System

Abstract

To provide a system for supporting carrier formation of an individual.SOLUTION: A system includes: means for acquiring needs of carrier orientation or skill improvement in a questionnaire form regarding a user who is an employee; means for generating and proposing a training program, which is recommended to the user, for the user by using a prompt sentence, which instructs the generation of the training program, and a generative AI model on the basis of the acquired data; and means for adjusting the proposed training program on the basis of progress management or feedback of the training program.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a lack of efficient means to objectively grasp employees' capabilities and support their individual career development. There is also a need for a way to generate easy-to-understand phrases and expressions that take into account each individual's thoughts and personality. [Means for solving the problem]

[0005] It provides a means to analyze individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualize them in a radar chart. It also provides a means to give advice on improving capabilities based on career aspirations, and a means to generate easy-to-understand phrases and expressions that take into account an individual's way of thinking and personality. It also provides a means to accumulate data from organizations with many employees and based on long-term employment, and to improve the accuracy of the model. This will realize a system that supports individuals' career development based on the results of an analysis of their capabilities. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] As one embodiment of the present invention, an AI system for analyzing individual capabilities is provided. This system analyzes individual capabilities from social media posts, texts in communication tools, proposal documents, etc., and visualizes them in a radar chart. Specifically, it uses a text analysis algorithm to evaluate the communication skills, problem-solving abilities, etc. of each employee. "Example 2"

[0029] Furthermore, in one embodiment of the present invention, advice on skill development is provided based on the employee's career aspirations and other needs. Specifically, the system investigates the employee's career aspirations and skill development needs and provides personalized advice based on the results. For example, advice such as recommending a specific management training program to an employee who wants to improve their management skills is provided.

[0030] "Example 3"

[0031] In one embodiment of the present invention, AI generates easy-to-understand phrases and expressions that take into account an individual's thoughts and personality. Specifically, the AI ​​analyzes an employee's personality traits and communication style and generates appropriate phrases and expressions. For example, for an employee who prefers direct feedback, the AI ​​provides advice using specific and clear language.

[0032] The processing flow of each embodiment will be described below.

[0033] "Example 1"

[0034] Step 1: The AI ​​system analyzes an individual's capabilities based on social media posts, text in communication tools, proposal materials, etc.

[0035] Step 2: Use text analysis algorithms to assess each employee's communication skills, problem-solving abilities, etc.

[0036] Step 3: Visualize the evaluation results in a radar chart to understand the capabilities of each employee.

[0037] Step 1: Assess employees' career aspirations and development needs.

[0038] Step 2: Generate personalized advice based on the findings.

[0039] Step 3: Provide advice, such as recommending specific management training programs for employees who want to improve their management skills.

[0040] "Example 3"

[0041] Step 1: Analyze your employees' personality traits and communication styles.

[0042] Step 2: Based on the analysis results, the AI ​​generates appropriate expressions and phrases.

[0043] Step 3: For example, if an employee prefers direct feedback, provide specific, clearly worded advice.

[0044] Example 1

[0045] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0046] Conventional individual ability evaluation systems have difficulty accurately analyzing and visualizing individual abilities from a variety of data sources, such as social media posts, text from communication tools, and proposal materials. It has also been difficult to provide advice that takes into account an individual's career aspirations and personality. Furthermore, for organizations with many employees, improving the accuracy of the model through long-term data accumulation has been a challenge.

[0047] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0048] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing them in a radar chart; means for collecting and cleansing text data; means for evaluating individual capabilities using a text analysis algorithm; means for generating the evaluation results as a radar chart and providing them to the user; means for providing advice on improving capabilities based on career aspirations, etc.; and means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality. This makes it possible to analyze and visualize individual capabilities from multiple perspectives. It also makes it possible to provide advice that takes into account the individual's career aspirations and personality, and to improve the accuracy of the model by accumulating data from the entire organization.

[0049] "SNS" stands for Social Networking Service, an online platform for users to share information and interact over the Internet.

[0050] A "communication tool" is any software or application that users use to exchange messages or information.

[0051] A "proposal" is a document or presentation created to explain a specific purpose or proposal.

[0052] "Individual capabilities" refer to the skills, knowledge, experience, and characteristics possessed by a particular individual.

[0053] A "radar chart" is a graph format that arranges multiple evaluation items radially and visually shows the evaluation score for each item.

[0054] "Text data" is information expressed as a string of characters, and includes social media posts, messages on communication tools, proposal documents, etc.

[0055] "Cleansing" is a data preprocessing process that involves removing unnecessary information and noise and preparing the data in a format suitable for analysis.

[0056] A "text analysis algorithm" is a computational method or model for analyzing text data and understanding its meaning and structure.

[0057] "Evaluation results" are evaluation scores and analysis results regarding an individual's abilities obtained by a text analysis algorithm.

[0058] "Career aspirations" refer to the goals and directions that individuals have regarding their occupations and careers.

[0059] "Advice" is advice or suggestions given for a specific purpose or problem.

[0060] "Thoughts and personality" refers to an individual's way of thinking, behavioral patterns, and personality traits.

[0061] "Phrases and expressions" are the choice of words and expressions used to convey a particular message or information.

[0062] "Data storage" refers to the storage of collected data and its retention for later analysis or use.

[0063] "Model accuracy" is an indicator of how accurately an analytical algorithm or predictive model produces results.

[0064] This invention relates to an AI system for analyzing and visualizing individual abilities. A specific embodiment of this system will be described below.

[0065] Hardware and software used

[0066] Hardware: High-performance servers (e.g., high-performance servers)

[0067] Software: text analysis algorithms (e.g., Python's NLTK library), databases (e.g., MySQL®), visualization tools (e.g., D3.js)

[0068] System configuration

[0069] 1. Data Collection

[0070] The server collects text data from the APIs of social networking sites and log files of communication tools. For example, it uses the Twitter API to retrieve posts from a specific user.

[0071] 2. Data Preprocessing

[0072] The server cleanses the collected text data and removes unnecessary information. Specifically, it uses a Python regular expression library to remove unnecessary strings from the text data.

[0073] 3. Text Analysis

[0074] The server uses text analysis algorithms to extract keywords and phrases from the text data and evaluate their individual capabilities, for example, using the NLTK library for sentiment analysis and topic modeling.

[0075] 4. Visualization of evaluation results

[0076] The server generates the evaluation results as a radar chart and provides it to the user. Specifically, it uses D3.js to graphically display the evaluation results.

[0077] 5. Providing results

[0078] The server sends the generated radar chart to the user's device, which displays the results, for example, through a web browser.

[0079] Specific examples

[0080] For example, when analyzing employee A's social media posts and internal chat logs, the following prompt sentences are input into the generative AI model.

[0081] Example prompt sentence:

[0082] Analyze Employee A's social media posts and internal chat logs and evaluate the following abilities.

[0083] Communication skills

[0084] problem solving ability

[0085] leadership

[0086] creativity

[0087] Display the evaluation results in a radar chart.

[0088] Based on this prompt, the server analyzes Employee A's text data and calculates an evaluation score for each ability. Finally, the evaluation results are visualized as a radar chart and provided to the user. In this way, the user can visually grasp Employee A's abilities.

[0089] This system makes it possible to analyze and visualize an individual's capabilities from multiple angles, and also provides advice that takes into account an individual's career aspirations and personality. It also makes it possible to accumulate data from across the organization to improve the accuracy of the model.

[0090] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0091] Step 1:

[0092] Data collection

[0093] The server collects text data from the APIs of social networking sites and log files of communication tools. For example, it uses the Twitter API to retrieve posts from a specific user.

[0094] Input: Log files from SNS APIs and communication tools

[0095] Output: Collected text data

[0096] Specific operation: The server sends a request to the Twitter API and receives the user's posted data in JSON format.

[0097] Step 2:

[0098] Data Preprocessing

[0099] The server cleanses the collected text data and removes unnecessary information. Specifically, it uses a Python regular expression library to remove unnecessary strings from the text data.

[0100] Input: Collected text data

[0101] Output: Cleansed text data

[0102] What it does: The server uses Python's regular expression library to remove HTML tags and special characters.

[0103] Step 3:

[0104] Text analytics

[0105] The server uses text analysis algorithms to extract keywords and phrases from the text data and evaluate their individual capabilities, for example, using the NLTK library for sentiment analysis and topic modeling.

[0106] Input: Cleansed text data

[0107] Output: Ability assessment score

[0108] What it does: The server uses the NLTK library to tokenize the text data and extract sentiment scores and topics.

[0109] Step 4:

[0110] Visualization of evaluation results

[0111] The server generates the evaluation results as a radar chart and provides it to the user. Specifically, it uses D3.js to graphically display the evaluation results.

[0112] Input: Ability assessment score

[0113] Output: Radar chart

[0114] Specific operation: The server passes the evaluation results in JSON format to D3.js and generates a radar chart.

[0115] Step 5:

[0116] Providing results

[0117] The server sends the generated radar chart to the user's device, where the results are displayed, for example, through a web browser.

[0118] Input: Radar chart

[0119] Output: Radar chart displayed on the user's device

[0120] Specific operation: The server sends an HTML page containing a radar chart to the user's device as an HTTP response.

[0121] (Application example 1)

[0122] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0123] Previous systems lacked the means to analyze individual capabilities and assign appropriate tasks. They also did not provide a concrete method for visualizing employees' skill sets and allowing managers to assign appropriate tasks. This made it difficult to make the most of employees' capabilities and hindered efficient business operations.

[0124] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0125] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing advice on improving capabilities based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account individual thoughts and personalities, means for analyzing communication tools and proposal materials used by employees in a factory, evaluating each employee's capabilities, and assigning appropriate tasks, means for visualizing employee skill sets in a radar chart, and means for a manager to assign appropriate tasks based on the employee's skill set. This makes it possible to efficiently analyze employee capabilities and assign appropriate tasks.

[0126] "SNS" is an abbreviation for social networking service, an online platform for users to share information and communicate.

[0127] A "communication tool" is software or an application that individuals or groups use to exchange information and communicate.

[0128] A "proposal" is a document or presentation created to explain and propose a particular idea or plan.

[0129] "Individual ability" is a comprehensive assessment of a specific person's skills, knowledge, experience, aptitude, etc.

[0130] A "radar chart" is a graph that visually represents the overall balance and characteristics by arranging multiple evaluation items radially and connecting the evaluation values ​​of each item.

[0131] "Career aspirations" refers to the career goals and direction that an individual wants to achieve in the future.

[0132] "Advice for improving skills" refers to providing specific suggestions and guidance to improve an individual's skills and knowledge.

[0133] "Individual thoughts and personality" refers to the way of thinking, behavioral patterns, and personality traits of a particular person.

[0134] "Easy-to-understand phrases and expressions" are words and expressions that convey information in a way that is easy for the recipient to understand and accept.

[0135] "Factory workers" refers to workers engaged in manufacturing and production industries.

[0136] "Assigning appropriate tasks" means assigning the most appropriate work or tasks to employees based on their abilities and skills.

[0137] A "skill set" refers to the set of skills and abilities that a particular person possesses.

[0138] "Administrator" refers to a person in charge of managing and supervising an organization or team.

[0139] The system for implementing this invention has a function to analyze individual capabilities from social media posts, text on communication tools, proposal documents, etc., and visualize them in a radar chart. It also includes a function to analyze communication tools and proposal documents used by employees in a factory, evaluate the capabilities of each employee, and assign appropriate tasks.

[0140] System Program

[0141] The server runs a program that performs the following processes: First, it collects text data such as social media posts, text from communication tools, and proposal materials. Next, it uses a text analysis algorithm to analyze the collected text data and evaluates each employee's communication skills, problem-solving ability, and other aspects. Based on the results of this evaluation, it generates and visualizes a radar chart.

[0142] Hardware and software used

[0143] This system uses the following hardware and software:

[0144] Hardware: Server, client terminal (PC, smartphone, tablet, etc.)

[0145] Software: text analysis algorithms (e.g., spacy), vectorization tools (e.g., TfidfVectorizer), dimensionality reduction tools (e.g., PCA), visualization tools (e.g., matplotlib)

[0146] Data processing and calculation

[0147] The server first collects text data and analyzes it using a text analysis algorithm. The analysis results are vectorized and then dimension-reduced using a dimension reduction tool. Next, a radar chart is generated and visualized based on the reduced data. This allows managers to visually check employees' skill sets and assign appropriate tasks.

[0148] Specific examples

[0149] For example, if Employee A makes a new proposal in a factory, the proposal text is analyzed and Employee A's communication skills and problem-solving abilities are evaluated. The evaluation results are visualized in a radar chart, and managers can use the results to assign appropriate tasks to Employee A.

[0150] Prompt Sentence Examples

[0151] Examples of prompts to input to a generative AI model include:

[0152] "Analyze Employee A's proposal and evaluate his / her communication skills and problem-solving abilities."

[0153] Using this prompt, the AI ​​model can evaluate employee capabilities and visualize the results in a radar chart.

[0154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0155] Step 1:

[0156] The server collects text data such as social media posts, text from communication tools, proposal materials, etc. This involves using APIs to obtain data from each platform. The input is text data from each platform, and the output is a list of the collected text data.

[0157] Step 2:

[0158] The server analyzes the collected text data using a text analysis algorithm (e.g., spacy). Specifically, it tokenizes the text data and analyzes the part of speech and meaning of each token. The input is a list of collected text data, and the output is a list of analyzed text data.

[0159] Step 3:

[0160] The server converts the parsed text data into numerical vectors using a vectorization tool (e.g., TfidfVectorizer), which makes the text data in a format that can be processed by a machine learning model. The input is a list of parsed text data, and the output is a list of numerical vectors.

[0161] Step 4:

[0162] The server reduces the dimension of the vectorized data using a dimension reduction tool (e.g., PCA), which makes it easier to visualize the data. The input is a list of numeric vectors, and the output is a list of dimension-reduced data.

[0163] Step 5:

[0164] The server generates and visualizes radar charts based on the dimension-reduced data. Specifically, it uses visualization tools such as matplotlib to draw the radar charts. The input is a list of dimension-reduced data, and the output is an image of the radar chart.

[0165] Step 6:

[0166] The server provides the generated radar chart to the manager, helping the manager assign appropriate tasks based on the employee's skill set. The input is the radar chart image, and the output is a report that the manager can review.

[0167] Step 7:

[0168] The user (manager) assigns appropriate tasks to employees based on the radar chart provided. Specifically, by referring to the radar chart, the user selects the tasks that best suit each employee's skill set. The input is an image of the radar chart and employee skill set information, and the output is a list of assigned tasks.

[0169] Example 2

[0170] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0171] Providing optimal advice to individual employees regarding their career aspirations and skill development needs has been difficult using conventional methods. Organizations with many employees, in particular, need systems that can efficiently collect and analyze data on each employee and provide appropriate advice. It is also important to generate easily understood phrases and expressions that take into account the employee's way of thinking and personality. Effective methods are needed to solve these challenges.

[0172] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0173] In this invention, the server includes means for analyzing individual abilities and visualizing them in a radar chart, means for providing ability improvement advice based on career aspirations, means for generating easy-to-understand phrases and expressions that take into account individual thoughts and personality, means for collecting and preprocessing employee data, means for inputting the preprocessed data into a generative AI model to generate advice, and means for providing the generated advice to employees. This makes it possible to efficiently provide individually optimal advice in response to employees' career aspirations and ability improvement needs.

[0174] "A means of analyzing individual capabilities and visualizing them in a radar chart" is a method for collecting employee skills and performance data, analyzing it, and displaying it visually.

[0175] "Means of providing advice on improving skills based on career aspirations" refers to a method of suggesting appropriate skill development and training based on employees' career goals and aspirations.

[0176] "Means of generating easy-to-understand phrases and expressions that take into account individual thoughts and personalities" is a method of generating optimal words and expressions that suit the personality and communication style of employees.

[0177] "Means for collecting and preprocessing employee data" refers to a method for collecting various data obtained from employees and arranging it into an analyzable format.

[0178] "Means for inputting preprocessed data into a generative AI model to generate advice" refers to a method for inputting preprocessed data into an AI model, which results in the generation of specific advice for employees.

[0179] "Means for providing generated advice to employees" means a method for communicating the advice generated by the AI ​​model to employees.

[0180] This invention is a system that provides optimal advice to each employee based on their career aspirations and needs for improving their skills. This system operates in cooperation with a server, a terminal, and a user.

[0181] The server first collects employee data, including information entered by the employee about their career aspirations and development needs, past performance data, evaluation data, etc. The collected data is then stored in a database.

[0182] The server then preprocesses the collected data. This includes data cleaning, normalization, and feature extraction. For example, it tokenizes text data entered by employees and removes unnecessary spaces and special characters. It also quantifies past performance data and converts it into a format that can be input into the model.

[0183] The preprocessed data is input into a generative AI model. Here, for example, OpenAI's GPT-4 (registered trademark) is used. This model generates optimal advice for employees based on the input data. Specifically, it uses the following prompt sentence:

[0184] Example prompt sentence:

[0185] Employee A wants to improve his management skills. Based on Employee A's past evaluation and performance data, please recommend the most appropriate management training program.

[0186] Based on this prompt, the generative AI model generates the following advice:

[0187] Employee A is recommended to take the Leadership Fundamentals course to strengthen his leadership skills, which will focus on effective team management and communication skills.

[0188] The generated advice is sent from the server to the terminal. The terminal displays the received advice to the user. The user can check the displayed advice and make an action plan if necessary. For example, the user can take steps to participate in a basic leadership course.

[0189] This system makes it possible to efficiently provide optimal advice to employees on their career aspirations and skill development needs, and also increases the receptivity of advice by generating easy-to-understand phrases and expressions that take into account the employee's way of thinking and personality.

[0190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0191] Step 1:

[0192] The server collects data from users (employees) regarding their career aspirations and needs for improving their skills.

[0193] Input: Information about your career aspirations and development needs entered by you through your device.

[0194] Output: The collected data is stored in a database.

[0195] Specific operation: The user inputs "I want to improve my management skills" through the terminal. The server stores this information in the database.

[0196] Step 2:

[0197] The server pre-processes the collected data.

[0198] Input: Information stored in a database about the user's career aspirations and development needs.

[0199] Output: The preprocessed data is converted into a format that can be fed into a generative AI model.

[0200] What it does: The server tokenizes the text data entered by the user, removing unnecessary spaces and special characters, and converts past performance data into a numerical format that can be fed into the model.

[0201] Step 3:

[0202] The server inputs the preprocessed data into a generative AI model.

[0203] Input: Preprocessed data.

[0204] Output: The data that is input into a generative AI model.

[0205] Specific operation: The server inputs the following prompt sentence into the generative AI model:

[0206] Employee A wants to improve his management skills. Based on Employee A's past evaluation and performance data, please recommend the most appropriate management training program.

[0207] Step 4:

[0208] The generative AI model generates advice based on input data.

[0209] Input: The data fed into a generative AI model.

[0210] Output: The generated advice.

[0211] Specific behavior: The generative AI model generates advice such as the following:

[0212] Employee A is recommended to take the Leadership Fundamentals course to strengthen his leadership skills, which will focus on effective team management and communication skills.

[0213] Step 5:

[0214] The server transmits the generated advice to the user's terminal.

[0215] Input: The generated advice.

[0216] Output: Advice sent to the user's terminal.

[0217] Specific operation: The server sends the generated advice to the terminal, and the terminal displays it to the user as follows:

[0218] Recommended Tip: To strengthen your leadership skills, we recommend taking our Leadership Fundamentals course, which focuses on effective team management and communication skills.

[0219] Step 6:

[0220] The user checks the advice displayed on the terminal and makes an action plan if necessary.

[0221] Input: The advice displayed on the terminal.

[0222] Output: User's action plan.

[0223] Specific operations: The user clicks on the link to apply for the "Leadership Basics Course" through the terminal and enters the required information into the application form.

[0224] (Application example 2)

[0225] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0226] There is a need to accurately grasp employees' career aspirations and skill development needs and provide individually optimized training programs. However, conventional systems make it difficult to respond to the diverse needs of employees, and progress management and feedback on training programs are insufficient, preventing effective skill development. For this reason, a new system is needed to support employees' career development and improve productivity throughout the organization.

[0227] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0228] In this invention, the server includes: means for acquiring, in the form of a questionnaire, information about the career aspirations or skill improvement needs of an employee user; a prompt for generating a training program recommended for the user based on the acquired data; a generative AI model for generating and proposing the training program to the user; and a means for adjusting the training program based on progress management or feedback on the proposed training program. This enables the server to meet diverse employee needs and provide individually optimized training programs. The server also includes: a prompt for analyzing individual abilities based on messages posted by the user on a social networking site, messages sent by the user via a communication tool, or materials generated by the user, and visualizing the analyzed individual abilities in a radar chart; a generative AI model for presenting the visualized radar chart to the user; a prompt for generating ability improvement advice based on the acquired career aspiration data and the results of the analysis of the individual abilities; and a generative AI model for generating ability improvement advice and presenting it to the user.

[0229] "SNS" is an abbreviation for Social Networking Service, an online platform for users to share information and communicate over the Internet.

[0230] "Communication tools" are software or applications that allow users to send and receive messages and share information.

[0231] A "proposal" is a document or presentation created to explain a specific purpose or proposal.

[0232] "Individual capabilities" refers to the knowledge, skills, experience, and other characteristics possessed by a particular person.

[0233] A "radar chart" is a graph that visually represents the overall balance and characteristics by arranging multiple evaluation items radially and connecting the evaluation values ​​of each item.

[0234] "Career aspirations" refer to the goals and direction an individual wants to achieve in their professional life.

[0235] "Skills improvement advice" refers to specific guidance and suggestions to further develop an individual's skills and knowledge.

[0236] "Individual thoughts and personality" refers to the way of thinking, behavioral patterns, and psychological characteristics of a particular person.

[0237] "Easy-to-understand phrases and expressions" refers to the use of language and expressions that convey information in a way that is easy for the recipient to understand and accept.

[0238] A "questionnaire format" is a survey method for obtaining answers to questions, and is mainly a multiple choice format or a free description format.

[0239] A "generative AI model" refers to an algorithm or system that uses artificial intelligence techniques to generate new information or suggestions from data.

[0240] A "personalized training program" refers to an education or training plan that is customized to meet individual needs and characteristics.

[0241] "Progress management" refers to the process of monitoring the progress of planned activities or projects and making adjustments as needed.

[0242] "Feedback" refers to information that provides evaluation and opinion on specific actions or results and encourages improvement and growth.

[0243] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. The server collects employees' career aspirations and skill improvement needs in the form of a questionnaire, and recommends personalized training programs using a generative AI model based on the collected data. The terminal provides an interface for employees to answer the questionnaire and manage the progress of the training program. The user uses the system as an employee, inputs information to support their own career development, and receives feedback.

[0244] The server first collects information about employees' career aspirations and skill development needs in the form of a questionnaire. This questionnaire is provided to employees via devices such as smartphones or tablets. Questions in the questionnaire include current skills, skills they would like to improve, and career goals.

[0245] The collected data is input into a generative AI model on the server. The generative AI model generates an optimal training program based on the employee's career aspirations and skill improvement needs. In this process, the generative AI model generates and proposes a training program suitable for the employee using a prompt statement that instructs the user to generate a recommended training program based on the acquired data.

[0246] For example, if an employee wants to improve their "advanced management skills," they can input the following prompt into the generative AI model:

[0247] Employee career aspirations: Improving management skills Current skills: Basic management skills Skills I'd like to improve: Advanced management skills Suggest a suitable training program for this employee.

[0248] Based on this prompt, the generative AI model suggests specific training programs, such as a "leadership training course" or a "project management certification program."

[0249] The proposed training program is provided to the employee through the terminal. The employee takes the training program on the terminal, the server manages the training progress and generates feedback, and the terminal can receive the generated feedback. The server collects progress data and adjusts the training program as needed.

[0250] This system makes it possible to respond to the diverse needs of employees and provide individually tailored training programs, thereby supporting employees' career development and improving productivity across the organization.

[0251] The server also analyzes individual abilities from statements posted by employee users on SNS, statements sent by users using a communication tool, or proposal materials generated by users, and presents the visualized radar chart to the user using a prompt statement instructing the system to visualize the analyzed individual abilities in a radar chart and the generative AI model. Specifically, the server generates the visualized radar chart using a prompt statement including data representing statements posted by employee users on SNS, statements sent by users using a communication tool, or proposal materials generated by users, and the prompt statement instructing the system to analyze individual abilities and visualize the analyzed individual abilities in a radar chart and the generative AI model.

[0252] The server also generates and presents advice for improving skills to the user using a prompt statement and a generative AI model that instructs the server to generate advice for improving skills based on the career-oriented data obtained in the form of a questionnaire and the results of an analysis of the individual's skills.

[0253] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0254] Step 1:

[0255] The server collects information about employees' career aspirations and skill improvement needs in the form of a questionnaire.

[0256] Input: Survey data answered by employees via smartphones or tablets.

[0257] Data processing: The survey responses are stored in a database to extract each employee's career aspirations and skill improvement needs.

[0258] Output: Career aspirations and skill development needs data for each employee.

[0259] Step 2:

[0260] The server inputs prompt sentences into the generative AI model based on the collected data.

[0261] Input: Employee career aspirations and skill development needs data.

[0262] Data processing: Generate prompt sentences and input them into the generative AI model.

[0263] Output: Training program suggestions from the generative AI model.

[0264] Step 3:

[0265] The server sends training program suggestions obtained from the generative AI model to the device.

[0266] Input: Training program suggestions from a generative AI model.

[0267] Data processing: Organize training program proposals for each employee and send them to the terminal.

[0268] Output: Training program suggestions displayed on the terminal.

[0269] Step 4:

[0270] The terminal displays training program suggestions to employees and manages their progress.

[0271] Input: Training program suggestions sent by the server.

[0272] Data Processing: Record the progress of training programs and gather employee feedback.

[0273] Output: Employee training program progress data and feedback.

[0274] Step 5:

[0275] The server adjusts the training program based on progress data and feedback collected from the device.

[0276] Input: Progress data and feedback sent from the device.

[0277] Data Processing: Analyze progress data and feedback and update training programs as needed.

[0278] Output: Updated training program proposal.

[0279] Step 6:

[0280] The server again transmits the updated training program proposal to the terminal and notifies the employee.

[0281] Input: Updated training program proposal.

[0282] Data processing: Updated training programs are organized for each employee and sent to the terminal.

[0283] Output: Updated training program proposal displayed on the terminal.

[0284] The server may further include means for analyzing the user's emotions using an emotion engine described below, and may adjust the training program according to the user's emotions when adjusting the training program. Specifically, if the user's emotion is "anger" or "sadness," the pace of the training program may be adjusted to slow down.

[0285] Example 3

[0286] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0287] Providing appropriate feedback based on employees' personality traits and communication styles contributes to improving employee motivation and work efficiency. However, conventional systems have difficulty generating personalized feedback for individual employees, and can only provide general advice. Furthermore, in organizations with many employees, it is difficult to grasp the personality traits of each employee in detail and provide feedback based on that.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0289] In this invention, the server includes a means for inputting employee personality trait data, a means for analyzing the personality trait data, a means for inputting a prompt sentence to the generative AI model, a means for the generative AI model to generate appropriate feedback, and a means for providing the generated feedback, thereby enabling the generation of personalized feedback based on the employee's personality traits and communication style.

[0290] "SNS" is an abbreviation for social networking services, an online platform for people to share information and communicate over the Internet.

[0291] A "communication tool" is software or an application that individuals or groups use to exchange information and communicate.

[0292] A "proposal" is a document created to explain a particular idea or plan and to seek approval or support.

[0293] "Individual capabilities" refers to the knowledge, skills, experience, and other characteristics possessed by a particular person.

[0294] A "radar chart" is a graphical format for visually comparing multiple variables, plotting data points on axes radiating from a center.

[0295] "Career aspirations" refer to the goals and direction that an individual wants to achieve in their professional life.

[0296] "Skill development advice" refers to specific suggestions and guidance to further develop an individual's skills and knowledge.

[0297] "Individual thoughts and personality" refers to the cognitive tendencies and behavioral patterns of a particular person.

[0298] "Easy-to-understand phrases and expressions" refers to the use of language and expressions that convey information in a way that is easy for the recipient to understand and accept.

[0299] "Employee personality trait data" refers to information about employees' personalities and behavioral patterns, and is obtained from psychological assessment tools, survey results, etc.

[0300] A "generative AI model" refers to an algorithm or system that has been trained to perform a specific task using artificial intelligence techniques.

[0301] A "prompt" is an instruction given to a generative AI model to generate a specific output.

[0302] "Feedback" refers to evaluation or advice given in response to a specific action or outcome.

[0303] The present invention provides a system for providing personalized feedback based on an employee's personality traits and communication style. Specific embodiments of the system are described below.

[0304] First, users input personality trait data for employees. This data is collected using psychological assessment tools such as the Big Five personality trait model and the Myers-Briggs Type Indicator (MBTI), as well as questionnaire results. Users enter information such as "Employee A is extroverted and prefers direct feedback" through a web form.

[0305] The server then receives the personality data entered by the user and analyzes it. The server references the employee's personality data stored in a database to identify the employee's communication style and feedback preferences. This analysis is performed using data analysis software and algorithms.

[0306] The server then uses the analysis results to create a prompt to be input to the generative AI model, such as "Employee A is extroverted and prefers direct feedback. Please provide specific and clear feedback on the progress of his recent project." This prompt is then sent to the generative AI model (e.g., OpenAI's GPT-4).

[0307] The generative AI model generates appropriate feedback based on the prompt received from the server. The generative AI model uses natural language processing technology to create feedback that matches the employee's personality traits. The generated feedback is then sent back to the server.

[0308] Finally, the server provides the feedback received from the generative AI model to the user. Specifically, the server sends the feedback to the user via a web interface or email. The user can review the provided feedback and communicate it to Employee A.

[0309] As a concrete example, if employee A has the personality traits of "extroverted and prefers direct feedback," the server will input the following prompt sentence into the generative AI model:

[0310] Example prompt sentence:

[0311] "Employee A is extroverted and enjoys direct feedback. Please provide specific and clear feedback on his recent project progress."

[0312] Based on this prompt, the generative AI model generates the following feedback:

[0313] "Employee A, your recent project progress has been excellent. Your data analysis was particularly accurate. However, please report back to us sooner next time."

[0314] In this way, the server can generate appropriate feedback based on the employee's personality traits and provide it to the user. This system is expected to improve employee motivation and work efficiency. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0315] Step 1:

[0316] The user inputs personality trait data for the employee.

[0317] Specifically, a user accesses a web form, enters employee personality trait data, for example, "Employee A is extroverted and prefers direct feedback," and clicks the submit button.

[0318] Input: Employee personality trait data (e.g., "Employee A is extroverted and prefers direct feedback")

[0319] Output: Personality trait data is sent to the server

[0320] Step 2:

[0321] The server receives the personality trait data sent by the user and stores it in a database.

[0322] Specifically, the server analyzes the received data and stores it in a database to identify employee personality traits.

[0323] Input: User-submitted personality trait data

[0324] Output: Personality trait data stored in a database

[0325] Step 3:

[0326] The server analyzes the stored personality trait data.

[0327] Specifically, the server references employee personality trait data stored in a database to identify the employee's communication style and feedback preferences.

[0328] Input: Personality trait data stored in a database

[0329] Output: Identification of employee communication styles and feedback preferences

[0330] Step 4:

[0331] Based on the analysis results, the server creates prompt sentences to be input into the generative AI model.

[0332] Specifically, the server generates a prompt such as, "Employee A is extroverted and prefers direct feedback. Please provide specific and clear feedback on the progress of his recent project."

[0333] Input: Identification of employee communication styles and feedback preferences

[0334] Output: A prompt to be input to the generative AI model

[0335] Step 5:

[0336] The server inputs prompt sentences into the generative AI model, causing it to generate appropriate feedback.

[0337] Specifically, the server sends prompt sentences to a generative AI model (e.g., OpenAI's GPT-4) to generate feedback.

[0338] Input: A prompt to be input to the generative AI model

[0339] Output: Feedback sent back from the generative AI model

[0340] Step 6:

[0341] The server provides the feedback received from the generative AI model to the user.

[0342] Specifically, the server sends the feedback to the user via a web interface or email, and the user can review the provided feedback and communicate it to Employee A.

[0343] Input: Feedback sent back from the generative AI model

[0344] Output: Feedback provided to the user

[0345] The above is the specific processing flow of the program of this system.

[0346] (Application example 3)

[0347] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0348] Providing appropriate feedback and instructions tailored to each employee's personality traits and communication style contributes to improving employee motivation and work efficiency. However, conventional systems often fail to provide personalized communication to individual employees, resulting in only general feedback and instructions. As a result, the feedback and instructions received by employees may be ineffective. The present invention aims to solve these problems and provide a system that provides feedback and instructions tailored to each employee.

[0349] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0350] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc. and visualizing the results in a radar chart, means for providing ability improvement advice based on career aspirations, etc., means for generating easy-to-understand phrases and expressions taking into account individual thoughts and personalities, means for analyzing employee personality traits and communication styles and generating appropriate phrases and expressions based thereon, and means for generating appropriate phrases using a generative AI model. This makes it possible to provide personalized feedback and instructions to each employee.

[0351] "SNS" stands for Social Networking Service, an online platform for people to share information and interact over the Internet.

[0352] "Communication tools" is a general term for software and applications used by individuals and groups to exchange information.

[0353] A "proposal" is a document or presentation created to explain and gain support for a particular idea or plan.

[0354] "Individual capabilities" refers to the knowledge, skills, experience, and other characteristics possessed by a particular person.

[0355] A "radar chart" is a diagram that visually represents multiple variables on a single graph, with each variable arranged radially.

[0356] "Career aspirations" refer to the goals and direction that an individual wants to achieve in their professional life.

[0357] "Skill development advice" refers to specific suggestions and guidance to further develop an individual's skills and knowledge.

[0358] "Individual thoughts and personality" refers to the way of thinking, behavioral patterns, and psychological characteristics of a particular person.

[0359] "Easy-to-understand phrases and expressions" refers to the use of language and expressions that convey information in a way that is easy for the recipient to understand and accept.

[0360] "Employee personality traits" refer to the psychological characteristics and behavioral patterns possessed by a particular employee.

[0361] "Communication style" refers to the particular way or attitude that an individual uses when exchanging information with others.

[0362] A "generative AI model" refers to an algorithm or system that is trained to perform a specific task using artificial intelligence.

[0363] A "prompt" is an instruction or question input to a generative AI model to obtain a specific output.

[0364] The system for implementing the present invention is designed to provide appropriate feedback and instructions based on the personality traits and communication styles of employees. A specific embodiment of this system will be described below.

[0365] System configuration

[0366] The system consists of the following main components:

[0367] 1. Data collection module: Collects data from social media posts, text from communication tools, proposal materials, etc.

[0368] 2. Ability analysis module: Analyzes collected data and visualizes individual abilities in a radar chart.

[0369] 3. Advice generation module: Provides advice on improving skills based on career aspirations, etc.

[0370] 4. Communication Style Analysis Module: Analyze employee personality traits and communication styles.

[0371] 5. Generative AI model: An AI model for generating appropriate phrases and expressions.

[0372] 6. Feedback generation module: Uses generative AI models to generate appropriate feedback and instructions for employees.

[0373] Program processing

[0374] The server first uses a data collection module to collect data from social media posts, text from communication tools, proposal materials, etc. This data is analyzed by a skills analysis module, and individual skills are visualized in a radar chart. Next, an advice generation module generates advice for skill improvement based on career aspirations, etc.

[0375] The communication style analysis module then analyzes the employee's personality traits and communication style, and the generative AI model uses the results to generate appropriate phrases and expressions. Finally, the feedback generation module uses the generative AI model to generate appropriate feedback and instructions for the employee.

[0376] Hardware and software used

[0377] Hardware: Internet-connected computers or robots

[0378] Software: Python, OpenAI API

[0379] Specific examples

[0380] For example, if employee Taro Tanaka prefers direct feedback, he could input the following prompt into the generative AI model:

[0381] The employee's name is Taro Tanaka. He prefers a direct communication style. Generate the following message in the appropriate wording: Please improve the work procedure.

[0382] When this prompt is input into the generative AI model, it generates direct and clear feedback, such as, "Mr. Tanaka, please make specific improvements to your work procedures." In this way, the system can provide personalized feedback and instructions to each employee.

[0383] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0384] Step 1:

[0385] The server uses a data collection module to collect data from social media posts, text from communication tools, proposal materials, etc. It receives employees' social media account information and communication tool login information as input, and generates the collected text data as output. This data serves as the basis for analyzing employees' abilities and personality traits.

[0386] Step 2:

[0387] The server uses a competency analysis module to analyze the collected text data. As input, it receives the text data collected in step 1 and analyzes individual competencies using natural language processing technology. As output, it generates data that visualizes the analysis results in the form of a radar chart. This data is used to visually grasp employee competencies.

[0388] Step 3:

[0389] The server uses an advice generation module to generate skill improvement advice based on career aspirations, etc. It receives as input the radar chart data generated in step 2 and the employee's career aspirations, and generates specific skill improvement advice as output. This advice serves as a guideline for employees to improve their skills.

[0390] Step 4:

[0391] The server uses the communication style analysis module to analyze the employee's personality traits and communication style. It receives the employee's personality diagnostic test results and past communication history as input, and generates the analysis results of the employee's personality traits and communication style as output. This analysis result becomes the basis for generating feedback and instructions appropriate for the employee.

[0392] Step 5:

[0393] The server uses a generative AI model to generate appropriate phrases and expressions. As input, it receives the results of the personality trait and communication style analysis generated in step 4, as well as the message that will be the basis for feedback and instructions, and as output, it generates phrases and expressions that are appropriate for the employee. In this process, the generative AI model generates appropriate feedback and instructions based on the prompt sentence.

[0394] Step 6:

[0395] The server uses a feedback generation module to provide employees with appropriate phrases and expressions obtained from the generative AI model. As input, it receives the feedback and instructions generated in step 5, and as output, it provides personalized feedback and instructions to employees. This feedback and instructions contributes to improving employee motivation and work efficiency.

[0396] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0397] "Example 1"

[0398] One embodiment of the present invention is a system that incorporates an emotion engine. This system analyzes an individual's abilities based on the user's social media posts, text on communication tools, proposal materials, etc., and visualizes them in a radar chart. It also provides advice on improving abilities based on career aspirations, etc. It also generates easy-to-understand phrases and expressions that take into account the individual's thoughts and personality. In this system, the emotion engine recognizes the user's emotions and provides advice based on those emotions. For example, if the user is feeling frustrated, it analyzes the cause and provides specific advice to resolve it.

[0399] "Example 2"

[0400] Another example is a system that accumulates data from organizations with many employees and long-term employment to improve the accuracy of the model. This system incorporates an emotion engine to take into account changes in employees' emotions. For example, if an employee is feeling stressed, the system analyzes the cause of that stress and provides specific advice to alleviate the stress.

[0401] "Example 3"

[0402] Another example is a system that supports individual career development based on the results of an individual's ability analysis. This system incorporates an emotion engine to take into account changes in employees' emotions. For example, if an employee is considering a career change, the system analyzes the emotional background that led to that decision and provides specific advice for the new career path.

[0403] The processing flow of each embodiment will be described below.

[0404] "Example 1"

[0405] Step 1: Analyze individual capabilities based on users' social media posts, text in communication tools, proposal materials, etc.

[0406] Step 2: Visualize the analysis results in a radar chart.

[0407] Step 3: Provide advice on improving skills based on career aspirations, etc.

[0408] Step 4: Use the emotion engine to recognize the user's emotions and provide advice based on those emotions.

[0409] "Example 2"

[0410] Step 1: Collect data on organizations with large numbers of employees and long-term employment.

[0411] Step 2: Improve the accuracy of the model based on the accumulated data.

[0412] Step 3: Use an emotion engine to take into account changes in employee emotions.

[0413] Step 4: If the employee is stressed, analyze the causes of the stress and provide specific advice to reduce it.

[0414] "Example 3"

[0415] Step 1: Support individual career development based on the results of individual ability analysis.

[0416] Step 2: Use an emotion engine to take into account changes in employee emotions.

[0417] Step 3: If the employee is considering a career change, analyze the emotional context that led to that decision.

[0418] Step 4: Provide specific advice for a new career path.

[0419] Example 1

[0420] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0421] In today's business environment, it is important to accurately evaluate individual capabilities and provide appropriate feedback. However, traditional methods make it difficult to comprehensively analyze an individual's capabilities and provide advice tailored to their emotions. It is also difficult to provide feedback that takes into account an individual's career aspirations and personality. This creates the challenge of being unable to effectively support employee capability improvement and career development.

[0422] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0423] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing ability improvement advice based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality, means for analyzing the user's emotions using an emotion engine and providing advice based on those emotions, means for evaluating the communication skills and problem-solving abilities of each employee using a text analysis algorithm, and means for generating a radar chart based on the analysis results and sending it to the user's terminal. This makes it possible to comprehensively analyze an individual's capabilities and provide appropriate advice based on their emotions.

[0424] "SNS" stands for Social Networking Service, an online platform for users to share information and interact over the Internet.

[0425] A "communication tool" is any software or application that users use to exchange messages or information.

[0426] A "proposal document" is a document or presentation created to explain the contents of a proposal for a business or project.

[0427] "Individual abilities" refers to a comprehensive assessment of the user's skills, knowledge, problem-solving ability, communication ability, etc.

[0428] A "radar chart" is a graph format for visually displaying multiple evaluation items, and visualizes the evaluation values ​​of each item by plotting them radially.

[0429] "Career aspirations" refers to the goals and directions that users have regarding their occupations and careers.

[0430] An "emotion engine" is an algorithm or system that analyzes emotions from a user's text and behavior and generates feedback and advice based on those emotions.

[0431] A "text analysis algorithm" is an algorithm that uses natural language processing technology to analyze text data and extract meaning, emotions, skills, etc.

[0432] "User's device" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0433] "Advice" refers to specific suggestions or guidance provided to help a user improve their skills or solve a problem.

[0434] This invention relates to an AI system that analyzes individual abilities and provides advice based on emotions. This system analyzes individual abilities from social media posts, text on communication tools, proposal materials, etc., and visualizes them in a radar chart. It also provides advice on skill improvement based on career aspirations and generates easy-to-understand phrases and expressions that take into account the individual's thoughts and personality.

[0435] Hardware and software used

[0436] Hardware: Servers, terminals (PCs, smartphones)

[0437] Software: Text analysis algorithms, sentiment engines, radar chart generation tools

[0438] System Overview

[0439] 1. Data Collection

[0440] The content posted by users on social media, text written on communication tools, and proposal materials are uploaded to the server.

[0441] The server collects this data and stores it in a database.

[0442] 2. Text Analysis

[0443] The server uses text analysis algorithms to analyze the collected data.

[0444] The server evaluates each employee's communication skills, problem-solving abilities, and more.

[0445] 3. Emotion analysis

[0446] The server uses an emotion engine to analyze the user's emotions.

[0447] If the user feels frustrated, the server analyzes the cause and generates specific advice to resolve the issue.

[0448] 4. Visualization

[0449] The server generates a radar chart based on the analysis results.

[0450] The server sends the radar chart to the user's terminal and displays it.

[0451] 5. Providing advice

[0452] The server generates advice for improving skills based on the user's career aspirations.

[0453] The server generates and provides to users easy-to-understand phrases and expressions that take into account the individual's thoughts and personality.

[0454] Specific examples

[0455] For example, if a user posts on social media, "Work hasn't been going well lately," the server analyzes the post and determines that the user is feeling frustrated. The server uses an emotion engine to analyze the cause of the frustration and generates specific advice, such as "You might want to reconsider your task priorities." The server provides this advice to the user and visualizes the user's abilities in a radar chart.

[0456] Prompt Sentence Examples

[0457] Analyze users' social media posts and text on communication tools to evaluate their individual abilities. Visualize the evaluation results in a radar chart and provide advice based on the user's emotions.

[0458] In this way, the system can analyze the user's abilities and provide appropriate advice according to their emotions.

[0459] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0460] Step 1: Data collection

[0461] The content posted by users on social media, conversation history on communication tools, and proposal materials are uploaded to the server.

[0462] Input: User social media posts, text from communication tools, proposal materials

[0463] Specific behavior: A user posts on social media, "My project hasn't been going well lately."

[0464] The server collects this data in real time and stores it in a database.

[0465] Output: User posts, text, and documents stored in a database

[0466] Specific operation: The server detects the user's post and stores it in the database.

[0467] Step 2: Text analysis

[0468] The server uses text analysis algorithms to analyze the collected data.

[0469] Input: User posts, text, and materials stored in the database

[0470] Specific behavior: The server analyzes posts such as "The project is not going well" and evaluates the user's problem-solving ability.

[0471] The server evaluates each employee's communication skills, problem-solving abilities, and more.

[0472] Output: Evaluation results of the user's communication skills and problem-solving ability

[0473] Specific operation: The server scores the user's problem-solving ability based on the analysis results.

[0474] Step 3: Sentiment Analysis

[0475] The server uses an emotion engine to analyze the user's emotions.

[0476] Input: Text analysis results

[0477] What it does: The server detects frustration from user posts.

[0478] If the user feels frustrated, the server analyzes the cause and generates specific advice to resolve the issue.

[0479] Output: User sentiment analysis results and specific advice

[0480] Specific behavior: The server generates advice such as "You may want to reconsider your task priorities."

[0481] Step 4: Visualization

[0482] The server generates a radar chart based on the analysis results.

[0483] Input: User's ability assessment results

[0484] Specific operation: The server converts the user's ability evaluation results into a radar chart.

[0485] The server sends the radar chart to the user's terminal and displays it.

[0486] Output: Radar chart displayed on the user's device

[0487] Specific operation: The server sends the generated radar chart to the user's smartphone and displays it in the app.

[0488] Step 5: Providing advice

[0489] The server generates advice for improving skills based on the user's career aspirations.

[0490] Input: User's career aspirations

[0491] Specific operation: The server considers the user's career aspirations and generates advice such as "You might want to take leadership training."

[0492] The server generates and provides to users easy-to-understand phrases and expressions that take into account the individual's thoughts and personality.

[0493] Output: Specific advice given to the user

[0494] Specific behavior: The server provides advice in a way that matches the user's personality.

[0495] (Application example 1)

[0496] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0497] It is difficult to evaluate employees' work efficiency and skills in real time and provide appropriate feedback. It is also difficult to recognize employees' emotions and provide advice accordingly. This results in a problem where employees are not given sufficient support for improving their abilities and career development.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0499] In this invention, the server includes: means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart; means for providing ability improvement advice based on career aspirations; means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality; means for analyzing employees' words and actions while working and evaluating their work efficiency and skills; means for recognizing employees' emotions using an emotion engine and providing appropriate advice; and means for visualizing the analysis results in a radar chart. This makes it possible to evaluate employees' work efficiency and skills in real time and provide appropriate feedback. Furthermore, by recognizing employees' emotions and providing appropriate advice, effective support for employee ability improvement and career development is provided.

[0500] "SNS" is an abbreviation for social networking services, an online platform for people to share information and interact over the Internet.

[0501] "Communication tools" is a general term for software and applications that individuals and groups use to exchange information and communicate.

[0502] A "proposal" is a document or presentation created to explain and propose a particular idea or plan.

[0503] "Individual ability" is a general term for the skills, knowledge, experience, aptitude, etc. that a particular person possesses.

[0504] A "radar chart" is a type of graph that visually represents the overall balance and characteristics by arranging multiple evaluation items radially and connecting the evaluation values ​​of each item.

[0505] "Career" refers to the experience, achievements, and work history that an individual accumulates throughout their working life.

[0506] "Intention" refers to the awareness or willingness to move in a particular direction or towards a goal.

[0507] "Improvement advice" refers to specific advice or suggestions for improving an individual's skills or knowledge.

[0508] "Thinking" is the mental process of thinking, judging, and understanding things.

[0509] "Personality" refers to the psychological characteristics that characterize an individual's tendencies to act, feel, and think.

[0510] "Phrases and expressions" refer to the use of words and expressions to convey a particular meaning or intention.

[0511] An "employee" is someone who is employed by a company or organization and performs work.

[0512] "Work efficiency" is an index that indicates how much work can be completed effectively within a certain time.

[0513] A "skill" is an ability or technique for effectively performing a particular task or activity.

[0514] An "emotion engine" is an algorithm or system that analyzes and recognizes human emotions from data such as text and voice.

[0515] "Feedback" is the evaluation or opinion given in response to a particular action or result.

[0516] The following system configuration will be described as an embodiment of the present invention.

[0517] The server runs a program that analyzes individual capabilities from social media posts, text on communication tools, proposal documents, etc., and visualizes them in a radar chart. This program uses a text analysis algorithm to evaluate each employee's communication skills, problem-solving ability, etc. Specifically, the server uses a Python program and libraries such as spacy, TextBlob, and matplotlib.

[0518] The server uses an emotion engine to recognize the employee's emotions and provide advice based on those emotions. The emotion engine analyzes emotions from text data and returns a numerical value indicating whether the emotion is positive or negative. Based on the analysis results, if an employee is feeling frustrated, the server analyzes the cause and provides specific advice to resolve the frustration.

[0519] The server analyzes the words and actions of employees while they are working to evaluate their work efficiency and skills. For example, if an employee says, "Today, I came up with a new solution to solve the problem. I discussed it with my team members and demonstrated leadership," the server analyzes this text to evaluate the employee's problem-solving ability and leadership and visualizes it in a radar chart.

[0520] Based on the analysis results, the server provides advice to support individual career development, thereby effectively supporting employee skill development and career development.

[0521] As a concrete example, consider the case of analyzing conversations between employees at work in a factory. If an employee says, "Today, I came up with a new solution to solve a problem. I discussed it with my team members and demonstrated leadership," this text can be analyzed to evaluate the employee's problem-solving ability and leadership, and visualize the results in a radar chart.

[0522] Example prompts to input to a generative AI model:

[0523] Analyze conversations between employees at work in your factory, evaluate their communication skills, problem-solving ability, and leadership skills, and visualize the results in a radar chart.

[0524] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0525] Step 1:

[0526] The server collects text data such as social media posts, text from communication tools, and proposal materials.

[0527] Input: Text data such as social media posts, communication tool text, proposal materials, etc.

[0528] Output: Collected text data

[0529] Specific operation: The server automatically collects various text data through APIs and database connections.

[0530] Step 2:

[0531] The server inputs the collected text data into a text analysis algorithm to analyze the individual's abilities.

[0532] Input: Collected text data

[0533] Output: Analyzed ability data (e.g., communication skills, problem-solving ability, leadership)

[0534] Specific operation: The server uses the spacy library to analyze text data, extract specific keywords and phrases, and evaluate capabilities.

[0535] Step 3:

[0536] The server generates a radar chart based on the analyzed ability data.

[0537] Input: Parsed ability data

[0538] Output: Radar chart image

[0539] Specific operation: The server uses the matplotlib library to generate a radar chart that visually represents the analysis results.

[0540] Step 4:

[0541] The server uses an emotion engine to analyze emotions from the text data.

[0542] Input: Collected text data

[0543] Output: Sentiment analysis results (e.g., positivity, negativity)

[0544] Specific operation: The server uses the TextBlob library to analyze the sentiment of the text data and return a numerical value indicating the degree of positivity or negativity.

[0545] Step 5:

[0546] The server generates appropriate advice for the employee based on the emotion analysis results.

[0547] Input: Sentiment analysis results

[0548] Output: Advice statement

[0549] Specific operation: The server generates advice based on the employee's emotions based on the results of emotion analysis. For example, if the employee is feeling frustrated, it analyzes the cause and provides specific advice to resolve the frustration.

[0550] Step 6:

[0551] The server provides analysis results and advice as feedback to employees.

[0552] Input: Radar chart image, advice text

[0553] Output: Feedback to employee

[0554] Specific operation: The server sends the generated radar chart and advice text to the employee's terminal and provides feedback.

[0555] Step 7:

[0556] The server stores the employee feedback results in a database and uses it to improve the accuracy of the model.

[0557] Input: Employee feedback results

[0558] Output: Updated database

[0559] Specific operation: The server stores the feedback results in a database and improves the accuracy of the model for future analysis.

[0560] Example 2

[0561] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0562] It was difficult with conventional systems to provide personalized advice tailored to employees' career aspirations and needs for skill development. It was also difficult to provide specific advice to reduce stress while taking into account changes in employees' emotions. Furthermore, there was a lack of systems for efficiently collecting and analyzing employee input information. To solve these issues, a comprehensive system that takes into account changes in employees' career aspirations and emotions is needed.

[0563] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0564] In this invention, the server includes means for analyzing individual abilities and visualizing them in a radar chart, means for providing advice for improving abilities based on career aspirations, means for generating easy-to-understand phrases and expressions that take into account individual thoughts and personality, means for investigating employees' career aspirations and needs for improving abilities and providing personalized advice, means for providing advice for reducing stress based on changes in employees' emotions, means for generating personalized advice using a generative AI model, means for inputting self-evaluations, career aspirations, and needs for improving abilities using a terminal, and means for the server to store and analyze the input information in a database. This makes it possible to provide personalized advice based on employees' career aspirations and needs for improving abilities, and also to provide specific advice for reducing stress that takes into account changes in employees' emotions.

[0565] "Means for analyzing individual abilities and visualizing them in a radar chart" is a method for collecting data on an individual's skills and abilities, analyzing it, and displaying it visually.

[0566] "Means for providing advice on improving skills based on career aspirations" refers to a method of providing specific advice for improving skills based on an individual's career goals and aspirations.

[0567] "Means for generating easy-to-understand phrases and expressions that take into account an individual's thinking and personality" is a method for generating phrases and expressions that are effectively conveyed, taking into account an individual's thinking patterns and personality characteristics.

[0568] "Means of investigating employees' career aspirations and needs for skill development and providing personalized advice" refers to a method of investigating employees' career goals and needs for skill development and providing individually optimized advice based on that.

[0569] "Means of taking into account changes in employees' emotions and providing advice to reduce stress" refers to a method of monitoring employees' emotional state and stress levels and providing specific advice to reduce stress based on that.

[0570] "Means for generating personalized advice using a generative AI model" means a method for using a generative AI model to generate personalized advice tailored to individual needs and circumstances.

[0571] The "means for inputting self-evaluation, career aspirations, and ability improvement needs using a terminal" is a method for a user to input self-evaluation, career goals, and ability improvement needs using a terminal.

[0572] "Means for the server to save the input information in a database and analyze it" refers to a method by which the server saves the information input by the user in a database and analyzes the data.

[0573] This invention is a system that provides personalized advice to employees based on their career aspirations and needs for improving their skills. The system consists of three main components: a server, a terminal, and a user.

[0574] server

[0575] The server serves as a central point for investigating employees' career aspirations and development needs and providing personalized advice. The server uses the following hardware and software:

[0576] Hardware: A server machine with a powerful processor, sufficient memory, and ample storage.

[0577] Software: Relational database management systems (RDBMS) such as MySQL or PostgreSQL, generative AI models (e.g., GPT-3®), and sentiment analysis APIs (e.g., IBM Watson® or Microsoft® Azure® sentiment analysis APIs).

[0578] The server stores the information entered by the user in a database and analyzes it. Based on the analysis results, it uses a generative AI model to generate personalized advice. For example, if an employee wants to "improve their management skills," the server will recommend an appropriate management training program.

[0579] Terminal

[0580] The terminal provides an interface for users to input their self-evaluation, career aspirations, and ability improvement needs. The terminal uses the following hardware and software.

[0581] Hardware: Devices such as computers, tablets, and smartphones.

[0582] Software: A web application built using a front-end framework such as React or Angular.

[0583] Users input their self-evaluation, career aspirations, and skill improvement needs through their terminal. The input information is sent from the terminal to the server. For example, if a user inputs "I want to improve my management skills," the information is sent to the server and analyzed.

[0584] User

[0585] Users (employees) use a terminal to input their self-evaluation, career aspirations, and needs for improving their abilities. Based on the information they input, the user receives advice from the server. For example, if the user is feeling stressed, they can follow the advice from the server and take specific measures to reduce stress.

[0586] Specific examples

[0587] Below is an example of a prompt sentence to input to the generative AI model.

[0588] "Employee A wants to improve his management skills. Please recommend an appropriate training program, taking into account his past performance data and feedback."

[0589] When this prompt sentence is input into the generative AI model, the model generates advice recommending the most suitable management training program for employee A. For example, the generated advice might be, "Leadership training program X is suitable for employee A. This program provides specific methods for improving team management and communication skills."

[0590] In this way, the system provides personalized advice tailored to employees' career aspirations and development needs, helping them grow.

[0591] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0592] Step 1:

[0593] Users use the terminal to input self-evaluation, career aspirations, and needs for skill improvement.

[0594] Input: The user types "I want to improve my management skills" into the terminal interface.

[0595] Specific actions: The user enters self-evaluation and career goals into a form on the device and clicks the submit button.

[0596] Output: The entered information is saved on the device.

[0597] Step 2:

[0598] The terminal transmits the input information to the server.

[0599] Input: User input: "I want to improve my management skills."

[0600] Specific operation: The device sends the entered information to the server in JSON format. For example, it sends the data {"user_id": 123, "career_goal": "Improve management skills"}.

[0601] Output: The server receives the information.

[0602] Step 3:

[0603] The server stores the received data in a database.

[0604] Input: User's career goal information sent from the device.

[0605] Specific operation: The server stores the received data in a relational database such as MySQL or PostgreSQL, for example, by executing the SQL query INSERT INTO user_data (user_id, career_goal) VALUES (123, 'Improve management skills').

[0606] Output: Information is stored in a database.

[0607] Step 4:

[0608] The server retrieves data from the database and inputs prompt statements into the generative AI model.

[0609] Input: User's career goal information stored in the database.

[0610] Specific operation: The server retrieves user data from the database and inputs a prompt statement to the generative AI model. For example, it executes the SQL query SELECT FROM user_data WHERE user_id = 123 and generates a prompt statement based on the retrieved data: "Employee A wants to improve his management skills. Please recommend an appropriate training program based on his past performance data and feedback."

[0611] Output: A prompt sentence is generated that is fed into the generative AI model.

[0612] Step 5:

[0613] Generative AI models generate personalized advice.

[0614] Input: The prompt text entered by the server.

[0615] Specific behavior: The generative AI model generates personalized advice based on the input prompt, for example, "Employee A would be well suited to leadership training program X. This program provides specific ways to improve team management and communication skills."

[0616] Output: A personalized advice is generated.

[0617] Step 6:

[0618] The server transmits the generated advice to the terminal.

[0619] Input: Personalized advice generated by a generative AI model.

[0620] Specific operation: The server sends the generated advice to the terminal. For example, it sends the following data: {"user_id": 123, "advice": "Leadership training program X is suitable for you."}

[0621] Output: The device receives the advice.

[0622] Step 7:

[0623] The terminal displays the advice to the user.

[0624] Input: Personalized advice sent by the server.

[0625] Specific action: The device displays the advice received from the server to the user. For example, it displays a message on the device screen saying, "Leadership training program X is suitable for you. This program provides specific methods for improving team management and communication skills."

[0626] Output: User confirms advice.

[0627] (Application example 2)

[0628] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0629] Providing personalized advice tailored to employees' career aspirations and skill development needs has been a difficult task with conventional systems. There is also a need to monitor changes in employees' emotions in real time and provide specific advice to reduce stress. This is expected to improve employee satisfaction and productivity.

[0630] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0631] In this invention, the server includes means for analyzing an individual's abilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing ability improvement advice based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality, means for monitoring the individual's emotions in real time using an emotion recognition engine, and means for providing advice for reducing stress based on the individual's emotion data. This makes it possible to provide personalized advice according to the employee's career aspirations and ability improvement needs, and specific advice for reducing stress that takes into account changes in emotions.

[0632] "SNS" is an abbreviation for social networking service, an online platform for users to share information and communicate over the Internet.

[0633] "Communication tools" is a general term for software and applications that allow users to exchange messages and information with each other.

[0634] A "proposal" is a document or presentation created to explain a specific purpose or proposal.

[0635] "Individual capabilities" is a general term for the skills, knowledge, experience, etc. possessed by a particular person.

[0636] A "radar chart" is a graph format in which multiple evaluation items are arranged radially and the evaluation values ​​of each item are connected to illustrate the graph.

[0637] "Career orientation" refers to an individual having a career goal or direction.

[0638] "Improvement advice" refers to specific suggestions or guidance for improving an individual's skills or knowledge.

[0639] "Individual thoughts and personality" is a general term for the way of thinking, behavioral patterns, and personality traits of a particular person.

[0640] "Easy-to-understand phrases and expressions" refers to words and expressions that are easy for the recipient to understand and accept.

[0641] An "emotion recognition engine" is software or algorithms that analyze and recognize an individual's emotional state from video and audio data.

[0642] "Real-time monitoring" refers to the immediate monitoring and acquisition of data on ongoing situations and conditions.

[0643] "Stress reduction advice" refers to specific suggestions or guidance to reduce the stress an individual is experiencing.

[0644] The following system configuration is used as an embodiment of the present invention: The system is composed of three main elements: a server, a terminal, and a user.

[0645] server

[0646] The server runs a program that has the following functions:

[0647] 1. Ability analysis: The server analyzes individual abilities from social media posts, text from communication tools, proposal documents, etc., and visualizes them in a radar chart. This is done using natural language processing technology.

[0648] 2. Career Advice: The server generates personalized advice for skill development based on an individual's career aspirations. Using a generative AI model, the server makes suggestions for training programs and skill development that are optimal for each individual's career aspirations.

[0649] 3. Emotion Monitoring: The server uses an emotion recognition engine to monitor individual emotions in real time, collecting individual emotional data and providing advice to reduce stress.

[0650] Terminal

[0651] Terminals refer to devices such as smart glasses and smartphones. These terminals have the following functions:

[0652] 1. Video capture: Use the camera in the smart glasses to capture video and send it to the server.

[0653] 2. Real-time display: Advice and analysis results received from the server are displayed in real time.

[0654] User

[0655] A user is an employee who uses the system. A user uses the system in the following steps:

[0656] 1. Data entry: Enter social media posts, communication tool text, proposal materials, etc. into the system.

[0657] 2. Receiving advice: The device receives and confirms personalized advice and emotion monitoring results sent from the server.

[0658] Hardware and software used

[0659] Hardware: smart glasses, smartphone, server

[0660] Software: OpenCV (video capture and image processing), EmotionRecognizer (emotion recognition engine), CareerAdviceModel (career advice model)

[0661] Specific examples

[0662] If Employee A wears the smart glasses and selects "improving management skills" as his or her career goal, the smart glasses will monitor Employee A's emotions in real time and provide advice such as "take a break to relax" if Employee A is feeling stressed.

[0663] Prompt Sentence Examples

[0664] Create a program that generates personalized advice based on employees' career aspirations and emotion data. Use smart glasses to capture video and use an emotion recognition engine to predict employees' emotions. Use a career advice model to generate advice based on employees' career aspirations and emotion data.

[0665] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0666] Step 1:

[0667] Users input social media posts, communication tool text, proposal materials, etc. into the system. This data is sent to the server. The input data includes text and image information.

[0668] Step 2:

[0669] The server analyzes the received data and evaluates the individual's abilities. Specifically, it uses natural language processing technology to analyze the text data and extract the individual's skills and knowledge. The analysis results are visualized in the form of a radar chart. The input data is text information, and the output is a radar chart.

[0670] Step 3:

[0671] The server generates personalized advice for improving skills based on an individual's career aspirations. Using a generative AI model, it proposes training programs and skill improvement methods that are optimal for the individual's career aspirations. The input data is career aspiration information, and the output is advice information.

[0672] Step 4:

[0673] The device (smart glasses) uses a camera to capture the user's image and transmits it to the server. The input data is the image information, and the output is the transmission of the image data to the server.

[0674] Step 5:

[0675] The server uses an emotion recognition engine to monitor the user's emotions in real time from the received video data. Specifically, it analyzes the video data and predicts the user's emotional state from their facial expressions and movements. The input data is video information, and the output is emotional state information.

[0676] Step 6:

[0677] The server provides advice for stress reduction based on the user's emotional data. It analyzes the emotional state information and generates specific stress reduction measures if the user is feeling stressed. The input data is the emotional state information, and the output is stress reduction advice.

[0678] Step 7:

[0679] The device displays the advice and analysis results received from the server to the user in real time. Specifically, the advice information and radar charts are displayed on the smart glasses display. The input data is the advice information from the server, and the output is the display to the user.

[0680] Step 8:

[0681] The user checks the advice and analysis results displayed on the device and adjusts their behavior as necessary. Specifically, they can take actions such as taking a break to reduce stress. The input data is the information displayed on the device, and the output is the user's behavior.

[0682] Example 3

[0683] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0684] It was difficult for conventional systems to provide appropriate feedback and advice based on employees' personality traits and communication styles. Furthermore, career development support based on the results of individual ability analysis could not take into account emotional changes, making it difficult to contribute to improving employee satisfaction and performance. Furthermore, for organizations with many employees, accumulating data and improving the accuracy of models was a challenge.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0686] In this invention, the server includes means for inputting data on an individual's personality traits and communication style, means for receiving the input data and analyzing the personality traits and communication style, means for generating and inputting prompt sentences to the generative AI model based on the analysis results, means for the generative AI model to generate appropriate phrases and expressions, and means for providing the generated phrases and expressions. This makes it possible to provide appropriate feedback and advice according to the employee's personality traits and communication style.

[0687] "Individual personality traits" are the personality characteristics and tendencies of an individual that are identified based on psychological assessment tools and questionnaire results.

[0688] "Communication style" refers to the characteristics of an individual's method, attitude, and choice of language when exchanging information with others.

[0689] "Means for inputting data" refers to the interface or method by which a user provides the required information to the system.

[0690] "Means for receiving and analyzing data" refers to the processes and technologies by which the server receives information provided by users and analyzes it to extract useful information.

[0691] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate appropriate output based on input data.

[0692] A "prompt" is an instruction or question input to a generative AI model, which serves as the basis for the model to generate appropriate output.

[0693] "Means for generating phrases and expressions" refers to the processes and techniques that enable a generative AI model to generate appropriate feedback and advice based on a prompt.

[0694] "Means for providing" refers to a method or interface for displaying or communicating the generated feedback or advice to the user.

[0695] The present invention is a system that provides appropriate feedback and advice according to the personality traits and communication styles of employees. A specific embodiment of this system is described below.

[0696] First, users input data about their employees' personality traits and communication styles. This includes psychological assessment tools and questionnaire results. Specifically, users can input results from the Big Five personality trait assessment and the Myers-Briggs Type Indicator (MBTI). Users input this data through a web interface.

[0697] The server then receives the data entered by the user and analyzes their personality traits and communication style. The server then uses Python scripts to analyze this data and identify the employee's characteristics. For example, it analyzes the results of a Big Five personality trait assessment to determine whether the employee is an extrovert or an introvert.

[0698] The server then generates a prompt sentence based on the analysis results and inputs it to the generative AI model. For example, OpenAI's GPT-4 is used as the generative AI model. The server generates the prompt sentence using a Python script and inputs it to the generative AI model via an API. Examples of prompt sentences include:

[0699] "Employee A scores high on Extraversion and low on Emotional Stability on the Big Five personality trait assessment. He also prefers direct feedback. Please provide him with feedback on his recent project."

[0700] The generative AI model generates appropriate phrases and expressions for the employee based on the prompt text input from the server. The generated feedback is returned to the server in JSON format. For example, the following feedback may be generated:

[0701] "Employee A, your proactive attitude on the recent project is much appreciated. However, I think you could do better next time if you were a little more calm."

[0702] Finally, the server provides the generated phrases and expressions to the user, who can then review the displayed feedback through a web interface and provide appropriate advice to the employee.

[0703] This system makes it possible to provide appropriate feedback and advice based on an employee's personality traits and communication style. Furthermore, by combining it with an emotion engine, it is possible to provide career development support that takes into account changes in an employee's emotions. For example, if an employee is considering a career change, the system can analyze the emotional background that led to that decision and provide specific advice for the new career path.

[0704] Furthermore, in organizations with many employees, it is important to accumulate data and improve the accuracy of the model. This system accumulates data from organizations that are based on long-term employment and includes means to improve the accuracy of the model, making it possible to provide more accurate feedback and advice. The flow of the identification process in Example 3 will be described with reference to FIG. 21.

[0705] Step 1:

[0706] Users input data about employees' personality traits and communication styles. Specifically, they input Big Five personality trait assessment and MBTI results through a web interface. Input data includes the employee's name, assessment results, and past feedback history. This provides the system with the basic data to understand the employee's characteristics.

[0707] Step 2:

[0708] The server receives data entered by the user. It then uses a Python script to analyze personality traits and communication styles. Specifically, it analyzes the results of the Big Five personality trait assessment to determine whether the employee is extroverted or introverted, and whether they have high or low emotional stability. By analyzing the input data, the server identifies the employee's personality traits and generates analysis results.

[0709] Step 3:

[0710] The server generates and inputs prompts to the generative AI model based on the analysis results. Specifically, the prompts are generated using a Python script and input to the generative AI model via an API. An example of a prompt might be, "Employee A is high in extraversion and low in emotional stability on the Big Five personality trait assessment. He also prefers direct feedback. Please provide him with feedback on his recent project." This provides the generative AI model with the basic information it needs to generate appropriate feedback.

[0711] Step 4:

[0712] The generative AI model generates phrases and expressions appropriate for the employee based on the prompt text input from the server. Specifically, the generative AI model (for example, OpenAI's GPT-4) analyzes the prompt text and generates feedback according to the employee's characteristics. The generated feedback is returned to the server in JSON format. For example, feedback such as, "Employee A, your proactive attitude in the recent project has been highly praised. However, if you respond more calmly next time, you will achieve better results."

[0713] Step 5:

[0714] The server receives the feedback returned by the generative AI model and provides it to the user. Specifically, it displays the generated feedback through a web interface. The user can check the displayed feedback and provide appropriate advice to the employee. This makes it possible to provide appropriate feedback and advice based on the employee's personality traits and communication style.

[0715] (Application example 3)

[0716] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0717] Providing appropriate feedback that takes into account employees' personality traits and emotional state is important for improving employee motivation and career development. However, conventional systems have had issues with providing personalized feedback to individual employees and being unable to provide advice that takes emotional changes into account. Furthermore, there is no established method for providing feedback using generative AI models, and there is a need for a means to automatically generate appropriate feedback based on employees' personality traits and emotional state.

[0718] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0719] In this invention, the server includes means for analyzing an individual's abilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing advice on improving abilities based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality, means for analyzing the employee's personality traits and emotional state and generating appropriate feedback, and means for providing feedback based on prompt sentences using a generative AI model. This makes it possible to provide personalized feedback based on the employee's personality traits and emotional state.

[0720] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[0721] A "communication tool" is any software or application that users use to exchange messages or information.

[0722] A "proposal" is a document created to explain and propose a particular idea or plan.

[0723] "Individual capabilities" is a collective term for the skills, knowledge, and experience possessed by a particular person.

[0724] A "radar chart" is a graph that shows multiple evaluation items arranged radially and connects the evaluation values ​​of each item.

[0725] "Career aspirations" refer to the hopes and goals that individuals have for their future careers or jobs.

[0726] "Improvement advice" is specific advice or suggestions for improving an individual's skills or knowledge.

[0727] "Individual thoughts and personality" refers to the way of thinking and personality traits of a particular person.

[0728] "Easy-to-understand phrases and expressions" are words and phrases that are easy for the other person to understand and accept.

[0729] "Employee personality traits" refer to the personality characteristics and tendencies possessed by a particular employee.

[0730] An "emotional state" is an individual's emotional state or mood at a particular moment.

[0731] "Feedback" is the evaluation or opinion given in response to a particular action or result.

[0732] A "generative AI model" is a model that uses artificial intelligence to generate output for a specific task.

[0733] A "prompt" is an instruction or question that is input into a generative AI model.

[0734] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. The server has the function of analyzing an individual's abilities from social media posts, text from communication tools, proposal materials, etc., and visualizing them in a radar chart. It also has the function of providing advice on improving abilities based on career aspirations, etc. Furthermore, it includes a function to generate easy-to-understand phrases and expressions that take into account the individual's thoughts and personality.

[0735] The server uses a generative AI model to analyze the employee's personality traits and emotional state and generate appropriate feedback. This generative AI model provides feedback based on prompt sentences. Specifically, it inputs data about the employee's personality traits and emotional state and generates appropriate feedback based on that.

[0736] The terminal is a device used by employees that receives feedback provided by the server. Terminals include smartphones, tablets, PCs, etc. Employees can check the feedback through the terminal and take necessary actions.

[0737] Users are employees who use the system and receive feedback. Users receive feedback based on their personality traits and emotional state, and can use that feedback to improve themselves and shape their careers.

[0738] As a concrete example, here is an example of a prompt sentence that generates feedback when an employee is considering a career change.

[0739] Prompt Sentence Examples

[0740] Employee Name: Taro Tanaka

[0741] Personality trait: Prefers direct feedback

[0742] Emotional state: Considering a career change

[0743] Provide appropriate feedback to this employee.

[0744] By inputting this prompt sentence into a generative AI model, appropriate feedback can be provided to employees. The server generates the feedback using OpenAI's API and provides it to the user via their device. This makes it possible to provide personalized feedback based on the employee's personality traits and emotional state.

[0745] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0746] Step 1:

[0747] The server analyzes individual capabilities from social media posts, text from communication tools, proposal materials, etc. It receives employee posts and text data as input and analyzes the text data using natural language processing technology. It generates data indicating employee capabilities as output. Specifically, it extracts keywords and phrases from the text data and uses them to evaluate employee skills and knowledge.

[0748] Step 2:

[0749] The server visualizes the analyzed ability data in a radar chart. As input, it receives the ability data generated in step 1 and creates a radar chart using a graph generation library. As output, it generates a radar chart that is visually easy to understand. Specifically, the evaluation values ​​of each ability item are arranged radially and the evaluation values ​​are connected to illustrate the chart.

[0750] Step 3:

[0751] The server provides advice on skill improvement based on career aspirations, etc. It receives the employee's career aspiration data and the data from Steps 1 and 2 as input, and generates specific advice using an advice generation algorithm. The output is advice for skill improvement provided to the employee. Specifically, it compares the employee's current abilities with their career aspirations and suggests ways to improve the necessary skills and knowledge.

[0752] Step 4:

[0753] The server generates easy-to-understand phrases and expressions that take into account the individual's thoughts and personality. As input, it receives the employee's personality trait data and the advice data from Step 3, and uses natural language generation technology to generate appropriate phrases. As output, it provides personalized feedback to the employee. Specifically, it selects words that are easy to understand and accept, in line with the employee's personality traits.

[0754] Step 5:

[0755] The server analyzes the employee's personality traits and emotional state and generates appropriate feedback. It receives the employee's personality trait data and emotional state data as input and generates feedback using a generative AI model. It provides specific feedback to the employee as output. Specifically, it takes the employee's emotional state into consideration and provides feedback with appropriate timing and content.

[0756] Step 6:

[0757] The server uses a generative AI model to provide feedback based on the prompt. As input, it receives the prompt and employee data and inputs them into the generative AI model. As output, it provides the generated feedback. Specifically, it uses the following prompt:

[0758] Employee Name: Taro Tanaka

[0759] Personality trait: Prefers direct feedback

[0760] Emotional state: Considering a career change

[0761] Provide appropriate feedback to this employee.

[0762] Step 7:

[0763] The terminal receives the feedback provided by the server and displays it to the user. As input, it receives feedback data from the server and displays it through a user interface. As output, the user can check the feedback and take necessary actions. Specifically, the feedback content is displayed on the screen and provided in a format that is easy for the user to understand.

[0764] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0765] The data generation model 58 is a so-called generative AI (Artificial Intelligence).

[0766] An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[0767] , obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0768] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0769] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0770] [Second embodiment]

[0771] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0772] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0773] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0774] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0775] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0776] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0777] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0778] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0779] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0780] The storage 32 stores a data generation model 58 and an emotion identification model 59.

[0781] The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0782] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0783] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0784] "Example 1"

[0785] As one embodiment of the present invention, an AI system for analyzing individual capabilities is provided. This system analyzes individual capabilities from social media posts, texts in communication tools, proposal documents, etc., and visualizes them in a radar chart. Specifically, it uses a text analysis algorithm to evaluate the communication skills, problem-solving abilities, etc. of each employee. "Example 2"

[0786] Furthermore, in one embodiment of the present invention, advice on skill development is provided based on the employee's career aspirations and other needs. Specifically, the system investigates the employee's career aspirations and skill development needs and provides personalized advice based on the results. For example, advice such as recommending a specific management training program to an employee who wants to improve their management skills is provided.

[0787] "Example 3"

[0788] In one embodiment of the present invention, AI generates easy-to-understand phrases and expressions that take into account an individual's thoughts and personality. Specifically, the AI ​​analyzes an employee's personality traits and communication style and generates appropriate phrases and expressions. For example, for an employee who prefers direct feedback, the AI ​​provides advice using specific and clear language.

[0789] The processing flow of each embodiment will be described below.

[0790] "Example 1"

[0791] Step 1: The AI ​​system analyzes an individual's capabilities based on social media posts, text in communication tools, proposal materials, etc.

[0792] Step 2: Use text analysis algorithms to assess each employee's communication skills, problem-solving abilities, etc.

[0793] Step 3: Visualize the evaluation results in a radar chart to understand the capabilities of each employee.

[0794] Step 1: Assess employees' career aspirations and development needs.

[0795] Step 2: Generate personalized advice based on the findings.

[0796] Step 3: Provide advice, such as recommending specific management training programs for employees who want to improve their management skills.

[0797] "Example 3"

[0798] Step 1: Analyze your employees' personality traits and communication styles.

[0799] Step 2: Based on the analysis results, the AI ​​generates appropriate expressions and phrases.

[0800] Step 3: For example, if an employee prefers direct feedback, provide specific, clearly worded advice.

[0801] Example 1

[0802] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0803] Conventional individual ability evaluation systems have difficulty accurately analyzing and visualizing individual abilities from a variety of data sources, such as social media posts, text from communication tools, and proposal materials. It has also been difficult to provide advice that takes into account an individual's career aspirations and personality. Furthermore, for organizations with many employees, improving the accuracy of the model through long-term data accumulation has been a challenge.

[0804] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0805] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing them in a radar chart; means for collecting and cleansing text data; means for evaluating individual capabilities using a text analysis algorithm; means for generating the evaluation results as a radar chart and providing them to the user; means for providing advice on improving capabilities based on career aspirations, etc.; and means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality. This makes it possible to analyze and visualize individual capabilities from multiple perspectives. It also makes it possible to provide advice that takes into account the individual's career aspirations and personality, and to improve the accuracy of the model by accumulating data from the entire organization.

[0806] "SNS" stands for Social Networking Service, an online platform for users to share information and interact over the Internet.

[0807] A "communication tool" is any software or application that users use to exchange messages or information.

[0808] A "proposal" is a document or presentation created to explain a specific purpose or proposal.

[0809] "Individual capabilities" refer to the skills, knowledge, experience, and characteristics possessed by a particular individual.

[0810] A "radar chart" is a graph format that arranges multiple evaluation items radially and visually shows the evaluation score for each item.

[0811] "Text data" is information expressed as a string of characters, and includes social media posts, messages on communication tools, proposal documents, etc.

[0812] "Cleansing" is a data preprocessing process that involves removing unnecessary information and noise and preparing the data in a format suitable for analysis.

[0813] A "text analysis algorithm" is a computational method or model for analyzing text data and understanding its meaning and structure.

[0814] "Evaluation results" are evaluation scores and analysis results regarding an individual's abilities obtained by a text analysis algorithm.

[0815] "Career aspirations" refer to the goals and directions that individuals have regarding their occupations and careers.

[0816] "Advice" is advice or suggestions given for a specific purpose or problem.

[0817] "Thoughts and personality" refers to an individual's way of thinking, behavioral patterns, and personality traits.

[0818] "Phrases and expressions" are the choice of words and expressions used to convey a particular message or information.

[0819] "Data storage" refers to the storage of collected data and its retention for later analysis or use.

[0820] "Model accuracy" is an indicator of how accurately an analytical algorithm or predictive model produces results.

[0821] This invention relates to an AI system for analyzing and visualizing individual abilities. A specific embodiment of this system will be described below.

[0822] Hardware and software used

[0823] Hardware: High-performance servers (e.g., high-performance servers)

[0824] Software: text analysis algorithms (e.g., Python's NLTK library), databases (e.g., MySQL), visualization tools (e.g., D3.js)

[0825] System configuration

[0826] 1. Data Collection

[0827] The server collects text data from the APIs of social networking sites and log files of communication tools. For example, it uses the Twitter API to retrieve posts from a specific user.

[0828] 2. Data Preprocessing

[0829] The server cleanses the collected text data and removes unnecessary information. Specifically, it uses a Python regular expression library to remove unnecessary strings from the text data.

[0830] 3. Text Analysis

[0831] The server uses text analysis algorithms to extract keywords and phrases from the text data and evaluate their individual capabilities, for example, using the NLTK library for sentiment analysis and topic modeling.

[0832] 4. Visualization of evaluation results

[0833] The server generates the evaluation results as a radar chart and provides it to the user. Specifically, it uses D3.js to graphically display the evaluation results.

[0834] 5. Providing results

[0835] The server sends the generated radar chart to the user's device, which displays the results, for example, through a web browser.

[0836] Specific examples

[0837] For example, when analyzing employee A's social media posts and internal chat logs, the following prompt sentences are input into the generative AI model.

[0838] Example prompt sentence:

[0839] Analyze Employee A's social media posts and internal chat logs and evaluate the following abilities.

[0840] Communication skills

[0841] problem solving ability

[0842] leadership

[0843] creativity

[0844] Display the evaluation results in a radar chart.

[0845] Based on this prompt, the server analyzes Employee A's text data and calculates an evaluation score for each ability. Finally, the evaluation results are visualized as a radar chart and provided to the user. In this way, the user can visually grasp Employee A's abilities.

[0846] This system makes it possible to analyze and visualize an individual's capabilities from multiple angles, and also provides advice that takes into account an individual's career aspirations and personality. It also makes it possible to accumulate data from across the organization to improve the accuracy of the model.

[0847] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0848] Step 1:

[0849] Data collection

[0850] The server collects text data from the APIs of social networking sites and log files of communication tools. For example, it uses the Twitter API to retrieve posts from a specific user.

[0851] Input: Log files from SNS APIs and communication tools

[0852] Output: Collected text data

[0853] Specific operation: The server sends a request to the Twitter API and receives the user's posted data in JSON format.

[0854] Step 2:

[0855] Data Preprocessing

[0856] The server cleanses the collected text data and removes unnecessary information. Specifically, it uses a Python regular expression library to remove unnecessary strings from the text data.

[0857] Input: Collected text data

[0858] Output: Cleansed text data

[0859] What it does: The server uses Python's regular expression library to remove HTML tags and special characters.

[0860] Step 3:

[0861] Text analytics

[0862] The server uses text analysis algorithms to extract keywords and phrases from the text data and evaluate their individual capabilities, for example, using the NLTK library for sentiment analysis and topic modeling.

[0863] Input: Cleansed text data

[0864] Output: Ability assessment score

[0865] What it does: The server uses the NLTK library to tokenize the text data and extract sentiment scores and topics.

[0866] Step 4:

[0867] Visualization of evaluation results

[0868] The server generates the evaluation results as a radar chart and provides it to the user. Specifically, it uses D3.js to graphically display the evaluation results.

[0869] Input: Ability assessment score

[0870] Output: Radar chart

[0871] Specific operation: The server passes the evaluation results in JSON format to D3.js and generates a radar chart.

[0872] Step 5:

[0873] Providing results

[0874] The server sends the generated radar chart to the user's device, where the results are displayed, for example, through a web browser.

[0875] Input: Radar chart

[0876] Output: Radar chart displayed on the user's device

[0877] Specific operation: The server sends an HTML page containing a radar chart to the user's device as an HTTP response.

[0878] (Application example 1)

[0879] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0880] Previous systems lacked the means to analyze individual capabilities and assign appropriate tasks. They also did not provide a concrete method for visualizing employees' skill sets and allowing managers to assign appropriate tasks. This made it difficult to make the most of employees' capabilities and hindered efficient business operations.

[0881] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0882] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing advice on improving capabilities based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account individual thoughts and personalities, means for analyzing communication tools and proposal materials used by employees in a factory, evaluating each employee's capabilities, and assigning appropriate tasks, means for visualizing employee skill sets in a radar chart, and means for a manager to assign appropriate tasks based on the employee's skill set. This makes it possible to efficiently analyze employee capabilities and assign appropriate tasks.

[0883] "SNS" is an abbreviation for social networking service, an online platform for users to share information and communicate.

[0884] A "communication tool" is software or an application that individuals or groups use to exchange information and communicate.

[0885] A "proposal" is a document or presentation created to explain and propose a particular idea or plan.

[0886] "Individual ability" is a comprehensive assessment of a specific person's skills, knowledge, experience, aptitude, etc.

[0887] A "radar chart" is a graph that visually represents the overall balance and characteristics by arranging multiple evaluation items radially and connecting the evaluation values ​​of each item.

[0888] "Career aspirations" refers to the career goals and direction that an individual wants to achieve in the future.

[0889] "Advice for improving skills" refers to providing specific suggestions and guidance to improve an individual's skills and knowledge.

[0890] "Individual thoughts and personality" refers to the way of thinking, behavioral patterns, and personality traits of a particular person.

[0891] "Easy-to-understand phrases and expressions" are words and expressions that convey information in a way that is easy for the recipient to understand and accept.

[0892] "Factory workers" refers to workers engaged in manufacturing and production industries.

[0893] "Assigning appropriate tasks" means assigning the most appropriate work or tasks to employees based on their abilities and skills.

[0894] A "skill set" refers to the set of skills and abilities that a particular person possesses.

[0895] "Administrator" refers to a person in charge of managing and supervising an organization or team.

[0896] The system for implementing this invention has a function to analyze individual capabilities from social media posts, text on communication tools, proposal documents, etc., and visualize them in a radar chart. It also includes a function to analyze communication tools and proposal documents used by employees in a factory, evaluate the capabilities of each employee, and assign appropriate tasks.

[0897] System Program

[0898] The server runs a program that performs the following processes: First, it collects text data such as social media posts, text from communication tools, and proposal materials. Next, it uses a text analysis algorithm to analyze the collected text data and evaluates each employee's communication skills, problem-solving ability, and other aspects. Based on the results of this evaluation, it generates and visualizes a radar chart.

[0899] Hardware and software used

[0900] This system uses the following hardware and software:

[0901] Hardware: Server, client terminal (PC, smartphone, tablet, etc.)

[0902] Software: text analysis algorithms (e.g., spacy), vectorization tools (e.g., TfidfVectorizer), dimensionality reduction tools (e.g., PCA), visualization tools (e.g., matplotlib)

[0903] Data processing and calculation

[0904] The server first collects text data and analyzes it using a text analysis algorithm. The analysis results are vectorized and then dimension-reduced using a dimension reduction tool. Next, a radar chart is generated and visualized based on the reduced data. This allows managers to visually check employees' skill sets and assign appropriate tasks.

[0905] Specific examples

[0906] For example, if Employee A makes a new proposal in a factory, the proposal text is analyzed and Employee A's communication skills and problem-solving abilities are evaluated. The evaluation results are visualized in a radar chart, and managers can use the results to assign appropriate tasks to Employee A.

[0907] Prompt Sentence Examples

[0908] Examples of prompts to input to a generative AI model include:

[0909] "Analyze Employee A's proposal and evaluate his / her communication skills and problem-solving abilities."

[0910] Using this prompt, the AI ​​model can evaluate employee capabilities and visualize the results in a radar chart.

[0911] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0912] Step 1:

[0913] The server collects text data such as social media posts, text from communication tools, proposal materials, etc. This involves using APIs to obtain data from each platform. The input is text data from each platform, and the output is a list of the collected text data.

[0914] Step 2:

[0915] The server analyzes the collected text data using a text analysis algorithm (e.g., spacy). Specifically, it tokenizes the text data and analyzes the part of speech and meaning of each token. The input is a list of collected text data, and the output is a list of analyzed text data.

[0916] Step 3:

[0917] The server converts the parsed text data into numerical vectors using a vectorization tool (e.g., TfidfVectorizer), which makes the text data in a format that can be processed by a machine learning model. The input is a list of parsed text data, and the output is a list of numerical vectors.

[0918] Step 4:

[0919] The server reduces the dimension of the vectorized data using a dimension reduction tool (e.g., PCA), which makes it easier to visualize the data. The input is a list of numeric vectors, and the output is a list of dimension-reduced data.

[0920] Step 5:

[0921] The server generates and visualizes radar charts based on the dimension-reduced data. Specifically, it uses visualization tools such as matplotlib to draw the radar charts. The input is a list of dimension-reduced data, and the output is an image of the radar chart.

[0922] Step 6:

[0923] The server provides the generated radar chart to the manager, helping the manager assign appropriate tasks based on the employee's skill set. The input is the radar chart image, and the output is a report that the manager can review.

[0924] Step 7:

[0925] The user (manager) assigns appropriate tasks to employees based on the radar chart provided. Specifically, by referring to the radar chart, the user selects the tasks that best suit each employee's skill set. The input is an image of the radar chart and employee skill set information, and the output is a list of assigned tasks.

[0926] Example 2

[0927] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0928] Providing optimal advice to individual employees regarding their career aspirations and skill development needs has been difficult using conventional methods. Organizations with many employees, in particular, need systems that can efficiently collect and analyze data on each employee and provide appropriate advice. It is also important to generate easily understood phrases and expressions that take into account the employee's way of thinking and personality. Effective methods are needed to solve these challenges.

[0929] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0930] In this invention, the server includes means for analyzing individual abilities and visualizing them in a radar chart, means for providing ability improvement advice based on career aspirations, means for generating easy-to-understand phrases and expressions that take into account individual thoughts and personality, means for collecting and preprocessing employee data, means for inputting the preprocessed data into a generative AI model to generate advice, and means for providing the generated advice to employees. This makes it possible to efficiently provide individually optimal advice in response to employees' career aspirations and ability improvement needs.

[0931] "A means of analyzing individual capabilities and visualizing them in a radar chart" is a method for collecting employee skills and performance data, analyzing it, and displaying it visually.

[0932] "Means of providing advice on improving skills based on career aspirations" refers to a method of suggesting appropriate skill development and training based on employees' career goals and aspirations.

[0933] "Means of generating easy-to-understand phrases and expressions that take into account individual thoughts and personalities" is a method of generating optimal words and expressions that suit the personality and communication style of employees.

[0934] "Means for collecting and preprocessing employee data" refers to a method for collecting various data obtained from employees and arranging it into an analyzable format.

[0935] "Means for inputting preprocessed data into a generative AI model to generate advice" refers to a method for inputting preprocessed data into an AI model, which results in the generation of specific advice for employees.

[0936] "Means for providing generated advice to employees" means a method for communicating the advice generated by the AI ​​model to employees.

[0937] This invention is a system that provides optimal advice to each employee based on their career aspirations and needs for improving their skills. This system operates in cooperation with a server, a terminal, and a user.

[0938] The server first collects employee data, including information entered by the employee about their career aspirations and development needs, past performance data, evaluation data, etc. The collected data is then stored in a database.

[0939] The server then preprocesses the collected data. This includes data cleaning, normalization, and feature extraction. For example, it tokenizes text data entered by employees and removes unnecessary spaces and special characters. It also quantifies past performance data and converts it into a format that can be input into the model.

[0940] The preprocessed data is then input into a generative AI model. For example, we use OpenAI's GPT-4. This model generates optimal advice for employees based on the input data. Specifically, it uses the following prompt sentence:

[0941] Example prompt sentence:

[0942] Employee A wants to improve his management skills. Based on Employee A's past evaluation and performance data, please recommend the most appropriate management training program.

[0943] Based on this prompt, the generative AI model generates the following advice:

[0944] Employee A is recommended to take the Leadership Fundamentals course to strengthen his leadership skills, which will focus on effective team management and communication skills.

[0945] The generated advice is sent from the server to the terminal. The terminal displays the received advice to the user. The user can check the displayed advice and make an action plan if necessary. For example, the user can take steps to participate in a basic leadership course.

[0946] This system makes it possible to efficiently provide optimal advice to employees on their career aspirations and skill development needs, and also increases the receptivity of advice by generating easy-to-understand phrases and expressions that take into account the employee's way of thinking and personality.

[0947] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0948] Step 1:

[0949] The server collects data from users (employees) regarding their career aspirations and needs for improving their skills.

[0950] Input: Information about your career aspirations and development needs entered by you through your device.

[0951] Output: The collected data is stored in a database.

[0952] Specific operation: The user inputs "I want to improve my management skills" through the terminal. The server stores this information in the database.

[0953] Step 2:

[0954] The server pre-processes the collected data.

[0955] Input: Information stored in a database about the user's career aspirations and development needs.

[0956] Output: The preprocessed data is converted into a format that can be fed into a generative AI model.

[0957] What it does: The server tokenizes the text data entered by the user, removing unnecessary spaces and special characters, and converts past performance data into a numerical format that can be fed into the model.

[0958] Step 3:

[0959] The server inputs the preprocessed data into a generative AI model.

[0960] Input: Preprocessed data.

[0961] Output: The data that is input into a generative AI model.

[0962] Specific operation: The server inputs the following prompt sentence into the generative AI model:

[0963] Employee A wants to improve his management skills. Based on Employee A's past evaluation and performance data, please recommend the most appropriate management training program.

[0964] Step 4:

[0965] The generative AI model generates advice based on input data.

[0966] Input: The data fed into a generative AI model.

[0967] Output: The generated advice.

[0968] Specific behavior: The generative AI model generates advice such as the following:

[0969] Employee A is recommended to take the Leadership Fundamentals course to strengthen his leadership skills, which will focus on effective team management and communication skills.

[0970] Step 5:

[0971] The server transmits the generated advice to the user's terminal.

[0972] Input: The generated advice.

[0973] Output: Advice sent to the user's terminal.

[0974] Specific operation: The server sends the generated advice to the terminal, and the terminal displays it to the user as follows:

[0975] Recommended Tip: To strengthen your leadership skills, we recommend taking our Leadership Fundamentals course, which focuses on effective team management and communication skills.

[0976] Step 6:

[0977] The user checks the advice displayed on the terminal and makes an action plan if necessary.

[0978] Input: The advice displayed on the terminal.

[0979] Output: User's action plan.

[0980] Specific operations: The user clicks on the link to apply for the "Leadership Basics Course" through the terminal and enters the required information into the application form.

[0981] (Application example 2)

[0982] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0983] There is a need to accurately grasp employees' career aspirations and skill development needs and provide individually optimized training programs. However, conventional systems make it difficult to respond to the diverse needs of employees, and progress management and feedback on training programs are insufficient, preventing effective skill development. For this reason, a new system is needed to support employees' career development and improve productivity throughout the organization.

[0984] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0985] There is a need to accurately grasp employees' career aspirations and skill development needs and provide individually optimized training programs. However, conventional systems make it difficult to respond to the diverse needs of employees, and progress management and feedback on training programs are insufficient, preventing effective skill development. For this reason, a new system is needed to support employees' career development and improve productivity throughout the organization.

[0986] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0987] In this invention, the server includes: means for acquiring, in the form of a questionnaire, information about the career aspirations or skill improvement needs of an employee user; a prompt for generating a training program recommended for the user based on the acquired data; a generative AI model for generating and proposing the training program to the user; and a means for adjusting the training program based on progress management or feedback on the proposed training program. This enables the server to meet diverse employee needs and provide individually optimized training programs. The server also includes: a prompt for analyzing individual abilities based on messages posted by the user on a social networking site, messages sent by the user via a communication tool, or materials generated by the user, and visualizing the analyzed individual abilities in a radar chart; a generative AI model for presenting the visualized radar chart to the user; a prompt for generating ability improvement advice based on the acquired career aspiration data and the results of the analysis of the individual abilities; and a generative AI model for generating ability improvement advice and presenting it to the user.

[0988] "SNS" is an abbreviation for Social Networking Service, an online platform for users to share information and communicate over the Internet.

[0989] "Communication tools" are software or applications that allow users to send and receive messages and share information.

[0990] A "proposal" is a document or presentation created to explain a specific purpose or proposal.

[0991] "Individual capabilities" refers to the knowledge, skills, experience, and other characteristics possessed by a particular person.

[0992] A "radar chart" is a graph that visually represents the overall balance and characteristics by arranging multiple evaluation items radially and connecting the evaluation values ​​of each item.

[0993] "Career aspirations" refer to the goals and direction an individual wants to achieve in their professional life.

[0994] "Skills improvement advice" refers to specific guidance and suggestions to further develop an individual's skills and knowledge.

[0995] "Individual thoughts and personality" refers to the way of thinking, behavioral patterns, and psychological characteristics of a particular person.

[0996] "Easy-to-understand phrases and expressions" refers to the use of language and expressions that convey information in a way that is easy for the recipient to understand and accept.

[0997] A "questionnaire format" is a survey method for obtaining answers to questions, and is mainly a multiple choice format or a free description format.

[0998] A "generative AI model" refers to an algorithm or system that uses artificial intelligence techniques to generate new information or suggestions from data.

[0999] A "personalized training program" refers to an education or training plan that is customized to meet individual needs and characteristics.

[1000] "Progress management" refers to the process of monitoring the progress of planned activities or projects and making adjustments as needed.

[1001] "Feedback" refers to information that provides evaluation and opinion on specific actions or results and encourages improvement and growth.

[1002] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. The server collects employees' career aspirations and skill improvement needs in the form of a questionnaire, and recommends personalized training programs using a generative AI model based on the collected data. The terminal provides an interface for employees to answer the questionnaire and manage the progress of the training program. The user uses the system as an employee, inputs information to support their own career development, and receives feedback.

[1003] The server first collects information about employees' career aspirations and skill development needs in the form of a questionnaire. This questionnaire is provided to employees via devices such as smartphones or tablets. Questions in the questionnaire include current skills, skills they would like to improve, and career goals.

[1004] The collected data is input into a generative AI model on the server. The generative AI model generates an optimal training program based on the employee's career aspirations and skill improvement needs. In this process, the generative AI model generates and proposes a training program suitable for the employee using a prompt statement that instructs the user to generate a recommended training program based on the acquired data.

[1005] For example, if an employee wants to improve their "advanced management skills," they can input the following prompt into the generative AI model:

[1006] Employee career aspirations: Improving management skills Current skills: Basic management skills Skills I'd like to improve: Advanced management skills Suggest a suitable training program for this employee.

[1007] Based on this prompt, the generative AI model suggests specific training programs, such as a "leadership training course" or a "project management certification program."

[1008] The proposed training program is provided to the employee through the terminal. The employee takes the training program on the terminal, the server manages the training progress and generates feedback, and the terminal can receive the generated feedback. The server collects progress data and adjusts the training program as needed.

[1009] This system makes it possible to respond to the diverse needs of employees and provide individually tailored training programs, thereby supporting employees' career development and improving productivity across the organization.

[1010] The server also analyzes individual abilities from statements posted by employee users on SNS, statements sent by users using a communication tool, or proposal materials generated by users, and presents the visualized radar chart to the user using a prompt statement instructing the system to visualize the analyzed individual abilities in a radar chart and the generative AI model. Specifically, the server generates the visualized radar chart using a prompt statement including data representing statements posted by employee users on SNS, statements sent by users using a communication tool, or proposal materials generated by users, and the prompt statement instructing the system to analyze individual abilities and visualize the analyzed individual abilities in a radar chart and the generative AI model.

[1011] The server also generates and presents advice for improving skills to the user using a prompt statement and a generative AI model that instructs the server to generate advice for improving skills based on the career-oriented data obtained in the form of a questionnaire and the results of an analysis of the individual's skills.

[1012] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1013] Step 1:

[1014] The server collects information about employees' career aspirations and skill improvement needs in the form of a questionnaire.

[1015] Input: Survey data answered by employees via smartphones or tablets.

[1016] Data processing: The survey responses are stored in a database to extract each employee's career aspirations and skill improvement needs.

[1017] Output: Career aspirations and skill development needs data for each employee.

[1018] Step 2:

[1019] The server inputs prompt sentences into the generative AI model based on the collected data.

[1020] Input: Employee career aspirations and skill development needs data.

[1021] Data processing: Generate prompt sentences and input them into the generative AI model.

[1022] Output: Training program suggestions from the generative AI model.

[1023] Step 3:

[1024] The server sends training program suggestions obtained from the generative AI model to the device.

[1025] Input: Training program suggestions from a generative AI model.

[1026] Data processing: Organize training program proposals for each employee and send them to the terminal.

[1027] Output: Training program suggestions displayed on the terminal.

[1028] Step 4:

[1029] The terminal displays training program suggestions to employees and manages their progress.

[1030] Input: Training program suggestions sent by the server.

[1031] Data Processing: Record the progress of training programs and gather employee feedback.

[1032] Output: Employee training program progress data and feedback.

[1033] Step 5:

[1034] The server adjusts the training program based on progress data and feedback collected from the device.

[1035] Input: Progress data and feedback sent from the device.

[1036] Data Processing: Analyze progress data and feedback and update training programs as needed.

[1037] Output: Updated training program proposal.

[1038] Step 6:

[1039] The server again transmits the updated training program proposal to the terminal and notifies the employee.

[1040] Input: Updated training program proposal.

[1041] Data processing: Updated training programs are organized for each employee and sent to the terminal.

[1042] Output: Updated training program proposal displayed on the terminal.

[1043] The server may further include means for analyzing the user's emotions using an emotion engine described below, and may adjust the training program according to the user's emotions when adjusting the training program. Specifically, if the user's emotion is "anger" or "sadness," the pace of the training program may be adjusted to slow down.

[1044] Example 3

[1045] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1046] Providing appropriate feedback based on employees' personality traits and communication styles contributes to improving employee motivation and work efficiency. However, conventional systems have difficulty generating personalized feedback for individual employees, and can only provide general advice. Furthermore, in organizations with many employees, it is difficult to grasp the personality traits of each employee in detail and provide feedback based on that.

[1047] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1048] In this invention, the server includes a means for inputting employee personality trait data, a means for analyzing the personality trait data, a means for inputting a prompt sentence to the generative AI model, a means for the generative AI model to generate appropriate feedback, and a means for providing the generated feedback, thereby enabling the generation of personalized feedback based on the employee's personality traits and communication style.

[1049] "SNS" is an abbreviation for social networking services, an online platform for people to share information and communicate over the Internet.

[1050] A "communication tool" is software or an application that individuals or groups use to exchange information and communicate.

[1051] A "proposal" is a document created to explain a particular idea or plan and to seek approval or support.

[1052] "Individual capabilities" refers to the knowledge, skills, experience, and other characteristics possessed by a particular person.

[1053] A "radar chart" is a graphical format for visually comparing multiple variables, plotting data points on axes radiating from a center.

[1054] "Career aspirations" refer to the goals and direction that an individual wants to achieve in their professional life.

[1055] "Skill development advice" refers to specific suggestions and guidance to further develop an individual's skills and knowledge.

[1056] "Individual thoughts and personality" refers to the cognitive tendencies and behavioral patterns of a particular person.

[1057] "Easy-to-understand phrases and expressions" refers to the use of language and expressions that convey information in a way that is easy for the recipient to understand and accept.

[1058] "Employee personality trait data" refers to information about employees' personalities and behavioral patterns, and is obtained from psychological assessment tools, survey results, etc.

[1059] A "generative AI model" refers to an algorithm or system that has been trained to perform a specific task using artificial intelligence techniques.

[1060] A "prompt" is an instruction given to a generative AI model to generate a specific output.

[1061] "Feedback" refers to evaluation or advice given in response to a specific action or outcome.

[1062] The present invention is a system for providing personalized feedback based on an employee's personality traits and communication style. Specific embodiments of this system are described below.

[1063] First, users input personality trait data for employees. This data is collected using psychological assessment tools such as the Big Five personality trait model and the Myers-Briggs Type Indicator (MBTI), as well as questionnaire results. Users enter information such as "Employee A is extroverted and prefers direct feedback" through a web form.

[1064] The server then receives the personality data entered by the user and analyzes it. The server references the employee's personality data stored in a database to identify the employee's communication style and feedback preferences. This analysis is performed using data analysis software and algorithms.

[1065] The server then uses the analysis results to create a prompt to be input to the generative AI model, such as "Employee A is extroverted and prefers direct feedback. Please provide specific and clear feedback on the progress of his recent project." This prompt is then sent to the generative AI model (e.g., OpenAI's GPT-4).

[1066] The generative AI model generates appropriate feedback based on the prompt received from the server. The generative AI model uses natural language processing technology to create feedback that matches the employee's personality traits. The generated feedback is then sent back to the server.

[1067] Finally, the server provides the feedback received from the generative AI model to the user. Specifically, the server sends the feedback to the user via a web interface or email. The user can review the provided feedback and communicate it to Employee A.

[1068] As a concrete example, if employee A has the personality traits of "extroverted and prefers direct feedback," the server will input the following prompt sentence into the generative AI model:

[1069] Example prompt sentence:

[1070] "Employee A is extroverted and enjoys direct feedback. Please provide specific and clear feedback on his recent project progress."

[1071] Based on this prompt, the generative AI model generates the following feedback:

[1072] "Employee A, your recent project progress has been excellent. Your data analysis was particularly accurate. However, please report back to us sooner next time."

[1073] In this way, the server can generate appropriate feedback based on the employee's personality traits and provide it to the user. This system is expected to improve employee motivation and work efficiency. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[1074] Step 1:

[1075] The user inputs personality trait data for the employee.

[1076] Specifically, a user accesses a web form, enters employee personality trait data, for example, "Employee A is extroverted and prefers direct feedback," and clicks the submit button.

[1077] Input: Employee personality trait data (e.g., "Employee A is extroverted and prefers direct feedback")

[1078] Output: Personality trait data is sent to the server

[1079] Step 2:

[1080] The server receives the personality trait data sent by the user and stores it in a database.

[1081] Specifically, the server analyzes the received data and stores it in a database to identify employee personality traits.

[1082] Input: User-submitted personality trait data

[1083] Output: Personality trait data stored in a database

[1084] Step 3:

[1085] The server analyzes the stored personality trait data.

[1086] Specifically, the server references employee personality trait data stored in a database to identify the employee's communication style and feedback preferences.

[1087] Input: Personality trait data stored in a database

[1088] Output: Identification of employee communication styles and feedback preferences

[1089] Step 4:

[1090] Based on the analysis results, the server creates prompt sentences to be input into the generative AI model.

[1091] Specifically, the server generates a prompt such as, "Employee A is extroverted and prefers direct feedback. Please provide specific and clear feedback on the progress of his recent project."

[1092] Input: Identification of employee communication styles and feedback preferences

[1093] Output: A prompt to be input to the generative AI model

[1094] Step 5:

[1095] The server inputs prompt sentences into the generative AI model, causing it to generate appropriate feedback.

[1096] Specifically, the server sends prompt sentences to a generative AI model (e.g., OpenAI's GPT-4) to generate feedback.

[1097] Input: A prompt to be input to the generative AI model

[1098] Output: Feedback sent back from the generative AI model

[1099] Step 6:

[1100] The server provides the feedback received from the generative AI model to the user.

[1101] Specifically, the server sends the feedback to the user via a web interface or email, and the user can review the provided feedback and communicate it to Employee A.

[1102] Input: Feedback sent back from the generative AI model

[1103] Output: Feedback provided to the user

[1104] The above is the specific processing flow of the program of this system.

[1105] (Application example 3)

[1106] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1107] Providing appropriate feedback and instructions tailored to each employee's personality traits and communication style contributes to improving employee motivation and work efficiency. However, conventional systems often fail to provide personalized communication to individual employees, resulting in only general feedback and instructions. As a result, the feedback and instructions received by employees may be ineffective. The present invention aims to solve these problems and provide a system that provides feedback and instructions tailored to each employee.

[1108] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1109] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc. and visualizing the results in a radar chart, means for providing ability improvement advice based on career aspirations, etc., means for generating easy-to-understand phrases and expressions taking into account individual thoughts and personalities, means for analyzing employee personality traits and communication styles and generating appropriate phrases and expressions based thereon, and means for generating appropriate phrases using a generative AI model. This makes it possible to provide personalized feedback and instructions to each employee.

[1110] "SNS" stands for Social Networking Service, an online platform for people to share information and interact over the Internet.

[1111] "Communication tools" is a general term for software and applications used by individuals and groups to exchange information.

[1112] A "proposal" is a document or presentation created to explain and gain support for a particular idea or plan.

[1113] "Individual capabilities" refers to the knowledge, skills, experience, and other characteristics possessed by a particular person.

[1114] A "radar chart" is a diagram that visually represents multiple variables on a single graph, with each variable arranged radially.

[1115] "Career aspirations" refer to the goals and direction that an individual wants to achieve in their professional life.

[1116] "Skill development advice" refers to specific suggestions and guidance to further develop an individual's skills and knowledge.

[1117] "Individual thoughts and personality" refers to the way of thinking, behavioral patterns, and psychological characteristics of a particular person.

[1118] "Easy-to-understand phrases and expressions" refers to the use of language and expressions that convey information in a way that is easy for the recipient to understand and accept.

[1119] "Employee personality traits" refer to the psychological characteristics and behavioral patterns possessed by a particular employee.

[1120] "Communication style" refers to the particular way or attitude that an individual uses when exchanging information with others.

[1121] A "generative AI model" refers to an algorithm or system that is trained to perform a specific task using artificial intelligence.

[1122] A "prompt" is an instruction or question input to a generative AI model to obtain a specific output.

[1123] The system for implementing the present invention is designed to provide appropriate feedback and instructions based on the personality traits and communication styles of employees. A specific embodiment of this system will be described below.

[1124] System configuration

[1125] The system consists of the following main components:

[1126] 1. Data collection module: Collects data from social media posts, text from communication tools, proposal materials, etc.

[1127] 2. Ability analysis module: Analyzes collected data and visualizes individual abilities in a radar chart.

[1128] 3. Advice generation module: Provides advice on improving skills based on career aspirations, etc.

[1129] 4. Communication Style Analysis Module: Analyze employee personality traits and communication styles.

[1130] 5. Generative AI model: An AI model for generating appropriate phrases and expressions.

[1131] 6. Feedback generation module: Uses generative AI models to generate appropriate feedback and instructions for employees.

[1132] Program processing

[1133] The server first uses a data collection module to collect data from social media posts, text from communication tools, proposal materials, etc. This data is analyzed by a skills analysis module, and individual skills are visualized in a radar chart. Next, an advice generation module generates advice for skill improvement based on career aspirations, etc.

[1134] The communication style analysis module then analyzes the employee's personality traits and communication style, and the generative AI model uses the results to generate appropriate phrases and expressions. Finally, the feedback generation module uses the generative AI model to generate appropriate feedback and instructions for the employee.

[1135] Hardware and software used

[1136] Hardware: Internet-connected computers or robots

[1137] Software: Python, OpenAI API

[1138] Specific examples

[1139] For example, if employee Taro Tanaka prefers direct feedback, he could input the following prompt into the generative AI model:

[1140] The employee's name is Taro Tanaka. He prefers a direct communication style. Generate the following message in the appropriate wording: Please improve the work procedure.

[1141] When this prompt is input into the generative AI model, it generates direct and clear feedback, such as, "Mr. Tanaka, please make specific improvements to your work procedures." In this way, the system can provide personalized feedback and instructions to each employee.

[1142] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1143] Step 1:

[1144] The server uses a data collection module to collect data from social media posts, text from communication tools, proposal materials, etc. It receives employees' social media account information and communication tool login information as input, and generates the collected text data as output. This data serves as the basis for analyzing employees' abilities and personality traits.

[1145] Step 2:

[1146] The server uses a competency analysis module to analyze the collected text data. As input, it receives the text data collected in step 1 and analyzes individual competencies using natural language processing technology. As output, it generates data that visualizes the analysis results in the form of a radar chart. This data is used to visually grasp employee competencies.

[1147] Step 3:

[1148] The server uses an advice generation module to generate skill improvement advice based on career aspirations, etc. It receives as input the radar chart data generated in step 2 and the employee's career aspirations, and generates specific skill improvement advice as output. This advice serves as a guideline for employees to improve their skills.

[1149] Step 4:

[1150] The server uses the communication style analysis module to analyze the employee's personality traits and communication style. It receives the employee's personality diagnostic test results and past communication history as input, and generates the analysis results of the employee's personality traits and communication style as output. This analysis result becomes the basis for generating feedback and instructions appropriate for the employee.

[1151] Step 5:

[1152] The server uses a generative AI model to generate appropriate phrases and expressions. As input, it receives the results of the personality trait and communication style analysis generated in step 4, as well as the message that will be the basis for feedback and instructions, and as output, it generates phrases and expressions that are appropriate for the employee. In this process, the generative AI model generates appropriate feedback and instructions based on the prompt sentence.

[1153] Step 6:

[1154] The server uses a feedback generation module to provide employees with appropriate phrases and expressions obtained from the generative AI model. As input, it receives the feedback and instructions generated in step 5, and as output, it provides personalized feedback and instructions to employees. This feedback and instructions contributes to improving employee motivation and work efficiency.

[1155] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1156] "Example 1"

[1157] One embodiment of the present invention is a system that incorporates an emotion engine. This system analyzes an individual's abilities based on the user's social media posts, text on communication tools, proposal materials, etc., and visualizes them in a radar chart. It also provides advice on improving abilities based on career aspirations, etc. It also generates easy-to-understand phrases and expressions that take into account the individual's thoughts and personality. In this system, the emotion engine recognizes the user's emotions and provides advice based on those emotions. For example, if the user is feeling frustrated, it analyzes the cause and provides specific advice to resolve it.

[1158] "Example 2"

[1159] Another example is a system that accumulates data from organizations with many employees and long-term employment to improve the accuracy of the model. This system incorporates an emotion engine to take into account changes in employees' emotions. For example, if an employee is feeling stressed, the system analyzes the cause of that stress and provides specific advice to alleviate the stress.

[1160] "Example 3"

[1161] Another example is a system that supports individual career development based on the results of an individual's ability analysis. This system incorporates an emotion engine to take into account changes in employees' emotions. For example, if an employee is considering a career change, the system analyzes the emotional background that led to that decision and provides specific advice for the new career path.

[1162] The processing flow of each embodiment will be described below.

[1163] "Example 1"

[1164] Step 1: Analyze individual capabilities based on users' social media posts, text in communication tools, proposal materials, etc.

[1165] Step 2: Visualize the analysis results in a radar chart.

[1166] Step 3: Provide advice on improving skills based on career aspirations, etc.

[1167] Step 4: Use the emotion engine to recognize the user's emotions and provide advice based on those emotions.

[1168] "Example 2"

[1169] Step 1: Collect data on organizations with large numbers of employees and long-term employment.

[1170] Step 2: Improve the accuracy of the model based on the accumulated data.

[1171] Step 3: Use an emotion engine to take into account changes in employee emotions.

[1172] Step 4: If the employee is stressed, analyze the causes of the stress and provide specific advice to reduce it.

[1173] "Example 3"

[1174] Step 1: Support individual career development based on the results of individual ability analysis.

[1175] Step 2: Use an emotion engine to take into account changes in employee emotions.

[1176] Step 3: If the employee is considering a career change, analyze the emotional context that led to that decision.

[1177] Step 4: Provide specific advice for a new career path.

[1178] Example 1

[1179] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1180] In today's business environment, it is important to accurately evaluate individual capabilities and provide appropriate feedback. However, traditional methods make it difficult to comprehensively analyze an individual's capabilities and provide advice tailored to their emotions. It is also difficult to provide feedback that takes into account an individual's career aspirations and personality. This creates the challenge of being unable to effectively support employee capability improvement and career development.

[1181] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1182] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing ability improvement advice based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality, means for analyzing the user's emotions using an emotion engine and providing advice based on those emotions, means for evaluating the communication skills and problem-solving abilities of each employee using a text analysis algorithm, and means for generating a radar chart based on the analysis results and sending it to the user's terminal. This makes it possible to comprehensively analyze an individual's capabilities and provide appropriate advice based on their emotions.

[1183] "SNS" stands for Social Networking Service, an online platform for users to share information and interact over the Internet.

[1184] A "communication tool" is any software or application that users use to exchange messages or information.

[1185] A "proposal document" is a document or presentation created to explain the contents of a proposal for a business or project.

[1186] "Individual abilities" refers to a comprehensive assessment of the user's skills, knowledge, problem-solving ability, communication ability, etc.

[1187] A "radar chart" is a graph format for visually displaying multiple evaluation items, and visualizes the evaluation values ​​of each item by plotting them radially.

[1188] "Career aspirations" refers to the goals and directions that users have regarding their occupations and careers.

[1189] An "emotion engine" is an algorithm or system that analyzes emotions from a user's text and behavior and generates feedback and advice based on those emotions.

[1190] A "text analysis algorithm" is an algorithm that uses natural language processing technology to analyze text data and extract meaning, emotions, skills, etc.

[1191] "User's device" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[1192] "Advice" refers to specific suggestions or guidance provided to help a user improve their skills or solve a problem.

[1193] This invention relates to an AI system that analyzes individual abilities and provides advice based on emotions. This system analyzes individual abilities from social media posts, text on communication tools, proposal materials, etc., and visualizes them in a radar chart. It also provides advice on skill improvement based on career aspirations and generates easy-to-understand phrases and expressions that take into account the individual's thoughts and personality.

[1194] Hardware and software used

[1195] Hardware: Servers, terminals (PCs, smartphones)

[1196] Software: Text analysis algorithms, sentiment engines, radar chart generation tools

[1197] System Overview

[1198] 1. Data Collection

[1199] The content posted by users on social media, text written on communication tools, and proposal materials are uploaded to the server.

[1200] The server collects this data and stores it in a database.

[1201] 2. Text Analysis

[1202] The server uses text analysis algorithms to analyze the collected data.

[1203] The server evaluates each employee's communication skills, problem-solving abilities, and more.

[1204] 3. Emotion analysis

[1205] The server uses an emotion engine to analyze the user's emotions.

[1206] If the user feels frustrated, the server analyzes the cause and generates specific advice to resolve the issue.

[1207] 4. Visualization

[1208] The server generates a radar chart based on the analysis results.

[1209] The server sends the radar chart to the user's terminal and displays it.

[1210] 5. Providing advice

[1211] The server generates advice for improving skills based on the user's career aspirations.

[1212] The server generates and provides to users easy-to-understand phrases and expressions that take into account the individual's thoughts and personality.

[1213] Specific examples

[1214] For example, if a user posts on social media, "Work hasn't been going well lately," the server analyzes the post and determines that the user is feeling frustrated. The server uses an emotion engine to analyze the cause of the frustration and generates specific advice, such as "You might want to reconsider your task priorities." The server provides this advice to the user and visualizes the user's abilities in a radar chart.

[1215] Prompt Sentence Examples

[1216] Analyze users' social media posts and text on communication tools to evaluate their individual abilities. Visualize the evaluation results in a radar chart and provide advice based on the user's emotions.

[1217] In this way, the system can analyze the user's abilities and provide appropriate advice according to their emotions.

[1218] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1219] Step 1: Data collection

[1220] The content posted by users on social media, conversation history on communication tools, and proposal materials are uploaded to the server.

[1221] Input: User social media posts, text from communication tools, proposal materials

[1222] Specific behavior: A user posts on social media, "My project hasn't been going well lately."

[1223] The server collects this data in real time and stores it in a database.

[1224] Output: User posts, text, and documents stored in a database

[1225] Specific operation: The server detects the user's post and stores it in the database.

[1226] Step 2: Text analysis

[1227] The server uses text analysis algorithms to analyze the collected data.

[1228] Input: User posts, text, and materials stored in the database

[1229] Specific behavior: The server analyzes posts such as "The project is not going well" and evaluates the user's problem-solving ability.

[1230] The server evaluates each employee's communication skills, problem-solving abilities, and more.

[1231] Output: Evaluation results of the user's communication skills and problem-solving ability

[1232] Specific operation: The server scores the user's problem-solving ability based on the analysis results.

[1233] Step 3: Sentiment Analysis

[1234] The server uses an emotion engine to analyze the user's emotions.

[1235] Input: Text analysis results

[1236] What it does: The server detects frustration from user posts.

[1237] If the user feels frustrated, the server analyzes the cause and generates specific advice to resolve the issue.

[1238] Output: User sentiment analysis results and specific advice

[1239] Specific behavior: The server generates advice such as "You may want to reconsider your task priorities."

[1240] Step 4: Visualization

[1241] The server generates a radar chart based on the analysis results.

[1242] Input: User's ability assessment results

[1243] Specific operation: The server converts the user's ability evaluation results into a radar chart.

[1244] The server sends the radar chart to the user's terminal and displays it.

[1245] Output: Radar chart displayed on the user's device

[1246] Specific operation: The server sends the generated radar chart to the user's smartphone and displays it in the app.

[1247] Step 5: Providing advice

[1248] The server generates advice for improving skills based on the user's career aspirations.

[1249] Input: User's career aspirations

[1250] Specific operation: The server considers the user's career aspirations and generates advice such as "You might want to take leadership training."

[1251] The server generates and provides to users easy-to-understand phrases and expressions that take into account the individual's thoughts and personality.

[1252] Output: Specific advice given to the user

[1253] Specific behavior: The server provides advice in a way that matches the user's personality.

[1254] (Application example 1)

[1255] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1256] It is difficult to evaluate employees' work efficiency and skills in real time and provide appropriate feedback. It is also difficult to recognize employees' emotions and provide advice accordingly. This results in a problem where employees are not given sufficient support for improving their abilities and career development.

[1257] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1258] In this invention, the server includes: means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart; means for providing ability improvement advice based on career aspirations; means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality; means for analyzing employees' words and actions while working and evaluating their work efficiency and skills; means for recognizing employees' emotions using an emotion engine and providing appropriate advice; and means for visualizing the analysis results in a radar chart. This makes it possible to evaluate employees' work efficiency and skills in real time and provide appropriate feedback. Furthermore, by recognizing employees' emotions and providing appropriate advice, effective support for employee ability improvement and career development is provided.

[1259] "SNS" is an abbreviation for social networking services, an online platform for people to share information and interact over the Internet.

[1260] "Communication tools" is a general term for software and applications used by individuals and groups to exchange information and communicate.

[1261] A "proposal" is a document or presentation created to explain and propose a particular idea or plan.

[1262] "Individual ability" is a general term for the skills, knowledge, experience, aptitude, etc. that a particular person possesses.

[1263] A radar chart is a type of graph that visually represents the overall balance and characteristics by arranging multiple evaluation items radially and connecting the evaluation values ​​of each item.

[1264] "Career" refers to the experience, achievements, and work history that an individual accumulates throughout their working life.

[1265] "Intention" refers to the awareness or willingness to move in a particular direction or towards a goal.

[1266] "Improvement advice" refers to specific advice or suggestions for improving an individual's skills or knowledge.

[1267] "Thinking" is the mental process of thinking, judging, and understanding things.

[1268] "Personality" refers to the psychological characteristics that characterize an individual's tendencies to act, feel, and think.

[1269] "Phrases and expressions" refer to the use of words and expressions to convey a particular meaning or intention.

[1270] An "employee" is someone who is employed by a company or organization and performs work.

[1271] "Work efficiency" is an index that indicates how much work can be completed effectively within a certain time.

[1272] A "skill" is an ability or technique for effectively performing a particular task or activity.

[1273] An "emotion engine" is an algorithm or system that analyzes and recognizes human emotions from data such as text and voice.

[1274] "Feedback" is the evaluation or opinion given in response to a particular action or result.

[1275] The following system configuration will be described as an embodiment of the present invention.

[1276] The server runs a program that analyzes individual capabilities from social media posts, text on communication tools, proposal documents, etc., and visualizes them in a radar chart. This program uses a text analysis algorithm to evaluate each employee's communication skills, problem-solving ability, etc. Specifically, the server uses a Python program and libraries such as spacy, TextBlob, and matplotlib.

[1277] The server uses an emotion engine to recognize the employee's emotions and provide advice based on those emotions. The emotion engine analyzes emotions from text data and returns a numerical value indicating whether the emotion is positive or negative. Based on the analysis results, if an employee is feeling frustrated, the server analyzes the cause and provides specific advice to resolve the frustration.

[1278] The server analyzes the words and actions of employees while they are working to evaluate their work efficiency and skills. For example, if an employee says, "Today, I came up with a new solution to solve the problem. I discussed it with my team members and demonstrated leadership," the server analyzes this text to evaluate the employee's problem-solving ability and leadership and visualizes it in a radar chart.

[1279] Based on the analysis results, the server provides advice to support individual career development, thereby effectively supporting employee skill development and career development.

[1280] As a concrete example, consider the case of analyzing conversations between employees at work in a factory. If an employee says, "Today, I came up with a new solution to solve a problem. I discussed it with my team members and demonstrated leadership," this text can be analyzed to evaluate the employee's problem-solving ability and leadership, and visualize the results in a radar chart.

[1281] Example prompts to input to a generative AI model:

[1282] Analyze conversations between employees at work in your factory, evaluate their communication skills, problem-solving ability, and leadership skills, and visualize the results in a radar chart.

[1283] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1284] Step 1:

[1285] The server collects text data such as social media posts, text from communication tools, and proposal materials.

[1286] Input: Text data such as social media posts, communication tool text, proposal materials, etc.

[1287] Output: Collected text data

[1288] Specific operation: The server automatically collects various text data through APIs and database connections.

[1289] Step 2:

[1290] The server inputs the collected text data into a text analysis algorithm to analyze the individual's abilities.

[1291] Input: Collected text data

[1292] Output: Analyzed ability data (e.g., communication skills, problem-solving ability, leadership)

[1293] Specific operation: The server uses the spacy library to analyze text data, extract specific keywords and phrases, and evaluate capabilities.

[1294] Step 3:

[1295] The server generates a radar chart based on the analyzed ability data.

[1296] Input: Parsed ability data

[1297] Output: Radar chart image

[1298] Specific operation: The server uses the matplotlib library to generate a radar chart that visually represents the analysis results.

[1299] Step 4:

[1300] The server uses an emotion engine to analyze emotions from the text data.

[1301] Input: Collected text data

[1302] Output: Sentiment analysis results (e.g., positivity, negativity)

[1303] Specific operation: The server uses the TextBlob library to analyze the sentiment of the text data and return a numerical value indicating the degree of positivity or negativity.

[1304] Step 5:

[1305] The server generates appropriate advice for the employee based on the emotion analysis results.

[1306] Input: Sentiment analysis results

[1307] Output: Advice statement

[1308] Specific operation: The server generates advice based on the employee's emotions based on the results of emotion analysis. For example, if the employee is feeling frustrated, it analyzes the cause and provides specific advice to resolve the frustration.

[1309] Step 6:

[1310] The server provides analysis results and advice as feedback to employees.

[1311] Input: Radar chart image, advice text

[1312] Output: Feedback to employee

[1313] Specific operation: The server sends the generated radar chart and advice text to the employee's terminal and provides feedback.

[1314] Step 7:

[1315] The server stores the employee feedback results in a database and uses them to improve the accuracy of the model.

[1316] Input: Employee feedback results

[1317] Output: Updated database

[1318] Specific operation: The server stores the feedback results in a database and improves the accuracy of the model for future analysis.

[1319] Example 2

[1320] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1321] It was difficult with conventional systems to provide personalized advice tailored to employees' career aspirations and needs for skill development. It was also difficult to provide specific advice to reduce stress while taking into account changes in employees' emotions. Furthermore, there was a lack of systems for efficiently collecting and analyzing employee input information. To solve these issues, a comprehensive system that takes into account changes in employees' career aspirations and emotions is needed.

[1322] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1323] In this invention, the server includes means for analyzing individual abilities and visualizing them in a radar chart, means for providing advice for improving abilities based on career aspirations, means for generating easy-to-understand phrases and expressions that take into account individual thoughts and personality, means for investigating employees' career aspirations and needs for improving abilities and providing personalized advice, means for providing advice for reducing stress based on changes in employees' emotions, means for generating personalized advice using a generative AI model, means for inputting self-evaluations, career aspirations, and needs for improving abilities using a terminal, and means for the server to store and analyze the input information in a database. This makes it possible to provide personalized advice based on employees' career aspirations and needs for improving abilities, and also to provide specific advice for reducing stress that takes into account changes in employees' emotions.

[1324] "Means for analyzing individual abilities and visualizing them in a radar chart" is a method for collecting data on an individual's skills and abilities, analyzing it, and displaying it visually.

[1325] "Means for providing advice on improving skills based on career aspirations" refers to a method of providing specific advice for improving skills based on an individual's career goals and aspirations.

[1326] "Means for generating easy-to-understand phrases and expressions that take into account an individual's thinking and personality" is a method for generating phrases and expressions that are effectively conveyed, taking into account an individual's thinking patterns and personality characteristics.

[1327] "Means of investigating employees' career aspirations and needs for skill development and providing personalized advice" refers to a method of investigating employees' career goals and needs for skill development and providing individually optimized advice based on that.

[1328] "Means of taking into account changes in employees' emotions and providing advice to reduce stress" refers to a method of monitoring employees' emotional state and stress levels and providing specific advice to reduce stress based on that.

[1329] "Means for generating personalized advice using a generative AI model" means a method for using a generative AI model to generate personalized advice tailored to individual needs and circumstances.

[1330] The "means for inputting self-evaluation, career aspirations, and ability improvement needs using a terminal" is a method for a user to input self-evaluation, career goals, and ability improvement needs using a terminal.

[1331] "Means for the server to save the input information in a database and analyze it" refers to a method by which the server saves the information input by the user in a database and analyzes the data.

[1332] This invention is a system that provides personalized advice to employees based on their career aspirations and needs for improving their skills. The system consists of three main components: a server, a terminal, and a user.

[1333] server

[1334] The server serves as a central point for investigating employees' career aspirations and development needs and providing personalized advice. The server uses the following hardware and software:

[1335] Hardware: A server machine with a powerful processor, sufficient memory, and ample storage.

[1336] Software: A relational database management system (RDBMS) such as MySQL or PostgreSQL, a generative AI model (e.g., GPT-3), and a sentiment analysis API (e.g., IBM Watson or Microsoft Azure sentiment analysis API).

[1337] The server stores the information entered by the user in a database and analyzes it. Based on the analysis results, it uses a generative AI model to generate personalized advice. For example, if an employee wants to "improve their management skills," the server will recommend an appropriate management training program.

[1338] Terminal

[1339] The terminal provides an interface for users to input their self-evaluation, career aspirations, and ability improvement needs. The terminal uses the following hardware and software.

[1340] Hardware: Devices such as computers, tablets, and smartphones.

[1341] Software: A web application built using a front-end framework such as React or Angular.

[1342] Users input their self-evaluation, career aspirations, and skill improvement needs through their terminal. The input information is sent from the terminal to the server. For example, if a user inputs "I want to improve my management skills," the information is sent to the server and analyzed.

[1343] User

[1344] Users (employees) use a terminal to input their self-evaluation, career aspirations, and needs for improving their abilities. Based on the information they input, the user receives advice from the server. For example, if the user is feeling stressed, they can follow the advice from the server and take specific measures to reduce stress.

[1345] Specific examples

[1346] Below is an example of a prompt sentence to input to the generative AI model.

[1347] "Employee A wants to improve his management skills. Please recommend an appropriate training program, taking into account his past performance data and feedback."

[1348] When this prompt sentence is input into the generative AI model, the model generates advice recommending the most suitable management training program for employee A. For example, the generated advice might be, "Leadership training program X is suitable for employee A. This program provides specific methods for improving team management and communication skills."

[1349] In this way, the system provides personalized advice tailored to employees' career aspirations and development needs, helping them grow.

[1350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1351] Step 1:

[1352] Users use the terminal to input self-evaluation, career aspirations, and needs for skill improvement.

[1353] Input: The user types "I want to improve my management skills" into the terminal interface.

[1354] Specific actions: The user enters self-evaluation and career goals into a form on the device and clicks the submit button.

[1355] Output: The entered information is saved on the device.

[1356] Step 2:

[1357] The terminal transmits the input information to the server.

[1358] Input: User input: "I want to improve my management skills."

[1359] Specific operation: The device sends the entered information to the server in JSON format. For example, it sends the data {"user_id": 123, "career_goal": "Improve management skills"}.

[1360] Output: The server receives the information.

[1361] Step 3:

[1362] The server stores the received data in a database.

[1363] Input: User's career goal information sent from the device.

[1364] Specific operation: The server stores the received data in a relational database such as MySQL or PostgreSQL, for example, by executing the SQL query INSERT INTO user_data (user_id, career_goal) VALUES (123, 'Improve management skills').

[1365] Output: Information is stored in a database.

[1366] Step 4:

[1367] The server retrieves data from the database and inputs prompt statements into the generative AI model.

[1368] Input: User's career goal information stored in the database.

[1369] Specific operation: The server retrieves user data from the database and inputs a prompt statement to the generative AI model. For example, it executes the SQL query SELECT FROM user_data WHERE user_id = 123 and generates a prompt statement based on the retrieved data: "Employee A wants to improve his management skills. Please recommend an appropriate training program based on his past performance data and feedback."

[1370] Output: A prompt sentence is generated that is fed into the generative AI model.

[1371] Step 5:

[1372] Generative AI models generate personalized advice.

[1373] Input: The prompt text entered by the server.

[1374] Specific behavior: The generative AI model generates personalized advice based on the input prompt, for example, "Employee A would be well suited to leadership training program X. This program provides specific ways to improve team management and communication skills."

[1375] Output: A personalized advice is generated.

[1376] Step 6:

[1377] The server transmits the generated advice to the terminal.

[1378] Input: Personalized advice generated by a generative AI model.

[1379] Specific operation: The server sends the generated advice to the terminal. For example, it sends the following data: {"user_id": 123, "advice": "Leadership training program X is suitable for you."}

[1380] Output: The device receives the advice.

[1381] Step 7:

[1382] The terminal displays the advice to the user.

[1383] Input: Personalized advice sent by the server.

[1384] Specific action: The device displays the advice received from the server to the user. For example, it displays a message on the device screen saying, "Leadership training program X is suitable for you. This program provides specific methods for improving team management and communication skills."

[1385] Output: User confirms advice.

[1386] (Application example 2)

[1387] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1388] Providing personalized advice tailored to employees' career aspirations and skill development needs has been a difficult task with conventional systems. There is also a need to monitor changes in employees' emotions in real time and provide specific advice to reduce stress. This is expected to improve employee satisfaction and productivity.

[1389] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1390] In this invention, the server includes means for analyzing an individual's abilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing ability improvement advice based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality, means for monitoring the individual's emotions in real time using an emotion recognition engine, and means for providing advice for reducing stress based on the individual's emotion data. This makes it possible to provide personalized advice according to the employee's career aspirations and ability improvement needs, and specific advice for reducing stress that takes into account changes in emotions.

[1391] "SNS" is an abbreviation for social networking service, an online platform for users to share information and communicate over the Internet.

[1392] "Communication tools" is a general term for software and applications that allow users to exchange messages and information with each other.

[1393] A "proposal" is a document or presentation created to explain a specific purpose or proposal.

[1394] "Individual capabilities" is a general term for the skills, knowledge, experience, etc. possessed by a particular person.

[1395] A "radar chart" is a graph format in which multiple evaluation items are arranged radially and the evaluation values ​​of each item are connected to illustrate the graph.

[1396] "Career orientation" refers to an individual having a career goal or direction.

[1397] "Improvement advice" refers to specific suggestions or guidance for improving an individual's skills or knowledge.

[1398] "Individual thoughts and personality" is a general term for the way of thinking, behavioral patterns, and personality traits of a particular person.

[1399] "Easy-to-understand phrases and expressions" refers to words and expressions that are easy for the recipient to understand and accept.

[1400] An "emotion recognition engine" is software or algorithms that analyze and recognize an individual's emotional state from video and audio data.

[1401] "Real-time monitoring" refers to the immediate monitoring and acquisition of data on ongoing situations and conditions.

[1402] "Stress reduction advice" refers to specific suggestions or guidance to reduce the stress an individual is experiencing.

[1403] The following system configuration is used as an embodiment of the present invention: The system is composed of three main elements: a server, a terminal, and a user.

[1404] server

[1405] The server runs a program that has the following functions:

[1406] 1. Ability analysis: The server analyzes individual abilities from social media posts, text from communication tools, proposal documents, etc., and visualizes them in a radar chart. This is done using natural language processing technology.

[1407] 2. Career Advice: The server generates personalized advice for skill development based on an individual's career aspirations. Using a generative AI model, the server makes suggestions for training programs and skill development that are optimal for each individual's career aspirations.

[1408] 3. Emotion Monitoring: The server uses an emotion recognition engine to monitor individual emotions in real time, collecting individual emotional data and providing advice to reduce stress.

[1409] Terminal

[1410] Terminals refer to devices such as smart glasses and smartphones. These terminals have the following functions:

[1411] 1. Video capture: Use the camera in the smart glasses to capture video and send it to the server.

[1412] 2. Real-time display: Advice and analysis results received from the server are displayed in real time.

[1413] User

[1414] A user is an employee who uses the system. A user uses the system in the following steps:

[1415] 1. Data entry: Enter social media posts, communication tool text, proposal materials, etc. into the system.

[1416] 2. Receiving advice: The device receives and confirms personalized advice and emotion monitoring results sent from the server.

[1417] Hardware and software used

[1418] Hardware: smart glasses, smartphone, server

[1419] Software: OpenCV (video capture and image processing), EmotionRecognizer (emotion recognition engine), CareerAdviceModel (career advice model)

[1420] Specific examples

[1421] If Employee A wears the smart glasses and selects "improving management skills" as his or her career goal, the smart glasses will monitor Employee A's emotions in real time and provide advice such as "take a break to relax" if Employee A is feeling stressed.

[1422] Prompt Sentence Examples

[1423] Create a program that generates personalized advice based on employees' career aspirations and emotion data. Use smart glasses to capture video and use an emotion recognition engine to predict employees' emotions. Use a career advice model to generate advice based on employees' career aspirations and emotion data.

[1424] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1425] Step 1:

[1426] Users input social media posts, communication tool text, proposal materials, etc. into the system. This data is sent to the server. The input data includes text and image information.

[1427] Step 2:

[1428] The server analyzes the received data and evaluates the individual's abilities. Specifically, it uses natural language processing technology to analyze the text data and extract the individual's skills and knowledge. The analysis results are visualized in the form of a radar chart. The input data is text information, and the output is a radar chart.

[1429] Step 3:

[1430] The server generates personalized advice for improving skills based on an individual's career aspirations. Using a generative AI model, it proposes training programs and skill improvement methods that are optimal for the individual's career aspirations. The input data is career aspiration information, and the output is advice information.

[1431] Step 4:

[1432] The device (smart glasses) uses a camera to capture the user's image and transmits it to the server. The input data is the image information, and the output is the transmission of the image data to the server.

[1433] Step 5:

[1434] The server uses an emotion recognition engine to monitor the user's emotions in real time from the received video data. Specifically, it analyzes the video data and predicts the user's emotional state from their facial expressions and movements. The input data is video information, and the output is emotional state information.

[1435] Step 6:

[1436] The server provides advice for stress reduction based on the user's emotional data. It analyzes the emotional state information and generates specific stress reduction measures if the user is feeling stressed. The input data is the emotional state information, and the output is stress reduction advice.

[1437] Step 7:

[1438] The device displays the advice and analysis results received from the server to the user in real time. Specifically, the advice information and radar charts are displayed on the smart glasses display. The input data is the advice information from the server, and the output is the display to the user.

[1439] Step 8:

[1440] The user checks the advice and analysis results displayed on the device and adjusts their behavior as necessary. Specifically, they can take actions such as taking a break to reduce stress. The input data is the information displayed on the device, and the output is the user's behavior.

[1441] Example 3

[1442] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1443] It was difficult for conventional systems to provide appropriate feedback and advice based on employees' personality traits and communication styles. Furthermore, career development support based on the results of individual ability analysis could not take into account emotional changes, making it difficult to contribute to improving employee satisfaction and performance. Furthermore, for organizations with many employees, accumulating data and improving the accuracy of models was a challenge.

[1444] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1445] In this invention, the server includes means for inputting data on an individual's personality traits and communication style, means for receiving the input data and analyzing the personality traits and communication style, means for generating and inputting prompt sentences to the generative AI model based on the analysis results, means for the generative AI model to generate appropriate phrases and expressions, and means for providing the generated phrases and expressions. This makes it possible to provide appropriate feedback and advice according to the employee's personality traits and communication style.

[1446] "Individual personality traits" are the personality characteristics and tendencies of an individual that are identified based on psychological assessment tools and questionnaire results.

[1447] "Communication style" refers to the characteristics of an individual's method, attitude, and choice of language when exchanging information with others.

[1448] "Means for inputting data" refers to the interface or method by which a user provides the required information to the system.

[1449] "Means for receiving and analyzing data" refers to the processes and technologies by which the server receives information provided by users and analyzes it to extract useful information.

[1450] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate appropriate output based on input data.

[1451] A "prompt" is an instruction or question input to a generative AI model, which serves as the basis for the model to generate appropriate output.

[1452] "Means for generating phrases and expressions" refers to the processes and techniques that enable a generative AI model to generate appropriate feedback and advice based on a prompt.

[1453] "Means for providing" refers to a method or interface for displaying or communicating the generated feedback or advice to the user.

[1454] The present invention is a system that provides appropriate feedback and advice according to the personality traits and communication styles of employees. A specific embodiment of this system is described below.

[1455] First, users input data about their employees' personality traits and communication styles. This includes psychological assessment tools and questionnaire results. Specifically, users can input results from the Big Five personality trait assessment and the Myers-Briggs Type Indicator (MBTI). Users input this data through a web interface.

[1456] The server then receives the data entered by the user and analyzes their personality traits and communication style. The server then uses Python scripts to analyze this data and identify the employee's characteristics. For example, it analyzes the results of a Big Five personality trait assessment to determine whether the employee is an extrovert or an introvert.

[1457] The server then generates a prompt sentence based on the analysis results and inputs it to the generative AI model. For example, OpenAI's GPT-4 is used as the generative AI model. The server generates the prompt sentence using a Python script and inputs it to the generative AI model via an API. Examples of prompt sentences include:

[1458] "Employee A scores high on Extraversion and low on Emotional Stability on the Big Five personality trait assessment. He also prefers direct feedback. Please provide him with feedback on his recent project."

[1459] The generative AI model generates appropriate phrases and expressions for the employee based on the prompt text input from the server. The generated feedback is returned to the server in JSON format. For example, the following feedback may be generated:

[1460] "Employee A, your proactive attitude on the recent project is much appreciated. However, I think you could do better next time if you were a little more calm."

[1461] Finally, the server provides the generated phrases and expressions to the user, who can then review the displayed feedback through a web interface and provide appropriate advice to the employee.

[1462] This system makes it possible to provide appropriate feedback and advice based on an employee's personality traits and communication style. Furthermore, by combining it with an emotion engine, it is possible to provide career development support that takes into account changes in an employee's emotions. For example, if an employee is considering a career change, the system can analyze the emotional background that led to that decision and provide specific advice for the new career path.

[1463] Furthermore, in organizations with many employees, it is important to accumulate data and improve the accuracy of the model. This system accumulates data from organizations that are based on long-term employment and includes means to improve the accuracy of the model, making it possible to provide more accurate feedback and advice. The flow of the identification process in Example 3 will be described with reference to FIG. 21.

[1464] Step 1:

[1465] Users input data about employees' personality traits and communication styles. Specifically, they input Big Five personality trait assessment and MBTI results through a web interface. Input data includes the employee's name, assessment results, and past feedback history. This provides the system with the basic data to understand the employee's characteristics.

[1466] Step 2:

[1467] The server receives data entered by the user. It then uses a Python script to analyze personality traits and communication styles. Specifically, it analyzes the results of the Big Five personality trait assessment to determine whether the employee is extroverted or introverted, and whether they have high or low emotional stability. By analyzing the input data, the server identifies the employee's personality traits and generates analysis results.

[1468] Step 3:

[1469] The server generates and inputs prompts to the generative AI model based on the analysis results. Specifically, the prompts are generated using a Python script and input to the generative AI model via an API. An example of a prompt might be, "Employee A is high in extraversion and low in emotional stability on the Big Five personality trait assessment. He also prefers direct feedback. Please provide him with feedback on his recent project." This provides the generative AI model with the basic information it needs to generate appropriate feedback.

[1470] Step 4:

[1471] The generative AI model generates phrases and expressions appropriate for the employee based on the prompt text input from the server. Specifically, the generative AI model (for example, OpenAI's GPT-4) analyzes the prompt text and generates feedback according to the employee's characteristics. The generated feedback is returned to the server in JSON format. For example, feedback such as, "Employee A, your proactive attitude in the recent project has been highly praised. However, if you respond more calmly next time, you will achieve better results."

[1472] Step 5:

[1473] The server receives the feedback returned by the generative AI model and provides it to the user. Specifically, it displays the generated feedback through a web interface. The user can check the displayed feedback and provide appropriate advice to the employee. This makes it possible to provide appropriate feedback and advice based on the employee's personality traits and communication style.

[1474] (Application example 3)

[1475] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1476] Providing appropriate feedback that takes into account employees' personality traits and emotional state is important for improving employee motivation and career development. However, conventional systems have had issues with providing personalized feedback to individual employees and being unable to provide advice that takes emotional changes into account. Furthermore, there is no established method for providing feedback using generative AI models, and there is a need for a means to automatically generate appropriate feedback based on employees' personality traits and emotional state.

[1477] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1478] In this invention, the server includes means for analyzing an individual's abilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing advice on improving abilities based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality, means for analyzing the employee's personality traits and emotional state and generating appropriate feedback, and means for providing feedback based on prompt sentences using a generative AI model. This makes it possible to provide personalized feedback based on the employee's personality traits and emotional state.

[1479] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[1480] A "communication tool" is any software or application that users use to exchange messages or information.

[1481] A "proposal" is a document created to explain and propose a particular idea or plan.

[1482] "Individual capabilities" is a collective term for the skills, knowledge, and experience possessed by a particular person.

[1483] A "radar chart" is a graph that shows multiple evaluation items arranged radially and connects the evaluation values ​​of each item.

[1484] "Career aspirations" refer to the hopes and goals that individuals have for their future careers or jobs.

[1485] "Improvement advice" is specific advice or suggestions for improving an individual's skills or knowledge.

[1486] "Individual thoughts and personality" refers to the way of thinking and personality traits of a particular person.

[1487] "Easy-to-understand phrases and expressions" are words and phrases that are easy for the other person to understand and accept.

[1488] "Employee personality traits" refer to the personality characteristics and tendencies possessed by a particular employee.

[1489] An "emotional state" is an individual's emotional state or mood at a particular moment.

[1490] "Feedback" is the evaluation or opinion given in response to a particular action or result.

[1491] A "generative AI model" is a model that uses artificial intelligence to generate output for a specific task.

[1492] A "prompt" is an instruction or question that is input into a generative AI model.

[1493] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. The server has the function of analyzing an individual's abilities from social media posts, text from communication tools, proposal materials, etc., and visualizing them in a radar chart. It also has the function of providing advice on improving abilities based on career aspirations, etc. Furthermore, it includes a function to generate easy-to-understand phrases and expressions that take into account the individual's thoughts and personality.

[1494] The server uses a generative AI model to analyze the employee's personality traits and emotional state and generate appropriate feedback. This generative AI model provides feedback based on prompt sentences. Specifically, it inputs data about the employee's personality traits and emotional state and generates appropriate feedback based on that.

[1495] The terminal is a device used by employees that receives feedback provided by the server. Terminals include smartphones, tablets, PCs, etc. Employees can check the feedback through the terminal and take necessary actions.

[1496] Users are employees who use the system and receive feedback. Users receive feedback based on their personality traits and emotional state, and can use that feedback to improve themselves and shape their careers.

[1497] As a concrete example, here is an example of a prompt sentence that generates feedback when an employee is considering a career change.

[1498] Prompt Sentence Examples

[1499] Employee Name: Taro Tanaka

[1500] Personality trait: Prefers direct feedback

[1501] Emotional state: Considering a career change

[1502] Provide appropriate feedback to this employee.

[1503] By inputting this prompt sentence into a generative AI model, appropriate feedback can be provided to employees. The server generates the feedback using OpenAI's API and provides it to the user via their device. This makes it possible to provide personalized feedback based on the employee's personality traits and emotional state.

[1504] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1505] Step 1:

[1506] The server analyzes individual capabilities from social media posts, text from communication tools, proposal materials, etc. It receives employee posts and text data as input and analyzes the text data using natural language processing technology. It generates data indicating employee capabilities as output. Specifically, it extracts keywords and phrases from the text data and uses them to evaluate employee skills and knowledge.

[1507] Step 2:

[1508] The server visualizes the analyzed ability data in a radar chart. As input, it receives the ability data generated in step 1 and creates a radar chart using a graph generation library. As output, it generates a radar chart that is visually easy to understand. Specifically, the evaluation values ​​of each ability item are arranged radially and the evaluation values ​​are connected to illustrate the chart.

[1509] Step 3:

[1510] The server provides advice on skill improvement based on career aspirations, etc. It receives the employee's career aspiration data and the data from Steps 1 and 2 as input, and generates specific advice using an advice generation algorithm. The output is advice for skill improvement provided to the employee. Specifically, it compares the employee's current abilities with their career aspirations and suggests ways to improve the necessary skills and knowledge.

[1511] Step 4:

[1512] The server generates easy-to-understand phrases and expressions that take into account the individual's thoughts and personality. As input, it receives the employee's personality trait data and the advice data from Step 3, and uses natural language generation technology to generate appropriate phrases. As output, it provides personalized feedback to the employee. Specifically, it selects words that are easy to understand and accept, in line with the employee's personality traits.

[1513] Step 5:

[1514] The server analyzes the employee's personality traits and emotional state and generates appropriate feedback. It receives the employee's personality trait data and emotional state data as input and generates feedback using a generative AI model. It provides specific feedback to the employee as output. Specifically, it takes the employee's emotional state into consideration and provides feedback with appropriate timing and content.

[1515] Step 6:

[1516] The server uses a generative AI model to provide feedback based on the prompt. As input, it receives the prompt and employee data and inputs them into the generative AI model. As output, it provides the generated feedback. Specifically, it uses the following prompt:

[1517] Employee Name: Taro Tanaka

[1518] Personality trait: Prefers direct feedback

[1519] Emotional state: Considering a career change

[1520] Provide appropriate feedback to this employee.

[1521] Step 7:

[1522] The terminal receives the feedback provided by the server and displays it to the user. As input, it receives feedback data from the server and displays it through a user interface. As output, the user can check the feedback and take necessary actions. Specifically, the feedback content is displayed on the screen and provided in a format that is easy for the user to understand.

[1523] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1524] The data generation model 58 is a so-called generative AI (Artificial Intelligence).

[1525] An example of a data generation model58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[1526] , obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1527] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1528] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1529] [Third embodiment]

[1530] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1531] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1532] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1533] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1534] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1535] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1536] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1537] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1538] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1539] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1540] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1541] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1542] "Example 1"

[1543] As one embodiment of the present invention, an AI system for analyzing individual capabilities is provided. This system analyzes individual capabilities from social media posts, texts in communication tools, proposal documents, etc., and visualizes them in a radar chart. Specifically, it uses a text analysis algorithm to evaluate the communication skills, problem-solving abilities, etc. of each employee. "Example 2"

[1544] Furthermore, in one embodiment of the present invention, advice on skill development is provided based on the employee's career aspirations and other needs. Specifically, the system investigates the employee's career aspirations and skill development needs and provides personalized advice based on the results. For example, advice such as recommending a specific management training program to an employee who wants to improve their management skills is provided.

[1545] "Example 3"

[1546] In one embodiment of the present invention, AI generates easy-to-understand phrases and expressions that take into account an individual's thoughts and personality. Specifically, the AI ​​analyzes an employee's personality traits and communication style and generates appropriate phrases and expressions. For example, for an employee who prefers direct feedback, the AI ​​provides advice using specific and clear language.

[1547] The processing flow of each embodiment will be described below.

[1548] "Example 1"

[1549] Step 1: The AI ​​system analyzes an individual's capabilities based on social media posts, text in communication tools, proposal materials, etc.

[1550] Step 2: Use text analysis algorithms to assess each employee's communication skills, problem-solving abilities, etc.

[1551] Step 3: Visualize the evaluation results in a radar chart to understand the capabilities of each employee.

[1552] Step 1: Assess employees' career aspirations and development needs.

[1553] Step 2: Generate personalized advice based on the findings.

[1554] Step 3: Provide advice, such as recommending specific management training programs for employees who want to improve their management skills.

[1555] "Example 3"

[1556] Step 1: Analyze your employees' personality traits and communication styles.

[1557] Step 2: Based on the analysis results, the AI ​​generates appropriate expressions and phrases.

[1558] Step 3: For example, if an employee prefers direct feedback, provide specific, clearly worded advice.

[1559] Example 1

[1560] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1561] Conventional individual ability evaluation systems have difficulty accurately analyzing and visualizing individual abilities from a variety of data sources, such as social media posts, text from communication tools, and proposal materials. It has also been difficult to provide advice that takes into account an individual's career aspirations and personality. Furthermore, for organizations with many employees, improving the accuracy of the model through long-term data accumulation has been a challenge.

[1562] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1563] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing them in a radar chart; means for collecting and cleansing text data; means for evaluating individual capabilities using a text analysis algorithm; means for generating the evaluation results as a radar chart and providing them to the user; means for providing advice on improving capabilities based on career aspirations, etc.; and means for generating easy-to-understand phrases and expressions that take into account the individual's thoughts and personality. This makes it possible to analyze and visualize individual capabilities from multiple perspectives. It also makes it possible to provide advice that takes into account the individual's career aspirations and personality, and to improve the accuracy of the model by accumulating data from the entire organization.

[1564] "SNS" stands for Social Networking Service, an online platform for users to share information and interact over the Internet.

[1565] A "communication tool" is any software or application that users use to exchange messages or information.

[1566] A "proposal" is a document or presentation created to explain a specific purpose or proposal.

[1567] "Individual capabilities" refer to the skills, knowledge, experience, and characteristics possessed by a particular individual.

[1568] A "radar chart" is a graph format that arranges multiple evaluation items radially and visually shows the evaluation score for each item.

[1569] "Text data" is information expressed as a string of characters, and includes social media posts, messages on communication tools, proposal documents, etc.

[1570] "Cleansing" is a data preprocessing process that involves removing unnecessary information and noise and preparing the data in a format suitable for analysis.

[1571] A "text analysis algorithm" is a computational method or model for analyzing text data and understanding its meaning and structure.

[1572] "Evaluation results" are evaluation scores and analysis results regarding an individual's abilities obtained by a text analysis algorithm.

[1573] "Career aspirations" refer to the goals and directions that individuals have regarding their occupations and careers.

[1574] "Advice" is advice or suggestions given for a specific purpose or problem.

[1575] "Thoughts and personality" refers to an individual's way of thinking, behavioral patterns, and personality traits.

[1576] "Phrases and expressions" are the choice of words and expressions used to convey a particular message or information.

[1577] "Data storage" refers to the storage of collected data and its retention for later analysis or use.

[1578] "Model accuracy" is an indicator of how accurately an analytical algorithm or predictive model produces results.

[1579] This invention relates to an AI system for analyzing and visualizing individual abilities. A specific embodiment of this system will be described below.

[1580] Hardware and software used

[1581] Hardware: High-performance servers (e.g., high-performance servers)

[1582] Software: text analysis algorithms (e.g., Python's NLTK library), databases (e.g., MySQL), visualization tools (e.g., D3.js)

[1583] System configuration

[1584] 1. Data Collection

[1585] The server collects text data from the APIs of social networking sites and log files of communication tools. For example, it uses the Twitter API to retrieve posts from a specific user.

[1586] 2. Data Preprocessing

[1587] The server cleanses the collected text data and removes unnecessary information. Specifically, it uses a Python regular expression library to remove unnecessary strings from the text data.

[1588] 3. Text Analysis

[1589] The server uses text analysis algorithms to extract keywords and phrases from the text data and evaluate their individual capabilities, for example, using the NLTK library for sentiment analysis and topic modeling.

[1590] 4. Visualization of evaluation results

[1591] The server generates the evaluation results as a radar chart and provides it to the user. Specifically, it uses D3.js to graphically display the evaluation results.

[1592] 5. Providing results

[1593] The server sends the generated radar chart to the user's device, where the results are displayed, for example, through a web browser.

[1594] Specific examples

[1595] For example, when analyzing employee A's social media posts and internal chat logs, the following prompt sentences are input into the generative AI model.

[1596] Example prompt sentence:

[1597] Analyze Employee A's social media posts and internal chat logs and evaluate the following abilities.

[1598] Communication skills

[1599] problem solving ability

[1600] leadership

[1601] creativity

[1602] Display the evaluation results in a radar chart.

[1603] Based on this prompt, the server analyzes Employee A's text data and calculates an evaluation score for each ability. Finally, the evaluation results are visualized as a radar chart and provided to the user. In this way, the user can visually grasp Employee A's abilities.

[1604] This system makes it possible to analyze and visualize an individual's capabilities from multiple angles, and also provides advice that takes into account an individual's career aspirations and personality. It also makes it possible to accumulate data from across the organization to improve the accuracy of the model.

[1605] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1606] Step 1:

[1607] Data collection

[1608] The server collects text data from the APIs of social networking sites and log files of communication tools. For example, it uses the Twitter API to retrieve posts from a specific user.

[1609] Input: Log files from SNS APIs and communication tools

[1610] Output: Collected text data

[1611] Specific operation: The server sends a request to the Twitter API and receives the user's posted data in JSON format.

[1612] Step 2:

[1613] Data Preprocessing

[1614] The server cleanses the collected text data and removes unnecessary information. Specifically, it uses a Python regular expression library to remove unnecessary strings from the text data.

[1615] Input: Collected text data

[1616] Output: Cleansed text data

[1617] What it does: The server uses Python's regular expression library to remove HTML tags and special characters.

[1618] Step 3:

[1619] Text analytics

[1620] The server uses text analysis algorithms to extract keywords and phrases from the text data and evaluate their individual capabilities, for example, using the NLTK library for sentiment analysis and topic modeling.

[1621] Input: Cleansed text data

[1622] Output: Ability assessment score

[1623] What it does: The server uses the NLTK library to tokenize the text data and extract sentiment scores and topics.

[1624] Step 4:

[1625] Visualization of evaluation results

[1626] The server generates the evaluation results as a radar chart and provides it to the user. Specifically, it uses D3.js to graphically display the evaluation results.

[1627] Input: Ability assessment score

[1628] Output: Radar chart

[1629] Specific operation: The server passes the evaluation results in JSON format to D3.js and generates a radar chart.

[1630] Step 5:

[1631] Providing results

[1632] The server sends the generated radar chart to the user's device, where the results are displayed, for example, through a web browser.

[1633] Input: Radar chart

[1634] Output: Radar chart displayed on the user's device

[1635] Specific operation: The server sends an HTML page containing a radar chart to the user's device as an HTTP response.

[1636] (Application example 1)

[1637] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1638] Previous systems lacked the means to analyze individual capabilities and assign appropriate tasks. They also did not provide a concrete method for visualizing employees' skill sets and allowing managers to assign appropriate tasks. This made it difficult to make the most of employees' capabilities and hindered efficient business operations.

[1639] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1640] In this invention, the server includes means for analyzing individual capabilities from social media posts, text on communication tools, proposal materials, etc., and visualizing the results in a radar chart, means for providing advice on improving capabilities based on career aspirations, etc., means for generating easy-to-understand phrases and expressions that take into account individual thoughts and personalities, means for analyzing communication tools and proposal materials used by employees in a factory, evaluating each employee's capabilities, and assigning appropriate tasks, means for visualizing employee skill sets in a radar chart, and means for a manager to assign appropriate tasks based on the employee's skill set. This makes it possible to efficiently analyze employee capabilities and assign appropriate tasks.

[1641] "SNS" is an abbreviation for social networking service, an online platform for users to share information and communicate.

[1642] A "communication tool" is software or an application that individuals or groups use to exchange information and communicate.

[1643] A "proposal" is a document or presentation created to explain and propose a particular idea or plan.

[1644] "Individual ability" is a comprehensive assessment of a specific person's skills, knowledge, experience, aptitude, etc.

[1645] A "radar chart" is a graph that visually represents the overall balance and characteristics by arranging multiple evaluation items radially and connecting the evaluation values ​​of each item.

[1646] "Career aspirations" refers to the career goals and direction that an individual wants to achieve in the future.

[1647] "Advice for improving skills" refers to providing specific suggestions and guidance to improve an individual's skills and knowledge.

[1648] "Individual thoughts and personality" refers to the way of thinking, behavioral patterns, and personality traits of a particular person.

[1649] "Easy-to-understand phrases and expressions" are words and expressions that convey information in a way that is easy for the recipient to understand and accept.

[1650] "Factory workers" refers to workers engaged in manufacturing and production industries.

[1651] "Assigning appropriate tasks" means assigning the most appropriate work or tasks to employees based on their abilities and skills.

[1652] A "skill set" refers to the set of skills and abilities that a particular person possesses.

[1653] "Administrator" refers to a person in charge of managing and supervising an organization or team.

[1654] The system for implementing this invention has a function to analyze individual capabilities from social media posts, text on communication tools, proposal documents, etc., and visualize them in a radar chart. It also includes a function to analyze communication tools and proposal documents used by employees in a factory, evaluate the capabilities of each employee, and assign appropriate tasks.

[1655] System Program

[1656] The server runs a program that performs the following processes: First, it collects text data such as social media posts, text from communication tools, and proposal materials. Next, it uses a text analysis algorithm to analyze the collected text data and evaluates each employee's communication skills, problem-solving ability, and other aspects. Based on the results of this evaluation, it generates and visualizes a radar chart.

[1657] Hardware and software used

[1658] This system uses the following hardware and software:

[1659] Hardware: Server, client terminal (PC, smartphone, tablet, etc.)

[1660] Software: text analysis algorithms (e.g., spacy), vectorization tools (e.g., TfidfVectorizer), dimensionality reduction tools (e.g., PCA), visualization tools (e.g., matplotlib)

[1661] Data processing and calculation

[1662] The server first collects text data and analyzes it using a text analysis algorithm. The analysis results are vectorized and then dimension-reduced using a dimension reduction tool. Next, a radar chart is generated and visualized based on the reduced data. This allows managers to visually check employees' skill sets and assign appropriate tasks.

[1663] Specific examples

[1664] For example, if Employee A makes a new proposal in a factory, the proposal text is analyzed and Employee A's communication skills and problem-solving abilities are evaluated. The evaluation results are visualized in a radar chart, and managers can use the results to assign appropriate tasks to Employee A.

[1665] Prompt Sentence Examples

[1666] Examples of prompts to input to a generative AI model include:

[1667] "Analyze Employee A's proposal and evaluate his / her communication skills and problem-solving abilities."

[1668] Using this prompt, the AI ​​model can evaluate employee capabilities and visualize the results in a radar chart.

[1669] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1670] Step 1:

[1671] The server collects text data such as social media posts, text from communication tools, proposal materials, etc. This involves using APIs to obtain data from each platform. The input is text data from each platform, and the output is a list of the collected text data.

[1672] Step 2:

[1673] The server analyzes the collected text data using a text analysis algorithm (e.g., spacy). Specifically, it tokenizes the text data and analyzes the part of speech and meaning of each token. The input is a list of collected text data, and the output is a list of analyzed text data.

[1674] Step 3:

[1675] The server converts the parsed text data into numerical vectors using a vectorization tool (e.g., TfidfVectorizer), which makes the text data in a format that can be processed by a machine learning model. The input is a list of parsed text data, and the output is a list of numerical vectors.

[1676] Step 4:

[1677] The server reduces the dimension of the vectorized data using a dimension reduction tool (e.g., PCA), which makes it easier to visualize the data. The input is a list of numeric vectors, and the output is a list of dimension-reduced data.

[1678] Step 5:

[1679] The server generates and visualizes radar charts based on the dimension-reduced data. Specifically, it uses visualization tools such as matplotlib to draw the radar charts. The input is a list of dimension-reduced data, and the output is an image of the radar chart.

[1680] Step 6:

[1681] The server provides the generated radar chart to the manager, helping the manager assign appropriate tasks based on the employee's skill set. The input is the radar chart image, and the output is a report that the manager can review.

[1682] Step 7:

[1683] The user (manager) assigns appropriate tasks to employees based on the radar chart provided. Specifically, by referring to the radar chart, the user selects the tasks that best suit each employee's skill set. The input is an image of the radar chart and employee skill set information, and the output is a list of assigned tasks.

[1684] Example 2

[1685] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1686] Providing optimal advice to individual employees regarding their career aspirations and skill development needs has been difficult using conventional methods. Organizations with many employees, in particular, need systems that can efficiently collect and analyze data on each employee and provide appropriate advice. It is also important to generate easily understood phrases and expressions that take into account the employee's way of thinking and personality. Effective methods are needed to solve these challenges.

[1687] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1688] In this invention, the server includes means for analyzing individual abilities and visualizing them in a radar chart, means for providing ability improvement advice based on career aspirations, means for generating easy-to-understand phrases and expressions that take into account individual thoughts and personality, means for collecting and preprocessing employee data, means for inputting the preprocessed data into a generative AI model to generate advice, and means for providing the generated advice to employees. This makes it possible to efficiently provide individually optimal advice in response to employees' career aspirations and ability improvement needs.

[1689] "A means of analyzing individual capabilities and visualizing them in a radar chart" is a method for collecting employee skills and performance data, analyzing it, and displaying it visually.

[1690] "Means of providing advice on improving skills based on career aspirations" refers to a method of suggesting appropriate skill development and training based on employees' career goals and aspirations.

[1691] "Means of generating easy-to-understand phrases and expressions that take into account individual thoughts and personalities" is a method of generating optimal words and expressions that suit the personality and communication style of employees.

[1692] "Means for collecting and preprocessing employee data" refers to a method for collecting various data obtained from employees and arranging it into an analyzable format.

[1693] "Means for inputting preprocessed data into a generative AI model to generate advice" refers to a method for inputting preprocessed data into an AI model, which results in the generation of specific advice for employees.

[1694] "Means for providing generated advice to employees" means a method for communicating the advice generated by the AI ​​model to employees.

[1695] This invention is a system that provides optimal advice to each employee based on their career aspirations and needs for improving their skills. This system operates in cooperation with a server, a terminal, and a user.

[1696] The server first collects employee data, including information entered by the employee about their career aspirations and development needs, past performance data, evaluation data, etc. The collected data is then stored in a database.

[1697] The server then preprocesses the collected data. This includes data cleaning, normalization, and feature extraction. For example, it tokenizes text data entered by employees and removes unnecessary spaces and special characters. It also quantifies past performance data and converts it into a format that can be input into the model.

[1698] The preprocessed data is then input into a generative AI model. For example, we use OpenAI's GPT-4. This model generates optimal advice for employees based on the input data. Specifically, it uses the following prompt sentence:

[1699] Example prompt sentence:

[1700] Employee A wants to improve his management skills. Based on Employee A's past evaluation and performance data, please recommend the most appropriate management training program.

[1701] Based on this prompt, the generative AI model generates the following advice:

[1702] Employee A is recommended to take the Leadership Fundamentals course to strengthen his leadership skills, which will focus on effective team management and communication skills.

[1703] The generated advice is sent from the server to the terminal. The terminal displays the received advice to the user. The user can check the displayed advice and make an action plan if necessary. For example, the user can take steps to participate in a basic leadership course.

[1704] This system makes it possible to efficiently provide optimal advice to employees on their career aspirations and skill development needs, and also increases the receptivity of advice by generating easy-to-understand phrases and expressions that take into account the employee's way of thinking and personality.

[1705] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1706] Step 1:

[1707] The server collects data from users (employees) regarding their career aspirations and needs for improving their skills.

[1708] Input: Information about your career aspirations and development needs entered by you through your device.

[1709] Output: The collected data is stored in a database.

[1710] Specific operation: The user inputs "I want to improve my management skills" through the terminal. The server stores this information in the database.

[1711] Step 2:

[1712] The server pre-processes the collected data.

[1713] Input: Information stored in a database about the user's career aspirations and development needs.

[1714] Output: The preprocessed data is converted into a format that can be fed into a generative AI model.

[1715] What it does: The server tokenizes the text data entered by the user, removing unnecessary spaces and special characters, and converts past performance data into a numerical format that can be fed into the model.

[1716] Step 3:

[1717] The server inputs the preprocessed data into a generative AI model.

[1718] Input: Preprocessed data.

[1719] Output: The data that is input into a generative AI model.

[1720] Specific operation: The server inputs the following prompt sentence into the generative AI model:

[1721] Employee A wants to improve his management skills. Based on Employee A's past evaluation and performance data, please recommend the most appropriate management training program.

[1722] Step 4:

[1723] The generative AI model generates advice based on input data.

[1724] Input: The data fed into a generative AI model.

[1725] Output: The generated advice.

[1726] Specific behavior: The generative AI model generates advice such as the following:

[1727] Employee A is recommended to take the Leadership Fundamentals course to strengthen his leadership skills, which will focus on effective team management and communication skills.

[1728] Step 5:

[1729] The server transmits the generated advice to the user's terminal.

[1730] Input: The generated advice.

[1731] Output: Advice sent to the user's terminal.

[1732] Specific operation: The server sends the generated advice to the terminal, and the terminal displays it to the user as follows:

[1733] Recommended Tip: To strengthen your leadership skills, we recommend taking our Leadership Fundamentals course, which focuses on effective team management and communication skills.

[1734] Step 6:

[1735] The user checks the advice displayed on the terminal and makes an action plan if necessary.

[1736] Input: The advice displayed on the terminal.

[1737] Output: User's action plan.

[1738] Specific operations: The user clicks on the link to apply for the "Leadership Basics Course" through the terminal and enters the required information into the application form.

[1739] (Application example 2)

[1740] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1741] There is a need to accurately grasp employees' career aspirations and skill development needs and provide individually optimized training programs. However, conventional systems make it difficult to respond to the diverse needs of employees, and progress management and feedback on training programs are insufficient, preventing effective skill development. For this reason, a new system is needed to support employees' career development and improve productivity throughout the organization.

[1742] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1743] There is a need to accurately grasp employees' career aspirations and skill development needs and provide individually optimized training programs. However, conventional systems make it difficult to respond to the diverse needs of employees, and progress management and feedback on training programs are insufficient, preventing effective skill development. For this reason, a new system is needed to support employees' career development and improve productivity throughout the organization.

[1744] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1745] In this invention, the server includes: means for acquiring, in the form of a questionnaire, information about the career aspirations or skill improvement needs of an employee user; a prompt for generating a training program recommended for the user based on the acquired data; a generative AI model for generating and proposing the training program to the user; and a means for adjusting the training program based on progress management or feedback on the proposed training program. This enables the server to meet diverse employee needs and provide individually optimized training programs. The server also includes: a prompt for analyzing individual abilities based on messages posted by the user on a social networking site, messages sent by the user via a communication tool, or materials generated by the user, and visualizing the analyzed individual abilities in a radar chart; a generative AI model for presenting the visualized radar chart to the user; a prompt for generating ability improvement advice based on the acquired career aspiration data and the results of the analysis of the individual abilities; and a generative AI model for generating ability improvement advice and presenting it to the user.

[1746] "SNS" is an abbreviation for Social Networking Service, an online platform for users to share information and communicate over the Internet.

[1747] "Communication tools" are software or applications that allow users to send and receive messages and share information.

[1748] A "proposal" is a document or presentation created to explain a specific purpose or proposal.

[1749] "Individual capabilities" refers to the knowledge, skills, experience, and other characteristics possessed by a particular person.

[1750] A "radar chart" is a graph that visually represents the overall balance and characteristics by arranging multiple evaluation items radially and connecting the evaluation values ​​of each item.

[1751] "Career aspirations" refer to the goals and direction an individual wants to achieve in their professional life.

[1752] "Skills improvement advice" refers to specific guidance and suggestions to further develop an individual's skills and knowledge.

[1753] "Individual thoughts and personality" refers to the way of thinking, behavioral patterns, and psychological characteristics of a particular person.

[1754] "Easy-to-understand phrases and expressions" refers to the use of language and expressions that convey information in a way that is easy for the recipient to understand and accept.

[1755] A "questionnaire format" is a survey method for obtaining answers to questions, and is mainly a multiple choice format or a free description format.

[1756] A "generative AI model" refers to an algorithm or system that uses artificial intelligence techniques to generate new information or suggestions from data.

[1757] A "personalized training program" refers to an education or training plan that is customized to meet individual needs and characteristics.

[1758] "Progress management" refers to the process of monitoring the progress of planned activities or projects and making adjustments as needed.

[1759] "Feedback" refers to information that provides evaluation and opinion on specific actions or results and encourages improvement and growth.

[1760] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. The server collects employees' career aspirations and skill improvement needs in the form of a questionnaire, and recommends personalized training programs using a generative AI model based on the collected data. The terminal provides an interface for employees to answer the questionnaire and manage the progress of the training program. The user uses the system as an employee, inputs information to support their own career development, and receives feedback.

[1761] The server first collects information about employees' career aspirations and skill development needs in the form of a questionnaire. This questionnaire is provided to employees via devices such as smartphones or tablets. Questions in the questionnaire include current skills, skills they would like to improve, and career goals.

[1762] The collected data is input into a generative AI model on the server. The generative AI model generates an optimal training program based on the employee's career aspirations and skill improvement needs. In this process, the generative AI model generates and proposes a training program suitable for the employee using a prompt statement that instructs the user to generate a recommended training program based on the acquired data.

[1763] For example, if an employee wants to improve their "advanced management skills," they can input the following prompt into the generative AI model:

[1764] Employee career aspirations: Improving management skills Current skills: Basic management skills Skills I'd like to improve: Advanced management skills Suggest a suitable training program for this employee.

[1765] Based on this prompt, the generative AI model suggests specific training programs, such as a "leadership training course" or a "project management certification program."

[1766] The proposed training program is provided to the employee through the terminal. The employee takes the training program on the terminal, the server manages the training progress and generates feedback, and the terminal can receive the generated feedback. The server collects progress data and adjusts the training program as needed.

[1767] This system makes it possible to respond to the diverse needs of employees and provide individually tailored training programs, thereby supporting employees' career development and improving productivity across the organization.

[1768] The server also analyzes individual abilities from statements posted by employee users on SNS, statements sent by users using a communication tool, or proposal materials generated by users, and presents the visualized radar chart to the user using a prompt statement instructing the system to visualize the analyzed individual abilities in a radar chart and the generative AI model. Specifically, the server generates the visualized radar chart using a prompt statement including data representing statements posted by employee users on SNS, statements sent by users using a communication tool, or proposal materials generated by users, and the prompt statement instructing the system to analyze individual abilities and visualize the analyzed individual abilities in a radar chart and the generative AI model.

[1769] The server also generates and presents advice for improving skills to the user using a prompt statement and a generative AI model that instructs the server to generate advice for improving skills based on the career-oriented data obtained in the form of a questionnaire and the results of an analysis of the individual's skills.

[1770] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1771] Step 1:

[1772] The server collects information about employees' career aspirations and skill improvement needs in the form of a questionnaire.

[1773] Input: Survey data answered by employees via smartphones or tablets.

[1774] Data processing: The survey responses are stored in a database to extract each employee's career aspirations and skill improvement needs.

[1775] Output: Career aspirations and skill development needs data for each employee.

[1776] Step 2:

[1777] The server inputs prompt sentences into the generative AI model based on the collected data.

[1778] Input: Employee career aspirations and skill development needs data.

[1779] Data processing: Generate prompt sentences and input them into the generative AI model.

[1780] Output: Training program suggestions from the generative AI model.

[1781] Step 3:

[1782] The server sends training program suggestions obtained from the generative AI model to the device.

[1783] Input: Training program suggestions from a generative AI model.

[1784] Data processing: Organize training program proposals for each employee and send them to the terminal.

[1785] Output: Training program suggestions displayed on the terminal.

[1786] Step 4:

[1787] The terminal displays training program suggestions to employees and manages their progress.

[1788] Input: Training program suggestions sent by the server.

[1789] Data Processing: Record the progress of training programs and gather employee feedback.

[1790] Output: Employee training program progress data and feedback.

[1791] Step 5:

[1792] The server adjusts the training program based on progress data and feedback collected from the device.

[1793] Input: Progress data and feedback sent from the device.

[1794] Data Processing: Analyze progress data and feedback and update training programs as needed.

[1795] Output: Updated training program proposal.

[1796] Step 6:

[1797] The server again transmits the updated training program proposal to the terminal and notifies the employee.

[1798] Input: Updated training program proposal.

[1799] Data processing: Updated training programs are organized for each employee and sent to the terminal.

[1800] Output: Updated training program proposal displayed on the terminal.

[1801] The server may further include means for analyzing the user's emotions using an emotion engine described below, and may adjust the training program according to the user's emotions when adjusting the training program. Specifically, if the user's emotion is "anger" or "sadness," the pace of the training program may be adjusted to slow down.

[1802] Example 3

[1803] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1804] Providing appropriate feedback based on employees' personality traits and communication styles contributes to improving employee motivation and work efficiency. However, conventional systems have difficulty generating personalized feedback for individual employees, and can only provide general advice. Furthermore, in organizations with many employees, it is difficult to grasp the personality traits of each employee in detail and provide feedback based on that.

[1805] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1806] In this invention, the server includes a means for inputting employee personality trait data, a means for analyzing the personality trait data, a means for inputting a prompt sentence to the generative AI model, a means for the generative AI model to generate appropriate feedback, and a means for providing the generated feedback, thereby enabling the generation of personalized feedback based on the employee's personality traits and communication style.

[1807] "SNS" is an abbreviation for social networking services, an online platform for people to share information and communicate over the Internet.

[1808] A "communication tool" is software or an application that individuals or groups use to exchange information and communicate.

[1809] A "proposal" is a document created to explain a particular idea or plan and to seek approval or support.

[1810] "Individual capabilities" refers to the knowledge, skills, experience, and other characteristics possessed by a particular person.

[1811] A "radar chart" is a graphical format for visually comparing multiple variables, plotting data points on axes radiating from a center.

[1812] "Career aspirations" refer to the goals and direction that an individual wants to achieve in their professional life.

[1813] "Skill development advice" refers to specific suggestions and guidance to further develop an individual's skills and knowledge.

[1814] "Individual thoughts and personality" refers to the cognitive tendencies and behavioral patterns of a particular person.

[1815] "Easy-to-understand phrases and expressions" refers to the use of language and expressions that convey information in a way that is easy for the recipient to understand and accept.

[1816] "Employee personality trait data" refers to information about employees' personalities and behavioral patterns, and is obtained from psychological assessment tools, survey results, etc.

[1817] A "generative AI model" refers to an algorithm or system that has been trained to perform a specific task using artificial intelligence techniques.

[1818] A "prompt" is an instruction given to a generative AI model to generate a specific output.

[1819] "Feedback" refers to evaluation or advice given in response to a specific action or outcome.

[1820] The present invention is a system for providing personalized feedback based on an employee's personality traits and communication style. Specific embodiments of this system are described below.

[1821] First, users input personality trait data for employees. This data is collected using psychological assessment tools such as the Big Five personality trait model and the Myers-Briggs Type Indicator (MBTI), as well as questionnaire results. Users enter information such as "Employee A is extroverted and prefers direct feedback" through a web form.

[1822] The server then receives the personality data entered by the user and analyzes it. The server references the employee's personality data stored in a database to identify the employee's communication style and feedback preferences. This analysis is performed using data analysis software and algorithms.

[1823] The server then uses the analysis results to create a prompt to be input to the generative AI model, such as "E...

Claims

1. A means for acquiring information on career aspirations or skill improvement needs of employees in the form of a questionnaire; A means for generating and proposing a training program to the user using a prompt sentence that instructs the user to generate a training program recommended for the user based on the acquired data and a generative AI model; means for adjusting the proposed training program based on progress management or feedback of the proposed training program; A system including:

2. a means for analyzing an individual's ability from a statement posted by the user on a social networking site, a statement sent by the user via a communication tool, or a document generated by the user, and presenting the visualized radar chart to the user using a prompt statement instructing the user to visualize the analyzed individual's ability in a radar chart and the generative AI model; a prompt sentence for instructing to generate advice for improving skills based on the acquired career-oriented data and the result of analyzing the individual's skills, and a means for generating advice for improving skills using the generative AI model and presenting the advice to the user; The system of claim 1 further comprising:

3. means for analyzing emotions of the user using an emotion engine; 2. The system of claim 1, wherein the means for making an adjustment adjusts the training program in response to an emotion of the user.

Images