system

Abstract

We provide the system. [Solution] Means for collecting personnel-related information, A means for generating a feature vector using the aforementioned personnel-related information, A means for constructing a predictive model based on the aforementioned feature vectors, A means of receiving current skills and desired career paths entered by employees, A means for generating the optimal placement and additional skills acquisition plan for an employee using the aforementioned predictive model, A system including means for visually displaying the generated plan.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional personnel consultation system, career advice for individual employees depends on the experience and knowledge of personnel staff, and there is a problem that the accuracy and usefulness lack consistency. Also, the specific required skills and experience for each department are not clear, and it is difficult for employees themselves to autonomously plan and execute a career path. For this reason, there has been a problem that the number of employees who are not assigned to an appropriate department and cannot fully demonstrate their abilities has been increasing.

Means for Solving the Problems

[0005] This invention provides a system that collects employee personnel-related information, generates feature vectors based on this information, and constructs a predictive model. This system receives employee input regarding desired skills and career paths, and uses the constructed predictive model to propose optimal placements and skill acquisition plans. Furthermore, it includes a function to collect employee feedback and use it to improve the accuracy of the predictive model. In this way, it makes it easier for each employee to autonomously plan a career path suited to them, and enables the organization as a whole to promote the right people in the right places.

[0006] "Personnel-related information" refers to the collection of all data used in human resource management, including employee skill sets, job activity data, performance evaluations, and self-reported information.

[0007] "Means" refers to elements or combinations for realizing a specific function, and in this invention, this includes hardware and software for performing functions such as information gathering, data processing, model building, and advice generation.

[0008] A "feature vector" is a vector-based data format used in data analysis and machine learning to convert multiple data points into a specific numerical format for input into a mathematical model.

[0009] A "predictive model" is a mathematical or statistical model built to infer or predict outcomes for a given input based on historical data.

[0010] "Assigned department" refers to the specific department or team within the organization where an employee will perform their duties.

[0011] A "skills acquisition plan" refers to a plan for the education and training necessary for employees to improve their abilities, specifically outlining which skills they should acquire and how.

[0012] "Feedback" refers to advice, opinions, and information provided by employees regarding their experience using the system, and is used to improve the system. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] To implement this invention, coordination between the server, terminal, and user is necessary. First, the server automatically collects HR-related information about employees from the company's HR system and database. This includes skill sets, self-reported data, and performance history. The server processes this information and generates feature vectors. Based on these, a predictive model is trained using a machine learning algorithm.

[0035] When a user requests career counseling, they can input their current skills and desired career path through their device. The device receives this data and sends it to a server. The server feeds the received data into a predictive model to generate the optimal placement for the user and a plan for acquiring any missing skills.

[0036] For example, if a user is feeling anxious about their career advancement within their current department, they can input their skills and desired department into their terminal. The server then uses a predictive model to generate detailed advice on the best new placement and new skills the user should acquire. This advice is displayed on the user's terminal, allowing them to plan a concrete career path based on it.

[0037] Furthermore, the server has the ability to collect user feedback and continuously improve the accuracy of the predictive model based on that feedback. This cycle enables efficient human resource utilization by placing the right people in the right positions throughout the entire organization.

[0038] This entire system, by integrating data processing on the server, the user interface on the terminal, and user feedback, can efficiently and effectively support employees' career development.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server collects HR-related information about employees from databases and HR systems. This includes employee skill sets, self-reported information, and performance history.

[0042] Step 2:

[0043] The server preprocesses the collected data and converts it into feature vectors that can be handled by machine learning algorithms. This process involves vectorizing text data and normalizing numerical data.

[0044] Step 3:

[0045] The server starts the generative AI learning process using pre-processed feature vectors. Here, a predictive model is built to clarify the skills and experience required in each department.

[0046] Step 4:

[0047] When a user requests career counseling, they input their current skills and desired career path through their device.

[0048] Step 5:

[0049] The terminal receives input from the user and sends it to the server.

[0050] Step 6:

[0051] The server inputs the data received from the user into a trained predictive model to generate advice on the optimal placement and skills the user should acquire.

[0052] Step 7:

[0053] The server sends the generated advice to the terminal.

[0054] Step 8:

[0055] The device displays the received advice to the user, who can then use this to develop a career plan.

[0056] Step 9:

[0057] Users send feedback regarding the validity and satisfaction level of the advice to the server via their device.

[0058] Step 10:

[0059] The server receives feedback and uses it to update the predictive model, improving its accuracy and effectiveness.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Traditional personnel placement systems often lagged behind in addressing employees' skills and career aspirations, and struggled to efficiently assign the right people to the right positions. As a result, the ability to offer individualized skill development and career path suggestions was limited, making it difficult to maximize the overall efficiency of human resources within the organization.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for acquiring personnel information, means for generating characteristic vectors, and means for constructing an analytical model. This enables the provision of more accurate and personalized placement and capability acquisition plans for individual employees, and efficient human resource utilization across the entire organization.

[0065] "Personnel information" refers to information about individuals within an organization, such as their skills, career history, and performance evaluations.

[0066] A "characteristic vector" is a vector that numerically represents an employee's skills and abilities, and is used as input data for a model.

[0067] An "analytical model" refers to a model that uses mathematical and statistical processing to analyze and predict data based on characteristic vectors.

[0068] "Assignment" refers to assigning individual employees to specific jobs or responsibilities in a way that is suitable for them.

[0069] A "skills acquisition plan" refers to a plan or proposal for employees to effectively acquire skills they lack.

[0070] "Response" refers to feedback and opinions from individuals regarding plans and suggestions provided by the system.

[0071] This invention is a system implemented through cooperation between a server, a terminal, and a user. The server utilizes existing human resources systems and databases to automatically collect personnel information from a company. The data includes employee skills, self-assessments, and work history. Based on this information, the server generates characteristic vectors. The generation of characteristic vectors involves vectorizing text data, encoding categorical data, and standardizing numerical data.

[0072] The server uses these characteristic vectors to build an analysis model. This model utilizes a generative AI model to perform data analysis and predictions based on the received data.

[0073] Users can use a specific device to input data about their current skills and desired career path. For example, a user might input a prompt such as: "Current skills: Programming, team leadership. Desired career path: Project management. Goal: Become a team manager within two years."

[0074] The terminal sends the entered information to the server, which uses an analysis model to generate an optimal deployment and capability acquisition plan for the user. This information is then returned to the terminal and displayed visually to the user.

[0075] Furthermore, the server receives responses from users and uses them to improve the accuracy of the analysis model. Through this continuous feedback loop, the model is constantly updated, enabling it to provide more refined advice.

[0076] Through such collaborative systems, organizations can address the individual needs of their employees and promote efficient talent allocation and skill development.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user uses a terminal to input data on their current skills and desired career path. This input includes skill set, desired department, and career goals. For example, they might enter a prompt such as, "Current skills: Programming, team leadership. Desired career path: Project management. Goal: Become a team manager within 2 years." The data entered here is then sent to the next process.

[0080] Step 2:

[0081] The terminal formats the data received from the user and prepares it for transmission to the server. Specifically, it converts the input data into a format that the server can understand and sends it to the server using a secure communication protocol. The input is the user's skills and preferences, and the output is the formatted data to be sent to the server.

[0082] Step 3:

[0083] Before feeding the received data into the analysis model, the server retrieves existing personnel information. The server collects employee information from the company's database, including skill data and job evaluations of other employees. As a data processing step, this information is cross-referenced to generate appropriate trait vectors. Here, the input is personnel information from the database, and the output is the trait vectors.

[0084] Step 4:

[0085] The server uses the generated characteristic vectors to build or update an analysis model. This model utilizes a generated AI model to perform data analysis. Specifically, vector information is input to the model, and the model predicts the optimal placement and capacity enhancement plan. The input is characteristic vectors, and the output is the prediction result.

[0086] Step 5:

[0087] The server sends the prediction results obtained by the model to the user's terminal as a suggestion. The server then formats the output to present this information in a format that is easy for the user to understand. The input is the prediction result from the model, and the output is the suggestion provided to the user.

[0088] Step 6:

[0089] The user reviews the proposed plan on their device and enters feedback. The device then sends this feedback back to the server. Specifically, the user describes the usefulness and areas for improvement of the plan. The input is the user's feedback on the proposal, and the output is the feedback data sent to the server.

[0090] Step 7:

[0091] The server receives feedback from users and uses it to improve the accuracy of the analysis model. Based on the feedback, the server adjusts the model, improving the accuracy and adaptability of its suggestions. The input is the feedback, and the output is the improved analysis model.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In the field of long-term care, there is a lack of mechanisms to efficiently support the skill development and career advancement of staff. Therefore, there is a need to provide concrete guidelines for staff to choose the optimal career path and acquire the necessary skills. Traditional methods struggle to provide results that consider diverse skill sets and career goals, and staff members bear a heavy burden of individually gathering information and making decisions.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes a device for collecting personnel information, a device for generating feature data using the personnel information, and a device for constructing a predictive algorithm based on the feature data. This makes it possible for care workers to automatically obtain specific guidelines for selecting the optimal role based on their own skills and career goals, and for acquiring the necessary skills.

[0097] "Personnel information" refers to all information about users, including their skills, experience, and career aspirations.

[0098] "Feature data" refers to a specific dataset generated to analyze personnel information and use it in predictive models.

[0099] A "predictive algorithm" refers to a computational method used to determine the optimal career path and skill plan for a user based on characteristic data.

[0100] "Users" refers to individuals who use this system to pursue career development or skill improvement.

[0101] "Visualization" refers to the process of graphically displaying generated plans and information in a way that is easy for users to understand.

[0102] The term "caregiving field" refers to the entire professional domain and environment used by caregiving staff.

[0103] The system for implementing this invention is designed to support the career development of care workers. This system is primarily realized through the collaboration of a server, terminals, and users.

[0104] The server connects with various databases and management systems of care facilities to collect personnel information about care workers. The collected data is processed using programming languages ​​such as Python and R. Specifically, libraries such as Pandas and NumPy are used to format the data and generate feature data.

[0105] After generating feature data, the server uses scikit-learn and TENSORFLOW® to build a predictive algorithm. This algorithm generates optimal roles and additional skill acquisition plans based on the user's current skills and career path. The generated plans are visualized in a dashboard format and displayed on the caregiver's terminal.

[0106] The terminal provides an interface for care workers to input their current skills and future career goals. It will be developed for iOS or Android® devices using programming languages ​​such as Swift or Kotlin. The application on the terminal collects the input data and sends it to the server in real time. Based on this information, the server can use the feedback to improve the accuracy of its predictive model.

[0107] As a concrete example, when a user aiming to become a caregiver uses the app to input their strengths and areas of interest, the server analyzes that information and visualizes and presents specific advice, such as "take a dementia care specialist training course."

[0108] By utilizing generative AI models, employees can easily access optimal plans for skill acquisition and career development, even outside of business hours. As part of this system, prompts such as the following are used to input into the generative AI model:

[0109] "We are developing a career development support app for care workers. Please suggest the optimal skill set and acquisition plan to help the target staff member achieve their career goals. The current skill set is 'Skills A, B, C,' and the target career goal is 'Dementia Care Manager.'"

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] Users use a device to input their current skill set, past career experience, and future career goals. This input data is collected in various formats, including text, radio buttons, and dropdown menus. This data is then prepared for transmission to the server.

[0113] Step 2:

[0114] The server receives the data sent from the terminal. The entered skill and career data is organized into a dataframe using the Pandas library. This is a necessary step for subsequent data analysis processing.

[0115] Step 3:

[0116] The server generates feature data based on the received data. Specifically, it normalizes numerical data and vectorizes text data. Here, it uses scikit-learn's functions to encode categorical data. As a result of this process, an optimal dataset is generated as input for the predictive model.

[0117] Step 4:

[0118] Using the generated feature data, the server executes a prediction algorithm. This algorithm, built with TensorFlow, estimates the optimal career plan and additional skill acquisition plan for the user. This provides specific advice and clearly indicates the actions needed for the next steps.

[0119] Step 5:

[0120] The server generates a career plan and advice, which is then returned to the device. The device receives this information and visualizes it in a way that is easy for the user to understand. The information is presented in a visually intuitive interface using dashboards and graphs.

[0121] Step 6:

[0122] Users review the plan provided on their device and submit feedback. The feedback data is returned to the server and used to improve the accuracy of the prediction algorithm, thereby ensuring continuous improvement of the overall system accuracy.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] To implement this invention, it is necessary to build a system that supports employee career development and incorporate an emotion engine. This system has the function of recognizing the user's emotions using the emotion engine and supplementing the input information.

[0125] First, the server automatically collects HR-related information. This information includes employee skill sets, self-reported information, and performance history. The server preprocesses the collected information, generates feature vectors, and builds a predictive model using a machine learning algorithm. This model predicts which skills and experience are needed in which departments.

[0126] When a user requests career counseling, they input their current skills and desired career path via their device. In addition, an emotion engine acquires emotional data in real time from the user's facial expressions and voice, evaluating their stress and motivation levels. The device then transmits this data to a server.

[0127] The server comprehensively considers user input and data from the emotion engine to generate optimal placement and skill acquisition plans based on predictive models. This includes factors such as stress levels and adaptability due to emotional states. The generated advice is provided to the user via the terminal, allowing the user to refine their career plan based on the displayed information.

[0128] For example, if a user is experiencing workplace stress, the emotion engine recognizes this and the server suggests a work assignment or training plan that will reduce the stress level. In this way, emotional data is reflected in the user's career advice, making it possible to provide more personalized support.

[0129] Furthermore, the server improves the accuracy of its predictive model by collecting user feedback. This feedback includes the output of the emotion engine, which is also used to improve the model. Through this iterative process, the system is expected to further promote the placement of the right people in the right places and improve the overall productivity of the organization.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server periodically collects HR-related information, including employee skills, performance history, and self-reported information, from HR databases and various systems. This data is acquired securely and efficiently because it is necessary for subsequent analysis and model building.

[0133] Step 2:

[0134] The server preprocesses the collected data. This vectorizes text data, encodes categorical data, and normalizes numerical data. This data is then organized into feature vectors, enabling analysis by machine learning algorithms.

[0135] Step 3:

[0136] The server uses pre-processed feature vectors to run machine learning algorithms and build predictive models. These models can identify which skills and experiences are valued in which departments.

[0137] Step 4:

[0138] When a user requests career counseling, they input their current skills and desired career path via a terminal. Simultaneously, an emotion engine activates in response to the user's input, analyzing their facial expressions and voice to acquire emotional data.

[0139] Step 5:

[0140] The device transmits the acquired user skill information and emotional data to the server. Emotional data, including the user's stress level and motivation, is a crucial element in career advice.

[0141] Step 6:

[0142] The server combines information submitted by the user with emotional data from the emotion engine to generate an optimal placement and skills acquisition plan based on a predictive model. This plan takes into account stress reduction and adaptation strategies tailored to the user's emotional state.

[0143] Step 7:

[0144] The terminal displays advice received from the server to the user in a visually easy-to-understand format. Users can review suggested assignments and skill acquisition plans and develop their own career plans.

[0145] Step 8:

[0146] Based on the suggested advice, users decide on actions to consider a more appropriate career path and send feedback to the server via their device.

[0147] Step 9:

[0148] The server aggregates user feedback and analysis results from the sentiment engine, and continuously improves the accuracy of the predictive model. In this way, the system can provide more accurate and effective advice over time.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In today's workplace, employee career development is a crucial element in improving organizational productivity. However, when assignments and skill development plans are created without considering employees' emotional states and stress levels, problems arise such as low employee retention and performance mismatches. Therefore, there is a need for more personalized career support systems that take employees' emotions into account.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for automatically collecting personnel-related data, means for preprocessing the data to generate feature vectors, means for constructing a predictive model using a machine learning algorithm, means for sentiment analysis to acquire user sentiment data in real time, and means for generating the optimal placement and skill acquisition plan based on the data. This makes it possible to provide an appropriate career plan that takes into account the user's emotional state.

[0154] "Personnel-related data" refers to information about employees, including skill sets, self-reported information, and performance review history.

[0155] A "feature vector" is a mathematical representation created from pre-processed data and is used in machine learning algorithms.

[0156] A "machine learning algorithm" is a computational method for learning patterns from data and building predictive models.

[0157] A "predictive model" is a mathematical model constructed to predict future outcomes based on feature vectors.

[0158] "Emotional analysis means" refers to technologies and devices that analyze a user's facial expressions and voice to acquire emotional data.

[0159] "Assigned department" refers to the specific job or duties that an employee is assigned to.

[0160] A "skills acquisition plan" is a plan or program designed to help employees acquire the necessary skills.

[0161] "Feedback" refers to the opinions and reactions provided by employees, which are used to improve the system.

[0162] This system utilizes various hardware and software to support employee career development. The server handles primary processing, collecting and managing HR-related data. Meanwhile, terminals function as the user interface, supporting data entry and sentiment data acquisition. Specific implementations are described below.

[0163] The server uses a database management system to collect and automatically store HR-related information. This information includes employee skill sets, self-reported information, and performance history. The server preprocesses this information and generates feature vectors using programming languages ​​such as Python and R. The generated feature vectors are analyzed by machine learning algorithms to build predictive models. This allows for the prediction of the optimal skills and experience for each department.

[0164] When a user requests career counseling, they use a terminal to input their current skills and desired career path. The terminal then activates emotion analysis software using a facial recognition camera and microphone, acquiring emotional data in real time from the user's facial expressions and voice. This emotional data is used to evaluate the user's stress levels and motivation. The input information and emotional data from the terminal are sent to a server and used for comprehensive analysis.

[0165] Based on this information, the server uses a predictive model to generate the optimal placement and skills acquisition plan for the user. This generation process takes into account the user's emotional state, incorporating elements to reduce stress and enhance adaptability. The generated career plan is visually displayed on the device, allowing the user to further refine their career plan.

[0166] For example, if a user experiences stress in their current workplace, the emotion engine analyzes this, and the server suggests a suitable assignment or training plan to reduce stress. In this way, emotional data is reflected in the user's career advice, enabling the provision of more personalized support.

[0167] A concrete example of a prompt message for a generating AI model is, "Based on the user's emotional data, create the optimal job suggestion to reduce stress." Based on this prompt message, the server generates a career plan that reflects the user's emotional state.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server accesses HR-related databases to automatically collect employee skill sets, self-reported information, and performance history. Input is raw data from the database, and output is a list of pre-processed data. The server filters the collected data and standardizes its format to make it processable.

[0171] Step 2:

[0172] The server generates feature vectors based on the preprocessed data. The input is the data list obtained in step 1, and the output is the feature vectors. By using techniques such as data categorization and numerical normalization, and utilizing the Python NumPy library to vectorize the data, it is converted into a format that can be used by the learning model.

[0173] Step 3:

[0174] The server uses a machine learning algorithm to build a predictive model from feature vectors. The input is the feature vectors generated in step 2, and the output is the completed predictive model. Using libraries such as Scikit-learn, the predictive model is trained to predict which departments require the appropriate skills and experience.

[0175] Step 4:

[0176] Users use a terminal to input their current skills information and career path aspirations. Input is manually entered data, and output is the input data in digital format. The terminal provides form entries and dropdown menus to collect information accurately and efficiently.

[0177] Step 5:

[0178] The device launches emotion analysis software and acquires emotional data through the user's facial expressions and voice. The input is the user's voice and video data, and the output is data indicating their emotional state. The device uses the camera and microphone to analyze the data and an emotion engine to evaluate stress and motivation.

[0179] Step 6:

[0180] The server receives user input information and emotional data, and uses this to generate the optimal placement and skill acquisition plan. The input is the output data from steps 4 and 5, and the predictive model from step 3, and the output is the placement plan and skill acquisition plan. The server comprehensively analyzes this data to generate the optimal plan tailored to each user's emotional state.

[0181] Step 7:

[0182] The terminal visually displays the generated plan to the user. The input is the output plan from step 6, and the output is the visual presentation to the user. The terminal displays this information using a graphical user interface, which the user can use to refine their career plan.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] In employee career development, a challenge exists in that current career plans are limited to individual employees' skill sets and aspirations, with little consideration given to their emotions or stress levels. As a result, employees may be assigned to departments that are not optimal for them, potentially leading to decreased overall organizational productivity. Furthermore, improvements in the accuracy of feedback-based models are limited, highlighting the need for more personalized career support.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for recognizing an employee's emotions using an emotion engine, means for collecting HR-related information and generating feature vectors, and means for generating optimal placement and skill acquisition plans for employees using predictive models and emotion data. This enables more appropriate career advice and placement suggestions based on the employee's emotional state.

[0188] "Personnel-related information" refers to information related to career development, including employee skill sets, performance history, and self-reported information.

[0189] A "feature vector" is a set of data obtained by quantifying personnel-related information and converting it into a format that can be used by machine learning models.

[0190] A "predictive model" is a machine learning algorithm built to calculate the optimal placement and skill acquisition plan for employees.

[0191] An "emotion engine" is software that analyzes a user's emotions from their facial expressions and voice, and acquires that data.

[0192] "Assignment location" refers to the appropriate department or position within the organization to which an employee should be assigned.

[0193] A "skills acquisition plan" is a set of guidance policies and training schedules designed to help employees acquire the skills and knowledge they will need in the future.

[0194] "Visual and auditory output means" refers to devices or software systems for displaying the generated carrier plan on a display or presenting it audibly.

[0195] This invention utilizes a system incorporating an emotion engine to support employees' career development. This system includes a server, terminals, and the emotion engine.

[0196] The server automatically collects HR-related information and generates feature vectors for each employee. This includes employee skill sets, performance history, and self-reported information. Next, the server uses machine learning algorithms to build a predictive model. This model calculates the optimal placement and skill acquisition plan for each employee based on the feature vectors.

[0197] The terminal receives the user's current skill information and desired career path. During this process, an emotion engine analyzes the user's facial expressions and voice, acquiring emotional data in real time. The emotion engine can utilize, for example, facial expression analysis software or voice emotion analysis software.

[0198] The server generates an effective career plan in its predictive model based on input data from the terminal and emotional data from the emotion engine. This plan takes into account the user's stress level and motivation level. The generated plan is presented to the user visually and audibly through the terminal.

[0199] As a concrete example, when a user interacts with the system, the robot can read the user's facial expressions and, if it detects that the user is experiencing stress, it can suggest assigning them to a new project that will alleviate that stress. In this way, emotional data is transformed into more personalized career advice, which is expected to improve user satisfaction and work efficiency.

[0200] Examples of prompt statements include the following:

[0201] "The user is seeking career counseling. Their current skill set is {User's Skill Set}, and their emotional state is {User's Emotional State}. Please propose the best career plan for this user."

[0202] This system integrates emotional data and personnel information to help create an environment where employees can perform at their best in the roles best suited to them.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The server automatically collects HR-related information from various data sources. This information includes employee skill sets, performance history, and self-reported information. This information is stored in a database and prepared for the generation of feature vectors used in subsequent processing.

[0206] Step 2:

[0207] The server generates feature vectors using the collected personnel-related information. This process involves vectorizing text data, encoding categorical information, and normalizing numerical data. The resulting feature vectors are then used to build machine learning models.

[0208] Step 3:

[0209] The server uses the generated feature vectors as input to build a predictive model using a machine learning algorithm. This predictive model is used to predict the optimal placement and skill acquisition plan for each employee. This model building process is iteratively executed based on training data to improve accuracy.

[0210] Step 4:

[0211] Users enter their current skills and desired career path via their device to receive career counseling. This input data is sent from the device to the server, where it is prepared for processing by a predictive model.

[0212] Step 5:

[0213] The emotion engine analyzes the user's facial expressions and voice in real time to acquire emotional data. This acquired emotional data is used to evaluate the user's stress level and motivation status. This information is also sent to the server and integrated with other carrier information.

[0214] Step 6:

[0215] The server integrates predictive models and sentiment data to generate an optimal career plan for the user. This plan takes emotional states into account and provides specific suggestions for placement and skill acquisition. These suggestions are generated by the AI ​​model.

[0216] Step 7:

[0217] The device presents the generated career plan to the user visually and audibly. Prompts are used to help the user consider the next steps based on their specific career plan. This process provides support tailored to the user's individual needs.

[0218] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0219] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0220] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0224] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0225] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0226] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0227] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0228] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0229] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0230] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0231] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0232] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0233] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0234] To implement this invention, coordination between the server, terminal, and user is necessary. First, the server automatically collects HR-related information about employees from the company's HR system and database. This includes skill sets, self-reported data, and performance history. The server processes this information and generates feature vectors. Based on these, a predictive model is trained using a machine learning algorithm.

[0235] When a user requests career counseling, they can input their current skills and desired career path through their device. The device receives this data and sends it to a server. The server feeds the received data into a predictive model to generate the optimal placement for the user and a plan for acquiring any missing skills.

[0236] For example, if a user is feeling anxious about their career advancement within their current department, they can input their skills and desired department into their terminal. The server then uses a predictive model to generate detailed advice on the best new placement and new skills the user should acquire. This advice is displayed on the user's terminal, allowing them to plan a concrete career path based on it.

[0237] Furthermore, the server has the ability to collect user feedback and continuously improve the accuracy of the predictive model based on that feedback. This cycle enables efficient human resource utilization by placing the right people in the right positions throughout the entire organization.

[0238] This entire system, by integrating data processing on the server, the user interface on the terminal, and user feedback, can efficiently and effectively support employees' career development.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The server collects HR-related information about employees from databases and HR systems. This includes employee skill sets, self-reported information, and performance history.

[0242] Step 2:

[0243] The server preprocesses the collected data and converts it into feature vectors that can be handled by machine learning algorithms. This process involves vectorizing text data and normalizing numerical data.

[0244] Step 3:

[0245] The server starts the generative AI learning process using pre-processed feature vectors. Here, a predictive model is built to clarify the skills and experience required in each department.

[0246] Step 4:

[0247] When a user requests career counseling, they input their current skills and desired career path through their device.

[0248] Step 5:

[0249] The terminal receives input from the user and sends it to the server.

[0250] Step 6:

[0251] The server inputs the data received from the user into a trained predictive model to generate advice on the optimal placement and skills the user should acquire.

[0252] Step 7:

[0253] The server sends the generated advice to the terminal.

[0254] Step 8:

[0255] The device displays the received advice to the user, who can then use this to develop a career plan.

[0256] Step 9:

[0257] Users send feedback regarding the validity and satisfaction level of the advice to the server via their device.

[0258] Step 10:

[0259] The server receives feedback and uses it to update the predictive model, improving its accuracy and effectiveness.

[0260] (Example 1)

[0261] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0262] Traditional personnel placement systems often lagged behind in addressing employees' skills and career aspirations, and struggled to efficiently assign the right people to the right positions. As a result, the ability to offer individualized skill development and career path suggestions was limited, making it difficult to maximize the overall efficiency of human resources within the organization.

[0263] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0264] In this invention, the server includes means for acquiring personnel information, means for generating characteristic vectors, and means for constructing an analytical model. This enables the provision of more accurate and personalized placement and capability acquisition plans for individual employees, and efficient human resource utilization across the entire organization.

[0265] "Personnel information" refers to information about individuals within an organization, such as their skills, career history, and performance evaluations.

[0266] A "characteristic vector" is a vector that numerically represents an employee's skills and abilities, and is used as input data for a model.

[0267] An "analytical model" refers to a model that uses mathematical and statistical processing to analyze and predict data based on characteristic vectors.

[0268] "Assignment" refers to assigning individual employees to specific jobs or responsibilities in a way that is suitable for them.

[0269] A "skills acquisition plan" refers to a plan or proposal for employees to effectively acquire skills they lack.

[0270] "Response" refers to feedback and opinions from individuals regarding plans and suggestions provided by the system.

[0271] This invention is a system implemented through cooperation between a server, a terminal, and a user. The server utilizes existing human resources systems and databases to automatically collect personnel information from a company. The data includes employee skills, self-assessments, and work history. Based on this information, the server generates characteristic vectors. The generation of characteristic vectors involves vectorizing text data, encoding categorical data, and standardizing numerical data.

[0272] The server uses these characteristic vectors to build an analysis model. This model utilizes a generative AI model to perform data analysis and predictions based on the received data.

[0273] Users can use a specific device to input data about their current skills and desired career path. For example, a user might input a prompt such as: "Current skills: Programming, team leadership. Desired career path: Project management. Goal: Become a team manager within two years."

[0274] The terminal sends the entered information to the server, which uses an analysis model to generate an optimal deployment and capability acquisition plan for the user. This information is then returned to the terminal and displayed visually to the user.

[0275] Furthermore, the server receives responses from users and uses them to improve the accuracy of the analysis model. Through this continuous feedback loop, the model is constantly updated, enabling it to provide more refined advice.

[0276] With such a collaborative system, an organization can meet the individual needs of employees and promote efficient personnel allocation and capacity development.

[0277] The flow of the specific process in Example 1 will be described using FIG. 11.

[0278] Step 1:

[0279] The user uses the terminal to input data on current skill information and the desired career path. This input includes skill sets, desired departments, and career goals. As an example, prompt sentences such as "Current skills: programming, team leadership. Desired career path: project management. Goal: become a team manager within two years." are input. The input data here is sent to the next process.

[0280] Step 2:

[0281] The terminal formats the data received from the user and prepares it for transmission to the server. Specifically, the input data is converted into a format understandable by the server and sent to the server using a secure communication protocol. The input is the user's skills and desires, and the output is the formatted data for transmission to the server.

[0282] Step 3:

[0283] Before the server inputs the received data into the analysis model, it acquires existing personnel information. The server collects employee information from the company's database, which includes the skill data and job evaluations of other employees. As data processing, these pieces of information are collated to generate appropriate characteristic vectors. The input here is the personnel information from the database, and the output is the characteristic vector.

[0284] Step 4:

[0285] The server uses the generated feature vectors to construct or update an analysis model. This model utilizes a generative AI model to perform data analysis. As a specific operation, vector information is input into the model, and the model predicts an optimal layout and a capacity enhancement plan. The input is the feature vector, and the output is the prediction result.

[0286] Step 5:

[0287] The server transmits the prediction results obtained by the model to the terminal as proposals for the user. The server formats the output to provide this information to the user in an understandable form. The input is the prediction result from the model, and the output is the proposal provided to the user.

[0288] Step 6:

[0289] The user checks the proposed plan on the terminal and inputs feedback. The terminal transmits this feedback to the server again. As a specific operation, the user describes the usefulness and improvement points of the plan. The input is the user's feedback on the proposal, and the output is the feedback data to the server.

[0290] Step 7:

[0291] The server receives the feedback from the user and uses it to improve the accuracy of the analysis model. The server adjusts the model based on the feedback to improve the accuracy and adaptability of the proposal. The input is the feedback, and the output is the improved analysis model.

[0292] (Application Example 1)

[0293] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0294] In the field of long-term care, there is a lack of mechanisms to efficiently support the skill development and career advancement of staff. Therefore, there is a need to provide concrete guidelines for staff to choose the optimal career path and acquire the necessary skills. Traditional methods struggle to provide results that consider diverse skill sets and career goals, and staff members bear a heavy burden of individually gathering information and making decisions.

[0295] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0296] In this invention, the server includes a device for collecting personnel information, a device for generating feature data using the personnel information, and a device for constructing a predictive algorithm based on the feature data. This makes it possible for care workers to automatically obtain specific guidelines for selecting the optimal role based on their own skills and career goals, and for acquiring the necessary skills.

[0297] "Personnel information" refers to all information about users, including their skills, experience, and career aspirations.

[0298] "Feature data" refers to a specific dataset generated to analyze personnel information and use it in predictive models.

[0299] A "predictive algorithm" refers to a computational method used to determine the optimal career path and skill plan for a user based on characteristic data.

[0300] "Users" refers to individuals who use this system to pursue career development or skill improvement.

[0301] "Visualization" refers to the process of graphically displaying generated plans and information in a way that is easy for users to understand.

[0302] The term "caregiving field" refers to the entire professional domain and environment used by caregiving staff.

[0303] The system for implementing this invention is designed to support the career development of caregiving staff. This system is mainly realized through the collaboration of a server, terminals, and users.

[0304] The server collaborates with various databases and the management systems of care facilities to collect personnel information regarding caregiving staff. The collected data is processed using programming languages such as Python and R. Specifically, libraries such as Pandas and NumPy are used to format the data and perform the process of generating feature data.

[0305] After generating the feature data, the server constructs a prediction algorithm using scikit - learn or TensorFlow. Based on the current skills and career paths input by the user, the algorithm generates an optimal role and an additional skill acquisition plan. The generated plan is visualized in a dashboard format and displayed on the terminals of caregiving staff.

[0306] The terminal provides an interface for caregiving staff to input their current skills and future career goals. It is developed for iOS or Android devices using programming languages such as Swift and Kotlin. The application on the terminal collects the input data and sends it to the server in real - time. Based on this information, the server can utilize the feedback to improve the accuracy of the prediction model.

[0307] As a specific example, when a user aiming for a new caregiving position uses the app to input their strengths and areas of interest, the server analyzes this information and presents a visualization of specific advice such as "Enroll in a training course for becoming a dementia care specialist".

[0308] By leveraging the generative AI model, it is possible for staff to easily confirm the optimal plan for skill acquisition and career development even outside business hours. As part of this system, the following prompt texts for inputting into the generative AI model are also used:

[0309] "We are developing a career development support app for care workers. Please suggest the optimal skill set and acquisition plan to help the target staff member achieve their career goals. The current skill set is 'Skills A, B, C,' and the target career goal is 'Dementia Care Manager.'"

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] Users use a device to input their current skill set, past career experience, and future career goals. This input data is collected in various formats, including text, radio buttons, and dropdown menus. This data is then prepared for transmission to the server.

[0313] Step 2:

[0314] The server receives the data sent from the terminal. The entered skill and career data is organized into a dataframe using the Pandas library. This is a necessary step for subsequent data analysis processing.

[0315] Step 3:

[0316] The server generates feature data based on the received data. Specifically, it normalizes numerical data and vectorizes text data. Here, it uses scikit-learn's functions to encode categorical data. As a result of this process, an optimal dataset is generated as input for the predictive model.

[0317] Step 4:

[0318] Using the generated feature data, the server executes a prediction algorithm. This algorithm, built with TensorFlow, estimates the optimal career plan and additional skill acquisition plan for the user. This provides specific advice and clearly indicates the actions needed for the next steps.

[0319] Step 5:

[0320] The server generates a career plan and advice, which is then returned to the device. The device receives this information and visualizes it in a way that is easy for the user to understand. The information is presented in a visually intuitive interface using dashboards and graphs.

[0321] Step 6:

[0322] Users review the plan provided on their device and submit feedback. The feedback data is returned to the server and used to improve the accuracy of the prediction algorithm, thereby ensuring continuous improvement of the overall system accuracy.

[0323] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0324] To implement this invention, it is necessary to build a system that supports employee career development and incorporate an emotion engine. This system has the function of recognizing the user's emotions using the emotion engine and supplementing the input information.

[0325] First, the server automatically collects HR-related information. This information includes employee skill sets, self-reported information, and performance history. The server preprocesses the collected information, generates feature vectors, and builds a predictive model using a machine learning algorithm. This model predicts which skills and experience are needed in which departments.

[0326] When a user requests career counseling, they input their current skills and desired career path via their device. In addition, an emotion engine acquires emotional data in real time from the user's facial expressions and voice, evaluating their stress and motivation levels. The device then transmits this data to a server.

[0327] The server comprehensively considers user input and data from the emotion engine to generate optimal placement and skill acquisition plans based on predictive models. This includes factors such as stress levels and adaptability due to emotional states. The generated advice is provided to the user via the terminal, allowing the user to refine their career plan based on the displayed information.

[0328] For example, if a user is experiencing workplace stress, the emotion engine recognizes this and the server suggests a work assignment or training plan that will reduce the stress level. In this way, emotional data is reflected in the user's career advice, making it possible to provide more personalized support.

[0329] Furthermore, the server improves the accuracy of its predictive model by collecting user feedback. This feedback includes the output of the emotion engine, which is also used to improve the model. Through this iterative process, the system is expected to further promote the placement of the right people in the right places and improve the overall productivity of the organization.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The server periodically collects HR-related information, including employee skills, performance history, and self-reported information, from HR databases and various systems. This data is acquired securely and efficiently because it is necessary for subsequent analysis and model building.

[0333] Step 2:

[0334] The server preprocesses the collected data. This vectorizes text data, encodes categorical data, and normalizes numerical data. This data is then organized into feature vectors, enabling analysis by machine learning algorithms.

[0335] Step 3:

[0336] The server uses pre-processed feature vectors to run machine learning algorithms and build predictive models. These models can identify which skills and experiences are valued in which departments.

[0337] Step 4:

[0338] When a user requests career counseling, they input their current skills and desired career path via a terminal. Simultaneously, an emotion engine activates in response to the user's input, analyzing their facial expressions and voice to acquire emotional data.

[0339] Step 5:

[0340] The device transmits the acquired user skill information and emotional data to the server. Emotional data, including the user's stress level and motivation, is a crucial element in career advice.

[0341] Step 6:

[0342] The server combines information submitted by the user with emotional data from the emotion engine to generate an optimal placement and skills acquisition plan based on a predictive model. This plan takes into account stress reduction and adaptation strategies tailored to the user's emotional state.

[0343] Step 7:

[0344] The terminal displays advice received from the server to the user in a visually easy-to-understand format. Users can review suggested assignments and skill acquisition plans and develop their own career plans.

[0345] Step 8:

[0346] Based on the suggested advice, users decide on actions to consider a more appropriate career path and send feedback to the server via their device.

[0347] Step 9:

[0348] The server aggregates user feedback and analysis results from the sentiment engine, and continuously improves the accuracy of the predictive model. In this way, the system can provide more accurate and effective advice over time.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0351] In today's workplace, employee career development is a crucial element in improving organizational productivity. However, when assignments and skill development plans are created without considering employees' emotional states and stress levels, problems arise such as low employee retention and performance mismatches. Therefore, there is a need for more personalized career support systems that take employees' emotions into account.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for automatically collecting personnel-related data, means for preprocessing the data to generate feature vectors, means for constructing a predictive model using a machine learning algorithm, means for sentiment analysis to acquire user sentiment data in real time, and means for generating the optimal placement and skill acquisition plan based on the data. This makes it possible to provide an appropriate career plan that takes into account the user's emotional state.

[0354] "Personnel-related data" refers to information about employees, including skill sets, self-reported information, and performance review history.

[0355] A "feature vector" is a mathematical representation created from pre-processed data and is used in machine learning algorithms.

[0356] A "machine learning algorithm" is a computational method for learning patterns from data and building predictive models.

[0357] A "predictive model" is a mathematical model constructed to predict future outcomes based on feature vectors.

[0358] "Emotional analysis means" refers to technologies and devices that analyze a user's facial expressions and voice to acquire emotional data.

[0359] "Assigned department" refers to the specific job or duties that an employee is assigned to.

[0360] A "skills acquisition plan" is a plan or program designed to help employees acquire the necessary skills.

[0361] "Feedback" refers to the opinions and reactions provided by employees, which are used to improve the system.

[0362] This system utilizes various hardware and software to support employee career development. The server handles primary processing, collecting and managing HR-related data. Meanwhile, terminals function as the user interface, supporting data entry and sentiment data acquisition. Specific implementations are described below.

[0363] The server uses a database management system to collect and automatically store HR-related information. This information includes employee skill sets, self-reported information, and performance history. The server preprocesses this information and generates feature vectors using programming languages ​​such as Python and R. The generated feature vectors are analyzed by machine learning algorithms to build predictive models. This allows for the prediction of the optimal skills and experience for each department.

[0364] When a user requests career counseling, they use a terminal to input their current skills and desired career path. The terminal then activates emotion analysis software using a facial recognition camera and microphone, acquiring emotional data in real time from the user's facial expressions and voice. This emotional data is used to evaluate the user's stress levels and motivation. The input information and emotional data from the terminal are sent to a server and used for comprehensive analysis.

[0365] Based on this information, the server uses a predictive model to generate the optimal placement and skills acquisition plan for the user. This generation process takes into account the user's emotional state, incorporating elements to reduce stress and enhance adaptability. The generated career plan is visually displayed on the device, allowing the user to further refine their career plan.

[0366] For example, if a user experiences stress in their current workplace, the emotion engine analyzes this, and the server suggests a suitable assignment or training plan to reduce stress. In this way, emotional data is reflected in the user's career advice, enabling the provision of more personalized support.

[0367] A concrete example of a prompt message for a generating AI model is, "Based on the user's emotional data, create the optimal job suggestion to reduce stress." Based on this prompt message, the server generates a career plan that reflects the user's emotional state.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The server accesses HR-related databases to automatically collect employee skill sets, self-reported information, and performance history. Input is raw data from the database, and output is a list of pre-processed data. The server filters the collected data and standardizes its format to make it processable.

[0371] Step 2:

[0372] The server generates feature vectors based on the preprocessed data. The input is the data list obtained in step 1, and the output is the feature vectors. By using techniques such as data categorization and numerical normalization, and utilizing the Python NumPy library to vectorize the data, it is converted into a format that can be used by the learning model.

[0373] Step 3:

[0374] The server uses a machine learning algorithm to build a predictive model from feature vectors. The input is the feature vectors generated in step 2, and the output is the completed predictive model. Using libraries such as Scikit-learn, the predictive model is trained to predict which departments require the appropriate skills and experience.

[0375] Step 4:

[0376] Users use a terminal to input their current skills information and career path aspirations. Input is manually entered data, and output is the input data in digital format. The terminal provides form entries and dropdown menus to collect information accurately and efficiently.

[0377] Step 5:

[0378] The device launches emotion analysis software and acquires emotional data through the user's facial expressions and voice. The input is the user's voice and video data, and the output is data indicating their emotional state. The device uses the camera and microphone to analyze the data and an emotion engine to evaluate stress and motivation.

[0379] Step 6:

[0380] The server receives user input information and emotional data, and uses this to generate the optimal placement and skill acquisition plan. The input is the output data from steps 4 and 5, and the predictive model from step 3, and the output is the placement plan and skill acquisition plan. The server comprehensively analyzes this data to generate the optimal plan tailored to each user's emotional state.

[0381] Step 7:

[0382] The terminal visually displays the generated plan to the user. The input is the output plan from step 6, and the output is the visual presentation to the user. The terminal displays this information using a graphical user interface, which the user can use to refine their career plan.

[0383] (Application Example 2)

[0384] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0385] In employee career development, a challenge exists in that current career plans are limited to individual employees' skill sets and aspirations, with little consideration given to their emotions or stress levels. As a result, employees may be assigned to departments that are not optimal for them, potentially leading to decreased overall organizational productivity. Furthermore, improvements in the accuracy of feedback-based models are limited, highlighting the need for more personalized career support.

[0386] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0387] In this invention, the server includes means for recognizing an employee's emotions using an emotion engine, means for collecting HR-related information and generating feature vectors, and means for generating optimal placement and skill acquisition plans for employees using predictive models and emotion data. This enables more appropriate career advice and placement suggestions based on the employee's emotional state.

[0388] "Personnel-related information" refers to information related to career development, including employee skill sets, performance history, and self-reported information.

[0389] A "feature vector" is a set of data obtained by quantifying personnel-related information and converting it into a format that can be used by machine learning models.

[0390] A "predictive model" is a machine learning algorithm built to calculate the optimal placement and skill acquisition plan for employees.

[0391] An "emotion engine" is software that analyzes a user's emotions from their facial expressions and voice, and acquires that data.

[0392] "Assignment location" refers to the appropriate department or position within the organization to which an employee should be assigned.

[0393] A "skills acquisition plan" is a set of guidance policies and training schedules designed to help employees acquire the skills and knowledge they will need in the future.

[0394] "Visual and auditory output means" refers to devices or software systems for displaying the generated carrier plan on a display or presenting it audibly.

[0395] This invention utilizes a system incorporating an emotion engine to support employees' career development. This system includes a server, terminals, and the emotion engine.

[0396] The server automatically collects HR-related information and generates feature vectors for each employee. This includes employee skill sets, performance history, and self-reported information. Next, the server uses machine learning algorithms to build a predictive model. This model calculates the optimal placement and skill acquisition plan for each employee based on the feature vectors.

[0397] The terminal receives the user's current skill information and desired career path. During this process, an emotion engine analyzes the user's facial expressions and voice, acquiring emotional data in real time. The emotion engine can utilize, for example, facial expression analysis software or voice emotion analysis software.

[0398] The server generates an effective career plan in its predictive model based on input data from the terminal and emotional data from the emotion engine. This plan takes into account the user's stress level and motivation level. The generated plan is presented to the user visually and audibly through the terminal.

[0399] As a concrete example, when a user interacts with the system, the robot can read the user's facial expressions and, if it detects that the user is experiencing stress, it can suggest assigning them to a new project that will alleviate that stress. In this way, emotional data is transformed into more personalized career advice, which is expected to improve user satisfaction and work efficiency.

[0400] Examples of prompt statements include the following:

[0401] "The user is seeking career counseling. Their current skill set is {User's Skill Set}, and their emotional state is {User's Emotional State}. Please propose the best career plan for this user."

[0402] This system integrates emotional data and personnel information to help create an environment where employees can perform at their best in the roles best suited to them.

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The server automatically collects HR-related information from various data sources. This information includes employee skill sets, performance history, and self-reported information. This information is stored in a database and prepared for the generation of feature vectors used in subsequent processing.

[0406] Step 2:

[0407] The server generates feature vectors using the collected personnel-related information. This process involves vectorizing text data, encoding categorical information, and normalizing numerical data. The resulting feature vectors are then used to build machine learning models.

[0408] Step 3:

[0409] The server uses the generated feature vectors as input to build a predictive model using a machine learning algorithm. This predictive model is used to predict the optimal placement and skill acquisition plan for each employee. This model building process is iteratively executed based on training data to improve accuracy.

[0410] Step 4:

[0411] Users enter their current skills and desired career path via their device to receive career counseling. This input data is sent from the device to the server, where it is prepared for processing by a predictive model.

[0412] Step 5:

[0413] The emotion engine analyzes the user's facial expressions and voice in real time to acquire emotional data. This acquired emotional data is used to evaluate the user's stress level and motivation status. This information is also sent to the server and integrated with other carrier information.

[0414] Step 6:

[0415] The server integrates predictive models and sentiment data to generate an optimal career plan for the user. This plan takes emotional states into account and provides specific suggestions for placement and skill acquisition. These suggestions are generated by the AI ​​model.

[0416] Step 7:

[0417] The device presents the generated career plan to the user visually and audibly. Prompts are used to help the user consider the next steps based on their specific career plan. This process provides support tailored to the user's individual needs.

[0418] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0419] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0420] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0424] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0425] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0426] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0427] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0428] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0429] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0430] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0431] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0432] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0433] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0434] To implement this invention, coordination between the server, terminal, and user is necessary. First, the server automatically collects HR-related information about employees from the company's HR system and database. This includes skill sets, self-reported data, and performance history. The server processes this information and generates feature vectors. Based on these, a predictive model is trained using a machine learning algorithm.

[0435] When a user requests career counseling, they can input their current skills and desired career path through their device. The device receives this data and sends it to a server. The server feeds the received data into a predictive model to generate the optimal placement for the user and a plan for acquiring any missing skills.

[0436] For example, if a user is feeling anxious about their career advancement within their current department, they can input their skills and desired department into their terminal. The server then uses a predictive model to generate detailed advice on the best new placement and new skills the user should acquire. This advice is displayed on the user's terminal, allowing them to plan a concrete career path based on it.

[0437] Furthermore, the server has the ability to collect user feedback and continuously improve the accuracy of the predictive model based on that feedback. This cycle enables efficient human resource utilization by placing the right people in the right positions throughout the entire organization.

[0438] This entire system, by integrating data processing on the server, the user interface on the terminal, and user feedback, can efficiently and effectively support employees' career development.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The server collects HR-related information about employees from databases and HR systems. This includes employee skill sets, self-reported information, and performance history.

[0442] Step 2:

[0443] The server preprocesses the collected data and converts it into feature vectors that can be handled by machine learning algorithms. This process involves vectorizing text data and normalizing numerical data.

[0444] Step 3:

[0445] The server starts the generative AI learning process using pre-processed feature vectors. Here, a predictive model is built to clarify the skills and experience required in each department.

[0446] Step 4:

[0447] When a user requests career counseling, they input their current skills and desired career path through their device.

[0448] Step 5:

[0449] The terminal receives input from the user and sends it to the server.

[0450] Step 6:

[0451] The server inputs the data received from the user into a trained predictive model to generate advice on the optimal placement and skills the user should acquire.

[0452] Step 7:

[0453] The server sends the generated advice to the terminal.

[0454] Step 8:

[0455] The device displays the received advice to the user, who can then use this to develop a career plan.

[0456] Step 9:

[0457] Users send feedback regarding the validity and satisfaction level of the advice to the server via their device.

[0458] Step 10:

[0459] The server receives feedback and uses it to update the predictive model, improving its accuracy and effectiveness.

[0460] (Example 1)

[0461] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0462] Traditional personnel placement systems often lagged behind in addressing employees' skills and career aspirations, and struggled to efficiently assign the right people to the right positions. As a result, the ability to offer individualized skill development and career path suggestions was limited, making it difficult to maximize the overall efficiency of human resources within the organization.

[0463] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0464] In this invention, the server includes means for acquiring personnel information, means for generating characteristic vectors, and means for constructing an analytical model. This enables the provision of more accurate and personalized placement and capability acquisition plans for individual employees, and efficient human resource utilization across the entire organization.

[0465] "Personnel information" refers to information about individuals within an organization, such as their skills, career history, and performance evaluations.

[0466] A "characteristic vector" is a vector that numerically represents an employee's skills and abilities, and is used as input data for a model.

[0467] An "analytical model" refers to a model that uses mathematical and statistical processing to analyze and predict data based on characteristic vectors.

[0468] "Assignment" refers to assigning individual employees to specific jobs or responsibilities in a way that is suitable for them.

[0469] A "skills acquisition plan" refers to a plan or proposal for employees to effectively acquire skills they lack.

[0470] "Response" refers to feedback and opinions from individuals regarding plans and suggestions provided by the system.

[0471] This invention is a system implemented through cooperation between a server, a terminal, and a user. The server utilizes existing human resources systems and databases to automatically collect personnel information from a company. The data includes employee skills, self-assessments, and work history. Based on this information, the server generates characteristic vectors. The generation of characteristic vectors involves vectorizing text data, encoding categorical data, and standardizing numerical data.

[0472] The server uses these characteristic vectors to build an analysis model. This model utilizes a generative AI model to perform data analysis and predictions based on the received data.

[0473] Users can use a specific device to input data about their current skills and desired career path. For example, a user might input a prompt such as: "Current skills: Programming, team leadership. Desired career path: Project management. Goal: Become a team manager within two years."

[0474] The terminal sends the entered information to the server, which uses an analysis model to generate an optimal deployment and capability acquisition plan for the user. This information is then returned to the terminal and displayed visually to the user.

[0475] Furthermore, the server receives responses from users and uses them to improve the accuracy of the analysis model. Through this continuous feedback loop, the model is constantly updated, enabling it to provide more refined advice.

[0476] Through such collaborative systems, organizations can address the individual needs of their employees and promote efficient talent allocation and skill development.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user uses a terminal to input data on their current skills and desired career path. This input includes skill set, desired department, and career goals. For example, they might enter a prompt such as, "Current skills: Programming, team leadership. Desired career path: Project management. Goal: Become a team manager within 2 years." The data entered here is then sent to the next process.

[0480] Step 2:

[0481] The terminal formats the data received from the user and prepares it for transmission to the server. Specifically, it converts the input data into a format that the server can understand and sends it to the server using a secure communication protocol. The input is the user's skills and preferences, and the output is the formatted data to be sent to the server.

[0482] Step 3:

[0483] Before feeding the received data into the analysis model, the server retrieves existing personnel information. The server collects employee information from the company's database, including skill data and job evaluations of other employees. As a data processing step, this information is cross-referenced to generate appropriate trait vectors. Here, the input is personnel information from the database, and the output is the trait vectors.

[0484] Step 4:

[0485] The server uses the generated characteristic vectors to build or update an analysis model. This model utilizes a generated AI model to perform data analysis. Specifically, vector information is input to the model, and the model predicts the optimal placement and capacity enhancement plan. The input is characteristic vectors, and the output is the prediction result.

[0486] Step 5:

[0487] The server sends the prediction results obtained by the model to the user's terminal as a suggestion. The server then formats the output to present this information in a format that is easy for the user to understand. The input is the prediction result from the model, and the output is the suggestion provided to the user.

[0488] Step 6:

[0489] The user reviews the proposed plan on their device and enters feedback. The device then sends this feedback back to the server. Specifically, the user describes the usefulness and areas for improvement of the plan. The input is the user's feedback on the proposal, and the output is the feedback data sent to the server.

[0490] Step 7:

[0491] The server receives feedback from users and uses it to improve the accuracy of the analysis model. Based on the feedback, the server adjusts the model, improving the accuracy and adaptability of its suggestions. The input is the feedback, and the output is the improved analysis model.

[0492] (Application Example 1)

[0493] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0494] In the field of long-term care, there is a lack of mechanisms to efficiently support the skill development and career advancement of staff. Therefore, there is a need to provide concrete guidelines for staff to choose the optimal career path and acquire the necessary skills. Traditional methods struggle to provide results that consider diverse skill sets and career goals, and staff members bear a heavy burden of individually gathering information and making decisions.

[0495] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0496] In this invention, the server includes a device for collecting personnel information, a device for generating feature data using the personnel information, and a device for constructing a predictive algorithm based on the feature data. This makes it possible for care workers to automatically obtain specific guidelines for selecting the optimal role based on their own skills and career goals, and for acquiring the necessary skills.

[0497] "Personnel information" refers to all information about users, including their skills, experience, and career aspirations.

[0498] "Feature data" refers to a specific dataset generated to analyze personnel information and use it in predictive models.

[0499] A "predictive algorithm" refers to a computational method used to determine the optimal career path and skill plan for a user based on characteristic data.

[0500] "Users" refers to individuals who use this system to pursue career development or skill improvement.

[0501] "Visualization" refers to the process of graphically displaying generated plans and information in a way that is easy for users to understand.

[0502] The term "caregiving field" refers to the entire professional domain and environment used by caregiving staff.

[0503] The system for implementing this invention is designed to support the career development of care workers. This system is primarily realized through the collaboration of a server, terminals, and users.

[0504] The server connects with various databases and management systems of care facilities to collect personnel information about care workers. The collected data is processed using programming languages ​​such as Python and R. Specifically, libraries such as Pandas and NumPy are used to format the data and generate feature data.

[0505] After generating feature data, the server uses scikit-learn and TensorFlow to build a prediction algorithm. This algorithm generates optimal roles and additional skill acquisition plans based on the user's current skills and career path. The generated plans are visualized in a dashboard format and displayed on the caregiver's terminal.

[0506] The terminal provides an interface for care workers to input their current skills and future career goals. It is developed for iOS or Android devices using programming languages ​​such as Swift or Kotlin. The application on the terminal collects the input data and sends it to the server in real time. Based on this information, the server can use the feedback to improve the accuracy of its predictive model.

[0507] As a concrete example, when a user aiming to become a caregiver uses the app to input their strengths and areas of interest, the server analyzes that information and visualizes and presents specific advice, such as "take a dementia care specialist training course."

[0508] By utilizing generative AI models, employees can easily access optimal plans for skill acquisition and career development, even outside of business hours. As part of this system, prompts such as the following are used to input into the generative AI model:

[0509] "We are developing a career development support app for care workers. Please suggest the optimal skill set and acquisition plan to help the target staff member achieve their career goals. The current skill set is 'Skills A, B, C,' and the target career goal is 'Dementia Care Manager.'"

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] Users use a device to input their current skill set, past career experience, and future career goals. This input data is collected in various formats, including text, radio buttons, and dropdown menus. This data is then prepared for transmission to the server.

[0513] Step 2:

[0514] The server receives the data sent from the terminal. The entered skill and career data is organized into a dataframe using the Pandas library. This is a necessary step for subsequent data analysis processing.

[0515] Step 3:

[0516] The server generates feature data based on the received data. Specifically, it normalizes numerical data and vectorizes text data. Here, it uses scikit-learn's functions to encode categorical data. As a result of this process, an optimal dataset is generated as input for the predictive model.

[0517] Step 4:

[0518] Using the generated feature data, the server executes a prediction algorithm. This algorithm, built with TensorFlow, estimates the optimal career plan and additional skill acquisition plan for the user. This provides specific advice and clearly indicates the actions needed for the next steps.

[0519] Step 5:

[0520] The server generates a career plan and advice, which is then returned to the device. The device receives this information and visualizes it in a way that is easy for the user to understand. The information is presented in a visually intuitive interface using dashboards and graphs.

[0521] Step 6:

[0522] Users review the plan provided on their device and submit feedback. The feedback data is returned to the server and used to improve the accuracy of the prediction algorithm, thereby ensuring continuous improvement of the overall system accuracy.

[0523] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0524] To implement this invention, it is necessary to build a system that supports employee career development and incorporate an emotion engine. This system has the function of recognizing the user's emotions using the emotion engine and supplementing the input information.

[0525] First, the server automatically collects HR-related information. This information includes employee skill sets, self-reported information, and performance history. The server preprocesses the collected information, generates feature vectors, and builds a predictive model using a machine learning algorithm. This model predicts which skills and experience are needed in which departments.

[0526] When a user requests career counseling, they input their current skills and desired career path via their device. In addition, an emotion engine acquires emotional data in real time from the user's facial expressions and voice, evaluating their stress and motivation levels. The device then transmits this data to a server.

[0527] The server comprehensively considers user input and data from the emotion engine to generate optimal placement and skill acquisition plans based on predictive models. This includes factors such as stress levels and adaptability due to emotional states. The generated advice is provided to the user via the terminal, allowing the user to refine their career plan based on the displayed information.

[0528] For example, if a user is experiencing workplace stress, the emotion engine recognizes this and the server suggests a work assignment or training plan that will reduce the stress level. In this way, emotional data is reflected in the user's career advice, making it possible to provide more personalized support.

[0529] Furthermore, the server improves the accuracy of its predictive model by collecting user feedback. This feedback includes the output of the emotion engine, which is also used to improve the model. Through this iterative process, the system is expected to further promote the placement of the right people in the right places and improve the overall productivity of the organization.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The server periodically collects HR-related information, including employee skills, performance history, and self-reported information, from HR databases and various systems. This data is acquired securely and efficiently because it is necessary for subsequent analysis and model building.

[0533] Step 2:

[0534] The server preprocesses the collected data. This vectorizes text data, encodes categorical data, and normalizes numerical data. This data is then organized into feature vectors, enabling analysis by machine learning algorithms.

[0535] Step 3:

[0536] The server uses pre-processed feature vectors to run machine learning algorithms and build predictive models. These models can identify which skills and experiences are valued in which departments.

[0537] Step 4:

[0538] When a user requests career counseling, they input their current skills and desired career path via a terminal. Simultaneously, an emotion engine activates in response to the user's input, analyzing their facial expressions and voice to acquire emotional data.

[0539] Step 5:

[0540] The device transmits the acquired user skill information and emotional data to the server. Emotional data, including the user's stress level and motivation, is a crucial element in career advice.

[0541] Step 6:

[0542] The server combines information submitted by the user with emotional data from the emotion engine to generate an optimal placement and skills acquisition plan based on a predictive model. This plan takes into account stress reduction and adaptation strategies tailored to the user's emotional state.

[0543] Step 7:

[0544] The terminal displays advice received from the server to the user in a visually easy-to-understand format. Users can review suggested assignments and skill acquisition plans and develop their own career plans.

[0545] Step 8:

[0546] Based on the suggested advice, users decide on actions to consider a more appropriate career path and send feedback to the server via their device.

[0547] Step 9:

[0548] The server aggregates user feedback and analysis results from the sentiment engine, and continuously improves the accuracy of the predictive model. In this way, the system can provide more accurate and effective advice over time.

[0549] (Example 2)

[0550] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0551] In today's workplace, employee career development is a crucial element in improving organizational productivity. However, when assignments and skill development plans are created without considering employees' emotional states and stress levels, problems arise such as low employee retention and performance mismatches. Therefore, there is a need for more personalized career support systems that take employees' emotions into account.

[0552] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0553] In this invention, the server includes means for automatically collecting personnel-related data, means for preprocessing the data to generate feature vectors, means for constructing a predictive model using a machine learning algorithm, means for sentiment analysis to acquire user sentiment data in real time, and means for generating the optimal placement and skill acquisition plan based on the data. This makes it possible to provide an appropriate career plan that takes into account the user's emotional state.

[0554] "Personnel-related data" refers to information about employees, including skill sets, self-reported information, and performance review history.

[0555] A "feature vector" is a mathematical representation created from pre-processed data and is used in machine learning algorithms.

[0556] A "machine learning algorithm" is a computational method for learning patterns from data and building predictive models.

[0557] A "predictive model" is a mathematical model constructed to predict future outcomes based on feature vectors.

[0558] "Emotional analysis means" refers to technologies and devices that analyze a user's facial expressions and voice to acquire emotional data.

[0559] "Assigned department" refers to the specific job or duties that an employee is assigned to.

[0560] A "skills acquisition plan" is a plan or program designed to help employees acquire the necessary skills.

[0561] "Feedback" refers to the opinions and reactions provided by employees, which are used to improve the system.

[0562] This system utilizes various hardware and software to support employee career development. The server handles primary processing, collecting and managing HR-related data. Meanwhile, terminals function as the user interface, supporting data entry and sentiment data acquisition. Specific implementations are described below.

[0563] The server uses a database management system to collect and automatically store HR-related information. This information includes employee skill sets, self-reported information, and performance history. The server preprocesses this information and generates feature vectors using programming languages ​​such as Python and R. The generated feature vectors are analyzed by machine learning algorithms to build predictive models. This allows for the prediction of the optimal skills and experience for each department.

[0564] When a user requests career counseling, they use a terminal to input their current skills and desired career path. The terminal then activates emotion analysis software using a facial recognition camera and microphone, acquiring emotional data in real time from the user's facial expressions and voice. This emotional data is used to evaluate the user's stress levels and motivation. The input information and emotional data from the terminal are sent to a server and used for comprehensive analysis.

[0565] Based on this information, the server uses a predictive model to generate the optimal placement and skills acquisition plan for the user. This generation process takes into account the user's emotional state, incorporating elements to reduce stress and enhance adaptability. The generated career plan is visually displayed on the device, allowing the user to further refine their career plan.

[0566] For example, if a user experiences stress in their current workplace, the emotion engine analyzes this, and the server suggests a suitable assignment or training plan to reduce stress. In this way, emotional data is reflected in the user's career advice, enabling the provision of more personalized support.

[0567] A concrete example of a prompt message for a generating AI model is, "Based on the user's emotional data, create the optimal job suggestion to reduce stress." Based on this prompt message, the server generates a career plan that reflects the user's emotional state.

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The server accesses HR-related databases to automatically collect employee skill sets, self-reported information, and performance history. Input is raw data from the database, and output is a list of pre-processed data. The server filters the collected data and standardizes its format to make it processable.

[0571] Step 2:

[0572] The server generates feature vectors based on the preprocessed data. The input is the data list obtained in step 1, and the output is the feature vectors. By using techniques such as data categorization and numerical normalization, and utilizing the Python NumPy library to vectorize the data, it is converted into a format that can be used by the learning model.

[0573] Step 3:

[0574] The server uses a machine learning algorithm to build a predictive model from feature vectors. The input is the feature vectors generated in step 2, and the output is the completed predictive model. Using libraries such as Scikit-learn, the predictive model is trained to predict which departments require the appropriate skills and experience.

[0575] Step 4:

[0576] Users use a terminal to input their current skills information and career path aspirations. Input is manually entered data, and output is the input data in digital format. The terminal provides form entries and dropdown menus to collect information accurately and efficiently.

[0577] Step 5:

[0578] The device launches emotion analysis software and acquires emotional data through the user's facial expressions and voice. The input is the user's voice and video data, and the output is data indicating their emotional state. The device uses the camera and microphone to analyze the data and an emotion engine to evaluate stress and motivation.

[0579] Step 6:

[0580] The server receives user input information and emotional data, and uses this to generate the optimal placement and skill acquisition plan. The input is the output data from steps 4 and 5, and the predictive model from step 3, and the output is the placement plan and skill acquisition plan. The server comprehensively analyzes this data to generate the optimal plan tailored to each user's emotional state.

[0581] Step 7:

[0582] The terminal visually displays the generated plan to the user. The input is the output plan from step 6, and the output is the visual presentation to the user. The terminal displays this information using a graphical user interface, which the user can use to refine their career plan.

[0583] (Application Example 2)

[0584] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0585] In employee career development, a challenge exists in that current career plans are limited to individual employees' skill sets and aspirations, with little consideration given to their emotions or stress levels. As a result, employees may be assigned to departments that are not optimal for them, potentially leading to decreased overall organizational productivity. Furthermore, improvements in the accuracy of feedback-based models are limited, highlighting the need for more personalized career support.

[0586] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0587] In this invention, the server includes means for recognizing an employee's emotions using an emotion engine, means for collecting HR-related information and generating feature vectors, and means for generating optimal placement and skill acquisition plans for employees using predictive models and emotion data. This enables more appropriate career advice and placement suggestions based on the employee's emotional state.

[0588] "Personnel-related information" refers to information related to career development, including employee skill sets, performance history, and self-reported information.

[0589] A "feature vector" is a set of data obtained by quantifying personnel-related information and converting it into a format that can be used by machine learning models.

[0590] A "predictive model" is a machine learning algorithm built to calculate the optimal placement and skill acquisition plan for employees.

[0591] An "emotion engine" is software that analyzes a user's emotions from their facial expressions and voice, and acquires that data.

[0592] "Assignment location" refers to the appropriate department or position within the organization to which an employee should be assigned.

[0593] A "skills acquisition plan" is a set of guidance policies and training schedules designed to help employees acquire the skills and knowledge they will need in the future.

[0594] "Visual and auditory output means" refers to devices or software systems for displaying the generated carrier plan on a display or presenting it audibly.

[0595] This invention utilizes a system incorporating an emotion engine to support employees' career development. This system includes a server, terminals, and the emotion engine.

[0596] The server automatically collects HR-related information and generates feature vectors for each employee. This includes employee skill sets, performance history, and self-reported information. Next, the server uses machine learning algorithms to build a predictive model. This model calculates the optimal placement and skill acquisition plan for each employee based on the feature vectors.

[0597] The terminal receives the user's current skill information and desired career path. During this process, an emotion engine analyzes the user's facial expressions and voice, acquiring emotional data in real time. The emotion engine can utilize, for example, facial expression analysis software or voice emotion analysis software.

[0598] The server generates an effective career plan in its predictive model based on input data from the terminal and emotional data from the emotion engine. This plan takes into account the user's stress level and motivation level. The generated plan is presented to the user visually and audibly through the terminal.

[0599] As a concrete example, when a user interacts with the system, the robot can read the user's facial expressions and, if it detects that the user is experiencing stress, it can suggest assigning them to a new project that will alleviate that stress. In this way, emotional data is transformed into more personalized career advice, which is expected to improve user satisfaction and work efficiency.

[0600] Examples of prompt statements include the following:

[0601] "The user is seeking career counseling. Their current skill set is {User's Skill Set}, and their emotional state is {User's Emotional State}. Please propose the best career plan for this user."

[0602] This system integrates emotional data and personnel information to help create an environment where employees can perform at their best in the roles best suited to them.

[0603] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0604] Step 1:

[0605] The server automatically collects HR-related information from various data sources. This information includes employee skill sets, performance history, and self-reported information. This information is stored in a database and prepared for the generation of feature vectors used in subsequent processing.

[0606] Step 2:

[0607] The server generates feature vectors using the collected personnel-related information. This process involves vectorizing text data, encoding categorical information, and normalizing numerical data. The resulting feature vectors are then used to build machine learning models.

[0608] Step 3:

[0609] The server uses the generated feature vectors as input to build a predictive model using a machine learning algorithm. This predictive model is used to predict the optimal placement and skill acquisition plan for each employee. This model building process is iteratively executed based on training data to improve accuracy.

[0610] Step 4:

[0611] Users enter their current skills and desired career path via their device to receive career counseling. This input data is sent from the device to the server, where it is prepared for processing by a predictive model.

[0612] Step 5:

[0613] The emotion engine analyzes the user's facial expressions and voice in real time to acquire emotional data. This acquired emotional data is used to evaluate the user's stress level and motivation status. This information is also sent to the server and integrated with other carrier information.

[0614] Step 6:

[0615] The server integrates predictive models and sentiment data to generate an optimal career plan for the user. This plan takes emotional states into account and provides specific suggestions for placement and skill acquisition. These suggestions are generated by the AI ​​model.

[0616] Step 7:

[0617] The device presents the generated career plan to the user visually and audibly. Prompts are used to help the user consider the next steps based on their specific career plan. This process provides support tailored to the user's individual needs.

[0618] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0619] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0620] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0624] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0625] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0626] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0627] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0628] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0629] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0630] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0631] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0632] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0633] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0634] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0635] To implement this invention, coordination between the server, terminal, and user is necessary. First, the server automatically collects HR-related information about employees from the company's HR system and database. This includes skill sets, self-reported data, and performance history. The server processes this information and generates feature vectors. Based on these, a predictive model is trained using a machine learning algorithm.

[0636] When a user requests career counseling, they can input their current skills and desired career path through their device. The device receives this data and sends it to a server. The server feeds the received data into a predictive model to generate the optimal placement for the user and a plan for acquiring any missing skills.

[0637] For example, if a user is feeling anxious about their career advancement within their current department, they can input their skills and desired department into their terminal. The server then uses a predictive model to generate detailed advice on the best new placement and new skills the user should acquire. This advice is displayed on the user's terminal, allowing them to plan a concrete career path based on it.

[0638] Furthermore, the server has the ability to collect user feedback and continuously improve the accuracy of the predictive model based on that feedback. This cycle enables efficient human resource utilization by placing the right people in the right positions throughout the entire organization.

[0639] This entire system, by integrating data processing on the server, the user interface on the terminal, and user feedback, can efficiently and effectively support employees' career development.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The server collects HR-related information about employees from databases and HR systems. This includes employee skill sets, self-reported information, and performance history.

[0643] Step 2:

[0644] The server preprocesses the collected data and converts it into feature vectors that can be handled by machine learning algorithms. This process involves vectorizing text data and normalizing numerical data.

[0645] Step 3:

[0646] The server starts the generative AI learning process using pre-processed feature vectors. Here, a predictive model is built to clarify the skills and experience required in each department.

[0647] Step 4:

[0648] When a user requests career counseling, they input their current skills and desired career path through their device.

[0649] Step 5:

[0650] The terminal receives input from the user and sends it to the server.

[0651] Step 6:

[0652] The server inputs the data received from the user into a trained predictive model to generate advice on the optimal placement and skills the user should acquire.

[0653] Step 7:

[0654] The server sends the generated advice to the terminal.

[0655] Step 8:

[0656] The device displays the received advice to the user, who can then use this to develop a career plan.

[0657] Step 9:

[0658] Users send feedback regarding the validity and satisfaction level of the advice to the server via their device.

[0659] Step 10:

[0660] The server receives feedback and uses it to update the predictive model, improving its accuracy and effectiveness.

[0661] (Example 1)

[0662] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] Traditional personnel placement systems often lagged behind in addressing employees' skills and career aspirations, and struggled to efficiently assign the right people to the right positions. As a result, the ability to offer individualized skill development and career path suggestions was limited, making it difficult to maximize the overall efficiency of human resources within the organization.

[0664] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0665] In this invention, the server includes means for acquiring personnel information, means for generating characteristic vectors, and means for constructing an analytical model. This enables the provision of more accurate and personalized placement and capability acquisition plans for individual employees, and efficient human resource utilization across the entire organization.

[0666] "Personnel information" refers to information about individuals within an organization, such as their skills, career history, and performance evaluations.

[0667] A "characteristic vector" is a vector that numerically represents an employee's skills and abilities, and is used as input data for a model.

[0668] An "analytical model" refers to a model that uses mathematical and statistical processing to analyze and predict data based on characteristic vectors.

[0669] "Assignment" refers to assigning individual employees to specific jobs or responsibilities in a way that is suitable for them.

[0670] A "skills acquisition plan" refers to a plan or proposal for employees to effectively acquire skills they lack.

[0671] "Response" refers to feedback and opinions from individuals regarding plans and suggestions provided by the system.

[0672] This invention is a system implemented through cooperation between a server, a terminal, and a user. The server utilizes existing human resources systems and databases to automatically collect personnel information from a company. The data includes employee skills, self-assessments, and work history. Based on this information, the server generates characteristic vectors. The generation of characteristic vectors involves vectorizing text data, encoding categorical data, and standardizing numerical data.

[0673] The server uses these characteristic vectors to build an analysis model. This model utilizes a generative AI model to perform data analysis and predictions based on the received data.

[0674] Users can use a specific device to input data about their current skills and desired career path. For example, a user might input a prompt such as: "Current skills: Programming, team leadership. Desired career path: Project management. Goal: Become a team manager within two years."

[0675] The terminal sends the entered information to the server, which uses an analysis model to generate an optimal deployment and capability acquisition plan for the user. This information is then returned to the terminal and displayed visually to the user.

[0676] Furthermore, the server receives responses from users and uses them to improve the accuracy of the analysis model. Through this continuous feedback loop, the model is constantly updated, enabling it to provide more refined advice.

[0677] Through such collaborative systems, organizations can address the individual needs of their employees and promote efficient talent allocation and skill development.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The user uses a terminal to input data on their current skills and desired career path. This input includes skill set, desired department, and career goals. For example, they might enter a prompt such as, "Current skills: Programming, team leadership. Desired career path: Project management. Goal: Become a team manager within 2 years." The data entered here is then sent to the next process.

[0681] Step 2:

[0682] The terminal formats the data received from the user and prepares it for transmission to the server. Specifically, it converts the input data into a format that the server can understand and sends it to the server using a secure communication protocol. The input is the user's skills and preferences, and the output is the formatted data to be sent to the server.

[0683] Step 3:

[0684] Before feeding the received data into the analysis model, the server retrieves existing personnel information. The server collects employee information from the company's database, including skill data and job evaluations of other employees. As a data processing step, this information is cross-referenced to generate appropriate trait vectors. Here, the input is personnel information from the database, and the output is the trait vectors.

[0685] Step 4:

[0686] The server uses the generated characteristic vectors to build or update an analysis model. This model utilizes a generated AI model to perform data analysis. Specifically, vector information is input to the model, and the model predicts the optimal placement and capacity enhancement plan. The input is characteristic vectors, and the output is the prediction result.

[0687] Step 5:

[0688] The server sends the prediction results obtained by the model to the user's terminal as a suggestion. The server then formats the output to present this information in a format that is easy for the user to understand. The input is the prediction result from the model, and the output is the suggestion provided to the user.

[0689] Step 6:

[0690] The user reviews the proposed plan on their device and enters feedback. The device then sends this feedback back to the server. Specifically, the user describes the usefulness and areas for improvement of the plan. The input is the user's feedback on the proposal, and the output is the feedback data sent to the server.

[0691] Step 7:

[0692] The server receives feedback from users and uses it to improve the accuracy of the analysis model. Based on the feedback, the server adjusts the model, improving the accuracy and adaptability of its suggestions. The input is the feedback, and the output is the improved analysis model.

[0693] (Application Example 1)

[0694] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] In the field of long-term care, there is a lack of mechanisms to efficiently support the skill development and career advancement of staff. Therefore, there is a need to provide concrete guidelines for staff to choose the optimal career path and acquire the necessary skills. Traditional methods struggle to provide results that consider diverse skill sets and career goals, and staff members bear a heavy burden of individually gathering information and making decisions.

[0696] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0697] In this invention, the server includes a device for collecting personnel information, a device for generating feature data using the personnel information, and a device for constructing a predictive algorithm based on the feature data. This makes it possible for care workers to automatically obtain specific guidelines for selecting the optimal role based on their own skills and career goals, and for acquiring the necessary skills.

[0698] "Personnel information" refers to all information about users, including their skills, experience, and career aspirations.

[0699] "Feature data" refers to a specific dataset generated to analyze personnel information and use it in predictive models.

[0700] A "predictive algorithm" refers to a computational method used to determine the optimal career path and skill plan for a user based on characteristic data.

[0701] "Users" refers to individuals who use this system to pursue career development or skill improvement.

[0702] "Visualization" refers to the process of graphically displaying generated plans and information in a way that is easy for users to understand.

[0703] The term "caregiving field" refers to the entire professional domain and environment used by caregiving staff.

[0704] The system for implementing this invention is designed to support the career development of care workers. This system is primarily realized through the collaboration of a server, terminals, and users.

[0705] The server connects with various databases and management systems of care facilities to collect personnel information about care workers. The collected data is processed using programming languages ​​such as Python and R. Specifically, libraries such as Pandas and NumPy are used to format the data and generate feature data.

[0706] After generating feature data, the server uses scikit-learn and TensorFlow to build a prediction algorithm. This algorithm generates optimal roles and additional skill acquisition plans based on the user's current skills and career path. The generated plans are visualized in a dashboard format and displayed on the caregiver's terminal.

[0707] The terminal provides an interface for care workers to input their current skills and future career goals. It is developed for iOS or Android devices using programming languages ​​such as Swift or Kotlin. The application on the terminal collects the input data and sends it to the server in real time. Based on this information, the server can use the feedback to improve the accuracy of its predictive model.

[0708] As a concrete example, when a user aiming to become a caregiver uses the app to input their strengths and areas of interest, the server analyzes that information and visualizes and presents specific advice, such as "take a dementia care specialist training course."

[0709] By utilizing generative AI models, employees can easily access optimal plans for skill acquisition and career development, even outside of business hours. As part of this system, prompts such as the following are used to input into the generative AI model:

[0710] "We are developing a career development support app for care workers. Please suggest the optimal skill set and acquisition plan to help the target staff member achieve their career goals. The current skill set is 'Skills A, B, C,' and the target career goal is 'Dementia Care Manager.'"

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] Users use a device to input their current skill set, past career experience, and future career goals. This input data is collected in various formats, including text, radio buttons, and dropdown menus. This data is then prepared for transmission to the server.

[0714] Step 2:

[0715] The server receives the data sent from the terminal. The entered skill and career data is organized into a dataframe using the Pandas library. This is a necessary step for subsequent data analysis processing.

[0716] Step 3:

[0717] The server generates feature data based on the received data. Specifically, it normalizes numerical data and vectorizes text data. Here, it uses scikit-learn's functions to encode categorical data. As a result of this process, an optimal dataset is generated as input for the predictive model.

[0718] Step 4:

[0719] Using the generated feature data, the server executes a prediction algorithm. This algorithm, built with TensorFlow, estimates the optimal career plan and additional skill acquisition plan for the user. This provides specific advice and clearly indicates the actions needed for the next steps.

[0720] Step 5:

[0721] The server generates a career plan and advice, which is then returned to the device. The device receives this information and visualizes it in a way that is easy for the user to understand. The information is presented in a visually intuitive interface using dashboards and graphs.

[0722] Step 6:

[0723] Users review the plan provided on their device and submit feedback. The feedback data is returned to the server and used to improve the accuracy of the prediction algorithm, thereby ensuring continuous improvement of the overall system accuracy.

[0724] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0725] To implement this invention, it is necessary to build a system that supports employee career development and incorporate an emotion engine. This system has the function of recognizing the user's emotions using the emotion engine and supplementing the input information.

[0726] First, the server automatically collects HR-related information. This information includes employee skill sets, self-reported information, and performance history. The server preprocesses the collected information, generates feature vectors, and builds a predictive model using a machine learning algorithm. This model predicts which skills and experience are needed in which departments.

[0727] When a user requests career counseling, they input their current skills and desired career path via their device. In addition, an emotion engine acquires emotional data in real time from the user's facial expressions and voice, evaluating their stress and motivation levels. The device then transmits this data to a server.

[0728] The server comprehensively considers user input and data from the emotion engine to generate optimal placement and skill acquisition plans based on predictive models. This includes factors such as stress levels and adaptability due to emotional states. The generated advice is provided to the user via the terminal, allowing the user to refine their career plan based on the displayed information.

[0729] For example, if a user is experiencing workplace stress, the emotion engine recognizes this and the server suggests a work assignment or training plan that will reduce the stress level. In this way, emotional data is reflected in the user's career advice, making it possible to provide more personalized support.

[0730] Furthermore, the server improves the accuracy of its predictive model by collecting user feedback. This feedback includes the output of the emotion engine, which is also used to improve the model. Through this iterative process, the system is expected to further promote the placement of the right people in the right places and improve the overall productivity of the organization.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The server periodically collects HR-related information, including employee skills, performance history, and self-reported information, from HR databases and various systems. This data is acquired securely and efficiently because it is necessary for subsequent analysis and model building.

[0734] Step 2:

[0735] The server preprocesses the collected data. This vectorizes text data, encodes categorical data, and normalizes numerical data. This data is then organized into feature vectors, enabling analysis by machine learning algorithms.

[0736] Step 3:

[0737] The server uses pre-processed feature vectors to run machine learning algorithms and build predictive models. These models can identify which skills and experiences are valued in which departments.

[0738] Step 4:

[0739] When a user requests career counseling, they input their current skills and desired career path via a terminal. Simultaneously, an emotion engine activates in response to the user's input, analyzing their facial expressions and voice to acquire emotional data.

[0740] Step 5:

[0741] The device transmits the acquired user skill information and emotional data to the server. Emotional data, including the user's stress level and motivation, is a crucial element in career advice.

[0742] Step 6:

[0743] The server combines information submitted by the user with emotional data from the emotion engine to generate an optimal placement and skills acquisition plan based on a predictive model. This plan takes into account stress reduction and adaptation strategies tailored to the user's emotional state.

[0744] Step 7:

[0745] The terminal displays advice received from the server to the user in a visually easy-to-understand format. Users can review suggested assignments and skill acquisition plans and develop their own career plans.

[0746] Step 8:

[0747] Based on the suggested advice, users decide on actions to consider a more appropriate career path and send feedback to the server via their device.

[0748] Step 9:

[0749] The server aggregates user feedback and analysis results from the sentiment engine, and continuously improves the accuracy of the predictive model. In this way, the system can provide more accurate and effective advice over time.

[0750] (Example 2)

[0751] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0752] In today's workplace, employee career development is a crucial element in improving organizational productivity. However, when assignments and skill development plans are created without considering employees' emotional states and stress levels, problems arise such as low employee retention and performance mismatches. Therefore, there is a need for more personalized career support systems that take employees' emotions into account.

[0753] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0754] In this invention, the server includes means for automatically collecting personnel-related data, means for preprocessing the data to generate feature vectors, means for constructing a predictive model using a machine learning algorithm, means for sentiment analysis to acquire user sentiment data in real time, and means for generating the optimal placement and skill acquisition plan based on the data. This makes it possible to provide an appropriate career plan that takes into account the user's emotional state.

[0755] "Personnel-related data" refers to information about employees, including skill sets, self-reported information, and performance review history.

[0756] A "feature vector" is a mathematical representation created from pre-processed data and is used in machine learning algorithms.

[0757] A "machine learning algorithm" is a computational method for learning patterns from data and building predictive models.

[0758] A "predictive model" is a mathematical model constructed to predict future outcomes based on feature vectors.

[0759] "Emotional analysis means" refers to technologies and devices that analyze a user's facial expressions and voice to acquire emotional data.

[0760] "Assigned department" refers to the specific job or duties that an employee is assigned to.

[0761] A "skills acquisition plan" is a plan or program designed to help employees acquire the necessary skills.

[0762] "Feedback" refers to the opinions and reactions provided by employees, which are used to improve the system.

[0763] This system utilizes various hardware and software to support employee career development. The server handles primary processing, collecting and managing HR-related data. Meanwhile, terminals function as the user interface, supporting data entry and sentiment data acquisition. Specific implementations are described below.

[0764] The server uses a database management system to collect and automatically store HR-related information. This information includes employee skill sets, self-reported information, and performance history. The server preprocesses this information and generates feature vectors using programming languages ​​such as Python and R. The generated feature vectors are analyzed by machine learning algorithms to build predictive models. This allows for the prediction of the optimal skills and experience for each department.

[0765] When a user requests career counseling, they use a terminal to input their current skills and desired career path. The terminal then activates emotion analysis software using a facial recognition camera and microphone, acquiring emotional data in real time from the user's facial expressions and voice. This emotional data is used to evaluate the user's stress levels and motivation. The input information and emotional data from the terminal are sent to a server and used for comprehensive analysis.

[0766] Based on this information, the server uses a predictive model to generate the optimal placement and skills acquisition plan for the user. This generation process takes into account the user's emotional state, incorporating elements to reduce stress and enhance adaptability. The generated career plan is visually displayed on the device, allowing the user to further refine their career plan.

[0767] For example, if a user experiences stress in their current workplace, the emotion engine analyzes this, and the server suggests a suitable assignment or training plan to reduce stress. In this way, emotional data is reflected in the user's career advice, enabling the provision of more personalized support.

[0768] A concrete example of a prompt message for a generating AI model is, "Based on the user's emotional data, create the optimal job suggestion to reduce stress." Based on this prompt message, the server generates a career plan that reflects the user's emotional state.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The server accesses HR-related databases to automatically collect employee skill sets, self-reported information, and performance history. Input is raw data from the database, and output is a list of pre-processed data. The server filters the collected data and standardizes its format to make it processable.

[0772] Step 2:

[0773] The server generates feature vectors based on the preprocessed data. The input is the data list obtained in step 1, and the output is the feature vectors. By using techniques such as data categorization and numerical normalization, and utilizing the Python NumPy library to vectorize the data, it is converted into a format that can be used by the learning model.

[0774] Step 3:

[0775] The server uses a machine learning algorithm to build a predictive model from feature vectors. The input is the feature vectors generated in step 2, and the output is the completed predictive model. Using libraries such as Scikit-learn, the predictive model is trained to predict which departments require the appropriate skills and experience.

[0776] Step 4:

[0777] Users use a terminal to input their current skills information and career path aspirations. Input is manually entered data, and output is the input data in digital format. The terminal provides form entries and dropdown menus to collect information accurately and efficiently.

[0778] Step 5:

[0779] The device launches emotion analysis software and acquires emotional data through the user's facial expressions and voice. The input is the user's voice and video data, and the output is data indicating their emotional state. The device uses the camera and microphone to analyze the data and an emotion engine to evaluate stress and motivation.

[0780] Step 6:

[0781] The server receives user input information and emotional data, and uses this to generate the optimal placement and skill acquisition plan. The input is the output data from steps 4 and 5, and the predictive model from step 3, and the output is the placement plan and skill acquisition plan. The server comprehensively analyzes this data to generate the optimal plan tailored to each user's emotional state.

[0782] Step 7:

[0783] The terminal visually displays the generated plan to the user. The input is the output plan from step 6, and the output is the visual presentation to the user. The terminal displays this information using a graphical user interface, which the user can use to refine their career plan.

[0784] (Application Example 2)

[0785] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0786] In employee career development, a challenge exists in that current career plans are limited to individual employees' skill sets and aspirations, with little consideration given to their emotions or stress levels. As a result, employees may be assigned to departments that are not optimal for them, potentially leading to decreased overall organizational productivity. Furthermore, improvements in the accuracy of feedback-based models are limited, highlighting the need for more personalized career support.

[0787] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0788] In this invention, the server includes means for recognizing an employee's emotions using an emotion engine, means for collecting HR-related information and generating feature vectors, and means for generating optimal placement and skill acquisition plans for employees using predictive models and emotion data. This enables more appropriate career advice and placement suggestions based on the employee's emotional state.

[0789] "Personnel-related information" refers to information related to career development, including employee skill sets, performance history, and self-reported information.

[0790] A "feature vector" is a set of data obtained by quantifying personnel-related information and converting it into a format that can be used by machine learning models.

[0791] A "predictive model" is a machine learning algorithm built to calculate the optimal placement and skill acquisition plan for employees.

[0792] An "emotion engine" is software that analyzes a user's emotions from their facial expressions and voice, and acquires that data.

[0793] "Assignment location" refers to the appropriate department or position within the organization to which an employee should be assigned.

[0794] A "skills acquisition plan" is a set of guidance policies and training schedules designed to help employees acquire the skills and knowledge they will need in the future.

[0795] "Visual and auditory output means" refers to devices or software systems for displaying the generated carrier plan on a display or presenting it audibly.

[0796] This invention utilizes a system incorporating an emotion engine to support employees' career development. This system includes a server, terminals, and the emotion engine.

[0797] The server automatically collects HR-related information and generates feature vectors for each employee. This includes employee skill sets, performance history, and self-reported information. Next, the server uses machine learning algorithms to build a predictive model. This model calculates the optimal placement and skill acquisition plan for each employee based on the feature vectors.

[0798] The terminal receives the user's current skill information and desired career path. During this process, an emotion engine analyzes the user's facial expressions and voice, acquiring emotional data in real time. The emotion engine can utilize, for example, facial expression analysis software or voice emotion analysis software.

[0799] The server generates an effective career plan in its predictive model based on input data from the terminal and emotional data from the emotion engine. This plan takes into account the user's stress level and motivation level. The generated plan is presented to the user visually and audibly through the terminal.

[0800] As a concrete example, when a user interacts with the system, the robot can read the user's facial expressions and, if it detects that the user is experiencing stress, it can suggest assigning them to a new project that will alleviate that stress. In this way, emotional data is transformed into more personalized career advice, which is expected to improve user satisfaction and work efficiency.

[0801] Examples of prompt statements include the following:

[0802] "The user is seeking career counseling. Their current skill set is {User's Skill Set}, and their emotional state is {User's Emotional State}. Please propose the best career plan for this user."

[0803] This system integrates emotional data and personnel information to help create an environment where employees can perform at their best in the roles best suited to them.

[0804] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0805] Step 1:

[0806] The server automatically collects HR-related information from various data sources. This information includes employee skill sets, performance history, and self-reported information. This information is stored in a database and prepared for the generation of feature vectors used in subsequent processing.

[0807] Step 2:

[0808] The server generates feature vectors using the collected personnel-related information. This process involves vectorizing text data, encoding categorical information, and normalizing numerical data. The resulting feature vectors are then used to build machine learning models.

[0809] Step 3:

[0810] The server uses the generated feature vectors as input to build a predictive model using a machine learning algorithm. This predictive model is used to predict the optimal placement and skill acquisition plan for each employee. This model building process is iteratively executed based on training data to improve accuracy.

[0811] Step 4:

[0812] Users enter their current skills and desired career path via their device to receive career counseling. This input data is sent from the device to the server, where it is prepared for processing by a predictive model.

[0813] Step 5:

[0814] The emotion engine analyzes the user's facial expressions and voice in real time to acquire emotional data. This acquired emotional data is used to evaluate the user's stress level and motivation status. This information is also sent to the server and integrated with other carrier information.

[0815] Step 6:

[0816] The server integrates predictive models and sentiment data to generate an optimal career plan for the user. This plan takes emotional states into account and provides specific suggestions for placement and skill acquisition. These suggestions are generated by the AI ​​model.

[0817] Step 7:

[0818] The device presents the generated career plan to the user visually and audibly. Prompts are used to help the user consider the next steps based on their specific career plan. This process provides support tailored to the user's individual needs.

[0819] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0820] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0821] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0822] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0823] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0824] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0825] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0826] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0827] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0828] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0829] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0830] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0831] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0832] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0833] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0834] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0835] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0836] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0837] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0838] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0839] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0840] The following is further disclosed regarding the embodiments described above.

[0841] (Claim 1)

[0842] Means for collecting personnel-related information,

[0843] A means for generating a feature vector using the aforementioned personnel-related information,

[0844] A means for constructing a predictive model based on the aforementioned feature vectors,

[0845] A means of receiving current skills and desired career paths entered by employees,

[0846] A means for generating the optimal placement and additional skills acquisition plan for an employee using the aforementioned predictive model,

[0847] A system including means for visually displaying the generated plan.

[0848] (Claim 2)

[0849] The system according to claim 1, further comprising means for collecting feedback from employees and improving the accuracy of the predictive model.

[0850] (Claim 3)

[0851] The system according to claim 1, comprising means for vectorizing text data, encoding categorical data, and normalizing numerical data when generating the feature vectors.

[0852] "Example 1"

[0853] (Claim 1)

[0854] Means for obtaining personnel information,

[0855] A means for generating characteristic vectors using the aforementioned personnel information,

[0856] A means for constructing an analytical model based on the aforementioned characteristic vector,

[0857] A means of receiving the current skills and desired career path entered by an individual,

[0858] Means for creating an optimal placement and additional skills acquisition plan for an individual using the aforementioned analysis model,

[0859] Means for displaying the created plan,

[0860] A system including means for obtaining an individual's response to the aforementioned plan.

[0861] (Claim 2)

[0862] The system according to claim 1, comprising means for collecting responses from individuals and improving the accuracy of the analysis model.

[0863] (Claim 3)

[0864] The system according to claim 1, comprising means for vectorizing character information, encoding classification information, and standardizing numerical information when generating the characteristic vector.

[0865] "Application Example 1"

[0866] (Claim 1)

[0867] A device for collecting personnel information,

[0868] A device that generates characteristic data using the aforementioned personnel information,

[0869] A device for constructing a prediction algorithm based on the aforementioned feature data,

[0870] A device that receives the user's current skills and desired career path,

[0871] A device that uses the aforementioned prediction algorithm to generate an optimal role and additional skills acquisition plan for the user,

[0872] A device for visualizing the generated plan,

[0873] A system that includes a device that provides specific advice on acquiring skills and qualifications in the field of elderly care.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising means for collecting feedback from users and improving the accuracy of the prediction algorithm.

[0876] (Claim 3)

[0877] The system according to claim 1, comprising a device for generating the aforementioned feature data, which converts numerical information into a vector, encodes classification information, and standardizes the numerical information.

[0878] "Example 2 of combining an emotion engine"

[0879] (Claim 1)

[0880] A means of automatically collecting HR-related data,

[0881] A means for preprocessing the aforementioned personnel-related data to generate a feature vector,

[0882] A means for constructing a predictive model using a machine learning algorithm with the aforementioned feature vectors,

[0883] A means of receiving the skill information and career path preferences entered by the user,

[0884] A means for acquiring user emotional data using emotion analysis tools,

[0885] A means for generating the optimal placement and skill acquisition plan for a user using the predictive model based on the aforementioned emotional data and user input data,

[0886] A system including means for visually displaying the generated plan.

[0887] (Claim 2)

[0888] The system according to claim 1, comprising means for acquiring user emotional data in real time using an emotion engine and reflecting it in the predictive model to provide a more personalized plan.

[0889] (Claim 3)

[0890] The system according to claim 1, further comprising means for collecting employee feedback and sentiment analysis results to improve the accuracy of the predictive model.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] Means for collecting personnel-related information,

[0894] A means for generating a feature vector using the aforementioned personnel-related information,

[0895] A means for constructing a predictive model based on the aforementioned feature vectors,

[0896] A means of receiving current skills and desired career paths entered by employees,

[0897] A means of recognizing employees' emotions using an emotion engine,

[0898] A means for generating optimal placements and additional skill acquisition plans for employees using the aforementioned predictive model and sentiment data,

[0899] A system including output means for visually and audibly presenting the generated plan.

[0900] (Claim 2)

[0901] The system according to claim 1, further comprising means for collecting feedback from employees and improving the accuracy of the predictive model and the emotion recognition algorithm.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising means for vectorizing text data, encoding categorical data, and normalizing numerical data in generating the feature vectors, and also comprising means for processing sentiment data. [Explanation of symbols]

[0904] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting personnel-related information, A means for generating a feature vector using the aforementioned personnel-related information, A means for constructing a predictive model based on the aforementioned feature vectors, A means of receiving current skills and desired career paths entered by employees, A means for generating the optimal placement and additional skills acquisition plan for an employee using the aforementioned predictive model, A system including means for visually displaying the generated plan.

2. The system according to claim 1, further comprising means for collecting feedback from employees and improving the accuracy of the predictive model.

3. The system according to claim 1, comprising means for vectorizing text data, encoding categorical data, and normalizing numerical data when generating the feature vectors.

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