system

Abstract

We provide the system. [Solution] Means of obtaining user information, A means of using a generative model that analyzes the user information and generates an optimal career plan for the user, Means for providing the generated occupational plan to the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Many job seekers find it difficult to objectively evaluate their own skills and aptitudes, and it is also difficult to make a reliable career choice among diverse information. As a result, they cannot select an appropriate career path, leading to problems such as a decrease in job satisfaction and success.

Means for Solving the Problems

[0005] To address this challenge, the present invention provides a system that uses a generative model to collect and analyze user information to generate an optimal career plan. Specifically, it evaluates the user's skills and aptitudes and presents multiple career options based on the results, thereby providing the user with a highly reliable career plan. Furthermore, the career plan includes recommended industries, methods for acquiring skills, and market trend analysis, providing concrete guidance for the user to make a satisfying career choice in the future.

[0006] "User information" refers to data provided by users, such as skills, interests, work history, educational background, and career goals.

[0007] A "generative model" refers to an algorithm or machine learning model that analyzes given data and generates optimized results based on that data.

[0008] A "career plan" outlines a proposed future career path for the user, including recommended industries, job types, methods for acquiring necessary skills, and market trends.

[0009] "Analysis" refers to a series of processes that involve thoroughly analyzing collected data and extracting information from that data.

[0010] "Recommended industries" refer to industry sectors that are considered most suitable for the user's future career path, based on their skills and interests.

[0011] "Market trend analysis" refers to the process of investigating and evaluating current and future trends in a specific industry or occupation, and providing information based on that data. [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0015] In the following embodiments, the labeled 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, the labeled 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, the labeled 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 labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[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 relates to a system that suggests the optimal career path based on the user's skills and interests. The system aims to provide users with a reliable career plan through the coordinated operation of a server and a terminal. Specifically, the terminal collects user information such as skills, interests, and past experience, and transmits this information to the server. The server analyzes this information using a generative model and generates an optimal career plan for the user. The generated career plan includes recommended industries, methods for acquiring necessary skills, and market trend analysis.

[0034] The terminal displays a career plan sent from the server to the user in an easy-to-understand manner. Based on this information, the user can plan their future career actions. As a specific example, consider a case where the user is interested in the field of digital marketing. In this case, the terminal collects detailed information from the user, such as "digital skills" and "past experience," and transmits it to the server. The server generates a career plan incorporating the latest market trends and methods for acquiring digital tools related to that field, and recommends it to the user as the optimal career path. In this way, it supports users in finding a career direction that aligns with their skills and market needs.

[0035] The following describes the processing flow.

[0036] Step 1:

[0037] The device displays a prompt screen to the user, prompting them to enter information such as skills, interests, and career goals. As the user enters this information, the device receives it in real time and temporarily stores it in a database.

[0038] Step 2:

[0039] The terminal packages the collected user information and sends it to the server via a secure communication protocol. During this process, formatting and encryption are applied to maintain data integrity.

[0040] Step 3:

[0041] The server receives user information sent from the terminal and runs a generative model. The generative model uses a pre-trained machine learning algorithm to analyze the user's skills and aptitudes.

[0042] Step 4:

[0043] Based on the analysis results, the server generates a career plan best suited to the user. This plan includes recommended industries, methods for improving necessary skills, and relevant market trends.

[0044] Step 5:

[0045] The server sends the generated job plan to the terminal. Secure communication methods are used during transmission to prevent data tampering.

[0046] Step 6:

[0047] The terminal displays a career plan received from the server to the user. This display allows the user to consider their career path based on the presented information and plan their future actions.

[0048] (Example 1)

[0049] 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."

[0050] Traditional career planning systems have a problem in that they can only provide general career information without adequately considering the diverse skills, interests, and past experiences of users. As a result, it is difficult for users to find the optimal career path, and there is a challenge in obtaining a career plan that is suited to their skills and interests.

[0051] 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.

[0052] In this invention, the server includes a device for collecting user information, a device for analyzing the user information and generating an optimal career plan using a generative model, and a device for transmitting the generated career plan to the user terminal. This makes it possible to generate and present an optimal career plan based on the user's skills, interests, and past experience.

[0053] "User information" refers to data about the user's skills, interests, and past experiences.

[0054] A "generative model" is an algorithm used to analyze user information and generate an optimal career plan.

[0055] A "career plan" is a career path plan that includes suggested industries, methods for acquiring necessary skills, and market trend analysis, all of which are presented to the user.

[0056] An "apparatus" is a machine or system configured to perform a specific process or operation.

[0057] A "user terminal" is a device that users operate to input information or view their career plans.

[0058] In order to implement this invention, the user's terminal and the server must work together. First, the user inputs user information such as their skills, interests, and past experiences via the terminal. This input is done using a web form or a dedicated application interface. After collecting this information, the terminal sends it to the server using the HTTPS protocol for secure communication.

[0059] The server uses a generative AI model built with Python and TENSORFLOW® to analyze the received user information. This generative model receives the input information as prompts and generates an optimal career plan. Specifically, if a user inputs that they are interested in "digital marketing," the generative model will receive the prompt, "Please suggest the skills and market trends necessary for a career in the digital marketing field."

[0060] The generated career plan includes recommended industries, methods for acquiring skills, and market trend analysis. This information is useful to the user as an actionable plan. The server converts the generated career plan back into JSON format and sends it back to the user's terminal.

[0061] On the device, a web interface or mobile application UI is used to display the received career plan in a user-friendly format. Based on this, users can identify career directions that match their skills and market needs, and develop concrete action plans to achieve them. For example, a user interested in digital marketing can consider learning suggested digital tools or taking online courses.

[0062] In this way, this system supports users in maximizing their potential and forming effective career paths.

[0063] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0064] Step 1:

[0065] Users input their skills, interests, and past experiences using their own devices. This information is entered via web forms or dedicated apps on the device, and the data format is JSON. The entered information is organized as the user's personal data.

[0066] Step 2:

[0067] The terminal sends the collected user information to the server. Specifically, the data is transmitted securely using the HTTPS protocol. The input here is user information in JSON format, which is securely transferred to the server.

[0068] Step 3:

[0069] The server analyzes the received user information. A generative AI model, built using Python and TensorFlow, analyzes the input JSON data. In this process, the generative AI model is given the prompt "Please suggest the optimal career plan in the user's specified area of ​​interest," and the analysis results are generated. The output is a basic career plan proposal.

[0070] Step 4:

[0071] The server converts the generated career plan into JSON format and sends it to the user's terminal. The converted data is delivered to the terminal via a secure protocol. The input to this process is the analysis result, and the output is career plan data that the user can visually understand.

[0072] Step 5:

[0073] The terminal displays the received career plan to the user. Specifically, it presents information on the terminal's display using visual methods such as HTML / CSS and graphs. Based on the plan provided, the user can decide on specific next steps. The output is clearly presented career path information.

[0074] (Application Example 1)

[0075] 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."

[0076] Finding the optimal career path based on one's skills and interests is a crucial challenge in today's world. However, providing specific career plans tailored to individual users is not easy, and many systems fail to take into account specific qualifications or market trends. As a result, users may not receive sufficient information when making their career choices.

[0077] 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.

[0078] In this invention, the server includes means for acquiring user information, means for using a generative model that analyzes the user information to generate an optimal career plan for the user, means for transmitting the generated career plan to a visual display device and presenting it visually to the user, and means for the generative model to generate the career plan using information on qualifications and skill development trends related to occupations in a specific field. This enables users to find a career path that aligns with their skills and market needs, and to make career choices based on specific qualification acquisition information and market trends.

[0079] "Means of acquiring user information" refers to a mechanism for collecting information from individual users, such as their skills, interests, and past experiences.

[0080] "Methods using generative models" refer to mechanisms that utilize models used to analyze collected user information and generate optimal career plans based on the results.

[0081] "Means for transmitting the generated job plan to a visual display device and presenting it visually to the user" refers to a mechanism that displays the generated job plan on a terminal or device, presenting the information in a way that the user can visually understand.

[0082] "Means for generating career plans using information on trends in qualifications and skills development related to occupations in specific fields" refers to a mechanism for generating more specific and useful career plans by utilizing information on how to acquire qualifications and necessary skills in a particular occupational field.

[0083] As a form for carrying out the invention, the system that realizes this application example is designed to optimize the user's career plan. This system consists of a user terminal and a server. The details are described below.

[0084] First, users input their skills, interests, and past experiences using their smartphones or other devices. The device collects this information and sends it to the server. React Native is used as the front-end technology for information collection, and the user interface is built around it. This makes it easy for users to provide information.

[0085] On the server side, the received user information is analyzed. This analysis uses a generative AI model, such as OpenAI's GPT-based model. This model generates an optimal career plan based on the user information. This career plan includes specific occupational fields, required qualifications, skills to be acquired, and the latest market trends.

[0086] The generated career plan is sent to the device and presented to the user visually in an easy-to-understand format. The user receives this information and can make the necessary decisions regarding their career choices.

[0087] For example, if a user is interested in the security field, the system provides information such as potential qualifications the user might need (e.g., international information security certifications) and current market trends. In this way, the user can create a career plan that aligns their skill set with market needs.

[0088] The generative AI model performs analysis using user input as prompts. A specific example of a prompt might be: "The user is interested in cybersecurity and has 3 years of IT experience. Please suggest the best career plan for him, including necessary qualifications and market trends." Following this prompt, the model outputs an optimized career plan.

[0089] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0090] Step 1:

[0091] The user enters their carrier-related information.

[0092] Users input their skills, interests, and past experiences using smartphones or other devices. This input information is temporarily stored on the device and converted into a data structure. This input data forms the basis for analysis in the next step.

[0093] Step 2:

[0094] The terminal sends the input information to the server.

[0095] The device sends the collected user information to the server via a REST API. The HTTPS protocol is used to ensure data security. The transmitted data includes user-specific elements (skills, interests, experience) and is transferred to the server in JSON format.

[0096] Step 3:

[0097] The server analyzes the information and generates the optimal career plan.

[0098] The server applies a generative AI model to analyze the received user information. This model uses an OpenAI GPT-based model and uses the user information as prompts to generate an optimal career plan. Based on the analysis of the input data, it generates recommended career fields, required qualifications, learning methods, and market trends.

[0099] Step 4:

[0100] The server sends the job plan to the terminal.

[0101] The generated career plan is sent from the server to the user's terminal. A REST API is used for this secure data transfer. The resulting data is delivered to the terminal in a visually accessible format (e.g., JSON, XML).

[0102] Step 5:

[0103] The device visually presents the user with a career plan.

[0104] The device displays the received career plan on the user interface. The React Native framework enables this, providing users with intuitively understandable information. Based on the presented plan, users are supported in making decisions about their own careers.

[0105] 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.

[0106] This invention relates to a system that recognizes a user's skills and emotional state and provides an optimal career plan based on them. This system incorporates an emotion engine that collects the user's emotional information and utilizes it in generating career plans to provide more personalized suggestions.

[0107] First, the device prompts the user to enter information such as their skills, interests, and career goals. The user enters this information and also provides emotional data such as facial expressions and voice tone to enable sentiment analysis. The device temporarily stores this information and sends it to the server.

[0108] The server uses a generative model to analyze the received user information and create an optimal career plan for the user. Here, an emotion engine is utilized to perform analysis that takes the user's emotional state into account. For example, if a user expresses positive feelings towards a particular occupation, a career plan is generated that prioritizes recommending that occupation.

[0109] The generated career plan includes detailed information on recommended industries and job types, required skills, and how to acquire them, as well as an analysis of market trends. This detailed plan is sent to the user's device and displayed to them.

[0110] As a concrete example, consider a case where a user is interested in the IT industry and shows a positive attitude towards data science. In this case, the server generates a data science-related career plan and presents it to the user, including learning courses for new programming languages ​​and relevant market trends.

[0111] This system allows users to make career choices based on their skills, interests, and emotions, enabling them to create more accurate and satisfying career plans.

[0112] The following describes the processing flow.

[0113] Step 1:

[0114] The device displays a series of questions to the user, prompting them to input skills, interests, career goals, and emotional data. Emotional data is collected using a camera and microphone to analyze the user's facial expressions and voice tone.

[0115] Step 2:

[0116] The device temporarily stores all collected user information and sentiment data, then encrypts and transmits it to the server. The transmitted data is protected by security measures to safeguard personal information.

[0117] Step 3:

[0118] The server activates a generative model to analyze the received data. The generative model first analyzes information such as skills and interests, and then uses an emotion engine to analyze emotional data.

[0119] Step 4:

[0120] The generative model creates an optimal career plan for the user based on the results of data analysis. Based on the analysis results of the emotion engine, it prioritizes occupations and fields in which the user expresses more positive emotions.

[0121] Step 5:

[0122] The server sends the generated career plan to the terminal. This career plan includes recommended industries, job types, steps to acquire the necessary skills, and market trends.

[0123] Step 6:

[0124] The terminal displays a career plan sent from the server to the user. Based on the presented career plan, the user can consider their future career plan and create a concrete action plan.

[0125] (Example 2)

[0126] 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".

[0127] Providing personalized career plans tailored to each user's skills, interests, and even emotional state presents a challenge that transcends the limitations of conventional technologies. This challenge necessitates a more precise understanding of users' emotions and their reflection in career choices. Career recommendations that ignore emotions may not fully meet users' needs, potentially leading to unsatisfactory career development.

[0128] 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.

[0129] In this invention, the server includes means for acquiring user characteristic information and emotional information, means for using a generative AI model that analyzes the acquired information to generate a career plan that takes into account the user's emotional state, and means for visually displaying the generated career plan to the user. This makes it possible to provide a more accurate career plan based on the individual user's skills and emotions.

[0130] "User characteristic information" refers to personal information about the user, such as their skills, interests, and career goals.

[0131] "Emotional information" refers to data that indicates the user's emotional state, and is obtained through facial expressions, tone of voice, and other means.

[0132] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to analyze input information and generate data suitable for a specific purpose.

[0133] A "career plan" is a proposal that includes suitable occupations for the user, necessary skills, methods for acquiring those skills, and market trends.

[0134] "Means of visual display" refers to technologies and methods for displaying information on a screen as graphics or text in order to make the generated information easier to understand.

[0135] This invention relates to a system that acquires user characteristic information and emotional state, and provides an optimal career plan based on that information. This system mainly consists of a terminal, a server, and a generative AI model.

[0136] First, the device displays prompts for the user to input information such as skills, interests, and career goals. The user then inputs the necessary information and provides emotional information such as facial expressions and voice tone using the camera and microphone. This allows the device to acquire detailed characteristic and emotional information about the user.

[0137] Next, the terminal temporarily stores this information and sends it to the server using a communication protocol such as SSL / TLS to maintain security.

[0138] The server uses a generative AI model to analyze the received information. The generative AI model performs data calculations to generate the most appropriate career plan based on the user's characteristics and emotional state. In particular, the emotion engine plays a crucial role in the analysis process, allowing for the prioritization of careers that elicit positive emotions.

[0139] The generated career plan includes recommended industries and job types, required skills, methods for acquiring those skills, and market trend analysis. The server sends this information to the terminal, which displays it visually to the user as graphics and text. This helps the user in making career choices and gain a clearer understanding of their career direction.

[0140] As a concrete example, consider a case where a user is interested in the IT industry and shows a particularly positive attitude towards data science. In this case, the server can generate a career plan related to data science and present it to the user along with information on how to learn relevant programming languages ​​and market trends. An example of a prompt might be, "Please tell us your current skills and areas of interest. We will also capture your emotions through facial expressions and voice, and propose a career plan based on that."

[0141] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0142] Step 1:

[0143] The device displays prompts for the user to input their skills, interests, and career goals. The user then enters the necessary information in text format. The camera and microphone are also activated to record the user's facial expressions and voice tone. The data entered includes the user's characteristics and emotional information.

[0144] Step 2:

[0145] The device temporarily stores the acquired characteristic and sentiment information in local storage. Here, the data format is prepared and ready for transmission. Specifically, text data is converted to JSON format, and sentiment data is summarized as statistical features. This ensures the data has a structure suitable for transmission to the server.

[0146] Step 3:

[0147] The terminal sends data to the server using the SSL / TLS protocol. Encoding is applied to enhance the security of the data transmission. The input data includes user characteristic information and emotional information, and once this is securely sent to the server, step 2 (data preparation) is complete.

[0148] Step 4:

[0149] The server passes the received data to a generating AI model for analysis. Here, data calculations are performed based on user characteristic information and emotional information, while comparing it with a vast amount of historical data. In particular, an emotion engine is used to calculate an emotional score for specific occupations and analyze suitability for those occupations, thereby selecting a suitable occupation.

[0150] Step 5:

[0151] The server creates an optimal career plan for the user based on the analysis results of the generated AI model. This plan includes recommended industries and job types, required skills, learning methods, and market trend analysis results. The career plan resulting from the data processing is output as text and graphics.

[0152] Step 6:

[0153] The server sends the generated career plan to the terminal. Again, the data is encrypted using the SSL / TLS protocol. Once the career plan data successfully reaches the terminal, the user can receive career information best suited to them.

[0154] Step 7:

[0155] The terminal decodes the received career plan and displays it visually to the user. The display format is a dashboard incorporating graphs and charts, designed to allow users to quickly understand the information. Ultimately, this enables users to decide on their next career step with a concrete action plan and market conditions in mind.

[0156] (Application Example 2)

[0157] 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".

[0158] Conventional career planning support systems often make career suggestions without considering the user's emotional state, resulting in a failure to adequately address the individual needs of users. Furthermore, there was a lack of means to show how the proposed career plan fits into the user's daily life. This invention aims to provide a career plan tailored to the user by utilizing emotional data, and to support the actual application of that plan in daily life.

[0159] 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.

[0160] In this invention, the server includes means for acquiring user information and emotional state; means for using a generative model that analyzes the user information and emotional data to generate an optimal career plan for the user; means for providing the generated career plan to an output device; and means for displaying the presented career plan to the user in real time using a mobile in-home device. This makes it possible to provide a personalized career plan based on the user's emotional state and to smoothly integrate that plan into daily life.

[0161] "User information" refers to information about the user, such as their skills, interests, and career goals, that is necessary to generate a career plan.

[0162] "Emotional state" refers to data that indicates the state of emotions and mood, analyzed from the user's facial expressions, tone of voice, and other factors.

[0163] A "generative model" is a computational model that includes algorithms for generating an optimal career plan based on user information and emotional state.

[0164] An "output device" refers to a device that has an interface for displaying the generated job plan to the user.

[0165] "In-home mobile devices" refer to devices such as robots that are mobile for the purpose of conveying information to users within the home environment.

[0166] "Emotional data" refers to information that represents the user's emotional state, and includes data such as voice tone and facial expression recognition results.

[0167] A "career plan" is a proposal document that takes into account the user's skills and emotional state, and includes information on recommended occupations, methods for acquiring skills, and market trends.

[0168] The system implementing this invention acquires user usage information and emotional state, analyzes this information, and provides the user with an optimal career plan.

[0169] The server receives usage information, including skills, interests, and career goals, based on user input. It also uses cameras and microphones to acquire emotional information such as facial expressions and voice tone. This utilizes facial recognition technologies such as OpenCV and speech recognition APIs (e.g., Google® Speech-to-Text).

[0170] The server uses this information to run a generative AI model that generates an optimal career plan for the user. The generative model analyzes the user's skill set and emotional data to suggest appropriate occupations and learning methods. The technology used here includes sentiment analysis APIs (e.g., Microsoft® Azure® sentiment analysis service).

[0171] The generated career plan is presented to the user through an output device. Mobile devices such as robots installed in the home provide this information to the user in real time and play a role in explaining the career plan in more detail.

[0172] As a concrete example, let's consider a scenario where a user is considering a career change into a new technological field. For instance, if the user shows interest in the IT field and has a positive attitude towards artificial intelligence, the server would suggest career possibilities in the data science field. Along with this, information on necessary skill acquisition courses and market trends would also be provided.

[0173] By utilizing generative AI models, the following prompt statements can be used:

[0174] "User skills: Programming, data analysis"

[0175] Interest: AI, new technology

[0176] Emotional state: Positive (based on voice tone and facial expression analysis)

[0177] Plan to be generated: Create an AI-related career plan, including necessary skills and market trends.

[0178] This system allows users in different emotional states to receive career plans tailored to their individual needs, making career choices more effective and satisfying.

[0179] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0180] Step 1:

[0181] The device displays prompts to the user, prompting them to enter usage information such as skills, interests, and career goals. Once the user completes the input, the device activates the camera and microphone to record the user's facial expressions and tone of voice. This data is temporarily stored as usage information and emotional data.

[0182] Step 2:

[0183] The device sends temporarily stored usage information and emotional data to the server. The server then begins analysis based on the received data. A key aspect of this process is the use of speech recognition APIs and facial recognition software (e.g., Google Speech-to-Text, OpenCV) to analyze the user's emotions and skills.

[0184] Step 3:

[0185] The server utilizes a generative AI model to generate an optimal career plan using the received user information and emotional information as input data. During this process, emotional states are considered using an emotion analysis API (e.g., Microsoft Azure's emotion analysis service). This step outputs the recommended career plan as a result of the analysis.

[0186] Step 4:

[0187] The server transmits the generated job plan to a terminal or in-home output device. This allows the user to access a personalized job plan in real time. In-home mobile devices (e.g., robots) play a role in providing visual and auditory information to the user, explaining the proposed plan in detail.

[0188] Step 5:

[0189] Based on the career plan they receive, users determine their next course of action. The plan from the server includes specific skill acquisition methods and market trends, which users can refer to when making career decisions.

[0190] This allows users to receive a career plan optimized to their own emotional state, which can then be used to help them make future career choices.

[0191] 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.

[0192] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0193] 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.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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".

[0207] This invention relates to a system that suggests the optimal career path based on the user's skills and interests. The system aims to provide users with a reliable career plan through the coordinated operation of a server and a terminal. Specifically, the terminal collects user information such as skills, interests, and past experience, and transmits this information to the server. The server analyzes this information using a generative model and generates an optimal career plan for the user. The generated career plan includes recommended industries, methods for acquiring necessary skills, and market trend analysis.

[0208] The terminal displays a career plan sent from the server to the user in an easy-to-understand manner. Based on this information, the user can plan their future career actions. As a specific example, consider a case where the user is interested in the field of digital marketing. In this case, the terminal collects detailed information from the user, such as "digital skills" and "past experience," and transmits it to the server. The server generates a career plan incorporating the latest market trends and methods for acquiring digital tools related to that field, and recommends it to the user as the optimal career path. In this way, it supports users in finding a career direction that aligns with their skills and market needs.

[0209] The following describes the processing flow.

[0210] Step 1:

[0211] The device displays a prompt screen to the user, prompting them to enter information such as skills, interests, and career goals. As the user enters this information, the device receives it in real time and temporarily stores it in a database.

[0212] Step 2:

[0213] The terminal packages the collected user information and sends it to the server via a secure communication protocol. During this process, formatting and encryption are applied to maintain data integrity.

[0214] Step 3:

[0215] The server receives user information sent from the terminal and runs a generative model. The generative model uses a pre-trained machine learning algorithm to analyze the user's skills and aptitudes.

[0216] Step 4:

[0217] Based on the analysis results, the server generates a career plan best suited to the user. This plan includes recommended industries, methods for improving necessary skills, and relevant market trends.

[0218] Step 5:

[0219] The server sends the generated job plan to the terminal. Secure communication methods are used during transmission to prevent data tampering.

[0220] Step 6:

[0221] The terminal displays a career plan received from the server to the user. This display allows the user to consider their career path based on the presented information and plan their future actions.

[0222] (Example 1)

[0223] 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."

[0224] Traditional career planning systems have a problem in that they can only provide general career information without adequately considering the diverse skills, interests, and past experiences of users. As a result, it is difficult for users to find the optimal career path, and there is a challenge in obtaining a career plan that is suited to their skills and interests.

[0225] 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.

[0226] In this invention, the server includes a device for collecting user information, a device for analyzing the user information and generating an optimal career plan using a generative model, and a device for transmitting the generated career plan to the user terminal. This makes it possible to generate and present an optimal career plan based on the user's skills, interests, and past experience.

[0227] "User information" refers to data about the user's skills, interests, and past experiences.

[0228] A "generative model" is an algorithm used to analyze user information and generate an optimal career plan.

[0229] A "career plan" is a career path plan that includes suggested industries, methods for acquiring necessary skills, and market trend analysis, all of which are presented to the user.

[0230] An "apparatus" is a machine or system configured to perform a specific process or operation.

[0231] A "user terminal" is a device that users operate to input information or view their career plans.

[0232] In order to implement this invention, the user's terminal and the server must work together. First, the user inputs user information such as their skills, interests, and past experiences via the terminal. This input is done using a web form or a dedicated application interface. After collecting this information, the terminal sends it to the server using the HTTPS protocol for secure communication.

[0233] The server uses a generative AI model built with Python and TensorFlow to analyze the received user information. This generative model receives the input information as prompts and generates an optimal career plan. Specifically, if a user inputs that they are interested in "digital marketing," the generative model will receive the prompt, "Please suggest the skills and market trends necessary for a career in the digital marketing field."

[0234] The generated career plan includes recommended industries, methods for acquiring skills, and market trend analysis. This information is useful to the user as an actionable plan. The server converts the generated career plan back into JSON format and sends it back to the user's terminal.

[0235] On the device, a web interface or mobile application UI is used to display the received career plan in a user-friendly format. Based on this, users can identify career directions that match their skills and market needs, and develop concrete action plans to achieve them. For example, a user interested in digital marketing can consider learning suggested digital tools or taking online courses.

[0236] In this way, this system supports users in maximizing their potential and forming effective career paths.

[0237] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0238] Step 1:

[0239] Users input their skills, interests, and past experiences using their own devices. This information is entered via web forms or dedicated apps on the device, and the data format is JSON. The entered information is organized as the user's personal data.

[0240] Step 2:

[0241] The terminal sends the collected user information to the server. Specifically, the data is transmitted securely using the HTTPS protocol. The input here is user information in JSON format, which is securely transferred to the server.

[0242] Step 3:

[0243] The server analyzes the received user information. A generative AI model, built using Python and TensorFlow, analyzes the input JSON data. In this process, the generative AI model is given the prompt "Please suggest the optimal career plan in the user's specified area of ​​interest," and the analysis results are generated. The output is a basic career plan proposal.

[0244] Step 4:

[0245] The server converts the generated career plan into JSON format and sends it to the user's terminal. The converted data is delivered to the terminal via a secure protocol. The input to this process is the analysis result, and the output is career plan data that the user can visually understand.

[0246] Step 5:

[0247] The terminal displays the received career plan to the user. Specifically, it presents information on the terminal's display using visual methods such as HTML / CSS and graphs. Based on the plan provided, the user can decide on specific next steps. The output is clearly presented career path information.

[0248] (Application Example 1)

[0249] 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."

[0250] Finding the optimal career path based on one's skills and interests is a crucial challenge in today's world. However, providing specific career plans tailored to individual users is not easy, and many systems fail to take into account specific qualifications or market trends. As a result, users may not receive sufficient information when making their career choices.

[0251] 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.

[0252] In this invention, the server includes means for acquiring user information, means for using a generative model that analyzes the user information to generate an optimal career plan for the user, means for transmitting the generated career plan to a visual display device and presenting it visually to the user, and means for the generative model to generate the career plan using information on qualifications and skill development trends related to occupations in a specific field. This enables users to find a career path that aligns with their skills and market needs, and to make career choices based on specific qualification acquisition information and market trends.

[0253] "Means of acquiring user information" refers to a mechanism for collecting information from individual users, such as their skills, interests, and past experiences.

[0254] "Methods using generative models" refer to mechanisms that utilize models used to analyze collected user information and generate optimal career plans based on the results.

[0255] "Means for transmitting the generated job plan to a visual display device and presenting it visually to the user" refers to a mechanism that displays the generated job plan on a terminal or device, presenting the information in a way that the user can visually understand.

[0256] "Means for generating career plans using information on trends in qualifications and skills development related to occupations in specific fields" refers to a mechanism for generating more specific and useful career plans by utilizing information on how to acquire qualifications and necessary skills in a particular occupational field.

[0257] As a form for carrying out the invention, the system that realizes this application example is designed to optimize the user's career plan. This system consists of a user terminal and a server. The details are described below.

[0258] First, users input their skills, interests, and past experiences using their smartphones or other devices. The device collects this information and sends it to the server. React Native is used as the front-end technology for information collection, and the user interface is built around it. This makes it easy for users to provide information.

[0259] On the server side, the received user information is analyzed. A generative AI model is used for this analysis, for example, OpenAI's GPT-based model. This model generates an optimal career plan based on the user information. This career plan includes specific occupational fields, required qualifications, skills to be acquired, and the latest market trends.

[0260] The generated career plan is sent to the device and presented to the user visually in an easy-to-understand format. The user receives this information and can make the necessary decisions regarding their career choices.

[0261] For example, if a user is interested in the security field, the system provides information such as potential qualifications the user might need (e.g., international information security certifications) and current market trends. In this way, the user can create a career plan that aligns their skill set with market needs.

[0262] The generative AI model performs analysis using user input as prompts. A specific example of a prompt might be: "The user is interested in cybersecurity and has 3 years of IT experience. Please suggest the best career plan for him, including necessary qualifications and market trends." Following this prompt, the model outputs an optimized career plan.

[0263] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0264] Step 1:

[0265] The user enters their carrier-related information.

[0266] Users input their skills, interests, and past experiences using smartphones or other devices. This input information is temporarily stored on the device and converted into a data structure. This input data forms the basis for analysis in the next step.

[0267] Step 2:

[0268] The terminal sends the input information to the server.

[0269] The device sends the collected user information to the server via a REST API. The HTTPS protocol is used to ensure data security. The transmitted data includes user-specific elements (skills, interests, experience) and is transferred to the server in JSON format.

[0270] Step 3:

[0271] The server analyzes the information and generates the optimal career plan.

[0272] The server applies a generative AI model to analyze the received user information. This model uses an OpenAI GPT-based model and uses the user information as prompts to generate an optimal career plan. Based on the analysis of the input data, it generates recommended career fields, required qualifications, learning methods, and market trends.

[0273] Step 4:

[0274] The server sends the job plan to the terminal.

[0275] The generated career plan is sent from the server to the user's terminal. A REST API is used for this secure data transfer. The resulting data is delivered to the terminal in a visually accessible format (e.g., JSON, XML).

[0276] Step 5:

[0277] The device visually presents the user with a career plan.

[0278] The device displays the received career plan on the user interface. The React Native framework enables this, providing users with intuitively understandable information. Based on the presented plan, users are supported in making decisions about their own careers.

[0279] 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.

[0280] This invention relates to a system that recognizes a user's skills and emotional state and provides an optimal career plan based on them. This system incorporates an emotion engine that collects the user's emotional information and utilizes it in generating career plans to provide more personalized suggestions.

[0281] First, the device prompts the user to enter information such as their skills, interests, and career goals. The user enters this information and also provides emotional data such as facial expressions and voice tone to enable sentiment analysis. The device temporarily stores this information and sends it to the server.

[0282] The server uses a generative model to analyze the received user information and create an optimal career plan for the user. Here, an emotion engine is utilized to perform analysis that takes the user's emotional state into account. For example, if a user expresses positive feelings towards a particular occupation, a career plan is generated that prioritizes recommending that occupation.

[0283] The generated career plan includes detailed information about recommended industries, job types, required skills, and acquisition methods, and further includes the analysis results of market trends. This detailed plan is transmitted to the terminal and displayed to the user.

[0284] As a specific example, consider the case where the user is interested in the IT industry and shows a positive sentiment in data science. In this case, the server generates a career plan related to data science and presents it to the user, including a learning course for a new programming language and relevant market trends.

[0285] With this system, the user can make a career choice based on their skills, interests, and emotions, and obtain a more accurate and satisfactory career plan.

[0286] The processing flow will be described below.

[0287] Step 1:

[0288] The terminal displays a series of questions to the user and asks them to input skills, interests, career goals, and emotion data. The emotion data is obtained using a camera or microphone for analyzing the user's expression and voice tone.

[0289] Step 2:

[0290] The terminal temporarily stores all the user information and emotion data collected and encrypts and transmits it to the server. Security measures for personal information protection are implemented for the transmitted data.

[0291] Step 3:

[0292] The server activates a generation model to analyze the received data. The generation model first analyzes information such as skills and interests, and then analyzes the emotion data using an emotion engine.

[0293] Step 4:

[0294] The generative model creates an optimal career plan for the user based on the results of data analysis. Based on the analysis results of the emotion engine, it prioritizes occupations and fields in which the user expresses more positive emotions.

[0295] Step 5:

[0296] The server sends the generated career plan to the terminal. This career plan includes recommended industries, job types, steps to acquire the necessary skills, and market trends.

[0297] Step 6:

[0298] The terminal displays a career plan sent from the server to the user. Based on the presented career plan, the user can consider their future career plan and create a concrete action plan.

[0299] (Example 2)

[0300] 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".

[0301] Providing personalized career plans tailored to each user's skills, interests, and even emotional state presents a challenge that transcends the limitations of conventional technologies. This challenge necessitates a more precise understanding of users' emotions and their reflection in career choices. Career recommendations that ignore emotions may not fully meet users' needs, potentially leading to unsatisfactory career development.

[0302] 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.

[0303] In this invention, the server includes means for acquiring the characteristic information and emotional information of the user, means for using a generation AI model that analyzes the acquired information and generates a career plan considering the emotional state of the user, and means for visually displaying the generated career plan to the user. As a result, it becomes possible to provide a more accurate career plan based on the skills and emotions of individual users.

[0304] The "characteristic information of the user" refers to information about the individual such as the user's skills, interests, career goals, etc.

[0305] The "emotional information" is data indicating the emotional state of the user, and is acquired through expressions, voice tones, etc.

[0306] The "generation AI model" refers to an algorithm or system using artificial intelligence technology for analyzing the input information and generating data suitable for a specific application.

[0307] The "career plan" indicates a proposal including a career suitable for the user, necessary skills, acquisition methods, and market trends.

[0308] The "means for visual display" refers to a technology or method for displaying information as graphics or text on the screen in order to make the generated information easier to view.

[0309] This invention relates to a system that acquires the characteristic information and emotional state of the user and provides an optimal career plan based on it. This system is mainly composed of a terminal, a server, and a generation AI model. [[ID=2​​​​​ Next, the terminal temporarily stores this information and sends it to the server using a communication protocol such as SSL / TLS to maintain security.

[0312] The server uses a generative AI model to analyze the received information. The generative AI model performs data calculations to generate the most appropriate career plan based on the user's characteristics and emotional state. In particular, the emotion engine plays a crucial role in the analysis process, allowing for the prioritization of careers that elicit positive emotions.

[0313] The generated career plan includes recommended industries and job types, required skills, methods for acquiring those skills, and market trend analysis. The server sends this information to the terminal, which displays it visually to the user as graphics and text. This helps the user in making career choices and gain a clearer understanding of their career direction.

[0314] As a concrete example, consider a case where a user is interested in the IT industry and shows a particularly positive attitude towards data science. In this case, the server can generate a career plan related to data science and present it to the user along with information on how to learn relevant programming languages ​​and market trends. An example of a prompt might be, "Please tell us your current skills and areas of interest. We will also capture your emotions through facial expressions and voice, and propose a career plan based on that."

[0315] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0316] Step 1:

[0317] The device displays prompts for the user to input their skills, interests, and career goals. The user then enters the necessary information in text format. The camera and microphone are also activated to record the user's facial expressions and voice tone. The data entered includes the user's characteristics and emotional information.

[0318] Step 2:

[0319] The device temporarily stores the acquired characteristic and sentiment information in local storage. Here, the data format is prepared and ready for transmission. Specifically, text data is converted to JSON format, and sentiment data is summarized as statistical features. This ensures the data has a structure suitable for transmission to the server.

[0320] Step 3:

[0321] The terminal sends data to the server using the SSL / TLS protocol. Encoding is applied to enhance the security of the data transmission. The input data includes user characteristic information and emotional information, and once this is securely sent to the server, step 2 (data preparation) is complete.

[0322] Step 4:

[0323] The server passes the received data to a generating AI model for analysis. Here, data calculations are performed based on user characteristic information and emotional information, while comparing it with a vast amount of historical data. In particular, an emotion engine is used to calculate an emotional score for specific occupations and analyze suitability for those occupations, thereby selecting a suitable occupation.

[0324] Step 5:

[0325] The server creates an optimal career plan for the user based on the analysis results of the generated AI model. This plan includes recommended industries and job types, required skills, learning methods, and market trend analysis results. The career plan resulting from the data processing is output as text and graphics.

[0326] Step 6:

[0327] The server sends the generated career plan to the terminal. Again, the data is encrypted using the SSL / TLS protocol. Once the career plan data successfully reaches the terminal, the user can receive career information best suited to them.

[0328] Step 7:

[0329] The terminal decodes the received career plan and displays it visually to the user. The display format is a dashboard incorporating graphs and charts, designed to allow users to quickly understand the information. Ultimately, this enables users to decide on their next career step with a concrete action plan and market conditions in mind.

[0330] (Application Example 2)

[0331] 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."

[0332] Conventional career planning support systems often make career suggestions without considering the user's emotional state, resulting in a failure to adequately address the individual needs of users. Furthermore, there was a lack of means to show how the proposed career plan fits into the user's daily life. This invention aims to provide a career plan tailored to the user by utilizing emotional data, and to support the actual application of that plan in daily life.

[0333] 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.

[0334] In this invention, the server includes means for acquiring user information and emotional state; means for using a generative model that analyzes the user information and emotional data to generate an optimal career plan for the user; means for providing the generated career plan to an output device; and means for displaying the presented career plan to the user in real time using a mobile in-home device. This makes it possible to provide a personalized career plan based on the user's emotional state and to smoothly integrate that plan into daily life.

[0335] "User information" refers to information about the user, such as their skills, interests, and career goals, that is necessary to generate a career plan.

[0336] "Emotional state" refers to data that indicates the state of emotions and mood, analyzed from the user's facial expressions, tone of voice, and other factors.

[0337] A "generative model" is a computational model that includes algorithms for generating an optimal career plan based on user information and emotional state.

[0338] An "output device" refers to a device that has an interface for displaying the generated job plan to the user.

[0339] "In-home mobile devices" refer to devices such as robots that are mobile for the purpose of conveying information to users within the home environment.

[0340] "Emotional data" refers to information that represents the user's emotional state, and includes data such as voice tone and facial expression recognition results.

[0341] A "career plan" is a proposal document that takes into account the user's skills and emotional state, and includes information on recommended occupations, methods for acquiring skills, and market trends.

[0342] The system implementing this invention acquires user usage information and emotional state, analyzes this information, and provides the user with an optimal career plan.

[0343] The server receives usage information, including skills, interests, and career goals, based on user input. It also uses the camera and microphone to acquire emotional information such as facial expressions and voice tone. This utilizes facial recognition technologies such as OpenCV and speech recognition APIs (e.g., Google Speech-to-Text).

[0344] The server uses this information to run a generative AI model that generates an optimal career plan for the user. The generative model analyzes the user's skill set and emotional data to suggest appropriate occupations and learning methods. The technology used here includes sentiment analysis APIs (e.g., Microsoft Azure's sentiment analysis service).

[0345] The generated career plan is presented to the user through an output device. Mobile devices such as robots installed in the home provide this information to the user in real time and play a role in explaining the career plan in more detail.

[0346] As a concrete example, let's consider a scenario where a user is considering a career change into a new technological field. For instance, if the user shows interest in the IT field and has a positive attitude towards artificial intelligence, the server would suggest career possibilities in the data science field. Along with this, information on necessary skill acquisition courses and market trends would also be provided.

[0347] By utilizing generative AI models, the following prompt statements can be used:

[0348] "User skills: Programming, data analysis"

[0349] Interest: AI, new technology

[0350] Emotional state: Positive (based on voice tone and facial expression analysis)

[0351] Plan to be generated: Create an AI-related career plan, including necessary skills and market trends.

[0352] This system allows users in different emotional states to receive career plans tailored to their individual needs, making career choices more effective and satisfying.

[0353] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0354] Step 1:

[0355] The device displays prompts to the user, prompting them to enter usage information such as skills, interests, and career goals. Once the user completes the input, the device activates the camera and microphone to record the user's facial expressions and tone of voice. This data is temporarily stored as usage information and emotional data.

[0356] Step 2:

[0357] The device sends temporarily stored usage information and emotional data to the server. The server then begins analysis based on the received data. A key aspect of this process is the use of speech recognition APIs and facial recognition software (e.g., Google Speech-to-Text, OpenCV) to analyze the user's emotions and skills.

[0358] Step 3:

[0359] The server utilizes a generative AI model to generate an optimal career plan using the received user information and emotional information as input data. During this process, emotional states are considered using an emotion analysis API (e.g., Microsoft Azure's emotion analysis service). This step outputs the recommended career plan as a result of the analysis.

[0360] Step 4:

[0361] The server transmits the generated job plan to a terminal or in-home output device. This allows the user to access a personalized job plan in real time. In-home mobile devices (e.g., robots) play a role in providing visual and auditory information to the user, explaining the proposed plan in detail.

[0362] Step 5:

[0363] Based on the career plan they receive, users determine their next course of action. The plan from the server includes specific skill acquisition methods and market trends, which users can refer to when making career decisions.

[0364] This allows users to receive a career plan optimized to their own emotional state, which can then be used to help them make future career choices.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] [Third Embodiment]

[0369] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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".

[0381] This invention relates to a system that suggests the optimal career path based on the user's skills and interests. The system aims to provide users with a reliable career plan through the coordinated operation of a server and a terminal. Specifically, the terminal collects user information such as skills, interests, and past experience, and transmits this information to the server. The server analyzes this information using a generative model and generates an optimal career plan for the user. The generated career plan includes recommended industries, methods for acquiring necessary skills, and market trend analysis.

[0382] The terminal displays a career plan sent from the server to the user in an easy-to-understand manner. Based on this information, the user can plan their future career actions. As a specific example, consider a case where the user is interested in the field of digital marketing. In this case, the terminal collects detailed information from the user, such as "digital skills" and "past experience," and transmits it to the server. The server generates a career plan incorporating the latest market trends and methods for acquiring digital tools related to that field, and recommends it to the user as the optimal career path. In this way, it supports users in finding a career direction that aligns with their skills and market needs.

[0383] The following describes the processing flow.

[0384] Step 1:

[0385] The device displays a prompt screen to the user, prompting them to enter information such as skills, interests, and career goals. As the user enters this information, the device receives it in real time and temporarily stores it in a database.

[0386] Step 2:

[0387] The terminal packages the collected user information and sends it to the server via a secure communication protocol. During this process, formatting and encryption are applied to maintain data integrity.

[0388] Step 3:

[0389] The server receives user information sent from the terminal and runs a generative model. The generative model uses a pre-trained machine learning algorithm to analyze the user's skills and aptitudes.

[0390] Step 4:

[0391] Based on the analysis results, the server generates a career plan best suited to the user. This plan includes recommended industries, methods for improving necessary skills, and relevant market trends.

[0392] Step 5:

[0393] The server sends the generated job plan to the terminal. Secure communication methods are used during transmission to prevent data tampering.

[0394] Step 6:

[0395] The terminal displays a career plan received from the server to the user. This display allows the user to consider their career path based on the presented information and plan their future actions.

[0396] (Example 1)

[0397] 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."

[0398] Traditional career planning systems have a problem in that they can only provide general career information without adequately considering the diverse skills, interests, and past experiences of users. As a result, it is difficult for users to find the optimal career path, and there is a challenge in obtaining a career plan that is suited to their skills and interests.

[0399] 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.

[0400] In this invention, the server includes a device for collecting user information, a device for analyzing the user information and generating an optimal career plan using a generative model, and a device for transmitting the generated career plan to the user terminal. This makes it possible to generate and present an optimal career plan based on the user's skills, interests, and past experience.

[0401] "User information" refers to data about the user's skills, interests, and past experiences.

[0402] A "generative model" is an algorithm used to analyze user information and generate an optimal career plan.

[0403] A "career plan" is a career path plan that includes suggested industries, methods for acquiring necessary skills, and market trend analysis, all of which are presented to the user.

[0404] An "apparatus" is a machine or system configured to perform a specific process or operation.

[0405] A "user terminal" is a device that users operate to input information or view their career plans.

[0406] In order to implement this invention, the user's terminal and the server must work together. First, the user inputs user information such as their skills, interests, and past experiences via the terminal. This input is done using a web form or a dedicated application interface. After collecting this information, the terminal sends it to the server using the HTTPS protocol for secure communication.

[0407] The server uses a generative AI model built with Python and TensorFlow to analyze the received user information. This generative model receives the input information as prompts and generates an optimal career plan. Specifically, if a user inputs that they are interested in "digital marketing," the generative model will receive the prompt, "Please suggest the skills and market trends necessary for a career in the digital marketing field."

[0408] The generated career plan includes recommended industries, methods for acquiring skills, and market trend analysis. This information is useful to the user as an actionable plan. The server converts the generated career plan back into JSON format and sends it back to the user's terminal.

[0409] On the device, a web interface or mobile application UI is used to display the received career plan in a user-friendly format. Based on this, users can identify career directions that match their skills and market needs, and develop concrete action plans to achieve them. For example, a user interested in digital marketing can consider learning suggested digital tools or taking online courses.

[0410] In this way, this system supports users in maximizing their potential and forming effective career paths.

[0411] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0412] Step 1:

[0413] Users input their skills, interests, and past experiences using their own devices. This information is entered via web forms or dedicated apps on the device, and the data format is JSON. The entered information is organized as the user's personal data.

[0414] Step 2:

[0415] The terminal sends the collected user information to the server. Specifically, the data is transmitted securely using the HTTPS protocol. The input here is user information in JSON format, which is securely transferred to the server.

[0416] Step 3:

[0417] The server analyzes the received user information. A generative AI model, built using Python and TensorFlow, analyzes the input JSON data. In this process, the generative AI model is given the prompt "Please suggest the optimal career plan in the user's specified area of ​​interest," and the analysis results are generated. The output is a basic career plan proposal.

[0418] Step 4:

[0419] The server converts the generated career plan into JSON format and sends it to the user's terminal. The converted data is delivered to the terminal via a secure protocol. The input to this process is the analysis result, and the output is career plan data that the user can visually understand.

[0420] Step 5:

[0421] The terminal displays the received career plan to the user. Specifically, it presents information on the terminal's display using visual methods such as HTML / CSS and graphs. Based on the plan provided, the user can decide on specific next steps. The output is clearly presented career path information.

[0422] (Application Example 1)

[0423] 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."

[0424] Finding the optimal career path based on one's skills and interests is a crucial challenge in today's world. However, providing specific career plans tailored to individual users is not easy, and many systems fail to take into account specific qualifications or market trends. As a result, users may not receive sufficient information when making their career choices.

[0425] 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.

[0426] In this invention, the server includes means for acquiring user information, means for using a generative model that analyzes the user information to generate an optimal career plan for the user, means for transmitting the generated career plan to a visual display device and presenting it visually to the user, and means for the generative model to generate the career plan using information on qualifications and skill development trends related to occupations in a specific field. This enables users to find a career path that aligns with their skills and market needs, and to make career choices based on specific qualification acquisition information and market trends.

[0427] "Means of acquiring user information" refers to a mechanism for collecting information from individual users, such as their skills, interests, and past experiences.

[0428] "Methods using generative models" refer to mechanisms that utilize models used to analyze collected user information and generate optimal career plans based on the results.

[0429] "Means for transmitting the generated job plan to a visual display device and presenting it visually to the user" refers to a mechanism that displays the generated job plan on a terminal or device, presenting the information in a way that the user can visually understand.

[0430] "Means for generating career plans using information on trends in qualifications and skills development related to occupations in specific fields" refers to a mechanism for generating more specific and useful career plans by utilizing information on how to acquire qualifications and necessary skills in a particular occupational field.

[0431] As a form for carrying out the invention, the system that realizes this application example is designed to optimize the user's career plan. This system consists of a user terminal and a server. The details are described below.

[0432] First, users input their skills, interests, and past experiences using their smartphones or other devices. The device collects this information and sends it to the server. React Native is used as the front-end technology for information collection, and the user interface is built around it. This makes it easy for users to provide information.

[0433] On the server side, the received user information is analyzed. A generative AI model is used for this analysis, for example, OpenAI's GPT-based model. This model generates an optimal career plan based on the user information. This career plan includes specific occupational fields, required qualifications, skills to be acquired, and the latest market trends.

[0434] The generated career plan is sent to the device and presented to the user visually in an easy-to-understand format. The user receives this information and can make the necessary decisions regarding their career choices.

[0435] For example, if a user is interested in the security field, the system provides information such as potential qualifications the user might need (e.g., international information security certifications) and current market trends. In this way, the user can create a career plan that aligns their skill set with market needs.

[0436] The generative AI model performs analysis using user input as prompts. A specific example of a prompt might be: "The user is interested in cybersecurity and has 3 years of IT experience. Please suggest the best career plan for him, including necessary qualifications and market trends." Following this prompt, the model outputs an optimized career plan.

[0437] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0438] Step 1:

[0439] The user enters their carrier-related information.

[0440] Users input their skills, interests, and past experiences using smartphones or other devices. This input information is temporarily stored on the device and converted into a data structure. This input data forms the basis for analysis in the next step.

[0441] Step 2:

[0442] The terminal sends the input information to the server.

[0443] The device sends the collected user information to the server via a REST API. The HTTPS protocol is used to ensure data security. The transmitted data includes user-specific elements (skills, interests, experience) and is transferred to the server in JSON format.

[0444] Step 3:

[0445] The server analyzes the information and generates the optimal career plan.

[0446] The server applies a generative AI model to analyze the received user information. This model uses an OpenAI GPT-based model and uses the user information as prompts to generate an optimal career plan. Based on the analysis of the input data, it generates recommended career fields, required qualifications, learning methods, and market trends.

[0447] Step 4:

[0448] The server sends the job plan to the terminal.

[0449] The generated career plan is sent from the server to the user's terminal. A REST API is used for this secure data transfer. The resulting data is delivered to the terminal in a visually accessible format (e.g., JSON, XML).

[0450] Step 5:

[0451] The device visually presents the user with a career plan.

[0452] The device displays the received career plan on the user interface. The React Native framework enables this, providing users with intuitively understandable information. Based on the presented plan, users are supported in making decisions about their own careers.

[0453] 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.

[0454] This invention relates to a system that recognizes a user's skills and emotional state and provides an optimal career plan based on them. This system incorporates an emotion engine that collects the user's emotional information and utilizes it in generating career plans to provide more personalized suggestions.

[0455] First, the device prompts the user to enter information such as their skills, interests, and career goals. The user enters this information and also provides emotional data such as facial expressions and voice tone to enable sentiment analysis. The device temporarily stores this information and sends it to the server.

[0456] The server uses a generative model to analyze the received user information and create an optimal career plan for the user. Here, an emotion engine is utilized to perform analysis that takes the user's emotional state into account. For example, if a user expresses positive feelings towards a particular occupation, a career plan is generated that prioritizes recommending that occupation.

[0457] The generated career plan includes detailed information on recommended industries and job types, required skills, and how to acquire them, as well as an analysis of market trends. This detailed plan is sent to the user's device and displayed to them.

[0458] As a concrete example, consider a case where a user is interested in the IT industry and shows a positive attitude towards data science. In this case, the server generates a data science-related career plan and presents it to the user, including learning courses for new programming languages ​​and relevant market trends.

[0459] This system allows users to make career choices based on their skills, interests, and emotions, enabling them to create more accurate and satisfying career plans.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The device displays a series of questions to the user, prompting them to input skills, interests, career goals, and emotional data. Emotional data is collected using a camera and microphone to analyze the user's facial expressions and voice tone.

[0463] Step 2:

[0464] The device temporarily stores all collected user information and sentiment data, then encrypts and transmits it to the server. The transmitted data is protected by security measures to safeguard personal information.

[0465] Step 3:

[0466] The server activates a generative model to analyze the received data. The generative model first analyzes information such as skills and interests, and then uses an emotion engine to analyze emotional data.

[0467] Step 4:

[0468] The generative model creates an optimal career plan for the user based on the results of data analysis. Based on the analysis results of the emotion engine, it prioritizes occupations and fields in which the user expresses more positive emotions.

[0469] Step 5:

[0470] The server sends the generated career plan to the terminal. This career plan includes recommended industries, job types, steps to acquire the necessary skills, and market trends.

[0471] Step 6:

[0472] The terminal displays a career plan sent from the server to the user. Based on the presented career plan, the user can consider their future career plan and create a concrete action plan.

[0473] (Example 2)

[0474] 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."

[0475] Providing personalized career plans tailored to each user's skills, interests, and even emotional state presents a challenge that transcends the limitations of conventional technologies. This challenge necessitates a more precise understanding of users' emotions and their reflection in career choices. Career recommendations that ignore emotions may not fully meet users' needs, potentially leading to unsatisfactory career development.

[0476] 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.

[0477] In this invention, the server includes means for acquiring user characteristic information and emotional information, means for using a generative AI model that analyzes the acquired information to generate a career plan that takes into account the user's emotional state, and means for visually displaying the generated career plan to the user. This makes it possible to provide a more accurate career plan based on the individual user's skills and emotions.

[0478] "User characteristic information" refers to personal information about the user, such as their skills, interests, and career goals.

[0479] "Emotional information" refers to data that indicates the user's emotional state, and is obtained through facial expressions, tone of voice, and other means.

[0480] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to analyze input information and generate data suitable for a specific purpose.

[0481] A "career plan" is a proposal that includes suitable occupations for the user, necessary skills, methods for acquiring those skills, and market trends.

[0482] "Means of visual display" refers to technologies and methods for displaying information on a screen as graphics or text in order to make the generated information easier to understand.

[0483] This invention relates to a system that acquires user characteristic information and emotional state, and provides an optimal career plan based on that information. This system mainly consists of a terminal, a server, and a generative AI model.

[0484] First, the device displays prompts for the user to input information such as skills, interests, and career goals. The user then inputs the necessary information and provides emotional information such as facial expressions and voice tone using the camera and microphone. This allows the device to acquire detailed characteristic and emotional information about the user.

[0485] Next, the terminal temporarily stores this information and sends it to the server using a communication protocol such as SSL / TLS to maintain security.

[0486] The server uses a generative AI model to analyze the received information. The generative AI model performs data calculations to generate the most appropriate career plan based on the user's characteristics and emotional state. In particular, the emotion engine plays a crucial role in the analysis process, allowing for the prioritization of careers that elicit positive emotions.

[0487] The generated career plan includes recommended industries and job types, required skills, methods for acquiring those skills, and market trend analysis. The server sends this information to the terminal, which displays it visually to the user as graphics and text. This helps the user in making career choices and gain a clearer understanding of their career direction.

[0488] As a concrete example, consider a case where a user is interested in the IT industry and shows a particularly positive attitude towards data science. In this case, the server can generate a career plan related to data science and present it to the user along with information on how to learn relevant programming languages ​​and market trends. An example of a prompt might be, "Please tell us your current skills and areas of interest. We will also capture your emotions through facial expressions and voice, and propose a career plan based on that."

[0489] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0490] Step 1:

[0491] The device displays prompts for the user to input their skills, interests, and career goals. The user then enters the necessary information in text format. The camera and microphone are also activated to record the user's facial expressions and voice tone. The data entered includes the user's characteristics and emotional information.

[0492] Step 2:

[0493] The device temporarily stores the acquired characteristic and sentiment information in local storage. Here, the data format is prepared and ready for transmission. Specifically, text data is converted to JSON format, and sentiment data is summarized as statistical features. This ensures the data has a structure suitable for transmission to the server.

[0494] Step 3:

[0495] The terminal sends data to the server using the SSL / TLS protocol. Encoding is applied to enhance the security of the data transmission. The input data includes user characteristic information and emotional information, and once this is securely sent to the server, step 2 (data preparation) is complete.

[0496] Step 4:

[0497] The server passes the received data to a generating AI model for analysis. Here, data calculations are performed based on user characteristic information and emotional information, while comparing it with a vast amount of historical data. In particular, an emotion engine is used to calculate an emotional score for specific occupations and analyze suitability for those occupations, thereby selecting a suitable occupation.

[0498] Step 5:

[0499] The server creates an optimal career plan for the user based on the analysis results of the generated AI model. This plan includes recommended industries and job types, required skills, learning methods, and market trend analysis results. The career plan resulting from the data processing is output as text and graphics.

[0500] Step 6:

[0501] The server sends the generated career plan to the terminal. Again, the data is encrypted using the SSL / TLS protocol. Once the career plan data successfully reaches the terminal, the user can receive career information best suited to them.

[0502] Step 7:

[0503] The terminal decodes the received career plan and displays it visually to the user. The display format is a dashboard incorporating graphs and charts, designed to allow users to quickly understand the information. Ultimately, this enables users to decide on their next career step with a concrete action plan and market conditions in mind.

[0504] (Application Example 2)

[0505] 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."

[0506] Conventional career planning support systems often make career suggestions without considering the user's emotional state, resulting in a failure to adequately address the individual needs of users. Furthermore, there was a lack of means to show how the proposed career plan fits into the user's daily life. This invention aims to provide a career plan tailored to the user by utilizing emotional data, and to support the actual application of that plan in daily life.

[0507] 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.

[0508] In this invention, the server includes means for acquiring user information and emotional state; means for using a generative model that analyzes the user information and emotional data to generate an optimal career plan for the user; means for providing the generated career plan to an output device; and means for displaying the presented career plan to the user in real time using a mobile in-home device. This makes it possible to provide a personalized career plan based on the user's emotional state and to smoothly integrate that plan into daily life.

[0509] "User information" refers to information about the user, such as their skills, interests, and career goals, that is necessary to generate a career plan.

[0510] "Emotional state" refers to data that indicates the state of emotions and mood, analyzed from the user's facial expressions, tone of voice, and other factors.

[0511] A "generative model" is a computational model that includes algorithms for generating an optimal career plan based on user information and emotional state.

[0512] An "output device" refers to a device that has an interface for displaying the generated job plan to the user.

[0513] "In-home mobile devices" refer to devices such as robots that are mobile for the purpose of conveying information to users within the home environment.

[0514] "Emotional data" refers to information that represents the user's emotional state, and includes data such as voice tone and facial expression recognition results.

[0515] A "career plan" is a proposal document that takes into account the user's skills and emotional state, and includes information on recommended occupations, methods for acquiring skills, and market trends.

[0516] The system implementing this invention acquires user usage information and emotional state, analyzes this information, and provides the user with an optimal career plan.

[0517] The server receives usage information, including skills, interests, and career goals, based on user input. It also uses the camera and microphone to acquire emotional information such as facial expressions and voice tone. This utilizes facial recognition technologies such as OpenCV and speech recognition APIs (e.g., Google Speech-to-Text).

[0518] The server uses this information to run a generative AI model that generates an optimal career plan for the user. The generative model analyzes the user's skill set and emotional data to suggest appropriate occupations and learning methods. The technology used here includes sentiment analysis APIs (e.g., Microsoft Azure's sentiment analysis service).

[0519] The generated career plan is presented to the user through an output device. Mobile devices such as robots installed in the home provide this information to the user in real time and play a role in explaining the career plan in more detail.

[0520] As a concrete example, let's consider a scenario where a user is considering a career change into a new technological field. For instance, if the user shows interest in the IT field and has a positive attitude towards artificial intelligence, the server would suggest career possibilities in the data science field. Along with this, information on necessary skill acquisition courses and market trends would also be provided.

[0521] By utilizing generative AI models, the following prompt statements can be used:

[0522] "User skills: Programming, data analysis"

[0523] Interest: AI, new technology

[0524] Emotional state: Positive (based on voice tone and facial expression analysis)

[0525] Plan to be generated: Create an AI-related career plan, including necessary skills and market trends.

[0526] This system allows users in different emotional states to receive career plans tailored to their individual needs, making career choices more effective and satisfying.

[0527] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0528] Step 1:

[0529] The device displays prompts to the user, prompting them to enter usage information such as skills, interests, and career goals. Once the user completes the input, the device activates the camera and microphone to record the user's facial expressions and tone of voice. This data is temporarily stored as usage information and emotional data.

[0530] Step 2:

[0531] The device sends temporarily stored usage information and emotional data to the server. The server then begins analysis based on the received data. A key aspect of this process is the use of speech recognition APIs and facial recognition software (e.g., Google Speech-to-Text, OpenCV) to analyze the user's emotions and skills.

[0532] Step 3:

[0533] The server utilizes a generative AI model to generate an optimal career plan using the received user information and emotional information as input data. During this process, emotional states are considered using an emotion analysis API (e.g., Microsoft Azure's emotion analysis service). This step outputs the recommended career plan as a result of the analysis.

[0534] Step 4:

[0535] The server transmits the generated job plan to a terminal or in-home output device. This allows the user to access a personalized job plan in real time. In-home mobile devices (e.g., robots) play a role in providing visual and auditory information to the user, explaining the proposed plan in detail.

[0536] Step 5:

[0537] Based on the career plan they receive, users determine their next course of action. The plan from the server includes specific skill acquisition methods and market trends, which users can refer to when making career decisions.

[0538] This allows users to receive a career plan optimized to their own emotional state, which can then be used to help them make future career choices.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] [Fourth Embodiment]

[0543] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0544] 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.

[0545] 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).

[0546] 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.

[0547] 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.

[0548] 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).

[0549] 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.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] 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".

[0556] This invention relates to a system that suggests the optimal career path based on the user's skills and interests. The system aims to provide users with a reliable career plan through the coordinated operation of a server and a terminal. Specifically, the terminal collects user information such as skills, interests, and past experience, and transmits this information to the server. The server analyzes this information using a generative model and generates an optimal career plan for the user. The generated career plan includes recommended industries, methods for acquiring necessary skills, and market trend analysis.

[0557] The terminal displays a career plan sent from the server to the user in an easy-to-understand manner. Based on this information, the user can plan their future career actions. As a specific example, consider a case where the user is interested in the field of digital marketing. In this case, the terminal collects detailed information from the user, such as "digital skills" and "past experience," and transmits it to the server. The server generates a career plan incorporating the latest market trends and methods for acquiring digital tools related to that field, and recommends it to the user as the optimal career path. In this way, it supports users in finding a career direction that aligns with their skills and market needs.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The device displays a prompt screen to the user, prompting them to enter information such as skills, interests, and career goals. As the user enters this information, the device receives it in real time and temporarily stores it in a database.

[0561] Step 2:

[0562] The terminal packages the collected user information and sends it to the server via a secure communication protocol. During this process, formatting and encryption are applied to maintain data integrity.

[0563] Step 3:

[0564] The server receives user information sent from the terminal and runs a generative model. The generative model uses a pre-trained machine learning algorithm to analyze the user's skills and aptitudes.

[0565] Step 4:

[0566] Based on the analysis results, the server generates a career plan best suited to the user. This plan includes recommended industries, methods for improving necessary skills, and relevant market trends.

[0567] Step 5:

[0568] The server sends the generated job plan to the terminal. Secure communication methods are used during transmission to prevent data tampering.

[0569] Step 6:

[0570] The terminal displays a career plan received from the server to the user. This display allows the user to consider their career path based on the presented information and plan their future actions.

[0571] (Example 1)

[0572] 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".

[0573] Traditional career planning systems have a problem in that they can only provide general career information without adequately considering the diverse skills, interests, and past experiences of users. As a result, it is difficult for users to find the optimal career path, and there is a challenge in obtaining a career plan that is suited to their skills and interests.

[0574] 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.

[0575] In this invention, the server includes a device for collecting user information, a device for analyzing the user information and generating an optimal career plan using a generative model, and a device for transmitting the generated career plan to the user terminal. This makes it possible to generate and present an optimal career plan based on the user's skills, interests, and past experience.

[0576] "User information" refers to data about the user's skills, interests, and past experiences.

[0577] A "generative model" is an algorithm used to analyze user information and generate an optimal career plan.

[0578] A "career plan" is a career path plan that includes suggested industries, methods for acquiring necessary skills, and market trend analysis, all of which are presented to the user.

[0579] An "apparatus" is a machine or system configured to perform a specific process or operation.

[0580] A "user terminal" is a device that users operate to input information or view their career plans.

[0581] In order to implement this invention, the user's terminal and the server must work together. First, the user inputs user information such as their skills, interests, and past experiences via the terminal. This input is done using a web form or a dedicated application interface. After collecting this information, the terminal sends it to the server using the HTTPS protocol for secure communication.

[0582] The server uses a generative AI model built with Python and TensorFlow to analyze the received user information. This generative model receives the input information as prompts and generates an optimal career plan. Specifically, if a user inputs that they are interested in "digital marketing," the generative model will receive the prompt, "Please suggest the skills and market trends necessary for a career in the digital marketing field."

[0583] The generated career plan includes recommended industries, methods for acquiring skills, and market trend analysis. This information is useful to the user as an actionable plan. The server converts the generated career plan back into JSON format and sends it back to the user's terminal.

[0584] On the device, a web interface or mobile application UI is used to display the received career plan in a user-friendly format. Based on this, users can identify career directions that match their skills and market needs, and develop concrete action plans to achieve them. For example, a user interested in digital marketing can consider learning suggested digital tools or taking online courses.

[0585] In this way, this system supports users in maximizing their potential and forming effective career paths.

[0586] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0587] Step 1:

[0588] Users input their skills, interests, and past experiences using their own devices. This information is entered via web forms or dedicated apps on the device, and the data format is JSON. The entered information is organized as the user's personal data.

[0589] Step 2:

[0590] The terminal sends the collected user information to the server. Specifically, the data is transmitted securely using the HTTPS protocol. The input here is user information in JSON format, which is securely transferred to the server.

[0591] Step 3:

[0592] The server analyzes the received user information. A generative AI model, built using Python and TensorFlow, analyzes the input JSON data. In this process, the generative AI model is given the prompt "Please suggest the optimal career plan in the user's specified area of ​​interest," and the analysis results are generated. The output is a basic career plan proposal.

[0593] Step 4:

[0594] The server converts the generated career plan into JSON format and sends it to the user's terminal. The converted data is delivered to the terminal via a secure protocol. The input to this process is the analysis result, and the output is career plan data that the user can visually understand.

[0595] Step 5:

[0596] The terminal displays the received career plan to the user. Specifically, it presents information on the terminal's display using visual methods such as HTML / CSS and graphs. Based on the plan provided, the user can decide on specific next steps. The output is clearly presented career path information.

[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 robot 414 as the "terminal".

[0599] Finding the optimal career path based on one's skills and interests is a crucial challenge in today's world. However, providing specific career plans tailored to individual users is not easy, and many systems fail to take into account specific qualifications or market trends. As a result, users may not receive sufficient information when making their career choices.

[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 means for acquiring user information, means for using a generative model that analyzes the user information to generate an optimal career plan for the user, means for transmitting the generated career plan to a visual display device and presenting it visually to the user, and means for the generative model to generate the career plan using information on qualifications and skill development trends related to occupations in a specific field. This enables users to find a career path that aligns with their skills and market needs, and to make career choices based on specific qualification acquisition information and market trends.

[0602] "Means of acquiring user information" refers to a mechanism for collecting information from individual users, such as their skills, interests, and past experiences.

[0603] "Methods using generative models" refer to mechanisms that utilize models used to analyze collected user information and generate optimal career plans based on the results.

[0604] "Means for transmitting the generated job plan to a visual display device and presenting it visually to the user" refers to a mechanism that displays the generated job plan on a terminal or device, presenting the information in a way that the user can visually understand.

[0605] "Means for generating career plans using information on trends in qualifications and skills development related to occupations in specific fields" refers to a mechanism for generating more specific and useful career plans by utilizing information on how to acquire qualifications and necessary skills in a particular occupational field.

[0606] As a form for carrying out the invention, the system that realizes this application example is designed to optimize the user's career plan. This system consists of a user terminal and a server. The details are described below.

[0607] First, users input their skills, interests, and past experiences using their smartphones or other devices. The device collects this information and sends it to the server. React Native is used as the front-end technology for information collection, and the user interface is built around it. This makes it easy for users to provide information.

[0608] On the server side, the received user information is analyzed. A generative AI model is used for this analysis, for example, OpenAI's GPT-based model. This model generates an optimal career plan based on the user information. This career plan includes specific occupational fields, required qualifications, skills to be acquired, and the latest market trends.

[0609] The generated career plan is sent to the device and presented to the user visually in an easy-to-understand format. The user receives this information and can make the necessary decisions regarding their career choices.

[0610] For example, if a user is interested in the security field, the system provides information such as potential qualifications the user might need (e.g., international information security certifications) and current market trends. In this way, the user can create a career plan that aligns their skill set with market needs.

[0611] The generative AI model performs analysis using user input as prompts. A specific example of a prompt might be: "The user is interested in cybersecurity and has 3 years of IT experience. Please suggest the best career plan for him, including necessary qualifications and market trends." Following this prompt, the model outputs an optimized career plan.

[0612] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0613] Step 1:

[0614] The user enters their carrier-related information.

[0615] Users input their skills, interests, and past experiences using smartphones or other devices. This input information is temporarily stored on the device and converted into a data structure. This input data forms the basis for analysis in the next step.

[0616] Step 2:

[0617] The terminal sends the input information to the server.

[0618] The device sends the collected user information to the server via a REST API. The HTTPS protocol is used to ensure data security. The transmitted data includes user-specific elements (skills, interests, experience) and is transferred to the server in JSON format.

[0619] Step 3:

[0620] The server analyzes the information and generates the optimal career plan.

[0621] The server applies a generative AI model to analyze the received user information. This model uses an OpenAI GPT-based model and uses the user information as prompts to generate an optimal career plan. Based on the analysis of the input data, it generates recommended career fields, required qualifications, learning methods, and market trends.

[0622] Step 4:

[0623] The server sends the job plan to the terminal.

[0624] The generated career plan is sent from the server to the user's terminal. A REST API is used for this secure data transfer. The resulting data is delivered to the terminal in a visually accessible format (e.g., JSON, XML).

[0625] Step 5:

[0626] The device visually presents the user with a career plan.

[0627] The device displays the received career plan on the user interface. The React Native framework enables this, providing users with intuitively understandable information. Based on the presented plan, users are supported in making decisions about their own careers.

[0628] 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.

[0629] This invention relates to a system that recognizes a user's skills and emotional state and provides an optimal career plan based on them. This system incorporates an emotion engine that collects the user's emotional information and utilizes it in generating career plans to provide more personalized suggestions.

[0630] First, the device prompts the user to enter information such as their skills, interests, and career goals. The user enters this information and also provides emotional data such as facial expressions and voice tone to enable sentiment analysis. The device temporarily stores this information and sends it to the server.

[0631] The server uses a generative model to analyze the received user information and create an optimal career plan for the user. Here, an emotion engine is utilized to perform analysis that takes the user's emotional state into account. For example, if a user expresses positive feelings towards a particular occupation, a career plan is generated that prioritizes recommending that occupation.

[0632] The generated career plan includes detailed information on recommended industries and job types, required skills, and how to acquire them, as well as an analysis of market trends. This detailed plan is sent to the user's device and displayed to them.

[0633] As a concrete example, consider a case where a user is interested in the IT industry and shows a positive attitude towards data science. In this case, the server generates a data science-related career plan and presents it to the user, including learning courses for new programming languages ​​and relevant market trends.

[0634] This system allows users to make career choices based on their skills, interests, and emotions, enabling them to create more accurate and satisfying career plans.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The device displays a series of questions to the user, prompting them to input skills, interests, career goals, and emotional data. Emotional data is collected using a camera and microphone to analyze the user's facial expressions and voice tone.

[0638] Step 2:

[0639] The device temporarily stores all collected user information and sentiment data, then encrypts and transmits it to the server. The transmitted data is protected by security measures to safeguard personal information.

[0640] Step 3:

[0641] The server activates a generative model to analyze the received data. The generative model first analyzes information such as skills and interests, and then uses an emotion engine to analyze emotional data.

[0642] Step 4:

[0643] The generative model creates an optimal career plan for the user based on the results of data analysis. Based on the analysis results of the emotion engine, it prioritizes occupations and fields in which the user expresses more positive emotions.

[0644] Step 5:

[0645] The server sends the generated career plan to the terminal. This career plan includes recommended industries, job types, steps to acquire the necessary skills, and market trends.

[0646] Step 6:

[0647] The terminal displays a career plan sent from the server to the user. Based on the presented career plan, the user can consider their future career plan and create a concrete action plan.

[0648] (Example 2)

[0649] 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".

[0650] Providing personalized career plans tailored to each user's skills, interests, and even emotional state presents a challenge that transcends the limitations of conventional technologies. This challenge necessitates a more precise understanding of users' emotions and their reflection in career choices. Career recommendations that ignore emotions may not fully meet users' needs, potentially leading to unsatisfactory career development.

[0651] 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.

[0652] In this invention, the server includes means for acquiring user characteristic information and emotional information, means for using a generative AI model that analyzes the acquired information to generate a career plan that takes into account the user's emotional state, and means for visually displaying the generated career plan to the user. This makes it possible to provide a more accurate career plan based on the individual user's skills and emotions.

[0653] "User characteristic information" refers to personal information about the user, such as their skills, interests, and career goals.

[0654] "Emotional information" refers to data that indicates the user's emotional state, and is obtained through facial expressions, tone of voice, and other means.

[0655] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to analyze input information and generate data suitable for a specific purpose.

[0656] A "career plan" is a proposal that includes suitable occupations for the user, necessary skills, methods for acquiring those skills, and market trends.

[0657] "Means of visual display" refers to technologies and methods for displaying information on a screen as graphics or text in order to make the generated information easier to understand.

[0658] This invention relates to a system that acquires user characteristic information and emotional state, and provides an optimal career plan based on that information. This system mainly consists of a terminal, a server, and a generative AI model.

[0659] First, the device displays prompts for the user to input information such as skills, interests, and career goals. The user then inputs the necessary information and provides emotional information such as facial expressions and voice tone using the camera and microphone. This allows the device to acquire detailed characteristic and emotional information about the user.

[0660] Next, the terminal temporarily stores this information and sends it to the server using a communication protocol such as SSL / TLS to maintain security.

[0661] The server uses a generative AI model to analyze the received information. The generative AI model performs data calculations to generate the most appropriate career plan based on the user's characteristics and emotional state. In particular, the emotion engine plays a crucial role in the analysis process, allowing for the prioritization of careers that elicit positive emotions.

[0662] The generated career plan includes recommended industries and job types, required skills, methods for acquiring those skills, and market trend analysis. The server sends this information to the terminal, which displays it visually to the user as graphics and text. This helps the user in making career choices and gain a clearer understanding of their career direction.

[0663] As a concrete example, consider a case where a user is interested in the IT industry and shows a particularly positive attitude towards data science. In this case, the server can generate a career plan related to data science and present it to the user along with information on how to learn relevant programming languages ​​and market trends. An example of a prompt might be, "Please tell us your current skills and areas of interest. We will also capture your emotions through facial expressions and voice, and propose a career plan based on that."

[0664] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0665] Step 1:

[0666] The device displays prompts for the user to input their skills, interests, and career goals. The user then enters the necessary information in text format. The camera and microphone are also activated to record the user's facial expressions and voice tone. The data entered includes the user's characteristics and emotional information.

[0667] Step 2:

[0668] The device temporarily stores the acquired characteristic and sentiment information in local storage. Here, the data format is prepared and ready for transmission. Specifically, text data is converted to JSON format, and sentiment data is summarized as statistical features. This ensures the data has a structure suitable for transmission to the server.

[0669] Step 3:

[0670] The terminal sends data to the server using the SSL / TLS protocol. Encoding is applied to enhance the security of the data transmission. The input data includes user characteristic information and emotional information, and once this is securely sent to the server, step 2 (data preparation) is complete.

[0671] Step 4:

[0672] The server passes the received data to a generating AI model for analysis. Here, data calculations are performed based on user characteristic information and emotional information, while comparing it with a vast amount of historical data. In particular, an emotion engine is used to calculate an emotional score for specific occupations and analyze suitability for those occupations, thereby selecting a suitable occupation.

[0673] Step 5:

[0674] The server creates an optimal career plan for the user based on the analysis results of the generated AI model. This plan includes recommended industries and job types, required skills, learning methods, and market trend analysis results. The career plan resulting from the data processing is output as text and graphics.

[0675] Step 6:

[0676] The server sends the generated career plan to the terminal. Again, the data is encrypted using the SSL / TLS protocol. Once the career plan data successfully reaches the terminal, the user can receive career information best suited to them.

[0677] Step 7:

[0678] The terminal decodes the received career plan and displays it visually to the user. The display format is a dashboard incorporating graphs and charts, designed to allow users to quickly understand the information. Ultimately, this enables users to decide on their next career step with a concrete action plan and market conditions in mind.

[0679] (Application Example 2)

[0680] 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".

[0681] Conventional career planning support systems often make career suggestions without considering the user's emotional state, resulting in a failure to adequately address the individual needs of users. Furthermore, there was a lack of means to show how the proposed career plan fits into the user's daily life. This invention aims to provide a career plan tailored to the user by utilizing emotional data, and to support the actual application of that plan in daily life.

[0682] 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.

[0683] In this invention, the server includes means for acquiring user information and emotional state; means for using a generative model that analyzes the user information and emotional data to generate an optimal career plan for the user; means for providing the generated career plan to an output device; and means for displaying the presented career plan to the user in real time using a mobile in-home device. This makes it possible to provide a personalized career plan based on the user's emotional state and to smoothly integrate that plan into daily life.

[0684] "User information" refers to information about the user, such as their skills, interests, and career goals, that is necessary to generate a career plan.

[0685] "Emotional state" refers to data that indicates the state of emotions and mood, analyzed from the user's facial expressions, tone of voice, and other factors.

[0686] A "generative model" is a computational model that includes algorithms for generating an optimal career plan based on user information and emotional state.

[0687] An "output device" refers to a device that has an interface for displaying the generated job plan to the user.

[0688] "In-home mobile devices" refer to devices such as robots that are mobile for the purpose of conveying information to users within the home environment.

[0689] "Emotional data" refers to information that represents the user's emotional state, and includes data such as voice tone and facial expression recognition results.

[0690] A "career plan" is a proposal document that takes into account the user's skills and emotional state, and includes information on recommended occupations, methods for acquiring skills, and market trends.

[0691] The system implementing this invention acquires user usage information and emotional state, analyzes this information, and provides the user with an optimal career plan.

[0692] The server receives usage information, including skills, interests, and career goals, based on user input. It also uses the camera and microphone to acquire emotional information such as facial expressions and voice tone. This utilizes facial recognition technologies such as OpenCV and speech recognition APIs (e.g., Google Speech-to-Text).

[0693] The server uses this information to run a generative AI model that generates an optimal career plan for the user. The generative model analyzes the user's skill set and emotional data to suggest appropriate occupations and learning methods. The technology used here includes sentiment analysis APIs (e.g., Microsoft Azure's sentiment analysis service).

[0694] The generated career plan is presented to the user through an output device. Mobile devices such as robots installed in the home provide this information to the user in real time and play a role in explaining the career plan in more detail.

[0695] As a concrete example, let's consider a scenario where a user is considering a career change into a new technological field. For instance, if the user shows interest in the IT field and has a positive attitude towards artificial intelligence, the server would suggest career possibilities in the data science field. Along with this, information on necessary skill acquisition courses and market trends would also be provided.

[0696] By utilizing generative AI models, the following prompt statements can be used:

[0697] "User skills: Programming, data analysis"

[0698] Interest: AI, new technology

[0699] Emotional state: Positive (based on voice tone and facial expression analysis)

[0700] Plan to be generated: Create an AI-related career plan, including necessary skills and market trends.

[0701] This system allows users in different emotional states to receive career plans tailored to their individual needs, making career choices more effective and satisfying.

[0702] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0703] Step 1:

[0704] The device displays prompts to the user, prompting them to enter usage information such as skills, interests, and career goals. Once the user completes the input, the device activates the camera and microphone to record the user's facial expressions and tone of voice. This data is temporarily stored as usage information and emotional data.

[0705] Step 2:

[0706] The device sends temporarily stored usage information and emotional data to the server. The server then begins analysis based on the received data. A key aspect of this process is the use of speech recognition APIs and facial recognition software (e.g., Google Speech-to-Text, OpenCV) to analyze the user's emotions and skills.

[0707] Step 3:

[0708] The server utilizes a generative AI model to generate an optimal career plan using the received user information and emotional information as input data. During this process, emotional states are considered using an emotion analysis API (e.g., Microsoft Azure's emotion analysis service). This step outputs the recommended career plan as a result of the analysis.

[0709] Step 4:

[0710] The server transmits the generated job plan to a terminal or in-home output device. This allows the user to access a personalized job plan in real time. In-home mobile devices (e.g., robots) play a role in providing visual and auditory information to the user, explaining the proposed plan in detail.

[0711] Step 5:

[0712] Based on the career plan they receive, users determine their next course of action. The plan from the server includes specific skill acquisition methods and market trends, which users can refer to when making career decisions.

[0713] This allows users to receive a career plan optimized to their own emotional state, which can then be used to help them make future career choices.

[0714] 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.

[0715] 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.

[0716] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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."

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] The following is further disclosed regarding the embodiments described above.

[0736] (Claim 1)

[0737] Means of obtaining user information,

[0738] A means of using a generative model that analyzes the user information and generates an optimal career plan for the user,

[0739] Means for providing the generated occupational plan to the user terminal,

[0740] A system that includes this.

[0741] (Claim 2)

[0742] The aforementioned generation model includes means for evaluating the user's skills and aptitude,

[0743] The system according to claim 1, characterized by comprising means for presenting multiple occupational options based on the evaluation results.

[0744] (Claim 3)

[0745] The system according to claim 1, characterized in that the career plan includes recommended industries, methods for acquiring skills, and an analysis of market trends.

[0746] "Example 1"

[0747] (Claim 1)

[0748] A device for collecting user information,

[0749] A device that analyzes the aforementioned user information and generates an optimal occupational plan using a generative model,

[0750] A device that transmits the generated work plan to a user terminal,

[0751] A display device for presenting the aforementioned vocational plan to the user,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, characterized in that the generation model includes means for evaluating the user's skills, interests, and past experience and generating multiple career plans.

[0755] (Claim 3)

[0756] The system according to claim 1, characterized in that the career plan includes recommended industries, methods for acquiring necessary skills, and an analysis of market trends.

[0757] "Application Example 1"

[0758] (Claim 1)

[0759] Means of obtaining user information,

[0760] A means of using a generative model that analyzes the user information and generates an optimal career plan for the user,

[0761] A means for transmitting the generated occupational plan to a visual display device and presenting it visually to the user,

[0762] The generation model includes means for generating a career plan using information on trends in qualifications and skills development related to occupations in a specific field,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The aforementioned generation model includes means for evaluating the user's skills and aptitude,

[0766] The system according to claim 1, characterized in that it includes means for presenting multiple occupational options, including qualifications and market trend information, based on the aforementioned evaluation results.

[0767] (Claim 3)

[0768] The system according to claim 1, characterized in that the occupational plan includes recommended industrial fields, methods for acquiring skills, information on obtaining qualifications, and an analysis of market trends.

[0769] "Example 2 of combining an emotion engine"

[0770] (Claim 1)

[0771] Means for acquiring user characteristic information and emotional information,

[0772] A means of using a generative AI model that analyzes the acquired information and generates a career plan that takes into account the emotional state of the user,

[0773] A means for visually displaying the generated occupational plan to the user,

[0774] A system that includes this.

[0775] (Claim 2)

[0776] The aforementioned generation AI model includes means for evaluating the user's skills and emotional state,

[0777] The system according to claim 1, characterized by comprising means for providing high-priority occupational options based on the aforementioned evaluation results.

[0778] (Claim 3)

[0779] The system according to claim 1, characterized in that the career plan includes recommended fields, methods for acquiring skills, and market trend analysis.

[0780] "Application example 2 when combining with an emotional engine"

[0781] (Claim 1)

[0782] Means for acquiring user information and emotional state,

[0783] A means of using a generative model that analyzes the user information and emotional data to generate an optimal career plan for the user,

[0784] Means for providing the generated occupational plan to an output device,

[0785] A means of displaying a career plan presented to a user in real time using a mobile device within the home,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The aforementioned generation model includes means for evaluating the user's skills and aptitude,

[0789] The system according to claim 1, further comprising means for presenting multiple occupational options based on the aforementioned evaluation results, and characterized in that it makes occupational suggestions based on analysis results using the user's emotional data.

[0790] (Claim 3)

[0791] The system according to claim 1, characterized in that the career plan includes, in addition to, an analysis of recommended industry areas, methods for acquiring skills, and market trends, it also provides feedback based on emotional data. [Explanation of Symbols]

[0792] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining user information, A means of using a generative model that analyzes the user information and generates an optimal career plan for the user, Means for providing the generated occupational plan to the user terminal, A system that includes this.

2. The aforementioned generation model includes means for evaluating the user's skills and aptitude, The system according to claim 1, characterized by comprising means for presenting multiple occupational options based on the evaluation results.

3. The system according to claim 1, characterized in that the career plan includes recommended industries, methods for acquiring skills, and an analysis of market trends.

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