methods: Provide employees with a form to periodically enter their progress.

[0624] User

[0625] Users (employees and raters) interact with the system through the following means:

[0626] Employees: Enter their own performance and progress, and check the set goals and evaluation results.

[0627] Evaluator: Review employee evaluation data and provide feedback as needed.

[0628] Examples and prompts

[0629] A specific example of use would be when a sales department employee is set a goal of "acquiring five new customers" and enters their progress into the system.

[0630] Examples:

[0631] Employee A reports that he has acquired two new clients over the past three months, and an interim evaluation is generated based on that data. Based on this interim evaluation, the server provides feedback such as, "You're making good progress, but you need to put in a little more effort to achieve your goals."

[0632] Example prompt sentence:

[0633] Goal: Acquire 5 new customers

[0634] Progress: Acquired 2 companies in 3 months

[0635] Generate mid-term evaluation feedback.

[0636] By inputting this prompt into a generative AI model, specific feedback is generated.

[0637] As described above, the evaluation management system of the present invention consistently automates the entire process from employee goal setting to progress management and evaluation, achieving fair and efficient evaluation. This system is expected to improve employee motivation and overall corporate performance.

[0638] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0639] Step 1: Collect and store initial data

[0640] The server provides a web form exclusively for administrators, and collects data such as company information, organizational goals, and each employee's grade, rank, job type, and job description. The required information is entered into the input form and stored in a database.

[0641] Input: Company information, organizational goals, employee data

[0642] Output: Initial data stored in the database

[0643] How it works: An administrator accesses a web form and enters the required information in real time. The entered data is immediately validated and any incomplete fields are flagged with a warning. After validation is complete, the data is saved to the database.

[0644] Step 2: AI-driven goal setting

[0645] The server uses a generative AI model to set appropriate goals for each employee, based on past evaluation and performance data, and performs analysis using Python-based random forests and neural networks.

[0646] Input: Past evaluation data, performance data

[0647] Output: Goals for each employee

[0648] How it works: The server retrieves past performance data from the database, runs the generative AI model, and generates appropriate goals for each employee. The generated goals are then stored in the database.

[0649] Step 3: Review your goals and request revisions

[0650] Users (employees) log in to the system from their own terminals, check the set goals, and input correction requests as necessary.

[0651] Input: Employee Goals

[0652] Output: Correction request

[0653] Specific operation: An employee logs in to the system and checks the presented goals. If any corrections are needed, they fill out a form with the desired corrections and submit it to the server.

[0654] The server runs the AI ​​again based on the correction request and corrects the goal if necessary.

[0655] Input: Correction request

[0656] Output: revised target

[0657] Specific operation: The server verifies the received correction request and re-evaluates it using the generative AI model. The re-evaluated goal is generated and saved in the database.

[0658] Step 4: Monitor progress and collect data

[0659] The terminal provides a form for employees to periodically enter their progress, which is used as evaluation material.

[0660] Input: Progress data (e.g., acquisition of two new customers)

[0661] Output: Save progress data to database

[0662] Specific operation: Employees periodically access their terminals and enter their progress information into a form. The entered data is sent to the server and stored in a database.

[0663] The server collects and monitors progress data in real time.

[0664] Input: Progress data

[0665] Output: Real-time progress monitoring

[0666] Specific operation: Stores progress data in a database and displays progress in real time on a dashboard.

[0667] Step 5: Input of mid-term evaluation and feedback

[0668] Users (employees) enter interim evaluations and report progress.

[0669] Input: Interim evaluation data (e.g., 3 new customers acquired)

[0670] Output: Interim evaluation input data

[0671] Specific operation: Employees enter their progress into the interim evaluation form from their terminal and send it to the server.

[0672] The server aggregates the intermediate evaluations and generates feedback using an AI model.

[0673] Input: Interim evaluation data

[0674] Output: Feedback

[0675] Specific operation: The server collects intermediate evaluation data, runs the generative AI model to perform evaluation, and generates specific feedback that is sent to the employee.

[0676] Step 6: Calculate the final rating

[0677] At the end of the evaluation period, the server calculates a final evaluation based on all collected data.

[0678] Input: Collected data (progress data, interim evaluation data)

[0679] Output: Final evaluation result

[0680] Specific operation: The server processes all collected data in a batch and generates a final quantitative evaluation, which is then stored in a database.

[0681] Step 7: Notification and confirmation of final evaluation results

[0682] The user (employee) checks the final evaluation results on their own device.

[0683] Input: Final evaluation result

[0684] Output: Display of evaluation results

[0685] Specific operation: An employee logs in to the system and checks the evaluation results. For example, the evaluation shows, "You have achieved results that far exceed the targets, so you are a candidate for promotion."

[0686] Step 8: Save the data and reflect it in the next period

[0687] The server stores the final evaluation results and performs data analysis to reflect them in setting goals for the next period.

[0688] Input: Final evaluation result

[0689] Output: Analysis data for setting next goals

[0690] Specific operation: The server stores the final evaluation results in a database and analyzes the data using an analytical engine. Based on the analysis results, data for setting the next goal is provided to the generative AI model.

[0691] (Application example 1)

[0692] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0693] Managing and optimizing the productivity of work equipment (such as robots) in factories is important in modern manufacturing, but traditional methods have the drawback of requiring a great deal of effort to manually set goals and manage progress, and are prone to subjective judgment. Another issue is that the inability to grasp goal achievement status and evaluation results in real time makes it difficult to adapt to situations that require rapid response.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0695] In this invention, the server includes artificial intelligence means for automatically setting appropriate targets for work equipment using past evaluation data and performance data, user interface means for the work equipment manager to check the set targets and input correction requests as necessary, artificial intelligence means for accepting correction requests and performing re-evaluation, database means for collecting progress status of work equipment and storing the data, and evaluation means for automatically calculating interim and final evaluations and generating feedback. This makes it possible to automate production target setting and improve the efficiency of progress management.

[0696] "Past evaluation data" is data based on targets and performance that the work equipment has achieved in the past.

[0697] "Performance data" is specific numerical data relating to the productivity and efficiency of work equipment.

[0698] "Appropriate targets" are reasonable and achievable production targets set based on the performance of work equipment and past performance.

[0699] "Artificial intelligence means" is a system that uses artificial intelligence algorithms to analyze data and set and reassess goals.

[0700] The "user interface means" refers to an interface means through which the administrator interacts with the system, and is used to confirm goals and input correction requests.

[0701] A "modification request" is input information that allows an administrator to request a change to a set goal.

[0702] "Database means" refers to a database system for storing and managing progress data, etc.

[0703] "Mid-term evaluation" is the process of evaluating the progress of work equipment midway through the evaluation period.

[0704] "Final evaluation" is the process of evaluating the final performance of the work equipment at the end of the evaluation period.

[0705] The "evaluation means" is a means for automatically calculating intermediate and final evaluations of the work equipment based on the data and generating feedback.

[0706] "Feedback" is information that provides comments and suggestions regarding the productivity and efficiency of work equipment based on the evaluation results.

[0707] The system for realizing the present invention is a rating management system in which a server, terminals, and users work together. This system is composed of the following elements:

[0708] System configuration overview

[0709] 1. Server: The server includes an artificial intelligence means for setting appropriate targets for work equipment using past evaluation data and performance data, a database means for collecting and storing data, and an evaluation means for automatically calculating intermediate and final evaluations and generating feedback.

[0710] 2. Terminal: The terminal has a user interface for the user (administrator) to check the set goals and input correction requests as necessary. It also includes an interface for checking the evaluation results and receiving feedback.

[0711] 3. User: As an administrator, the user interacts with the system using a terminal, setting goals, checking evaluation results, and receiving feedback.

[0712] Program processing overview

[0713] Data Processing Description

[0714] 1. Initial data collection and storage

[0715] Server: The manager uses a smartphone to input information about the work equipment (model number, functions, capacity, etc.) and production targets (production volume of part A, etc.), and stores the data on the server. The collected data is saved in a database.

[0716] 2. AI-driven goal setting

[0717] Server: The server uses past production and performance data from the work equipment to set optimal production targets using artificial intelligence means. For example, an AI model is used to generate a target such as "produce 50 units of part A per hour."

[0718] 3. Review goals and request revisions

[0719] User: The administrator uses a smartphone to check the set goals and input correction requests if necessary. The correction requests are sent to the server for re-evaluation.

[0720] 4. Progress monitoring and data collection

[0721] Work equipment: Work equipment periodically sends production data to the server, which collects the progress data and stores it in a database, allowing real-time progress monitoring.

[0722] 5. Calculation of Interim and Final Grades

[0723] Server: The server automatically calculates interim and final evaluations based on the collected data and generates feedback. For example, the interim evaluation generates feedback such as "Production of part A is proceeding as planned."

[0724] 6. Notification and confirmation of evaluation results

[0725] User: The manager uses a smartphone to check the final evaluation results and feedback. The evaluation result may be, for example, "Part A was produced beyond the target, so the evaluation is A."

[0726] Examples of concrete examples and prompts

[0727] Examples:

[0728] For example, if a factory manager uses a smartphone to enter a prompt such as, "To achieve this year's goal, please set the target production volume for part A on this factory robot production line," the AI ​​model will generate a goal such as, "The target production volume for part A is 50 units per hour."

[0729] In this way, an AI-based production activity evaluation and management system can improve production efficiency within a factory and reduce management efforts. The specific technologies used include smartphones, work equipment, and servers as hardware, and databases (MySQL, etc.), AI models (TensorFlow), and smartphone applications (compatible with iOS and Android) as software.

[0730] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0731] Step 1:

[0732] Initial data collection and storage

[0733] The server provides an input form for the manager to use a smartphone to enter information about the work equipment (model number, functions, capacity, etc.) and production targets (production volume of part A, etc.). The entered data is sent to the server and stored in a database. The input at this time is the basic data and target data for the work equipment, and the output is the initial data for each work equipment stored in the database.

[0734] Step 2:

[0735] AI-driven goal setting

[0736] The server inputs past production data and performance data of the work equipment into an AI model (such as TensorFlow). The AI ​​analyzes the data and sets optimal production targets for each piece of work equipment. For example, based on the input data, it generates a target such as "produce 50 units of part A per hour." The input at this time is past evaluation data and performance data, and the output is the newly set target data.

[0737] Step 3:

[0738] Review goals and request revisions

[0739] The user uses a smartphone to check the target sent from the server. If necessary, the user inputs a correction request and sends it to the server. For example, the user inputs a request such as "I would like to change the target for part A from 50 units per hour to 45 units." The input in this case is the target data and correction request confirmed by the user, and the output is the correction request sent to the server.

[0740] Step 4:

[0741] Processing correction requests

[0742] The server re-inputs the received correction request into the AI ​​model and re-evaluates the goal. As a result of the re-evaluation, new goal data is generated and notified to the user. The input at this time is the correction request, and the output is the re-evaluated new goal data.

[0743] Step 5:

[0744] Progress monitoring and data collection

[0745] The work equipment sends production data (e.g., "Produce 48 units of part A in 1 hour") to the server at specified intervals. The server stores the sent data in a database and monitors the progress in real time. The input at this time is the progress data from the work equipment, and the output is the progress data stored in the database.

[0746] Step 6:

[0747] Mid-term assessment calculation and feedback

[0748] The server calculates an interim evaluation based on the collected progress data and generates feedback. For example, it generates feedback such as "Currently, production of part A is proceeding as planned" and notifies the user. The input at this time is the progress data, and the output is the interim evaluation and feedback data.

[0749] Step 7:

[0750] Final rating calculation

[0751] At the end of the evaluation period, the server calculates the final evaluation based on the collected data. For example, it generates a final evaluation such as "Part A was produced beyond the target, so the evaluation is A." The input at this time is all the progress data during the evaluation period, and the output is the final evaluation data.

[0752] Step 8:

[0753] Notification and confirmation of evaluation results

[0754] The user uses a smartphone to check the final evaluation results and feedback sent from the server. The input is the final evaluation data and feedback data, and the output is the evaluation results checked by the user.

[0755] Step 9:

[0756] Saving evaluation results and reflecting them in the next period

[0757] The server saves the final evaluation results in a database and performs data analysis to reflect them in setting the next target. For example, it analyzes the "appropriateness of the production target for part A" and uses the data as reference data for setting the next target. The input at this time is the final evaluation data, and the output is the data analysis results and the saved evaluation data.

[0758] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0759] As an embodiment for realizing the present invention, a specific system configuration and program processing will be described below.

[0760] System configuration overview

[0761] This system is an evaluation management system that operates in cooperation with the server, terminals, and users, and by combining it with an emotion engine, the emotional state of employees is reflected in the evaluation process. The entire system consists of the following main elements:

[0762] 1. Server

[0763] 2. Terminal

[0764] 3. Users (Employees and Raters)

[0765] 4. Emotion Engine

[0766] Program processing overview

[0767] Initial data collection and storage

[0768] server

[0769] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores the data in a database. The administrator enters this information via a web interface.

[0770] AI-driven goal setting

[0771] server

[0772] The system uses AI algorithms based on past evaluation data and performance data to automatically set appropriate goals for each employee. For example, it uses past goal achievement data to generate goals for a specific employee, such as "acquire five new customers" or "increase sales by 20% year-on-year."

[0773] Collecting and reflecting emotional data

[0774] server

[0775] An emotion engine is run to collect employee emotional data and reflect it in the evaluation process. The emotion engine recognizes emotions from employees' facial expressions and voices, and stores the data in a database.

[0776] User (Employee)

[0777] The system also has a function to input emotional states on the employee's own device. For example, when receiving feedback, the system prompts the employee to "enter their current emotional state," and the employee can input their emotional state.

[0778] Review goals and request revisions

[0779] User (Employee)

[0780] Check the set goals on your device and enter correction requests as necessary. For example, enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[0781] server

[0782] It accepts revision requests and runs AI algorithms to re-evaluate them, taking into account sentiment data collected by the sentiment engine.

[0783] Progress monitoring and data collection

[0784] Terminal

[0785] Employees periodically enter progress data, such as the progress of a sales activity, such as "acquiring two new clients."

[0786] server

[0787] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[0788] Interim evaluation input and feedback

[0789] User (Employee)

[0790] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[0791] server

[0792] Intermediate evaluation data is compiled and feedback is generated. For example, "The acquisition of three new customers is going smoothly. Sales targets are as expected." The content of the feedback is adjusted based on the emotional data.

[0793] Final rating calculation

[0794] server

[0795] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For a specific employee, the evaluation result is calculated as "Acquired six new clients and increased sales by 25% compared to the previous year."

[0796] Notification and confirmation of evaluation results

[0797] User (Employee)

[0798] The employee checks the final evaluation results on their own device. Feedback is displayed along with the evaluation results. For example, the evaluation may say, "You have significantly exceeded your targets and are a candidate for promotion." The emotion engine adjusts the timing and method of notification of the evaluation results based on the employee's emotional state.

[0799] Saving data and reflecting it in the next period

[0800] server

[0801] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[0802] The above process can improve the sense of satisfaction and fairness of traditional evaluation methods, as well as improve the efficiency of the entire evaluation system. Furthermore, by taking into account the emotional state of employees, motivation can be further increased.

[0803] The processing flow will be explained below.

[0804] Step 1:

[0805] server

[0806] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores it in the database. Specifically, the administrator enters and registers the information through a web interface.

[0807] Step 2:

[0808] server

[0809] The system uses AI algorithms based on past evaluation data and performance data to automatically set appropriate goals for each employee. Specifically, it analyzes past goal achievement data and generates goals such as "acquire five new customers" or "increase sales by 20% year-on-year."

[0810] Step 3:

[0811] server

[0812] An emotion engine is used to collect employee emotion data, including data obtained through facial expression recognition and voice analysis of employees.

[0813] Step 4:

[0814] User (Employee)

[0815] Check the set goal on your device and enter a request for correction if necessary. For example, you can enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[0816] Step 5:

[0817] server

[0818] Along with the sentiment data collected by the sentiment engine, an AI algorithm is run to reevaluate the modification request, which could result in a change such as "modify new customer goal from 5 to 3."

[0819] Step 6:

[0820] Terminal

[0821] Employees periodically enter progress data, such as sales activity reports like "two new clients acquired."

[0822] Step 7:

[0823] server

[0824] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[0825] Step 8:

[0826] User (Employee)

[0827] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[0828] Step 9:

[0829] server

[0830] Intermediate evaluation data is compiled and feedback is generated. Emotional data is referenced and the feedback content is adapted to the employee's emotional state. For example, the generated feedback might be, "The acquisition of three new clients is going smoothly. Sales targets are as expected."

[0831] Step 10:

[0832] server

[0833] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data, and the employee's evaluation result is "Acquired six new clients and increased sales by 25% compared to the previous year."

[0834] Step 11:

[0835] User (Employee)

[0836] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[0837] Step 12:

[0838] server

[0839] The final evaluation results are stored in a database and analyzed to reflect them in setting goals for the next period. Emotional data is also included in the analysis, and for example, the "appropriateness of the target for the number of new customers acquired" is evaluated and used as a reference for setting future goals.

[0840] Step 13:

[0841] server

[0842] The timing and method of notifying the employee of the evaluation results can be adjusted based on the employee's emotional state as recognized by the emotion engine. For example, if the emotional data indicates that the employee is feeling stressed, the notification of the evaluation results can be adjusted to be more gentle.

[0843] Example 2

[0844] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0845] In conventional goal setting and evaluation systems, goals are set and evaluations are performed without taking into account the emotional state of employees, resulting in problems with employee motivation and stress management. Furthermore, the goal setting and revision process is inefficient, making it difficult to increase employees' sense of satisfaction and fairness. Furthermore, feedback is uniform and not adjusted to suit each employee's individual state, resulting in the inability to provide effective feedback.

[0846] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0847] In this invention, the server includes: an artificial intelligence unit that automatically sets appropriate goals for employees using past evaluation data and performance data; a user interface unit that allows employees to input their emotional state and collect that data; an emotion analysis unit that reflects the collected emotional data in the evaluation process; a user interface unit that allows employees to check their set goals and input correction requests as necessary; an artificial intelligence unit that accepts correction requests and performs reevaluations; a database unit that collects and stores data on employee progress; an evaluation unit that automatically calculates interim and final evaluations and generates feedback; and a unit that adjusts the content of the feedback based on the emotional data. This enables goal setting and evaluation to take employees' emotional states into consideration, contributing to improved employee motivation and stress management. Furthermore, an efficient and convincing goal setting and correction process is realized, allowing for effective feedback tailored to each individual employee.

[0848] "Artificial intelligence means" refers to functions that analyze past evaluation data and performance data, automatically set appropriate goals for employees, and re-evaluate them after receiving requests for corrections.

[0849] "User interface means" refers to devices or software that provide an interface through which employees can input their emotional state and goal modification requests and view evaluation results and feedback.

[0850] "Emotion analysis means" refers to the function of analyzing emotional data collected from employees and reflecting it in the evaluation process and feedback.

[0851] "Database means" refers to a system that stores information such as employee progress and evaluation data, and allows for efficient management, retrieval, and use of that information.

[0852] "Assessment tool" refers to a system or algorithm that includes functionality for automatically calculating interim and final assessments and generating feedback.

[0853] "Emotional Data" refers to emotional states entered by employees and emotional data collected and analyzed by emotion analysis tools.

[0854] "Goal setting" refers to the process of defining specific work goals for employees to achieve.

[0855] "Modification request" refers to a request entered by an employee when they wish to modify a set goal.

[0856] "Feedback" refers to comments and evaluations provided based on an employee's progress toward achieving goals and evaluation results.

[0857] The present invention is a system that utilizes past evaluation data and performance data to set appropriate goals and evaluate employees, taking into account their emotional state. This system operates using multiple means, with a server, terminals, and users working together.

[0858] Server processing

[0859] server

[0860] As an initial setup, managers enter company information, employee job descriptions, goals, past performance data, etc. through a web interface and store them in a database. The AI ​​tool analyzes this data and automatically sets appropriate goals for each employee.

[0861] As a specific example, the server analyzes the performance data of employee A for the past three years and sends the goal "acquire five new clients" as a prompt to the generative AI model. The prompt sentence of the generative AI model is "Please set the next goal for employee A."

[0862] User Action

[0863] User (Employee)

[0864] Employees can review their goals on their own devices and input their emotional data. By inputting their emotional state, data is collected that is reflected in the evaluation process. Employees can also input requests to modify their goals.

[0865] For example, after confirming the target, employee B inputs into the terminal, "Five new customers is too many, so I would like to change it to three." The server receives this request and uses AI tools to reevaluate.

[0866] Processing by the terminal

[0867] Terminal

[0868] The terminals where employees input their daily work progress send the collected data to a server in real time. This data is stored in a database and used to monitor and evaluate progress. The terminals also have a user interface means for displaying the evaluation results and feedback.

[0869] For example, employee C enters "two new clients acquired" into the terminal. This information is sent to the server and stored in the database.

[0870] Feedback generation and notification

[0871] server

[0872] The evaluation method automatically calculates interim and final evaluations based on the collected data and generates feedback. During this process, the collected emotional data is reflected in the feedback content. The timing and method of notification of the feedback are also adjusted based on the emotional data.

[0873] As a specific example, the server generates feedback such as "Goals are being achieved smoothly, and sales are as expected" based on progress data such as "Three new customers have been acquired."

[0874] Saving data and reflecting it in the next period

[0875] server

[0876] The final evaluation results are stored in a database and analyzed as reference data for setting the next goal, which will result in appropriate goal setting for the next period.

[0877] As a concrete example, the server analyzes the "target appropriateness of the number of new customers acquired" and uses it as the basic data for goal setting to be applied in the next evaluation period. It sends a prompt to the generative AI model saying, "Please analyze the appropriateness of the number of new customers acquired in order to set the next goal."

[0878] In this way, the present invention can realize a more convincing and fair evaluation system that takes into account the emotional state of employees, which is more effective than conventional evaluation methods in that it contributes to improving employee motivation and stress management.

[0879] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0880] Step 1:

[0881] Initial data collection and storage

[0882] The server receives company and employee information entered by the administrator through a web interface and stores the data in a database in real time. Specific inputs include organizational structure, organizational goals, employee names, job titles, and job descriptions. The server converts this input data into JSON format and stores it in the database.

[0883] Specific behavior:

[0884] When an administrator opens the administration screen in a browser, enters data into each field, and clicks the "Submit" button, the entered data is sent to the server, which receives the data and stores it in a database.

[0885] Step 2:

[0886] AI-driven goal setting

[0887] The server uses a generative AI model to analyze past evaluation and performance data stored in the database. Specifically, it analyzes each employee's past goal achievement rate and performance to generate appropriate goals for the next period.

[0888] Input: Past evaluation data, performance data

[0889] Data processing: A prompt sentence is generated based on the input data for the generative AI model and sent to the AI ​​model.

[0890] Output: Appropriate goal setting data

[0891] Specific behavior:

[0892] The server generates a prompt saying, "Please generate an appropriate goal based on employee A's past evaluation data," and sends it to the generative AI model. The AI ​​model generates a goal, "Acquire five new customers," and sends it back to the server.

[0893] Step 3:

[0894] Collecting and reflecting emotional data

[0895] Users (employees) input their emotional state during the evaluation process or when receiving feedback. The data is sent to the server in real time and stored in a database. The server then reflects the emotional data in the evaluation process.

[0896] Input: Emotional state input data

[0897] Data processing: Analyze sentiment data and incorporate it into the evaluation process.

[0898] Output: Emotion data reflected in the evaluation

[0899] Specific behavior:

[0900] When an employee sees a prompt on the terminal asking, "Please enter your current emotional state," they enter "I feel stressed," and click the "Submit" button. The entered emotional data is sent to the server and stored in a database.

[0901] Step 4:

[0902] Check your goals and enter correction requests

[0903] The user (employee) checks the set goals on the terminal and inputs correction requests as necessary. The server receives this request and uses the AI ​​model to reassess.

[0904] Input: Goal setting data, correction request data

[0905] Data processing: Reevaluation process using generative AI models

[0906] Output: Reassessed goal setting data

[0907] Specific behavior:

[0908] The employee opens the goal confirmation screen, checks the goal "Acquire 5 new customers," enters "Five new customers is too many, so I would like to change it to three," and submits it. The server receives this request and sends a reevaluation prompt to the generative AI model: "Please reevaluate Employee A's new goal setting." The AI ​​model generates a new appropriate goal and notifies the server.

[0909] Step 5:

[0910] Progress monitoring and data collection

[0911] The terminal provides a screen where employees can input their daily work progress. The progress data entered by employees is sent to the server in real time and stored in a database. The server uses this data to monitor the progress.

[0912] Input: Progress input data

[0913] Data processing: progress data collection and real-time monitoring

[0914] Output: Progress data

[0915] Specific behavior:

[0916] An employee types "Acquired two new clients" into a terminal and clicks the send button. The entered data is sent to the server and saved in a database. The server uses the data to monitor progress in real time.

[0917] Step 6:

[0918] Enter mid-term evaluations and generate feedback

[0919] The server calculates intermediate evaluations based on the collected progress data and generates feedback, which is adjusted based on the emotional data.

[0920] Input: progress data, emotional state data

[0921] Data processing: Calculating intermediate assessments and generating feedback

[0922] Output: Regulated feedback data

[0923] Specific behavior:

[0924] The server performs an interim evaluation based on progress data such as "three new clients acquired," and generates feedback such as "goals are being achieved smoothly." The content of the feedback and the timing of notifications are adjusted based on the emotional data.

[0925] Step 7:

[0926] Calculation and notification of final grade

[0927] At the end of the evaluation period, the server automatically calculates the final evaluation based on all collected data and notifies the user along with feedback. The timing and method of notification are also adjusted based on the emotional data.

[0928] Input: progress data, emotional state data

[0929] Data processing: Calculating final grades and generating feedback

[0930] Output: Final evaluation result data and feedback data

[0931] Specific behavior:

[0932] At the end of the period, the server consolidates all the aggregated data and sends a prompt to the generative AI model saying, "Please generate the final evaluation result for employee F." The final evaluation and feedback are generated and notified to the employee's device.

[0933] Step 8:

[0934] Confirmation of evaluation results and reflection on the next period

[0935] The user (employee) checks the evaluation results and feedback on the terminal, and the server stores the results in a database and reflects them in setting goals for the next period.

[0936] Input: Final evaluation result data

[0937] Data processing: Data storage and analysis for setting next goals

[0938] Output: Saved final evaluation results

[0939] Specific behavior:

[0940] The employee opens the evaluation results screen and checks the evaluation result: "Acquired six new customers, increased sales by 25% compared to the previous year." The server saves this data in a database and analyzes it for use in setting goals for the next period. The server sends a prompt to the AI ​​model saying, "Please analyze the appropriateness of the number of new customers acquired in order to set goals for the next period," and sets goals based on the results.

[0941] (Application example 2)

[0942] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0943] Conventional employee evaluation systems have problems in that the appropriateness of set goals and the quality of feedback have a significant impact on employee motivation and performance. Furthermore, because they were unable to take into account employees' emotions and stress levels, it was difficult to appropriately adjust workloads and maintain motivation. This led to issues such as a lack of fairness and a sense of satisfaction in evaluations, which ultimately led to a decline in work efficiency.

[0944] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: artificial intelligence means for automatically setting appropriate goals for employees using past evaluation data and performance data; user interface means for employees to check the set goals and input correction requests as necessary; artificial intelligence means for accepting correction requests and performing reevaluations; emotion recognition means for collecting employee emotion data and adjusting work instructions and feedback based on the data; database means for collecting employee progress and storing the data; and evaluation means for automatically calculating interim and final evaluations and generating feedback. This enables appropriate goal setting and workload adjustment taking into account employees' emotions and stress levels, thereby improving the sense of satisfaction and fairness of evaluations and optimizing work efficiency and maintaining employee motivation.

[0945] "Artificial intelligence means" refers to technology that analyzes past evaluation data and performance data and automatically sets appropriate goals for employees.

[0946] The "user interface means" is an interface through which employees can interact with the system, and is a function that allows employees to confirm set goals and input correction requests.

[0947] "Emotion recognition means" is a technology that collects emotional data from employees' facial expressions and voices, and adjusts work instructions and feedback based on that data.

[0948] "Database means" is a system for efficiently collecting and storing employee progress and emotional data, and has the function of centrally managing data.

[0949] The "evaluation tool" is a technology that automatically generates feedback and evaluates employees based on collected mid-term and final evaluation data.

[0950] "Emotional data" refers to data obtained from an employee's facial expressions, voice, and other emotional expressions, and is information that quantifies or qualitatively evaluates the employee's emotional state.

[0951] The present invention relates to a system for improving the sense of fairness and satisfaction of evaluations by reflecting emotional data in the performance evaluations of employees. Specific embodiments for carrying out the present invention will be described in detail below.

[0952] System Configuration

[0953] This system is an evaluation management system that operates in cooperation with a server, terminals, and users (employees and evaluators), and by combining it with an emotion engine, reflects the employee's emotional state in the evaluation process. The entire system includes the following elements:

[0954] Server: The server is the core of the system and includes artificial intelligence means, database means, and evaluation means. Specifically, the server analyzes past evaluation data and performance data to set appropriate goals for employees, accepts correction requests, and performs reevaluations. It also has the function of collecting employee emotional data using emotion recognition means and adjusting work instructions and feedback based on that data.

[0955] Terminal: A device used by the employee and evaluator that contains a user interface through which the employee can view set goals, input correction requests, report progress, and view feedback.

[0956] Users (employees and evaluators): Users access the system and input their own work status and emotional data. Employees report their progress and emotional state, and evaluators provide appropriate feedback based on the collected data.

[0957] Hardware and software used

[0958] Hardware

[0959] Camera: Used to capture employee facial expressions.

[0960] Computer terminal: Used to operate the user interface.

[0961] Server machine: Used to process and store data centrally.

[0962] software

[0963] OpenCV: Used to acquire and process camera images.

[0964] EmotionRecognitionEngine: Functions as an emotion recognition engine, analyzing employees' emotional states from facial expressions and voice data. For example, "Face++" or "Microsoft Azure Emotion API."

[0965] RobotController: Uses a robot control library such as ROS to transmit work instructions and feedback to the robot.

[0966] DatabaseClient: Use a database client such as "SQLite" to efficiently store emotion data and rating data.

[0967] Artificial intelligence model: A generative AI model that analyzes past evaluation data and performance data to generate appropriate goals.

[0968] Explanation of program processing

[0969] Server: The server first uses the administrator's input form to collect company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores them in a database. Next, it uses an AI algorithm to set appropriate goals based on past evaluation data and performance data. It also runs an emotion recognition engine to collect employee emotional data and reflect it in the evaluation process. Finally, it automatically calculates interim and final evaluations and generates feedback.

[0970] Terminal: Employees use the terminal to check the set goals and input correction requests as necessary. Progress data and emotional state are also entered through this terminal. Employees also use this terminal to check evaluation results and feedback.

[0971] Users: Employees and evaluators access the system through a user interface to input and confirm the necessary data. In particular, real-time feedback and work adjustments based on emotional data are possible.

[0972] Specific examples

[0973] As a specific use case, the following prompts can be input to the generative AI model:

[0974] Please provide an example of a system that uses emotion recognition data from employees to enable factory robots to provide appropriate work instructions and feedback. Please also explain how workloads are adjusted when stress levels are high, and how positive feedback is provided when happiness levels are low.

[0975] This prompt can be fed into a generative AI model to generate additional ideas for similar implementations and improvements.

[0976] As described above, the present invention provides an evaluation management system based on employee emotional data, and is expected to improve employee motivation and performance.

[0977] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0978] Step 1:

[0979] The server uses the administrator's input form to collect data such as company information, organizational goals, each employee's grade, rank, job type, and job description, and stores it in a database. This prepares the initial data necessary for evaluation. The input data is the information entered by the administrator, and the output is the prepared data stored in the database.

[0980] Step 2:

[0981] The server runs an artificial intelligence model based on past evaluation data and performance data, automatically setting appropriate goals for each employee. For example, by analyzing past goal achievement data, goals such as "acquire five new clients" or "increase sales by 20% compared to the previous year" are generated for a specific employee. The input data are past evaluation data and performance data, and the output is the appropriate goal generated for each employee.

[0982] Step 3:

[0983] Using a terminal, an employee checks the set targets and inputs correction requests as necessary. For example, they can input a comment such as, "Five new customers is too many, so I would like to reduce it to three." The input data is the displayed targets and correction requests, and the output shows the state after the correction requests have been input.

[0984] Step 4:

[0985] The server accepts the correction request and re-runs the AI ​​model to re-evaluate it. The input data is the correction request entered by the employee, and the output is the re-evaluated goal. This re-evaluation also takes into account the employee's emotional data.

[0986] Step 5:

[0987] The server uses a camera and an emotion recognition engine to collect emotional data from employees' facial expressions and voices. This emotional data is stored in a database. The input data is camera footage and audio data, and the output is analyzed emotional data.

[0988] Step 6:

[0989] The server adjusts work instructions and feedback based on the employee's emotional data. For example, if the stress level is high, it reduces the workload, and if the happiness level is low, it provides positive feedback. The input data is emotional data, and the output is adjusted work instructions and feedback.

[0990] Step 7:

[0991] Employees periodically input progress data using terminals. For example, the progress of sales activities such as "acquiring two new clients" is entered. The input data is specific information indicating the progress, and the output is progress data stored in a database.

[0992] Step 8:

[0993] The server collects the entered progress data and stores it in a database, allowing employees' progress toward their goals to be monitored in real time. The input data is the progress data entered by the employees, and the output is the progress data stored in the database.

[0994] Step 9:

[0995] Employees use terminals to input interim evaluations and report the progress of each goal. For example, they input progress data such as "three new clients acquired." The input data is the progress data as interim evaluations, and the output is the interim evaluation data sent to the server.

[0996] Step 10:

[0997] The server aggregates the interim evaluation data and generates feedback. For example, the feedback might be something like, "The acquisition of three new customers is going smoothly. Sales targets are as expected." The content of the feedback is adjusted based on the emotional data. The input data is the interim evaluation data, and the output is the generated feedback.

[0998] Step 11:

[0999] At the end of the evaluation period, the server automatically calculates the final evaluation based on the collected data. For example, the evaluation result calculated is "6 new customers acquired, sales increased 25% compared to the previous year." The input data is all the collected evaluation data, and the output is the final evaluation result.

[1000] Step 12:

[1001] The employee uses a terminal to check the final evaluation results. Feedback is displayed along with the evaluation results. For example, an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion" may be displayed. The input data is the final evaluation results sent from the server, and the output is the state of the evaluation results as viewed by the employee.

[1002] Step 13:

[1003] The server saves the final evaluation results in a database and performs data analysis to reflect them in setting goals for the next period. For example, it analyzes the "target appropriateness of the number of new customers acquired" and uses it as reference data for setting goals for the next period. The input data is the final evaluation results, and the output is the analyzed reference data.

[1004] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1005] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1006] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1007] [Third embodiment]

[1008] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1009] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1010] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1011] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1012] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1013] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1014] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1015] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1016] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1017] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1018] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1019] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1020] As an embodiment for realizing the present invention, a specific system configuration and program processing will be described below.

[1021] System configuration overview

[1022] This system is a reputation management system in which the server, terminals, and users work together. The entire system is composed of the following main elements:

[1023] 1. Server

[1024] 2. Terminal

[1025] 3. Users (Employees and Raters)

[1026] Program processing overview

[1027] Initial data collection and storage

[1028] server

[1029] The administrator uses the input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores it in the database. This allows all initial data to be managed within the system.

[1030] AI-driven goal setting

[1031] server

[1032] The system uses AI algorithms based on past evaluation data and employee performance data to automatically set appropriate goals for each employee. For example, it generates goals such as "acquire five new customers" or "increase sales by 20% year-on-year" by taking into account past goal achievement data.

[1033] Review goals and request revisions

[1034] User (Employee)

[1035] Check the set goal on your device and enter a request for correction if necessary. For example, enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[1036] server

[1037] The AI ​​will accept requests for revisions and review the targets again. For example, it may "revise the new customer target from five companies to three companies" taking into account the overall situation of the sales department.

[1038] Progress monitoring and data collection

[1039] Terminal

[1040] Employees periodically enter progress data, such as "two new clients acquired" as a sales activity report.

[1041] server

[1042] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[1043] Interim evaluation input and feedback

[1044] User (Employee)

[1045] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[1046] server

[1047] The interim evaluations are compiled and feedback is generated. For example, feedback such as "The acquisition of three new clients is going well. Sales targets are on track" is sent to employees.

[1048] Final rating calculation

[1049] server

[1050] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For example, the evaluation is calculated as "6 new customers acquired, sales increased 25% compared to the previous year."

[1051] Notification and confirmation of final evaluation results

[1052] User (Employee)

[1053] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[1054] Saving data and reflecting it in the next period

[1055] server

[1056] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[1057] The above process will improve the sense of satisfaction and fairness of the traditional evaluation method, as well as improve the efficiency of the entire evaluation system. It will also increase employee motivation.

[1058] The processing flow will be explained below.

[1059] Step 1:

[1060] server

[1061] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores the data in a database. Specifically, the administrator enters values ​​for each item via a web interface and registers it.

[1062] Step 2:

[1063] server

[1064] The AI ​​algorithm is run based on past evaluation data and each employee's performance data to automatically set appropriate goals for each employee. For example, past goal achievement data can be input into a machine learning model to generate goals for a specific employee, such as "acquire five new customers" or "increase sales by 20% compared to the previous year."

[1065] Step 3:

[1066] User (Employee)

[1067] Check the goals set on your device and enter correction requests as necessary. For example, you can enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[1068] Step 4:

[1069] server

[1070] An AI algorithm is run to accept and reevaluate requests for revisions. For example, the system may consider the overall situation of the sales department and make a change such as "revise the new customer target from five companies to three companies."

[1071] Step 5:

[1072] Terminal

[1073] Employees periodically enter progress data, such as the progress of a sales activity, such as "acquiring two new clients."

[1074] Step 6:

[1075] server

[1076] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[1077] Step 7:

[1078] User (Employee)

[1079] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[1080] Step 8:

[1081] server

[1082] The interim evaluation data is compiled and feedback is generated. The feedback is then communicated to the employee. For example, the feedback might be something like, "The acquisition of three new clients is going smoothly. Sales targets are on track."

[1083] Step 9:

[1084] server

[1085] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For a specific employee, the evaluation result is calculated as "Acquired six new clients and increased sales by 25% compared to the previous year."

[1086] Step 10:

[1087] User (Employee)

[1088] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[1089] Step 11:

[1090] server

[1091] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[1092] Example 1

[1093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1094] In conventional employee evaluation systems, evaluation settings and progress management are done manually, which creates issues with the fairness and efficiency of evaluations. It is also difficult to set appropriate goals and monitor progress in real time, which leads to a lack of satisfaction with evaluations and improved employee motivation. Furthermore, it is difficult to reflect evaluation results in setting goals for the next period.

[1095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1096] In this invention, the server includes an artificial intelligence unit that automatically sets appropriate goals for employees using past evaluation data and performance data, a user interface unit that allows employees to check the set goals and input revision requests as necessary, and an artificial intelligence unit that accepts revision requests and performs reevaluation. This improves the fairness and efficiency of evaluations and enables appropriate goal setting and real-time monitoring of progress. It also makes it easier to reflect evaluation results in setting next-period goals, contributing to improved employee motivation.

[1097] "Artificial intelligence tools" are algorithms or programs that automatically set appropriate goals for employees based on past evaluation data and performance data, and then reevaluate them.

[1098] "User interface means" refers to an interface through which employees can access the system, check the set goals, input correction requests as necessary, and check the evaluation results.

[1099] The "database means" is a database system for storing collected progress data and evaluation data, and for saving and managing necessary information.

[1100] An "assessment tool" is an algorithm or program that automatically calculates interim and final assessments and generates feedback.

[1101] A "final rating tool" is an algorithm or program that automatically calculates a final rating based on the data collected at the end of the rating period.

[1102] A "generative AI model" is an artificial intelligence model used to analyze employee performance data in real time and monitor progress.

[1103] "Monitoring Measures" means systems or tools for analyzing collected progress data in real time and monitoring employee progress.

[1104] "Input means" refers to a form or interface that allows employees to enter progress information and send the data to the server.

[1105] "Data storage and analysis means" refers to a system for storing the final evaluation results and analyzing the data to reflect them in setting goals for the next period.

[1106] The evaluation management system of the present invention functions in cooperation with the server, terminals, and users to efficiently and fairly evaluate employee performance. It is realized by using "artificial intelligence means," "user interface means," "database means," "evaluation means," etc. The specific configuration and processing flow of the system are explained below.

[1107] System Configuration

[1108] server

[1109] The server is the core of the system and is responsible for:

[1110] Artificial intelligence measures: Using generative AI models (e.g., random forests or neural networks) to automatically set appropriate goals for employees based on past appraisal and performance data.

[1111] Database means: Collected data is stored using a database system such as MySQL or PostgreSQL.

[1112] Assessment instruments: Automatically calculate mid-term and final grades and generate feedback.

[1113] Monitoring measures: Analyze employee performance data in real time to monitor progress.

[1114] Terminal

[1115] The devices are used by employees and assessors and perform the following functions:

[1116] User interface means: Provide an interface for employees to check the set goals, input correction requests, and check the evaluation results.

[1117] Input methods: Provide employees with a form to periodically enter their progress.

[1118] User

[1119] Users (employees and raters) interact with the system through the following means:

[1120] Employees: Enter their own performance and progress, and check the set goals and evaluation results.

[1121] Evaluator: Review employee evaluation data and provide feedback as needed.

[1122] Examples and prompts

[1123] A specific example of use would be when a sales department employee is set a goal of "acquiring five new customers" and enters their progress into the system.

[1124] Examples:

[1125] Employee A reports that he has acquired two new clients over the past three months, and an interim evaluation is generated based on that data. Based on this interim evaluation, the server provides feedback such as, "You're making good progress, but you need to put in a little more effort to achieve your goals."

[1126] Example prompt sentence:

[1127] Goal: Acquire 5 new customers

[1128] Progress: Acquired 2 companies in 3 months

[1129] Generate mid-term evaluation feedback.

[1130] By inputting this prompt into a generative AI model, specific feedback is generated.

[1131] As described above, the evaluation management system of the present invention consistently automates the entire process from employee goal setting to progress management and evaluation, achieving fair and efficient evaluation. This system is expected to improve employee motivation and overall corporate performance.

[1132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1133] Step 1: Collect and store initial data

[1134] The server provides a web form exclusively for administrators, and collects data such as company information, organizational goals, and each employee's grade, rank, job type, and job description. The required information is entered into the input form and stored in a database.

[1135] Input: Company information, organizational goals, employee data

[1136] Output: Initial data stored in the database

[1137] How it works: An administrator accesses a web form and enters the required information in real time. The entered data is immediately validated and any incomplete fields are flagged with a warning. After validation is complete, the data is saved to the database.

[1138] Step 2: AI-driven goal setting

[1139] The server uses a generative AI model to set appropriate goals for each employee, based on past evaluation and performance data, and performs analysis using Python-based random forests and neural networks.

[1140] Input: Past evaluation data, performance data

[1141] Output: Goals for each employee

[1142] How it works: The server retrieves past performance data from the database, runs the generative AI model, and generates appropriate goals for each employee. The generated goals are then stored in the database.

[1143] Step 3: Review your goals and request revisions

[1144] Users (employees) log in to the system from their own terminals, check the set goals, and input correction requests as necessary.

[1145] Input: Employee Goals

[1146] Output: Correction request

[1147] Specific operation: An employee logs in to the system and checks the presented goals. If any corrections are needed, they fill out a form with the desired corrections and submit it to the server.

[1148] The server runs the AI ​​again based on the correction request and corrects the goal if necessary.

[1149] Input: Correction request

[1150] Output: revised target

[1151] Specific operation: The server verifies the received correction request and re-evaluates it using the generative AI model. The re-evaluated goal is generated and saved in the database.

[1152] Step 4: Monitor progress and collect data

[1153] The terminal provides a form for employees to periodically enter their progress, which is used as evaluation material.

[1154] Input: Progress data (e.g., acquisition of two new customers)

[1155] Output: Save progress data to database

[1156] Specific operation: Employees periodically access their terminals and enter their progress information into a form. The entered data is sent to the server and stored in a database.

[1157] The server collects and monitors progress data in real time.

[1158] Input: Progress data

[1159] Output: Real-time progress monitoring

[1160] Specific operation: Stores progress data in a database and displays progress in real time on a dashboard.

[1161] Step 5: Input of mid-term evaluation and feedback

[1162] Users (employees) enter interim evaluations and report progress.

[1163] Input: Interim evaluation data (e.g., 3 new customers acquired)

[1164] Output: Interim evaluation input data

[1165] Specific operation: Employees enter their progress into the interim evaluation form from their terminal and send it to the server.

[1166] The server aggregates the intermediate evaluations and generates feedback using an AI model.

[1167] Input: Interim evaluation data

[1168] Output: Feedback

[1169] Specific operation: The server collects intermediate evaluation data, runs the generative AI model to perform evaluation, and generates specific feedback that is sent to the employee.

[1170] Step 6: Calculate the final rating

[1171] At the end of the evaluation period, the server calculates a final evaluation based on all collected data.

[1172] Input: Collected data (progress data, interim evaluation data)

[1173] Output: Final evaluation result

[1174] Specific operation: The server processes all collected data in a batch and generates a final quantitative evaluation, which is then stored in a database.

[1175] Step 7: Notification and confirmation of final evaluation results

[1176] The user (employee) checks the final evaluation results on their own device.

[1177] Input: Final evaluation result

[1178] Output: Display of evaluation results

[1179] Specific operation: An employee logs in to the system and checks the evaluation results. For example, the evaluation shows, "You have achieved results that far exceed the targets, so you are a candidate for promotion."

[1180] Step 8: Save the data and reflect it in the next period

[1181] The server stores the final evaluation results and performs data analysis to reflect them in setting goals for the next period.

[1182] Input: Final evaluation result

[1183] Output: Analysis data for setting next goals

[1184] Specific operation: The server stores the final evaluation results in a database and analyzes the data using an analytical engine. Based on the analysis results, data for setting the next goal is provided to the generative AI model.

[1185] (Application example 1)

[1186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1187] Managing and optimizing the productivity of work equipment (such as robots) in factories is important in modern manufacturing, but traditional methods have the drawback of requiring a great deal of effort to manually set goals and manage progress, and are prone to subjective judgment. Another issue is that the inability to grasp goal achievement status and evaluation results in real time makes it difficult to adapt to situations that require rapid response.

[1188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1189] In this invention, the server includes artificial intelligence means for automatically setting appropriate targets for work equipment using past evaluation data and performance data, user interface means for the work equipment manager to check the set targets and input correction requests as necessary, artificial intelligence means for accepting correction requests and performing re-evaluation, database means for collecting progress status of work equipment and storing the data, and evaluation means for automatically calculating interim and final evaluations and generating feedback. This makes it possible to automate production target setting and improve the efficiency of progress management.

[1190] "Past evaluation data" is data based on targets and performance that the work equipment has achieved in the past.

[1191] "Performance data" is specific numerical data relating to the productivity and efficiency of work equipment.

[1192] "Appropriate targets" are reasonable and achievable production targets set based on the performance of work equipment and past performance.

[1193] "Artificial intelligence means" is a system that uses artificial intelligence algorithms to analyze data and set and reassess goals.

[1194] The "user interface means" refers to an interface means through which the administrator interacts with the system, and is used to confirm goals and input correction requests.

[1195] A "modification request" is input information that allows an administrator to request a change to a set goal.

[1196] "Database means" refers to a database system for storing and managing progress data, etc.

[1197] "Mid-term evaluation" is the process of evaluating the progress of work equipment midway through the evaluation period.

[1198] "Final evaluation" is the process of evaluating the final performance of the work equipment at the end of the evaluation period.

[1199] The "evaluation means" is a means for automatically calculating intermediate and final evaluations of the work equipment based on the data and generating feedback.

[1200] "Feedback" is information that provides comments and suggestions regarding the productivity and efficiency of work equipment based on the evaluation results.

[1201] The system for realizing the present invention is a rating management system in which a server, terminals, and users work together. This system is composed of the following elements:

[1202] System configuration overview

[1203] 1. Server: The server includes an artificial intelligence means for setting appropriate targets for work equipment using past evaluation data and performance data, a database means for collecting and storing data, and an evaluation means for automatically calculating intermediate and final evaluations and generating feedback.

[1204] 2. Terminal: The terminal has a user interface for the user (administrator) to check the set goals and input correction requests as necessary. It also includes an interface for checking the evaluation results and receiving feedback.

[1205] 3. User: As an administrator, the user interacts with the system using a terminal, setting goals, checking evaluation results, and receiving feedback.

[1206] Program processing overview

[1207] Data Processing Description

[1208] 1. Initial data collection and storage

[1209] Server: The manager uses a smartphone to input information about the work equipment (model number, functions, capacity, etc.) and production targets (production volume of part A, etc.), and stores the data on the server. The collected data is saved in a database.

[1210] 2. AI-driven goal setting

[1211] Server: The server uses past production and performance data from the work equipment to set optimal production targets using artificial intelligence means. For example, an AI model is used to generate a target such as "produce 50 units of part A per hour."

[1212] 3. Review goals and request revisions

[1213] User: The administrator uses a smartphone to check the set goals and input correction requests if necessary. The correction requests are sent to the server for re-evaluation.

[1214] 4. Progress monitoring and data collection

[1215] Work equipment: Work equipment periodically sends production data to the server, which collects the progress data and stores it in a database, allowing real-time progress monitoring.

[1216] 5. Calculation of Interim and Final Grades

[1217] Server: The server automatically calculates interim and final evaluations based on the collected data and generates feedback. For example, the interim evaluation generates feedback such as "Production of part A is proceeding as planned."

[1218] 6. Notification and confirmation of evaluation results

[1219] User: The manager uses a smartphone to check the final evaluation results and feedback. The evaluation result may be, for example, "Part A was produced beyond the target, so the evaluation is A."

[1220] Examples of concrete examples and prompts

[1221] Examples:

[1222] For example, if a factory manager uses a smartphone to enter a prompt such as, "To achieve this year's goal, please set the target production volume for part A on this factory robot production line," the AI ​​model will generate a goal such as, "The target production volume for part A is 50 units per hour."

[1223] In this way, an AI-based production activity evaluation and management system can improve production efficiency within a factory and reduce management efforts. The specific technologies used include smartphones, work equipment, and servers as hardware, and databases (MySQL, etc.), AI models (TensorFlow), and smartphone applications (compatible with iOS and Android) as software.

[1224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1225] Step 1:

[1226] Initial data collection and storage

[1227] The server provides an input form for the manager to use a smartphone to enter information about the work equipment (model number, functions, capacity, etc.) and production targets (production volume of part A, etc.). The entered data is sent to the server and stored in a database. The input at this time is the basic data and target data for the work equipment, and the output is the initial data for each work equipment stored in the database.

[1228] Step 2:

[1229] AI-driven goal setting

[1230] The server inputs past production data and performance data of the work equipment into an AI model (such as TensorFlow). The AI ​​analyzes the data and sets optimal production targets for each piece of work equipment. For example, based on the input data, it generates a target such as "produce 50 units of part A per hour." The input at this time is past evaluation data and performance data, and the output is the newly set target data.

[1231] Step 3:

[1232] Review goals and request revisions

[1233] The user uses a smartphone to check the target sent from the server. If necessary, the user inputs a correction request and sends it to the server. For example, the user inputs a request such as "I would like to change the target for part A from 50 units per hour to 45 units." The input in this case is the target data and correction request confirmed by the user, and the output is the correction request sent to the server.

[1234] Step 4:

[1235] Processing correction requests

[1236] The server re-inputs the received correction request into the AI ​​model and re-evaluates the goal. As a result of the re-evaluation, new goal data is generated and notified to the user. The input at this time is the correction request, and the output is the re-evaluated new goal data.

[1237] Step 5:

[1238] Progress monitoring and data collection

[1239] The work equipment sends production data (e.g., "Produce 48 units of part A in 1 hour") to the server at specified intervals. The server stores the sent data in a database and monitors the progress in real time. The input at this time is the progress data from the work equipment, and the output is the progress data stored in the database.

[1240] Step 6:

[1241] Mid-term assessment calculation and feedback

[1242] The server calculates an interim evaluation based on the collected progress data and generates feedback. For example, it generates feedback such as "Currently, production of part A is proceeding as planned" and notifies the user. The input at this time is the progress data, and the output is the interim evaluation and feedback data.

[1243] Step 7:

[1244] Final rating calculation

[1245] At the end of the evaluation period, the server calculates the final evaluation based on the collected data. For example, it generates a final evaluation such as "Part A was produced beyond the target, so the evaluation is A." The input at this time is all the progress data during the evaluation period, and the output is the final evaluation data.

[1246] Step 8:

[1247] Notification and confirmation of evaluation results

[1248] The user uses a smartphone to check the final evaluation results and feedback sent from the server. The input is the final evaluation data and feedback data, and the output is the evaluation results checked by the user.

[1249] Step 9:

[1250] Saving evaluation results and reflecting them in the next period

[1251] The server saves the final evaluation results in a database and performs data analysis to reflect them in setting the next target. For example, it analyzes the "appropriateness of the production target for part A" and uses the data as reference data for setting the next target. The input at this time is the final evaluation data, and the output is the data analysis results and the saved evaluation data.

[1252] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1253] As an embodiment for realizing the present invention, a specific system configuration and program processing will be described below.

[1254] System configuration overview

[1255] This system is an evaluation management system that operates in cooperation with the server, terminals, and users, and by combining it with an emotion engine, the emotional state of employees is reflected in the evaluation process. The entire system consists of the following main elements:

[1256] 1. Server

[1257] 2. Terminal

[1258] 3. Users (Employees and Raters)

[1259] 4. Emotion Engine

[1260] Program processing overview

[1261] Initial data collection and storage

[1262] server

[1263] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores the data in a database. The administrator enters this information via a web interface.

[1264] AI-driven goal setting

[1265] server

[1266] The system uses AI algorithms based on past evaluation data and performance data to automatically set appropriate goals for each employee. For example, it uses past goal achievement data to generate goals for a specific employee, such as "acquire five new customers" or "increase sales by 20% year-on-year."

[1267] Collecting and reflecting emotional data

[1268] server

[1269] An emotion engine is run to collect employee emotional data and reflect it in the evaluation process. The emotion engine recognizes emotions from employees' facial expressions and voices, and stores the data in a database.

[1270] User (Employee)

[1271] The system also has a function to input emotional states on the employee's own device. For example, when receiving feedback, the system prompts the employee to "enter their current emotional state," and the employee can input their emotional state.

[1272] Review goals and request revisions

[1273] User (Employee)

[1274] Check the set goals on your device and enter correction requests as necessary. For example, enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[1275] server

[1276] It accepts revision requests and runs AI algorithms to re-evaluate them, taking into account sentiment data collected by the sentiment engine.

[1277] Progress monitoring and data collection

[1278] Terminal

[1279] Employees periodically enter progress data, such as the progress of a sales activity, such as "acquiring two new clients."

[1280] server

[1281] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[1282] Interim evaluation input and feedback

[1283] User (Employee)

[1284] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[1285] server

[1286] Intermediate evaluation data is compiled and feedback is generated. For example, "The acquisition of three new customers is going smoothly. Sales targets are as expected." The content of the feedback is adjusted based on the emotional data.

[1287] Final rating calculation

[1288] server

[1289] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For a specific employee, the evaluation result is calculated as "Acquired six new clients and increased sales by 25% compared to the previous year."

[1290] Notification and confirmation of evaluation results

[1291] User (Employee)

[1292] The employee checks the final evaluation results on their own device. Feedback is displayed along with the evaluation results. For example, the evaluation may say, "You have significantly exceeded your targets and are a candidate for promotion." The emotion engine adjusts the timing and method of notification of the evaluation results based on the employee's emotional state.

[1293] Saving data and reflecting it in the next period

[1294] server

[1295] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[1296] The above process can improve the sense of satisfaction and fairness of traditional evaluation methods, as well as improve the efficiency of the entire evaluation system. Furthermore, by taking into account the emotional state of employees, motivation can be further increased.

[1297] The processing flow will be explained below.

[1298] Step 1:

[1299] server

[1300] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores it in the database. Specifically, the administrator enters and registers the information through a web interface.

[1301] Step 2:

[1302] server

[1303] The system uses AI algorithms based on past evaluation data and performance data to automatically set appropriate goals for each employee. Specifically, it analyzes past goal achievement data and generates goals such as "acquire five new customers" or "increase sales by 20% year-on-year."

[1304] Step 3:

[1305] server

[1306] An emotion engine is used to collect employee emotion data, including data obtained through facial expression recognition and voice analysis of employees.

[1307] Step 4:

[1308] User (Employee)

[1309] Check the set goal on your device and enter a request for correction if necessary. For example, you can enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[1310] Step 5:

[1311] server

[1312] Along with the sentiment data collected by the sentiment engine, an AI algorithm is run to reevaluate the modification request, which could result in a change such as "modify new customer goal from 5 to 3."

[1313] Step 6:

[1314] Terminal

[1315] Employees periodically enter progress data, such as sales activity reports like "two new clients acquired."

[1316] Step 7:

[1317] server

[1318] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[1319] Step 8:

[1320] User (Employee)

[1321] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[1322] Step 9:

[1323] server

[1324] Intermediate evaluation data is compiled and feedback is generated. Emotional data is referenced and the feedback content is adapted to the employee's emotional state. For example, the generated feedback might be, "The acquisition of three new clients is going smoothly. Sales targets are as expected."

[1325] Step 10:

[1326] server

[1327] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data, and the employee's evaluation result is "Acquired six new clients and increased sales by 25% compared to the previous year."

[1328] Step 11:

[1329] User (Employee)

[1330] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[1331] Step 12:

[1332] server

[1333] The final evaluation results are stored in a database and analyzed to reflect them in setting goals for the next period. Emotional data is also included in the analysis, and for example, the "appropriateness of the target for the number of new customers acquired" is evaluated and used as a reference for setting future goals.

[1334] Step 13:

[1335] server

[1336] The timing and method of notifying the employee of the evaluation results can be adjusted based on the employee's emotional state as recognized by the emotion engine. For example, if the emotional data indicates that the employee is feeling stressed, the notification of the evaluation results can be adjusted to be more gentle.

[1337] Example 2

[1338] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1339] In conventional goal setting and evaluation systems, goals are set and evaluations are performed without taking into account the emotional state of employees, resulting in problems with employee motivation and stress management. Furthermore, the goal setting and revision process is inefficient, making it difficult to increase employees' sense of satisfaction and fairness. Furthermore, feedback is uniform and not adjusted to suit each employee's individual state, resulting in the inability to provide effective feedback.

[1340] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1341] In this invention, the server includes: an artificial intelligence unit that automatically sets appropriate goals for employees using past evaluation data and performance data; a user interface unit that allows employees to input their emotional state and collect that data; an emotion analysis unit that reflects the collected emotional data in the evaluation process; a user interface unit that allows employees to check their set goals and input correction requests as necessary; an artificial intelligence unit that accepts correction requests and performs reevaluations; a database unit that collects and stores data on employee progress; an evaluation unit that automatically calculates interim and final evaluations and generates feedback; and a unit that adjusts the content of the feedback based on the emotional data. This enables goal setting and evaluation to take employees' emotional states into consideration, contributing to improved employee motivation and stress management. Furthermore, an efficient and convincing goal setting and correction process is realized, allowing for effective feedback tailored to each individual employee.

[1342] "Artificial intelligence means" refers to functions that analyze past evaluation data and performance data, automatically set appropriate goals for employees, and re-evaluate them after receiving requests for corrections.

[1343] "User interface means" refers to devices or software that provide an interface through which employees can input their emotional state and goal modification requests and view evaluation results and feedback.

[1344] "Emotion analysis means" refers to the function of analyzing emotional data collected from employees and reflecting it in the evaluation process and feedback.

[1345] "Database means" refers to a system that stores information such as employee progress and evaluation data, and allows for efficient management, retrieval, and use of that information.

[1346] "Assessment tool" refers to a system or algorithm that includes functionality for automatically calculating interim and final assessments and generating feedback.

[1347] "Emotional Data" refers to emotional states entered by employees and emotional data collected and analyzed by emotion analysis tools.

[1348] "Goal setting" refers to the process of defining specific work goals for employees to achieve.

[1349] "Modification request" refers to a request entered by an employee when they wish to modify a set goal.

[1350] "Feedback" refers to comments and evaluations provided based on an employee's progress toward achieving goals and evaluation results.

[1351] The present invention is a system that utilizes past evaluation data and performance data to set appropriate goals and evaluate employees, taking into account their emotional state. This system operates using multiple means, with a server, terminals, and users working together.

[1352] Server processing

[1353] server

[1354] As an initial setup, managers enter company information, employee job descriptions, goals, past performance data, etc. through a web interface and store them in a database. The AI ​​tool analyzes this data and automatically sets appropriate goals for each employee.

[1355] As a specific example, the server analyzes the performance data of employee A for the past three years and sends the goal "acquire five new clients" as a prompt to the generative AI model. The prompt sentence of the generative AI model is "Please set the next goal for employee A."

[1356] User Action

[1357] User (Employee)

[1358] Employees can review their goals on their own devices and input their emotional data. By inputting their emotional state, data is collected that is reflected in the evaluation process. Employees can also input requests to modify their goals.

[1359] For example, after confirming the target, employee B inputs into the terminal, "Five new customers is too many, so I would like to change it to three." The server receives this request and uses AI tools to reevaluate.

[1360] Processing by the terminal

[1361] Terminal

[1362] The terminals where employees input their daily work progress send the collected data to a server in real time. This data is stored in a database and used to monitor and evaluate progress. The terminals also have a user interface means for displaying the evaluation results and feedback.

[1363] For example, employee C enters "two new clients acquired" into the terminal. This information is sent to the server and stored in the database.

[1364] Feedback generation and notification

[1365] server

[1366] The evaluation method automatically calculates interim and final evaluations based on the collected data and generates feedback. During this process, the collected emotional data is reflected in the feedback content. The timing and method of notification of the feedback are also adjusted based on the emotional data.

[1367] As a specific example, the server generates feedback such as "Goals are being achieved smoothly, and sales are as expected" based on progress data such as "Three new customers have been acquired."

[1368] Saving data and reflecting it in the next period

[1369] server

[1370] The final evaluation results are stored in a database and analyzed as reference data for setting the next goal, which will result in appropriate goal setting for the next period.

[1371] As a concrete example, the server analyzes the "target appropriateness of the number of new customers acquired" and uses it as the basic data for goal setting to be applied in the next evaluation period. It sends a prompt to the generative AI model saying, "Please analyze the appropriateness of the number of new customers acquired in order to set the next goal."

[1372] In this way, the present invention can realize a more convincing and fair evaluation system that takes into account the emotional state of employees, which is more effective than conventional evaluation methods in that it contributes to improving employee motivation and stress management.

[1373] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1374] Step 1:

[1375] Initial data collection and storage

[1376] The server receives company and employee information entered by the administrator through a web interface and stores the data in a database in real time. Specific inputs include organizational structure, organizational goals, employee names, job titles, and job descriptions. The server converts this input data into JSON format and stores it in the database.

[1377] Specific behavior:

[1378] When an administrator opens the administration screen in a browser, enters data into each field, and clicks the "Submit" button, the entered data is sent to the server, which receives the data and stores it in a database.

[1379] Step 2:

[1380] AI-driven goal setting

[1381] The server uses a generative AI model to analyze past evaluation and performance data stored in the database. Specifically, it analyzes each employee's past goal achievement rate and performance to generate appropriate goals for the next period.

[1382] Input: Past evaluation data, performance data

[1383] Data processing: A prompt sentence is generated based on the input data for the generative AI model and sent to the AI ​​model.

[1384] Output: Appropriate goal setting data

[1385] Specific behavior:

[1386] The server generates a prompt saying, "Please generate an appropriate goal based on employee A's past evaluation data," and sends it to the generative AI model. The AI ​​model generates a goal, "Acquire five new customers," and sends it back to the server.

[1387] Step 3:

[1388] Collecting and reflecting emotional data

[1389] Users (employees) input their emotional state during the evaluation process or when receiving feedback. The data is sent to the server in real time and stored in a database. The server then reflects the emotional data in the evaluation process.

[1390] Input: Emotional state input data

[1391] Data processing: Analyze sentiment data and incorporate it into the evaluation process.

[1392] Output: Emotion data reflected in the evaluation

[1393] Specific behavior:

[1394] When an employee sees a prompt on the terminal asking, "Please enter your current emotional state," they enter "I feel stressed," and click the "Submit" button. The entered emotional data is sent to the server and stored in a database.

[1395] Step 4:

[1396] Check your goals and enter correction requests

[1397] The user (employee) checks the set goals on the terminal and inputs correction requests as necessary. The server receives this request and uses the AI ​​model to reassess.

[1398] Input: Goal setting data, correction request data

[1399] Data processing: Reevaluation process using generative AI models

[1400] Output: Reassessed goal setting data

[1401] Specific behavior:

[1402] The employee opens the goal confirmation screen, checks the goal "Acquire 5 new customers," enters "Five new customers is too many, so I would like to change it to three," and submits it. The server receives this request and sends a reevaluation prompt to the generative AI model: "Please reevaluate Employee A's new goal setting." The AI ​​model generates a new appropriate goal and notifies the server.

[1403] Step 5:

[1404] Progress monitoring and data collection

[1405] The terminal provides a screen where employees can input their daily work progress. The progress data entered by employees is sent to the server in real time and stored in a database. The server uses this data to monitor the progress.

[1406] Input: Progress input data

[1407] Data processing: progress data collection and real-time monitoring

[1408] Output: Progress data

[1409] Specific behavior:

[1410] An employee types "Acquired two new clients" into a terminal and clicks the send button. The entered data is sent to the server and saved in a database. The server uses the data to monitor progress in real time.

[1411] Step 6:

[1412] Enter mid-term evaluations and generate feedback

[1413] The server calculates intermediate evaluations based on the collected progress data and generates feedback, which is adjusted based on the emotional data.

[1414] Input: progress data, emotional state data

[1415] Data processing: Calculating intermediate assessments and generating feedback

[1416] Output: Regulated feedback data

[1417] Specific behavior:

[1418] The server performs an interim evaluation based on progress data such as "three new clients acquired," and generates feedback such as "goals are being achieved smoothly." The content of the feedback and the timing of notifications are adjusted based on the emotional data.

[1419] Step 7:

[1420] Calculation and notification of final grade

[1421] At the end of the evaluation period, the server automatically calculates the final evaluation based on all collected data and notifies the user along with feedback. The timing and method of notification are also adjusted based on the emotional data.

[1422] Input: progress data, emotional state data

[1423] Data processing: Calculating final grades and generating feedback

[1424] Output: Final evaluation result data and feedback data

[1425] Specific behavior:

[1426] At the end of the period, the server consolidates all the aggregated data and sends a prompt to the generative AI model saying, "Please generate the final evaluation result for employee F." The final evaluation and feedback are generated and notified to the employee's device.

[1427] Step 8:

[1428] Confirmation of evaluation results and reflection on the next period

[1429] The user (employee) checks the evaluation results and feedback on the terminal, and the server stores the results in a database and reflects them in setting goals for the next period.

[1430] Input: Final evaluation result data

[1431] Data processing: Data storage and analysis for setting next goals

[1432] Output: Saved final evaluation results

[1433] Specific behavior:

[1434] The employee opens the evaluation results screen and checks the evaluation result: "Acquired six new customers, increased sales by 25% compared to the previous year." The server saves this data in a database and analyzes it for use in setting goals for the next period. The server sends a prompt to the AI ​​model saying, "Please analyze the appropriateness of the number of new customers acquired in order to set goals for the next period," and sets goals based on the results.

[1435] (Application example 2)

[1436] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1437] Conventional employee evaluation systems have problems in that the appropriateness of set goals and the quality of feedback have a significant impact on employee motivation and performance. Furthermore, because they were unable to take into account employees' emotions and stress levels, it was difficult to appropriately adjust workloads and maintain motivation. This led to issues such as a lack of fairness and a sense of satisfaction in evaluations, which ultimately led to a decline in work efficiency.

[1438] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: artificial intelligence means for automatically setting appropriate goals for employees using past evaluation data and performance data; user interface means for employees to check the set goals and input correction requests as necessary; artificial intelligence means for accepting correction requests and performing reevaluations; emotion recognition means for collecting employee emotion data and adjusting work instructions and feedback based on the data; database means for collecting employee progress and storing the data; and evaluation means for automatically calculating interim and final evaluations and generating feedback. This enables appropriate goal setting and workload adjustment taking into account employees' emotions and stress levels, thereby improving the sense of satisfaction and fairness of evaluations and optimizing work efficiency and maintaining employee motivation.

[1439] "Artificial intelligence means" refers to technology that analyzes past evaluation data and performance data and automatically sets appropriate goals for employees.

[1440] The "user interface means" is an interface through which employees can interact with the system, and is a function that allows employees to confirm set goals and input correction requests.

[1441] "Emotion recognition means" is a technology that collects emotional data from employees' facial expressions and voices, and adjusts work instructions and feedback based on that data.

[1442] "Database means" is a system for efficiently collecting and storing employee progress and emotional data, and has the function of centrally managing data.

[1443] The "evaluation tool" is a technology that automatically generates feedback and evaluates employees based on collected mid-term and final evaluation data.

[1444] "Emotional data" refers to data obtained from an employee's facial expressions, voice, and other emotional expressions, and is information that quantifies or qualitatively evaluates the employee's emotional state.

[1445] The present invention relates to a system for improving the sense of fairness and satisfaction of evaluations by reflecting emotional data in the performance evaluations of employees. Specific embodiments for carrying out the present invention will be described in detail below.

[1446] System Configuration

[1447] This system is an evaluation management system that operates in cooperation with a server, terminals, and users (employees and evaluators), and by combining it with an emotion engine, reflects the employee's emotional state in the evaluation process. The entire system includes the following elements:

[1448] Server: The server is the core of the system and includes artificial intelligence means, database means, and evaluation means. Specifically, the server analyzes past evaluation data and performance data to set appropriate goals for employees, accepts correction requests, and performs reevaluations. It also has the function of collecting employee emotional data using emotion recognition means and adjusting work instructions and feedback based on that data.

[1449] Terminal: A device used by the employee and evaluator that contains a user interface through which the employee can view set goals, input correction requests, report progress, and view feedback.

[1450] Users (employees and evaluators): Users access the system and input their own work status and emotional data. Employees report their progress and emotional state, and evaluators provide appropriate feedback based on the collected data.

[1451] Hardware and software used

[1452] Hardware

[1453] Camera: Used to capture employee facial expressions.

[1454] Computer terminal: Used to operate the user interface.

[1455] Server machine: Used to process and store data centrally.

[1456] software

[1457] OpenCV: Used to acquire and process camera images.

[1458] EmotionRecognitionEngine: Functions as an emotion recognition engine, analyzing employees' emotional states from facial expressions and voice data. For example, "Face++" or "Microsoft Azure Emotion API."

[1459] RobotController: Uses a robot control library such as ROS to transmit work instructions and feedback to the robot.

[1460] DatabaseClient: Use a database client such as "SQLite" to efficiently store emotion data and rating data.

[1461] Artificial intelligence model: A generative AI model that analyzes past evaluation data and performance data to generate appropriate goals.

[1462] Explanation of program processing

[1463] Server: The server first uses the administrator's input form to collect company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores them in a database. Next, it uses an AI algorithm to set appropriate goals based on past evaluation data and performance data. It also runs an emotion recognition engine to collect employee emotional data and reflect it in the evaluation process. Finally, it automatically calculates interim and final evaluations and generates feedback.

[1464] Terminal: Employees use the terminal to check the set goals and input correction requests as necessary. Progress data and emotional state are also entered through this terminal. Employees also use this terminal to check evaluation results and feedback.

[1465] Users: Employees and evaluators access the system through a user interface to input and confirm the necessary data. In particular, real-time feedback and work adjustments based on emotional data are possible.

[1466] Specific examples

[1467] As a specific use case, the following prompts can be input to the generative AI model:

[1468] Please provide an example of a system that uses emotion recognition data from employees to enable factory robots to provide appropriate work instructions and feedback. Please also explain how workloads are adjusted when stress levels are high, and how positive feedback is provided when happiness levels are low.

[1469] This prompt can be fed into a generative AI model to generate additional ideas for similar implementations and improvements.

[1470] As described above, the present invention provides an evaluation management system based on employee emotional data, and is expected to improve employee motivation and performance.

[1471] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1472] Step 1:

[1473] The server uses the administrator's input form to collect data such as company information, organizational goals, each employee's grade, rank, job type, and job description, and stores it in a database. This prepares the initial data necessary for evaluation. The input data is the information entered by the administrator, and the output is the prepared data stored in the database.

[1474] Step 2:

[1475] The server runs an artificial intelligence model based on past evaluation data and performance data, automatically setting appropriate goals for each employee. For example, by analyzing past goal achievement data, goals such as "acquire five new clients" or "increase sales by 20% compared to the previous year" are generated for a specific employee. The input data are past evaluation data and performance data, and the output is the appropriate goal generated for each employee.

[1476] Step 3:

[1477] Using a terminal, an employee checks the set targets and inputs correction requests as necessary. For example, they can input a comment such as, "Five new customers is too many, so I would like to reduce it to three." The input data is the displayed targets and correction requests, and the output shows the state after the correction requests have been input.

[1478] Step 4:

[1479] The server accepts the correction request and re-runs the AI ​​model to re-evaluate it. The input data is the correction request entered by the employee, and the output is the re-evaluated goal. This re-evaluation also takes into account the employee's emotional data.

[1480] Step 5:

[1481] The server uses a camera and an emotion recognition engine to collect emotional data from employees' facial expressions and voices. This emotional data is stored in a database. The input data is camera footage and audio data, and the output is analyzed emotional data.

[1482] Step 6:

[1483] The server adjusts work instructions and feedback based on the employee's emotional data. For example, if the stress level is high, it reduces the workload, and if the happiness level is low, it provides positive feedback. The input data is emotional data, and the output is adjusted work instructions and feedback.

[1484] Step 7:

[1485] Employees periodically input progress data using terminals. For example, the progress of sales activities such as "acquiring two new clients" is entered. The input data is specific information indicating the progress, and the output is progress data stored in a database.

[1486] Step 8:

[1487] The server collects the entered progress data and stores it in a database, allowing employees' progress toward their goals to be monitored in real time. The input data is the progress data entered by the employees, and the output is the progress data stored in the database.

[1488] Step 9:

[1489] Employees use terminals to input interim evaluations and report the progress of each goal. For example, they input progress data such as "three new clients acquired." The input data is the progress data as interim evaluations, and the output is the interim evaluation data sent to the server.

[1490] Step 10:

[1491] The server aggregates the interim evaluation data and generates feedback. For example, the feedback might be something like, "The acquisition of three new customers is going smoothly. Sales targets are as expected." The content of the feedback is adjusted based on the emotional data. The input data is the interim evaluation data, and the output is the generated feedback.

[1492] Step 11:

[1493] At the end of the evaluation period, the server automatically calculates the final evaluation based on the collected data. For example, the evaluation result calculated is "6 new customers acquired, sales increased 25% compared to the previous year." The input data is all the collected evaluation data, and the output is the final evaluation result.

[1494] Step 12:

[1495] The employee uses a terminal to check the final evaluation results. Feedback is displayed along with the evaluation results. For example, an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion" may be displayed. The input data is the final evaluation results sent from the server, and the output is the state of the evaluation results as viewed by the employee.

[1496] Step 13:

[1497] The server saves the final evaluation results in a database and performs data analysis to reflect them in setting goals for the next period. For example, it analyzes the "target appropriateness of the number of new customers acquired" and uses it as reference data for setting goals for the next period. The input data is the final evaluation results, and the output is the analyzed reference data.

[1498] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1499] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1500] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1501] [Fourth embodiment]

[1502] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1503] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1504] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1505] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1506] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1507] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1508] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1509] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1510] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1511] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1512] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1513] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1514] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1515] As an embodiment for realizing the present invention, a specific system configuration and program processing will be described below.

[1516] System configuration overview

[1517] This system is a reputation management system in which the server, terminals, and users work together. The entire system is composed of the following main elements:

[1518] 1. Server

[1519] 2. Terminal

[1520] 3. Users (Employees and Raters)

[1521] Program processing overview

[1522] Initial data collection and storage

[1523] server

[1524] The administrator uses the input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores it in the database. This allows all initial data to be managed within the system.

[1525] AI-driven goal setting

[1526] server

[1527] The system uses AI algorithms based on past evaluation data and employee performance data to automatically set appropriate goals for each employee. For example, it generates goals such as "acquire five new customers" or "increase sales by 20% year-on-year" by taking into account past goal achievement data.

[1528] Review goals and request revisions

[1529] User (Employee)

[1530] Check the set goal on your device and enter a request for correction if necessary. For example, enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[1531] server

[1532] The AI ​​will accept requests for revisions and review the targets again. For example, it may "revise the new customer target from five companies to three companies" taking into account the overall situation of the sales department.

[1533] Progress monitoring and data collection

[1534] Terminal

[1535] Employees periodically enter progress data, such as "two new clients acquired" as a sales activity report.

[1536] server

[1537] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[1538] Interim evaluation input and feedback

[1539] User (Employee)

[1540] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[1541] server

[1542] The interim evaluations are compiled and feedback is generated. For example, feedback such as "The acquisition of three new clients is going well. Sales targets are on track" is sent to employees.

[1543] Final rating calculation

[1544] server

[1545] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For example, the evaluation is calculated as "6 new customers acquired, sales increased 25% compared to the previous year."

[1546] Notification and confirmation of final evaluation results

[1547] User (Employee)

[1548] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[1549] Saving data and reflecting it in the next period

[1550] server

[1551] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[1552] The above process will improve the sense of satisfaction and fairness of the traditional evaluation method, as well as improve the efficiency of the entire evaluation system. It will also increase employee motivation.

[1553] The processing flow will be explained below.

[1554] Step 1:

[1555] server

[1556] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores the data in a database. Specifically, the administrator enters values ​​for each item via a web interface and registers it.

[1557] Step 2:

[1558] server

[1559] The AI ​​algorithm is run based on past evaluation data and each employee's performance data to automatically set appropriate goals for each employee. For example, past goal achievement data can be input into a machine learning model to generate goals for a specific employee, such as "acquire five new customers" or "increase sales by 20% compared to the previous year."

[1560] Step 3:

[1561] User (Employee)

[1562] Check the goals set on your device and enter correction requests as necessary. For example, you can enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[1563] Step 4:

[1564] server

[1565] An AI algorithm is run to accept and reevaluate requests for revisions. For example, the system may consider the overall situation of the sales department and make a change such as "revise the new customer target from five companies to three companies."

[1566] Step 5:

[1567] Terminal

[1568] Employees periodically enter progress data, such as the progress of a sales activity, such as "acquiring two new clients."

[1569] Step 6:

[1570] server

[1571] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[1572] Step 7:

[1573] User (Employee)

[1574] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[1575] Step 8:

[1576] server

[1577] The interim evaluation data is compiled and feedback is generated. The feedback is then communicated to the employee. For example, the feedback might be something like, "The acquisition of three new clients is going smoothly. Sales targets are on track."

[1578] Step 9:

[1579] server

[1580] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For a specific employee, the evaluation result is calculated as "Acquired six new clients and increased sales by 25% compared to the previous year."

[1581] Step 10:

[1582] User (Employee)

[1583] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[1584] Step 11:

[1585] server

[1586] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[1587] Example 1

[1588] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1589] In conventional employee evaluation systems, evaluation settings and progress management are done manually, which creates issues with the fairness and efficiency of evaluations. It is also difficult to set appropriate goals and monitor progress in real time, which leads to a lack of satisfaction with evaluations and improved employee motivation. Furthermore, it is difficult to reflect evaluation results in setting goals for the next period.

[1590] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1591] In this invention, the server includes an artificial intelligence unit that automatically sets appropriate goals for employees using past evaluation data and performance data, a user interface unit that allows employees to check the set goals and input revision requests as necessary, and an artificial intelligence unit that accepts revision requests and performs reevaluation. This improves the fairness and efficiency of evaluations and enables appropriate goal setting and real-time monitoring of progress. It also makes it easier to reflect evaluation results in setting next-period goals, contributing to improved employee motivation.

[1592] "Artificial intelligence tools" are algorithms or programs that automatically set appropriate goals for employees based on past evaluation data and performance data, and then reevaluate them.

[1593] "User interface means" refers to an interface through which employees can access the system, check the set goals, input correction requests as necessary, and check the evaluation results.

[1594] The "database means" is a database system for storing collected progress data and evaluation data, and for saving and managing necessary information.

[1595] An "assessment tool" is an algorithm or program that automatically calculates interim and final assessments and generates feedback.

[1596] A "final rating tool" is an algorithm or program that automatically calculates a final rating based on the data collected at the end of the rating period.

[1597] A "generative AI model" is an artificial intelligence model used to analyze employee performance data in real time and monitor progress.

[1598] "Monitoring Measures" means systems or tools for analyzing collected progress data in real time and monitoring employee progress.

[1599] "Input means" refers to a form or interface that allows employees to enter progress information and send the data to the server.

[1600] "Data storage and analysis means" refers to a system for storing the final evaluation results and analyzing the data to reflect them in setting goals for the next period.

[1601] The evaluation management system of the present invention functions in cooperation with the server, terminals, and users to efficiently and fairly evaluate employee performance. It is realized by using "artificial intelligence means," "user interface means," "database means," "evaluation means," etc. The specific configuration and processing flow of the system are explained below.

[1602] System Configuration

[1603] server

[1604] The server is the core of the system and is responsible for:

[1605] Artificial intelligence measures: Using generative AI models (e.g., random forests or neural networks) to automatically set appropriate goals for employees based on past appraisal and performance data.

[1606] Database means: Collected data is stored using a database system such as MySQL or PostgreSQL.

[1607] Assessment instruments: Automatically calculate mid-term and final grades and generate feedback.

[1608] Monitoring measures: Analyze employee performance data in real time to monitor progress.

[1609] Terminal

[1610] The devices are used by employees and assessors and perform the following functions:

[1611] User interface means: Provide an interface for employees to check the set goals, input correction requests, and check the evaluation results.

[1612] Input methods: Provide employees with a form to periodically enter their progress.

[1613] User

[1614] Users (employees and raters) interact with the system through the following means:

[1615] Employees: Enter their own performance and progress, and check the set goals and evaluation results.

[1616] Evaluator: Review employee evaluation data and provide feedback as needed.

[1617] Examples and prompts

[1618] A specific example of use would be when a sales department employee is set a goal of "acquiring five new customers" and enters their progress into the system.

[1619] Examples:

[1620] Employee A reports that he has acquired two new clients over the past three months, and an interim evaluation is generated based on that data. Based on this interim evaluation, the server provides feedback such as, "You're making good progress, but you need to put in a little more effort to achieve your goals."

[1621] Example prompt sentence:

[1622] Goal: Acquire 5 new customers

[1623] Progress: Acquired 2 companies in 3 months

[1624] Generate mid-term evaluation feedback.

[1625] By inputting this prompt into a generative AI model, specific feedback is generated.

[1626] As described above, the evaluation management system of the present invention consistently automates the entire process from employee goal setting to progress management and evaluation, achieving fair and efficient evaluation. This system is expected to improve employee motivation and overall corporate performance.

[1627] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1628] Step 1: Collect and store initial data

[1629] The server provides a web form exclusively for administrators, and collects data such as company information, organizational goals, and each employee's grade, rank, job type, and job description. The required information is entered into the input form and stored in a database.

[1630] Input: Company information, organizational goals, employee data

[1631] Output: Initial data stored in the database

[1632] How it works: An administrator accesses a web form and enters the required information in real time. The entered data is immediately validated and any incomplete fields are flagged with a warning. After validation is complete, the data is saved to the database.

[1633] Step 2: AI-driven goal setting

[1634] The server uses a generative AI model to set appropriate goals for each employee, based on past evaluation and performance data, and performs analysis using Python-based random forests and neural networks.

[1635] Input: Past evaluation data, performance data

[1636] Output: Goals for each employee

[1637] How it works: The server retrieves past performance data from the database, runs the generative AI model, and generates appropriate goals for each employee. The generated goals are then stored in the database.

[1638] Step 3: Review your goals and request revisions

[1639] Users (employees) log in to the system from their own terminals, check the set goals, and input correction requests as necessary.

[1640] Input: Employee Goals

[1641] Output: Correction request

[1642] Specific operation: An employee logs in to the system and checks the presented goals. If any corrections are needed, they fill out a form with the desired corrections and submit it to the server.

[1643] The server runs the AI ​​again based on the correction request and corrects the goal if necessary.

[1644] Input: Correction request

[1645] Output: revised target

[1646] Specific operation: The server verifies the received correction request and re-evaluates it using the generative AI model. The re-evaluated goal is generated and saved in the database.

[1647] Step 4: Monitor progress and collect data

[1648] The terminal provides a form for employees to periodically enter their progress, which is used as evaluation material.

[1649] Input: Progress data (e.g., acquisition of two new customers)

[1650] Output: Save progress data to database

[1651] Specific operation: Employees periodically access their terminals and enter their progress information into a form. The entered data is sent to the server and stored in a database.

[1652] The server collects and monitors progress data in real time.

[1653] Input: Progress data

[1654] Output: Real-time progress monitoring

[1655] Specific operation: Stores progress data in a database and displays progress in real time on a dashboard.

[1656] Step 5: Input of mid-term evaluation and feedback

[1657] Users (employees) enter interim evaluations and report progress.

[1658] Input: Interim evaluation data (e.g., 3 new customers acquired)

[1659] Output: Interim evaluation input data

[1660] Specific operation: Employees enter their progress into the interim evaluation form from their terminal and send it to the server.

[1661] The server aggregates the intermediate evaluations and generates feedback using an AI model.

[1662] Input: Interim evaluation data

[1663] Output: Feedback

[1664] Specific operation: The server collects intermediate evaluation data, runs the generative AI model to perform evaluation, and generates specific feedback that is sent to the employee.

[1665] Step 6: Calculate the final rating

[1666] At the end of the evaluation period, the server calculates a final evaluation based on all collected data.

[1667] Input: Collected data (progress data, interim evaluation data)

[1668] Output: Final evaluation result

[1669] Specific operation: The server processes all collected data in a batch and generates a final quantitative evaluation, which is then stored in a database.

[1670] Step 7: Notification and confirmation of final evaluation results

[1671] The user (employee) checks the final evaluation results on their own device.

[1672] Input: Final evaluation result

[1673] Output: Display of evaluation results

[1674] Specific operation: An employee logs in to the system and checks the evaluation results. For example, the evaluation shows, "You have achieved results that far exceed the targets, so you are a candidate for promotion."

[1675] Step 8: Save the data and reflect it in the next period

[1676] The server stores the final evaluation results and performs data analysis to reflect them in setting goals for the next period.

[1677] Input: Final evaluation result

[1678] Output: Analysis data for setting next goals

[1679] Specific operation: The server stores the final evaluation results in a database and analyzes the data using an analytical engine. Based on the analysis results, data for setting the next goal is provided to the generative AI model.

[1680] (Application example 1)

[1681] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1682] Managing and optimizing the productivity of work equipment (such as robots) in factories is important in modern manufacturing, but traditional methods have the drawback of requiring a great deal of effort to manually set goals and manage progress, and are prone to subjective judgment. Another issue is that the inability to grasp goal achievement status and evaluation results in real time makes it difficult to adapt to situations that require rapid response.

[1683] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1684] In this invention, the server includes artificial intelligence means for automatically setting appropriate targets for work equipment using past evaluation data and performance data, user interface means for the work equipment manager to check the set targets and input correction requests as necessary, artificial intelligence means for accepting correction requests and performing re-evaluation, database means for collecting progress status of work equipment and storing the data, and evaluation means for automatically calculating interim and final evaluations and generating feedback. This makes it possible to automate production target setting and improve the efficiency of progress management.

[1685] "Past evaluation data" is data based on targets and performance that the work equipment has achieved in the past.

[1686] "Performance data" is specific numerical data relating to the productivity and efficiency of work equipment.

[1687] "Appropriate targets" are reasonable and achievable production targets set based on the performance of work equipment and past performance.

[1688] "Artificial intelligence means" is a system that uses artificial intelligence algorithms to analyze data and set and reassess goals.

[1689] The "user interface means" refers to an interface means through which the administrator interacts with the system, and is used to confirm goals and input correction requests.

[1690] A "modification request" is input information that allows an administrator to request a change to a set goal.

[1691] "Database means" refers to a database system for storing and managing progress data, etc.

[1692] "Mid-term evaluation" is the process of evaluating the progress of work equipment midway through the evaluation period.

[1693] "Final evaluation" is the process of evaluating the final performance of the work equipment at the end of the evaluation period.

[1694] The "evaluation means" is a means for automatically calculating intermediate and final evaluations of the work equipment based on the data and generating feedback.

[1695] "Feedback" is information that provides comments and suggestions regarding the productivity and efficiency of work equipment based on the evaluation results.

[1696] The system for realizing the present invention is a rating management system in which a server, terminals, and users work together. This system is composed of the following elements:

[1697] System configuration overview

[1698] 1. Server: The server includes an artificial intelligence means for setting appropriate targets for work equipment using past evaluation data and performance data, a database means for collecting and storing data, and an evaluation means for automatically calculating intermediate and final evaluations and generating feedback.

[1699] 2. Terminal: The terminal has a user interface for the user (administrator) to check the set goals and input correction requests as necessary. It also includes an interface for checking the evaluation results and receiving feedback.

[1700] 3. User: As an administrator, the user interacts with the system using a terminal, setting goals, checking evaluation results, and receiving feedback.

[1701] Program processing overview

[1702] Data Processing Description

[1703] 1. Initial data collection and storage

[1704] Server: The manager uses a smartphone to input information about the work equipment (model number, functions, capacity, etc.) and production targets (production volume of part A, etc.), and stores the data on the server. The collected data is saved in a database.

[1705] 2. AI-driven goal setting

[1706] Server: The server uses past production and performance data from the work equipment to set optimal production targets using artificial intelligence means. For example, an AI model is used to generate a target such as "produce 50 units of part A per hour."

[1707] 3. Review goals and request revisions

[1708] User: The administrator uses a smartphone to check the set goals and input correction requests if necessary. The correction requests are sent to the server for re-evaluation.

[1709] 4. Progress monitoring and data collection

[1710] Work equipment: Work equipment periodically sends production data to the server, which collects the progress data and stores it in a database, allowing real-time progress monitoring.

[1711] 5. Calculation of Interim and Final Grades

[1712] Server: The server automatically calculates interim and final evaluations based on the collected data and generates feedback. For example, the interim evaluation generates feedback such as "Production of part A is proceeding as planned."

[1713] 6. Notification and confirmation of evaluation results

[1714] User: The manager uses a smartphone to check the final evaluation results and feedback. The evaluation result may be, for example, "Part A was produced beyond the target, so the evaluation is A."

[1715] Examples of concrete examples and prompts

[1716] Examples:

[1717] For example, if a factory manager uses a smartphone to enter a prompt such as, "To achieve this year's goal, please set the target production volume for part A on this factory robot production line," the AI ​​model will generate a goal such as, "The target production volume for part A is 50 units per hour."

[1718] In this way, an AI-based production activity evaluation and management system can improve production efficiency within a factory and reduce management efforts. The specific technologies used include smartphones, work equipment, and servers as hardware, and databases (MySQL, etc.), AI models (TensorFlow), and smartphone applications (compatible with iOS and Android) as software.

[1719] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1720] Step 1:

[1721] Initial data collection and storage

[1722] The server provides an input form for the manager to use a smartphone to enter information about the work equipment (model number, functions, capacity, etc.) and production targets (production volume of part A, etc.). The entered data is sent to the server and stored in a database. The input at this time is the basic data and target data for the work equipment, and the output is the initial data for each work equipment stored in the database.

[1723] Step 2:

[1724] AI-driven goal setting

[1725] The server inputs past production data and performance data of the work equipment into an AI model (such as TensorFlow). The AI ​​analyzes the data and sets optimal production targets for each piece of work equipment. For example, based on the input data, it generates a target such as "produce 50 units of part A per hour." The input at this time is past evaluation data and performance data, and the output is the newly set target data.

[1726] Step 3:

[1727] Review goals and request revisions

[1728] The user uses a smartphone to check the target sent from the server. If necessary, the user inputs a correction request and sends it to the server. For example, the user inputs a request such as "I would like to change the target for part A from 50 units per hour to 45 units." The input in this case is the target data and correction request confirmed by the user, and the output is the correction request sent to the server.

[1729] Step 4:

[1730] Processing correction requests

[1731] The server re-inputs the received correction request into the AI ​​model and re-evaluates the goal. As a result of the re-evaluation, new goal data is generated and notified to the user. The input at this time is the correction request, and the output is the re-evaluated new goal data.

[1732] Step 5:

[1733] Progress monitoring and data collection

[1734] The work equipment sends production data (e.g., "Produce 48 units of part A in 1 hour") to the server at specified intervals. The server stores the sent data in a database and monitors the progress in real time. The input at this time is the progress data from the work equipment, and the output is the progress data stored in the database.

[1735] Step 6:

[1736] Mid-term assessment calculation and feedback

[1737] The server calculates an interim evaluation based on the collected progress data and generates feedback. For example, it generates feedback such as "Currently, production of part A is proceeding as planned" and notifies the user. The input at this time is the progress data, and the output is the interim evaluation and feedback data.

[1738] Step 7:

[1739] Final rating calculation

[1740] At the end of the evaluation period, the server calculates the final evaluation based on the collected data. For example, it generates a final evaluation such as "Part A was produced beyond the target, so the evaluation is A." The input at this time is all the progress data during the evaluation period, and the output is the final evaluation data.

[1741] Step 8:

[1742] Notification and confirmation of evaluation results

[1743] The user uses a smartphone to check the final evaluation results and feedback sent from the server. The input is the final evaluation data and feedback data, and the output is the evaluation results checked by the user.

[1744] Step 9:

[1745] Saving evaluation results and reflecting them in the next period

[1746] The server saves the final evaluation results in a database and performs data analysis to reflect them in setting the next target. For example, it analyzes the "appropriateness of the production target for part A" and uses the data as reference data for setting the next target. The input at this time is the final evaluation data, and the output is the data analysis results and the saved evaluation data.

[1747] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1748] As an embodiment for realizing the present invention, a specific system configuration and program processing will be described below.

[1749] System configuration overview

[1750] This system is an evaluation management system that operates in cooperation with the server, terminals, and users, and by combining it with an emotion engine, the emotional state of employees is reflected in the evaluation process. The entire system consists of the following main elements:

[1751] 1. Server

[1752] 2. Terminal

[1753] 3. Users (Employees and Raters)

[1754] 4. Emotion Engine

[1755] Program processing overview

[1756] Initial data collection and storage

[1757] server

[1758] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores the data in a database. The administrator enters this information via a web interface.

[1759] AI-driven goal setting

[1760] server

[1761] The system uses AI algorithms based on past evaluation data and performance data to automatically set appropriate goals for each employee. For example, it uses past goal achievement data to generate goals for a specific employee, such as "acquire five new customers" or "increase sales by 20% year-on-year."

[1762] Collecting and reflecting emotional data

[1763] server

[1764] An emotion engine is run to collect employee emotional data and reflect it in the evaluation process. The emotion engine recognizes emotions from employees' facial expressions and voices, and stores the data in a database.

[1765] User (Employee)

[1766] The system also has a function to input emotional states on the employee's own device. For example, when receiving feedback, the system prompts the employee to "enter their current emotional state," and the employee can input their emotional state.

[1767] Review goals and request revisions

[1768] User (Employee)

[1769] Check the set goals on your device and enter correction requests as necessary. For example, enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[1770] server

[1771] It accepts revision requests and runs AI algorithms to re-evaluate them, taking into account sentiment data collected by the sentiment engine.

[1772] Progress monitoring and data collection

[1773] Terminal

[1774] Employees periodically enter progress data, such as the progress of a sales activity, such as "acquiring two new clients."

[1775] server

[1776] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[1777] Interim evaluation input and feedback

[1778] User (Employee)

[1779] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[1780] server

[1781] Intermediate evaluation data is compiled and feedback is generated. For example, "The acquisition of three new customers is going smoothly. Sales targets are as expected." The content of the feedback is adjusted based on the emotional data.

[1782] Final rating calculation

[1783] server

[1784] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data. For a specific employee, the evaluation result is calculated as "Acquired six new clients and increased sales by 25% compared to the previous year."

[1785] Notification and confirmation of evaluation results

[1786] User (Employee)

[1787] The employee checks the final evaluation results on their own device. Feedback is displayed along with the evaluation results. For example, the evaluation may say, "You have significantly exceeded your targets and are a candidate for promotion." The emotion engine adjusts the timing and method of notification of the evaluation results based on the employee's emotional state.

[1788] Saving data and reflecting it in the next period

[1789] server

[1790] The final evaluation results are saved in a database and analyzed to reflect them in setting goals for the next period. For example, the "appropriateness of the target number of new customers acquired" is analyzed and used as reference data for setting goals for the next period.

[1791] The above process can improve the sense of satisfaction and fairness of traditional evaluation methods, as well as improve the efficiency of the entire evaluation system. Furthermore, by taking into account the emotional state of employees, motivation can be further increased.

[1792] The processing flow will be explained below.

[1793] Step 1:

[1794] server

[1795] The administrator uses an input form to collect data such as company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores it in the database. Specifically, the administrator enters and registers the information through a web interface.

[1796] Step 2:

[1797] server

[1798] The system uses AI algorithms based on past evaluation data and performance data to automatically set appropriate goals for each employee. Specifically, it analyzes past goal achievement data and generates goals such as "acquire five new customers" or "increase sales by 20% year-on-year."

[1799] Step 3:

[1800] server

[1801] An emotion engine is used to collect employee emotion data, including data obtained through facial expression recognition and voice analysis of employees.

[1802] Step 4:

[1803] User (Employee)

[1804] Check the set goal on your device and enter a request for correction if necessary. For example, you can enter a comment such as "Five new customers is too many, so I would like to reduce it to three."

[1805] Step 5:

[1806] server

[1807] Along with the sentiment data collected by the sentiment engine, an AI algorithm is run to reevaluate the modification request, which could result in a change such as "modify new customer goal from 5 to 3."

[1808] Step 6:

[1809] Terminal

[1810] Employees periodically enter progress data, such as sales activity reports like "two new clients acquired."

[1811] Step 7:

[1812] server

[1813] The entered progress data is collected and stored in a database, allowing real-time monitoring of employee progress toward their goals.

[1814] Step 8:

[1815] User (Employee)

[1816] Users enter interim evaluations on their own devices and report the progress of each goal. For example, they enter progress data such as "3 new clients acquired."

[1817] Step 9:

[1818] server

[1819] Intermediate evaluation data is compiled and feedback is generated. Emotional data is referenced and the feedback content is adapted to the employee's emotional state. For example, the generated feedback might be, "The acquisition of three new clients is going smoothly. Sales targets are as expected."

[1820] Step 10:

[1821] server

[1822] At the end of the evaluation period, the final evaluation is automatically calculated based on the collected data, and the employee's evaluation result is "Acquired six new clients and increased sales by 25% compared to the previous year."

[1823] Step 11:

[1824] User (Employee)

[1825] Check the final evaluation results on your device. Feedback will be displayed along with the evaluation results. For example, you may see an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion."

[1826] Step 12:

[1827] server

[1828] The final evaluation results are stored in a database and analyzed to reflect them in setting goals for the next period. Emotional data is also included in the analysis, and for example, the "appropriateness of the target for the number of new customers acquired" is evaluated and used as a reference for setting future goals.

[1829] Step 13:

[1830] server

[1831] The timing and method of notifying the employee of the evaluation results can be adjusted based on the employee's emotional state as recognized by the emotion engine. For example, if the emotional data indicates that the employee is feeling stressed, the notification of the evaluation results can be adjusted to be more gentle.

[1832] Example 2

[1833] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1834] In conventional goal setting and evaluation systems, goals are set and evaluations are performed without taking into account the emotional state of employees, resulting in problems with employee motivation and stress management. Furthermore, the goal setting and revision process is inefficient, making it difficult to increase employees' sense of satisfaction and fairness. Furthermore, feedback is uniform and not adjusted to suit each employee's individual state, resulting in the inability to provide effective feedback.

[1835] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1836] In this invention, the server includes: an artificial intelligence unit that automatically sets appropriate goals for employees using past evaluation data and performance data; a user interface unit that allows employees to input their emotional state and collect that data; an emotion analysis unit that reflects the collected emotional data in the evaluation process; a user interface unit that allows employees to check their set goals and input correction requests as necessary; an artificial intelligence unit that accepts correction requests and performs reevaluations; a database unit that collects and stores data on employee progress; an evaluation unit that automatically calculates interim and final evaluations and generates feedback; and a unit that adjusts the content of the feedback based on the emotional data. This enables goal setting and evaluation to take employees' emotional states into consideration, contributing to improved employee motivation and stress management. Furthermore, an efficient and convincing goal setting and correction process is realized, allowing for effective feedback tailored to each individual employee.

[1837] "Artificial intelligence means" refers to functions that analyze past evaluation data and performance data, automatically set appropriate goals for employees, and re-evaluate them after receiving requests for corrections.

[1838] "User interface means" refers to devices or software that provide an interface through which employees can input their emotional state and goal modification requests and view evaluation results and feedback.

[1839] "Emotion analysis means" refers to the function of analyzing emotional data collected from employees and reflecting it in the evaluation process and feedback.

[1840] "Database means" refers to a system that stores information such as employee progress and evaluation data, and allows for efficient management, retrieval, and use of that information.

[1841] "Assessment tool" refers to a system or algorithm that includes functionality for automatically calculating interim and final assessments and generating feedback.

[1842] "Emotional Data" refers to emotional states entered by employees and emotional data collected and analyzed by emotion analysis tools.

[1843] "Goal setting" refers to the process of defining specific work goals for employees to achieve.

[1844] "Modification request" refers to a request entered by an employee when they wish to modify a set goal.

[1845] "Feedback" refers to comments and evaluations provided based on an employee's progress toward achieving goals and evaluation results.

[1846] The present invention is a system that utilizes past evaluation data and performance data to set appropriate goals and evaluate employees, taking into account their emotional state. This system operates using multiple means, with a server, terminals, and users working together.

[1847] Server processing

[1848] server

[1849] As an initial setup, managers enter company information, employee job descriptions, goals, past performance data, etc. through a web interface and store them in a database. The AI ​​tool analyzes this data and automatically sets appropriate goals for each employee.

[1850] As a specific example, the server analyzes the performance data of employee A for the past three years and sends the goal "acquire five new clients" as a prompt to the generative AI model. The prompt sentence of the generative AI model is "Please set the next goal for employee A."

[1851] User Action

[1852] User (Employee)

[1853] Employees can review their goals on their own devices and input their emotional data. By inputting their emotional state, data is collected that is reflected in the evaluation process. Employees can also input requests to modify their goals.

[1854] For example, after confirming the target, employee B inputs into the terminal, "Five new customers is too many, so I would like to change it to three." The server receives this request and uses AI tools to reevaluate.

[1855] Processing by the terminal

[1856] Terminal

[1857] The terminals where employees input their daily work progress send the collected data to a server in real time. This data is stored in a database and used to monitor and evaluate progress. The terminals also have a user interface means for displaying the evaluation results and feedback.

[1858] For example, employee C enters "two new clients acquired" into the terminal. This information is sent to the server and stored in the database.

[1859] Feedback generation and notification

[1860] server

[1861] The evaluation method automatically calculates interim and final evaluations based on the collected data and generates feedback. During this process, the collected emotional data is reflected in the feedback content. The timing and method of notification of the feedback are also adjusted based on the emotional data.

[1862] As a specific example, the server generates feedback such as "Goals are being achieved smoothly, and sales are as expected" based on progress data such as "Three new customers have been acquired."

[1863] Saving data and reflecting it in the next period

[1864] server

[1865] The final evaluation results are stored in a database and analyzed as reference data for setting the next goal, which will result in appropriate goal setting for the next period.

[1866] As a concrete example, the server analyzes the "target appropriateness of the number of new customers acquired" and uses it as the basic data for goal setting to be applied in the next evaluation period. It sends a prompt to the generative AI model saying, "Please analyze the appropriateness of the number of new customers acquired in order to set the next goal."

[1867] In this way, the present invention can realize a more convincing and fair evaluation system that takes into account the emotional state of employees, which is more effective than conventional evaluation methods in that it contributes to improving employee motivation and stress management.

[1868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1869] Step 1:

[1870] Initial data collection and storage

[1871] The server receives company and employee information entered by the administrator through a web interface and stores the data in a database in real time. Specific inputs include organizational structure, organizational goals, employee names, job titles, and job descriptions. The server converts this input data into JSON format and stores it in the database.

[1872] Specific behavior:

[1873] When an administrator opens the administration screen in a browser, enters data into each field, and clicks the "Submit" button, the entered data is sent to the server, which receives the data and stores it in a database.

[1874] Step 2:

[1875] AI-driven goal setting

[1876] The server uses a generative AI model to analyze past evaluation and performance data stored in the database. Specifically, it analyzes each employee's past goal achievement rate and performance to generate appropriate goals for the next period.

[1877] Input: Past evaluation data, performance data

[1878] Data processing: A prompt sentence is generated based on the input data for the generative AI model and sent to the AI ​​model.

[1879] Output: Appropriate goal setting data

[1880] Specific behavior:

[1881] The server generates a prompt saying, "Please generate an appropriate goal based on employee A's past evaluation data," and sends it to the generative AI model. The AI ​​model generates a goal, "Acquire five new customers," and sends it back to the server.

[1882] Step 3:

[1883] Collecting and reflecting emotional data

[1884] Users (employees) input their emotional state during the evaluation process or when receiving feedback. The data is sent to the server in real time and stored in a database. The server then reflects the emotional data in the evaluation process.

[1885] Input: Emotional state input data

[1886] Data processing: Analyze sentiment data and incorporate it into the evaluation process.

[1887] Output: Emotion data reflected in the evaluation

[1888] Specific behavior:

[1889] When an employee sees a prompt on the terminal asking, "Please enter your current emotional state," they enter "I feel stressed," and click the "Submit" button. The entered emotional data is sent to the server and stored in a database.

[1890] Step 4:

[1891] Check your goals and enter correction requests

[1892] The user (employee) checks the set goals on the terminal and inputs correction requests as necessary. The server receives this request and uses the AI ​​model to reassess.

[1893] Input: Goal setting data, correction request data

[1894] Data processing: Reevaluation process using generative AI models

[1895] Output: Reassessed goal setting data

[1896] Specific behavior:

[1897] The employee opens the goal confirmation screen, checks the goal "Acquire 5 new customers," enters "Five new customers is too many, so I would like to change it to three," and submits it. The server receives this request and sends a reevaluation prompt to the generative AI model: "Please reevaluate Employee A's new goal setting." The AI ​​model generates a new appropriate goal and notifies the server.

[1898] Step 5:

[1899] Progress monitoring and data collection

[1900] The terminal provides a screen where employees can input their daily work progress. The progress data entered by employees is sent to the server in real time and stored in a database. The server uses this data to monitor the progress.

[1901] Input: Progress input data

[1902] Data processing: progress data collection and real-time monitoring

[1903] Output: Progress data

[1904] Specific behavior:

[1905] An employee types "Acquired two new clients" into a terminal and clicks the send button. The entered data is sent to the server and saved in a database. The server uses the data to monitor progress in real time.

[1906] Step 6:

[1907] Enter mid-term evaluations and generate feedback

[1908] The server calculates intermediate evaluations based on the collected progress data and generates feedback, which is adjusted based on the emotional data.

[1909] Input: progress data, emotional state data

[1910] Data processing: Calculating intermediate assessments and generating feedback

[1911] Output: Regulated feedback data

[1912] Specific behavior:

[1913] The server performs an interim evaluation based on progress data such as "three new clients acquired," and generates feedback such as "goals are being achieved smoothly." The content of the feedback and the timing of notifications are adjusted based on the emotional data.

[1914] Step 7:

[1915] Calculation and notification of final grade

[1916] At the end of the evaluation period, the server automatically calculates the final evaluation based on all collected data and notifies the user along with feedback. The timing and method of notification are also adjusted based on the emotional data.

[1917] Input: progress data, emotional state data

[1918] Data processing: Calculating final grades and generating feedback

[1919] Output: Final evaluation result data and feedback data

[1920] Specific behavior:

[1921] At the end of the period, the server consolidates all the aggregated data and sends a prompt to the generative AI model saying, "Please generate the final evaluation result for employee F." The final evaluation and feedback are generated and notified to the employee's device.

[1922] Step 8:

[1923] Confirmation of evaluation results and reflection on the next period

[1924] The user (employee) checks the evaluation results and feedback on the terminal, and the server stores the results in a database and reflects them in setting goals for the next period.

[1925] Input: Final evaluation result data

[1926] Data processing: Data storage and analysis for setting next goals

[1927] Output: Saved final evaluation results

[1928] Specific behavior:

[1929] The employee opens the evaluation results screen and checks the evaluation result: "Acquired six new customers, increased sales by 25% compared to the previous year." The server saves this data in a database and analyzes it for use in setting goals for the next period. The server sends a prompt to the AI ​​model saying, "Please analyze the appropriateness of the number of new customers acquired in order to set goals for the next period," and sets goals based on the results.

[1930] (Application example 2)

[1931] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1932] Conventional employee evaluation systems have problems in that the appropriateness of set goals and the quality of feedback have a significant impact on employee motivation and performance. Furthermore, because they were unable to take into account employees' emotions and stress levels, it was difficult to appropriately adjust workloads and maintain motivation. This led to issues such as a lack of fairness and a sense of satisfaction in evaluations, which ultimately led to a decline in work efficiency.

[1933] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: artificial intelligence means for automatically setting appropriate goals for employees using past evaluation data and performance data; user interface means for employees to check the set goals and input correction requests as necessary; artificial intelligence means for accepting correction requests and performing reevaluations; emotion recognition means for collecting employee emotion data and adjusting work instructions and feedback based on the data; database means for collecting employee progress and storing the data; and evaluation means for automatically calculating interim and final evaluations and generating feedback. This enables appropriate goal setting and workload adjustment taking into account employees' emotions and stress levels, thereby improving the sense of satisfaction and fairness of evaluations and optimizing work efficiency and maintaining employee motivation.

[1934] "Artificial intelligence means" refers to technology that analyzes past evaluation data and performance data and automatically sets appropriate goals for employees.

[1935] The "user interface means" is an interface through which employees can interact with the system, and is a function that allows employees to confirm set goals and input correction requests.

[1936] "Emotion recognition means" is a technology that collects emotional data from employees' facial expressions and voices, and adjusts work instructions and feedback based on that data.

[1937] "Database means" is a system for efficiently collecting and storing employee progress and emotional data, and has the function of centrally managing data.

[1938] The "evaluation tool" is a technology that automatically generates feedback and evaluates employees based on collected mid-term and final evaluation data.

[1939] "Emotional data" refers to data obtained from an employee's facial expressions, voice, and other emotional expressions, and is information that quantifies or qualitatively evaluates the employee's emotional state.

[1940] The present invention relates to a system for improving the sense of fairness and satisfaction of evaluations by reflecting emotional data in the performance evaluations of employees. Specific embodiments for carrying out the present invention will be described in detail below.

[1941] System Configuration

[1942] This system is an evaluation management system that operates in cooperation with a server, terminals, and users (employees and evaluators), and by combining it with an emotion engine, reflects the employee's emotional state in the evaluation process. The entire system includes the following elements:

[1943] Server: The server is the core of the system and includes artificial intelligence means, database means, and evaluation means. Specifically, the server analyzes past evaluation data and performance data to set appropriate goals for employees, accepts correction requests, and performs reevaluations. It also has the function of collecting employee emotional data using emotion recognition means and adjusting work instructions and feedback based on that data.

[1944] Terminal: A device used by the employee and evaluator that contains a user interface through which the employee can view set goals, input correction requests, report progress, and view feedback.

[1945] Users (employees and evaluators): Users access the system and input their own work status and emotional data. Employees report their progress and emotional state, and evaluators provide appropriate feedback based on the collected data.

[1946] Hardware and software used

[1947] Hardware

[1948] Camera: Used to capture employee facial expressions.

[1949] Computer terminal: Used to operate the user interface.

[1950] Server machine: Used to process and store data centrally.

[1951] software

[1952] OpenCV: Used to acquire and process camera images.

[1953] EmotionRecognitionEngine: Functions as an emotion recognition engine, analyzing employees' emotional states from facial expressions and voice data. For example, "Face++" or "Microsoft Azure Emotion API."

[1954] RobotController: Uses a robot control library such as ROS to transmit work instructions and feedback to the robot.

[1955] DatabaseClient: Use a database client such as "SQLite" to efficiently store emotion data and rating data.

[1956] Artificial intelligence model: A generative AI model that analyzes past evaluation data and performance data to generate appropriate goals.

[1957] Explanation of program processing

[1958] Server: The server first uses the administrator's input form to collect company information, organizational goals, and each employee's grade, rank, job type, and job description, and stores them in a database. Next, it uses an AI algorithm to set appropriate goals based on past evaluation data and performance data. It also runs an emotion recognition engine to collect employee emotional data and reflect it in the evaluation process. Finally, it automatically calculates interim and final evaluations and generates feedback.

[1959] Terminal: Employees use the terminal to check the set goals and input correction requests as necessary. Progress data and emotional state are also entered through this terminal. Employees also use this terminal to check evaluation results and feedback.

[1960] Users: Employees and evaluators access the system through a user interface to input and confirm the necessary data. In particular, real-time feedback and work adjustments based on emotional data are possible.

[1961] Specific examples

[1962] As a specific use case, the following prompts can be input to the generative AI model:

[1963] Please provide an example of a system that uses emotion recognition data from employees to enable factory robots to provide appropriate work instructions and feedback. Please also explain how workloads are adjusted when stress levels are high, and how positive feedback is provided when happiness levels are low.

[1964] This prompt can be fed into a generative AI model to generate additional ideas for similar implementations and improvements.

[1965] As described above, the present invention provides an evaluation management system based on employee emotional data, and is expected to improve employee motivation and performance.

[1966] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1967] Step 1:

[1968] The server uses the administrator's input form to collect data such as company information, organizational goals, each employee's grade, rank, job type, and job description, and stores it in a database. This prepares the initial data necessary for evaluation. The input data is the information entered by the administrator, and the output is the prepared data stored in the database.

[1969] Step 2:

[1970] The server runs an artificial intelligence model based on past evaluation data and performance data, automatically setting appropriate goals for each employee. For example, by analyzing past goal achievement data, goals such as "acquire five new clients" or "increase sales by 20% compared to the previous year" are generated for a specific employee. The input data are past evaluation data and performance data, and the output is the appropriate goal generated for each employee.

[1971] Step 3:

[1972] Using a terminal, an employee checks the set targets and inputs correction requests as necessary. For example, they can input a comment such as, "Five new customers is too many, so I would like to reduce it to three." The input data is the displayed targets and correction requests, and the output shows the state after the correction requests have been input.

[1973] Step 4:

[1974] The server accepts the correction request and re-runs the AI ​​model to re-evaluate it. The input data is the correction request entered by the employee, and the output is the re-evaluated goal. This re-evaluation also takes into account the employee's emotional data.

[1975] Step 5:

[1976] The server uses a camera and an emotion recognition engine to collect emotional data from employees' facial expressions and voices. This emotional data is stored in a database. The input data is camera footage and audio data, and the output is analyzed emotional data.

[1977] Step 6:

[1978] The server adjusts work instructions and feedback based on the employee's emotional data. For example, if the stress level is high, it reduces the workload, and if the happiness level is low, it provides positive feedback. The input data is emotional data, and the output is adjusted work instructions and feedback.

[1979] Step 7:

[1980] Employees periodically input progress data using terminals. For example, the progress of sales activities such as "acquiring two new clients" is entered. The input data is specific information indicating the progress, and the output is progress data stored in a database.

[1981] Step 8:

[1982] The server collects the entered progress data and stores it in a database, allowing employees' progress toward their goals to be monitored in real time. The input data is the progress data entered by the employees, and the output is the progress data stored in the database.

[1983] Step 9:

[1984] Employees use terminals to input interim evaluations and report the progress of each goal. For example, they input progress data such as "three new clients acquired." The input data is the progress data as interim evaluations, and the output is the interim evaluation data sent to the server.

[1985] Step 10:

[1986] The server aggregates the interim evaluation data and generates feedback. For example, the feedback might be something like, "The acquisition of three new customers is going smoothly. Sales targets are as expected." The content of the feedback is adjusted based on the emotional data. The input data is the interim evaluation data, and the output is the generated feedback.

[1987] Step 11:

[1988] At the end of the evaluation period, the server automatically calculates the final evaluation based on the collected data. For example, the evaluation result calculated is "6 new customers acquired, sales increased 25% compared to the previous year." The input data is all the collected evaluation data, and the output is the final evaluation result.

[1989] Step 12:

[1990] The employee uses a terminal to check the final evaluation results. Feedback is displayed along with the evaluation results. For example, an evaluation such as "You have achieved results that significantly exceeded the targets, so you are a candidate for promotion" may be displayed. The input data is the final evaluation results sent from the server, and the output is the state of the evaluation results as viewed by the employee.

[1991] Step 13:

[1992] The server saves the final evaluation results in a database and performs data analysis to reflect them in setting goals for the next period. For example, it analyzes the "target appropriateness of the number of new customers acquired" and uses it as reference data for setting goals for the next period. The input data is the final evaluation results, and the output is the analyzed reference data.

[1993] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1994] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1995] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1996] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1997] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1998] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1999] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2000] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2001] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2002] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2003] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2004] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2005] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 h...

Claims

1. Artificial intelligence means to automatically set appropriate goals for employees using past evaluation data and performance data; a user interface means for employees to review the established goals and input correction requests as needed; an artificial intelligence means for accepting and re-evaluating correction requests; a database means for collecting and storing employee progress data; an assessment instrument that automatically calculates interim and final grades and generates feedback; A system including:

2. The system of claim 1 further comprising a user interface means for employees to review the evaluation results and receive feedback.

3. 2. The system according to claim 1, further comprising a data storage and analysis means for storing the final evaluation results and reflecting them in the setting of the next goal.

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