system

Abstract

Provide a system. 【Solution means】 An information terminal means for inputting user attribute data, A computing device means for receiving and analyzing the attribute data, Means for designing a plurality of career paths for the user based on the generated analysis results, Means for outputting the career path to the user and obtaining opinions, Means for modifying and improving the career path based on the opinions, Means for creating a specific action plan based on the final career path, Means for cooperating with an auxiliary system and providing necessary support, Means for presenting a career improvement path specific to the care field, A system including the above.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern times, with the aging of life, the need for individuals to live a fulfilling life even after retirement has been increasing. However, many people cannot find their desired second career and do not know how to achieve it. Also, it is difficult to appropriately find opportunities for re-learning and networking in new fields, and as a result, cases of uneasiness in old age and a sense of isolation from society can be seen. The present invention aims to solve these problems and provide a system that enables an individual to construct an optimal career plan for themselves and receive support for its realization.

Means for Solving the Problems

[0005] This invention provides an interface for inputting user information, thereby collecting information about the individual's profile, interests, and desired career. The collected information is analyzed by a processing means, and multiple career plans based on the user are generated. The generated plans are then presented to the user, and the plans are modified and improved by collecting feedback from the user. Furthermore, a concrete action plan is formulated based on the optimized career plan, and additional educational opportunities and support are provided by collaborating with external services as needed. This configuration enables users to find a second career that suits them and receive concrete support to realize it.

[0006] An "interface means" is a functional part of a system that allows users to input information, and enables interaction through voice or text using natural language processing technology.

[0007] "Processing means" refers to the functional part of a system for analyzing information received from users, using machine learning algorithms to identify trends and needs and generate career plans.

[0008] A "career plan" refers to a set of multiple occupational or activity options proposed based on the user's wishes and analysis results, presenting a suitable second career path for the user.

[0009] "Feedback" refers to the opinions and responses provided by users to the system, and is information used to revise and improve the plan.

[0010] An "action plan" is a collection of specific action steps formulated based on an optimized career plan, providing detailed guidance for achieving goals.

[0011] "External services" refer to support organizations and resources outside the system, such as online education platforms and industry networks, that are integrated to provide users with additional educational opportunities and support. [Brief explanation of the drawing]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that provides users with personalized programs to find and implement the second career best suited to them. This system has the following functions:

[0034] 1. Enter user information

[0035] Terminal: Users enter their personal profile, past work history, and interests on the interface. The terminal converts this data into an appropriate format and sends it to the server.

[0036] 2. Data Analysis

[0037] Server: Based on the received information, it applies machine learning algorithms to analyze user characteristics. The server searches the database for highly relevant success stories and career paths and generates candidates.

[0038] 3. Generating and presenting a career plan

[0039] Server: Based on the analysis results, the generating AI creates multiple career plans. The server evaluates how well each plan matches the user's needs and assigns a score.

[0040] Device: Visualizes and presents carrier plans and their evaluations to the user. The user can select from the displayed options and view further details.

[0041] 4. Interaction with users

[0042] Users can provide various feedback on the presented career plan. They can also enter additional questions and request specific suggestions.

[0043] 5. Responding to feedback and improving the plan

[0044] Server: Receive user feedback and re-analyze it. Modify the plan as needed and provide new, optimized suggestions to the user.

[0045] 6. Formulation of an implementation plan

[0046] Server: Based on the ultimately selected career plan, generate a specific, step-by-step execution plan. This includes necessary resources and opportunities for skill development.

[0047] Terminal: Present the plan to the user and organize the information necessary for its execution.

[0048] 7. Integration with related services

[0049] Server: The program integrates with external educational platforms and industry networks to provide necessary support to users.

[0050] Terminal: Provides users with linked information and incorporates it into their plans.

[0051] Specific example:

[0052] For example, let's say Mr. C, a retired teacher, wants to make a new contribution to the field of education and uses this system. Through feedback, Mr. C requests support in launching a local education program. Based on this request, the system provides a partnership plan with local education support organizations and proposes concrete steps to Mr. C. As a result, Mr. C can start a project to contribute to his community.

[0053] Through the process described above, users can discover the second career best suited to their needs and receive concrete support to make it a reality.

[0054] The following describes the processing flow.

[0055] Step 1:

[0056] Users input personal profile information, work history, and interests using an interface on their device. The device collects this information in an appropriate format and sends it to the server.

[0057] Step 2:

[0058] The server analyzes the received user information using processing tools. It utilizes machine learning algorithms to analyze the characteristics of the user profile and identify their needs.

[0059] Step 3:

[0060] Based on the analysis results, the server's generating AI creates multiple career plans tailored to the user. It also references similar career paths and success stories from the database to enhance the suggestions.

[0061] Step 4:

[0062] The device receives carrier plans provided by the server, visualizes them, and presents them to the user. The user is shown detailed information about each plan and an evaluation score regarding its feasibility.

[0063] Step 5:

[0064] Users provide feedback on the displayed career plans. They can also send additional information or questions about plans they are interested in to the server via their device.

[0065] Step 6:

[0066] The server collects user feedback and re-evaluates the plan. If necessary, it incorporates the feedback and regenerates a plan that better suits the user.

[0067] Step 7:

[0068] Based on the final career plan, the server develops a detailed execution plan. This plan includes specific steps, recommended resources, and strategies for improving required skills.

[0069] Step 8:

[0070] The terminal presents the user with an execution plan and displays organized information about the preparatory steps required to execute the plan.

[0071] Step 9:

[0072] The server interacts with relevant external services and platforms to provide support for users in realizing their execution plans. The terminal displays this information to the user as needed, integrating it as part of the plan.

[0073] (Example 1)

[0074] 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."

[0075] In today's world, many individuals are exploring second careers, but they lack concrete methods for selecting and implementing the most suitable career plan. This problem makes re-employment or transitioning to a new career more difficult, increasing the time and effort required for individual career development.

[0076] 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.

[0077] In this invention, the server includes an interface means for inputting user data, a processing means for receiving the user data and analyzing its characteristics, and a means for creating multiple career plans for the user based on the generated characteristic analysis results. This makes it possible to select the optimal second career based on the individual's characteristics and to formulate a concrete plan for its implementation.

[0078] An "interface means" is a component that allows a user to input data and interact with the system.

[0079] "Processing means" refers to components that analyze received user data and perform calculations and analyses to identify user characteristics.

[0080] A "career plan" is a set of options for specific career changes and career development, formulated based on the user's characteristics analysis.

[0081] "Means of collecting opinions" are components that enable users to provide feedback on the career plan presented.

[0082] "Means of modification and improvement" refer to components used to change the work plan based on user feedback and make it more suitable.

[0083] "External information sources" refer to organizations or services that provide additional information or resources necessary to support users from outside the system, such as online learning platforms or industry networks.

[0084] This invention is a system that provides support to users in finding and implementing the optimal second career. The system takes user data as input, analyzes user characteristics based on that information, and performs a series of processes to generate an optimal career plan. The hardware and software used, as well as specific examples of operation, are described below.

[0085] Users use a device to enter their personal profile, past work history, and interests. This device is configured as a personal computer or mobile device and can input data using standard input devices (keyboard, touchscreen, etc.). The input data is formatted and sent to the server.

[0086] The server utilizes computing resources in the cloud to receive and store input data. For specific analysis, machine learning algorithms are employed. Clustering and classification models are used as machine learning models. These function as means of searching the database for past success stories and related career paths. A generative AI model generates an optimal career plan for the user based on the acquired data.

[0087] For example, if a user wants to work on a new project in the technology field, this system can analyze the user's work history and skills and present relevant project examples. It can also suggest other fields where the user might be more suitable. The generated plan is visualized on the user's device and provided to them.

[0088] An example of a prompt message is, "Based on the user's profile, suggest the best new career options for them."

[0089] Another important feature of this system is that the server connects with external educational platforms and industry networks. This connection allows users to access additional learning opportunities and industry support. The connection is primarily done through APIs and is designed to allow users to retrieve information seamlessly.

[0090] This specific structure enables the provision of flexible and effective career plans tailored to the user's needs.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1: Enter user information

[0093] Terminal: Users use the terminal's interface to enter their personal profile, past work history, and interests. The terminal retrieves this information and converts it into a dedicated data format. The converted data is then ready to be sent to the server.

[0094] Step 2: Send

[0095] Terminal: The entered data is sent to the server using security protocols such as SSL / TLS. This transmission process ensures the confidentiality and integrity of the data.

[0096] Step 3: Receiving and storing data

[0097] Server: Received data is temporarily stored in storage. Here, the data undergoes preprocessing for analysis. Preprocessing includes data cleaning and normalization to ensure that the analysis algorithm functions efficiently.

[0098] Step 4: Analyzing User Characteristics

[0099] Server: Machine learning algorithms (clustering models, classification models) are applied to the data. These algorithms extract user characteristics and generate relevant statistics. This allows for the development of an initial career plan based on the user's characteristics.

[0100] Step 5: Generating a Career Plan

[0101] Server: Uses a generative AI model to generate multiple career plans based on user characteristic data. A prompt is used as input, and the optimal suggestion is output. An example is, "Based on the user's profile, suggest the best new career options for them."

[0102] Step 6: Plan presentation and evaluation

[0103] Terminal: The generated career plan and its evaluation results are visualized and presented to the user. The user can select from the presented options and view the details.

[0104] Step 7: Gathering Feedback

[0105] User: Provide feedback on the presented plan. This feedback may include additional questions or specific requests.

[0106] Step 8: Revise and improve the plan

[0107] Server: User feedback is received and integrated with existing data. If necessary, analysis is performed again, and the career plan is revised and improved.

[0108] Step 9: Develop the final implementation plan

[0109] Server: Based on the finalized career plan, a specific action plan is created. This action plan includes the necessary resources and steps.

[0110] Step 10: Integration with external services

[0111] Server: Connects with external educational platforms and industry networks via APIs to obtain additional information and learning opportunities.

[0112] Terminal: Provides the collected information to the user and integrates it into the final plan.

[0113] (Application Example 1)

[0114] 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."

[0115] In the field of elderly care, it is a challenging task for individuals to leverage their skills and experience, find the optimal career advancement path, and develop concrete action plans based on that. In particular, there is a lack of mechanisms to effectively utilize specialized knowledge and resources in elderly care, which acts as a barrier when individuals explore new career paths.

[0116] 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.

[0117] In this invention, the server includes an information terminal means for inputting user attribute data, a computing device means for receiving and analyzing the attribute data, and means for presenting career advancement paths specific to the nursing care field. This enables users to design an optimal career path based on their experience and skills, and to create and implement a concrete action plan.

[0118] An "information terminal device" is a device that allows users to input their own attribute data, convert it into a digital format, and transmit it to an analysis platform.

[0119] The "computation device means" is a device that analyzes received attribute data using a machine learning algorithm and designs a career path suitable for the user.

[0120] A "career path" refers to the specific route or plan a user takes when advancing their career or transitioning to a new job.

[0121] "Opinions" refer to feedback and suggestions collected from users, and include their evaluations and desires regarding the designed career paths.

[0122] A "support system" is a system that collaborates with external educational infrastructure and industry networks to provide users with necessary support and additional learning opportunities.

[0123] "A career advancement path unique to the nursing care field" refers to specific career paths and opportunities within the nursing care industry that leverage acquired skills and work experience for career advancement.

[0124] The system for implementing this invention provides a platform for individuals engaged in caregiving to design and execute the optimal career advancement path. It mainly consists of an information terminal, a computing device, and an auxiliary system.

[0125] Users input attribute data such as their skills, work experience, and interests using an information terminal. The information terminal is implemented as a smartphone or tablet, and the input data is converted into an appropriate digital format.

[0126] The converted data is sent to a server, where a computing device analyzes it. The computing device is equipped with machine learning libraries such as TENSORFLOW® and database management systems such as MySQL®, which are used to analyze the user's data and generate the optimal career path.

[0127] The generated career path is transmitted to an information terminal and output to the user. The user provides feedback on this path, and the system improves the path based on that feedback. The final action plan proposes specific steps to the user, providing further details and support through an auxiliary system.

[0128] As a concrete example, consider the process by which Ms. A, who works at a nursing care facility, uses the application to aim for career advancement to a management position. Ms. A inputs her current skills and past work experience into the app and can find out the qualifications and experience required to become a "manager at a welfare facility."

[0129] The following are examples of prompts in which a generative AI model operates.

[0130] "Please tell me the specific steps I can take to advance my career in the caregiving profession. Current skill set: Nursing care, welfare management; Past work experience: 3 years at a care facility; Interests: Leadership, management positions."

[0131] In this way, the system can provide support to users in solving their career challenges.

[0132] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0133] Step 1:

[0134] The user uses an information terminal to input attribute data such as their skills, work experience, and interests. The information terminal then converts the entered data into JSON format and prepares it for transmission to the server.

[0135] Step 2:

[0136] The server receives JSON-formatted data from the information terminal. It then checks the integrity of the received data, verifying that there is no invalid or missing data. Data cleaning is performed as needed.

[0137] Step 3:

[0138] The server uses cleaned data and machine learning algorithms such as TensorFlow to analyze user characteristics. This analysis generates multiple optimal career paths for the user. The computing system retrieves similar success stories from the database and uses them to create career paths.

[0139] Step 4:

[0140] The server evaluates the generated career paths and assigns a score to each path. The score is an indicator of how well the path matches the user's attribute data. Based on this score, the server selects the highest-priority path.

[0141] Step 5:

[0142] The terminal sends the selected, high-priority occupational routes. The terminal presents the occupational routes to the user in a visualized format. The user provides opinions and feedback on the routes based on the visualized data.

[0143] Step 6:

[0144] The server analyzes the feedback received from users and determines the need for evaluation. Furthermore, it uses a generative AI model to extract elements that should be reflected from the feedback and revise the career path.

[0145] Step 7:

[0146] The server generates a specific action plan based on the finalized career path. This plan includes necessary skill development and educational opportunities. This action plan is provided in conjunction with auxiliary systems, integrating information and resources accessible to the user.

[0147] Step 8:

[0148] The device outputs an action plan to the user and presents the information necessary to execute the plan. The user can then proceed with preparing for career advancement according to the plan.

[0149] 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.

[0150] This invention is a system that helps users find the best second career for themselves and put it into action with support tailored to their emotional state. This system consists of the following components and procedures.

[0151] 1. User information input and sentiment recognition

[0152] Terminal: Users input personal profile information, work history, and interests through the interface. Along with this information, the terminal's emotion engine acquires emotional data from the user's facial expressions and voice, and sends this data to the server.

[0153] 2. Data Analysis

[0154] Server: The server processes and analyzes received user information and sentiment data. Using machine learning algorithms, it analyzes user sentiment and profile-based characteristics to identify needs.

[0155] 3. Creating a Career Plan

[0156] Server: Based on the analysis results, the generating AI creates multiple career plans optimized for the user. It incorporates data from the emotion engine to prioritize options that match the user's interests and current emotions.

[0157] 4. Plan presentation and feedback

[0158] Terminal: Visually displays the carrier plan sent from the server and continuously collects user reactions to the proposed plan along with sentiment data.

[0159] Users: Enter feedback about the plan and express their feelings to specifically indicate what attracts them or what makes them uneasy.

[0160] 5. Improvement through feedback

[0161] Server: Analyzes emotional data and feedback to identify which parts of the plan are appealing or deterrents to users, and then refines the plan based on the results.

[0162] 6. Formulation of an implementation plan

[0163] Server: Develop a detailed execution plan based on the final selected and refined career plan. The plan will include key steps, support measures, and plans for strengthening necessary skills.

[0164] 7. Integration with related services

[0165] Server: Collaborates with external educational platforms and industry networks to provide additional educational opportunities and networks to support users' second careers.

[0166] Terminal: Present information about these external services to the user and incorporate it into the execution plan.

[0167] Specific example:

[0168] For example, if person C wants to work in a new field but feels a little anxious about taking concrete action, the system receives emotional data along with C's input data. Understanding C's anxiety, the emotional engine prioritizes presenting stable options, receives feedback on the plan, and introduces networks and educational opportunities to specifically alleviate that anxiety. This allows C to confidently take the first step towards building a new career.

[0169] This system can propose and support the implementation of the most suitable career plan for the user through a process that takes the user's emotions into account.

[0170] The following describes the processing flow.

[0171] Step 1:

[0172] The user enters personal profile information through the interface on the device. The device uses its camera and microphone to record the user's facial expressions and voice, preparing the emotion engine to analyze this data.

[0173] Step 2:

[0174] The device sends recorded emotional data and user-entered information to the server. The data sent includes basic profile information and information about the user's emotional state.

[0175] Step 3:

[0176] The server analyzes the received information and uses machine learning algorithms to identify the user's emotional state and needs from their profile. Specifically, it determines which career paths align with the user's interests and which elements resonate emotionally.

[0177] Step 4:

[0178] Based on the analysis results, the server uses generative AI to create multiple career plans tailored to the user. It leverages data from the emotion engine to generate plans that prioritize elements that evoke positive emotions in the user.

[0179] Step 5:

[0180] The device presents the user with a generated carrier plan. This plan includes comments and recommendations tailored to the user's emotions and is displayed in a way that solicits additional feedback.

[0181] Step 6:

[0182] Users input feedback based on the presented plan, sending through their device to the server which elements they find emotionally appealing and which elements need improvement.

[0183] Step 7:

[0184] The server re-analyzes feedback and emotional data to create improved career plan suggestions. This provides a plan that better matches the user's emotional balance and needs.

[0185] Step 8:

[0186] The server develops a detailed execution plan based on the final career plan. This plan includes step-by-step instructions and approaches to necessary skill development.

[0187] Step 9:

[0188] The device presents the user with an action plan and provides information to take action according to the plan, as well as information on integration with external services. This allows the user to confidently begin concrete actions.

[0189] (Example 2)

[0190] 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".

[0191] Traditional career support systems struggled to take users' emotions into account, resulting in career plans that were not optimized for users' psychological state or interests. Furthermore, they lacked effective means of utilizing feedback to improve plans, failing to adequately support users in achieving their optimal career development. Additionally, insufficient collaboration with external educational institutions and networks limited opportunities for skill enhancement and network building.

[0192] 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.

[0193] In this invention, the server includes means for inputting the user's personal information and emotional data, means for emotion analysis for recognizing the user's emotional state from their facial expressions and voice, and means for generating multiple career plans suitable for the user based on the analysis results using a generative AI model. This makes it possible to effectively support the user's career development by providing an optimal career plan that takes the user's emotions into consideration, improving the plan using feedback on each plan, and coordinating with external resources.

[0194] An "input device" is a device used to collect personal information and emotional data from users.

[0195] An "emotion analysis device" is a device equipped with technology that processes a user's facial expressions and voice to recognize the user's emotional state.

[0196] A "data analysis device" is a computing device that receives user profile information and sentiment data and performs analysis using machine learning algorithms.

[0197] A "generative AI model" is an artificial intelligence model used to generate a career plan optimized for the user based on the data received.

[0198] A "presentation device" is a device that visually displays career plans to users and collects user feedback.

[0199] An "external educational network" refers to external educational resources that are linked to users to improve their skills and provide career support.

[0200] An "industry-related network" refers to a network of specialized fields and industries that collaborate to support users' career development.

[0201] One embodiment of the present invention provides a system for users to find the optimal career plan for themselves. This system combines numerous technical elements to generate an optimal career plan from the user's emotions and profile information, and aims to improve it based on user feedback.

[0202] First, the user inputs personal information, work history, and interests into the terminal through the interface. The terminal uses its camera and microphone to collect the user's facial expressions and voice, and transmits this information to an emotion analysis device. This analysis uses libraries for image processing and voice analysis (e.g., OpenCV or librosa).

[0203] Next, the server receives the data sent from the terminal and processes it using a data analysis device. In this process, machine learning algorithms (e.g., TensorFlow, scikit-learn) are applied using programming languages ​​such as Python to extract and analyze user characteristics.

[0204] Next, a generative AI model is used to automatically generate a personalized career plan based on the analysis results. The generated plan is optimized to include options that match the user's interests and current emotions. Specifically, natural language generation technology (e.g., GPT model) is utilized.

[0205] The device then visually presents the generated carrier plan to the user. The user provides feedback on the plan, expressing their emotions and communicating specific concerns and anxieties to the device.

[0206] Furthermore, the server re-analyzes feedback and sentiment data to improve the plan. By text-analyzing user opinions and creating newly enhanced plans using a generative AI model, it provides a more appropriate plan for the user. Natural language processing techniques (e.g., NLTK, spaCy) are used in this process.

[0207] Finally, the server connects with relevant external educational and industry-related networks to provide users with additional resources. API technology is used for this connection, and information to support users' career development is presented through their devices. This allows users to gain access to a wealth of learning opportunities.

[0208] Specific example: For instance, if a user is considering venturing into a new field but is unsure how to begin, the system identifies their concerns based on user input and emotional data, and generates a safe, step-by-step career plan. The user provides feedback, and the server adjusts the plan to establish an ideal path to their career.

[0209] Example of a prompt:

[0210] "I feel anxious about entering a new field, but what kind of career plan would suit me?"

[0211] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0212] Step 1:

[0213] The user accesses the interface and enters personal information, work history, and interests. The entered information is collected by the terminal. Simultaneously, the user's facial expressions and voice are recorded in real time through the terminal's camera and microphone and transmitted to an emotion analysis device. At this stage, the input device captures biometric data and provides raw data for analyzing the user's emotional state.

[0214] Step 2:

[0215] The device sends personal information and emotional data collected from the user to the server. The server receives this data using a data analysis device and analyzes the user's profile and emotional characteristics using machine learning algorithms within the program (e.g., scikit-learn, TensorFlow). Through this analysis, the user's behavioral tendencies and psychological state are extracted as features.

[0216] Step 3:

[0217] The server uses a generative AI model to generate a career plan tailored to the user based on the analysis results. This utilizes generative AI (e.g., the GPT model) to provide optimized options that align with the user's interests and emotions. Input data consists of the user's profile and emotional data, while the output is multiple personalized career plans.

[0218] Step 4:

[0219] The device visually presents the carrier plans received from the server to the user. The user reviews the plans displayed on the screen and provides feedback on the plans they like. During this process, the device records the user's reactions as sentiment data and prepares to send it to the server.

[0220] Step 5:

[0221] The server analyzes user feedback and new sentiment data to improve the plan. Using natural language processing techniques (e.g., NLTK, spaCy), it analyzes user feedback as text and applies the feedback to a generative AI model to refine the plan. This generates a career plan that better matches the user's needs.

[0222] Step 6:

[0223] The server develops an execution plan based on the improved career plan. This plan includes specific steps and resources for the user to acquire the necessary skills. The server connects with external educational and industry networks via APIs to extract useful information for the user.

[0224] Step 7:

[0225] The terminal notifies the user of information about external resources that the server has integrated with and presents a method for incorporating them into the execution plan. The user can then use this information to begin taking concrete actions to put the plan into action.

[0226] (Application Example 2)

[0227] 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".

[0228] In today's professional environment, individuals face a wide range of challenges when pursuing new careers. One of these is finding a career plan that suits their individual emotional state and interests, and obtaining the necessary learning opportunities and support. However, due to insufficient technological solutions, it remains difficult for individuals to make optimal career choices.

[0229] 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.

[0230] In this invention, the server includes input means for inputting user attribute information, processing means for receiving and analyzing the attribute information and emotional information, and generation means for generating multiple career options based on the analysis results. This makes it possible to propose an optimal career plan based on the individual's emotional state.

[0231] "Input methods for entering user attribute information" refer to means by which individuals provide information such as their profile, work history, interests, and preferences to a system.

[0232] "Emotional information" refers to data that indicates a user's emotional state, and is obtained from things like facial expressions and voice.

[0233] "Analysis processing means" refers to a means of analyzing the received user attribute information and emotional information to understand its characteristics and trends.

[0234] A "generation method" is a means of creating multiple career options based on the analysis results.

[0235] "Means of improvement for enhancement" refers to methods for adjusting carrier options and providing more suitable plans based on information obtained from user responses.

[0236] "Means for formulating an implementation scheme" refers to the means of planning specific action plans and steps based on an improved career plan.

[0237] "Means of collaborating with external educational resources and industry connections to provide additional support" refers to means of working with external educational institutions and industry networks to provide users with further learning opportunities and support.

[0238] The system that realizes this invention consists of a terminal that has a direct interface with the user and a server that stores and analyzes information.

[0239] The device is equipped with a touchscreen and a microphone capable of voice input as means of inputting user attribute information. The device provides the user's emotional information collected through these means to an emotion recognition system using a camera and microphone. The emotion recognition system analyzes the collected data using software such as OpenCV and TensorFlow.

[0240] Based on the analyzed data, the server generates multiple career options using a generative AI model. This generative AI utilizes OpenAI's GPT-based model, enabling suggestions tailored to each user's individual needs and emotional state. The generated options are presented to the user, and emotional data is used along with user feedback. This feedback information is then sent back to the server and used to improve the career options.

[0241] Furthermore, the server collaborates with external educational resources and industry networks to provide users with the learning opportunities and support necessary for an improved career plan. To achieve this, it interconnects with various platforms via APIs.

[0242] As a concrete example, user B inputs information about their areas of interest and current occupation into the system, exploring potential career paths. The terminal retrieves B's input information and emotional data and sends it to the server. The server generates multiple career plans based on the analysis and presents them to B. If B expresses interest in a career change to the entertainment industry, the system also offers suggestions such as skill development programs and networking events.

[0243] An example of a prompt message would be: "What kind of work environment does the user prefer? Based on their sentiment data and areas of interest, suggest the optimal career path."

[0244] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0245] Step 1:

[0246] The user enters profile information, work history, and areas of interest into the device. During this process, the device uses its camera and microphone to capture data on the user's facial expressions and voice, simultaneously collecting emotional information. The input data includes attribute information in text format and audio / video data.

[0247] Step 2:

[0248] The device sends acquired user attribute information and emotional information to the server. The server receives this data, analyzes the video data using OpenCV, and analyzes the emotional content of the audio data using TensorFlow. As a result of the analysis, the user's emotional state and attribute information are converted into numerical data.

[0249] Step 3:

[0250] The server generates multiple career options using a generative AI model based on numerically quantified information. In this process, it uses OpenAI's GPT-based model to propose a career plan that best suits the user's needs. The generated career options are output in text format.

[0251] Step 4:

[0252] The generated carrier options are sent to the device and visually presented to the user. The user provides feedback on the proposed plan, and sentiment data is collected again during this process. As output, the user's feedback and sentiment information are collected.

[0253] Step 5:

[0254] The server analyzes feedback and sentiment data received from the terminal to improve the carrier options. This improvement process again uses a generative AI model to suggest plans that better suit the user's needs. The improved options are then prepared for the next step.

[0255] Step 6:

[0256] Based on the final, improved career plan, the server connects with external educational resources and industry networks to obtain necessary support information. The server uses APIs to connect with external platforms and gather learning opportunities and network information to provide to the user. As output, a resource list is created to support the user in building their new career.

[0257] Step 7:

[0258] The terminal notifies the user of the resource list received from the server and proposes specific execution steps. This allows the user to clearly understand the steps to take the next action, and a concrete action plan is presented to the user as output.

[0259] 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.

[0260] Data generation model 58 is a type of 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.

[0261] 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.

[0262] [Second Embodiment]

[0263] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0264] 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.

[0265] 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).

[0266] 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.

[0267] 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.

[0268] 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).

[0269] 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.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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".

[0275] This invention is a system that provides users with personalized programs to find and implement the second career best suited to them. This system has the following functions:

[0276] 1. Enter user information

[0277] Terminal: Users enter their personal profile, past work history, and interests on the interface. The terminal converts this data into an appropriate format and sends it to the server.

[0278] 2. Data Analysis

[0279] Server: Based on the received information, apply machine learning algorithms to analyze user characteristics. The server searches the database for highly relevant success cases and career paths and generates candidates.

[0280] 3. Generation and Presentation of Career Plans

[0281] Server: Based on the analysis results, the generative AI creates multiple career plans. The server evaluates how well each plan meets the user's needs and assigns a score.

[0282] Terminal: Visualize the career plan and its evaluation and present it to the user. The user can select from the displayed options and view more details.

[0283] 4. Interaction with the User

[0284] User: Can provide various feedback on the presented career plans. Additionally, the user can input additional questions and seek suggestions in specific areas.

[0285] 5. Response to Feedback and Plan Improvement

[0286] Server: Receive feedback from the user and perform analysis again. Modify the plan as necessary and present a new optimized proposal to the user.

[0287] 6. Formulation of an Implementation Plan

[0288] Server: Based on the finally selected career plan, generate a specific step-by-step implementation plan. This includes the necessary resources and opportunities for skill improvement, etc.

[0289] Terminal: Show the plan to the user and organize the information necessary for implementation.

[0290] 7. Collaboration with Related Services

[0291] Server: The program integrates with external educational platforms and industry networks to provide necessary support to users.

[0292] Terminal: Provides users with linked information and incorporates it into their plans.

[0293] Specific example:

[0294] For example, let's say Mr. C, a retired teacher, wants to make a new contribution to the field of education and uses this system. Through feedback, Mr. C requests support in launching a local education program. Based on this request, the system provides a partnership plan with local education support organizations and proposes concrete steps to Mr. C. As a result, Mr. C can start a project to contribute to his community.

[0295] Through the process described above, users can discover the second career best suited to their needs and receive concrete support to make it a reality.

[0296] The following describes the processing flow.

[0297] Step 1:

[0298] Users input personal profile information, work history, and interests using an interface on their device. The device collects this information in an appropriate format and sends it to the server.

[0299] Step 2:

[0300] The server analyzes the received user information using processing tools. It utilizes machine learning algorithms to analyze the characteristics of the user profile and identify their needs.

[0301] Step 3:

[0302] Based on the analysis results, the server enables the generative AI to create multiple career plans suitable for the user. It also refers to similar career paths and success stories from the database to strengthen the proposed content.

[0303] Step 4:

[0304] The terminal receives the career plans provided by the server, visualizes them, and presents them to the user. The user is shown the detailed information of each plan and an evaluation score regarding feasibility.

[0305] Step 5:

[0306] The user inputs feedback on the presented career plans. Additionally, the user sends additional information and questions about the plans of interest to the server through the terminal.

[0307] Step 6:

[0308] The server aggregates the feedback from the user and re-evaluates the plans. If necessary, it reflects the content of the feedback and regenerates a more user-suited plan.

[0309] Step 7:

[0310] Based on the final career plans, the server formulates a detailed implementation plan. The plan includes specific steps, recommended resources, and improvement measures for the required skills.

[0311] Step 8:

[0312] The terminal presents the implementation plan to the user and organizes and displays information regarding the preparation stage for executing the plan.

[0313] Step 9:

[0314] The server interacts with relevant external services and platforms to provide support for users in realizing their execution plans. The terminal displays this information to the user as needed, integrating it as part of the plan.

[0315] (Example 1)

[0316] 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."

[0317] In today's world, many individuals are exploring second careers, but they lack concrete methods for selecting and implementing the most suitable career plan. This problem makes re-employment or transitioning to a new career more difficult, increasing the time and effort required for individual career development.

[0318] 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.

[0319] In this invention, the server includes an interface means for inputting user data, a processing means for receiving the user data and analyzing its characteristics, and a means for creating multiple career plans for the user based on the generated characteristic analysis results. This makes it possible to select the optimal second career based on the individual's characteristics and to formulate a concrete plan for its implementation.

[0320] An "interface means" is a component that allows a user to input data and interact with the system.

[0321] "Processing means" refers to components that analyze received user data and perform calculations and analyses to identify user characteristics.

[0322] A "career plan" is a set of options for specific career changes and career development, formulated based on the user's characteristics analysis.

[0323] "Means of collecting opinions" are components that enable users to provide feedback on the career plan presented.

[0324] "Means of modification and improvement" refer to components used to change the work plan based on user feedback and make it more suitable.

[0325] "External information sources" refer to organizations or services that provide additional information or resources necessary to support users from outside the system, such as online learning platforms or industry networks.

[0326] This invention is a system that provides support to users in finding and implementing the optimal second career. The system takes user data as input, analyzes user characteristics based on that information, and performs a series of processes to generate an optimal career plan. The hardware and software used, as well as specific examples of operation, are described below.

[0327] Users use a device to enter their personal profile, past work history, and interests. This device is configured as a personal computer or mobile device and can input data using standard input devices (keyboard, touchscreen, etc.). The input data is formatted and sent to the server.

[0328] The server utilizes computing resources in the cloud to receive and store input data. For specific analysis, machine learning algorithms are employed. Clustering and classification models are used as machine learning models. These function as means of searching the database for past success stories and related career paths. A generative AI model generates an optimal career plan for the user based on the acquired data.

[0329] For example, if a user wants to work on a new project in the technology field, this system can analyze the user's work history and skills and present relevant project examples. It can also suggest other fields where the user might be more suitable. The generated plan is visualized on the user's device and provided to them.

[0330] An example of a prompt message is, "Based on the user's profile, suggest the best new career options for them."

[0331] Another important feature of this system is that the server connects with external educational platforms and industry networks. This connection allows users to access additional learning opportunities and industry support. The connection is primarily done through APIs and is designed to allow users to retrieve information seamlessly.

[0332] This specific structure enables the provision of flexible and effective career plans tailored to the user's needs.

[0333] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0334] Step 1: Enter user information

[0335] Terminal: Users use the terminal's interface to enter their personal profile, past work history, and interests. The terminal retrieves this information and converts it into a dedicated data format. The converted data is then ready to be sent to the server.

[0336] Step 2: Send

[0337] Terminal: The entered data is sent to the server using security protocols such as SSL / TLS. This transmission process ensures the confidentiality and integrity of the data.

[0338] Step 3: Receiving and storing data

[0339] Server: Received data is temporarily stored in storage. Here, the data undergoes preprocessing for analysis. Preprocessing includes data cleaning and normalization to ensure that the analysis algorithm functions efficiently.

[0340] Step 4: Analyzing User Characteristics

[0341] Server: Machine learning algorithms (clustering models, classification models) are applied to the data. These algorithms extract user characteristics and generate relevant statistics. This allows for the development of an initial career plan based on the user's characteristics.

[0342] Step 5: Generating a Career Plan

[0343] Server: Uses a generative AI model to generate multiple career plans based on user characteristic data. A prompt is used as input, and the optimal suggestion is output. An example is, "Based on the user's profile, suggest the best new career options for them."

[0344] Step 6: Plan presentation and evaluation

[0345] Terminal: The generated career plan and its evaluation results are visualized and presented to the user. The user can select from the presented options and view the details.

[0346] Step 7: Gathering Feedback

[0347] User: Provide feedback on the presented plan. This feedback may include additional questions or specific requests.

[0348] Step 8: Revise and improve the plan

[0349] Server: User feedback is received and integrated with existing data. If necessary, analysis is performed again, and the career plan is revised and improved.

[0350] Step 9: Develop the final implementation plan

[0351] Server: Based on the finalized career plan, a specific action plan is created. This action plan includes the necessary resources and steps.

[0352] Step 10: Integration with external services

[0353] Server: Connects with external educational platforms and industry networks via APIs to obtain additional information and learning opportunities.

[0354] Terminal: Provides the collected information to the user and integrates it into the final plan.

[0355] (Application Example 1)

[0356] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0357] In the field of elderly care, it is a challenging task for individuals to leverage their skills and experience, find the optimal career advancement path, and develop concrete action plans based on that. In particular, there is a lack of mechanisms to effectively utilize specialized knowledge and resources in elderly care, which acts as a barrier when individuals explore new career paths.

[0358] 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.

[0359] In this invention, the server includes an information terminal means for inputting user attribute data, a computing device means for receiving and analyzing the attribute data, and means for presenting career advancement paths specific to the nursing care field. This enables users to design an optimal career path based on their experience and skills, and to create and implement a concrete action plan.

[0360] An "information terminal device" is a device that allows users to input their own attribute data, convert it into a digital format, and transmit it to an analysis platform.

[0361] The "computation device means" is a device that analyzes received attribute data using a machine learning algorithm and designs a career path suitable for the user.

[0362] A "career path" refers to the specific route or plan a user takes when advancing their career or transitioning to a new job.

[0363] "Opinions" refer to feedback and suggestions collected from users, and include their evaluations and desires regarding the designed career paths.

[0364] A "support system" is a system that collaborates with external educational infrastructure and industry networks to provide users with necessary support and additional learning opportunities.

[0365] "A career advancement path unique to the nursing care field" refers to specific career paths and opportunities within the nursing care industry that leverage acquired skills and work experience for career advancement.

[0366] The system for implementing this invention provides a platform for individuals engaged in caregiving to design and execute the optimal career advancement path. It mainly consists of an information terminal, a computing device, and an auxiliary system.

[0367] Users input attribute data such as their skills, work experience, and interests using an information terminal. The information terminal is implemented as a smartphone or tablet, and the input data is converted into an appropriate digital format.

[0368] The converted data is sent to a server, where a computing system analyzes it. The computing system is equipped with machine learning libraries such as TensorFlow and database management systems such as MySQL, which are used to analyze the user's data and generate the optimal career path.

[0369] The generated career path is transmitted to an information terminal and output to the user. The user provides feedback on this path, and the system improves the path based on that feedback. The final action plan proposes specific steps to the user, providing further details and support through an auxiliary system.

[0370] As a concrete example, consider the process by which Ms. A, who works at a nursing care facility, uses the application to aim for career advancement to a management position. Ms. A inputs her current skills and past work experience into the app and can find out the qualifications and experience required to become a "manager at a welfare facility."

[0371] The following are examples of prompts in which a generative AI model operates.

[0372] "Please tell me the specific steps I can take to advance my career in the caregiving profession. Current skill set: Nursing care, welfare management; Past work experience: 3 years at a care facility; Interests: Leadership, management positions."

[0373] In this way, the system can provide support to users in solving their career challenges.

[0374] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0375] Step 1:

[0376] The user uses an information terminal to input attribute data such as their skills, work experience, and interests. The information terminal then converts the entered data into JSON format and prepares it for transmission to the server.

[0377] Step 2:

[0378] The server receives JSON-formatted data from the information terminal. It then checks the integrity of the received data, verifying that there is no invalid or missing data. Data cleaning is performed as needed.

[0379] Step 3:

[0380] The server uses cleaned data and machine learning algorithms such as TensorFlow to analyze user characteristics. This analysis generates multiple optimal career paths for the user. The computing system retrieves similar success stories from the database and uses them to create career paths.

[0381] Step 4:

[0382] The server evaluates the generated career paths and assigns a score to each path. The score is an indicator of how well the path matches the user's attribute data. Based on this score, the server selects the highest-priority path.

[0383] Step 5:

[0384] The terminal sends the selected, high-priority occupational routes. The terminal presents the occupational routes to the user in a visualized format. The user provides opinions and feedback on the routes based on the visualized data.

[0385] Step 6:

[0386] The server analyzes the feedback received from users and determines the need for evaluation. Furthermore, it uses a generative AI model to extract elements that should be reflected from the feedback and revise the career path.

[0387] Step 7:

[0388] The server generates a specific action plan based on the finalized career path. This plan includes necessary skill development and educational opportunities. This action plan is provided in conjunction with auxiliary systems, integrating information and resources accessible to the user.

[0389] Step 8:

[0390] The device outputs an action plan to the user and presents the information necessary to execute the plan. The user can then proceed with preparing for career advancement according to the plan.

[0391] 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.

[0392] This invention is a system that helps users find the best second career for themselves and put it into action with support tailored to their emotional state. This system consists of the following components and procedures.

[0393] 1. User information input and sentiment recognition

[0394] Terminal: Users input personal profile information, work history, and interests through the interface. Along with this information, the terminal's emotion engine acquires emotional data from the user's facial expressions and voice, and sends this data to the server.

[0395] 2. Data Analysis

[0396] Server: The server processes and analyzes received user information and sentiment data. Using machine learning algorithms, it analyzes user sentiment and profile-based characteristics to identify needs.

[0397] 3. Creating a Career Plan

[0398] Server: Based on the analysis results, the generating AI creates multiple career plans optimized for the user. It incorporates data from the emotion engine to prioritize options that match the user's interests and current emotions.

[0399] 4. Plan presentation and feedback

[0400] Terminal: Visually displays the carrier plan sent from the server and continuously collects user reactions to the proposed plan along with sentiment data.

[0401] Users: Enter feedback about the plan and express their feelings to specifically indicate what attracts them or what makes them uneasy.

[0402] 5. Improvement through feedback

[0403] Server: Analyzes emotional data and feedback to identify which parts of the plan are appealing or deterrents to users, and then refines the plan based on the results.

[0404] 6. Formulation of an implementation plan

[0405] Server: Develop a detailed execution plan based on the final selected and refined career plan. The plan will include key steps, support measures, and plans for strengthening necessary skills.

[0406] 7. Integration with related services

[0407] Server: Collaborates with external educational platforms and industry networks to provide additional educational opportunities and networks to support users' second careers.

[0408] Terminal: Present information about these external services to the user and incorporate it into the execution plan.

[0409] Specific example:

[0410] For example, if person C wants to work in a new field but feels a little anxious about taking concrete action, the system receives emotional data along with C's input data. Understanding C's anxiety, the emotional engine prioritizes presenting stable options, receives feedback on the plan, and introduces networks and educational opportunities to specifically alleviate that anxiety. This allows C to confidently take the first step towards building a new career.

[0411] This system can propose and support the implementation of the most suitable career plan for the user through a process that takes the user's emotions into account.

[0412] The following describes the processing flow.

[0413] Step 1:

[0414] The user enters personal profile information through the interface on the device. The device uses its camera and microphone to record the user's facial expressions and voice, preparing the emotion engine to analyze this data.

[0415] Step 2:

[0416] The device sends recorded emotional data and user-entered information to the server. The data sent includes basic profile information and information about the user's emotional state.

[0417] Step 3:

[0418] The server analyzes the received information and uses machine learning algorithms to identify the user's emotional state and needs from their profile. Specifically, it determines which career paths align with the user's interests and which elements resonate emotionally.

[0419] Step 4:

[0420] Based on the analysis results, the server uses generative AI to create multiple career plans tailored to the user. It leverages data from the emotion engine to generate plans that prioritize elements that evoke positive emotions in the user.

[0421] Step 5:

[0422] The device presents the user with a generated carrier plan. This plan includes comments and recommendations tailored to the user's emotions and is displayed in a way that solicits additional feedback.

[0423] Step 6:

[0424] Users input feedback based on the presented plan, sending through their device to the server which elements they find emotionally appealing and which elements need improvement.

[0425] Step 7:

[0426] The server re-analyzes feedback and emotional data to create improved career plan suggestions. This provides a plan that better matches the user's emotional balance and needs.

[0427] Step 8:

[0428] The server develops a detailed execution plan based on the final career plan. This plan includes step-by-step instructions and approaches to necessary skill development.

[0429] Step 9:

[0430] The device presents the user with an action plan and provides information to take action according to the plan, as well as information on integration with external services. This allows the user to confidently begin concrete actions.

[0431] (Example 2)

[0432] 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".

[0433] Traditional career support systems struggled to take users' emotions into account, resulting in career plans that were not optimized for users' psychological state or interests. Furthermore, they lacked effective means of utilizing feedback to improve plans, failing to adequately support users in achieving their optimal career development. Additionally, insufficient collaboration with external educational institutions and networks limited opportunities for skill enhancement and network building.

[0434] 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.

[0435] In this invention, the server includes means for inputting the user's personal information and emotional data, means for emotion analysis for recognizing the user's emotional state from their facial expressions and voice, and means for generating multiple career plans suitable for the user based on the analysis results using a generative AI model. This makes it possible to effectively support the user's career development by providing an optimal career plan that takes the user's emotions into consideration, improving the plan using feedback on each plan, and coordinating with external resources.

[0436] An "input device" is a device used to collect personal information and emotional data from users.

[0437] An "emotion analysis device" is a device equipped with technology that processes a user's facial expressions and voice to recognize the user's emotional state.

[0438] A "data analysis device" is a computing device that receives user profile information and sentiment data and performs analysis using machine learning algorithms.

[0439] A "generative AI model" is an artificial intelligence model used to generate a career plan optimized for the user based on the data received.

[0440] A "presentation device" is a device that visually displays career plans to users and collects user feedback.

[0441] An "external educational network" refers to external educational resources that are linked to users to improve their skills and provide career support.

[0442] An "industry-related network" refers to a network of specialized fields and industries that collaborate to support users' career development.

[0443] One embodiment of the present invention provides a system for users to find the optimal career plan for themselves. This system combines numerous technical elements to generate an optimal career plan from the user's emotions and profile information, and aims to improve it based on user feedback.

[0444] First, the user inputs personal information, work history, and interests into the terminal through the interface. The terminal uses its camera and microphone to collect the user's facial expressions and voice, and transmits this information to an emotion analysis device. This analysis uses libraries for image processing and voice analysis (e.g., OpenCV or librosa).

[0445] Next, the server receives the data sent from the terminal and processes it using a data analysis device. In this process, machine learning algorithms (e.g., TensorFlow, scikit-learn) are applied using programming languages ​​such as Python to extract and analyze user characteristics.

[0446] Next, a generative AI model is used to automatically generate a personalized career plan based on the analysis results. The generated plan is optimized to include options that match the user's interests and current emotions. Specifically, natural language generation technology (e.g., GPT model) is utilized.

[0447] The device then visually presents the generated carrier plan to the user. The user provides feedback on the plan, expressing their emotions and communicating specific concerns and anxieties to the device.

[0448] Furthermore, the server re-analyzes feedback and sentiment data to improve the plan. By text-analyzing user opinions and creating newly enhanced plans using a generative AI model, it provides a more appropriate plan for the user. Natural language processing techniques (e.g., NLTK, spaCy) are used in this process.

[0449] Finally, the server connects with relevant external educational and industry-related networks to provide users with additional resources. API technology is used for this connection, and information to support users' career development is presented through their devices. This allows users to gain access to a wealth of learning opportunities.

[0450] Specific example: For instance, if a user is considering venturing into a new field but is unsure how to begin, the system identifies their concerns based on user input and emotional data, and generates a safe, step-by-step career plan. The user provides feedback, and the server adjusts the plan to establish an ideal path to their career.

[0451] Example of a prompt:

[0452] "I feel anxious about entering a new field, but what kind of career plan would suit me?"

[0453] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0454] Step 1:

[0455] The user accesses the interface and enters personal information, work history, and interests. The entered information is collected by the terminal. Simultaneously, the user's facial expressions and voice are recorded in real time through the terminal's camera and microphone and transmitted to an emotion analysis device. At this stage, the input device captures biometric data and provides raw data for analyzing the user's emotional state.

[0456] Step 2:

[0457] The device sends personal information and emotional data collected from the user to the server. The server receives this data using a data analysis device and analyzes the user's profile and emotional characteristics using machine learning algorithms within the program (e.g., scikit-learn, TensorFlow). Through this analysis, the user's behavioral tendencies and psychological state are extracted as features.

[0458] Step 3:

[0459] The server uses a generative AI model to generate a career plan tailored to the user based on the analysis results. This utilizes generative AI (e.g., the GPT model) to provide optimized options that align with the user's interests and emotions. Input data consists of the user's profile and emotional data, while the output is multiple personalized career plans.

[0460] Step 4:

[0461] The device visually presents the carrier plans received from the server to the user. The user reviews the plans displayed on the screen and provides feedback on the plans they like. During this process, the device records the user's reactions as sentiment data and prepares to send it to the server.

[0462] Step 5:

[0463] The server analyzes user feedback and new sentiment data to improve the plan. Using natural language processing techniques (e.g., NLTK, spaCy), it analyzes user feedback as text and applies the feedback to a generative AI model to refine the plan. This generates a career plan that better matches the user's needs.

[0464] Step 6:

[0465] The server develops an execution plan based on the improved career plan. This plan includes specific steps and resources for the user to acquire the necessary skills. The server connects with external educational and industry networks via APIs to extract useful information for the user.

[0466] Step 7:

[0467] The terminal notifies the user of information about external resources that the server has integrated with and presents a method for incorporating them into the execution plan. The user can then use this information to begin taking concrete actions to put the plan into action.

[0468] (Application Example 2)

[0469] 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."

[0470] In today's professional environment, individuals face a wide range of challenges when pursuing new careers. One of these is finding a career plan that suits their individual emotional state and interests, and obtaining the necessary learning opportunities and support. However, due to insufficient technological solutions, it remains difficult for individuals to make optimal career choices.

[0471] 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.

[0472] In this invention, the server includes input means for inputting user attribute information, processing means for receiving and analyzing the attribute information and emotional information, and generation means for generating multiple career options based on the analysis results. This makes it possible to propose an optimal career plan based on the individual's emotional state.

[0473] "Input methods for entering user attribute information" refer to means by which individuals provide information such as their profile, work history, interests, and preferences to a system.

[0474] "Emotional information" refers to data that indicates a user's emotional state, and is obtained from things like facial expressions and voice.

[0475] "Analysis processing means" refers to a means of analyzing the received user attribute information and emotional information to understand its characteristics and trends.

[0476] A "generation method" is a means of creating multiple career options based on the analysis results.

[0477] "Means of improvement for enhancement" refers to methods for adjusting carrier options and providing more suitable plans based on information obtained from user responses.

[0478] "Means for formulating an implementation scheme" refers to the means of planning specific action plans and steps based on an improved career plan.

[0479] "Means of collaborating with external educational resources and industry connections to provide additional support" refers to means of working with external educational institutions and industry networks to provide users with further learning opportunities and support.

[0480] The system that realizes this invention consists of a terminal that has a direct interface with the user and a server that stores and analyzes information.

[0481] The device is equipped with a touchscreen and a microphone capable of voice input as means of inputting user attribute information. The device provides the user's emotional information collected through these means to an emotion recognition system using a camera and microphone. The emotion recognition system analyzes the collected data using software such as OpenCV and TensorFlow.

[0482] Based on the analyzed data, the server generates multiple career options using a generative AI model. This generative AI utilizes OpenAI's GPT-based model, enabling suggestions tailored to each user's individual needs and emotional state. The generated options are presented to the user, and emotional data is used to gather user feedback. This feedback information is then sent back to the server and used to improve the career options.

[0483] Furthermore, the server collaborates with external educational resources and industry networks to provide users with the learning opportunities and support necessary for an improved career plan. To achieve this, it interconnects with various platforms via APIs.

[0484] As a concrete example, user B inputs information about their areas of interest and current occupation into the system, exploring potential career paths. The terminal retrieves B's input information and emotional data and sends it to the server. The server generates multiple career plans based on the analysis and presents them to B. If B expresses interest in a career change to the entertainment industry, the system also offers suggestions such as skill development programs and networking events.

[0485] An example of a prompt message would be: "What kind of work environment does the user prefer? Based on their sentiment data and areas of interest, suggest the optimal career path."

[0486] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0487] Step 1:

[0488] The user enters profile information, work history, and areas of interest into the device. During this process, the device uses its camera and microphone to capture data on the user's facial expressions and voice, simultaneously collecting emotional information. The input data includes attribute information in text format and audio / video data.

[0489] Step 2:

[0490] The device sends acquired user attribute information and emotional information to the server. The server receives this data, analyzes the video data using OpenCV, and analyzes the emotional content of the audio data using TensorFlow. As a result of the analysis, the user's emotional state and attribute information are converted into numerical data.

[0491] Step 3:

[0492] The server generates multiple career options using a generative AI model based on numerically quantified information. In this process, it uses OpenAI's GPT-based model to propose a career plan that best suits the user's needs. The generated career options are output in text format.

[0493] Step 4:

[0494] The generated carrier options are sent to the device and visually presented to the user. The user provides feedback on the proposed plan, and sentiment data is collected again during this process. As output, the user's feedback and sentiment information are collected.

[0495] Step 5:

[0496] The server analyzes feedback and sentiment data received from the terminal to improve the carrier options. This improvement process again uses a generative AI model to suggest plans that better suit the user's needs. The improved options are then prepared for the next step.

[0497] Step 6:

[0498] Based on the final, improved career plan, the server connects with external educational resources and industry networks to obtain necessary support information. The server uses APIs to connect with external platforms and gather learning opportunities and network information to provide to the user. As output, a resource list is created to support the user in building their new career.

[0499] Step 7:

[0500] The terminal notifies the user of the resource list received from the server and proposes specific execution steps. This allows the user to clearly understand the steps to take the next action, and a concrete action plan is presented to the user as output.

[0501] 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.

[0502] 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.

[0503] 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.

[0504] [Third Embodiment]

[0505] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0506] 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.

[0507] 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).

[0508] 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.

[0509] 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.

[0510] 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).

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] 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.

[0516] 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".

[0517] This invention is a system that provides users with personalized programs to find and implement the second career best suited to them. This system has the following functions:

[0518] 1. Enter user information

[0519] Terminal: Users enter their personal profile, past work history, and interests on the interface. The terminal converts this data into an appropriate format and sends it to the server.

[0520] 2. Data Analysis

[0521] Server: Based on the received information, it applies machine learning algorithms to analyze user characteristics. The server searches the database for highly relevant success stories and career paths and generates candidates.

[0522] 3. Generating and presenting a career plan

[0523] Server: Based on the analysis results, the generating AI creates multiple career plans. The server evaluates how well each plan matches the user's needs and assigns a score.

[0524] Device: Visualizes and presents carrier plans and their evaluations to the user. The user can select from the displayed options and view further details.

[0525] 4. Interaction with users

[0526] Users can provide various feedback on the presented career plan. They can also enter additional questions and request specific suggestions.

[0527] 5. Responding to feedback and improving the plan

[0528] Server: Receive user feedback and re-analyze it. Modify the plan as needed and provide new, optimized suggestions to the user.

[0529] 6. Formulation of an implementation plan

[0530] Server: Based on the ultimately selected career plan, generate a specific, step-by-step execution plan. This includes necessary resources and opportunities for skill development.

[0531] Terminal: Present the plan to the user and organize the information necessary for its execution.

[0532] 7. Integration with related services

[0533] Server: The program integrates with external educational platforms and industry networks to provide necessary support to users.

[0534] Terminal: Provides users with linked information and incorporates it into their plans.

[0535] Specific example:

[0536] For example, let's say Mr. C, a retired teacher, wants to make a new contribution to the field of education and uses this system. Through feedback, Mr. C requests support in launching a local education program. Based on this request, the system provides a partnership plan with local education support organizations and proposes concrete steps to Mr. C. As a result, Mr. C can start a project to contribute to his community.

[0537] Through the process described above, users can discover the second career best suited to their needs and receive concrete support to make it a reality.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] Users input personal profile information, work history, and interests using an interface on their device. The device collects this information in an appropriate format and sends it to the server.

[0541] Step 2:

[0542] The server analyzes the received user information using processing tools. It utilizes machine learning algorithms to analyze the characteristics of the user profile and identify their needs.

[0543] Step 3:

[0544] Based on the analysis results, the server's generating AI creates multiple career plans tailored to the user. It also references similar career paths and success stories from the database to enhance the suggestions.

[0545] Step 4:

[0546] The device receives carrier plans provided by the server, visualizes them, and presents them to the user. The user is shown detailed information about each plan and an evaluation score regarding its feasibility.

[0547] Step 5:

[0548] Users provide feedback on the displayed career plans. They can also send additional information or questions about plans they are interested in to the server via their device.

[0549] Step 6:

[0550] The server collects user feedback and re-evaluates the plan. If necessary, it incorporates the feedback and regenerates a plan that better suits the user.

[0551] Step 7:

[0552] Based on the final career plan, the server develops a detailed execution plan. This plan includes specific steps, recommended resources, and strategies for improving required skills.

[0553] Step 8:

[0554] The terminal presents the user with an execution plan and displays organized information about the preparatory steps required to execute the plan.

[0555] Step 9:

[0556] The server interacts with relevant external services and platforms to provide support for users in realizing their execution plans. The terminal displays this information to the user as needed, integrating it as part of the plan.

[0557] (Example 1)

[0558] 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."

[0559] In today's world, many individuals are exploring second careers, but they lack concrete methods for selecting and implementing the most suitable career plan. This problem makes re-employment or transitioning to a new career more difficult, increasing the time and effort required for individual career development.

[0560] 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.

[0561] In this invention, the server includes an interface means for inputting user data, a processing means for receiving the user data and analyzing its characteristics, and a means for creating multiple career plans for the user based on the generated characteristic analysis results. This makes it possible to select the optimal second career based on the individual's characteristics and to formulate a concrete plan for its implementation.

[0562] An "interface means" is a component that allows a user to input data and interact with the system.

[0563] "Processing means" refers to components that analyze received user data and perform calculations and analyses to identify user characteristics.

[0564] A "career plan" is a set of options for specific career changes and career development, formulated based on the user's characteristics analysis.

[0565] "Means of collecting opinions" are components that enable users to provide feedback on the career plan presented.

[0566] "Means of modification and improvement" refer to components used to change the work plan based on user feedback and make it more suitable.

[0567] "External information sources" refer to organizations or services that provide additional information or resources necessary to support users from outside the system, such as online learning platforms or industry networks.

[0568] This invention is a system that provides support to users in finding and implementing the optimal second career. The system takes user data as input, analyzes user characteristics based on that information, and performs a series of processes to generate an optimal career plan. The hardware and software used, as well as specific examples of operation, are described below.

[0569] Users use a device to enter their personal profile, past work history, and interests. This device is configured as a personal computer or mobile device and can input data using standard input devices (keyboard, touchscreen, etc.). The input data is formatted and sent to the server.

[0570] The server utilizes computing resources in the cloud to receive and store input data. For specific analysis, machine learning algorithms are employed. Clustering and classification models are used as machine learning models. These function as means of searching the database for past success stories and related career paths. A generative AI model generates an optimal career plan for the user based on the acquired data.

[0571] For example, if a user wants to work on a new project in the technology field, this system can analyze the user's work history and skills and present relevant project examples. It can also suggest other fields where the user might be more suitable. The generated plan is visualized on the user's device and provided to them.

[0572] An example of a prompt message is, "Based on the user's profile, suggest the best new career options for them."

[0573] Another important feature of this system is that the server connects with external educational platforms and industry networks. This connection allows users to access additional learning opportunities and industry support. The connection is primarily done through APIs and is designed to allow users to retrieve information seamlessly.

[0574] This specific structure enables the provision of flexible and effective career plans tailored to the user's needs.

[0575] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0576] Step 1: Enter user information

[0577] Terminal: Users use the terminal's interface to enter their personal profile, past work history, and interests. The terminal retrieves this information and converts it into a dedicated data format. The converted data is then ready to be sent to the server.

[0578] Step 2: Send

[0579] Terminal: The entered data is sent to the server using security protocols such as SSL / TLS. This transmission process ensures the confidentiality and integrity of the data.

[0580] Step 3: Receiving and storing data

[0581] Server: Received data is temporarily stored in storage. Here, the data undergoes preprocessing for analysis. Preprocessing includes data cleaning and normalization to ensure that the analysis algorithm functions efficiently.

[0582] Step 4: Analyzing User Characteristics

[0583] Server: Machine learning algorithms (clustering models, classification models) are applied to the data. These algorithms extract user characteristics and generate relevant statistics. This allows for the development of an initial career plan based on the user's characteristics.

[0584] Step 5: Generating a Career Plan

[0585] Server: Uses a generative AI model to generate multiple career plans based on user characteristic data. A prompt is used as input, and the optimal suggestion is output. An example is, "Based on the user's profile, suggest the best new career options for them."

[0586] Step 6: Plan presentation and evaluation

[0587] Terminal: The generated career plan and its evaluation results are visualized and presented to the user. The user can select from the presented options and view the details.

[0588] Step 7: Gathering Feedback

[0589] User: Provide feedback on the presented plan. This feedback may include additional questions or specific requests.

[0590] Step 8: Revise and improve the plan

[0591] Server: User feedback is received and integrated with existing data. If necessary, analysis is performed again, and the career plan is revised and improved.

[0592] Step 9: Develop the final implementation plan

[0593] Server: Based on the finalized career plan, a specific action plan is created. This action plan includes the necessary resources and steps.

[0594] Step 10: Integration with external services

[0595] Server: Connects with external educational platforms and industry networks via APIs to obtain additional information and learning opportunities.

[0596] Terminal: Provides the collected information to the user and integrates it into the final plan.

[0597] (Application Example 1)

[0598] 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."

[0599] In the field of elderly care, it is a challenging task for individuals to leverage their skills and experience, find the optimal career advancement path, and develop concrete action plans based on that. In particular, there is a lack of mechanisms to effectively utilize specialized knowledge and resources in elderly care, which acts as a barrier when individuals explore new career paths.

[0600] 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.

[0601] In this invention, the server includes an information terminal means for inputting user attribute data, a computing device means for receiving and analyzing the attribute data, and means for presenting career advancement paths specific to the nursing care field. This enables users to design an optimal career path based on their experience and skills, and to create and implement a concrete action plan.

[0602] An "information terminal device" is a device that allows users to input their own attribute data, convert it into a digital format, and transmit it to an analysis platform.

[0603] The "computation device means" is a device that analyzes received attribute data using a machine learning algorithm and designs a career path suitable for the user.

[0604] A "career path" refers to the specific route or plan a user takes when advancing their career or transitioning to a new job.

[0605] "Opinions" refer to feedback and suggestions collected from users, and include their evaluations and desires regarding the designed career paths.

[0606] A "support system" is a system that collaborates with external educational infrastructure and industry networks to provide users with necessary support and additional learning opportunities.

[0607] "A career advancement path unique to the nursing care field" refers to specific career paths and opportunities within the nursing care industry that leverage acquired skills and work experience for career advancement.

[0608] The system for implementing this invention provides a platform for individuals engaged in caregiving to design and execute the optimal career advancement path. It mainly consists of an information terminal, a computing device, and an auxiliary system.

[0609] Users input attribute data such as their skills, work experience, and interests using an information terminal. The information terminal is implemented as a smartphone or tablet, and the input data is converted into an appropriate digital format.

[0610] The converted data is sent to a server, where a computing system analyzes it. The computing system is equipped with machine learning libraries such as TensorFlow and database management systems such as MySQL, which are used to analyze the user's data and generate the optimal career path.

[0611] The generated career path is transmitted to an information terminal and output to the user. The user provides feedback on this path, and the system improves the path based on that feedback. The final action plan proposes specific steps to the user, providing further details and support through an auxiliary system.

[0612] As a concrete example, consider the process by which Ms. A, who works at a nursing care facility, uses the application to aim for career advancement to a management position. Ms. A inputs her current skills and past work experience into the app and can find out the qualifications and experience required to become a "manager at a welfare facility."

[0613] The following are examples of prompts in which a generative AI model operates.

[0614] "Please tell me the specific steps I can take to advance my career in the caregiving profession. Current skill set: Nursing care, welfare management; Past work experience: 3 years at a care facility; Interests: Leadership, management positions."

[0615] In this way, the system can provide support to users in solving their career challenges.

[0616] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0617] Step 1:

[0618] The user uses an information terminal to input attribute data such as their skills, work experience, and interests. The information terminal then converts the entered data into JSON format and prepares it for transmission to the server.

[0619] Step 2:

[0620] The server receives JSON-formatted data from the information terminal. It then checks the integrity of the received data, verifying that there is no invalid or missing data. Data cleaning is performed as needed.

[0621] Step 3:

[0622] The server uses cleaned data and machine learning algorithms such as TensorFlow to analyze user characteristics. This analysis generates multiple optimal career paths for the user. The computing system retrieves similar success stories from the database and uses them to create career paths.

[0623] Step 4:

[0624] The server evaluates the generated career paths and assigns a score to each path. The score is an indicator of how well the path matches the user's attribute data. Based on this score, the server selects the highest-priority path.

[0625] Step 5:

[0626] The terminal sends the selected, high-priority occupational routes. The terminal presents the occupational routes to the user in a visualized format. The user provides opinions and feedback on the routes based on the visualized data.

[0627] Step 6:

[0628] The server analyzes the feedback received from users and determines the need for evaluation. Furthermore, it uses a generative AI model to extract elements that should be reflected from the feedback and revise the career path.

[0629] Step 7:

[0630] The server generates a specific action plan based on the finalized career path. This plan includes necessary skill development and educational opportunities. This action plan is provided in conjunction with auxiliary systems, integrating information and resources accessible to the user.

[0631] Step 8:

[0632] The device outputs an action plan to the user and presents the information necessary to execute the plan. The user can then proceed with preparing for career advancement according to the plan.

[0633] 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.

[0634] This invention is a system that helps users find the best second career for themselves and put it into action with support tailored to their emotional state. This system consists of the following components and procedures.

[0635] 1. User information input and sentiment recognition

[0636] Terminal: Users input personal profile information, work history, and interests through the interface. Along with this information, the terminal's emotion engine acquires emotional data from the user's facial expressions and voice, and sends this data to the server.

[0637] 2. Data Analysis

[0638] Server: The server processes and analyzes received user information and sentiment data. Using machine learning algorithms, it analyzes user sentiment and profile-based characteristics to identify needs.

[0639] 3. Creating a Career Plan

[0640] Server: Based on the analysis results, the generating AI creates multiple career plans optimized for the user. It incorporates data from the emotion engine to prioritize options that match the user's interests and current emotions.

[0641] 4. Plan presentation and feedback

[0642] Terminal: Visually displays the carrier plan sent from the server and continuously collects user reactions to the proposed plan along with sentiment data.

[0643] Users: Enter feedback about the plan and express their feelings to specifically indicate what attracts them or what makes them uneasy.

[0644] 5. Improvement through feedback

[0645] Server: Analyzes emotional data and feedback to identify which parts of the plan are appealing or deterrents to users, and then refines the plan based on the results.

[0646] 6. Formulation of an implementation plan

[0647] Server: Develop a detailed execution plan based on the final selected and refined career plan. The plan will include key steps, support measures, and plans for strengthening necessary skills.

[0648] 7. Integration with related services

[0649] Server: Collaborates with external educational platforms and industry networks to provide additional educational opportunities and networks to support users' second careers.

[0650] Terminal: Present information about these external services to the user and incorporate it into the execution plan.

[0651] Specific example:

[0652] For example, if person C wants to work in a new field but feels a little anxious about taking concrete action, the system receives emotional data along with C's input data. Understanding C's anxiety, the emotional engine prioritizes presenting stable options, receives feedback on the plan, and introduces networks and educational opportunities to specifically alleviate that anxiety. This allows C to confidently take the first step towards building a new career.

[0653] This system can propose and support the implementation of the most suitable career plan for the user through a process that takes the user's emotions into account.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The user enters personal profile information through the interface on the device. The device uses its camera and microphone to record the user's facial expressions and voice, preparing the emotion engine to analyze this data.

[0657] Step 2:

[0658] The device sends recorded emotional data and user-entered information to the server. The data sent includes basic profile information and information about the user's emotional state.

[0659] Step 3:

[0660] The server analyzes the received information and uses machine learning algorithms to identify the user's emotional state and needs from their profile. Specifically, it determines which career paths align with the user's interests and which elements resonate emotionally.

[0661] Step 4:

[0662] Based on the analysis results, the server uses generative AI to create multiple career plans tailored to the user. It leverages data from the emotion engine to generate plans that prioritize elements that evoke positive emotions in the user.

[0663] Step 5:

[0664] The device presents the user with a generated carrier plan. This plan includes comments and recommendations tailored to the user's emotions and is displayed in a way that solicits additional feedback.

[0665] Step 6:

[0666] Users input feedback based on the presented plan, sending through their device to the server which elements they find emotionally appealing and which elements need improvement.

[0667] Step 7:

[0668] The server re-analyzes feedback and emotional data to create improved career plan suggestions. This provides a plan that better matches the user's emotional balance and needs.

[0669] Step 8:

[0670] The server develops a detailed execution plan based on the final career plan. This plan includes step-by-step instructions and approaches to necessary skill development.

[0671] Step 9:

[0672] The device presents the user with an action plan and provides information to take action according to the plan, as well as information on integration with external services. This allows the user to confidently begin concrete actions.

[0673] (Example 2)

[0674] 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."

[0675] Traditional career support systems struggled to take users' emotions into account, resulting in career plans that were not optimized for users' psychological state or interests. Furthermore, they lacked effective means of utilizing feedback to improve plans, failing to adequately support users in achieving their optimal career development. Additionally, insufficient collaboration with external educational institutions and networks limited opportunities for skill enhancement and network building.

[0676] 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.

[0677] In this invention, the server includes means for inputting the user's personal information and emotional data, means for emotion analysis for recognizing the user's emotional state from their facial expressions and voice, and means for generating multiple career plans suitable for the user based on the analysis results using a generative AI model. This makes it possible to effectively support the user's career development by providing an optimal career plan that takes the user's emotions into consideration, improving the plan using feedback on each plan, and coordinating with external resources.

[0678] An "input device" is a device used to collect personal information and emotional data from users.

[0679] An "emotion analysis device" is a device equipped with technology that processes a user's facial expressions and voice to recognize the user's emotional state.

[0680] A "data analysis device" is a computing device that receives user profile information and sentiment data and performs analysis using machine learning algorithms.

[0681] A "generative AI model" is an artificial intelligence model used to generate a career plan optimized for the user based on the data received.

[0682] A "presentation device" is a device that visually displays career plans to users and collects user feedback.

[0683] An "external educational network" refers to external educational resources that are linked to users to improve their skills and provide career support.

[0684] An "industry-related network" refers to a network of specialized fields and industries that collaborate to support users' career development.

[0685] One embodiment of the present invention provides a system for users to find the optimal career plan for themselves. This system combines numerous technical elements to generate an optimal career plan from the user's emotions and profile information, and aims to improve it based on user feedback.

[0686] First, the user inputs personal information, work history, and interests into the terminal through the interface. The terminal uses its camera and microphone to collect the user's facial expressions and voice, and transmits this information to an emotion analysis device. This analysis uses libraries for image processing and voice analysis (e.g., OpenCV or librosa).

[0687] Next, the server receives the data sent from the terminal and processes it using a data analysis device. In this process, machine learning algorithms (e.g., TensorFlow, scikit-learn) are applied using programming languages ​​such as Python to extract and analyze user characteristics.

[0688] Next, a generative AI model is used to automatically generate a personalized career plan based on the analysis results. The generated plan is optimized to include options that match the user's interests and current emotions. Specifically, natural language generation technology (e.g., GPT model) is utilized.

[0689] The device then visually presents the generated carrier plan to the user. The user provides feedback on the plan, expressing their emotions and communicating specific concerns and anxieties to the device.

[0690] Furthermore, the server re-analyzes feedback and sentiment data to improve the plan. By text-analyzing user opinions and creating newly enhanced plans using a generative AI model, it provides a more appropriate plan for the user. Natural language processing techniques (e.g., NLTK, spaCy) are used in this process.

[0691] Finally, the server connects with relevant external educational and industry-related networks to provide users with additional resources. API technology is used for this connection, and information to support users' career development is presented through their devices. This allows users to gain access to a wealth of learning opportunities.

[0692] Specific example: For instance, if a user is considering venturing into a new field but is unsure how to begin, the system identifies their concerns based on user input and emotional data, and generates a safe, step-by-step career plan. The user provides feedback, and the server adjusts the plan to establish an ideal path to their career.

[0693] Example of a prompt:

[0694] "I feel anxious about entering a new field, but what kind of career plan would suit me?"

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The user accesses the interface and enters personal information, work history, and interests. The entered information is collected by the terminal. Simultaneously, the user's facial expressions and voice are recorded in real time through the terminal's camera and microphone and transmitted to an emotion analysis device. At this stage, the input device captures biometric data and provides raw data for analyzing the user's emotional state.

[0698] Step 2:

[0699] The device sends personal information and emotional data collected from the user to the server. The server receives this data using a data analysis device and analyzes the user's profile and emotional characteristics using machine learning algorithms within the program (e.g., scikit-learn, TensorFlow). Through this analysis, the user's behavioral tendencies and psychological state are extracted as features.

[0700] Step 3:

[0701] The server uses a generative AI model to generate a career plan tailored to the user based on the analysis results. This utilizes generative AI (e.g., the GPT model) to provide optimized options that align with the user's interests and emotions. Input data consists of the user's profile and emotional data, while the output is multiple personalized career plans.

[0702] Step 4:

[0703] The device visually presents the carrier plans received from the server to the user. The user reviews the plans displayed on the screen and provides feedback on the plans they like. During this process, the device records the user's reactions as sentiment data and prepares to send it to the server.

[0704] Step 5:

[0705] The server analyzes user feedback and new sentiment data to improve the plan. Using natural language processing techniques (e.g., NLTK, spaCy), it analyzes user feedback as text and applies the feedback to a generative AI model to refine the plan. This generates a career plan that better matches the user's needs.

[0706] Step 6:

[0707] The server develops an execution plan based on the improved career plan. This plan includes specific steps and resources for the user to acquire the necessary skills. The server connects with external educational and industry networks via APIs to extract useful information for the user.

[0708] Step 7:

[0709] The terminal notifies the user of information about external resources that the server has integrated with and presents a method for incorporating them into the execution plan. The user can then use this information to begin taking concrete actions to put the plan into action.

[0710] (Application Example 2)

[0711] 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."

[0712] In today's professional environment, individuals face a wide range of challenges when pursuing new careers. One of these is finding a career plan that suits their individual emotional state and interests, and obtaining the necessary learning opportunities and support. However, due to insufficient technological solutions, it remains difficult for individuals to make optimal career choices.

[0713] 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.

[0714] In this invention, the server includes input means for inputting user attribute information, processing means for receiving and analyzing the attribute information and emotional information, and generation means for generating multiple career options based on the analysis results. This makes it possible to propose an optimal career plan based on the individual's emotional state.

[0715] "Input methods for entering user attribute information" refer to means by which individuals provide information such as their profile, work history, interests, and preferences to a system.

[0716] "Emotional information" refers to data that indicates a user's emotional state, and is obtained from things like facial expressions and voice.

[0717] "Analysis processing means" refers to a means of analyzing the received user attribute information and emotional information to understand its characteristics and trends.

[0718] A "generation method" is a means of creating multiple career options based on the analysis results.

[0719] "Means of improvement for enhancement" refers to methods for adjusting carrier options and providing more suitable plans based on information obtained from user responses.

[0720] "Means for formulating an implementation scheme" refers to the means of planning specific action plans and steps based on an improved career plan.

[0721] "Means of collaborating with external educational resources and industry connections to provide additional support" refers to means of working with external educational institutions and industry networks to provide users with further learning opportunities and support.

[0722] The system that realizes this invention consists of a terminal that has a direct interface with the user and a server that stores and analyzes information.

[0723] The device is equipped with a touchscreen and a microphone capable of voice input as means of inputting user attribute information. The device provides the user's emotional information collected through these means to an emotion recognition system using a camera and microphone. The emotion recognition system analyzes the collected data using software such as OpenCV and TensorFlow.

[0724] Based on the analyzed data, the server generates multiple career options using a generative AI model. This generative AI utilizes OpenAI's GPT-based model, enabling suggestions tailored to each user's individual needs and emotional state. The generated options are presented to the user, and emotional data is used to gather user feedback. This feedback information is then sent back to the server and used to improve the career options.

[0725] Furthermore, the server collaborates with external educational resources and industry networks to provide users with the learning opportunities and support necessary for an improved career plan. To achieve this, it interconnects with various platforms via APIs.

[0726] As a concrete example, user B inputs information about their areas of interest and current occupation into the system, exploring potential career paths. The terminal retrieves B's input information and emotional data and sends it to the server. The server generates multiple career plans based on the analysis and presents them to B. If B expresses interest in a career change to the entertainment industry, the system also offers suggestions such as skill development programs and networking events.

[0727] An example of a prompt message would be: "What kind of work environment does the user prefer? Based on their sentiment data and areas of interest, suggest the optimal career path."

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The user enters profile information, work history, and areas of interest into the device. During this process, the device uses its camera and microphone to capture data on the user's facial expressions and voice, simultaneously collecting emotional information. The input data includes attribute information in text format and audio / video data.

[0731] Step 2:

[0732] The device sends acquired user attribute information and emotional information to the server. The server receives this data, analyzes the video data using OpenCV, and analyzes the emotional content of the audio data using TensorFlow. As a result of the analysis, the user's emotional state and attribute information are converted into numerical data.

[0733] Step 3:

[0734] The server generates multiple career options using a generative AI model based on numerically quantified information. In this process, it uses OpenAI's GPT-based model to propose a career plan that best suits the user's needs. The generated career options are output in text format.

[0735] Step 4:

[0736] The generated carrier options are sent to the device and visually presented to the user. The user provides feedback on the proposed plan, and sentiment data is collected again during this process. As output, the user's feedback and sentiment information are collected.

[0737] Step 5:

[0738] The server analyzes feedback and sentiment data received from the terminal to improve the carrier options. This improvement process again uses a generative AI model to suggest plans that better suit the user's needs. The improved options are then prepared for the next step.

[0739] Step 6:

[0740] Based on the final, improved career plan, the server connects with external educational resources and industry networks to obtain necessary support information. The server uses APIs to connect with external platforms and gather learning opportunities and network information to provide to the user. As output, a resource list is created to support the user in building their new career.

[0741] Step 7:

[0742] The terminal notifies the user of the resource list received from the server and proposes specific execution steps. This allows the user to clearly understand the steps to take the next action, and a concrete action plan is presented to the user as output.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] [Fourth Embodiment]

[0747] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0748] 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.

[0749] 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).

[0750] 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.

[0751] 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.

[0752] 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).

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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".

[0760] This invention is a system that provides users with personalized programs to find and implement the second career best suited to them. This system has the following functions:

[0761] 1. Enter user information

[0762] Terminal: Users enter their personal profile, past work history, and interests on the interface. The terminal converts this data into an appropriate format and sends it to the server.

[0763] 2. Data Analysis

[0764] Server: Based on the received information, it applies machine learning algorithms to analyze user characteristics. The server searches the database for highly relevant success stories and career paths and generates candidates.

[0765] 3. Generating and presenting a career plan

[0766] Server: Based on the analysis results, the generating AI creates multiple career plans. The server evaluates how well each plan matches the user's needs and assigns a score.

[0767] Device: Visualizes and presents carrier plans and their evaluations to the user. The user can select from the displayed options and view further details.

[0768] 4. Interaction with users

[0769] Users can provide various feedback on the presented career plan. They can also enter additional questions and request specific suggestions.

[0770] 5. Responding to feedback and improving the plan

[0771] Server: Receive user feedback and re-analyze it. Modify the plan as needed and provide new, optimized suggestions to the user.

[0772] 6. Formulation of an implementation plan

[0773] Server: Based on the ultimately selected career plan, generate a specific, step-by-step execution plan. This includes necessary resources and opportunities for skill development.

[0774] Terminal: Present the plan to the user and organize the information necessary for its execution.

[0775] 7. Integration with related services

[0776] Server: The program integrates with external educational platforms and industry networks to provide necessary support to users.

[0777] Terminal: Provides users with linked information and incorporates it into their plans.

[0778] Specific example:

[0779] For example, let's say Mr. C, a retired teacher, wants to make a new contribution to the field of education and uses this system. Through feedback, Mr. C requests support in launching a local education program. Based on this request, the system provides a partnership plan with local education support organizations and proposes concrete steps to Mr. C. As a result, Mr. C can start a project to contribute to his community.

[0780] Through the process described above, users can discover the second career best suited to their needs and receive concrete support to make it a reality.

[0781] The following describes the processing flow.

[0782] Step 1:

[0783] Users input personal profile information, work history, and interests using an interface on their device. The device collects this information in an appropriate format and sends it to the server.

[0784] Step 2:

[0785] The server analyzes the received user information using processing tools. It utilizes machine learning algorithms to analyze the characteristics of the user profile and identify their needs.

[0786] Step 3:

[0787] Based on the analysis results, the server's generating AI creates multiple career plans tailored to the user. It also references similar career paths and success stories from the database to enhance the suggestions.

[0788] Step 4:

[0789] The device receives carrier plans provided by the server, visualizes them, and presents them to the user. The user is shown detailed information about each plan and an evaluation score regarding its feasibility.

[0790] Step 5:

[0791] Users provide feedback on the displayed career plans. They can also send additional information or questions about plans they are interested in to the server via their device.

[0792] Step 6:

[0793] The server collects user feedback and re-evaluates the plan. If necessary, it incorporates the feedback and regenerates a plan that better suits the user.

[0794] Step 7:

[0795] Based on the final career plan, the server develops a detailed execution plan. This plan includes specific steps, recommended resources, and strategies for improving required skills.

[0796] Step 8:

[0797] The terminal presents the user with an execution plan and displays organized information about the preparatory steps required to execute the plan.

[0798] Step 9:

[0799] The server interacts with relevant external services and platforms to provide support for users in realizing their execution plans. The terminal displays this information to the user as needed, integrating it as part of the plan.

[0800] (Example 1)

[0801] 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".

[0802] In today's world, many individuals are exploring second careers, but they lack concrete methods for selecting and implementing the most suitable career plan. This problem makes re-employment or transitioning to a new career more difficult, increasing the time and effort required for individual career development.

[0803] 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.

[0804] In this invention, the server includes an interface means for inputting user data, a processing means for receiving the user data and analyzing its characteristics, and a means for creating multiple career plans for the user based on the generated characteristic analysis results. This makes it possible to select the optimal second career based on the individual's characteristics and to formulate a concrete plan for its implementation.

[0805] An "interface means" is a component that allows a user to input data and interact with the system.

[0806] "Processing means" refers to components that analyze received user data and perform calculations and analyses to identify user characteristics.

[0807] A "career plan" is a set of options for specific career changes and career development, formulated based on the user's characteristics analysis.

[0808] "Means of collecting opinions" are components that enable users to provide feedback on the career plan presented.

[0809] "Means of modification and improvement" refer to components used to change the work plan based on user feedback and make it more suitable.

[0810] "External information sources" refer to organizations or services that provide additional information or resources necessary to support users from outside the system, such as online learning platforms or industry networks.

[0811] This invention is a system that provides support to users in finding and implementing the optimal second career. The system takes user data as input, analyzes user characteristics based on that information, and performs a series of processes to generate an optimal career plan. The hardware and software used, as well as specific examples of operation, are described below.

[0812] Users use a device to enter their personal profile, past work history, and interests. This device is configured as a personal computer or mobile device and can input data using standard input devices (keyboard, touchscreen, etc.). The input data is formatted and sent to the server.

[0813] The server utilizes computing resources in the cloud to receive and store input data. For specific analysis, machine learning algorithms are employed. Clustering and classification models are used as machine learning models. These function as means of searching the database for past success stories and related career paths. A generative AI model generates an optimal career plan for the user based on the acquired data.

[0814] For example, if a user wants to work on a new project in the technology field, this system can analyze the user's work history and skills and present relevant project examples. It can also suggest other fields where the user might be more suitable. The generated plan is visualized on the user's device and provided to them.

[0815] An example of a prompt message is, "Based on the user's profile, suggest the best new career options for them."

[0816] Another important feature of this system is that the server connects with external educational platforms and industry networks. This connection allows users to access additional learning opportunities and industry support. The connection is primarily done through APIs and is designed to allow users to retrieve information seamlessly.

[0817] This specific structure enables the provision of flexible and effective career plans tailored to the user's needs.

[0818] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0819] Step 1: Enter user information

[0820] Terminal: Users use the terminal's interface to enter their personal profile, past work history, and interests. The terminal retrieves this information and converts it into a dedicated data format. The converted data is then ready to be sent to the server.

[0821] Step 2: Send

[0822] Terminal: The entered data is sent to the server using security protocols such as SSL / TLS. This transmission process ensures the confidentiality and integrity of the data.

[0823] Step 3: Receiving and storing data

[0824] Server: Received data is temporarily stored in storage. Here, the data undergoes preprocessing for analysis. Preprocessing includes data cleaning and normalization to ensure that the analysis algorithm functions efficiently.

[0825] Step 4: Analyzing User Characteristics

[0826] Server: Machine learning algorithms (clustering models, classification models) are applied to the data. These algorithms extract user characteristics and generate relevant statistics. This allows for the development of an initial career plan based on the user's characteristics.

[0827] Step 5: Generating a Career Plan

[0828] Server: Uses a generative AI model to generate multiple career plans based on user characteristic data. A prompt is used as input, and the optimal suggestion is output. An example is, "Based on the user's profile, suggest the best new career options for them."

[0829] Step 6: Plan presentation and evaluation

[0830] Terminal: The generated career plan and its evaluation results are visualized and presented to the user. The user can select from the presented options and view the details.

[0831] Step 7: Gathering Feedback

[0832] User: Provide feedback on the presented plan. This feedback may include additional questions or specific requests.

[0833] Step 8: Revise and improve the plan

[0834] Server: User feedback is received and integrated with existing data. If necessary, analysis is performed again, and the career plan is revised and improved.

[0835] Step 9: Develop the final implementation plan

[0836] Server: Based on the finalized career plan, a specific action plan is created. This action plan includes the necessary resources and steps.

[0837] Step 10: Integration with external services

[0838] Server: Connects with external educational platforms and industry networks via APIs to obtain additional information and learning opportunities.

[0839] Terminal: Provides the collected information to the user and integrates it into the final plan.

[0840] (Application Example 1)

[0841] 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".

[0842] In the field of elderly care, it is a challenging task for individuals to leverage their skills and experience, find the optimal career advancement path, and develop concrete action plans based on that. In particular, there is a lack of mechanisms to effectively utilize specialized knowledge and resources in elderly care, which acts as a barrier when individuals explore new career paths.

[0843] 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.

[0844] In this invention, the server includes an information terminal means for inputting user attribute data, a computing device means for receiving and analyzing the attribute data, and means for presenting career advancement paths specific to the nursing care field. This enables users to design an optimal career path based on their experience and skills, and to create and implement a concrete action plan.

[0845] An "information terminal device" is a device that allows users to input their own attribute data, convert it into a digital format, and transmit it to an analysis platform.

[0846] The "computation device means" is a device that analyzes received attribute data using a machine learning algorithm and designs a career path suitable for the user.

[0847] A "career path" refers to the specific route or plan a user takes when advancing their career or transitioning to a new job.

[0848] "Opinions" refer to feedback and suggestions collected from users, and include their evaluations and desires regarding the designed career paths.

[0849] A "support system" is a system that collaborates with external educational infrastructure and industry networks to provide users with necessary support and additional learning opportunities.

[0850] "A career advancement path unique to the nursing care field" refers to specific career paths and opportunities within the nursing care industry that leverage acquired skills and work experience for career advancement.

[0851] The system for implementing this invention provides a platform for individuals engaged in caregiving to design and execute the optimal career advancement path. It mainly consists of an information terminal, a computing device, and an auxiliary system.

[0852] Users input attribute data such as their skills, work experience, and interests using an information terminal. The information terminal is implemented as a smartphone or tablet, and the input data is converted into an appropriate digital format.

[0853] The converted data is sent to a server, where a computing system analyzes it. The computing system is equipped with machine learning libraries such as TensorFlow and database management systems such as MySQL, which are used to analyze the user's data and generate the optimal career path.

[0854] The generated career path is transmitted to an information terminal and output to the user. The user provides feedback on this path, and the system improves the path based on that feedback. The final action plan proposes specific steps to the user, providing further details and support through an auxiliary system.

[0855] As a concrete example, consider the process by which Ms. A, who works at a nursing care facility, uses the application to aim for career advancement to a management position. Ms. A inputs her current skills and past work experience into the app and can find out the qualifications and experience required to become a "manager at a welfare facility."

[0856] The following are examples of prompts in which a generative AI model operates.

[0857] "Please tell me the specific steps I can take to advance my career in the caregiving profession. Current skill set: Nursing care, welfare management; Past work experience: 3 years at a care facility; Interests: Leadership, management positions."

[0858] In this way, the system can provide support to users in solving their career challenges.

[0859] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0860] Step 1:

[0861] The user uses an information terminal to input attribute data such as their skills, work experience, and interests. The information terminal then converts the entered data into JSON format and prepares it for transmission to the server.

[0862] Step 2:

[0863] The server receives JSON-formatted data from the information terminal. It then checks the integrity of the received data, verifying that there is no invalid or missing data. Data cleaning is performed as needed.

[0864] Step 3:

[0865] The server uses cleaned data and machine learning algorithms such as TensorFlow to analyze user characteristics. This analysis generates multiple optimal career paths for the user. The computing system retrieves similar success stories from the database and uses them to create career paths.

[0866] Step 4:

[0867] The server evaluates the generated career paths and assigns a score to each path. The score is an indicator of how well the path matches the user's attribute data. Based on this score, the server selects the highest-priority path.

[0868] Step 5:

[0869] The terminal sends the selected, high-priority occupational routes. The terminal presents the occupational routes to the user in a visualized format. The user provides opinions and feedback on the routes based on the visualized data.

[0870] Step 6:

[0871] The server analyzes the feedback received from users and determines the need for evaluation. Furthermore, it uses a generative AI model to extract elements that should be reflected from the feedback and revise the career path.

[0872] Step 7:

[0873] The server generates a specific action plan based on the finalized career path. This plan includes necessary skill development and educational opportunities. This action plan is provided in conjunction with auxiliary systems, integrating information and resources accessible to the user.

[0874] Step 8:

[0875] The device outputs an action plan to the user and presents the information necessary to execute the plan. The user can then proceed with preparing for career advancement according to the plan.

[0876] 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.

[0877] This invention is a system that helps users find the best second career for themselves and put it into action with support tailored to their emotional state. This system consists of the following components and procedures.

[0878] 1. User information input and sentiment recognition

[0879] Terminal: Users input personal profile information, work history, and interests through the interface. Along with this information, the terminal's emotion engine acquires emotional data from the user's facial expressions and voice, and sends this data to the server.

[0880] 2. Data Analysis

[0881] Server: The server processes and analyzes received user information and sentiment data. Using machine learning algorithms, it analyzes user sentiment and profile-based characteristics to identify needs.

[0882] 3. Creating a Career Plan

[0883] Server: Based on the analysis results, the generating AI creates multiple career plans optimized for the user. It incorporates data from the emotion engine to prioritize options that match the user's interests and current emotions.

[0884] 4. Plan presentation and feedback

[0885] Terminal: Visually displays the carrier plan sent from the server and continuously collects user reactions to the proposed plan along with sentiment data.

[0886] Users: Enter feedback about the plan and express their feelings to specifically indicate what attracts them or what makes them uneasy.

[0887] 5. Improvement through feedback

[0888] Server: Analyzes emotional data and feedback to identify which parts of the plan are appealing or deterrents to users, and then refines the plan based on the results.

[0889] 6. Formulation of an implementation plan

[0890] Server: Develop a detailed execution plan based on the final selected and refined career plan. The plan will include key steps, support measures, and plans for strengthening necessary skills.

[0891] 7. Integration with related services

[0892] Server: Collaborates with external educational platforms and industry networks to provide additional educational opportunities and networks to support users' second careers.

[0893] Terminal: Present information about these external services to the user and incorporate it into the execution plan.

[0894] Specific example:

[0895] For example, if person C wants to work in a new field but feels a little anxious about taking concrete action, the system receives emotional data along with C's input data. Understanding C's anxiety, the emotional engine prioritizes presenting stable options, receives feedback on the plan, and introduces networks and educational opportunities to specifically alleviate that anxiety. This allows C to confidently take the first step towards building a new career.

[0896] This system can propose and support the implementation of the most suitable career plan for the user through a process that takes the user's emotions into account.

[0897] The following describes the processing flow.

[0898] Step 1:

[0899] The user enters personal profile information through the interface on the device. The device uses its camera and microphone to record the user's facial expressions and voice, preparing the emotion engine to analyze this data.

[0900] Step 2:

[0901] The device sends recorded emotional data and user-entered information to the server. The data sent includes basic profile information and information about the user's emotional state.

[0902] Step 3:

[0903] The server analyzes the received information and uses machine learning algorithms to identify the user's emotional state and needs from their profile. Specifically, it determines which career paths align with the user's interests and which elements resonate emotionally.

[0904] Step 4:

[0905] Based on the analysis results, the server uses generative AI to create multiple career plans tailored to the user. It leverages data from the emotion engine to generate plans that prioritize elements that evoke positive emotions in the user.

[0906] Step 5:

[0907] The device presents the user with a generated carrier plan. This plan includes comments and recommendations tailored to the user's emotions and is displayed in a way that solicits additional feedback.

[0908] Step 6:

[0909] Users input feedback based on the presented plan, sending through their device to the server which elements they find emotionally appealing and which elements need improvement.

[0910] Step 7:

[0911] The server re-analyzes feedback and emotional data to create improved career plan suggestions. This provides a plan that better matches the user's emotional balance and needs.

[0912] Step 8:

[0913] The server develops a detailed execution plan based on the final career plan. This plan includes step-by-step instructions and approaches to necessary skill development.

[0914] Step 9:

[0915] The device presents the user with an action plan and provides information to take action according to the plan, as well as information on integration with external services. This allows the user to confidently begin concrete actions.

[0916] (Example 2)

[0917] 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".

[0918] Traditional career support systems struggled to take users' emotions into account, resulting in career plans that were not optimized for users' psychological state or interests. Furthermore, they lacked effective means of utilizing feedback to improve plans, failing to adequately support users in achieving their optimal career development. Additionally, insufficient collaboration with external educational institutions and networks limited opportunities for skill enhancement and network building.

[0919] 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.

[0920] In this invention, the server includes means for inputting the user's personal information and emotional data, means for emotion analysis for recognizing the user's emotional state from their facial expressions and voice, and means for generating multiple career plans suitable for the user based on the analysis results using a generative AI model. This makes it possible to effectively support the user's career development by providing an optimal career plan that takes the user's emotions into consideration, improving the plan using feedback on each plan, and coordinating with external resources.

[0921] An "input device" is a device used to collect personal information and emotional data from users.

[0922] An "emotion analysis device" is a device equipped with technology that processes a user's facial expressions and voice to recognize the user's emotional state.

[0923] A "data analysis device" is a computing device that receives user profile information and sentiment data and performs analysis using machine learning algorithms.

[0924] A "generative AI model" is an artificial intelligence model used to generate a career plan optimized for the user based on the data received.

[0925] A "presentation device" is a device that visually displays career plans to users and collects user feedback.

[0926] An "external educational network" refers to external educational resources that are linked to users to improve their skills and provide career support.

[0927] An "industry-related network" refers to a network of specialized fields and industries that collaborate to support users' career development.

[0928] One embodiment of the present invention provides a system for users to find the optimal career plan for themselves. This system combines numerous technical elements to generate an optimal career plan from the user's emotions and profile information, and aims to improve it based on user feedback.

[0929] First, the user inputs personal information, work history, and interests into the terminal through the interface. The terminal uses its camera and microphone to collect the user's facial expressions and voice, and transmits this information to an emotion analysis device. This analysis uses libraries for image processing and voice analysis (e.g., OpenCV or librosa).

[0930] Next, the server receives the data sent from the terminal and processes it using a data analysis device. In this process, machine learning algorithms (e.g., TensorFlow, scikit-learn) are applied using programming languages ​​such as Python to extract and analyze user characteristics.

[0931] Next, a generative AI model is used to automatically generate a personalized career plan based on the analysis results. The generated plan is optimized to include options that match the user's interests and current emotions. Specifically, natural language generation technology (e.g., GPT model) is utilized.

[0932] The device then visually presents the generated carrier plan to the user. The user provides feedback on the plan, expressing their emotions and communicating specific concerns and anxieties to the device.

[0933] Furthermore, the server re-analyzes feedback and sentiment data to improve the plan. By text-analyzing user opinions and creating newly enhanced plans using a generative AI model, it provides a more appropriate plan for the user. Natural language processing techniques (e.g., NLTK, spaCy) are used in this process.

[0934] Finally, the server connects with relevant external educational and industry-related networks to provide users with additional resources. API technology is used for this connection, and information to support users' career development is presented through their devices. This allows users to gain access to a wealth of learning opportunities.

[0935] Specific example: For instance, if a user is considering venturing into a new field but is unsure how to begin, the system identifies their concerns based on user input and emotional data, and generates a safe, step-by-step career plan. The user provides feedback, and the server adjusts the plan to establish an ideal path to their career.

[0936] Example of a prompt:

[0937] "I feel anxious about entering a new field, but what kind of career plan would suit me?"

[0938] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0939] Step 1:

[0940] The user accesses the interface and enters personal information, work history, and interests. The entered information is collected by the terminal. Simultaneously, the user's facial expressions and voice are recorded in real time through the terminal's camera and microphone and transmitted to an emotion analysis device. At this stage, the input device captures biometric data and provides raw data for analyzing the user's emotional state.

[0941] Step 2:

[0942] The device sends personal information and emotional data collected from the user to the server. The server receives this data using a data analysis device and analyzes the user's profile and emotional characteristics using machine learning algorithms within the program (e.g., scikit-learn, TensorFlow). Through this analysis, the user's behavioral tendencies and psychological state are extracted as features.

[0943] Step 3:

[0944] The server uses a generative AI model to generate a career plan tailored to the user based on the analysis results. This utilizes generative AI (e.g., the GPT model) to provide optimized options that align with the user's interests and emotions. Input data consists of the user's profile and emotional data, while the output is multiple personalized career plans.

[0945] Step 4:

[0946] The device visually presents the carrier plans received from the server to the user. The user reviews the plans displayed on the screen and provides feedback on the plans they like. During this process, the device records the user's reactions as sentiment data and prepares to send it to the server.

[0947] Step 5:

[0948] The server analyzes user feedback and new sentiment data to improve the plan. Using natural language processing techniques (e.g., NLTK, spaCy), it analyzes user feedback as text and applies the feedback to a generative AI model to refine the plan. This generates a career plan that better matches the user's needs.

[0949] Step 6:

[0950] The server develops an execution plan based on the improved career plan. This plan includes specific steps and resources for the user to acquire the necessary skills. The server connects with external educational and industry networks via APIs to extract useful information for the user.

[0951] Step 7:

[0952] The terminal notifies the user of information about external resources that the server has integrated with and presents a method for incorporating them into the execution plan. The user can then use this information to begin taking concrete actions to put the plan into action.

[0953] (Application Example 2)

[0954] 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".

[0955] In today's professional environment, individuals face a wide range of challenges when pursuing new careers. One of these is finding a career plan that suits their individual emotional state and interests, and obtaining the necessary learning opportunities and support. However, due to insufficient technological solutions, it remains difficult for individuals to make optimal career choices.

[0956] 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.

[0957] In this invention, the server includes input means for inputting user attribute information, processing means for receiving and analyzing the attribute information and emotional information, and generation means for generating multiple career options based on the analysis results. This makes it possible to propose an optimal career plan based on the individual's emotional state.

[0958] "Input methods for entering user attribute information" refer to means by which individuals provide information such as their profile, work history, interests, and preferences to a system.

[0959] "Emotional information" refers to data that indicates a user's emotional state, and is obtained from things like facial expressions and voice.

[0960] "Analysis processing means" refers to a means of analyzing the received user attribute information and emotional information to understand its characteristics and trends.

[0961] A "generation method" is a means of creating multiple career options based on the analysis results.

[0962] "Means of improvement for enhancement" refers to methods for adjusting carrier options and providing more suitable plans based on information obtained from user responses.

[0963] "Means for formulating an implementation scheme" refers to the means of planning specific action plans and steps based on an improved career plan.

[0964] "Means of collaborating with external educational resources and industry connections to provide additional support" refers to means of working with external educational institutions and industry networks to provide users with further learning opportunities and support.

[0965] The system that realizes this invention consists of a terminal that has a direct interface with the user and a server that stores and analyzes information.

[0966] The device is equipped with a touchscreen and a microphone capable of voice input as means of inputting user attribute information. The device provides the user's emotional information collected through these means to an emotion recognition system using a camera and microphone. The emotion recognition system analyzes the collected data using software such as OpenCV and TensorFlow.

[0967] Based on the analyzed data, the server generates multiple career options using a generative AI model. This generative AI utilizes OpenAI's GPT-based model, enabling suggestions tailored to each user's individual needs and emotional state. The generated options are presented to the user, and emotional data is used to gather user feedback. This feedback information is then sent back to the server and used to improve the career options.

[0968] Furthermore, the server collaborates with external educational resources and industry networks to provide users with the learning opportunities and support necessary for an improved career plan. To achieve this, it interconnects with various platforms via APIs.

[0969] As a concrete example, user B inputs information about their areas of interest and current occupation into the system, exploring potential career paths. The terminal retrieves B's input information and emotional data and sends it to the server. The server generates multiple career plans based on the analysis and presents them to B. If B expresses interest in a career change to the entertainment industry, the system also offers suggestions such as skill development programs and networking events.

[0970] An example of a prompt message would be: "What kind of work environment does the user prefer? Based on their sentiment data and areas of interest, suggest the optimal career path."

[0971] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0972] Step 1:

[0973] The user enters profile information, work history, and areas of interest into the device. During this process, the device uses its camera and microphone to capture data on the user's facial expressions and voice, simultaneously collecting emotional information. The input data includes attribute information in text format and audio / video data.

[0974] Step 2:

[0975] The device sends acquired user attribute information and emotional information to the server. The server receives this data, analyzes the video data using OpenCV, and analyzes the emotional content of the audio data using TensorFlow. As a result of the analysis, the user's emotional state and attribute information are converted into numerical data.

[0976] Step 3:

[0977] The server generates multiple career options using a generative AI model based on numerically quantified information. In this process, it uses OpenAI's GPT-based model to propose a career plan that best suits the user's needs. The generated career options are output in text format.

[0978] Step 4:

[0979] The generated carrier options are sent to the device and visually presented to the user. The user provides feedback on the proposed plan, and sentiment data is collected again during this process. As output, the user's feedback and sentiment information are collected.

[0980] Step 5:

[0981] The server analyzes feedback and sentiment data received from the terminal to improve the carrier options. This improvement process again uses a generative AI model to suggest plans that better suit the user's needs. The improved options are then prepared for the next step.

[0982] Step 6:

[0983] Based on the final, improved career plan, the server connects with external educational resources and industry networks to obtain necessary support information. The server uses APIs to connect with external platforms and gather learning opportunities and network information to provide to the user. As output, a resource list is created to support the user in building their new career.

[0984] Step 7:

[0985] The terminal notifies the user of the resource list received from the server and proposes specific execution steps. This allows the user to clearly understand the steps to take the next action, and a concrete action plan is presented to the user as output.

[0986] 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.

[0987] 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.

[0988] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0989] 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.

[0990] 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.

[0991] 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.

[0992] 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.

[0993] 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.

[0994] 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."

[0995] 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.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] 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.

[1007] The following is further disclosed regarding the embodiments described above.

[1008] (Claim 1)

[1009] An interface means for inputting user information,

[1010] Processing means for receiving and analyzing the aforementioned user information,

[1011] A means of creating multiple career plans for a user based on the generated analysis results,

[1012] A means of presenting the aforementioned career plan to the user and collecting feedback,

[1013] Based on the aforementioned feedback, means for modifying and improving the career plan,

[1014] A means of formulating a concrete action plan based on the final career plan,

[1015] A means of providing necessary support by collaborating with external services,

[1016] A system that includes this.

[1017] (Claim 2)

[1018] The system according to claim 1, wherein the interface means uses natural language processing technology to interact with the user via voice or text.

[1019] (Claim 3)

[1020] The system according to claim 1, wherein the means for integrating with external services is to integrate with online education platforms and industry networks to provide users with additional educational opportunities.

[1021] "Example 1"

[1022] (Claim 1)

[1023] An interface means for inputting user data,

[1024] A processing means for receiving the aforementioned user data and analyzing its characteristics,

[1025] A means for creating multiple career plans for a user based on the generated feature analysis results,

[1026] A means of visualizing and presenting the aforementioned career plan to the user and collecting their opinions,

[1027] Based on the above opinion, means for revising and improving the occupational plan,

[1028] A means of formulating a concrete action plan based on the final career plan,

[1029] A means of integrating necessary support by collaborating with external information sources,

[1030] A system that includes this.

[1031] (Claim 2)

[1032] The system according to claim 1, wherein the interface means uses natural language processing technology to interact with the user via voice or text.

[1033] (Claim 3)

[1034] The system according to claim 1, wherein the means for linking with external information sources links with online learning platforms and industrial networks to provide users with additional learning opportunities.

[1035] "Application Example 1"

[1036] (Claim 1)

[1037] An information terminal for inputting user attribute data,

[1038] A computing device means for receiving and analyzing the aforementioned attribute data,

[1039] A means for designing multiple career paths for the user based on the generated analysis results,

[1040] A means for outputting the aforementioned career path to the user and obtaining their opinion,

[1041] Based on the above opinion, means to modify and improve the career path,

[1042] A means of creating a concrete action plan based on the final career path,

[1043] A means of providing necessary support in conjunction with the support system,

[1044] A means of presenting career advancement paths specific to the nursing care field,

[1045] A system that includes this.

[1046] (Claim 2)

[1047] The system according to claim 1, wherein the information terminal means utilizes natural language understanding technology to communicate with the user via voice or text.

[1048] (Claim 3)

[1049] The system according to claim 1, wherein the means for coordinating with the auxiliary system cooperates with an electronic education infrastructure and a professional industry network to provide users with additional learning opportunities.

[1050] "Example 2 of combining an emotion engine"

[1051] (Claim 1)

[1052] A means equipped with an input device for inputting user personal information and emotional data,

[1053] Means comprising an emotion analysis device for recognizing the emotional state from the user's facial expressions and voice,

[1054] A means comprising a data analysis device for receiving user profile information and sentiment data and analyzing them using a machine learning algorithm,

[1055] A means of generating multiple career plans suitable for the user based on analysis results using a generative AI model,

[1056] A means comprising a display device for visually presenting the aforementioned career plan on the user's device and collecting feedback from the user,

[1057] By analyzing the collected feedback and emotional data, we can develop methods to revise or improve career plans.

[1058] A means of developing an actionable plan based on the improved final career plan,

[1059] By collaborating with external educational and industry-related networks, we provide users with additional opportunities for skill enhancement.

[1060] A system that includes this.

[1061] (Claim 2)

[1062] The system according to claim 1, wherein the input device uses natural language processing technology to interact with the user via voice or text.

[1063] (Claim 3)

[1064] The system according to claim 1, wherein the means for integrating with external services is integrated with educational platforms and industry networks to provide users with additional learning opportunities.

[1065] "Application example 2 when combining with an emotional engine"

[1066] (Claim 1)

[1067] An input method for entering user attribute information,

[1068] A processing means for receiving the aforementioned attribute information and emotional information and analyzing them,

[1069] A generation means that generates multiple career options based on the results of the analysis,

[1070] A means for presenting the aforementioned options to the user and obtaining a response,

[1071] Based on user responses, means of improvement to enhance the options,

[1072] Based on the improved options, means to formulate a detailed implementation scheme,

[1073] We will collaborate with external educational resources and industry connections to provide additional support.

[1074] Information systems including

[1075] (Claim 2)

[1076] The information system according to claim 1, wherein the input means uses machine learning technology to perform dialogue based on the user's emotional information.

[1077] (Claim 3)

[1078] The information system according to claim 1, wherein the means for coordinating with external resources cooperates with a digital education platform and a professional network to provide learning opportunities to users. [Explanation of Symbols]

[1079] 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. An information terminal for inputting user attribute data, A computing device means for receiving and analyzing the aforementioned attribute data, A means for designing multiple career paths for the user based on the generated analysis results, A means for outputting the aforementioned career path to the user and obtaining their opinion, Based on the aforementioned opinion, means to revise and improve the career path, A means of creating a concrete action plan based on the final career path, A means of providing necessary support in conjunction with the support system, A means of presenting career advancement paths specific to the nursing care field, A system that includes this.

2. The system according to claim 1, wherein the information terminal means utilizes natural language understanding technology to communicate with the user via voice or text.

3. The system according to claim 1, wherein the means for linking with the auxiliary system is linked with an electronic education infrastructure or a professional industry network to provide users with additional learning opportunities.

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