system

Abstract

We provide the system. [Solution] A means for providing a form on the user terminal for the user to input basic information and evaluation information, A means by which a user terminal transmits basic information and evaluation information entered by the user to a server, The server provides a means for storing basic information and evaluation information transmitted from the user terminal in a database, A means by which the server calculates the overall user evaluation based on the basic information and evaluation information stored in the above database, A means by which the server selects management candidates based on a calculated overall evaluation, A means by which the server generates feedback for the above-mentioned management candidate and sends it to the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional evaluation system, there is a problem that while active and superficially prominent talents are highly evaluated, potential talents who seem lackluster but have high abilities are often overlooked. It is difficult to maximize the performance of the entire organization with such an evaluation system. Also, in the selection of management position candidates that偏重 on existing evaluation criteria, diverse talents cannot be utilized.

Means for Solving the Problems

[0005] The present invention provides a system that allows a user terminal to input basic information and evaluation information, and a means for the user terminal to transmit the basic information and evaluation information input by the user to a server. Furthermore, the system includes means for the server to store the basic information and evaluation information transmitted from the user terminal in a database, means for calculating the user's overall evaluation based on the basic information and evaluation information stored in the database, means for selecting management candidates based on the calculated overall evaluation, and means for generating and transmitting feedback to the user terminal. By incorporating new indicators in addition to conventional evaluation criteria, the present invention provides a system that can appropriately evaluate diverse personnel by conducting multifaceted evaluations.

[0006] A "user terminal" is a device used by users to input evaluation information and basic information, and it is responsible for sending and receiving data with the server.

[0007] "Basic information" refers to fundamental data used for evaluation, such as personal information about the user and information about their job duties.

[0008] "Evaluation information" refers to data based on evaluations from the user themselves, colleagues, and supervisors, and includes detailed evaluation items such as skills, work performance, communication skills, supportive attitude, and creativity.

[0009] A "server" is a computer system that receives basic and evaluation information sent from user terminals, stores it in a database, and analyzes and evaluates this data.

[0010] A "database" is a storage system used to systematically store and manage basic and evaluation information received by a server.

[0011] "Overall evaluation" is an evaluation score calculated by the server based on basic information and evaluation information, which quantifies or represents the overall performance of the user.

[0012] A "management candidate" is a user whose overall evaluation by the server exceeds the standard score and who is deemed suitable for a management position.

[0013] "Feedback" refers to the evaluation results report generated by the server for the user, which includes details of the evaluation, strengths, weaknesses, and areas for improvement. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of a data processing system in Embodiment 2 when a sentiment engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] The present invention is a system for users to input their own evaluations and evaluations from others, and a server collects and analyzes this evaluation data to select candidates for management positions. An embodiment of this system will be described below.

[0036] 1. Enter user information

[0037] The terminal provides users with a survey form for entering basic and evaluation information. This form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. Users enter their information and send the data to the server by pressing the submit button.

[0038] 2. Collection of evaluation data

[0039] The server receives basic user information and evaluation information sent from the terminal. The received data is stored in a database. This database is designed to organize and store user-specific data.

[0040] 3. Analysis of evaluation data

[0041] The server analyzes the user's basic and evaluation information stored in the database. First, it calculates scores based on traditional evaluation metrics (performance achievement, skill evaluation, etc.). Then, it calculates scores for new evaluation metrics (communication skills, supportive attitude, creativity, etc.).

[0042] 4. Selection of management candidates

[0043] The server performs an overall evaluation based on the calculated scores and selects candidates for management positions. To be selected as a management candidate, users must meet certain criteria in both the traditional and new evaluation metrics. The server lists users with high overall evaluation scores and generates a list of management candidates.

[0044] 5. Providing feedback

[0045] The server generates a document providing detailed feedback to users selected as management candidates, along with their evaluation results. This feedback document includes detailed evaluation results (scores for each evaluation item, overall score, etc.), strengths and weaknesses, and areas for future improvement. The generated feedback document is sent to the terminal and notified to the user.

[0046] Specific example

[0047] For example, if a user (employee A) fills out an evaluation form, they would follow these steps:

[0048] 1. Enter basic information

[0049] Employee A enters their name, job title, and years of service into the form displayed on the terminal.

[0050] 2. Entering evaluation information

[0051] Employee A inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[0052] 3. Sending data

[0053] After completing the input, employee A presses the submit button, and the terminal sends it to the server.

[0054] 4. Data Processing

[0055] The server receives employee A's data and saves it to the database.

[0056] The server calculates an overall evaluation score based on the stored data.

[0057] 5. Selection of management candidates

[0058] The server determines whether employee A is suitable as a management candidate based on their overall evaluation score.

[0059] 6. Providing feedback

[0060] The server generates a detailed feedback document for employee A and sends it to their terminal.

[0061] The device displays a feedback notification to employee A.

[0062] Thus, the present invention enables comprehensive evaluation that incorporates new indicators in addition to conventional evaluation criteria, allowing for the appropriate evaluation of diverse personnel and maximizing the overall performance of the organization.

[0063] The following describes the processing flow.

[0064] Step 1:

[0065] The terminal displays a survey form for the user to input basic and evaluation information. The user enters basic information such as name, position, and years of service, as well as information related to evaluation items such as performance achievement, skill evaluation, communication skills, supportive spirit, and creativity into the form.

[0066] Step 2:

[0067] The user completes the survey form and presses the "Submit" button. The device sends the entered basic information and evaluation information to the server.

[0068] Step 3:

[0069] The server receives basic and evaluation information sent from the terminal. The received data is temporarily stored in the server's internal temporary memory.

[0070] Step 4:

[0071] The server stores the received basic and evaluation information in a database. During storage, the data is organized by user and associated using a unique user ID.

[0072] Step 5:

[0073] The server extracts basic and evaluation information stored in the database. The extracted data is then loaded back into temporary memory for the analysis process.

[0074] Step 6:

[0075] The server calculates evaluation scores based on conventional evaluation metrics. This is a process of individually evaluating and scoring items such as performance achievement and skill evaluation.

[0076] Step 7:

[0077] The server calculates an evaluation score based on new evaluation metrics (such as communication skills, supportiveness, and creativity). These are also scored separately.

[0078] Step 8:

[0079] The server calculates an overall evaluation score based on both traditional and new evaluation metrics. This includes a process of weighting each evaluation metric.

[0080] Step 9:

[0081] The server selects management candidates based on their overall performance score. Users whose overall performance score exceeds a pre-set standard are listed and designated as management candidates.

[0082] Step 10:

[0083] The server generates a detailed feedback document for each selected management candidate. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, and areas for improvement.

[0084] Step 11:

[0085] The server sends the generated feedback document to the terminal. The terminal displays a feedback notification to the user (management candidate). The user can then review the feedback content through the terminal.

[0086] (Example 1)

[0087] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] Traditional evaluation systems failed to adequately assess users as a whole, making it difficult to appropriately select candidates for management positions. Furthermore, the feedback was often insufficient, preventing users from accurately understanding their strengths and areas for improvement. In addition, evaluation bias existed, and the fairness of evaluations was not guaranteed.

[0089] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0090] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information from the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to use a generative AI model to generate feedback documents for users selected as management candidates; and means for the server to transmit the feedback documents generated by the generative AI model to the user terminal. This enables a multifaceted evaluation of the user, allowing for more appropriate selection of management candidates and the provision of detailed feedback.

[0091] A "user terminal" is a device used by a user to input information, and includes PCs, smartphones, and tablets.

[0092] A "form" refers to a screen or input fields in the form of a survey where users enter basic information and evaluation information.

[0093] "Basic information" refers to the user's basic personal information, such as name, job title, and years of service.

[0094] "Evaluation information" refers to information regarding evaluation items such as user performance, skills, communication ability, supportive attitude, and creativity.

[0095] A "server" refers to a central system that receives, stores, analyzes, and generates feedback on data.

[0096] A "database" refers to a system for organizing and storing basic user information and evaluation information.

[0097] "Overall rating" refers to the user's overall evaluation calculated based on both traditional and new evaluation metrics.

[0098] A "management candidate" refers to a user selected based on an overall evaluation as being suitable for a management position.

[0099] A "generative AI model" refers to an artificial intelligence model that analyzes user evaluation data and generates feedback documents.

[0100] A "feedback document" refers to a document that details the evaluation results, strengths, weaknesses, and areas for improvement of the user.

[0101] "Notifications" refer to messages or alerts that inform the user's device of feedback or other important information.

[0102] This invention relates to a system for users to input their own evaluations and evaluations from others, with a server collecting and analyzing this evaluation data to select management candidates. This system consists of a user terminal, a server, a database, and a generating AI model.

[0103] Specific system configuration

[0104] Hardware and software to be used

[0105] Device: A device used by a user to input information (such as a PC, smartphone, or tablet).

[0106] Server: A central system that receives, stores, analyzes, and generates feedback from data.

[0107] Database: A system for organizing and storing basic user information and evaluation information (e.g., MySQL®, PostgreSQL)

[0108] Generative AI Models: AI tools for analyzing user evaluation data and generating feedback documents (e.g., OpenAI® GPT model)

[0109] Specific operation of the system

[0110] 1. Enter user information

[0111] The device displays a survey-style form to the user. This form includes basic information that the user must enter (such as name, job title, and years of service).

[0112] Users enter information such as name, job title, years of service, achievements, skills, communication skills, supportive attitude, and creativity according to a specified format.

[0113] After the user has completed entering all the required information, they press the submit button. This sends the data to the server.

[0114] 2. Collection of evaluation data

[0115] The server receives user information sent from the terminal.

[0116] The server stores the received data in a database. This database is designed to systematically organize and store each user's information.

[0117] 3. Analysis of evaluation data

[0118] The server analyzes user information stored in the database. First, it calculates a score based on traditional evaluation metrics (performance achievement, skill evaluation).

[0119] The server also calculates scores for new evaluation metrics (communication skills, supportiveness, and creativity).

[0120] 4. Selection of management candidates

[0121] The server performs an overall evaluation based on the calculated scores for each evaluation metric.

[0122] The server lists users with high overall evaluation scores and generates a list of management candidates.

[0123] 5. Providing feedback

[0124] The server generates a feedback document containing detailed evaluation results for users selected as management candidates. This process utilizes a generative AI model.

[0125] The server sends the generated feedback document to the terminal and notifies the user.

[0126] This system ensures fairness in evaluations, enables proper assessment of diverse talent, and aims to improve the overall performance of the organization.

[0127] Specific example

[0128] For example, if a user (employee A) fills out an evaluation form, they would follow these steps:

[0129] Example of a prompt

[0130] 1. Enter basic information:

[0131] In the form displayed on the terminal, employee A enters the name "Taro Tanaka", the position "Senior Engineer", and the years of service "5 years".

[0132] 2. Entering evaluation information:

[0133] Employee A inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[0134] 3. Sending data:

[0135] After completing the input, employee A presses the submit button, and the terminal sends it to the server.

[0136] 4. Data processing:

[0137] The server receives employee A's data and saves it to the database.

[0138] The server calculates an overall evaluation score based on the stored data.

[0139] 5. Selection of management candidates:

[0140] The server determines whether employee A is suitable as a management candidate based on their overall evaluation score.

[0141] 6. Providing feedback:

[0142] The server generates a detailed feedback document for employee A and sends it to their terminal.

[0143] The device displays a feedback notification to employee A.

[0144] In this way, by using the system, evaluation bias can be reduced, diverse talent can be evaluated fairly and comprehensively, and the overall performance of the organization can be maximized.

[0145] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0146] Step 1: Enter user information

[0147] The terminal displays a survey form to the user. This form includes basic information that the user must enter (name, position, years of service, etc.) and evaluation information (performance, skills, communication skills, supportive attitude, creativity).

[0148] The user enters the required information and sends the data to the server by pressing the submit button.

[0149] Input: Basic information entered by the user (name, job title, years of service, etc.) and evaluation information (performance, skills, communication skills, supportive spirit, creativity).

[0150] Output: The basic information and evaluation information entered by the user are sent to the server.

[0151] Step 2: Collection of evaluation data

[0152] The server receives user information sent from the terminal.

[0153] The server stores the received data in a database. The database is designed to systematically organize and store each user's information.

[0154] Input: Basic user information and evaluation information sent from the terminal.

[0155] Output: User data is saved to the database.

[0156] Step 3: Analysis of evaluation data

[0157] The server analyzes user information stored in the database. First, it calculates a score based on conventional evaluation metrics (performance achievement, skill evaluation).

[0158] Next, we will calculate scores for the new evaluation metrics (communication skills, supportive spirit, and creativity).

[0159] Input: User's basic information and evaluation information stored in the database.

[0160] Output: Scores for each evaluation metric (traditional metric and new metric) are calculated.

[0161] Step 4: Selection of management candidates

[0162] The server performs an overall evaluation based on the calculated scores for each evaluation metric.

[0163] The server lists users with high overall evaluation scores and generates a list of management candidates.

[0164] Input: Scores for each evaluation metric (traditional metric and new metric).

[0165] Output: List of management candidates.

[0166] Step 5: Provide feedback

[0167] The server uses a generative AI model to generate feedback documents containing detailed evaluation results for users selected as management candidates.

[0168] The server sends the generated feedback document to the terminal and notifies the user.

[0169] Input: List of management candidates and each user's performance metric score.

[0170] Output: Feedback documents and notifications.

[0171] By incorporating specific actions into each step, the overall flow of the system becomes easier to understand, and it becomes clear how and which data is processed at which stage. This allows for more efficient system implementation and operation.

[0172] (Application Example 1)

[0173] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0174] Traditional management candidate selection systems had limited evaluation information, making it difficult to comprehensively assess the practical skills and on-site performance of operators and engineers, especially in factory settings. This prevented evaluations that reflected the realities of the workplace, resulting in the inability to appropriately select management candidates. Furthermore, insufficient feedback made it difficult for candidates to obtain concrete guidance for self-improvement. Additionally, the traditional system required users to input evaluation information cumbersomely, hindering efficient operation.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0176] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information to the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to generate feedback for the management candidates and transmit it to the user terminal; means for the user terminal to function as a tool for factory operators and engineers to input self-evaluations and peer evaluations; means for the user terminal to transmit self-evaluation and peer evaluation data to the server; and means for the server to generate a detailed feedback document based on the transmitted evaluation data and transmit a notification to the user terminal. This enables detailed evaluation and feedback based on the actual situation on site, and allows for the efficient selection of management candidates.

[0177] A "user terminal" is a device that a user uses to input and transmit information.

[0178] A "form" is an interface or screen used by users to input basic information and evaluation information.

[0179] "Basic information" refers to fundamental data such as the user's name, job title, and years of service.

[0180] "Evaluation information" refers to data related to user performance, such as achievement level, skill assessment, communication skills, supportive attitude, and creativity.

[0181] A "server" is a computing system that receives information sent from user terminals, stores it in a database, and performs analysis on it.

[0182] A "database" is a system used by a server to organize and store basic and evaluation information it receives.

[0183] "Overall rating" refers to the user's overall score calculated based on basic information and evaluation information.

[0184] A "management candidate" is an individual selected based on an overall evaluation to determine their suitability for a management position.

[0185] "Feedback" refers to a document containing detailed evaluation results and suggestions for improvement, generated based on the overall evaluation.

[0186] A "factory operator" is an employee who operates machinery and equipment on the factory floor.

[0187] An "engineer" is a professional employee who is responsible for technical tasks.

[0188] "Self-evaluation" is the act of a user evaluating their own performance.

[0189] "Colleague evaluation" is the act of a user evaluating the performance of other employees.

[0190] A "tool" is software or an application used for a specific purpose.

[0191] A "notification" is a message sent to inform a user of specific information.

[0192] This invention is a system for users to input self-assessments and peer assessments, and a server collects and analyzes this assessment data to select candidates for management positions.

[0193] 1. Enter user information

[0194] The user's device (e.g., a smartphone or tablet) provides a form for the user to input basic and evaluation information. This form allows the user to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. The user enters their information and sends the data to the server by pressing the submit button.

[0195] 2. Collection of evaluation data

[0196] The server receives basic user information and evaluation information sent from the user's terminal. The received data is stored in a database. This database is designed to organize and store user-specific data.

[0197] 3. Analysis of evaluation data

[0198] The server analyzes the user's basic and evaluation information stored in the database. First, it calculates scores based on traditional evaluation metrics (performance achievement, skill evaluation, etc.). Then, it calculates scores for new evaluation metrics (communication skills, supportive attitude, creativity, etc.). It also conducts a comprehensive evaluation that includes evaluation items specific to factory operators and engineers.

[0199] 4. Selection of management candidates

[0200] The server performs an overall evaluation based on the calculated scores and selects candidates for management positions. To be selected as a management candidate, individuals must meet certain criteria in both the traditional and new evaluation metrics, as well as evaluation items specific to factory operators and engineers. The server lists users with high overall evaluation scores and generates a list of management candidates.

[0201] 5. Providing feedback

[0202] The server generates a document providing detailed feedback to users selected as management candidates, along with their evaluation results. This feedback document includes detailed evaluation results (scores for each evaluation item, overall score, etc.), strengths and weaknesses, and areas for future improvement. The generated feedback document is sent to the user's terminal, and the user is notified.

[0203] Hardware and software to be used

[0204] The following hardware and software will be used to implement this system.

[0205] Hardware: Smartphones, tablets, and servers

[0206] Software: Django framework, database management system (e.g., PostgreSQL), generative AI model

[0207] Specific example

[0208] For example, when a factory operator (user) fills out an evaluation form, they would follow these steps:

[0209] 1. Enter basic information

[0210] The user (factory operator) opens the app on their smartphone and enters their name, job title, and years of service.

[0211] 2. Entering evaluation information

[0212] You will input your self-assessment based on the evaluation items provided by the app (performance achievement, skill evaluation, communication skills, supportive spirit, creativity).

[0213] You will also enter your evaluation of your colleagues.

[0214] 3. Sending data

[0215] After completing the input, the user presses the submit button, and the app sends it to the server.

[0216] 4. Data Processing

[0217] The server receives user data and stores it in the database.

[0218] The server calculates an overall evaluation score based on the stored data.

[0219] 5. Selection of management candidates

[0220] The server determines whether a user is suitable as a management candidate based on their overall evaluation score.

[0221] 6. Providing feedback

[0222] The server generates a detailed feedback document for the user and sends it to the user's terminal.

[0223] The user's device displays a feedback notification to the user.

[0224] This invention enables evaluation and feedback based on actual on-site conditions, allowing for the efficient selection of management candidates. Furthermore, it provides users with specific guidance for self-improvement through concrete feedback.

[0225] Example of a prompt

[0226] Please provide the following information regarding the management candidate selection system:

[0227] 1. Employee basic information (name, position, years of service)

[0228] 2. Self-evaluation (performance achievement, skill assessment, communication skills, supportive spirit, creativity)

[0229] 3. Peer evaluation (performance achievement, skill assessment, communication skills, supportive attitude, creativity)

[0230] Input endpoint: / api / submit_evaluation

[0231] Analysis endpoint: / api / analyze_evaluation

[0232] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0233] Step 1:

[0234] The user terminal provides a form for entering basic information and evaluation information.

[0235] Input: User's basic information (name, job title, years of service, etc.) and evaluation information (performance achievement, skill evaluation, communication skills, supportive attitude, creativity).

[0236] Output: Input form screen.

[0237] Specific operation: The user terminal displays a form for the user to fill out, and the user enters the necessary information there.

[0238] Step 2:

[0239] The user terminal sends basic information and evaluation information entered by the user to the server.

[0240] Input: Basic information and evaluation information entered by the user.

[0241] Output: Data sent to the server.

[0242] Specific operation: When the user completes filling out the form and presses the submit button, basic information and evaluation information are sent to the server.

[0243] Step 3:

[0244] The server stores basic information and evaluation information sent from the user terminal in a database.

[0245] Input: Basic information and evaluation information sent from the user's terminal.

[0246] Output: Data stored in the database.

[0247] Specific operation: The server organizes the received data and saves it to the appropriate field in the database.

[0248] Step 4:

[0249] The server calculates the user's overall rating based on the basic information and evaluation information stored in the database.

[0250] Input: Basic information and evaluation information stored in the database.

[0251] Output: Overall evaluation score.

[0252] Specific operation: The server calculates scores for each based on the traditional and new evaluation metrics, and then calculates an overall evaluation score.

[0253] Step 5:

[0254] The server selects management candidates based on a calculated overall evaluation.

[0255] Input: Overall rating score.

[0256] Output: List of management candidates.

[0257] Specific operation: The server sets criteria based on the overall evaluation score and lists users with high scores as candidates for management positions.

[0258] Step 6:

[0259] The server generates feedback for the management candidate mentioned above and sends it to the user's terminal.

[0260] Input: List of management candidates and evaluation data for each user.

[0261] Output: Feedback document and notification.

[0262] Specific operation: The server generates a detailed feedback document based on the evaluation results and sends a notification to the user's terminal.

[0263] Step 7:

[0264] The user's device displays a feedback notification to the user.

[0265] Input: Feedback notification sent from the server.

[0266] Output: Feedback notification screen.

[0267] Specific action: The user terminal notifies the user that feedback has arrived and displays the document.

[0268] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0269] This invention is an evaluation system that combines an emotion engine to recognize user emotions. By collecting and analyzing emotional data in addition to basic user information and evaluation information, the system provides a more multifaceted evaluation. Embodiments of this system will be described in detail below.

[0270] 1. Enter user information

[0271] The terminal provides users with a survey form for entering basic and evaluation information. This form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. Furthermore, while filling out the form and responding to specific questions, an emotion engine analyzes the user's facial expressions and voice, collecting emotional data. Users submit this information to the server by pressing the submit button.

[0272] 2. Collection of evaluation data

[0273] The server receives basic user information, evaluation information, and sentiment data sent from the terminal. The received data is temporarily stored in the server's internal temporary memory and then saved to a database. This database is designed to organize and store data for each user.

[0274] 3. Analysis of evaluation data

[0275] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. First, it calculates individual scores based on traditional evaluation metrics (performance achievement, skill assessment, etc.) and new evaluation metrics (communication skills, supportiveness, creativity, etc.).

[0276] Next, emotional data is analyzed to evaluate the user's emotional response in specific evaluation scenarios. This includes changes in the user's emotions while filling out a form and their responses to specific questions. This emotional data is scored in relation to each evaluation metric, contributing to a more multifaceted scoring.

[0277] 4. Selection of management candidates

[0278] The server calculates an overall evaluation score based on each calculated evaluation score (conventional metrics, new metrics, sentiment data score). The overall evaluation score is the result of weighting each score and serves as the criterion for selecting management position candidates.

[0279] List up users whose overall evaluation score exceeds the pre-set criteria and use them as management position candidates. By including sentiment data, it is possible to take into account the emotional suitability of users who are often overlooked in the conventional evaluation method.

[0280] 5. Provision of Feedback

[0281] The server generates a detailed feedback document for the selected management position candidates. This feedback document includes the detailed scores of each evaluation item, strengths and weaknesses, points for future improvement, as well as feedback based on sentiment data (e.g., emotional reactions to specific questions and changes in emotions at that time).

[0282] The generated feedback document is sent to the terminal and notified to the user. The user can check the feedback content through the terminal and use it as a reference in the future.

[0283] Specific Example

[0284] For example, when user (Employee B) fills out a questionnaire form, the following steps are taken:

[0285] 1. Input of Basic Information

[0286] Employee B inputs their name, position, and years of service into the form displayed on the terminal.

[0287] 2. Input of Evaluation Information

[0288] Regarding the evaluation items provided by the terminal (performance achievement, skill evaluation, communication ability, support spirit, creativity), Employee B inputs their self-evaluation.

[0289] 3. Collection of emotional data

[0290] While employee B is filling out the form, the emotion engine analyzes their facial expressions and voice to collect emotional data.

[0291] 4. Sending data

[0292] After the input and sentiment analysis are complete, employee B presses the send button, and the terminal sends it to the server.

[0293] 5. Data Processing

[0294] The server receives employee B's data and saves it to the database.

[0295] The server calculates an overall evaluation score based on the stored data.

[0296] 6. Selection of management candidates

[0297] The server determines whether employee B is suitable as a management candidate based on the overall evaluation score.

[0298] 7. Providing feedback

[0299] The server generates a detailed feedback document for employee B and sends it to their terminal.

[0300] The device displays a feedback notification to employee B.

[0301] Thus, the present invention enables comprehensive evaluation that incorporates emotional data in addition to conventional evaluation criteria, allowing for the appropriate evaluation of diverse personnel and maximizing the overall performance of the organization.

[0302] The following describes the processing flow.

[0303] Step 1:

[0304] The terminal displays a questionnaire form for the user to input basic information and evaluation information. The user inputs basic information such as name, position, and years of service, as well as information regarding evaluation items such as performance achievement, skill evaluation, communication ability, support spirit, and creativity into the form.

[0305] Step 2:

[0306] The emotion engine is activated and analyzes the user's facial expressions and voice while the user is inputting the questionnaire form. This analysis records the reactions to the input and emotional changes in response to specific questions.

[0307] Step 3:

[0308] The user completes the input in the questionnaire form and presses the "Send" button. The terminal sends the input basic information, evaluation information, and the emotion data obtained by the emotion engine to the server.

[0309] Step 4:

[0310] The server receives the basic information, evaluation information, and emotion data sent from the terminal. The received data is temporarily stored in the server's internal temporary memory and then saved to the database.

[0311] Step 5:

[0312] The server extracts the basic information, evaluation information, and emotion data stored in the database. The extracted data is read back into the temporary memory for analysis.

[0313] Step 6:

[0314] The server calculates an evaluation score based on conventional evaluation metrics (e.g., performance achievement and skill evaluation). This includes individual scores for each evaluation item.

[0315] Step 7:

[0316] The server calculates evaluation scores based on new evaluation metrics (e.g., communication skills, supportiveness, creativity). The server also calculates these scores individually.

[0317] Step 8:

[0318] The server analyzes emotional data obtained from the emotion engine and calculates an evaluation score based on that data. This includes changes in emotions and emotional responses to specific questions.

[0319] Step 9:

[0320] The server integrates traditional evaluation metrics, new evaluation metrics, and evaluation scores based on sentiment data to calculate the user's overall rating. The integrated overall rating score is a combination of weighted scores.

[0321] Step 10:

[0322] The server selects management candidates based on their overall evaluation score. Users whose overall evaluation score exceeds the set criteria are listed as management candidates.

[0323] Step 11:

[0324] The server generates detailed feedback documents for selected management candidates. These feedback documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data.

[0325] Step 12:

[0326] The server sends the generated feedback document to the terminal. The terminal displays a feedback notification to the user, allowing the user to review the feedback content.

[0327] (Example 2)

[0328] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0329] Traditional evaluation systems based solely on basic and performance data, failing to consider emotional aspects. Consequently, users' emotional aptitudes and responses in specific scenarios were rarely reflected in evaluations. This resulted in a lack of evaluation of emotional aptitude, communication skills, and supportiveness, particularly in the selection of management candidates, hindering a more multifaceted assessment.

[0330] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0331] In this invention, the server includes means for storing basic information, evaluation information, and emotional data transmitted from the user terminal in a database, means for analyzing emotional data from the user's facial expressions and voice using an emotion engine, and means for analyzing emotional data in association with each evaluation index. This makes it possible to perform a comprehensive evaluation based on emotional data in addition to basic information and conventional evaluation indexes.

[0332] A "user terminal" is an electronic device used by a user to input basic information and evaluation information, and includes devices such as personal computers, tablets, and smartphones.

[0333] A "server" is a central processing unit that has the function of receiving, storing, and analyzing data sent from user terminals, and generating and sending feedback.

[0334] "Basic information" refers to personal attributes and background information such as the user's name, job title, and years of service.

[0335] "Evaluation information" refers to data related to the user's job performance capabilities and characteristics, such as the user's achievement level, skill evaluation, communication skills, supportive attitude, and creativity.

[0336] "Emotional data" refers to data about a user's emotional state, obtained by analyzing their facial expressions and voice.

[0337] An "emotion engine" refers to a combination of software and hardware used to analyze a user's facial expressions and voice, and is a technology that recognizes the user's emotional state in real time.

[0338] A "database" is an information storage system used by a server to systematically store, manage, and retrieve basic information, evaluation information, and sentiment data received by the server.

[0339] The "Overall Rating Score" is a numerical value that represents the overall evaluation result of a user, calculated by the server based on basic information, evaluation information, and sentiment data.

[0340] A "management candidate" refers to a user who has been judged to have high suitability based on their overall evaluation score and who should be selected for a management position.

[0341] "Feedback" refers to documents and notifications that contain information such as evaluation results and areas for improvement, generated based on the overall evaluation score and sentiment data calculated by the server.

[0342] This invention is an evaluation system that combines an emotion engine that recognizes user emotions. By collecting and analyzing emotion data in addition to basic user information and evaluation information, the system provides a more multifaceted evaluation. This system is mainly implemented using a user terminal, a server, and an emotion engine.

[0343] First, the user terminal provides the user with a questionnaire form for inputting basic and evaluation information. This form is built using HTML, CSS, and JavaScript (registered trademarks), and users access it through a browser. The form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance achievement, skill evaluation, communication skills, supportiveness, and creativity.

[0344] While filling out the survey form and answering specific questions, the emotion engine analyzes the user's facial expressions and voice, collecting emotional data. The emotion engine utilizes software such as TENSORFLOW® and OpenCV to recognize the user's emotional state in real time. This data will later be used in the evaluation.

[0345] Once the user completes the survey and presses the submit button, the device converts the entered basic information, evaluation information, and collected sentiment data into JSON format and sends it to the server using HTTPS. The server receives this data and first stores it in temporary memory. Then, it migrates the data to a database (MySQL or PostgreSQL) and stores it organized by user.

[0346] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. Individual scores are calculated based on traditional evaluation metrics (e.g., performance achievement, skill evaluation) and new evaluation metrics (e.g., communication skills, supportiveness, creativity). Sentiment data is also analyzed to evaluate the user's emotional response in specific evaluation scenarios. This calculation is performed using a Python script. The sentiment data thus obtained is associated with each evaluation metric and used for a more refined overall evaluation.

[0347] Next, the server calculates an overall evaluation score based on the calculated evaluation scores (traditional indicators, new indicators, and sentiment data scores). This overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates. Users whose overall evaluation score exceeds the pre-set criteria are listed and become management candidates.

[0348] Furthermore, the server generates a detailed feedback document for the selected management candidates. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, and feedback based on sentiment data. The generated feedback document is sent to the terminal and notified to the user. The user can review the feedback content through the terminal and use it for future reference.

[0349] A specific example of a prompt would be: "Please describe a comprehensive evaluation system that uses user sentiment data. Please include specific evaluation criteria, methods for collecting sentiment data, and evaluation methods."

[0350] In this way, the present invention makes it possible to appropriately evaluate diverse personnel and maximize the overall performance of the organization by conducting a comprehensive evaluation that incorporates emotional data in addition to conventional evaluation criteria.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1: Entering user information and collecting sentiment data

[0353] Input: Users enter basic and evaluation information into the survey form using their device. Specifically, they enter their name, job title, years of service, and evaluation items (performance achievement, skill evaluation, communication skills, supportive spirit, creativity, etc.).

[0354] Operation: The device provides a survey form built with HTML, CSS, and JavaScript. When the user begins to input, the device's camera and microphone are activated, and an emotion engine (TensorFlow, OpenCV) analyzes the user's facial expressions and voice in real time to collect emotion data.

[0355] Output: The basic information and evaluation information entered by the user, along with the collected sentiment data, are saved on the device in JSON format.

[0356] Step 2: Send

[0357] Input: Basic information, evaluation information, and sentiment data (in JSON format) collected in Step 1.

[0358] Operation: Once the user completes the survey form and presses the submit button, the device sends this data to the server using HTTPS.

[0359] Output: The server receives basic information, rating information, and sentiment data in JSON format.

[0360] Step 3: Collection and storage of evaluation data

[0361] Input: Basic information, evaluation information, and sentiment data (in JSON format) sent to the server.

[0362] Operation: The server temporarily stores data in memory, then migrates it to a database (MySQL, PostgreSQL) and organizes and stores it for each user.

[0363] Output: Basic information, evaluation information, and sentiment data stored in the database.

[0364] Step 4: Analysis of evaluation data

[0365] Input: Basic information, evaluation information, and sentiment data extracted from the database.

[0366] Operation: The server extracts this data and calculates individual scores based on traditional evaluation metrics (e.g., performance, skill assessment) and new evaluation metrics (e.g., communication skills, supportiveness, creativity). It also analyzes emotional data and evaluates the user's emotional response in specific evaluation scenarios. This analysis process uses a Python script.

[0367] Output: Traditional evaluation metrics, new evaluation metrics, and individual scores based on sentiment data.

[0368] Step 5: Calculation of overall performance score and selection of management candidates

[0369] Input: Individual evaluation scores (traditional metrics, new metrics, sentiment data scores).

[0370] Operation: The server integrates and weights each evaluation score to calculate an overall evaluation score. Next, it lists users whose overall evaluation score exceeds a pre-set standard and selects them as management candidates.

[0371] Output: List of management candidates.

[0372] Step 6: Generating and providing feedback

[0373] Input: A list of management candidates and detailed scores for each evaluation item.

[0374] Operation: The server generates a detailed feedback document for management candidates. This document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, and feedback based on sentiment data. The generated feedback document is sent to the terminal and notified to the user.

[0375] Output: A feedback document is sent to the user's terminal, and a notification is displayed to the user.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0378] Traditional evaluation systems were limited to evaluations based on basic user information and evaluation data, and did not take into account the user's emotional state. This made it difficult to appropriately evaluate and respond quickly to customer satisfaction and emotional reactions, especially in physical stores. The lack of real-time information to evaluate and improve store service attitudes and atmosphere limited the ability to optimize the customer experience.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information from the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to generate feedback for the management candidates and transmit it to the user terminal; means for collecting customer emotional data in physical stores and providing information for evaluating and improving customer service attitudes; and means including an emotion recognition engine that analyzes customer facial expressions and voice to collect emotional data. This makes it possible to perform multifaceted evaluations, including the emotional state of the customer, and to improve customer satisfaction in physical stores.

[0381] A "user terminal" refers to an electronic device used by a user to input information. Examples include smartphones, tablets, and computers.

[0382] "Basic information" refers to information that identifies a user and forms the basis of their identity, such as their name, job title, and years of service.

[0383] "Evaluation information" refers to specific information used to evaluate a user, such as their performance, skills, communication abilities, supportive attitude, and creativity.

[0384] A "server" refers to a computer system that receives information sent from user terminals, stores it in a database, and performs analysis on it.

[0385] A "database" refers to a system that organizes and stores basic user information and evaluation data as a collection of structured data.

[0386] "Overall rating" refers to a comprehensive performance evaluation of a user, calculated based on their basic information and evaluation data.

[0387] A "management candidate" refers to a user selected by the server and evaluated as suitable for a management position.

[0388] "Feedback" refers to information used to convey user evaluations and areas for improvement.

[0389] An "emotion recognition engine" refers to technology that analyzes the facial expressions and voice of users and customers to understand their emotional state.

[0390] A "physical store" refers to a retail store that exists in a physical location and can be visited by customers.

[0391] "Emotional data" refers to information about emotions obtained as a result of analyzing a customer's facial expressions and voice.

[0392] "Customer service attitude" refers to the quality of service and the way in which employees interact with customers in a store.

[0393] To implement this invention, the following system configuration is adopted. The system includes a user terminal, a server, a database, and an emotion recognition engine.

[0394] 1. Enter user information

[0395] The user terminal provides the user with a form for entering basic and evaluation information. The form includes basic user information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. As the user fills out the form, an emotion recognition engine analyzes the user's facial expressions and voice, collecting emotional data. The user enters this information and sends the data to the server by pressing the submit button.

[0396] 2. Collection of evaluation data

[0397] The server receives basic information, evaluation information, and sentiment data sent from the user's terminal. The received data is temporarily stored in temporary memory before being saved to the database. The database is designed to organize and store data for each user.

[0398] 3. Analysis of evaluation data

[0399] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. It calculates individual scores based on traditional evaluation metrics (such as performance achievement and skill assessment) and new evaluation metrics (such as communication skills, supportiveness, and creativity). Furthermore, it analyzes sentiment data to assess the user's emotional response in specific evaluation scenarios. This includes changes in the user's emotions while filling out forms and their responses to specific questions. Each evaluation metric is correlated with sentiment data and scored to achieve a more multifaceted scoring system.

[0400] 4. Selection of management candidates

[0401] The server calculates an overall evaluation score based on each calculated evaluation score. This overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates. Users whose overall evaluation score exceeds a pre-set standard are listed and selected as management candidates.

[0402] 5. Providing feedback

[0403] The server generates detailed feedback documents for selected management candidates. These documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data. The generated feedback documents are sent to the user's terminal, allowing the user to review the feedback content.

[0404] Adding specific examples

[0405] For example, in a physical store, a sales associate wearing smart glasses analyzes the customer's facial expressions and voice using an emotion recognition engine. If a customer appears confused in the fitting room, the system detects that emotion and notifies the sales associate that "the customer is likely in distress." Based on this, the sales associate can provide support quickly.

[0406] Example of a prompt

[0407] As a specific example, when a customer is looking for items in a fitting room, if this system analyzes the customer's facial expression and determines that the customer is confused, please provide specific examples of what kind of notification is sent to the store staff and how they should respond.

[0408] The software used includes OpenCV for image processing, Dlib for facial landmark detection, TensorFlow for emotion recognition models, and Flask for data communication. This enables multifaceted evaluation, including customer emotional states, and can improve customer satisfaction in physical stores.

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1: Enter user information

[0411] The user enters basic information (name, position, years of service) and evaluation information (performance, skills, communication ability, supportive spirit, creativity) into a form provided by the terminal. During this process, an emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. The entered information and emotional data are stored in temporary memory. Basic information, evaluation information, and emotional data are obtained as input data.

[0412] Step 2: Send

[0413] When the user presses the submit button, the device sends the entered basic information, evaluation information, and collected sentiment data to the server. User information (basic information, evaluation information, sentiment data) is the input, and the server receives the data as output.

[0414] Step 3: Collection and storage of evaluation data

[0415] The server receives basic information, evaluation information, and sentiment data transmitted from the terminal. The received data is temporarily stored in temporary memory and then organized and stored in the database. The server's input is user information, and its output is storage in the database.

[0416] Step 4: Analysis of evaluation data

[0417] The server extracts basic information, evaluation information, and sentiment data stored in the database to perform a multifaceted evaluation. In addition to conventional evaluation metrics (performance achievement, skill evaluation), it calculates individual scores using new evaluation metrics (communication skills, supportive spirit, creativity) and sentiment data. The sentiment recognition engine provides the analysis results and generates the sentiment portion score in real time based on them. The input is user information from the database, and the output is the individual score for each evaluation metric and the overall evaluation score.

[0418] Step 5: Selection of management candidates

[0419] The server derives an overall evaluation score based on the calculated scores for each indicator. The overall evaluation score is calculated by assigning appropriate weights to each score. As a result, users whose overall evaluation score exceeds a pre-set standard are listed as management candidates. The input is the individual evaluation scores, and the output is a list of management candidates.

[0420] Step 6: Generate and send feedback

[0421] The server generates detailed feedback documents for selected management candidates. These documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data. The generated feedback documents are sent to the user's terminal for review. The input is the management candidate's score data, and the output is the feedback document.

[0422] Step 7: In-store evaluation and notification

[0423] In physical stores, a terminal worn by a sales associate wearing smart glasses analyzes the customer's facial expressions and voice in real time. An emotion recognition engine provides the analysis results, and when a specific emotional state is detected, the system sends a notification to the sales associate. For example, if it determines that "the customer is in distress," the sales associate will receive a notification stating "the customer needs assistance." The input is the customer's emotional data, and the output is a notification to the sales associate.

[0424] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0425] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0427] [Second Embodiment]

[0428] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0429] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0430] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0431] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0432] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0433] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0434] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0435] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0436] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0437] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0438] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0439] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0440] The present invention is a system for users to input their own evaluations and evaluations from others, and a server collects and analyzes this evaluation data to select candidates for management positions. An embodiment of this system will be described below.

[0441] 1. Enter user information

[0442] The terminal provides users with a survey form for entering basic and evaluation information. This form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. Users enter their information and send the data to the server by pressing the submit button.

[0443] 2. Collection of evaluation data

[0444] The server receives basic user information and evaluation information sent from the terminal. The received data is stored in a database. This database is designed to organize and store user-specific data.

[0445] 3. Analysis of evaluation data

[0446] The server analyzes the user's basic and evaluation information stored in the database. First, it calculates scores based on traditional evaluation metrics (performance achievement, skill evaluation, etc.). Then, it calculates scores for new evaluation metrics (communication skills, supportive attitude, creativity, etc.).

[0447] 4. Selection of management candidates

[0448] The server performs an overall evaluation based on the calculated scores and selects candidates for management positions. To be selected as a management candidate, users must meet certain criteria in both the traditional and new evaluation metrics. The server lists users with high overall evaluation scores and generates a list of management candidates.

[0449] 5. Providing feedback

[0450] The server generates a document providing detailed feedback to users selected as management candidates, along with their evaluation results. This feedback document includes detailed evaluation results (scores for each evaluation item, overall score, etc.), strengths and weaknesses, and areas for future improvement. The generated feedback document is sent to the terminal and notified to the user.

[0451] Specific example

[0452] For example, if a user (employee A) fills out an evaluation form, they would follow these steps:

[0453] 1. Enter basic information

[0454] Employee A enters their name, job title, and years of service into the form displayed on the terminal.

[0455] 2. Entering evaluation information

[0456] Employee A inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[0457] 3. Sending data

[0458] After completing the input, employee A presses the submit button, and the terminal sends it to the server.

[0459] 4. Data Processing

[0460] The server receives employee A's data and saves it to the database.

[0461] The server calculates an overall evaluation score based on the stored data.

[0462] 5. Selection of management candidates

[0463] The server determines whether employee A is suitable as a management candidate based on their overall evaluation score.

[0464] 6. Providing feedback

[0465] The server generates a detailed feedback document for employee A and sends it to their terminal.

[0466] The device displays a feedback notification to employee A.

[0467] Thus, the present invention enables comprehensive evaluation that incorporates new indicators in addition to conventional evaluation criteria, allowing for the appropriate evaluation of diverse personnel and maximizing the overall performance of the organization.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The terminal displays a survey form for the user to input basic and evaluation information. The user enters basic information such as name, position, and years of service, as well as information related to evaluation items such as performance achievement, skill evaluation, communication skills, supportive spirit, and creativity into the form.

[0471] Step 2:

[0472] The user completes the survey form and presses the "Submit" button. The device sends the entered basic information and evaluation information to the server.

[0473] Step 3:

[0474] The server receives basic and evaluation information sent from the terminal. The received data is temporarily stored in the server's internal temporary memory.

[0475] Step 4:

[0476] The server stores the received basic and evaluation information in a database. During storage, the data is organized by user and associated using a unique user ID.

[0477] Step 5:

[0478] The server extracts basic and evaluation information stored in the database. The extracted data is then loaded back into temporary memory for the analysis process.

[0479] Step 6:

[0480] The server calculates evaluation scores based on conventional evaluation metrics. This is a process of individually evaluating and scoring items such as performance achievement and skill evaluation.

[0481] Step 7:

[0482] The server calculates an evaluation score based on new evaluation metrics (such as communication skills, supportiveness, and creativity). These are also scored separately.

[0483] Step 8:

[0484] The server calculates an overall evaluation score based on both traditional and new evaluation metrics. This includes a process of weighting each evaluation metric.

[0485] Step 9:

[0486] The server selects management candidates based on their overall performance score. Users whose overall performance score exceeds a pre-set standard are listed and designated as management candidates.

[0487] Step 10:

[0488] The server generates a detailed feedback document for each selected management candidate. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, and areas for improvement.

[0489] Step 11:

[0490] The server sends the generated feedback document to the terminal. The terminal displays a feedback notification to the user (management candidate). The user can then review the feedback content through the terminal.

[0491] (Example 1)

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

[0493] Traditional evaluation systems failed to adequately assess users as a whole, making it difficult to appropriately select candidates for management positions. Furthermore, the feedback was often insufficient, preventing users from accurately understanding their strengths and areas for improvement. In addition, evaluation bias existed, and the fairness of evaluations was not guaranteed.

[0494] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0495] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information from the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to use a generative AI model to generate feedback documents for users selected as management candidates; and means for the server to transmit the feedback documents generated by the generative AI model to the user terminal. This enables a multifaceted evaluation of the user, allowing for more appropriate selection of management candidates and the provision of detailed feedback.

[0496] A "user terminal" is a device used by a user to input information, and includes PCs, smartphones, and tablets.

[0497] A "form" refers to a screen or input fields in the form of a survey where users enter basic information and evaluation information.

[0498] "Basic information" refers to the user's basic personal information, such as name, job title, and years of service.

[0499] "Evaluation information" refers to information regarding evaluation items such as user performance, skills, communication ability, supportive attitude, and creativity.

[0500] A "server" refers to a central system that receives, stores, analyzes, and generates feedback on data.

[0501] A "database" refers to a system for organizing and storing basic user information and evaluation information.

[0502] "Overall rating" refers to the user's overall evaluation calculated based on both traditional and new evaluation metrics.

[0503] A "management candidate" refers to a user selected based on an overall evaluation as being suitable for a management position.

[0504] A "generative AI model" refers to an artificial intelligence model that analyzes user evaluation data and generates feedback documents.

[0505] A "feedback document" refers to a document that details the evaluation results, strengths, weaknesses, and areas for improvement of the user.

[0506] "Notifications" refer to messages or alerts that inform the user's device of feedback or other important information.

[0507] This invention relates to a system for users to input their own evaluations and evaluations from others, with a server collecting and analyzing this evaluation data to select management candidates. This system consists of a user terminal, a server, a database, and a generating AI model.

[0508] Specific system configuration

[0509] Hardware and software to be used

[0510] Device: A device used by a user to input information (such as a PC, smartphone, or tablet).

[0511] Server: A central system that receives, stores, analyzes, and generates feedback from data.

[0512] Database: A system for organizing and storing basic user information and evaluation data (e.g., MySQL, PostgreSQL)

[0513] Generative AI Models: AI tools for analyzing user evaluation data and generating feedback documents (e.g., OpenAI GPT model)

[0514] Specific operation of the system

[0515] 1. Enter user information

[0516] The device displays a survey-style form to the user. This form includes basic information that the user must enter (such as name, job title, and years of service).

[0517] Users enter information such as name, job title, years of service, achievements, skills, communication skills, supportive attitude, and creativity according to a specified format.

[0518] After the user has completed entering all the required information, they press the submit button. This sends the data to the server.

[0519] 2. Collection of evaluation data

[0520] The server receives user information sent from the terminal.

[0521] The server stores the received data in a database. This database is designed to systematically organize and store each user's information.

[0522] 3. Analysis of evaluation data

[0523] The server analyzes user information stored in the database. First, it calculates a score based on traditional evaluation metrics (performance achievement, skill evaluation).

[0524] The server also calculates scores for new evaluation metrics (communication skills, supportiveness, and creativity).

[0525] 4. Selection of management candidates

[0526] The server performs an overall evaluation based on the calculated scores for each evaluation metric.

[0527] The server lists users with high overall evaluation scores and generates a list of management candidates.

[0528] 5. Providing feedback

[0529] The server generates a feedback document containing detailed evaluation results for users selected as management candidates. This process utilizes a generative AI model.

[0530] The server sends the generated feedback document to the terminal and notifies the user.

[0531] This system ensures fairness in evaluations, enables proper assessment of diverse talent, and aims to improve the overall performance of the organization.

[0532] Specific example

[0533] For example, if a user (employee A) fills out an evaluation form, they would follow these steps:

[0534] Example of a prompt

[0535] 1. Enter basic information:

[0536] In the form displayed on the terminal, employee A enters the name "Taro Tanaka", the position "Senior Engineer", and the years of service "5 years".

[0537] 2. Entering evaluation information:

[0538] Employee A inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[0539] 3. Sending data:

[0540] After completing the input, employee A presses the submit button, and the terminal sends it to the server.

[0541] 4. Data processing:

[0542] The server receives employee A's data and saves it to the database.

[0543] The server calculates an overall evaluation score based on the stored data.

[0544] 5. Selection of management candidates:

[0545] The server determines whether employee A is suitable as a management candidate based on their overall evaluation score.

[0546] 6. Providing feedback:

[0547] The server generates a detailed feedback document for employee A and sends it to their terminal.

[0548] The device displays a feedback notification to employee A.

[0549] In this way, by using the system, evaluation bias can be reduced, diverse talent can be evaluated fairly and comprehensively, and the overall performance of the organization can be maximized.

[0550] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0551] Step 1: Enter user information

[0552] The terminal displays a survey form to the user. This form includes basic information that the user must enter (name, position, years of service, etc.) and evaluation information (performance, skills, communication skills, supportive attitude, creativity).

[0553] The user enters the required information and sends the data to the server by pressing the submit button.

[0554] Input: Basic information entered by the user (name, job title, years of service, etc.) and evaluation information (performance, skills, communication skills, supportive spirit, creativity).

[0555] Output: The basic information and evaluation information entered by the user are sent to the server.

[0556] Step 2: Collection of evaluation data

[0557] The server receives user information sent from the terminal.

[0558] The server stores the received data in a database. The database is designed to systematically organize and store each user's information.

[0559] Input: Basic user information and evaluation information sent from the terminal.

[0560] Output: User data is saved to the database.

[0561] Step 3: Analysis of evaluation data

[0562] The server analyzes user information stored in the database. First, it calculates a score based on conventional evaluation metrics (performance achievement, skill evaluation).

[0563] Next, we will calculate scores for the new evaluation metrics (communication skills, supportive spirit, and creativity).

[0564] Input: User's basic information and evaluation information stored in the database.

[0565] Output: Scores for each evaluation metric (traditional metric and new metric) are calculated.

[0566] Step 4: Selection of management candidates

[0567] The server performs an overall evaluation based on the calculated scores for each evaluation metric.

[0568] The server lists users with high overall evaluation scores and generates a list of management candidates.

[0569] Input: Scores for each evaluation metric (traditional metric and new metric).

[0570] Output: List of management candidates.

[0571] Step 5: Provide feedback

[0572] The server uses a generative AI model to generate feedback documents containing detailed evaluation results for users selected as management candidates.

[0573] The server sends the generated feedback document to the terminal and notifies the user.

[0574] Input: List of management candidates and each user's performance metric score.

[0575] Output: Feedback documents and notifications.

[0576] By incorporating specific actions into each step, the overall flow of the system becomes easier to understand, and it becomes clear how and which data is processed at which stage. This allows for more efficient system implementation and operation.

[0577] (Application Example 1)

[0578] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0579] Traditional management candidate selection systems had limited evaluation information, making it difficult to comprehensively assess the practical skills and on-site performance of operators and engineers, especially in factory settings. This prevented evaluations that reflected the realities of the workplace, resulting in the inability to appropriately select management candidates. Furthermore, insufficient feedback made it difficult for candidates to obtain concrete guidance for self-improvement. Additionally, the traditional system required users to input evaluation information cumbersomely, hindering efficient operation.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0581] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information to the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to generate feedback for the management candidates and transmit it to the user terminal; means for the user terminal to function as a tool for factory operators and engineers to input self-evaluations and peer evaluations; means for the user terminal to transmit self-evaluation and peer evaluation data to the server; and means for the server to generate a detailed feedback document based on the transmitted evaluation data and transmit a notification to the user terminal. This enables detailed evaluation and feedback based on the actual situation on site, and allows for the efficient selection of management candidates.

[0582] A "user terminal" is a device that a user uses to input and transmit information.

[0583] A "form" is an interface or screen used by users to input basic information and evaluation information.

[0584] "Basic information" refers to fundamental data such as the user's name, job title, and years of service.

[0585] "Evaluation information" refers to data related to user performance, such as achievement level, skill assessment, communication skills, supportive attitude, and creativity.

[0586] A "server" is a computing system that receives information sent from user terminals, stores it in a database, and performs analysis on it.

[0587] A "database" is a system used by a server to organize and store basic and evaluation information it receives.

[0588] "Overall rating" refers to the user's overall score calculated based on basic information and evaluation information.

[0589] A "management candidate" is an individual selected based on an overall evaluation to determine their suitability for a management position.

[0590] "Feedback" refers to a document containing detailed evaluation results and suggestions for improvement, generated based on the overall evaluation.

[0591] A "factory operator" is an employee who operates machinery and equipment on the factory floor.

[0592] An "engineer" is a professional employee who is responsible for technical tasks.

[0593] "Self-evaluation" is the act of a user evaluating their own performance.

[0594] "Colleague evaluation" is the act of a user evaluating the performance of other employees.

[0595] A "tool" is software or an application used for a specific purpose.

[0596] A "notification" is a message sent to inform a user of specific information.

[0597] This invention is a system for users to input self-assessments and peer assessments, and a server collects and analyzes this assessment data to select candidates for management positions.

[0598] 1. Enter user information

[0599] The user's device (e.g., a smartphone or tablet) provides a form for the user to input basic and evaluation information. This form allows the user to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. The user enters their information and sends the data to the server by pressing the submit button.

[0600] 2. Collection of evaluation data

[0601] The server receives basic user information and evaluation information sent from the user's terminal. The received data is stored in a database. This database is designed to organize and store user-specific data.

[0602] 3. Analysis of evaluation data

[0603] The server analyzes the user's basic and evaluation information stored in the database. First, it calculates scores based on traditional evaluation metrics (performance achievement, skill evaluation, etc.). Then, it calculates scores for new evaluation metrics (communication skills, supportive attitude, creativity, etc.). It also conducts a comprehensive evaluation that includes evaluation items specific to factory operators and engineers.

[0604] 4. Selection of management candidates

[0605] The server performs an overall evaluation based on the calculated scores and selects candidates for management positions. To be selected as a management candidate, individuals must meet certain criteria in both the traditional and new evaluation metrics, as well as evaluation items specific to factory operators and engineers. The server lists users with high overall evaluation scores and generates a list of management candidates.

[0606] 5. Providing feedback

[0607] The server generates a document providing detailed feedback to users selected as management candidates, along with their evaluation results. This feedback document includes detailed evaluation results (scores for each evaluation item, overall score, etc.), strengths and weaknesses, and areas for future improvement. The generated feedback document is sent to the user's terminal, and the user is notified.

[0608] Hardware and software to be used

[0609] The following hardware and software will be used to implement this system.

[0610] Hardware: Smartphones, tablets, and servers

[0611] Software: Django framework, database management system (e.g., PostgreSQL), generative AI model

[0612] Specific example

[0613] For example, when a factory operator (user) fills out an evaluation form, they would follow these steps:

[0614] 1. Enter basic information

[0615] The user (factory operator) opens the app on their smartphone and enters their name, job title, and years of service.

[0616] 2. Entering evaluation information

[0617] You will input your self-assessment based on the evaluation items provided by the app (performance achievement, skill evaluation, communication skills, supportive spirit, creativity).

[0618] You will also enter your evaluation of your colleagues.

[0619] 3. Sending data

[0620] After completing the input, the user presses the submit button, and the app sends it to the server.

[0621] 4. Data Processing

[0622] The server receives user data and stores it in the database.

[0623] The server calculates an overall evaluation score based on the stored data.

[0624] 5. Selection of management candidates

[0625] The server determines whether a user is suitable as a management candidate based on their overall evaluation score.

[0626] 6. Providing feedback

[0627] The server generates a detailed feedback document for the user and sends it to the user's terminal.

[0628] The user's device displays a feedback notification to the user.

[0629] This invention enables evaluation and feedback based on actual on-site conditions, allowing for the efficient selection of management candidates. Furthermore, it provides users with specific guidance for self-improvement through concrete feedback.

[0630] Example of a prompt

[0631] Please provide the following information regarding the management candidate selection system:

[0632] 1. Employee basic information (name, position, years of service)

[0633] 2. Self-evaluation (performance achievement, skill assessment, communication skills, supportive spirit, creativity)

[0634] 3. Peer evaluation (performance achievement, skill assessment, communication skills, supportive attitude, creativity)

[0635] Input endpoint: / api / submit_evaluation

[0636] Analysis endpoint: / api / analyze_evaluation

[0637] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0638] Step 1:

[0639] The user terminal provides a form for entering basic information and evaluation information.

[0640] Input: User's basic information (name, job title, years of service, etc.) and evaluation information (performance achievement, skill evaluation, communication skills, supportive attitude, creativity).

[0641] Output: Input form screen.

[0642] Specific operation: The user terminal displays a form for the user to fill out, and the user enters the necessary information there.

[0643] Step 2:

[0644] The user terminal sends basic information and evaluation information entered by the user to the server.

[0645] Input: Basic information and evaluation information entered by the user.

[0646] Output: Data sent to the server.

[0647] Specific operation: When the user completes filling out the form and presses the submit button, basic information and evaluation information are sent to the server.

[0648] Step 3:

[0649] The server stores basic information and evaluation information sent from the user terminal in a database.

[0650] Input: Basic information and evaluation information sent from the user's terminal.

[0651] Output: Data stored in the database.

[0652] Specific operation: The server organizes the received data and saves it to the appropriate field in the database.

[0653] Step 4:

[0654] The server calculates the user's overall rating based on the basic information and evaluation information stored in the database.

[0655] Input: Basic information and evaluation information stored in the database.

[0656] Output: Overall evaluation score.

[0657] Specific operation: The server calculates scores for each based on the traditional and new evaluation metrics, and then calculates an overall evaluation score.

[0658] Step 5:

[0659] The server selects management candidates based on a calculated overall evaluation.

[0660] Input: Overall rating score.

[0661] Output: List of management candidates.

[0662] Specific operation: The server sets criteria based on the overall evaluation score and lists users with high scores as candidates for management positions.

[0663] Step 6:

[0664] The server generates feedback for the management candidate mentioned above and sends it to the user's terminal.

[0665] Input: List of management candidates and evaluation data for each user.

[0666] Output: Feedback document and notification.

[0667] Specific operation: The server generates a detailed feedback document based on the evaluation results and sends a notification to the user's terminal.

[0668] Step 7:

[0669] The user's device displays a feedback notification to the user.

[0670] Input: Feedback notification sent from the server.

[0671] Output: Feedback notification screen.

[0672] Specific action: The user terminal notifies the user that feedback has arrived and displays the document.

[0673] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0674] This invention is an evaluation system that combines an emotion engine to recognize user emotions. By collecting and analyzing emotional data in addition to basic user information and evaluation information, the system provides a more multifaceted evaluation. Embodiments of this system will be described in detail below.

[0675] 1. Enter user information

[0676] The terminal provides users with a survey form for entering basic and evaluation information. This form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. Furthermore, while filling out the form and responding to specific questions, an emotion engine analyzes the user's facial expressions and voice, collecting emotional data. Users submit this information to the server by pressing the submit button.

[0677] 2. Collection of evaluation data

[0678] The server receives basic user information, evaluation information, and sentiment data sent from the terminal. The received data is temporarily stored in the server's internal temporary memory and then saved to a database. This database is designed to organize and store data for each user.

[0679] 3. Analysis of evaluation data

[0680] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. First, it calculates individual scores based on traditional evaluation metrics (performance achievement, skill assessment, etc.) and new evaluation metrics (communication skills, supportiveness, creativity, etc.).

[0681] Next, emotional data is analyzed to evaluate the user's emotional response in specific evaluation scenarios. This includes changes in the user's emotions while filling out a form and their responses to specific questions. This emotional data is scored in relation to each evaluation metric, contributing to a more multifaceted scoring.

[0682] 4. Selection of management candidates

[0683] The server calculates an overall evaluation score based on each calculated evaluation score (traditional indicator, new indicator, and sentiment data score). The overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates.

[0684] Users whose overall evaluation score exceeds a pre-set standard are listed and designated as candidates for management positions. By including emotional data, it is possible to take into account users' emotional suitability, which is often overlooked in conventional evaluation methods.

[0685] 5. Providing feedback

[0686] The server generates a detailed feedback document for each selected management candidate. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, as well as feedback based on emotional data (for example, emotional responses to specific questions and changes in those emotions).

[0687] The generated feedback document is sent to the device and notified to the user. The user can review the feedback content through the device and use it for future reference.

[0688] Specific example

[0689] For example, if a user (employee B) fills out a survey form, they would follow these steps:

[0690] 1. Enter basic information

[0691] Employee B enters their name, job title, and years of service into the form displayed on the terminal.

[0692] 2. Entering evaluation information

[0693] Employee B inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[0694] 3. Collection of emotional data

[0695] While employee B is filling out the form, the emotion engine analyzes their facial expressions and voice to collect emotional data.

[0696] 4. Sending data

[0697] After the input and sentiment analysis are complete, employee B presses the send button, and the terminal sends it to the server.

[0698] 5. Data Processing

[0699] The server receives employee B's data and saves it to the database.

[0700] The server calculates an overall evaluation score based on the stored data.

[0701] 6. Selection of management candidates

[0702] The server determines whether employee B is suitable as a management candidate based on the overall evaluation score.

[0703] 7. Providing feedback

[0704] The server generates a detailed feedback document for employee B and sends it to their terminal.

[0705] The device displays a feedback notification to employee B.

[0706] Thus, the present invention enables comprehensive evaluation that incorporates emotional data in addition to conventional evaluation criteria, allowing for the appropriate evaluation of diverse personnel and maximizing the overall performance of the organization.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The terminal displays a survey form for the user to input basic and evaluation information. The user enters basic information such as name, position, and years of service, as well as information related to evaluation items such as performance achievement, skill evaluation, communication skills, supportiveness, and creativity into the form.

[0710] Step 2:

[0711] The emotion engine is activated and analyzes the user's facial expressions and voice while they fill out the survey form. This analysis records their reactions to the input and their emotional changes in response to specific questions.

[0712] Step 3:

[0713] The user completes the survey form and presses the "Submit" button. The device sends the entered basic information, evaluation information, and sentiment data acquired by the sentiment engine to the server.

[0714] Step 4:

[0715] The server receives basic information, evaluation information, and sentiment data sent from the terminal. The received data is temporarily stored in the server's internal temporary memory and then saved to the database.

[0716] Step 5:

[0717] The server extracts basic information, evaluation information, and sentiment data stored in the database. The extracted data is then loaded back into temporary memory for analysis.

[0718] Step 6:

[0719] The server calculates evaluation scores based on traditional evaluation metrics (e.g., performance achievement and skill assessment). This includes individual scores for each evaluation item.

[0720] Step 7:

[0721] The server calculates evaluation scores based on new evaluation metrics (e.g., communication skills, supportiveness, creativity). The server also calculates these scores individually.

[0722] Step 8:

[0723] The server analyzes emotional data obtained from the emotion engine and calculates an evaluation score based on that data. This includes changes in emotions and emotional responses to specific questions.

[0724] Step 9:

[0725] The server integrates traditional evaluation metrics, new evaluation metrics, and evaluation scores based on sentiment data to calculate the user's overall rating. The integrated overall rating score is a combination of weighted scores.

[0726] Step 10:

[0727] The server selects management candidates based on their overall evaluation score. Users whose overall evaluation score exceeds the set criteria are listed as management candidates.

[0728] Step 11:

[0729] The server generates detailed feedback documents for selected management candidates. These feedback documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data.

[0730] Step 12:

[0731] The server sends the generated feedback document to the terminal. The terminal displays a feedback notification to the user, allowing the user to review the feedback content.

[0732] (Example 2)

[0733] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0734] Traditional evaluation systems based solely on basic and performance data, failing to consider emotional aspects. Consequently, users' emotional aptitudes and responses in specific scenarios were rarely reflected in evaluations. This resulted in a lack of evaluation of emotional aptitude, communication skills, and supportiveness, particularly in the selection of management candidates, hindering a more multifaceted assessment.

[0735] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0736] In this invention, the server includes means for storing basic information, evaluation information, and emotional data transmitted from the user terminal in a database, means for analyzing emotional data from the user's facial expressions and voice using an emotion engine, and means for analyzing emotional data in association with each evaluation index. This makes it possible to perform a comprehensive evaluation based on emotional data in addition to basic information and conventional evaluation indexes.

[0737] A "user terminal" is an electronic device used by a user to input basic information and evaluation information, and includes devices such as personal computers, tablets, and smartphones.

[0738] A "server" is a central processing unit that has the function of receiving, storing, and analyzing data sent from user terminals, and generating and sending feedback.

[0739] "Basic information" refers to personal attributes and background information such as the user's name, job title, and years of service.

[0740] "Evaluation information" refers to data related to the user's job performance capabilities and characteristics, such as the user's achievement level, skill evaluation, communication skills, supportive attitude, and creativity.

[0741] "Emotional data" refers to data about a user's emotional state, obtained by analyzing their facial expressions and voice.

[0742] An "emotion engine" refers to a combination of software and hardware used to analyze a user's facial expressions and voice, and is a technology that recognizes the user's emotional state in real time.

[0743] A "database" is an information storage system used by a server to systematically store, manage, and retrieve basic information, evaluation information, and sentiment data received by the server.

[0744] The "Overall Rating Score" is a numerical value that represents the overall evaluation result of a user, calculated by the server based on basic information, evaluation information, and sentiment data.

[0745] A "management candidate" refers to a user who has been judged to have high suitability based on their overall evaluation score and who should be selected for a management position.

[0746] "Feedback" refers to documents and notifications that contain information such as evaluation results and areas for improvement, generated based on the overall evaluation score and sentiment data calculated by the server.

[0747] This invention is an evaluation system that combines an emotion engine that recognizes user emotions. By collecting and analyzing emotion data in addition to basic user information and evaluation information, the system provides a more multifaceted evaluation. This system is mainly implemented using a user terminal, a server, and an emotion engine.

[0748] First, the user terminal provides the user with a survey form for entering basic and evaluation information. This form is built using HTML, CSS, and JavaScript, and users access it through a browser. The form allows users to enter basic information (name, position, years of service, etc.) as well as evaluation items such as performance achievement, skill evaluation, communication skills, supportiveness, and creativity.

[0749] While filling out the survey form and answering specific questions, the emotion engine analyzes the user's facial expressions and voice, collecting emotional data. The emotion engine utilizes software such as TensorFlow and OpenCV to recognize the user's emotional state in real time. This data is later used in evaluations.

[0750] Once the user completes the survey and presses the submit button, the device converts the entered basic information, evaluation information, and collected sentiment data into JSON format and sends it to the server using HTTPS. The server receives this data and first stores it in temporary memory. Then, it migrates the data to a database (MySQL or PostgreSQL) and stores it organized by user.

[0751] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. Individual scores are calculated based on traditional evaluation metrics (e.g., performance achievement, skill evaluation) and new evaluation metrics (e.g., communication skills, supportiveness, creativity). Sentiment data is also analyzed to evaluate the user's emotional response in specific evaluation scenarios. This calculation is performed using a Python script. The sentiment data thus obtained is associated with each evaluation metric and used for a more refined overall evaluation.

[0752] Next, the server calculates an overall evaluation score based on the calculated evaluation scores (traditional indicators, new indicators, and sentiment data scores). This overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates. Users whose overall evaluation score exceeds the pre-set criteria are listed and become management candidates.

[0753] Furthermore, the server generates a detailed feedback document for the selected management candidates. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, and feedback based on sentiment data. The generated feedback document is sent to the terminal and notified to the user. The user can review the feedback content through the terminal and use it for future reference.

[0754] A specific example of a prompt would be: "Please describe a comprehensive evaluation system that uses user sentiment data. Please include specific evaluation criteria, methods for collecting sentiment data, and evaluation methods."

[0755] In this way, the present invention makes it possible to appropriately evaluate diverse personnel and maximize the overall performance of the organization by conducting a comprehensive evaluation that incorporates emotional data in addition to conventional evaluation criteria.

[0756] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0757] Step 1: Entering user information and collecting sentiment data

[0758] Input: Users enter basic and evaluation information into the survey form using their device. Specifically, they enter their name, job title, years of service, and evaluation items (performance achievement, skill evaluation, communication skills, supportive spirit, creativity, etc.).

[0759] Operation: The device provides a survey form built with HTML, CSS, and JavaScript. When the user begins to input, the device's camera and microphone are activated, and an emotion engine (TensorFlow, OpenCV) analyzes the user's facial expressions and voice in real time to collect emotion data.

[0760] Output: The basic information and evaluation information entered by the user, along with the collected sentiment data, are saved on the device in JSON format.

[0761] Step 2: Send

[0762] Input: Basic information, evaluation information, and sentiment data (in JSON format) collected in Step 1.

[0763] Operation: Once the user completes the survey form and presses the submit button, the device sends this data to the server using HTTPS.

[0764] Output: The server receives basic information, rating information, and sentiment data in JSON format.

[0765] Step 3: Collection and storage of evaluation data

[0766] Input: Basic information, evaluation information, and sentiment data (in JSON format) sent to the server.

[0767] Operation: The server temporarily stores data in memory, then migrates it to a database (MySQL, PostgreSQL) and organizes and stores it for each user.

[0768] Output: Basic information, evaluation information, and sentiment data stored in the database.

[0769] Step 4: Analysis of evaluation data

[0770] Input: Basic information, evaluation information, and sentiment data extracted from the database.

[0771] Operation: The server extracts this data and calculates individual scores based on traditional evaluation metrics (e.g., performance, skill assessment) and new evaluation metrics (e.g., communication skills, supportiveness, creativity). It also analyzes emotional data and evaluates the user's emotional response in specific evaluation scenarios. This analysis process uses a Python script.

[0772] Output: Traditional evaluation metrics, new evaluation metrics, and individual scores based on sentiment data.

[0773] Step 5: Calculation of overall performance score and selection of management candidates

[0774] Input: Individual evaluation scores (traditional metrics, new metrics, sentiment data scores).

[0775] Operation: The server integrates and weights each evaluation score to calculate an overall evaluation score. Next, it lists users whose overall evaluation score exceeds a pre-set standard and selects them as management candidates.

[0776] Output: List of management candidates.

[0777] Step 6: Generating and providing feedback

[0778] Input: A list of management candidates and detailed scores for each evaluation item.

[0779] Operation: The server generates a detailed feedback document for management candidates. This document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, and feedback based on sentiment data. The generated feedback document is sent to the terminal and notified to the user.

[0780] Output: A feedback document is sent to the user's terminal, and a notification is displayed to the user.

[0781] (Application Example 2)

[0782] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0783] Traditional evaluation systems were limited to evaluations based on basic user information and evaluation data, and did not take into account the user's emotional state. This made it difficult to appropriately evaluate and respond quickly to customer satisfaction and emotional reactions, especially in physical stores. The lack of real-time information to evaluate and improve store service attitudes and atmosphere limited the ability to optimize the customer experience.

[0784] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0785] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information from the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to generate feedback for the management candidates and transmit it to the user terminal; means for collecting customer emotional data in physical stores and providing information for evaluating and improving customer service attitudes; and means including an emotion recognition engine that analyzes customer facial expressions and voice to collect emotional data. This makes it possible to perform multifaceted evaluations, including the emotional state of the customer, and to improve customer satisfaction in physical stores.

[0786] A "user terminal" refers to an electronic device used by a user to input information. Examples include smartphones, tablets, and computers.

[0787] "Basic information" refers to information that identifies a user and forms the basis of their identity, such as their name, job title, and years of service.

[0788] "Evaluation information" refers to specific information used to evaluate a user, such as their performance, skills, communication abilities, supportive attitude, and creativity.

[0789] A "server" refers to a computer system that receives information sent from user terminals, stores it in a database, and performs analysis on it.

[0790] A "database" refers to a system that organizes and stores basic user information and evaluation data as a collection of structured data.

[0791] "Overall rating" refers to a comprehensive performance evaluation of a user, calculated based on their basic information and evaluation data.

[0792] A "management candidate" refers to a user selected by the server and evaluated as suitable for a management position.

[0793] "Feedback" refers to information used to convey user evaluations and areas for improvement.

[0794] An "emotion recognition engine" refers to technology that analyzes the facial expressions and voice of users and customers to understand their emotional state.

[0795] A "physical store" refers to a retail store that exists in a physical location and can be visited by customers.

[0796] "Emotional data" refers to information about emotions obtained as a result of analyzing a customer's facial expressions and voice.

[0797] "Customer service attitude" refers to the quality of service and the way in which employees interact with customers in a store.

[0798] To implement this invention, the following system configuration is adopted. The system includes a user terminal, a server, a database, and an emotion recognition engine.

[0799] 1. Enter user information

[0800] The user terminal provides the user with a form for entering basic and evaluation information. The form includes basic user information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. As the user fills out the form, an emotion recognition engine analyzes the user's facial expressions and voice, collecting emotional data. The user enters this information and sends the data to the server by pressing the submit button.

[0801] 2. Collection of evaluation data

[0802] The server receives basic information, evaluation information, and sentiment data sent from the user's terminal. The received data is temporarily stored in temporary memory before being saved to the database. The database is designed to organize and store data for each user.

[0803] 3. Analysis of evaluation data

[0804] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. It calculates individual scores based on traditional evaluation metrics (such as performance achievement and skill assessment) and new evaluation metrics (such as communication skills, supportiveness, and creativity). Furthermore, it analyzes sentiment data to assess the user's emotional response in specific evaluation scenarios. This includes changes in the user's emotions while filling out forms and their responses to specific questions. Each evaluation metric is correlated with sentiment data and scored to achieve a more multifaceted scoring system.

[0805] 4. Selection of management candidates

[0806] The server calculates an overall evaluation score based on each calculated evaluation score. This overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates. Users whose overall evaluation score exceeds a pre-set standard are listed and selected as management candidates.

[0807] 5. Providing feedback

[0808] The server generates detailed feedback documents for selected management candidates. These documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data. The generated feedback documents are sent to the user's terminal, allowing the user to review the feedback content.

[0809] Adding specific examples

[0810] For example, in a physical store, a sales associate wearing smart glasses analyzes the customer's facial expressions and voice using an emotion recognition engine. If a customer appears confused in the fitting room, the system detects that emotion and notifies the sales associate that "the customer is likely in distress." Based on this, the sales associate can provide support quickly.

[0811] Example of a prompt

[0812] As a specific example, when a customer is looking for items in a fitting room, if this system analyzes the customer's facial expression and determines that the customer is confused, please provide specific examples of what kind of notification is sent to the store staff and how they should respond.

[0813] The software used includes OpenCV for image processing, Dlib for facial landmark detection, TensorFlow for emotion recognition models, and Flask for data communication. This enables multifaceted evaluation, including customer emotional states, and can improve customer satisfaction in physical stores.

[0814] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0815] Step 1: Enter user information

[0816] The user enters basic information (name, position, years of service) and evaluation information (performance, skills, communication ability, supportive spirit, creativity) into a form provided by the terminal. During this process, an emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. The entered information and emotional data are stored in temporary memory. Basic information, evaluation information, and emotional data are obtained as input data.

[0817] Step 2: Send

[0818] When the user presses the submit button, the device sends the entered basic information, evaluation information, and collected sentiment data to the server. User information (basic information, evaluation information, sentiment data) is the input, and the server receives the data as output.

[0819] Step 3: Collection and storage of evaluation data

[0820] The server receives basic information, evaluation information, and sentiment data transmitted from the terminal. The received data is temporarily stored in temporary memory and then organized and stored in the database. The server's input is user information, and its output is storage in the database.

[0821] Step 4: Analysis of evaluation data

[0822] The server extracts basic information, evaluation information, and sentiment data stored in the database to perform a multifaceted evaluation. In addition to conventional evaluation metrics (performance achievement, skill evaluation), it calculates individual scores using new evaluation metrics (communication skills, supportive spirit, creativity) and sentiment data. The sentiment recognition engine provides the analysis results and generates the sentiment portion score in real time based on them. The input is user information from the database, and the output is the individual score for each evaluation metric and the overall evaluation score.

[0823] Step 5: Selection of management candidates

[0824] The server derives an overall evaluation score based on the calculated scores for each indicator. The overall evaluation score is calculated by assigning appropriate weights to each score. As a result, users whose overall evaluation score exceeds a pre-set standard are listed as management candidates. The input is the individual evaluation scores, and the output is a list of management candidates.

[0825] Step 6: Generate and send feedback

[0826] The server generates detailed feedback documents for selected management candidates. These documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data. The generated feedback documents are sent to the user's terminal for review. The input is the management candidate's score data, and the output is the feedback document.

[0827] Step 7: In-store evaluation and notification

[0828] In physical stores, a terminal worn by a sales associate wearing smart glasses analyzes the customer's facial expressions and voice in real time. An emotion recognition engine provides the analysis results, and when a specific emotional state is detected, the system sends a notification to the sales associate. For example, if it determines that "the customer is in distress," the sales associate will receive a notification stating "the customer needs assistance." The input is the customer's emotional data, and the output is a notification to the sales associate.

[0829] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0830] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0831] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0832] [Third Embodiment]

[0833] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0834] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0835] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0836] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0837] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0838] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0839] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0840] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0841] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0842] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0843] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0844] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0845] The present invention is a system for users to input their own evaluations and evaluations from others, and a server collects and analyzes this evaluation data to select candidates for management positions. An embodiment of this system will be described below.

[0846] 1. Enter user information

[0847] The terminal provides users with a survey form for entering basic and evaluation information. This form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. Users enter their information and send the data to the server by pressing the submit button.

[0848] 2. Collection of evaluation data

[0849] The server receives basic user information and evaluation information sent from the terminal. The received data is stored in a database. This database is designed to organize and store user-specific data.

[0850] 3. Analysis of evaluation data

[0851] The server analyzes the user's basic and evaluation information stored in the database. First, it calculates scores based on traditional evaluation metrics (performance achievement, skill evaluation, etc.). Then, it calculates scores for new evaluation metrics (communication skills, supportive attitude, creativity, etc.).

[0852] 4. Selection of management candidates

[0853] The server performs an overall evaluation based on the calculated scores and selects candidates for management positions. To be selected as a management candidate, users must meet certain criteria in both the traditional and new evaluation metrics. The server lists users with high overall evaluation scores and generates a list of management candidates.

[0854] 5. Providing feedback

[0855] The server generates a document providing detailed feedback to users selected as management candidates, along with their evaluation results. This feedback document includes detailed evaluation results (scores for each evaluation item, overall score, etc.), strengths and weaknesses, and areas for future improvement. The generated feedback document is sent to the terminal and notified to the user.

[0856] Specific example

[0857] For example, if a user (employee A) fills out an evaluation form, they would follow these steps:

[0858] 1. Enter basic information

[0859] Employee A enters their name, job title, and years of service into the form displayed on the terminal.

[0860] 2. Entering evaluation information

[0861] Employee A inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[0862] 3. Sending data

[0863] After completing the input, employee A presses the submit button, and the terminal sends it to the server.

[0864] 4. Data Processing

[0865] The server receives employee A's data and saves it to the database.

[0866] The server calculates an overall evaluation score based on the stored data.

[0867] 5. Selection of management candidates

[0868] The server determines whether employee A is suitable as a management candidate based on their overall evaluation score.

[0869] 6. Providing feedback

[0870] The server generates a detailed feedback document for employee A and sends it to their terminal.

[0871] The device displays a feedback notification to employee A.

[0872] Thus, the present invention enables comprehensive evaluation that incorporates new indicators in addition to conventional evaluation criteria, allowing for the appropriate evaluation of diverse personnel and maximizing the overall performance of the organization.

[0873] The following describes the processing flow.

[0874] Step 1:

[0875] The terminal displays a survey form for the user to input basic and evaluation information. The user enters basic information such as name, position, and years of service, as well as information related to evaluation items such as performance achievement, skill evaluation, communication skills, supportive spirit, and creativity into the form.

[0876] Step 2:

[0877] The user completes the survey form and presses the "Submit" button. The device sends the entered basic information and evaluation information to the server.

[0878] Step 3:

[0879] The server receives basic and evaluation information sent from the terminal. The received data is temporarily stored in the server's internal temporary memory.

[0880] Step 4:

[0881] The server stores the received basic and evaluation information in a database. During storage, the data is organized by user and associated using a unique user ID.

[0882] Step 5:

[0883] The server extracts basic and evaluation information stored in the database. The extracted data is then loaded back into temporary memory for the analysis process.

[0884] Step 6:

[0885] The server calculates evaluation scores based on conventional evaluation metrics. This is a process of individually evaluating and scoring items such as performance achievement and skill evaluation.

[0886] Step 7:

[0887] The server calculates an evaluation score based on new evaluation metrics (such as communication skills, supportiveness, and creativity). These are also scored separately.

[0888] Step 8:

[0889] The server calculates an overall evaluation score based on both traditional and new evaluation metrics. This includes a process of weighting each evaluation metric.

[0890] Step 9:

[0891] The server selects management candidates based on their overall performance score. Users whose overall performance score exceeds a pre-set standard are listed and designated as management candidates.

[0892] Step 10:

[0893] The server generates a detailed feedback document for each selected management candidate. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, and areas for improvement.

[0894] Step 11:

[0895] The server sends the generated feedback document to the terminal. The terminal displays a feedback notification to the user (management candidate). The user can then review the feedback content through the terminal.

[0896] (Example 1)

[0897] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0898] Traditional evaluation systems failed to adequately assess users as a whole, making it difficult to appropriately select candidates for management positions. Furthermore, the feedback was often insufficient, preventing users from accurately understanding their strengths and areas for improvement. In addition, evaluation bias existed, and the fairness of evaluations was not guaranteed.

[0899] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0900] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information from the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to use a generative AI model to generate feedback documents for users selected as management candidates; and means for the server to transmit the feedback documents generated by the generative AI model to the user terminal. This enables a multifaceted evaluation of the user, allowing for more appropriate selection of management candidates and the provision of detailed feedback.

[0901] A "user terminal" is a device used by a user to input information, and includes PCs, smartphones, and tablets.

[0902] A "form" refers to a screen or input fields in the form of a survey where users enter basic information and evaluation information.

[0903] "Basic information" refers to the user's basic personal information, such as name, job title, and years of service.

[0904] "Evaluation information" refers to information regarding evaluation items such as user performance, skills, communication ability, supportive attitude, and creativity.

[0905] A "server" refers to a central system that receives, stores, analyzes, and generates feedback on data.

[0906] A "database" refers to a system for organizing and storing basic user information and evaluation information.

[0907] "Overall rating" refers to the user's overall evaluation calculated based on both traditional and new evaluation metrics.

[0908] A "management candidate" refers to a user selected based on an overall evaluation as being suitable for a management position.

[0909] A "generative AI model" refers to an artificial intelligence model that analyzes user evaluation data and generates feedback documents.

[0910] A "feedback document" refers to a document that details the evaluation results, strengths, weaknesses, and areas for improvement of the user.

[0911] "Notifications" refer to messages or alerts that inform the user's device of feedback or other important information.

[0912] This invention relates to a system for users to input their own evaluations and evaluations from others, with a server collecting and analyzing this evaluation data to select management candidates. This system consists of a user terminal, a server, a database, and a generating AI model.

[0913] Specific system configuration

[0914] Hardware and software to be used

[0915] Device: A device used by a user to input information (such as a PC, smartphone, or tablet).

[0916] Server: A central system that receives, stores, analyzes, and generates feedback from data.

[0917] Database: A system for organizing and storing basic user information and evaluation data (e.g., MySQL, PostgreSQL)

[0918] Generative AI Models: AI tools for analyzing user evaluation data and generating feedback documents (e.g., OpenAI GPT model)

[0919] Specific operation of the system

[0920] 1. Enter user information

[0921] The device displays a survey-style form to the user. This form includes basic information that the user must enter (such as name, job title, and years of service).

[0922] Users enter information such as name, job title, years of service, achievements, skills, communication skills, supportive attitude, and creativity according to a specified format.

[0923] After the user has completed entering all the required information, they press the submit button. This sends the data to the server.

[0924] 2. Collection of evaluation data

[0925] The server receives user information sent from the terminal.

[0926] The server stores the received data in a database. This database is designed to systematically organize and store each user's information.

[0927] 3. Analysis of evaluation data

[0928] The server analyzes user information stored in the database. First, it calculates a score based on traditional evaluation metrics (performance achievement, skill evaluation).

[0929] The server also calculates scores for new evaluation metrics (communication skills, supportiveness, and creativity).

[0930] 4. Selection of management candidates

[0931] The server performs an overall evaluation based on the calculated scores for each evaluation metric.

[0932] The server lists users with high overall evaluation scores and generates a list of management candidates.

[0933] 5. Providing feedback

[0934] The server generates a feedback document containing detailed evaluation results for users selected as management candidates. This process utilizes a generative AI model.

[0935] The server sends the generated feedback document to the terminal and notifies the user.

[0936] This system ensures fairness in evaluations, enables proper assessment of diverse talent, and aims to improve the overall performance of the organization.

[0937] Specific example

[0938] For example, if a user (employee A) fills out an evaluation form, they would follow these steps:

[0939] Example of a prompt

[0940] 1. Enter basic information:

[0941] In the form displayed on the terminal, employee A enters the name "Taro Tanaka", the position "Senior Engineer", and the years of service "5 years".

[0942] 2. Entering evaluation information:

[0943] Employee A inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[0944] 3. Sending data:

[0945] After completing the input, employee A presses the submit button, and the terminal sends it to the server.

[0946] 4. Data processing:

[0947] The server receives employee A's data and saves it to the database.

[0948] The server calculates an overall evaluation score based on the stored data.

[0949] 5. Selection of management candidates:

[0950] The server determines whether employee A is suitable as a management candidate based on their overall evaluation score.

[0951] 6. Providing feedback:

[0952] The server generates a detailed feedback document for employee A and sends it to their terminal.

[0953] The device displays a feedback notification to employee A.

[0954] In this way, by using the system, evaluation bias can be reduced, diverse talent can be evaluated fairly and comprehensively, and the overall performance of the organization can be maximized.

[0955] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0956] Step 1: Enter user information

[0957] The terminal displays a survey form to the user. This form includes basic information that the user must enter (name, position, years of service, etc.) and evaluation information (performance, skills, communication skills, supportive attitude, creativity).

[0958] The user enters the required information and sends the data to the server by pressing the submit button.

[0959] Input: Basic information entered by the user (name, job title, years of service, etc.) and evaluation information (performance, skills, communication skills, supportive spirit, creativity).

[0960] Output: The basic information and evaluation information entered by the user are sent to the server.

[0961] Step 2: Collection of evaluation data

[0962] The server receives user information sent from the terminal.

[0963] The server stores the received data in a database. The database is designed to systematically organize and store each user's information.

[0964] Input: Basic user information and evaluation information sent from the terminal.

[0965] Output: User data is saved to the database.

[0966] Step 3: Analysis of evaluation data

[0967] The server analyzes user information stored in the database. First, it calculates a score based on conventional evaluation metrics (performance achievement, skill evaluation).

[0968] Next, we will calculate scores for the new evaluation metrics (communication skills, supportive spirit, and creativity).

[0969] Input: User's basic information and evaluation information stored in the database.

[0970] Output: Scores for each evaluation metric (traditional metric and new metric) are calculated.

[0971] Step 4: Selection of management candidates

[0972] The server performs an overall evaluation based on the calculated scores for each evaluation metric.

[0973] The server lists users with high overall evaluation scores and generates a list of management candidates.

[0974] Input: Scores for each evaluation metric (traditional metric and new metric).

[0975] Output: List of management candidates.

[0976] Step 5: Provide feedback

[0977] The server uses a generative AI model to generate feedback documents containing detailed evaluation results for users selected as management candidates.

[0978] The server sends the generated feedback document to the terminal and notifies the user.

[0979] Input: List of management candidates and each user's performance metric score.

[0980] Output: Feedback documents and notifications.

[0981] By incorporating specific actions into each step, the overall flow of the system becomes easier to understand, and it becomes clear how and which data is processed at which stage. This allows for more efficient system implementation and operation.

[0982] (Application Example 1)

[0983] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0984] Traditional management candidate selection systems had limited evaluation information, making it difficult to comprehensively assess the practical skills and on-site performance of operators and engineers, especially in factory settings. This prevented evaluations that reflected the realities of the workplace, resulting in the inability to appropriately select management candidates. Furthermore, insufficient feedback made it difficult for candidates to obtain concrete guidance for self-improvement. Additionally, the traditional system required users to input evaluation information cumbersomely, hindering efficient operation.

[0985] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0986] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information to the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to generate feedback for the management candidates and transmit it to the user terminal; means for the user terminal to function as a tool for factory operators and engineers to input self-evaluations and peer evaluations; means for the user terminal to transmit self-evaluation and peer evaluation data to the server; and means for the server to generate a detailed feedback document based on the transmitted evaluation data and transmit a notification to the user terminal. This enables detailed evaluation and feedback based on the actual situation on site, and allows for the efficient selection of management candidates.

[0987] A "user terminal" is a device that a user uses to input and transmit information.

[0988] A "form" is an interface or screen used by users to input basic information and evaluation information.

[0989] "Basic information" refers to fundamental data such as the user's name, job title, and years of service.

[0990] "Evaluation information" refers to data related to user performance, such as achievement level, skill assessment, communication skills, supportive attitude, and creativity.

[0991] A "server" is a computing system that receives information sent from user terminals, stores it in a database, and performs analysis on it.

[0992] A "database" is a system used by a server to organize and store basic and evaluation information it receives.

[0993] "Overall rating" refers to the user's overall score calculated based on basic information and evaluation information.

[0994] A "management candidate" is an individual selected based on an overall evaluation to determine their suitability for a management position.

[0995] "Feedback" refers to a document containing detailed evaluation results and suggestions for improvement, generated based on the overall evaluation.

[0996] A "factory operator" is an employee who operates machinery and equipment on the factory floor.

[0997] An "engineer" is a professional employee who is responsible for technical tasks.

[0998] "Self-evaluation" is the act of a user evaluating their own performance.

[0999] "Colleague evaluation" is the act of a user evaluating the performance of other employees.

[1000] A "tool" is software or an application used for a specific purpose.

[1001] A "notification" is a message sent to inform a user of specific information.

[1002] This invention is a system for users to input self-assessments and peer assessments, and a server collects and analyzes this assessment data to select candidates for management positions.

[1003] 1. Enter user information

[1004] The user's device (e.g., a smartphone or tablet) provides a form for the user to input basic and evaluation information. This form allows the user to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. The user enters their information and sends the data to the server by pressing the submit button.

[1005] 2. Collection of evaluation data

[1006] The server receives basic user information and evaluation information sent from the user's terminal. The received data is stored in a database. This database is designed to organize and store user-specific data.

[1007] 3. Analysis of evaluation data

[1008] The server analyzes the user's basic and evaluation information stored in the database. First, it calculates scores based on traditional evaluation metrics (performance achievement, skill evaluation, etc.). Then, it calculates scores for new evaluation metrics (communication skills, supportive attitude, creativity, etc.). It also conducts a comprehensive evaluation that includes evaluation items specific to factory operators and engineers.

[1009] 4. Selection of management candidates

[1010] The server performs an overall evaluation based on the calculated scores and selects candidates for management positions. To be selected as a management candidate, individuals must meet certain criteria in both the traditional and new evaluation metrics, as well as evaluation items specific to factory operators and engineers. The server lists users with high overall evaluation scores and generates a list of management candidates.

[1011] 5. Providing feedback

[1012] The server generates a document providing detailed feedback to users selected as management candidates, along with their evaluation results. This feedback document includes detailed evaluation results (scores for each evaluation item, overall score, etc.), strengths and weaknesses, and areas for future improvement. The generated feedback document is sent to the user's terminal, and the user is notified.

[1013] Hardware and software to be used

[1014] The following hardware and software will be used to implement this system.

[1015] Hardware: Smartphones, tablets, and servers

[1016] Software: Django framework, database management system (e.g., PostgreSQL), generative AI model

[1017] Specific example

[1018] For example, when a factory operator (user) fills out an evaluation form, they would follow these steps:

[1019] 1. Enter basic information

[1020] The user (factory operator) opens the app on their smartphone and enters their name, job title, and years of service.

[1021] 2. Entering evaluation information

[1022] You will input your self-assessment based on the evaluation items provided by the app (performance achievement, skill evaluation, communication skills, supportive spirit, creativity).

[1023] You will also enter your evaluation of your colleagues.

[1024] 3. Sending data

[1025] After completing the input, the user presses the submit button, and the app sends it to the server.

[1026] 4. Data Processing

[1027] The server receives user data and stores it in the database.

[1028] The server calculates an overall evaluation score based on the stored data.

[1029] 5. Selection of management candidates

[1030] The server determines whether a user is suitable as a management candidate based on their overall evaluation score.

[1031] 6. Providing feedback

[1032] The server generates a detailed feedback document for the user and sends it to the user's terminal.

[1033] The user's device displays a feedback notification to the user.

[1034] This invention enables evaluation and feedback based on actual on-site conditions, allowing for the efficient selection of management candidates. Furthermore, it provides users with specific guidance for self-improvement through concrete feedback.

[1035] Example of a prompt

[1036] Please provide the following information regarding the management candidate selection system:

[1037] 1. Employee basic information (name, position, years of service)

[1038] 2. Self-evaluation (performance achievement, skill assessment, communication skills, supportive spirit, creativity)

[1039] 3. Peer evaluation (performance achievement, skill assessment, communication skills, supportive attitude, creativity)

[1040] Input endpoint: / api / submit_evaluation

[1041] Analysis endpoint: / api / analyze_evaluation

[1042] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1043] Step 1:

[1044] The user terminal provides a form for entering basic information and evaluation information.

[1045] Input: User's basic information (name, job title, years of service, etc.) and evaluation information (performance achievement, skill evaluation, communication skills, supportive attitude, creativity).

[1046] Output: Input form screen.

[1047] Specific operation: The user terminal displays a form for the user to fill out, and the user enters the necessary information there.

[1048] Step 2:

[1049] The user terminal sends basic information and evaluation information entered by the user to the server.

[1050] Input: Basic information and evaluation information entered by the user.

[1051] Output: Data sent to the server.

[1052] Specific operation: When the user completes filling out the form and presses the submit button, basic information and evaluation information are sent to the server.

[1053] Step 3:

[1054] The server stores basic information and evaluation information sent from the user terminal in a database.

[1055] Input: Basic information and evaluation information sent from the user's terminal.

[1056] Output: Data stored in the database.

[1057] Specific operation: The server organizes the received data and saves it to the appropriate field in the database.

[1058] Step 4:

[1059] The server calculates the user's overall rating based on the basic information and evaluation information stored in the database.

[1060] Input: Basic information and evaluation information stored in the database.

[1061] Output: Overall evaluation score.

[1062] Specific operation: The server calculates scores for each based on the traditional and new evaluation metrics, and then calculates an overall evaluation score.

[1063] Step 5:

[1064] The server selects management candidates based on a calculated overall evaluation.

[1065] Input: Overall rating score.

[1066] Output: List of management candidates.

[1067] Specific operation: The server sets criteria based on the overall evaluation score and lists users with high scores as candidates for management positions.

[1068] Step 6:

[1069] The server generates feedback for the management candidate mentioned above and sends it to the user's terminal.

[1070] Input: List of management candidates and evaluation data for each user.

[1071] Output: Feedback document and notification.

[1072] Specific operation: The server generates a detailed feedback document based on the evaluation results and sends a notification to the user's terminal.

[1073] Step 7:

[1074] The user's device displays a feedback notification to the user.

[1075] Input: Feedback notification sent from the server.

[1076] Output: Feedback notification screen.

[1077] Specific action: The user terminal notifies the user that feedback has arrived and displays the document.

[1078] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1079] This invention is an evaluation system that combines an emotion engine to recognize user emotions. By collecting and analyzing emotional data in addition to basic user information and evaluation information, the system provides a more multifaceted evaluation. Embodiments of this system will be described in detail below.

[1080] 1. Enter user information

[1081] The terminal provides users with a survey form for entering basic and evaluation information. This form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. Furthermore, while filling out the form and responding to specific questions, an emotion engine analyzes the user's facial expressions and voice, collecting emotional data. Users submit this information to the server by pressing the submit button.

[1082] 2. Collection of evaluation data

[1083] The server receives basic user information, evaluation information, and sentiment data sent from the terminal. The received data is temporarily stored in the server's internal temporary memory and then saved to a database. This database is designed to organize and store data for each user.

[1084] 3. Analysis of evaluation data

[1085] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. First, it calculates individual scores based on traditional evaluation metrics (performance achievement, skill assessment, etc.) and new evaluation metrics (communication skills, supportiveness, creativity, etc.).

[1086] Next, emotional data is analyzed to evaluate the user's emotional response in specific evaluation scenarios. This includes changes in the user's emotions while filling out a form and their responses to specific questions. This emotional data is scored in relation to each evaluation metric, contributing to a more multifaceted scoring.

[1087] 4. Selection of management candidates

[1088] The server calculates an overall evaluation score based on each calculated evaluation score (traditional indicator, new indicator, and sentiment data score). The overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates.

[1089] Users whose overall evaluation score exceeds a pre-set standard are listed and designated as candidates for management positions. By including emotional data, it is possible to take into account users' emotional suitability, which is often overlooked in conventional evaluation methods.

[1090] 5. Providing feedback

[1091] The server generates a detailed feedback document for each selected management candidate. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, as well as feedback based on emotional data (for example, emotional responses to specific questions and changes in those emotions).

[1092] The generated feedback document is sent to the device and notified to the user. The user can review the feedback content through the device and use it for future reference.

[1093] Specific example

[1094] For example, if a user (employee B) fills out a survey form, they would follow these steps:

[1095] 1. Enter basic information

[1096] Employee B enters their name, job title, and years of service into the form displayed on the terminal.

[1097] 2. Entering evaluation information

[1098] Employee B inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[1099] 3. Collection of emotional data

[1100] While employee B is filling out the form, the emotion engine analyzes their facial expressions and voice to collect emotional data.

[1101] 4. Sending data

[1102] After the input and sentiment analysis are complete, employee B presses the send button, and the terminal sends it to the server.

[1103] 5. Data Processing

[1104] The server receives employee B's data and saves it to the database.

[1105] The server calculates an overall evaluation score based on the stored data.

[1106] 6. Selection of management candidates

[1107] The server determines whether employee B is suitable as a management candidate based on the overall evaluation score.

[1108] 7. Providing feedback

[1109] The server generates a detailed feedback document for employee B and sends it to their terminal.

[1110] The device displays a feedback notification to employee B.

[1111] Thus, the present invention enables comprehensive evaluation that incorporates emotional data in addition to conventional evaluation criteria, allowing for the appropriate evaluation of diverse personnel and maximizing the overall performance of the organization.

[1112] The following describes the processing flow.

[1113] Step 1:

[1114] The terminal displays a survey form for the user to input basic and evaluation information. The user enters basic information such as name, position, and years of service, as well as information related to evaluation items such as performance achievement, skill evaluation, communication skills, supportiveness, and creativity into the form.

[1115] Step 2:

[1116] The emotion engine is activated and analyzes the user's facial expressions and voice while they fill out the survey form. This analysis records their reactions to the input and their emotional changes in response to specific questions.

[1117] Step 3:

[1118] The user completes the survey form and presses the "Submit" button. The device sends the entered basic information, evaluation information, and sentiment data acquired by the sentiment engine to the server.

[1119] Step 4:

[1120] The server receives basic information, evaluation information, and sentiment data sent from the terminal. The received data is temporarily stored in the server's internal temporary memory and then saved to the database.

[1121] Step 5:

[1122] The server extracts basic information, evaluation information, and sentiment data stored in the database. The extracted data is then loaded back into temporary memory for analysis.

[1123] Step 6:

[1124] The server calculates evaluation scores based on traditional evaluation metrics (e.g., performance achievement and skill assessment). This includes individual scores for each evaluation item.

[1125] Step 7:

[1126] The server calculates evaluation scores based on new evaluation metrics (e.g., communication skills, supportiveness, creativity). The server also calculates these scores individually.

[1127] Step 8:

[1128] The server analyzes emotional data obtained from the emotion engine and calculates an evaluation score based on that data. This includes changes in emotions and emotional responses to specific questions.

[1129] Step 9:

[1130] The server integrates traditional evaluation metrics, new evaluation metrics, and evaluation scores based on sentiment data to calculate the user's overall rating. The integrated overall rating score is a combination of weighted scores.

[1131] Step 10:

[1132] The server selects management candidates based on their overall evaluation score. Users whose overall evaluation score exceeds the set criteria are listed as management candidates.

[1133] Step 11:

[1134] The server generates detailed feedback documents for selected management candidates. These feedback documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data.

[1135] Step 12:

[1136] The server sends the generated feedback document to the terminal. The terminal displays a feedback notification to the user, allowing the user to review the feedback content.

[1137] (Example 2)

[1138] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1139] Traditional evaluation systems based solely on basic and performance data, failing to consider emotional aspects. Consequently, users' emotional aptitudes and responses in specific scenarios were rarely reflected in evaluations. This resulted in a lack of evaluation of emotional aptitude, communication skills, and supportiveness, particularly in the selection of management candidates, hindering a more multifaceted assessment.

[1140] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1141] In this invention, the server includes means for storing basic information, evaluation information, and emotional data transmitted from the user terminal in a database, means for analyzing emotional data from the user's facial expressions and voice using an emotion engine, and means for analyzing emotional data in association with each evaluation index. This makes it possible to perform a comprehensive evaluation based on emotional data in addition to basic information and conventional evaluation indexes.

[1142] A "user terminal" is an electronic device used by a user to input basic information and evaluation information, and includes devices such as personal computers, tablets, and smartphones.

[1143] A "server" is a central processing unit that has the function of receiving, storing, and analyzing data sent from user terminals, and generating and sending feedback.

[1144] "Basic information" refers to personal attributes and background information such as the user's name, job title, and years of service.

[1145] "Evaluation information" refers to data related to the user's job performance capabilities and characteristics, such as the user's achievement level, skill evaluation, communication skills, supportive attitude, and creativity.

[1146] "Emotional data" refers to data about a user's emotional state, obtained by analyzing their facial expressions and voice.

[1147] An "emotion engine" refers to a combination of software and hardware used to analyze a user's facial expressions and voice, and is a technology that recognizes the user's emotional state in real time.

[1148] A "database" is an information storage system used by a server to systematically store, manage, and retrieve basic information, evaluation information, and sentiment data received by the server.

[1149] The "Overall Rating Score" is a numerical value that represents the overall evaluation result of a user, calculated by the server based on basic information, evaluation information, and sentiment data.

[1150] A "management candidate" refers to a user who has been judged to have high suitability based on their overall evaluation score and who should be selected for a management position.

[1151] "Feedback" refers to documents and notifications that contain information such as evaluation results and areas for improvement, generated based on the overall evaluation score and sentiment data calculated by the server.

[1152] This invention is an evaluation system that combines an emotion engine that recognizes user emotions. By collecting and analyzing emotion data in addition to basic user information and evaluation information, the system provides a more multifaceted evaluation. This system is mainly implemented using a user terminal, a server, and an emotion engine.

[1153] First, the user terminal provides the user with a survey form for entering basic and evaluation information. This form is built using HTML, CSS, and JavaScript, and users access it through a browser. The form allows users to enter basic information (name, position, years of service, etc.) as well as evaluation items such as performance achievement, skill evaluation, communication skills, supportiveness, and creativity.

[1154] While filling out the survey form and answering specific questions, the emotion engine analyzes the user's facial expressions and voice, collecting emotional data. The emotion engine utilizes software such as TensorFlow and OpenCV to recognize the user's emotional state in real time. This data is later used in evaluations.

[1155] Once the user completes the survey and presses the submit button, the device converts the entered basic information, evaluation information, and collected sentiment data into JSON format and sends it to the server using HTTPS. The server receives this data and first stores it in temporary memory. Then, it migrates the data to a database (MySQL or PostgreSQL) and stores it organized by user.

[1156] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. Individual scores are calculated based on traditional evaluation metrics (e.g., performance achievement, skill evaluation) and new evaluation metrics (e.g., communication skills, supportiveness, creativity). Sentiment data is also analyzed to evaluate the user's emotional response in specific evaluation scenarios. This calculation is performed using a Python script. The sentiment data thus obtained is associated with each evaluation metric and used for a more refined overall evaluation.

[1157] Next, the server calculates an overall evaluation score based on the calculated evaluation scores (traditional indicators, new indicators, and sentiment data scores). This overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates. Users whose overall evaluation score exceeds the pre-set criteria are listed and become management candidates.

[1158] Furthermore, the server generates a detailed feedback document for the selected management candidates. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, and feedback based on sentiment data. The generated feedback document is sent to the terminal and notified to the user. The user can review the feedback content through the terminal and use it for future reference.

[1159] A specific example of a prompt would be: "Please describe a comprehensive evaluation system that uses user sentiment data. Please include specific evaluation criteria, methods for collecting sentiment data, and evaluation methods."

[1160] In this way, the present invention makes it possible to appropriately evaluate diverse personnel and maximize the overall performance of the organization by conducting a comprehensive evaluation that incorporates emotional data in addition to conventional evaluation criteria.

[1161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1162] Step 1: Entering user information and collecting sentiment data

[1163] Input: Users enter basic and evaluation information into the survey form using their device. Specifically, they enter their name, job title, years of service, and evaluation items (performance achievement, skill evaluation, communication skills, supportive spirit, creativity, etc.).

[1164] Operation: The device provides a survey form built with HTML, CSS, and JavaScript. When the user begins to input, the device's camera and microphone are activated, and an emotion engine (TensorFlow, OpenCV) analyzes the user's facial expressions and voice in real time to collect emotion data.

[1165] Output: The basic information and evaluation information entered by the user, along with the collected sentiment data, are saved on the device in JSON format.

[1166] Step 2: Send

[1167] Input: Basic information, evaluation information, and sentiment data (in JSON format) collected in Step 1.

[1168] Operation: Once the user completes the survey form and presses the submit button, the device sends this data to the server using HTTPS.

[1169] Output: The server receives basic information, rating information, and sentiment data in JSON format.

[1170] Step 3: Collection and storage of evaluation data

[1171] Input: Basic information, evaluation information, and sentiment data (in JSON format) sent to the server.

[1172] Operation: The server temporarily stores data in memory, then migrates it to a database (MySQL, PostgreSQL) and organizes and stores it for each user.

[1173] Output: Basic information, evaluation information, and sentiment data stored in the database.

[1174] Step 4: Analysis of evaluation data

[1175] Input: Basic information, evaluation information, and sentiment data extracted from the database.

[1176] Operation: The server extracts this data and calculates individual scores based on traditional evaluation metrics (e.g., performance, skill assessment) and new evaluation metrics (e.g., communication skills, supportiveness, creativity). It also analyzes emotional data and evaluates the user's emotional response in specific evaluation scenarios. This analysis process uses a Python script.

[1177] Output: Traditional evaluation metrics, new evaluation metrics, and individual scores based on sentiment data.

[1178] Step 5: Calculation of overall performance score and selection of management candidates

[1179] Input: Individual evaluation scores (traditional metrics, new metrics, sentiment data scores).

[1180] Operation: The server integrates and weights each evaluation score to calculate an overall evaluation score. Next, it lists users whose overall evaluation score exceeds a pre-set standard and selects them as management candidates.

[1181] Output: List of management candidates.

[1182] Step 6: Generating and providing feedback

[1183] Input: A list of management candidates and detailed scores for each evaluation item.

[1184] Operation: The server generates a detailed feedback document for management candidates. This document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, and feedback based on sentiment data. The generated feedback document is sent to the terminal and notified to the user.

[1185] Output: A feedback document is sent to the user's terminal, and a notification is displayed to the user.

[1186] (Application Example 2)

[1187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1188] Traditional evaluation systems were limited to evaluations based on basic user information and evaluation data, and did not take into account the user's emotional state. This made it difficult to appropriately evaluate and respond quickly to customer satisfaction and emotional reactions, especially in physical stores. The lack of real-time information to evaluate and improve store service attitudes and atmosphere limited the ability to optimize the customer experience.

[1189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1190] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information from the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to generate feedback for the management candidates and transmit it to the user terminal; means for collecting customer emotional data in physical stores and providing information for evaluating and improving customer service attitudes; and means including an emotion recognition engine that analyzes customer facial expressions and voice to collect emotional data. This makes it possible to perform multifaceted evaluations, including the emotional state of the customer, and to improve customer satisfaction in physical stores.

[1191] A "user terminal" refers to an electronic device used by a user to input information. Examples include smartphones, tablets, and computers.

[1192] "Basic information" refers to information that identifies a user and forms the basis of their identity, such as their name, job title, and years of service.

[1193] "Evaluation information" refers to specific information used to evaluate a user, such as their performance, skills, communication abilities, supportive attitude, and creativity.

[1194] A "server" refers to a computer system that receives information sent from user terminals, stores it in a database, and performs analysis on it.

[1195] A "database" refers to a system that organizes and stores basic user information and evaluation data as a collection of structured data.

[1196] "Overall rating" refers to a comprehensive performance evaluation of a user, calculated based on their basic information and evaluation data.

[1197] A "management candidate" refers to a user selected by the server and evaluated as suitable for a management position.

[1198] "Feedback" refers to information used to convey user evaluations and areas for improvement.

[1199] An "emotion recognition engine" refers to technology that analyzes the facial expressions and voice of users and customers to understand their emotional state.

[1200] A "physical store" refers to a retail store that exists in a physical location and can be visited by customers.

[1201] "Emotional data" refers to information about emotions obtained as a result of analyzing a customer's facial expressions and voice.

[1202] "Customer service attitude" refers to the quality of service and the way in which employees interact with customers in a store.

[1203] To implement this invention, the following system configuration is adopted. The system includes a user terminal, a server, a database, and an emotion recognition engine.

[1204] 1. Enter user information

[1205] The user terminal provides the user with a form for entering basic and evaluation information. The form includes basic user information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. As the user fills out the form, an emotion recognition engine analyzes the user's facial expressions and voice, collecting emotional data. The user enters this information and sends the data to the server by pressing the submit button.

[1206] 2. Collection of evaluation data

[1207] The server receives basic information, evaluation information, and sentiment data sent from the user's terminal. The received data is temporarily stored in temporary memory before being saved to the database. The database is designed to organize and store data for each user.

[1208] 3. Analysis of evaluation data

[1209] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. It calculates individual scores based on traditional evaluation metrics (such as performance achievement and skill assessment) and new evaluation metrics (such as communication skills, supportiveness, and creativity). Furthermore, it analyzes sentiment data to assess the user's emotional response in specific evaluation scenarios. This includes changes in the user's emotions while filling out forms and their responses to specific questions. Each evaluation metric is correlated with sentiment data and scored to achieve a more multifaceted scoring system.

[1210] 4. Selection of management candidates

[1211] The server calculates an overall evaluation score based on each calculated evaluation score. This overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates. Users whose overall evaluation score exceeds a pre-set standard are listed and selected as management candidates.

[1212] 5. Providing feedback

[1213] The server generates detailed feedback documents for selected management candidates. These documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data. The generated feedback documents are sent to the user's terminal, allowing the user to review the feedback content.

[1214] Adding specific examples

[1215] For example, in a physical store, a sales associate wearing smart glasses analyzes the customer's facial expressions and voice using an emotion recognition engine. If a customer appears confused in the fitting room, the system detects that emotion and notifies the sales associate that "the customer is likely in distress." Based on this, the sales associate can provide support quickly.

[1216] Example of a prompt

[1217] As a specific example, when a customer is looking for items in a fitting room, if this system analyzes the customer's facial expression and determines that the customer is confused, please provide specific examples of what kind of notification is sent to the store staff and how they should respond.

[1218] The software used includes OpenCV for image processing, Dlib for facial landmark detection, TensorFlow for emotion recognition models, and Flask for data communication. This enables multifaceted evaluation, including customer emotional states, and can improve customer satisfaction in physical stores.

[1219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1220] Step 1: Enter user information

[1221] The user enters basic information (name, position, years of service) and evaluation information (performance, skills, communication ability, supportive spirit, creativity) into a form provided by the terminal. During this process, an emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. The entered information and emotional data are stored in temporary memory. Basic information, evaluation information, and emotional data are obtained as input data.

[1222] Step 2: Send

[1223] When the user presses the submit button, the device sends the entered basic information, evaluation information, and collected sentiment data to the server. User information (basic information, evaluation information, sentiment data) is the input, and the server receives the data as output.

[1224] Step 3: Collection and storage of evaluation data

[1225] The server receives basic information, evaluation information, and sentiment data transmitted from the terminal. The received data is temporarily stored in temporary memory and then organized and stored in the database. The server's input is user information, and its output is storage in the database.

[1226] Step 4: Analysis of evaluation data

[1227] The server extracts basic information, evaluation information, and sentiment data stored in the database to perform a multifaceted evaluation. In addition to conventional evaluation metrics (performance achievement, skill evaluation), it calculates individual scores using new evaluation metrics (communication skills, supportive spirit, creativity) and sentiment data. The sentiment recognition engine provides the analysis results and generates the sentiment portion score in real time based on them. The input is user information from the database, and the output is the individual score for each evaluation metric and the overall evaluation score.

[1228] Step 5: Selection of management candidates

[1229] The server derives an overall evaluation score based on the calculated scores for each indicator. The overall evaluation score is calculated by assigning appropriate weights to each score. As a result, users whose overall evaluation score exceeds a pre-set standard are listed as management candidates. The input is the individual evaluation scores, and the output is a list of management candidates.

[1230] Step 6: Generate and send feedback

[1231] The server generates detailed feedback documents for selected management candidates. These documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data. The generated feedback documents are sent to the user's terminal for review. The input is the management candidate's score data, and the output is the feedback document.

[1232] Step 7: In-store evaluation and notification

[1233] In physical stores, a terminal worn by a sales associate wearing smart glasses analyzes the customer's facial expressions and voice in real time. An emotion recognition engine provides the analysis results, and when a specific emotional state is detected, the system sends a notification to the sales associate. For example, if it determines that "the customer is in distress," the sales associate will receive a notification stating "the customer needs assistance." The input is the customer's emotional data, and the output is a notification to the sales associate.

[1234] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1235] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1236] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1237] [Fourth Embodiment]

[1238] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1239] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1240] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1241] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1242] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1243] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1244] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1245] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1246] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1247] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1248] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1249] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1250] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1251] The present invention is a system for users to input their own evaluations and evaluations from others, and a server collects and analyzes this evaluation data to select candidates for management positions. An embodiment of this system will be described below.

[1252] 1. Enter user information

[1253] The terminal provides users with a survey form for entering basic and evaluation information. This form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. Users enter their information and send the data to the server by pressing the submit button.

[1254] 2. Collection of evaluation data

[1255] The server receives basic user information and evaluation information sent from the terminal. The received data is stored in a database. This database is designed to organize and store user-specific data.

[1256] 3. Analysis of evaluation data

[1257] The server analyzes the user's basic and evaluation information stored in the database. First, it calculates scores based on traditional evaluation metrics (performance achievement, skill evaluation, etc.). Then, it calculates scores for new evaluation metrics (communication skills, supportive attitude, creativity, etc.).

[1258] 4. Selection of management candidates

[1259] The server performs an overall evaluation based on the calculated scores and selects candidates for management positions. To be selected as a management candidate, users must meet certain criteria in both the traditional and new evaluation metrics. The server lists users with high overall evaluation scores and generates a list of management candidates.

[1260] 5. Providing feedback

[1261] The server generates a document providing detailed feedback to users selected as management candidates, along with their evaluation results. This feedback document includes detailed evaluation results (scores for each evaluation item, overall score, etc.), strengths and weaknesses, and areas for future improvement. The generated feedback document is sent to the terminal and notified to the user.

[1262] Specific example

[1263] For example, if a user (employee A) fills out an evaluation form, they would follow these steps:

[1264] 1. Enter basic information

[1265] Employee A enters their name, job title, and years of service into the form displayed on the terminal.

[1266] 2. Entering evaluation information

[1267] Employee A inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[1268] 3. Sending data

[1269] After completing the input, employee A presses the submit button, and the terminal sends it to the server.

[1270] 4. Data Processing

[1271] The server receives employee A's data and saves it to the database.

[1272] The server calculates an overall evaluation score based on the stored data.

[1273] 5. Selection of management candidates

[1274] The server determines whether employee A is suitable as a management candidate based on their overall evaluation score.

[1275] 6. Providing feedback

[1276] The server generates a detailed feedback document for employee A and sends it to their terminal.

[1277] The device displays a feedback notification to employee A.

[1278] Thus, the present invention enables comprehensive evaluation that incorporates new indicators in addition to conventional evaluation criteria, allowing for the appropriate evaluation of diverse personnel and maximizing the overall performance of the organization.

[1279] The following describes the processing flow.

[1280] Step 1:

[1281] The terminal displays a survey form for the user to input basic and evaluation information. The user enters basic information such as name, position, and years of service, as well as information related to evaluation items such as performance achievement, skill evaluation, communication skills, supportive spirit, and creativity into the form.

[1282] Step 2:

[1283] The user completes the survey form and presses the "Submit" button. The device sends the entered basic information and evaluation information to the server.

[1284] Step 3:

[1285] The server receives basic and evaluation information sent from the terminal. The received data is temporarily stored in the server's internal temporary memory.

[1286] Step 4:

[1287] The server stores the received basic and evaluation information in a database. During storage, the data is organized by user and associated using a unique user ID.

[1288] Step 5:

[1289] The server extracts basic and evaluation information stored in the database. The extracted data is then loaded back into temporary memory for the analysis process.

[1290] Step 6:

[1291] The server calculates evaluation scores based on conventional evaluation metrics. This is a process of individually evaluating and scoring items such as performance achievement and skill evaluation.

[1292] Step 7:

[1293] The server calculates an evaluation score based on new evaluation metrics (such as communication skills, supportiveness, and creativity). These are also scored separately.

[1294] Step 8:

[1295] The server calculates an overall evaluation score based on both traditional and new evaluation metrics. This includes a process of weighting each evaluation metric.

[1296] Step 9:

[1297] The server selects management candidates based on their overall performance score. Users whose overall performance score exceeds a pre-set standard are listed and designated as management candidates.

[1298] Step 10:

[1299] The server generates a detailed feedback document for each selected management candidate. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, and areas for improvement.

[1300] Step 11:

[1301] The server sends the generated feedback document to the terminal. The terminal displays a feedback notification to the user (management candidate). The user can then review the feedback content through the terminal.

[1302] (Example 1)

[1303] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1304] Traditional evaluation systems failed to adequately assess users as a whole, making it difficult to appropriately select candidates for management positions. Furthermore, the feedback was often insufficient, preventing users from accurately understanding their strengths and areas for improvement. In addition, evaluation bias existed, and the fairness of evaluations was not guaranteed.

[1305] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1306] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information from the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to use a generative AI model to generate feedback documents for users selected as management candidates; and means for the server to transmit the feedback documents generated by the generative AI model to the user terminal. This enables a multifaceted evaluation of the user, allowing for more appropriate selection of management candidates and the provision of detailed feedback.

[1307] A "user terminal" is a device used by a user to input information, and includes PCs, smartphones, and tablets.

[1308] A "form" refers to a screen or input fields in the form of a survey where users enter basic information and evaluation information.

[1309] "Basic information" refers to the user's basic personal information, such as name, job title, and years of service.

[1310] "Evaluation information" refers to information regarding evaluation items such as user performance, skills, communication ability, supportive attitude, and creativity.

[1311] A "server" refers to a central system that receives, stores, analyzes, and generates feedback on data.

[1312] A "database" refers to a system for organizing and storing basic user information and evaluation information.

[1313] "Overall rating" refers to the user's overall evaluation calculated based on both traditional and new evaluation metrics.

[1314] A "management candidate" refers to a user selected based on an overall evaluation as being suitable for a management position.

[1315] A "generative AI model" refers to an artificial intelligence model that analyzes user evaluation data and generates feedback documents.

[1316] A "feedback document" refers to a document that details the evaluation results, strengths, weaknesses, and areas for improvement of the user.

[1317] "Notifications" refer to messages or alerts that inform the user's device of feedback or other important information.

[1318] This invention relates to a system for users to input their own evaluations and evaluations from others, with a server collecting and analyzing this evaluation data to select management candidates. This system consists of a user terminal, a server, a database, and a generating AI model.

[1319] Specific system configuration

[1320] Hardware and software to be used

[1321] Device: A device used by a user to input information (such as a PC, smartphone, or tablet).

[1322] Server: A central system that receives, stores, analyzes, and generates feedback from data.

[1323] Database: A system for organizing and storing basic user information and evaluation data (e.g., MySQL, PostgreSQL)

[1324] Generative AI Models: AI tools for analyzing user evaluation data and generating feedback documents (e.g., OpenAI GPT model)

[1325] Specific operation of the system

[1326] 1. Enter user information

[1327] The device displays a survey-style form to the user. This form includes basic information that the user must enter (such as name, job title, and years of service).

[1328] Users enter information such as name, job title, years of service, achievements, skills, communication skills, supportive attitude, and creativity according to a specified format.

[1329] After the user has completed entering all the required information, they press the submit button. This sends the data to the server.

[1330] 2. Collection of evaluation data

[1331] The server receives user information sent from the terminal.

[1332] The server stores the received data in a database. This database is designed to systematically organize and store each user's information.

[1333] 3. Analysis of evaluation data

[1334] The server analyzes user information stored in the database. First, it calculates a score based on traditional evaluation metrics (performance achievement, skill evaluation).

[1335] The server also calculates scores for new evaluation metrics (communication skills, supportiveness, and creativity).

[1336] 4. Selection of management candidates

[1337] The server performs an overall evaluation based on the calculated scores for each evaluation metric.

[1338] The server lists users with high overall evaluation scores and generates a list of management candidates.

[1339] 5. Providing feedback

[1340] The server generates a feedback document containing detailed evaluation results for users selected as management candidates. This process utilizes a generative AI model.

[1341] The server sends the generated feedback document to the terminal and notifies the user.

[1342] This system ensures fairness in evaluations, enables proper assessment of diverse talent, and aims to improve the overall performance of the organization.

[1343] Specific example

[1344] For example, if a user (employee A) fills out an evaluation form, they would follow these steps:

[1345] Example of a prompt

[1346] 1. Enter basic information:

[1347] In the form displayed on the terminal, employee A enters the name "Taro Tanaka", the position "Senior Engineer", and the years of service "5 years".

[1348] 2. Entering evaluation information:

[1349] Employee A inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[1350] 3. Sending data:

[1351] After completing the input, employee A presses the submit button, and the terminal sends it to the server.

[1352] 4. Data processing:

[1353] The server receives employee A's data and saves it to the database.

[1354] The server calculates an overall evaluation score based on the stored data.

[1355] 5. Selection of management candidates:

[1356] The server determines whether employee A is suitable as a management candidate based on their overall evaluation score.

[1357] 6. Providing feedback:

[1358] The server generates a detailed feedback document for employee A and sends it to their terminal.

[1359] The device displays a feedback notification to employee A.

[1360] In this way, by using the system, evaluation bias can be reduced, diverse talent can be evaluated fairly and comprehensively, and the overall performance of the organization can be maximized.

[1361] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1362] Step 1: Enter user information

[1363] The terminal displays a survey form to the user. This form includes basic information that the user must enter (name, position, years of service, etc.) and evaluation information (performance, skills, communication skills, supportive attitude, creativity).

[1364] The user enters the required information and sends the data to the server by pressing the submit button.

[1365] Input: Basic information entered by the user (name, job title, years of service, etc.) and evaluation information (performance, skills, communication skills, supportive spirit, creativity).

[1366] Output: The basic information and evaluation information entered by the user are sent to the server.

[1367] Step 2: Collection of evaluation data

[1368] The server receives user information sent from the terminal.

[1369] The server stores the received data in a database. The database is designed to systematically organize and store each user's information.

[1370] Input: Basic user information and evaluation information sent from the terminal.

[1371] Output: User data is saved to the database.

[1372] Step 3: Analysis of evaluation data

[1373] The server analyzes user information stored in the database. First, it calculates a score based on conventional evaluation metrics (performance achievement, skill evaluation).

[1374] Next, we will calculate scores for the new evaluation metrics (communication skills, supportive spirit, and creativity).

[1375] Input: User's basic information and evaluation information stored in the database.

[1376] Output: Scores for each evaluation metric (traditional metric and new metric) are calculated.

[1377] Step 4: Selection of management candidates

[1378] The server performs an overall evaluation based on the calculated scores for each evaluation metric.

[1379] The server lists users with high overall evaluation scores and generates a list of management candidates.

[1380] Input: Scores for each evaluation metric (traditional metric and new metric).

[1381] Output: List of management candidates.

[1382] Step 5: Provide feedback

[1383] The server uses a generative AI model to generate feedback documents containing detailed evaluation results for users selected as management candidates.

[1384] The server sends the generated feedback document to the terminal and notifies the user.

[1385] Input: List of management candidates and each user's performance metric score.

[1386] Output: Feedback documents and notifications.

[1387] By incorporating specific actions into each step, the overall flow of the system becomes easier to understand, and it becomes clear how and which data is processed at which stage. This allows for more efficient system implementation and operation.

[1388] (Application Example 1)

[1389] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1390] Traditional management candidate selection systems had limited evaluation information, making it difficult to comprehensively assess the practical skills and on-site performance of operators and engineers, especially in factory settings. This prevented evaluations that reflected the realities of the workplace, resulting in the inability to appropriately select management candidates. Furthermore, insufficient feedback made it difficult for candidates to obtain concrete guidance for self-improvement. Additionally, the traditional system required users to input evaluation information cumbersomely, hindering efficient operation.

[1391] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1392] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information to the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to generate feedback for the management candidates and transmit it to the user terminal; means for the user terminal to function as a tool for factory operators and engineers to input self-evaluations and peer evaluations; means for the user terminal to transmit self-evaluation and peer evaluation data to the server; and means for the server to generate a detailed feedback document based on the transmitted evaluation data and transmit a notification to the user terminal. This enables detailed evaluation and feedback based on the actual situation on site, and allows for the efficient selection of management candidates.

[1393] A "user terminal" is a device that a user uses to input and transmit information.

[1394] A "form" is an interface or screen used by users to input basic information and evaluation information.

[1395] "Basic information" refers to fundamental data such as the user's name, job title, and years of service.

[1396] "Evaluation information" refers to data related to user performance, such as achievement level, skill assessment, communication skills, supportive attitude, and creativity.

[1397] A "server" is a computing system that receives information sent from user terminals, stores it in a database, and performs analysis on it.

[1398] A "database" is a system used by a server to organize and store basic and evaluation information it receives.

[1399] "Overall rating" refers to the user's overall score calculated based on basic information and evaluation information.

[1400] A "management candidate" is an individual selected based on an overall evaluation to determine their suitability for a management position.

[1401] "Feedback" refers to a document containing detailed evaluation results and suggestions for improvement, generated based on the overall evaluation.

[1402] A "factory operator" is an employee who operates machinery and equipment on the factory floor.

[1403] An "engineer" is a professional employee who is responsible for technical tasks.

[1404] "Self-evaluation" is the act of a user evaluating their own performance.

[1405] "Colleague evaluation" is the act of a user evaluating the performance of other employees.

[1406] A "tool" is software or an application used for a specific purpose.

[1407] A "notification" is a message sent to inform a user of specific information.

[1408] This invention is a system for users to input self-assessments and peer assessments, and a server collects and analyzes this assessment data to select candidates for management positions.

[1409] 1. Enter user information

[1410] The user's device (e.g., a smartphone or tablet) provides a form for the user to input basic and evaluation information. This form allows the user to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. The user enters their information and sends the data to the server by pressing the submit button.

[1411] 2. Collection of evaluation data

[1412] The server receives basic user information and evaluation information sent from the user's terminal. The received data is stored in a database. This database is designed to organize and store user-specific data.

[1413] 3. Analysis of evaluation data

[1414] The server analyzes the user's basic and evaluation information stored in the database. First, it calculates scores based on traditional evaluation metrics (performance achievement, skill evaluation, etc.). Then, it calculates scores for new evaluation metrics (communication skills, supportive attitude, creativity, etc.). It also conducts a comprehensive evaluation that includes evaluation items specific to factory operators and engineers.

[1415] 4. Selection of management candidates

[1416] The server performs an overall evaluation based on the calculated scores and selects candidates for management positions. To be selected as a management candidate, individuals must meet certain criteria in both the traditional and new evaluation metrics, as well as evaluation items specific to factory operators and engineers. The server lists users with high overall evaluation scores and generates a list of management candidates.

[1417] 5. Providing feedback

[1418] The server generates a document providing detailed feedback to users selected as management candidates, along with their evaluation results. This feedback document includes detailed evaluation results (scores for each evaluation item, overall score, etc.), strengths and weaknesses, and areas for future improvement. The generated feedback document is sent to the user's terminal, and the user is notified.

[1419] Hardware and software to be used

[1420] The following hardware and software will be used to implement this system.

[1421] Hardware: Smartphones, tablets, and servers

[1422] Software: Django framework, database management system (e.g., PostgreSQL), generative AI model

[1423] Specific example

[1424] For example, when a factory operator (user) fills out an evaluation form, they would follow these steps:

[1425] 1. Enter basic information

[1426] The user (factory operator) opens the app on their smartphone and enters their name, job title, and years of service.

[1427] 2. Entering evaluation information

[1428] You will input your self-assessment based on the evaluation items provided by the app (performance achievement, skill evaluation, communication skills, supportive spirit, creativity).

[1429] You will also enter your evaluation of your colleagues.

[1430] 3. Sending data

[1431] After completing the input, the user presses the submit button, and the app sends it to the server.

[1432] 4. Data Processing

[1433] The server receives user data and stores it in the database.

[1434] The server calculates an overall evaluation score based on the stored data.

[1435] 5. Selection of management candidates

[1436] The server determines whether a user is suitable as a management candidate based on their overall evaluation score.

[1437] 6. Providing feedback

[1438] The server generates a detailed feedback document for the user and sends it to the user's terminal.

[1439] The user's device displays a feedback notification to the user.

[1440] This invention enables evaluation and feedback based on actual on-site conditions, allowing for the efficient selection of management candidates. Furthermore, it provides users with specific guidance for self-improvement through concrete feedback.

[1441] Example of a prompt

[1442] Please provide the following information regarding the management candidate selection system:

[1443] 1. Employee basic information (name, position, years of service)

[1444] 2. Self-evaluation (performance achievement, skill assessment, communication skills, supportive spirit, creativity)

[1445] 3. Peer evaluation (performance achievement, skill assessment, communication skills, supportive attitude, creativity)

[1446] Input endpoint: / api / submit_evaluation

[1447] Analysis endpoint: / api / analyze_evaluation

[1448] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1449] Step 1:

[1450] The user terminal provides a form for entering basic information and evaluation information.

[1451] Input: User's basic information (name, job title, years of service, etc.) and evaluation information (performance achievement, skill evaluation, communication skills, supportive attitude, creativity).

[1452] Output: Input form screen.

[1453] Specific operation: The user terminal displays a form for the user to fill out, and the user enters the necessary information there.

[1454] Step 2:

[1455] The user terminal sends basic information and evaluation information entered by the user to the server.

[1456] Input: Basic information and evaluation information entered by the user.

[1457] Output: Data sent to the server.

[1458] Specific operation: When the user completes filling out the form and presses the submit button, basic information and evaluation information are sent to the server.

[1459] Step 3:

[1460] The server stores basic information and evaluation information sent from the user terminal in a database.

[1461] Input: Basic information and evaluation information sent from the user's terminal.

[1462] Output: Data stored in the database.

[1463] Specific operation: The server organizes the received data and saves it to the appropriate field in the database.

[1464] Step 4:

[1465] The server calculates the user's overall rating based on the basic information and evaluation information stored in the database.

[1466] Input: Basic information and evaluation information stored in the database.

[1467] Output: Overall evaluation score.

[1468] Specific operation: The server calculates scores for each based on the traditional and new evaluation metrics, and then calculates an overall evaluation score.

[1469] Step 5:

[1470] The server selects management candidates based on a calculated overall evaluation.

[1471] Input: Overall rating score.

[1472] Output: List of management candidates.

[1473] Specific operation: The server sets criteria based on the overall evaluation score and lists users with high scores as candidates for management positions.

[1474] Step 6:

[1475] The server generates feedback for the management candidate mentioned above and sends it to the user's terminal.

[1476] Input: List of management candidates and evaluation data for each user.

[1477] Output: Feedback document and notification.

[1478] Specific operation: The server generates a detailed feedback document based on the evaluation results and sends a notification to the user's terminal.

[1479] Step 7:

[1480] The user's device displays a feedback notification to the user.

[1481] Input: Feedback notification sent from the server.

[1482] Output: Feedback notification screen.

[1483] Specific action: The user terminal notifies the user that feedback has arrived and displays the document.

[1484] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1485] This invention is an evaluation system that combines an emotion engine to recognize user emotions. By collecting and analyzing emotional data in addition to basic user information and evaluation information, the system provides a more multifaceted evaluation. Embodiments of this system will be described in detail below.

[1486] 1. Enter user information

[1487] The terminal provides users with a survey form for entering basic and evaluation information. This form allows users to input basic information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. Furthermore, while filling out the form and responding to specific questions, an emotion engine analyzes the user's facial expressions and voice, collecting emotional data. Users submit this information to the server by pressing the submit button.

[1488] 2. Collection of evaluation data

[1489] The server receives basic user information, evaluation information, and sentiment data sent from the terminal. The received data is temporarily stored in the server's internal temporary memory and then saved to a database. This database is designed to organize and store data for each user.

[1490] 3. Analysis of evaluation data

[1491] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. First, it calculates individual scores based on traditional evaluation metrics (performance achievement, skill assessment, etc.) and new evaluation metrics (communication skills, supportiveness, creativity, etc.).

[1492] Next, emotional data is analyzed to evaluate the user's emotional response in specific evaluation scenarios. This includes changes in the user's emotions while filling out a form and their responses to specific questions. This emotional data is scored in relation to each evaluation metric, contributing to a more multifaceted scoring.

[1493] 4. Selection of management candidates

[1494] The server calculates an overall evaluation score based on each calculated evaluation score (traditional indicator, new indicator, and sentiment data score). The overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates.

[1495] Users whose overall evaluation score exceeds a pre-set standard are listed and designated as candidates for management positions. By including emotional data, it is possible to take into account users' emotional suitability, which is often overlooked in conventional evaluation methods.

[1496] 5. Providing feedback

[1497] The server generates a detailed feedback document for each selected management candidate. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, as well as feedback based on emotional data (for example, emotional responses to specific questions and changes in those emotions).

[1498] The generated feedback document is sent to the device and notified to the user. The user can review the feedback content through the device and use it for future reference.

[1499] Specific example

[1500] For example, if a user (employee B) fills out a survey form, they would follow these steps:

[1501] 1. Enter basic information

[1502] Employee B enters their name, job title, and years of service into the form displayed on the terminal.

[1503] 2. Entering evaluation information

[1504] Employee B inputs their self-assessment based on the evaluation items provided by the terminal (performance achievement, skill evaluation, communication skills, supportive spirit, and creativity).

[1505] 3. Collection of emotional data

[1506] While employee B is filling out the form, the emotion engine analyzes their facial expressions and voice to collect emotional data.

[1507] 4. Sending data

[1508] After the input and sentiment analysis are complete, employee B presses the send button, and the terminal sends it to the server.

[1509] 5. Data Processing

[1510] The server receives employee B's data and saves it to the database.

[1511] The server calculates an overall evaluation score based on the stored data.

[1512] 6. Selection of management candidates

[1513] The server determines whether employee B is suitable as a management candidate based on the overall evaluation score.

[1514] 7. Providing feedback

[1515] The server generates a detailed feedback document for employee B and sends it to their terminal.

[1516] The device displays a feedback notification to employee B.

[1517] Thus, the present invention enables comprehensive evaluation that incorporates emotional data in addition to conventional evaluation criteria, allowing for the appropriate evaluation of diverse personnel and maximizing the overall performance of the organization.

[1518] The following describes the processing flow.

[1519] Step 1:

[1520] The terminal displays a survey form for the user to input basic and evaluation information. The user enters basic information such as name, position, and years of service, as well as information related to evaluation items such as performance achievement, skill evaluation, communication skills, supportiveness, and creativity into the form.

[1521] Step 2:

[1522] The emotion engine is activated and analyzes the user's facial expressions and voice while they fill out the survey form. This analysis records their reactions to the input and their emotional changes in response to specific questions.

[1523] Step 3:

[1524] The user completes the survey form and presses the "Submit" button. The device sends the entered basic information, evaluation information, and sentiment data acquired by the sentiment engine to the server.

[1525] Step 4:

[1526] The server receives basic information, evaluation information, and sentiment data sent from the terminal. The received data is temporarily stored in the server's internal temporary memory and then saved to the database.

[1527] Step 5:

[1528] The server extracts basic information, evaluation information, and sentiment data stored in the database. The extracted data is then loaded back into temporary memory for analysis.

[1529] Step 6:

[1530] The server calculates evaluation scores based on traditional evaluation metrics (e.g., performance achievement and skill assessment). This includes individual scores for each evaluation item.

[1531] Step 7:

[1532] The server calculates evaluation scores based on new evaluation metrics (e.g., communication skills, supportiveness, creativity). The server also calculates these scores individually.

[1533] Step 8:

[1534] The server analyzes emotional data obtained from the emotion engine and calculates an evaluation score based on that data. This includes changes in emotions and emotional responses to specific questions.

[1535] Step 9:

[1536] The server integrates traditional evaluation metrics, new evaluation metrics, and evaluation scores based on sentiment data to calculate the user's overall rating. The integrated overall rating score is a combination of weighted scores.

[1537] Step 10:

[1538] The server selects management candidates based on their overall evaluation score. Users whose overall evaluation score exceeds the set criteria are listed as management candidates.

[1539] Step 11:

[1540] The server generates detailed feedback documents for selected management candidates. These feedback documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data.

[1541] Step 12:

[1542] The server sends the generated feedback document to the terminal. The terminal displays a feedback notification to the user, allowing the user to review the feedback content.

[1543] (Example 2)

[1544] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1545] Traditional evaluation systems based solely on basic and performance data, failing to consider emotional aspects. Consequently, users' emotional aptitudes and responses in specific scenarios were rarely reflected in evaluations. This resulted in a lack of evaluation of emotional aptitude, communication skills, and supportiveness, particularly in the selection of management candidates, hindering a more multifaceted assessment.

[1546] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1547] In this invention, the server includes means for storing basic information, evaluation information, and emotional data transmitted from the user terminal in a database, means for analyzing emotional data from the user's facial expressions and voice using an emotion engine, and means for analyzing emotional data in association with each evaluation index. This makes it possible to perform a comprehensive evaluation based on emotional data in addition to basic information and conventional evaluation indexes.

[1548] A "user terminal" is an electronic device used by a user to input basic information and evaluation information, and includes devices such as personal computers, tablets, and smartphones.

[1549] A "server" is a central processing unit that has the function of receiving, storing, and analyzing data sent from user terminals, and generating and sending feedback.

[1550] "Basic information" refers to personal attributes and background information such as the user's name, job title, and years of service.

[1551] "Evaluation information" refers to data related to the user's job performance capabilities and characteristics, such as the user's achievement level, skill evaluation, communication skills, supportive attitude, and creativity.

[1552] "Emotional data" refers to data about a user's emotional state, obtained by analyzing their facial expressions and voice.

[1553] An "emotion engine" refers to a combination of software and hardware used to analyze a user's facial expressions and voice, and is a technology that recognizes the user's emotional state in real time.

[1554] A "database" is an information storage system used by a server to systematically store, manage, and retrieve basic information, evaluation information, and sentiment data received by the server.

[1555] The "Overall Rating Score" is a numerical value that represents the overall evaluation result of a user, calculated by the server based on basic information, evaluation information, and sentiment data.

[1556] A "management candidate" refers to a user who has been judged to have high suitability based on their overall evaluation score and who should be selected for a management position.

[1557] "Feedback" refers to documents and notifications that contain information such as evaluation results and areas for improvement, generated based on the overall evaluation score and sentiment data calculated by the server.

[1558] This invention is an evaluation system that combines an emotion engine that recognizes user emotions. By collecting and analyzing emotion data in addition to basic user information and evaluation information, the system provides a more multifaceted evaluation. This system is mainly implemented using a user terminal, a server, and an emotion engine.

[1559] First, the user terminal provides the user with a survey form for entering basic and evaluation information. This form is built using HTML, CSS, and JavaScript, and users access it through a browser. The form allows users to enter basic information (name, position, years of service, etc.) as well as evaluation items such as performance achievement, skill evaluation, communication skills, supportiveness, and creativity.

[1560] While filling out the survey form and answering specific questions, the emotion engine analyzes the user's facial expressions and voice, collecting emotional data. The emotion engine utilizes software such as TensorFlow and OpenCV to recognize the user's emotional state in real time. This data is later used in evaluations.

[1561] Once the user completes the survey and presses the submit button, the device converts the entered basic information, evaluation information, and collected sentiment data into JSON format and sends it to the server using HTTPS. The server receives this data and first stores it in temporary memory. Then, it migrates the data to a database (MySQL or PostgreSQL) and stores it organized by user.

[1562] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. Individual scores are calculated based on traditional evaluation metrics (e.g., performance achievement, skill evaluation) and new evaluation metrics (e.g., communication skills, supportiveness, creativity). Sentiment data is also analyzed to evaluate the user's emotional response in specific evaluation scenarios. This calculation is performed using a Python script. The sentiment data thus obtained is associated with each evaluation metric and used for a more refined overall evaluation.

[1563] Next, the server calculates an overall evaluation score based on the calculated evaluation scores (traditional indicators, new indicators, and sentiment data scores). This overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates. Users whose overall evaluation score exceeds the pre-set criteria are listed and become management candidates.

[1564] Furthermore, the server generates a detailed feedback document for the selected management candidates. This feedback document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, and feedback based on sentiment data. The generated feedback document is sent to the terminal and notified to the user. The user can review the feedback content through the terminal and use it for future reference.

[1565] A specific example of a prompt would be: "Please describe a comprehensive evaluation system that uses user sentiment data. Please include specific evaluation criteria, methods for collecting sentiment data, and evaluation methods."

[1566] In this way, the present invention makes it possible to appropriately evaluate diverse personnel and maximize the overall performance of the organization by conducting a comprehensive evaluation that incorporates emotional data in addition to conventional evaluation criteria.

[1567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1568] Step 1: Entering user information and collecting sentiment data

[1569] Input: Users enter basic and evaluation information into the survey form using their device. Specifically, they enter their name, job title, years of service, and evaluation items (performance achievement, skill evaluation, communication skills, supportive spirit, creativity, etc.).

[1570] Operation: The device provides a survey form built with HTML, CSS, and JavaScript. When the user begins to input, the device's camera and microphone are activated, and an emotion engine (TensorFlow, OpenCV) analyzes the user's facial expressions and voice in real time to collect emotion data.

[1571] Output: The basic information and evaluation information entered by the user, along with the collected sentiment data, are saved on the device in JSON format.

[1572] Step 2: Send

[1573] Input: Basic information, evaluation information, and sentiment data (in JSON format) collected in Step 1.

[1574] Operation: Once the user completes the survey form and presses the submit button, the device sends this data to the server using HTTPS.

[1575] Output: The server receives basic information, rating information, and sentiment data in JSON format.

[1576] Step 3: Collection and storage of evaluation data

[1577] Input: Basic information, evaluation information, and sentiment data (in JSON format) sent to the server.

[1578] Operation: The server temporarily stores data in memory, then migrates it to a database (MySQL, PostgreSQL) and organizes and stores it for each user.

[1579] Output: Basic information, evaluation information, and sentiment data stored in the database.

[1580] Step 4: Analysis of evaluation data

[1581] Input: Basic information, evaluation information, and sentiment data extracted from the database.

[1582] Operation: The server extracts this data and calculates individual scores based on traditional evaluation metrics (e.g., performance, skill assessment) and new evaluation metrics (e.g., communication skills, supportiveness, creativity). It also analyzes emotional data and evaluates the user's emotional response in specific evaluation scenarios. This analysis process uses a Python script.

[1583] Output: Traditional evaluation metrics, new evaluation metrics, and individual scores based on sentiment data.

[1584] Step 5: Calculation of overall performance score and selection of management candidates

[1585] Input: Individual evaluation scores (traditional metrics, new metrics, sentiment data scores).

[1586] Operation: The server integrates and weights each evaluation score to calculate an overall evaluation score. Next, it lists users whose overall evaluation score exceeds a pre-set standard and selects them as management candidates.

[1587] Output: List of management candidates.

[1588] Step 6: Generating and providing feedback

[1589] Input: A list of management candidates and detailed scores for each evaluation item.

[1590] Operation: The server generates a detailed feedback document for management candidates. This document includes detailed scores for each evaluation item, strengths and weaknesses, areas for future improvement, and feedback based on sentiment data. The generated feedback document is sent to the terminal and notified to the user.

[1591] Output: A feedback document is sent to the user's terminal, and a notification is displayed to the user.

[1592] (Application Example 2)

[1593] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1594] Traditional evaluation systems were limited to evaluations based on basic user information and evaluation data, and did not take into account the user's emotional state. This made it difficult to appropriately evaluate and respond quickly to customer satisfaction and emotional reactions, especially in physical stores. The lack of real-time information to evaluate and improve store service attitudes and atmosphere limited the ability to optimize the customer experience.

[1595] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1596] In this invention, the server includes means for providing a form for a user terminal to input basic information and evaluation information from the user; means for the user terminal to transmit the basic information and evaluation information input by the user to the server; means for the server to store the basic information and evaluation information transmitted from the user terminal in a database; means for the server to calculate the user's overall evaluation based on the basic information and evaluation information stored in the database; means for the server to select management candidates based on the calculated overall evaluation; means for the server to generate feedback for the management candidates and transmit it to the user terminal; means for collecting customer emotional data in physical stores and providing information for evaluating and improving customer service attitudes; and means including an emotion recognition engine that analyzes customer facial expressions and voice to collect emotional data. This makes it possible to perform multifaceted evaluations, including the emotional state of the customer, and to improve customer satisfaction in physical stores.

[1597] A "user terminal" refers to an electronic device used by a user to input information. Examples include smartphones, tablets, and computers.

[1598] "Basic information" refers to information that identifies a user and forms the basis of their identity, such as their name, job title, and years of service.

[1599] "Evaluation information" refers to specific information used to evaluate a user, such as their performance, skills, communication abilities, supportive attitude, and creativity.

[1600] A "server" refers to a computer system that receives information sent from user terminals, stores it in a database, and performs analysis on it.

[1601] A "database" refers to a system that organizes and stores basic user information and evaluation data as a collection of structured data.

[1602] "Overall rating" refers to a comprehensive performance evaluation of a user, calculated based on their basic information and evaluation data.

[1603] A "management candidate" refers to a user selected by the server and evaluated as suitable for a management position.

[1604] "Feedback" refers to information used to convey user evaluations and areas for improvement.

[1605] An "emotion recognition engine" refers to technology that analyzes the facial expressions and voice of users and customers to understand their emotional state.

[1606] A "physical store" refers to a retail store that exists in a physical location and can be visited by customers.

[1607] "Emotional data" refers to information about emotions obtained as a result of analyzing a customer's facial expressions and voice.

[1608] "Customer service attitude" refers to the quality of service and the way in which employees interact with customers in a store.

[1609] To implement this invention, the following system configuration is adopted. The system includes a user terminal, a server, a database, and an emotion recognition engine.

[1610] 1. Enter user information

[1611] The user terminal provides the user with a form for entering basic and evaluation information. The form includes basic user information (name, position, years of service, etc.) as well as evaluation items such as performance, skills, communication ability, supportiveness, and creativity. As the user fills out the form, an emotion recognition engine analyzes the user's facial expressions and voice, collecting emotional data. The user enters this information and sends the data to the server by pressing the submit button.

[1612] 2. Collection of evaluation data

[1613] The server receives basic information, evaluation information, and sentiment data sent from the user's terminal. The received data is temporarily stored in temporary memory before being saved to the database. The database is designed to organize and store data for each user.

[1614] 3. Analysis of evaluation data

[1615] The server extracts and analyzes basic information, evaluation information, and sentiment data stored in the database. It calculates individual scores based on traditional evaluation metrics (such as performance achievement and skill assessment) and new evaluation metrics (such as communication skills, supportiveness, and creativity). Furthermore, it analyzes sentiment data to assess the user's emotional response in specific evaluation scenarios. This includes changes in the user's emotions while filling out forms and their responses to specific questions. Each evaluation metric is correlated with sentiment data and scored to achieve a more multifaceted scoring system.

[1616] 4. Selection of management candidates

[1617] The server calculates an overall evaluation score based on each calculated evaluation score. This overall evaluation score is the result of weighting each score and serves as the criterion for selecting management candidates. Users whose overall evaluation score exceeds a pre-set standard are listed and selected as management candidates.

[1618] 5. Providing feedback

[1619] The server generates detailed feedback documents for selected management candidates. These documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data. The generated feedback documents are sent to the user's terminal, allowing the user to review the feedback content.

[1620] Adding specific examples

[1621] For example, in a physical store, a sales associate wearing smart glasses analyzes the customer's facial expressions and voice using an emotion recognition engine. If a customer appears confused in the fitting room, the system detects that emotion and notifies the sales associate that "the customer is likely in distress." Based on this, the sales associate can provide support quickly.

[1622] Example of a prompt

[1623] As a specific example, when a customer is looking for items in a fitting room, if this system analyzes the customer's facial expression and determines that the customer is confused, please provide specific examples of what kind of notification is sent to the store staff and how they should respond.

[1624] The software used includes OpenCV for image processing, Dlib for facial landmark detection, TensorFlow for emotion recognition models, and Flask for data communication. This enables multifaceted evaluation, including customer emotional states, and can improve customer satisfaction in physical stores.

[1625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1626] Step 1: Enter user information

[1627] The user enters basic information (name, position, years of service) and evaluation information (performance, skills, communication ability, supportive spirit, creativity) into a form provided by the terminal. During this process, an emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. The entered information and emotional data are stored in temporary memory. Basic information, evaluation information, and emotional data are obtained as input data.

[1628] Step 2: Send

[1629] When the user presses the submit button, the device sends the entered basic information, evaluation information, and collected sentiment data to the server. User information (basic information, evaluation information, sentiment data) is the input, and the server receives the data as output.

[1630] Step 3: Collection and storage of evaluation data

[1631] The server receives basic information, evaluation information, and sentiment data transmitted from the terminal. The received data is temporarily stored in temporary memory and then organized and stored in the database. The server's input is user information, and its output is storage in the database.

[1632] Step 4: Analysis of evaluation data

[1633] The server extracts basic information, evaluation information, and sentiment data stored in the database to perform a multifaceted evaluation. In addition to conventional evaluation metrics (performance achievement, skill evaluation), it calculates individual scores using new evaluation metrics (communication skills, supportive spirit, creativity) and sentiment data. The sentiment recognition engine provides the analysis results and generates the sentiment portion score in real time based on them. The input is user information from the database, and the output is the individual score for each evaluation metric and the overall evaluation score.

[1634] Step 5: Selection of management candidates

[1635] The server derives an overall evaluation score based on the calculated scores for each indicator. The overall evaluation score is calculated by assigning appropriate weights to each score. As a result, users whose overall evaluation score exceeds a pre-set standard are listed as management candidates. The input is the individual evaluation scores, and the output is a list of management candidates.

[1636] Step 6: Generate and send feedback

[1637] The server generates detailed feedback documents for selected management candidates. These documents include detailed scores for each evaluation item, strengths and weaknesses, areas for improvement, and feedback based on sentiment data. The generated feedback documents are sent to the user's terminal for review. The input is the management candidate's score data, and the output is the feedback document.

[1638] Step 7: In-store evaluation and notification

[1639] In physical stores, a terminal worn by a sales associate wearing smart glasses analyzes the customer's facial expressions and voice in real time. An emotion recognition engine provides the analysis results, and when a specific emotional state is detected, the system sends a notification to the sales associate. For example, if it determines that "the customer is in distress," the sales associate will receive a notification stating "the customer needs assistance." The input is the customer's emotional data, and the output is a notification to the sales associate.

[1640] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1641] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1642] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1643] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1644] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1645] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1646] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1647] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1648] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1649] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1650] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1651] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1652] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1653] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1654] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1655] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1656] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1657] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1658] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1659] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1660] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1661] The following is further disclosed regarding the embodiments described above.

[1662] (Claim 1)

[1663] A means by which a user terminal provides a form for the user to input basic information and evaluation information,

[1664] A means by which a user terminal transmits basic information and evaluation information entered by the user to a server,

[1665] The server provides a means for storing basic information and evaluation information transmitted from the user terminal in a database,

[1666] A means by which the server calculates the overall user evaluation based on the basic information and evaluation information stored in the above database,

[1667] A means by which the server selects management candidates based on a calculated overall evaluation,

[1668] A means by which the server generates feedback for the above-mentioned management candidate and sends it to the user terminal,

[1669] A system that includes this.

[1670] (Claim 2)

[1671] The above server further includes means for analyzing user basic information and evaluation information based on conventional and new indicators.

[1672] The system according to claim 1.

[1673] (Claim 3)

[1674] The above user terminal further includes means for displaying feedback notifications to the user.

[1675] The system according to claim 1.

[1676] "Example 1"

[1677] (Claim 1)

[1678] A means by which a user terminal provides a form for the user to input basic information and evaluation information,

[1679] A means by which a user terminal transmits basic information and evaluation information entered by the user to a server,

[1680] The server provides a means for storing basic information and evaluation information transmitted from the user terminal in a database,

[1681] A means by which the server calculates the overall user evaluation based on the basic information and evaluation information stored in the above database,

[1682] A means by which the server selects management candidates based on a calculated overall evaluation,

[1683] A means of using a generative AI model to generate feedback documents for users selected as management candidates,

[1684] The server provides a means for sending a feedback document generated by the above-mentioned AI model to the user terminal,

[1685] A system that includes this.

[1686] (Claim 2)

[1687] The system according to claim 1, further comprising means for the server to analyze the user's basic information and evaluation information based on conventional and new indicators.

[1688] (Claim 3)

[1689] The system according to claim 1, further comprising means for the user terminal to display a feedback notification to the user.

[1690] "Application Example 1"

[1691] (Claim 1)

[1692] A means by which a user terminal provides a form for the user to input basic information and evaluation information,

[1693] A means by which a user terminal transmits basic information and evaluation information entered by the user to a server,

[1694] The server provides a means for storing basic information and evaluation information transmitted from the user terminal in a database,

[1695] A means by which the server calculates the overall user evaluation based on the basic information and evaluation information stored in the above database,

[1696] A means by which the server selects management candidates based on a calculated overall evaluation,

[1697] A means by which the server generates feedback for the above-mentioned management candidate and sends it to the user terminal,

[1698] A means by which user terminals function as tools for factory operators and engineers to input self-assessments and peer assessments,

[1699] A means by which a user terminal transmits self-assessment and peer assessment data to a server,

[1700] A system that includes a means for a server to generate a detailed feedback document based on the transmitted evaluation data and send a notification to the user's terminal.

[1701] (Claim 2)

[1702] The system according to claim 1, further comprising means for analyzing the user's basic information and evaluation information based on conventional and new indicators, and newly designed evaluation indicators for robot operators and engineers.

[1703] (Claim 3)

[1704] The system according to claim 1, further comprising means for the user terminal to display a feedback notification to the user.

[1705] "Example 2 of combining an emotion engine"

[1706] (Claim 1)

[1707] A means by which a user terminal provides a form for the user to input basic information and evaluation information,

[1708] A means by which a user terminal transmits basic information and evaluation information entered by the user to a server,

[1709] The server provides means for storing basic information, evaluation information, and sentiment data transmitted from the user terminal in a database.

[1710] A means by which the server calculates the overall user rating based on the basic information, evaluation information, and sentiment data stored in the above database,

[1711] A means by which the server selects management candidates based on a calculated overall evaluation,

[1712] A means by which the server generates feedback for the above-mentioned management candidate and sends it to the user terminal,

[1713] A system that includes this.

[1714] (Claim 2)

[1715] The above server further includes means for analyzing emotional data from the user's facial expressions and voice using an emotion engine.

[1716] The system according to claim 1.

[1717] (Claim 3)

[1718] This further includes a means of analyzing the above emotional data in relation to each evaluation index.

[1719] The system according to claim 2.

[1720] (Claim 4)

[1721] The above user terminal further includes means for displaying feedback notifications to the user.

[1722] The system according to claim 1.

[1723] "Application example 2 when combining with an emotional engine"

[1724] (Claim 1)

[1725] A means by which a user terminal provides a form for the user to input basic information and evaluation information,

[1726] A means by which a user terminal transmits basic information and evaluation information entered by the user to a server,

[1727] The server provides a means for storing basic information and evaluation information transmitted from the user terminal in a database,

[1728] A means by which the server calculates the overall user evaluation based on the basic information and evaluation information stored in the above database,

[1729] A means by which the server selects management candidates based on a calculated overall evaluation,

[1730] A means by which the server generates feedback for the above-mentioned management candidate and sends it to the user terminal,

[1731] A means of collecting customer emotional data in physical stores and providing information to evaluate and improve customer service attitudes,

[1732] A means including an emotion recognition engine that analyzes customers' facial expressions and voice to collect emotional data,

[1733] A system that includes this.

[1734] (Claim 2)

[1735] The above server further includes means for analyzing user basic information and evaluation information based on conventional and new indicators.

[1736] The system according to claim 1.

[1737] (Claim 3)

[1738] The above user terminal further includes means for displaying feedback notifications to the user.

[1739] The system according to claim 1. [Explanation of symbols]

[1740] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means by which a user terminal provides a form for the user to input basic information and evaluation information, A means by which a user terminal transmits basic information and evaluation information entered by the user to a server, The server provides a means for storing basic information and evaluation information transmitted from the user terminal in a database, A means by which the server calculates the overall user evaluation based on the basic information and evaluation information stored in the above database, A means by which the server selects management candidates based on a calculated overall evaluation, A means by which the server generates feedback for the above-mentioned management candidate and sends it to the user terminal, A system that includes this.

2. The above server further includes means for analyzing user basic information and evaluation information based on conventional and new indicators. The system according to claim 1.

3. The above user terminal further includes means for displaying feedback notifications to the user. The system according to claim 1.

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