System

Abstract

To provide a system.SOLUTION: A system includes: a feeling engine for recognizing a feeling of a user; means for looking for a common point with a business world and occupation which exist in the world on the basis of a skill set of the user; suitable occupation generation means for inputting a prompt sentence for proposing a suitable occupation based on the skill set into a generative AI model to generate a suitable occupation based on the common point in consideration of the feeling of the user; and recommendation means for recommending the generated suitable occupation to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] It is difficult for people considering a career change to find the right job by finding commonalities between their skill set and existing industries and occupations. It is also difficult to recommend suitable jobs that take into account currently available job information. [Means for solving the problem]

[0005] As a means for solving this problem, the system of the present invention includes an emotion engine that recognizes the user's emotions, a means for searching for commonalities with existing industries and occupations based on the user's skill set, a suitable job generation means that generates a suitable job based on the commonalities taking the user's emotions into consideration by inputting prompt sentences to suggest suitable jobs based on the skill set into a generative AI model, and a recommendation means that recommends the generated suitable job to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0014] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0027] "Example 1"

[0028] In one embodiment of the present invention, a user inputs their own skill set into the system. This skill set specifically describes the user's skills, experience, and qualifications. For example, it may include knowledge of programming languages, project management experience, English proficiency, etc.

[0029] "Example 2"

[0030] Next, the system looks for commonalities with existing industries and occupations. This is done by comparing the user's skill set with the skill sets required for each industry or occupation and looking for matches or similarities. For example, if the user has knowledge of programming languages, the IT industry and software development occupations will be extracted as commonalities.

[0031] "Example 3"

[0032] The system also uses generative AI to generate suitable jobs. This AI generates new job combinations based on commonalities and recommends them to users. For example, if a user has knowledge of programming languages ​​and English skills, the AI ​​will generate a suitable job such as "overseas IT consultant."

[0033] "Example 4"

[0034] Generative AI also receives prompts from currently available job postings and generates suitable jobs based on those job postings. This allows it to recommend jobs based on the latest job postings to users. For example, for a user with a specific skill set currently in demand in the IT industry, it can generate suitable jobs based on the latest job postings that utilize those skill sets.

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: The user enters their own skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, they might enter "3 years of Java (registered trademark) development experience, PMP certification, TOEIC score 850."

[0038] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[0039] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[0040] "Example 2"

[0041] Step 1: The user enters their own skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, a user might enter "two years of experience in data analysis using Python, knowledge of database design, and presentation skills in English."

[0042] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[0043] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[0044] "Example 3"

[0045] Step 1: The user enters their skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, they might enter "5 years of development experience in C++, knowledge of network design, and ability to negotiate in Chinese."

[0046] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[0047] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[0048] Example 1

[0049] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0050] Previous systems struggled to effectively analyze users' skill sets and suggest appropriate career paths and skill acquisition. Furthermore, they lacked recommendations based on users' interests and preferences, making it difficult for users to make optimal suggestions. This made it difficult for users to make the most of their skill sets.

[0051] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0052] In this invention, the server includes means for receiving a user's skill set as input and saving the input skill set in a database, means for analyzing the saved skill set and classifying it by category, means for generating prompt sentences based on the analyzed skill set and inputting them into a generative AI model, and means for displaying suggestions generated by the generative AI model to the user. This makes it possible to effectively analyze the user's skill set and provide suggestions for appropriate career paths and skill acquisition.

[0053] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[0054] A "database" is a system for efficiently storing, managing, and retrieving data.

[0055] "Analysis" is the process of examining input data in detail to understand its meaning and structure.

[0056] A "category" is a group for classifying data based on specific criteria.

[0057] A "prompt" is text that is input to a generative AI model and contains instructions for the model to generate appropriate output.

[0058] A "generative AI model" is an artificial intelligence model that generates appropriate output based on input data.

[0059] A "Suggestion" is specific advice or recommendation provided to a user.

[0060] "Display" is the act of showing information to a user through a user interface.

[0061] This invention is a system that effectively analyzes a user's skill set and proposes appropriate career paths and skill acquisition. A specific embodiment of this system is described below.

[0062] First, users input their skill set into the system. This skill set specifically describes the user's skills, experience, and qualifications. For example, it may include knowledge of programming languages, project management experience, and English proficiency.

[0063] When a user inputs a skill set, the terminal sends this data to the server. The terminal is a device such as the user's PC or smartphone, and sends the input data to the server as an HTTP request. The data is sent in JSON format.

[0064] The server receives the data sent from the terminal and stores it in a database. The database uses a relational database management system (RDBMS) such as MySQL (registered trademark) or PostgreSQL. For example, it executes the following SQL query:

[0065] sql

[0066] INSERT INTO skill_sets (user_id, skills) VALUES (1, 'Python, Java, Project Management 3 years, TOEIC 850 points');

[0067] The server then analyzes the stored skill set and categorizes each skill into categories, using a programming language such as Python or Java and natural language processing libraries (e.g., NLTK or spaCy).

[0068] Based on the analyzed skill set, the server generates a prompt sentence and inputs it into a generative AI model. The generative AI model used is OpenAI's GPT-3 (registered trademark) or GPT-4 (registered trademark). An example of a prompt sentence is as follows:

[0069] The user's skill set is as follows:

[0070] Programming languages: Python, Java

[0071] Project Management: 3 years of experience

[0072] English proficiency: TOEIC 850 points

[0073] Suggest the next skills or career path that would be suitable for this user.

[0074] The generative AI model generates suggestions based on the input prompt and returns them to the server. Examples of generated suggestions are as follows:

[0075] Based on your skill set, your next skillset could include knowledge of data science and machine learning, and your career path could be to move from project manager to data scientist.

[0076] Finally, the server displays the suggestions returned by the generative AI model to the user, who can review them in their web browser.

[0077] In this way, users receive specific suggestions based on their skill set, allowing them to make the most of their skillset and plan appropriate career paths and skill acquisition.

[0078] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0079] Step 1:

[0080] The user enters a skillset.

[0081] Users input their skill set through the system's user interface. For example, they enter information such as "Python, Java, 3 years of project management, TOEIC score 850" into a form on a web browser. The input data is text information about the skill set.

[0082] Step 2:

[0083] The terminal sends the input data to the server.

[0084] The terminal (user's PC or smartphone) sends the entered skill set to the server as an HTTP request. At this time, the data is sent in JSON format. The input is the text information of the skill set entered by the user, and the output is the JSON data sent to the server.

[0085] Step 3:

[0086] The server receives the data and stores it in a database.

[0087] The server receives the data sent from the device and stores it in a database. The database uses an RDBMS such as MySQL or PostgreSQL. For example, it executes the following SQL query:

[0088] sql

[0089] INSERT INTO skill_sets (user_id, skills) VALUES (1, 'Python, Java, Project Management 3 years, TOEIC 850 points');

[0090] The input is JSON data sent from the device, and the output is a skillset record stored in a database.

[0091] Step 4:

[0092] The server analyzes the skill set and categorizes it.

[0093] The server analyzes the stored skill set and classifies each skill into a category. This process is performed using a programming language such as Python or Java, and a natural language processing library (e.g., NLTK or spaCy). The input is the skill set records stored in the database, and the output is the skill information classified by category.

[0094] Step 5:

[0095] The server inputs a prompt sentence into the generative AI model.

[0096] The server generates a prompt sentence based on the analyzed skill set and inputs it into the generative AI model. OpenAI's GPT-3 or GPT-4 is used as the generative AI model. An example of a prompt sentence is as follows:

[0097] The user's skill set is as follows:

[0098] Programming languages: Python, Java

[0099] Project Management: 3 years of experience

[0100] English proficiency: TOEIC 850 points

[0101] Suggest the next skills or career path that would be suitable for this user.

[0102] The input is skill information categorized by category, and the output is a prompt sentence that is input into the generative AI model.

[0103] Step 6:

[0104] The generative AI model generates suggestions and returns them to the server.

[0105] A generative AI model (e.g., OpenAI's GPT-4) generates suggestions based on the input prompt and returns them to the server. An example of a generated suggestion is as follows:

[0106] Based on your skill set, your next skillset could include knowledge of data science and machine learning, and your career path could be to move from project manager to data scientist.

[0107] The input is the prompt sentence and the output is the generated suggestion.

[0108] Step 7:

[0109] The server displays the suggestions to the user.

[0110] The server displays the suggestions returned by the generative AI model to the user, who can view them in a web browser. The input is the generated suggestions, and the output is the suggestions displayed to the user.

[0111] (Application example 1)

[0112] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0113] With conventional systems, it was difficult to properly utilize users' skill sets, optimize work, and monitor progress. Furthermore, tasks were not reassigned in real time based on user skills, making efficient work management impossible. This resulted in problems such as reduced work efficiency and difficulty in properly allocating personnel.

[0114] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0115] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, means for optimizing work based on the user's skill set, and means for monitoring work progress and reallocating tasks as necessary. This makes it possible to make the most of the user's skill set, enabling efficient work management and appropriate personnel allocation.

[0116] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[0117] "Commonalities with industries and occupations" refers to the points of agreement between the user's skill set and the required skills and conditions of existing industries and occupations.

[0118] "Generative AI methods" are artificial intelligence methods that generate suitable jobs based on the intersection of a user's skill set with industries and occupations.

[0119] "Means for recommending" refers to means for suggesting the generated suitable jobs to users.

[0120] A "means for optimizing work" is a means for maximizing the efficiency of work based on the user's skill set.

[0121] "Means for monitoring work progress" refers to means for monitoring the progress of work in real time.

[0122] The "means for reallocating tasks" refers to a means for reallocating tasks according to the progress of work.

[0123] To implement this invention, a system is required that provides an interface for users to input their own skill sets. This system is implemented as an application that runs on a terminal such as a smartphone or tablet. A specific embodiment of this system will be described below.

[0124] System configuration

[0125] The system consists of the following main components:

[0126] 1. User interface: Provides a screen for users to input their skill sets, such as knowledge of programming languages, project management experience, and English proficiency.

[0127] 2. Generative AI methods: Analyze the skillset entered by the user and find commonalities with existing industries and occupations. This is done using generative AI models.

[0128] 3. Recommendation method: The generative AI method recommends suitable jobs to the user. The recommendations are based on the user's interests and preferences.

[0129] 4. Work Optimizer: Optimize work based on the user's skill set. For example, generate instructions to optimize the operation of a factory robot.

[0130] 5. Progress monitoring measures: Monitor work progress in real time and reassign tasks as needed.

[0131] Hardware and Software

[0132] Hardware: smartphones, tablets, servers

[0133] Software: Python, JSON, generative AI models

[0134] Data processing and calculation

[0135] 1. User Interface: When a user enters a skill set, the data is sent to the server in JSON format.

[0136] 2. Generative AI method: The server analyzes the received skill set and generates suitable jobs using a generative AI model. At this time, the prompt sentence is "Please enter the user's skill set. For example, welding technology, machine operation experience, quality control knowledge, etc."

[0137] 3. Recommendation method: The generated suitable jobs are recommended based on the user's interests and preferences. The recommendation results are displayed in the user interface.

[0138] 4. Work optimization: Instructions are generated to optimize the operation of factory robots based on the user's skill set.

[0139] 5. Progress Monitoring Measures: Work progress is monitored in real time and tasks are reassigned as necessary.

[0140] Specific examples For example, if a user inputs "welding skills" and "quality control knowledge," the generative AI method generates suitable jobs based on these skill sets. The generated suitable jobs are recommended based on the user's interests and preferences. In addition, the operation of factory robots is optimized based on the user's skill set. Work progress is monitored in real time, and tasks are reallocated as necessary.

[0141] An example of a prompt sentence is "Please enter the user's skill set. For example, welding technology, machine operation experience, knowledge of quality control, etc."

[0142] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0143] Step 1:

[0144] The user enters the skillset using the terminal.

[0145] Input: User skill set (e.g. welding techniques, machine operation experience, quality control knowledge)

[0146] How it works: The user is presented with a screen to input their skillset through the user interface. The user inputs their skillset and presses the submit button.

[0147] Output: The input skillset is sent to the server in JSON format.

[0148] Step 2:

[0149] The server analyzes the received skillset.

[0150] Input: Skillset data in JSON format

[0151] Operation: The server parses the received skill set data and extracts each skill individually.

[0152] Output: Parsed skillset data (e.g. {'welding': True, 'machine_operation': False, 'quality_control': True})

[0153] Step 3:

[0154] The server generates suitable jobs using a generative AI model.

[0155] Input: Parsed skillset data

[0156] How it works: The server inputs the analyzed skill set data into the generative AI model along with the prompt, "Please enter the user's skill set. For example, welding techniques, machine operation experience, quality control knowledge, etc." The generative AI model then generates suitable jobs based on this.

[0157] Output: Generated job data (e.g., {'job_title': 'Welding engineer', 'industry': 'Manufacturing'})

[0158] Step 4:

[0159] The server recommends the generated suitable jobs to the user.

[0160] Input: Generated job aptitude data

[0161] How it works: The server filters the generated job data based on the user's interests and preferences to generate recommendation results.

[0162] Output: Recommendation results (e.g., {'recommended_jobs': ['Welding engineer', 'Quality control']})

[0163] Step 5:

[0164] The user receives the recommendations.

[0165] Input: Recommendation results

[0166] Operation: The device displays the recommended job results in the user interface. The user can then confirm the recommended job.

[0167] Output: User confirmation result (e.g., user selects the recommended suitable job)

[0168] Step 6:

[0169] The server optimizes work based on the user's skill set.

[0170] Input: User's skill set, selected suitable job

[0171] Operation: The server generates instructions to optimize the factory robot's operation based on the user's skill set and selected vocation.

[0172] Output: Optimized work instructions (e.g., {'robot_task': 'welding', 'parameters': {'speed': 'medium', 'precision': 'high'}})

[0173] Step 7:

[0174] The server monitors the progress of work and reassigns tasks as needed.

[0175] Input: Optimized work instructions, real-time work progress data

[0176] How it works: The server monitors the progress of work in real time and reallocates tasks as needed. For example, if progress is slow, it will reallocate the task to another robot or worker.

[0177] Output: Updated task assignment (e.g., {'robot_task': 'Welding', 'assigned_to': 'robot_2'})

[0178] Example 2

[0179] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0180] Conventional career recommendation systems have difficulty efficiently comparing a user's skill set with the skill sets of industries and occupations to recommend appropriate careers. Furthermore, they have not adequately generated suitable careers that take into account the user's skill set and job information, nor have they made recommendations based on the user's interests and preferences. This has made it difficult for users to find the career that best suits them, resulting in the problem of career mismatches.

[0181] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0182] In this invention, the server includes means for receiving a user's skill set as input, accessing a database to compare the skill set of each industry or occupation with the user's skill set, means for extracting industries or occupations with matching or similar skill sets, and means for recommending the extracted industries and occupations to the user, thereby enabling efficient recommendation of optimal occupations based on the user's skill set.

[0183] A "user's skill set" is the collection of knowledge, skills, experience, etc. that a user possesses.

[0184] A "database" is a collection of information that stores skill sets for each industry or occupation.

[0185] A "comparison means" is a method for comparing a user's skill set with skill sets in a database and evaluating the degree of match or similarity.

[0186] A "matching or similar skill set" is a skill set in an industry or occupation that has a high degree of match or similarity with the user's skill set.

[0187] The "extraction means" is a method for selecting industries or occupations that match or are similar to the user's skill set based on the comparison results.

[0188] "Recommendation methods" are methods of presenting extracted industries and occupations to users and recommending them as suitable jobs.

[0189] This invention is a system that receives a user's skill set as input, accesses a database to compare the skill sets of each industry or occupation with the user's skill set, extracts industries or occupations with matching or similar skill sets, and recommends the extracted industries and occupations to the user.

[0190] System configuration

[0191] Hardware

[0192] Server: Accesses the database to compare and extract skill sets.

[0193] Terminal: The device on which the user inputs the skillset and displays the results, such as a computer or smartphone.

[0194] software

[0195] Database management systems: Software for storing industry- or occupation-specific skill sets, such as MySQL or PostgreSQL.

[0196] Programming language: A language for manipulating and analyzing data, specifically Python.

[0197] Libraries: Libraries for efficient data manipulation and analysis, specifically Pandas and Scikit-learn.

[0198] Program processing

[0199] The server receives the skill set entered by the user from the terminal. Then, it accesses the database and compares the skill sets for each industry and occupation with the user's skill set. This comparison uses the Python Pandas library to manipulate and analyze the data. Specifically, it calculates the cosine similarity to evaluate the degree of match between the skill sets.

[0200] The server uses Scikit-learn to extract industries and occupations with skill sets that match or are similar to the user's skill set. For example, if a user has skills in "Python programming" and "data analysis," IT and software development occupations will be extracted.

[0201] The server sends the extracted list of industries and occupations to the device, which then analyzes the results and displays them to the user. For example, it might display information such as "Matching industries: IT industry" or "Matching occupations: software developer, data scientist."

[0202] Specific examples

[0203] For example, consider the following skill set entered by a user:

[0204] User Skill Set: Python Programming, Data Analysis

[0205] The server receives this skill set and compares it with the skill sets in its database. Based on the comparison, industries and occupations are extracted, such as:

[0206] Matching Industry: IT Industry

[0207] Matching occupations: Software developer, data scientist

[0208] The results are sent to the device and displayed to the user, allowing them to find the perfect job based on their skill set.

[0209] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0210] Step 1:

[0211] The user enters a skillset.

[0212] The user inputs their skill set into an input form on the device. For example, they input skills such as "Python programming" or "data analysis." The input skill set is temporarily stored in the device's memory.

[0213] Step 2:

[0214] The terminal transmits the input skill set to the server.

[0215] The terminal converts the skillset entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is sent to the server as a JSON-formatted string.

[0216] Step 3:

[0217] The server receives the skillset and accesses the database.

[0218] The server parses the received JSON data and extracts the user's skill set. Then, the server connects to a database to retrieve skill sets by industry and occupation. The data retrieved from the database is loaded into memory as a Pandas dataframe.

[0219] Step 4:

[0220] The server compares the user's skill set with the skill sets for each industry or occupation in the database.

[0221] The server uses the Pandas library to compare the skillsets in the database with the user's skillsets. Specifically, it calculates the cosine similarity to evaluate the match between the skillsets. The input data is the user's skillsets and the skillsets in the database, and the output data is the match score.

[0222] Step 5:

[0223] The server extracts industries and occupations with matching or similar skill sets.

[0224] The server uses Scikit-learn to extract industries and occupations with skill sets that match or are close to the user's skill set. Specifically, it selects industries and occupations with high match scores. The input data is the match score, and the output data is a list of matching industries and occupations.

[0225] Step 6:

[0226] The server sends the results to the terminal.

[0227] The server converts the extracted list of industries and occupations into JSON format and sends it to the terminal as an HTTP response. The input data is a list of matching industries and occupations, and the output data is a JSON-formatted string.

[0228] Step 7:

[0229] The terminal displays the results to the user.

[0230] The device parses the received JSON data and displays it to the user. Specifically, it displays information such as "Matching industry: IT industry" and "Matching occupation: software developer, data scientist." The input data is a string in JSON format, and the output data is text information displayed to the user.

[0231] (Application example 2)

[0232] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0233] Conventional career advice systems have difficulty effectively matching a user's skill set with the required skill sets of industries and occupations, and therefore have the problem of being unable to recommend the most suitable occupation for the user. Furthermore, they do not adequately consider the user's interests and preferences when recommending suitable occupations, which makes it difficult to increase user satisfaction.

[0234] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's ability set as input and searching for commonalities with existing fields and occupations, generative artificial intelligence means for generating suitable occupations based on the commonalities, means for recommending the generated suitable occupations to the user, and means for sending the user's ability set as a prompt to the generative artificial intelligence model to suggest optimal occupations. This makes it possible to effectively match the user's skill set with the required skill sets of industries and occupations and suggest optimal occupations to the user. It also makes it possible to recommend suitable occupations taking into account the user's interests and preferences, thereby increasing user satisfaction.

[0235] "User's competency set" refers to the knowledge, skills, experience, etc. possessed by the user, including the skills and qualifications required for a particular occupation or industry.

[0236] "Fields and occupations" are a general term for the various industries and occupations that exist in society, and refer to jobs and roles that require specific skill sets.

[0237] "Intersection tools" refer to methods and techniques for comparing a user's ability set with the skill sets required by a field or occupation to identify matches or similarities.

[0238] "Generative AI means" refers to AI technology that generates suitable jobs based on the intersection of a user's skill set with fields or occupations.

[0239] "Recommendation means" refers to methods and technologies for presenting the generated suitable jobs to the user and suggesting the most suitable occupation based on the user's interests and preferences.

[0240] "Generative AI model" refers to an AI model that takes a user's skill set as input and is used to suggest the most suitable occupation.

[0241] A "prompt" is a form of instruction or question input to a generative artificial intelligence model, and includes specific data such as a user's skill set or job information.

[0242] The system for implementing this invention receives a user's skill set as input, searches for commonalities with existing fields and occupations, and suggests the most suitable occupation. A specific embodiment of this system will be described below.

[0243] 1. System Configuration

[0244] The system consists of the following major components:

[0245] User devices (smartphones, smart glasses, head-mounted displays, etc.)

[0246] server

[0247] Generative AI models (OpenAI API, etc.)

[0248] 2. Program Processing

[0249] The server receives the user's ability set sent from the user's device and sends it as a prompt to the generative AI model. The generative AI model compares the user's ability set with the required skill set for the field or occupation and suggests the most suitable occupation. The suggested occupation is sent to the user's device and displayed to the user.

[0250] 3. Hardware and Software Used

[0251] Hardware: User devices (smartphones, smart glasses, head-mounted displays)

[0252] Software: OpenAI API, Python

[0253] 4. Data processing and calculation

[0254] The server receives the user's ability set in text format and sends it as a prompt to the generative AI model. The generative AI model performs data calculations based on the received prompt and generates the optimal occupation. The generated occupation is returned to the server in text format and sent to the user's device.

[0255] 5. Specific Examples

[0256] For example, if a user has the capability set "Programming languages: Python, JavaScript®; Data analysis; Machine learning," the system will send the following prompt to the generative artificial intelligence model:

[0257] Example prompt sentence:

[0258] User Skill Set: Programming languages: Python, JavaScript; Data Analysis; Machine Learning

[0259] Suggest suitable industries and occupations.

[0260] The generative AI model performs data calculations based on this prompt and generates a suggestion such as, "Data scientists and software engineers in the IT industry are suitable." This suggestion is sent to the user's device and displayed to the user.

[0261] In this way, it is possible to effectively match the user's ability set with the required skill set of a field or occupation, and suggest the most suitable occupation for the user.

[0262] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0263] Step 1:

[0264] The user inputs their own skill set using a terminal, which is then sent to the server in text format. The input data includes specific skills such as programming languages, data analysis, and machine learning.

[0265] Step 2:

[0266] The server then sends the received user skill set to the generative AI model as a prompt. The prompt is generated based on the user's skill set. For example, it could be in the format "User's skill set: Programming languages: Python, JavaScript; Data analysis; Machine learning. Please suggest suitable industries and occupations."

[0267] Step 3:

[0268] The generative AI model receives a prompt, compares the user's ability set with the required skill set for the field or occupation, performs data calculations, and generates the most suitable occupation. During this process, the generative AI model uses its internal database and pre-trained models to identify suitable occupations.

[0269] Step 4:

[0270] The generative AI model returns the generated suitable jobs in text format to the server. The output data includes specific job titles, such as "Data scientist or software engineer in the IT industry would be suitable."

[0271] Step 5:

[0272] The server sends the suitable jobs received from the generative AI model to the user's terminal. The user's terminal displays the suitable jobs received to the user. The user can check the displayed suitable jobs and use them as a reference for their own career path.

[0273] Step 6:

[0274] If the user requests more detailed information based on their interests and preferences, the server sends additional prompts to the generative artificial intelligence model, providing more specific career and job information. This process is repeated until the user is satisfied.

[0275] Example 3

[0276] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0277] Conventional career recommendation systems suggest suitable jobs based on the user's skill set, but because they cannot take into account the latest job information, it is difficult to suggest the most suitable job for the user.In addition, since recommendations do not reflect the user's interests and preferences, there is an issue of low user satisfaction.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0279] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, means for preprocessing data provided by the user, means for generating prompt sentences based on the preprocessed data, means for inputting the generated prompt sentences into the generative AI model, means for collecting job information on the Internet, means for generating new prompt sentences based on the collected job information, and means for generating suitable jobs taking the job information into consideration. This makes it possible to suggest suitable jobs taking the user's skill set and the latest job information into consideration.

[0280] "User skill set" refers to the knowledge, abilities, specific skills and experience possessed by the user.

[0281] "Intersections with industries and occupations" refers to the similarities between a user's skill set and the skills and knowledge required for a particular industry or occupation.

[0282] "Generative AI tools" refers to artificial intelligence functions that generate new job combinations and suitable jobs based on a user's skill set and other input data.

[0283] "Recommendation means" refers to the functions and methods for suggesting generated suitable jobs to users.

[0284] "Data preprocessing" refers to the process of converting data provided by users into a format that is easy for generative AI models to understand.

[0285] A "prompt" refers to an instruction or question that is input into a generative AI model.

[0286] A "generative AI model" refers to an artificial intelligence model that generates new information or suggestions based on an input prompt.

[0287] "Job information" refers to information published on the Internet or other media about the personnel sought by companies and organizations.

[0288] "New prompts" refer to new instructions or questions that are generated based on collected job information and are input into the generative AI model.

[0289] This invention is a system that generates suitable jobs based on a user's skill set and makes recommendations taking into account the latest job information. Specific embodiments of this system are described below.

[0290] System Overview

[0291] The system takes a user's skill set as input, uses a generative AI model to generate suitable jobs, and recommends them to the user. It also has the ability to collect the latest job information and generate suitable jobs based on that information.

[0292] Hardware and software used

[0293] Server: Processes data, runs generative AI models, and collects job information.

[0294] Terminal: Used by users to input their skill sets and view recommended suitable jobs.

[0295] Generative AI models: For example, using advanced natural language processing models such as OpenAI's GPT-4.

[0296] Data processing and calculation

[0297] 1. User data input: The user inputs their skill set (e.g., knowledge of programming languages ​​such as Python and Java, business-level English ability) through the terminal.

[0298] 2. Data preprocessing: The server receives the data provided by the user and converts it into a format that the generative AI model can understand, for example, JSON format.

[0299] 3. Prompt generation: The server generates a prompt based on the preprocessed data. For example, it generates the following prompt:

[0300] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[0301] 4. Input to the generative AI model: The server inputs the generated prompt sentence into the generative AI model, which then generates a suitable job based on the prompt sentence.

[0302] 5. Generating suitable jobs: The generative AI model generates suitable jobs for users based on the prompt, such as "IT consultant overseas."

[0303] 6. Job information collection: The server collects job information from the Internet, for example, scraping the latest job information from IT industry job sites.

[0304] 7. Generating suitable jobs based on job information: The server generates a new prompt based on the collected job information. For example, it generates the following prompt:

[0305] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[0306] The server then inputs this prompt sentence back into the generative AI model to generate a suitable job taking into account the job information.

[0307] 8. Result recommendation: The server recommends the generated suitable jobs to the user. The user can check the recommended jobs through the terminal.

[0308] Specific examples

[0309] For example, if a user has the following skill set:

[0310] Programming languages: Python, Java

[0311] Language ability: English (business level)

[0312] The server inputs the following prompt into the generative AI model:

[0313] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[0314] The generative AI model generates suitable jobs such as "IT consultant overseas." The server then collects the latest job information and generates prompts such as the following:

[0315] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[0316] The generative AI model generates "suitable jobs based on the latest IT consultant job information" based on this prompt sentence, and the server recommends the result to the user. The flow of the identification process in Example 3 will be described with reference to Fig. 15.

[0317] Step 1:

[0318] The user inputs their skill set through the terminal. For example, the user inputs "Knowledge of programming languages: Python, Java" and "Business-level English ability." The input data is sent to the server.

[0319] Step 2:

[0320] The server receives the data provided by the user and converts it into the required format. This preprocessing converts the input skill set into JSON format, making it understandable to the generative AI model. The input is the user's skill set, and the output is the preprocessed data.

[0321] Step 3:

[0322] The server generates a prompt based on the preprocessed data, for example:

[0323] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[0324] The input is the preprocessed data and the output is the generated prompt sentence.

[0325] Step 4:

[0326] The server inputs the generated prompt sentence into a generative AI model, such as OpenAI's GPT-4. The input is the generated prompt sentence, and the output is the suitable job generated by the generative AI model.

[0327] Step 5:

[0328] The generative AI model generates a suitable occupation for the user based on the prompt sentence. For example, it generates an occupation such as "IT consultant overseas." The input is the prompt sentence, and the output is the generated suitable occupation.

[0329] Step 6:

[0330] The server collects job information from the Internet. For example, it scrapes the latest job information from a job site in the IT industry. The input is a request to collect job information, and the output is the collected job information.

[0331] Step 7:

[0332] The server generates a new prompt based on the collected job information, for example:

[0333] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[0334] The input is the collected job information, and the output is the newly generated prompt.

[0335] Step 8:

[0336] The server inputs this new prompt sentence into the generative AI model again to generate a suitable job that takes the job information into account. The input is the newly generated prompt sentence, and the output is a suitable job that takes the job information into account.

[0337] Step 9:

[0338] The server recommends the generated suitable jobs to the user. The user can check the recommended jobs through their terminal. The input is the generated suitable jobs, and the output is the recommended results for the user.

[0339] (Application example 3)

[0340] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0341] While conventional career matching systems can recommend suitable jobs based on a user's skill set and interests, they lack the functionality to recommend products and services related to the generated suitable jobs, making it difficult for users to efficiently acquire the knowledge and tools necessary for a new career.

[0342] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0343] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a generative AI means for generating suitable jobs based on the commonalities, a means for recommending the generated suitable jobs to the user, and a means for recommending products related to the generated suitable jobs. This allows the user to efficiently acquire the knowledge and tools necessary for a new occupation.

[0344] A "user's skill set" is the collection of knowledge, skills, experience, etc. that a user possesses.

[0345] An "industry" is a group of businesses or organizations that conduct a particular type of economic activity.

[0346] An "occupation" is a job performed by people who have the skills and knowledge required to perform a particular task or role.

[0347] "Commonalities" are similarities or coincidences that exist between different elements or phenomena.

[0348] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[0349] A "job of choice" is a job that best suits a user's skill set and interests.

[0350] A "recommendation" is the act of suggesting specific information or options to a user.

[0351] A "product" is a good or service that a consumer can purchase.

[0352] "Job information" refers to information about job content and conditions that companies and organizations publish for new employment.

[0353] A "prompt" is an instruction or question input to a generative AI.

[0354] A system for carrying out this invention includes a generative AI means for receiving a user's skill set as input, searching for commonalities with existing industries and occupations, and generating suitable jobs based on those commonalities. It also includes a means for recommending the generated suitable jobs to the user and for recommending products related to the generated suitable jobs.

[0355] System Program

[0356] The system is implemented using the Python programming language and OpenAI's API. The server receives the user's skill set and the latest job information as input, and generates suitable jobs using a generative AI model. The generated suitable jobs are then recommended to the user based on their interests and preferences. Products related to the generated suitable jobs (books, tools, online courses, etc.) are also recommended.

[0357] Processing Description

[0358] The server receives the user's skill set and the latest job information as input. This data is then fed into a generative AI model using OpenAI's API as prompts. The generative AI model generates suitable jobs based on the input data. The generated suitable jobs are then recommended based on the user's interests and preferences. In addition, products related to the generated suitable jobs are also recommended.

[0359] Hardware and software used

[0360] Hardware: Servers, user devices (smartphones, PCs, etc.)

[0361] Software: Python, OpenAI API

[0362] Specific examples

[0363] For example, if a user has "knowledge of programming languages" and "English proficiency," the server inputs these skill sets and the latest job information as prompts into the generative AI model. The generative AI model generates a suitable job, such as "IT consultant overseas." Furthermore, it recommends products related to this suitable job, such as "books for IT consultants," "project management tools," and "online courses."

[0364] Example prompt sentence:

[0365] User skill set: Knowledge of programming languages, English proficiency

[0366] Latest Jobs: Overseas IT Consultant, Data Scientist

[0367] Generate suitable jobs based on this.

[0368] In this way, users can efficiently acquire the knowledge and tools necessary for a new profession.

[0369] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0370] Step 1:

[0371] The server receives the user's skill set and the latest job information as input. The user inputs their skill set (e.g., knowledge of programming languages, English ability) through a terminal and sends it to the server. The server also retrieves the latest job information from the database.

[0372] Input: User's skill set, latest job postings

[0373] Output: User skill sets and job listings

[0374] Step 2:

[0375] The server inputs the user's skill set and job information as prompts into the generative AI model, which then generates suitable jobs based on this data.

[0376] Input: User skill set and job posting combination

[0377] Output: Generated suitable jobs

[0378] How it works: The server uses the OpenAI API to send prompts to the generative AI model to generate suitable jobs.

[0379] Step 3:

[0380] The server recommends the generated suitable jobs to the user, who can then confirm the recommended suitable jobs through their device.

[0381] Input: Generated job titles

[0382] Output: Recommended jobs for the user

[0383] Specific operation: The server sends the generated suitable job to the user's device so that the user can check it.

[0384] Step 4:

[0385] The server recommends products related to the generated suitable job. Based on the generated suitable job, it searches a database for related products such as books, tools, and online courses and recommends them to the user.

[0386] Input: Generated job titles

[0387] Output: Recommended products

[0388] Specific operation: The server retrieves product information related to the generated suitable job from the database and sends it to the user's terminal.

[0389] Step 5:

[0390] Users can check the recommended suitable jobs and related products and purchase the necessary items. Users select the products and complete the purchase process via their device.

[0391] Input: Recommended products

[0392] Output: Purchased items

[0393] Specific actions: The user selects the product through the terminal and completes the purchase procedure.

[0394] In this way, users can efficiently acquire the knowledge and tools necessary for a new profession.

[0395] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0396] "Example 1"

[0397] One embodiment of the present invention is a system that combines an emotion engine that recognizes a user's emotions. This system receives a user's skill set as input and searches for commonalities with existing industries and occupations. The search for commonalities is performed using the emotion engine that recognizes the user's emotions. For example, if a user expresses the emotion "I like programming," the emotion engine analyzes this information and searches for commonalities with industries and occupations related to programming.

[0398] "Example 2"

[0399] Next, there is a generative AI method that generates suitable jobs based on commonalities. This generative AI method also uses an emotion engine that recognizes the user's emotions to generate suitable jobs that take the user's emotions into consideration. For example, if the user expresses the emotion "I like working cooperatively with others," the emotion engine analyzes this information and generates suitable jobs that require teamwork. "Example 3"

[0400] Finally, there is a way to recommend suitable jobs to users. This recommendation method also takes the user's emotions into consideration using an emotion engine that recognizes their feelings. For example, if a user expresses the emotion "I like trying new things," the emotion engine will analyze this information and recommend new industries or new occupations.

[0401] The processing flow of each embodiment will be described below.

[0402] "Example 1"

[0403] Step 1: Receive skillset input from the user.

[0404] Step 2: Use the emotion engine to recognize the user's emotions.

[0405] Step 3: Use the emotions you've identified to find commonalities between your skill set and your industry or occupation.

[0406] "Example 2"

[0407] Step 1: Receive skillset input from the user.

[0408] Step 2: Use the emotion engine to recognize the user's emotions.

[0409] Step 3: Generate suitable jobs based on the identified emotions and skill sets.

[0410] "Example 3"

[0411] Step 1: Receive skillset input from the user.

[0412] Step 2: Use the emotion engine to recognize the user's emotions.

[0413] Step 3: Generate suitable jobs based on the identified emotions and skill sets.

[0414] Step 4: Recommend the generated suitable jobs to the user.

[0415] Example 1

[0416] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0417] Conventional career recommendation systems recommend suitable jobs based on the user's skill set, but because they are unable to consider the user's emotions and interests, they have the problem of making it difficult to find the best job for the user. In addition, because they are unable to consider currently available job information, they are unable to recommend suitable jobs that are in line with the actual job market.

[0418] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0419] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a means for analyzing the user's emotions, a generative AI means for generating suitable jobs based on the commonalities and the analyzed emotions, and a means for recommending the generated suitable jobs to the user. This makes it possible to recommend suitable jobs that take into account not only the user's skill set but also their emotions and interests, and by taking into account currently available job information, it is possible to recommend suitable jobs that are in line with the actual job market.

[0420] "User skill set" refers to information that specifically describes the skills, experience, qualifications, etc. possessed by the user.

[0421] "Means for finding commonalities with industries and occupations" refers to methods and techniques for finding connections with existing industries and occupations based on the user's skill set.

[0422] "Means for analyzing user emotions" refers to methods or techniques for extracting emotions from user input and analyzing those emotions.

[0423] "Generative AI methods" are artificial intelligence technologies that generate suitable jobs based on the user's skill set and analyzed emotions.

[0424] "Recommendation means" refers to the methods and technologies used to present and recommend the generated suitable jobs to users.

[0425] "Job information" refers to information about currently available jobs, including details such as job content, required skills, and work location.

[0426] A "prompt" is input information that the generative AI means uses as a reference when generating suitable jobs.

[0427] A "suitable job" is a job that is best suited to the user, generated based on the user's skill set and emotions.

[0428] The present invention provides a system that generates suitable jobs based on the user's skill set and emotions and recommends them to the user. Specific embodiments of this system are described below.

[0429] First, a user uses a terminal to access the system's interface, which includes text boxes and drop-down menus where the user can input their skill set, which can include knowledge of programming languages, project management experience, qualifications, and even emotions (e.g., "I like programming").

[0430] The server then receives the skill set entered by the user and stores it in a database, where the skill sets for each user are organized and stored.

[0431] The server calls the emotion engine based on the stored skill set. The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to analyze emotions from the user's input. For example, it recognizes the emotion "I like programming."

[0432] The server then uses the emotion engine's analysis to present a list of industries and occupations that fit the user's skill set and emotions. For example, if a user likes programming, it will present occupations such as software development and IT consulting.

[0433] The server generates a list of industries and occupations in HTML format and displays it in the user's browser, allowing the user to consider their own career path based on the information presented.

[0434] As a concrete example, if a user enters the following skillset:

[0435] Programming languages: Python, Java

[0436] Experience: Project Management (5 years)

[0437] Qualification: PMP (Project Management Professional)

[0438] Emotion: I like programming

[0439] In this case, the server proceeds as follows:

[0440] 1. The user enters the above skill set.

[0441] 2. The server stores the entered skillset in a database.

[0442] 3. The emotion engine analyzes the emotion "I love programming."

[0443] 4. Based on the analysis results of the emotion engine, the server lists industries and occupations such as software development and IT consulting.

[0444] 5. The server presents the listed industries and occupations to the user.

[0445] Example prompts to input to a generative AI model:

[0446] The user entered the following skillset:

[0447] Programming languages: Python, Java

[0448] Experience: Project Management (5 years)

[0449] Qualification: PMP (Project Management Professional)

[0450] Emotion: I like programming

[0451] Based on this skill set and emotion, make a list of suitable industries and occupations.

[0452] This system makes it possible to recommend suitable jobs that take into account not only the user's skill set but also their emotions and interests.Furthermore, by taking into account currently available job information, it is possible to recommend suitable jobs that are in line with the actual job market.

[0453] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0454] Step 1:

[0455] The user enters a skillset.

[0456] Users access the system's interface using a terminal and enter their skill set using text boxes and drop-down menus. The input can include programming language knowledge, project management experience, qualifications, and even emotions (e.g., "I like programming"). The input data is sent to the server as an HTTP request.

[0457] Step 2:

[0458] The server receives the skillset and stores it in a database.

[0459] The server receives the HTTP request sent by the user and parses the skill set data. The parsed data is stored in a database along with the user ID. Storing the input data prepares the foundation for use in subsequent processing.

[0460] Step 3:

[0461] The server calls the emotion engine and analyzes the user's emotions.

[0462] The server passes the skillset stored in the database to the emotion engine. The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to extract and analyze emotions from the user's input. For example, it recognizes the emotion "I like programming." The analysis results are returned to the server.

[0463] Step 4:

[0464] The server lists appropriate industries and occupations based on the analysis results of the emotion engine.

[0465] The server uses a generative AI means to generate suitable jobs based on the analysis results returned by the emotion engine and the user's skill set. The generative AI means lists industries and occupations that are suitable for the user's skill set and emotions. For example, for a user who likes programming, occupations such as software development and IT consulting will be listed. The listed industries and occupations are stored on the server.

[0466] Step 5:

[0467] The server presents the user with a list of industries and occupations.

[0468] The server generates a list of industries and occupations in HTML format and displays it in the user's browser. The user can then use the information presented to consider their own career path. The displayed information is organized in a way that is easy for the user to understand.

[0469] (Application example 1)

[0470] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0471] Conventional skill matching systems suggest suitable jobs based on a user's skill set, but they are unable to consider the user's emotions and interests. This makes it difficult for users to find jobs that truly interest and satisfy them. Furthermore, for autonomous vehicles, there has been no system that suggests suitable driving tasks based on the driver's skill set and emotions. This has led to problems such as reduced driver satisfaction and efficiency.

[0472] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0473] In this invention, the server includes: means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations; generative AI means for generating suitable jobs based on the commonalities; means for generating suitable jobs based on the user's emotions using an emotion engine that recognizes the user's emotions; means for recommending the generated suitable jobs to the user; and means that is installed in the autonomous vehicle and suggests suitable driving tasks based on the user's skill set and emotions. This makes it possible to suggest suitable jobs and driving tasks that take the user's skill set and emotions into comprehensive consideration.

[0474] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[0475] "Means for finding commonalities with industries and occupations" refers to methods and techniques for finding connections with existing industries and occupations based on the user's skill set.

[0476] "Generative AI means" is an artificial intelligence technology that generates suitable jobs and driving tasks based on the user's skill set and emotional information.

[0477] An "emotion engine" is a technology that recognizes a user's emotions and analyzes information based on those emotions.

[0478] "Recommendation means" refers to methods and technologies for suggesting the generated suitable jobs or driving tasks to users.

[0479] An "autonomous vehicle" is a vehicle that drives autonomously using artificial intelligence and sensor technology.

[0480] A "driving task" refers to the specific driving operations and route selection that an autonomous vehicle performs.

[0481] The system for implementing this invention suggests suitable jobs and driving tasks based on the user's skill set and emotions. Specific embodiments of the system are described below.

[0482] System Configuration

[0483] The system consists of the following main components:

[0484] 1. User device: The device through which the user inputs their skillset and emotions. This can be a smartphone, tablet, or PC.

[0485] 2. Server: A central processing unit that receives the user's skill set and emotions and generates suitable jobs and driving tasks. It uses Python, the EmotionEngine library, and the SkillMatcher library.

[0486] 3. Self-driving vehicles: Vehicles equipped with on-board computers that perform driving tasks based on the skillset and emotions of the user.

[0487] Program processing

[0488] The server performs the process in the following steps.

[0489] 1. Skillset input: Receive the user's skillset from the user's device. The skillset is sent to the server in JSON format.

[0490] 2. Emotion Recognition: Using the EmotionEngine library, we recognize user emotions from text. For example, we analyze the text "I love programming" and recognize positive emotions.

[0491] 3. Job Generation: Using the SkillMatcher library, we generate suitable jobs based on the user's skill set and emotions. The generated suitable jobs are recommended based on the user's interests and preferences.

[0492] 4. Driving task suggestion: The system installed in the autonomous vehicle suggests suitable driving tasks based on the user's skill set and emotions.

[0493] Specific examples

[0494] For example, if a user types "I love programming," the emotion engine will recognize this "positive emotion" and suggest programming-related tasks (e.g., updating or debugging vehicle software).

[0495] Prompt Sentence Examples

[0496] Below are some example prompts for a generative AI model:

[0497] Enter the user's skill set, for example, knowledge of programming languages, project management experience, English proficiency, etc.

[0498] Next, enter your feelings in text, such as "I love programming."

[0499] Based on this information, the system suggests suitable driving tasks and routes.

[0500] In this way, it becomes possible to suggest suitable jobs or driving tasks that take into account the user's skill set and emotions comprehensively.

[0501] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0502] Step 1:

[0503] The user's device receives the user's skill set as input. The user then specifically describes their own skills, experience, qualifications, etc. For example, "knowledge of programming languages," "experience in project management," "English proficiency," etc. The input skill set is sent to the server in JSON format.

[0504] Step 2:

[0505] The server receives the skillset sent from the user terminal, analyzes the received skillset, and stores it in a database. The analyzed skillset is used in subsequent processing.

[0506] Step 3:

[0507] The user inputs the user's emotion in text form, such as "I like programming." The input emotion text is then sent to the server.

[0508] Step 4:

[0509] The server analyzes the user's emotion text using the EmotionEngine library. The emotion engine recognizes the user's emotion from the input text and classifies it into emotion categories such as positive, negative, and neutral. The analysis results are used in subsequent processing.

[0510] Step 5:

[0511] The server uses the SkillMatcher library to generate suitable jobs based on the user's skill set and emotions. It receives skill set and emotion information as input and generates suitable jobs using a generative AI model. The generated suitable jobs are recommended based on the user's interests and preferences.

[0512] Step 6:

[0513] The server sends the generated suitable jobs to the user's device, which then displays the recommended suitable jobs to the user. The user can review the suggested suitable jobs and select the occupation that interests them.

[0514] Step 7:

[0515] The autonomous vehicle proposes suitable driving tasks based on the user's skill set and emotions. The vehicle's on-board computer generates driving tasks based on the skill set and emotion information sent from the server. For example, tasks such as updating vehicle software or debugging are proposed.

[0516] Step 8:

[0517] The autonomous vehicle executes the proposed driving task, which is optimized based on the user's skill set and emotions, improving user satisfaction and efficiency.

[0518] Example 2

[0519] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0520] While conventional career recommendation systems can suggest suitable jobs based on a user's skill set, they have the problem of being unable to consider the user's emotions or work style preferences, making it difficult to find the best job for the user. Furthermore, they are also inadequate at generating suitable jobs that take into account currently available job information.

[0521] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0522] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a generative AI means for generating suitable jobs based on the commonalities, an emotion engine means for recognizing the user's emotions and generating suitable jobs taking those emotions into consideration, and a means for recommending the generated suitable jobs to the user. This makes it possible to suggest optimal jobs that take into consideration not only the user's skill set but also their emotions and work style preferences.

[0523] A "user's skill set" is the collection of knowledge, skills, and experience that a user possesses.

[0524] A "means for finding commonalities with industries or occupations" is a method or device for comparing a user's skill set with the skill sets required by each industry or occupation and finding matches or similarities.

[0525] "Generative AI tools" are methods or devices that use artificial intelligence technology to generate suitable jobs based on the intersection of a user's skill set with industries or occupations.

[0526] The "emotion engine means" is a method or device for recognizing a user's emotions and generating suitable jobs by taking those emotions into consideration.

[0527] The "means for recommending" refers to a method or device for presenting and suggesting the generated suitable jobs to the user.

[0528] "Job Information" means information about current employment opportunities, including details such as job type, location, and salary.

[0529] A "prompt" is input information that the generative AI means uses as a reference when generating suitable jobs.

[0530] The present invention is a system that suggests the most suitable occupation for a user by taking into consideration the user's skill set and emotions. Specific embodiments of this system will be described below.

[0531] Hardware and software used

[0532] Hardware: Servers, devices (users' PCs and smartphones)

[0533] Software: Generative AI model, emotion engine

[0534] System configuration

[0535] 1. Entering the user's skill set

[0536] Users use a terminal to input their skill set, such as knowledge of programming languages ​​(Python, Java) and project management experience (3 years).

[0537] 2. Submit and analyze your skillset

[0538] The device sends the input skill set to the server, which then passes the received data to the generative AI model and begins analysis.

[0539] 3. Extracting commonalities with industries and occupations

[0540] The server uses a generative AI model to compare the user's skill set with the skill sets required for each industry or occupation, extracting commonalities and similarities.

[0541] 4. User Emotion Input

[0542] Users use the device to input their feelings and work preferences, such as "I like working collaboratively with others."

[0543] 5. Generating suitable jobs

[0544] The server receives the user's emotional data and analyzes it using an emotion engine. The generative AI model combines the skill set and emotional data to generate a suitable occupation for the user.

[0545] 6. Displaying the results

[0546] The server sends the generated job candidates to the terminal and displays them to the user, who can then review the results and consider their next steps.

[0547] Specific examples

[0548] Example of user skill set input

[0549] Programming languages: Python, Java

[0550] Project Management: 3 years of experience

[0551] Communication skills: High

[0552] Example prompts for generative AI models

[0553] User skill set:

[0554] Programming languages: Python, Java

[0555] Project Management: 3 years of experience

[0556] Communication skills: High

[0557] User sentiment:

[0558] I like working collaboratively with people

[0559] Use this information to generate suitable occupations for your users.

[0560] Example of suitable job generation

[0561] IT Project Manager

[0562] Software Development Team Leader

[0563] The system helps users find the career that best suits them by taking into account their skill set and emotions and suggesting the most suitable careers.

[0564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0565] Step 1:

[0566] Entering user skill sets

[0567] How it works: A user uses a terminal to enter their skill set.

[0568] Input: Knowledge of programming languages ​​(Python, Java), project management experience (3 years), etc.

[0569] Output: The input skillset data.

[0570] Specific behavior: The user enters "Python, Java, 3 years of project management" into a dedicated web form and clicks the "Submit" button.

[0571] Step 2:

[0572] Skillset submission and analysis

[0573] How it works: The device sends the input skillset to the server, which then passes the received data to the generative AI model and begins analysis.

[0574] Input: Skillset data entered by the user.

[0575] Output: Skillset feature data as the analysis result.

[0576] Specific operation: The device sends the data "Python, Java, 3 years of project management" to the server, and the server inputs this data into the generative AI model.

[0577] Step 3:

[0578] Extracting commonalities with industries and occupations

[0579] How it works: The server uses a generative AI model to compare the user's skill set with the skill sets required for each industry or occupation, extracting commonalities and similarities.

[0580] Input: Parsed skillset feature data.

[0581] Output: Industry and occupation intersection data.

[0582] How it works: The server analyzes the skill set "Python, Java, 3 years of project management" and extracts occupations in the IT industry and software development as commonalities.

[0583] Step 4:

[0584] User emotion input

[0585] How it works: The user uses the device to input their emotions and work preferences.

[0586] Input: User sentiment data (e.g., "I like working collaboratively with others").

[0587] Output: The input emotion data.

[0588] Specific behavior: User types "I enjoy working with people" and clicks the "Submit" button.

[0589] Step 5:

[0590] Generating suitable jobs

[0591] How it works: The server receives the user's emotional data and analyzes it using the emotion engine. The generative AI model combines the skill set and emotional data to generate a suitable occupation for the user.

[0592] Input: Industry and occupation commonality data, user sentiment data.

[0593] Output: Job aptitude data.

[0594] Specific operation: The server uses an emotion engine to analyze emotional data such as "I like working collaboratively with others," and the generative AI model generates suitable jobs such as "IT project manager" and "software development team leader."

[0595] Step 6:

[0596] Displaying the results

[0597] How it works: The server sends the generated job listings to the device and displays them to the user, who can then review the results and consider their next steps.

[0598] Input: Job aptitude data.

[0599] Output: The job listing displayed to the user.

[0600] Specific operation: The server sends the result, such as "IT project manager" or "software development team leader," to the device, and the user confirms it on the screen.

[0601] (Application example 2)

[0602] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0603] While conventional career consulting systems can suggest suitable jobs based on a user's skill set, they face the challenge of being unable to suggest suitable jobs that take into account the user's emotions and interests. Furthermore, when users seek career advice in a virtual space, there is a lack of systems that can suggest suitable jobs in real time. This has led to problems such as reduced user satisfaction and reduced accuracy of suitable jobs.

[0604] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0605] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a generative AI means for generating suitable jobs based on the commonalities, a means for recommending the generated suitable jobs to the user, and an emotion engine for recognizing the user's emotions and generating suitable jobs taking those emotions into consideration. This makes it possible to suggest suitable jobs that take into consideration both the user's skill set and emotions.

[0606] A user's skill set is the collection of knowledge, skills, and experience that a user possesses.

[0607] An "industry or occupation intersection search" is a method or device for comparing a user's skill set with the skill sets of existing industries or occupations to find matches or similarities.

[0608] "Generative AI tools" are methods or devices that use artificial intelligence technology to generate suitable jobs based on the intersection of a user's skill set with industries or occupations.

[0609] The "means for recommending" refers to a method or device for suggesting the generated suitable jobs to the user.

[0610] An "emotion engine" is a technology or device that recognizes a user's emotions, analyzes them, and reflects them in generating suitable jobs.

[0611] "Job information" refers to information about currently available jobs, including details such as job content, required skills, and work location.

[0612] "Interests and preferences" refer to areas in which a user is particularly interested or has preferences, and are important factors in choosing a career.

[0613] A system for implementing this invention analyzes a user's skill set and emotions and suggests suitable jobs based on the analysis. The system includes: a means for receiving the user's skill set as input and searching for commonalities with industries or occupations; a generative AI means for generating suitable jobs based on the commonalities; a means for recommending the generated suitable jobs to the user; and an emotion engine for recognizing the user's emotions and generating suitable jobs taking those emotions into consideration.

[0614] Hardware and Software Configuration

[0615] Hardware:

[0616] Smartphone

[0617] Smart Glasses

[0618] head-mounted display

[0619] software:

[0620] OpenAI API

[0621] emotion_recognition library

[0622] skill_matching library

[0623] System Operation

[0624] 1. User Skillset and Emotion Input:

[0625] Users input their skills and emotions using a smartphone or head-mounted display. For example, a user can input "I have knowledge of programming languages" and express their emotion by saying "I like working collaboratively with others."

[0626] 2. Industry and occupational commonality analysis:

[0627] The server uses the skill_matching library to compare the user's skill set with existing industry and occupation skill sets and find commonalities.

[0628] 3. Job Creation:

[0629] The server uses generative AI methods to generate suitable jobs based on commonalities, using the OpenAI API to generate suitable jobs using the user's skill set and emotions as prompts.

[0630] 4. Use of Emotion Engine:

[0631] The server uses the emotion_recognition library to analyze the user's emotions and generate suitable jobs taking those emotions into consideration.

[0632] 5. Job Recommendations:

[0633] The server then recommends the generated suitable jobs to the user, based on the user's interests and preferences.

[0634] Specific examples

[0635] If a user inputs "I have knowledge of programming languages" and indicates the emotion "I like working collaboratively with others," the system will suggest occupations such as "Software development team leader."

[0636] Prompt Sentence Examples

[0637] User skill set: Knowledge of programming languages

[0638] User sentiment: I like working collaboratively with others

[0639] Please suggest suitable jobs.

[0640] In this way, the system can suggest suitable jobs that take into account the user's skill set and emotions simultaneously.

[0641] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0642] Step 1:

[0643] The user inputs their skill set and emotions using a smartphone or head-mounted display. The input skill set and emotions are sent to the server. Examples of input data include "knowledge of programming languages" and "I like working collaboratively with others."

[0644] Step 2:

[0645] The server uses the skill_matching library to compare the user's skill set with existing industry and occupation skill sets. Specifically, it matches the user's skill set with industry and occupation skill sets in the database to find matches or similarities. The input data is the user's skill set, and the output data is the intersection with the industry or occupation.

[0646] Step 3:

[0647] The server uses generative AI to generate suitable jobs based on the commonalities. It uses the OpenAI API to generate suitable jobs using the user's skill set and emotions as prompts. Specifically, it generates a prompt and sends it to the OpenAI API to receive suitable job suggestions. The input data are the commonalities and emotions, and the output data are suitable job suggestions.

[0648] Step 4:

[0649] The server analyzes the user's emotions using the emotion_recognition library. Specifically, it analyzes the emotional data entered by the user and takes those emotions into consideration when generating suitable jobs. The input data is the user's emotions, and the output data is a suitable job proposal that takes those emotions into consideration.

[0650] Step 5:

[0651] The server recommends the generated suitable jobs to the user. The recommendations are based on the user's interests and preferences. Specifically, the suitable job suggestions are sent to the user's device and displayed to the user. The input data are the suitable job suggestions, and the output data are the recommended information for the user.

[0652] In this way, the system can suggest suitable jobs that take into account the user's skill set and emotions simultaneously.

[0653] Example 3

[0654] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0655] Conventional career recommendation systems only consider the user's skill set and interests, making it difficult to provide recommendations that reflect the latest job information and the user's emotions. Furthermore, recommendations that do not consider the user's emotions can reduce user satisfaction. This makes it difficult to provide the most suitable career for the user.

[0656] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0657] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, emotion analysis means for analyzing the user's emotions and making recommendations taking those emotions into consideration, and means for acquiring the latest job information and generating suitable jobs based on that information. This makes it possible to recommend suitable jobs that take into consideration not only the user's skill set and interests but also the latest job information and the user's emotions.

[0658] A "user's skill set" is the specific set of skills, knowledge, and abilities that a user possesses.

[0659] "Methods for finding commonalities with industries and occupations" are methods and techniques for comparing a user's skill set with the required skills of existing industries and occupations to find common elements.

[0660] "Generative AI tools" are artificial intelligence technologies that generate suitable jobs based on a user's skill set, interests, and the latest job information.

[0661] "Recommendation means" refers to the methods and technologies used to present and suggest the generated suitable jobs to users.

[0662] "Emotion analysis means" refers to methods and technologies for analyzing emotions from user input and behavior and making recommendations that take those emotions into consideration.

[0663] "Means for obtaining the latest job information" refers to methods and technologies for collecting the latest job information from the Internet or databases.

[0664] A "prompt" is an instruction or question input to generative AI, and serves as the basic data for generating suitable jobs.

[0665] This invention is a system that recommends suitable jobs by taking into account a user's skill set, interests, the latest job information, and the user's emotions. A specific embodiment of this system will be described below.

[0666] First, a user uses a terminal to input their skills and interests. For example, they can input their skills in "Python programming" and "data analysis" and their interest in "remote work." This input data is then sent to the server.

[0667] The server receives the skill set and interest data submitted by the user and generates a prompt to send to a generative AI model, such as OpenAI's GPT-4. An example of a generated prompt is as follows:

[0668] User skills: Python programming, data analysis

[0669] User Interest: Remote Work

[0670] The server then sends the generated prompt to a generative AI model to generate a suitable job. The generative AI model analyzes the prompt and generates a suitable job based on the user's skill set and interests. For example, a suitable job such as "Data Scientist" or "AI Engineer" is generated.

[0671] Furthermore, the server retrieves the latest job information via the internet. For example, it retrieves job information for "data scientists who can work remotely." Then, it generates an additional prompt sentence based on the retrieved job information to send to the generative AI model. An example of the additional prompt sentence is as follows:

[0672] Latest Job Opening: Remote Data Scientist

[0673] The server sends this additional prompt to the generative AI model, which then regenerates suitable jobs that take into account the latest job information, enabling recommendations based on the latest job information to be provided to users.

[0674] Finally, the server uses an emotion engine to analyze the user's emotions. For example, if the user expresses an emotion such as "I like trying new things," the server analyzes that information. The emotion engine analyzes emotions from the user's input and past behavior, and makes recommendations taking those emotions into account.

[0675] The server generates recommendations that take the user's emotions into account based on the analyzed emotional information. For example, it may recommend new industries or new occupations. The generated suitable jobs are then displayed on the user's device, where the user can check the recommended suitable jobs.

[0676] In this way, suitable job recommendations are realized that comprehensively take into account the user's skill set and interests, the latest job information, and the user's emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0677] Step 1:

[0678] Users enter their skills and interests.

[0679] Users use the terminal to input their skillset (e.g., "Python programming," "data analysis") and interests (e.g., "remote work"), which is then sent to the server.

[0680] Input: User's skill set and interests

[0681] Output: User skill set and interest data sent to the server

[0682] Step 2:

[0683] The server receives the input data and generates a prompt for the generative AI model.

[0684] The server receives the skill set and interest data submitted by the user and generates prompts based on this data to send to the generative AI model.

[0685] Input: User skill set and interest data

[0686] Output: The prompt to send to the generative AI model

[0687] Specific operation: The server generates prompt sentences of the form "User skills: Python programming, data analysis" and "User interests: remote work".

[0688] Step 3:

[0689] The server sends prompts to the generative AI model to generate suitable jobs.

[0690] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-4), which analyzes the prompt and generates suitable jobs based on the user's skill set and interests.

[0691] Input: A prompt to send to the generative AI model

[0692] Output: A list of suitable jobs returned by the generative AI model

[0693] Specific operation: The generative AI model generates suitable jobs such as "data scientist" or "AI engineer."

[0694] Step 4:

[0695] The server retrieves the latest job information and generates additional prompts for the generative AI model.

[0696] The server retrieves the latest job information from the internet, then generates additional prompts based on the information to send to the generative AI model.

[0697] Input: Latest Jobs

[0698] Output: Additional prompts to send to the generative AI model

[0699] What it does: The server generates an additional prompt of the form "Latest job opening: Data scientist with remote work options."

[0700] Step 5:

[0701] The server sends additional prompts to the generative AI model to generate suitable jobs taking into account the latest job information.

[0702] The server sends additional prompts to the generative AI model, which then takes into account the latest job postings and regenerates suitable jobs based on the user's skill set and interests.

[0703] Input: Additional prompts to send to the generative AI model

[0704] Output: A list of suitable jobs, taking into account the latest job postings, returned by the generative AI model

[0705] Specific behavior: The generative AI model generates suitable jobs such as "data scientist with remote work capabilities."

[0706] Step 6:

[0707] The server analyzes the user's emotions using an emotion engine.

[0708] The server uses an emotion engine to analyze emotions from user input and past behavior. For example, if a user expresses an emotion such as "I like trying new things," the server analyzes that information.

[0709] Input: User input and past behavior data

[0710] Output: Analyzed user emotion information

[0711] Specific operation: The emotion engine analyzes the user's text input and behavioral history to identify emotions such as "I like challenges."

[0712] Step 7:

[0713] The server generates recommendations that take into account the user's emotions.

[0714] The server generates recommendations based on the analyzed emotional information, taking into account the user's emotions, such as recommending new industries or new job types.

[0715] Input: Analyzed user emotion information

[0716] Output: A list of recommendations that takes user sentiment into account

[0717] Specific operation: The server recommends suitable jobs that match the user's emotions, such as "data scientist in a new industry."

[0718] Step 8:

[0719] The server recommends the generated suitable jobs to the user.

[0720] The server recommends the generated suitable jobs to the user, who can then check the recommended suitable jobs through their device.

[0721] Input: Generated list of suitable jobs

[0722] Output: Information on suitable jobs recommended to the user

[0723] Specific operation: The server sends suitable job information to the terminal, and the user checks the information on the terminal.

[0724] (Application example 3)

[0725] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0726] Conventional career recommendation systems generate suitable jobs based on a user's skill set, interests, and preferences, but because they do not take into account behavioral data such as the user's purchase history, it is difficult to make more accurate recommendations. Furthermore, if the system does not generate suitable jobs that take into account currently available job information, it is unable to provide users with suitable jobs based on the latest job information. Furthermore, because recommendations do not take into account the user's emotions and interests, it is difficult to increase user satisfaction.

[0727] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0728] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, means for acquiring the user's purchase history, and means for generating suitable jobs based on the acquired purchase history. This enables highly accurate suitable job recommendations that take into account the user's skill set and purchase history.

[0729] A "user's skill set" is the collection of knowledge, skills, and experience that a user possesses.

[0730] An "industry" is a group of businesses or organizations that conduct a particular type of economic activity.

[0731] An "occupation" is a type of work with specific tasks or roles.

[0732] A "commonality" is a characteristic or property that exists in common among multiple elements.

[0733] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[0734] A "recommendation" is the act of recommending specific information or options to a user.

[0735] "Purchase history" is a record of products and services a user has purchased in the past.

[0736] "Job information" is information about the personnel that companies and organizations are looking for.

[0737] "Interests and preferences" are the things that a user is particularly interested in and the tendencies they like.

[0738] The system for implementing this invention generates and recommends suitable jobs based on a user's skill set and purchase history. A specific embodiment of this system is described below.

[0739] System Configuration

[0740] The system consists of the following main components:

[0741] 1. User device: The device used by the user, such as a smartphone or computer.

[0742] 2. Server: A central server that processes data and runs generative AI.

[0743] 3. Database: A database that stores user skill sets, purchase history, job information, etc.

[0744] Hardware and software used

[0745] Hardware: smartphones, PCs, servers

[0746] Software: Python, OpenAI API, database management system (e.g., MySQL)

[0747] Data processing and calculation

[0748] 1. Obtaining the user's skill set: The skill set entered from the user's terminal is sent to the server.

[0749] 2. Get purchase history: Get the user's purchase history from the database.

[0750] 3. Prompt generation: Based on the acquired skill set and purchase history, prompts are created to allow the generative AI to generate suitable jobs.

[0751] 4. Job Generation: Uses OpenAI's API to generate job prompts.

[0752] 5. Displaying recommendations: The generated suitable jobs are displayed on the user's device.

[0753] Specific examples

[0754] For example, if a user has purchased "programming books" and "English learning materials," the generative AI would generate suitable jobs using prompts like the following:

[0755] Prompt Sentence Examples

[0756] User purchase history: Programming books, English learning materials. Generate a suitable occupation for this user.

[0757] Based on this prompt, the generative AI generates suitable jobs such as "IT consultant overseas" and recommends them to the user.

[0758] In this way, it becomes possible to make highly accurate job recommendations that take into account the user's skill set and purchasing history.

[0759] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0760] Step 1:

[0761] A user terminal receives the user's skill set as input.

[0762] Input: The skill set the user entered into the terminal (e.g., programming, English)

[0763] Output: Skillset data is sent to the server.

[0764] Specific operation: The user enters their skillset into an input form on their smartphone or computer and presses the submit button.

[0765] Step 2:

[0766] The server retrieves the user's purchase history from the database.

[0767] Input: User ID

[0768] Output: Purchase history data (e.g. programming books, English learning materials)

[0769] What happens: The server queries the database to retrieve the purchase history associated with the user ID.

[0770] Step 3:

[0771] The server generates a prompt sentence based on the acquired skill set and purchase history.

[0772] Input: Skill set data, purchase history data

[0773] Output: Prompt statement (e.g., User's purchase history: programming books, English learning materials. Please generate a suitable occupation for this user.)

[0774] Specific operation: The server combines the skill set and purchase history to create a prompt for the generative AI to generate a suitable job.

[0775] Step 4:

[0776] The server uses generative AI to generate suitable jobs based on the prompt.

[0777] Input: prompt statement

[0778] Output: Generated suitable job (e.g. IT consultant overseas)

[0779] Specific operation: The server sends the prompt to the OpenAI API and receives the generated suitable job.

[0780] Step 5:

[0781] The server transmits the generated suitable jobs to the user terminal.

[0782] Input: Generated job titles

[0783] Output: Job listings displayed on the user's device

[0784] Specific operation: The server sends the generated suitable job to the user's device, which displays it.

[0785] Step 6:

[0786] The user terminal displays the generated suitable jobs to the user.

[0787] Input: Job information sent from the server

[0788] Output: Job listings displayed to the user

[0789] Specific operation: The user's device displays suitable job information on the screen, and the user confirms it.

[0790] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0791] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0792] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0793] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0794] [Second embodiment]

[0795] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0796] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0797] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0798] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0799] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0800] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0801] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0802] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0803] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0804] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0805] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0806] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0807] "Example 1"

[0808] In one embodiment of the present invention, a user inputs their own skill set into the system. This skill set specifically describes the user's skills, experience, and qualifications. For example, it may include knowledge of programming languages, project management experience, English proficiency, etc.

[0809] "Example 2"

[0810] Next, the system looks for commonalities with existing industries and occupations. This is done by comparing the user's skill set with the skill sets required for each industry or occupation and looking for matches or similarities. For example, if the user has knowledge of programming languages, the IT industry and software development occupations will be extracted as commonalities.

[0811] "Example 3"

[0812] The system also uses generative AI to generate suitable jobs. This AI generates new job combinations based on commonalities and recommends them to users. For example, if a user has knowledge of programming languages ​​and English skills, the AI ​​will generate a suitable job such as "overseas IT consultant."

[0813] "Example 4"

[0814] Generative AI also receives prompts from currently available job postings and generates suitable jobs based on those job postings. This allows it to recommend jobs based on the latest job postings to users. For example, for a user with a specific skill set currently in demand in the IT industry, it can generate suitable jobs based on the latest job postings that utilize those skill sets.

[0815] The processing flow of each embodiment will be described below.

[0816] "Example 1"

[0817] Step 1: The user enters their own skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, they might enter "3 years of Java development experience, PMP certification, TOEIC score 850."

[0818] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[0819] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[0820] "Example 2"

[0821] Step 1: The user enters their own skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, a user might enter "two years of experience in data analysis using Python, knowledge of database design, and presentation skills in English."

[0822] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[0823] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[0824] "Example 3"

[0825] Step 1: The user enters their skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, they might enter "5 years of development experience in C++, knowledge of network design, and ability to negotiate in Chinese."

[0826] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[0827] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[0828] Example 1

[0829] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0830] Previous systems struggled to effectively analyze users' skill sets and suggest appropriate career paths and skill acquisition. Furthermore, they lacked recommendations based on users' interests and preferences, making it difficult for users to make optimal suggestions. This made it difficult for users to make the most of their skill sets.

[0831] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0832] In this invention, the server includes means for receiving a user's skill set as input and saving the input skill set in a database, means for analyzing the saved skill set and classifying it by category, means for generating prompt sentences based on the analyzed skill set and inputting them into a generative AI model, and means for displaying suggestions generated by the generative AI model to the user. This makes it possible to effectively analyze the user's skill set and provide suggestions for appropriate career paths and skill acquisition.

[0833] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[0834] A "database" is a system for efficiently storing, managing, and retrieving data.

[0835] "Analysis" is the process of examining input data in detail to understand its meaning and structure.

[0836] A "category" is a group for classifying data based on specific criteria.

[0837] A "prompt" is text that is input to a generative AI model and contains instructions for the model to generate appropriate output.

[0838] A "generative AI model" is an artificial intelligence model that generates appropriate output based on input data.

[0839] A "Suggestion" is specific advice or recommendation provided to a user.

[0840] "Display" is the act of showing information to a user through a user interface.

[0841] This invention is a system that effectively analyzes a user's skill set and proposes appropriate career paths and skill acquisition. A specific embodiment of this system is described below.

[0842] First, users input their skill set into the system. This skill set specifically describes the user's skills, experience, and qualifications. For example, it may include knowledge of programming languages, project management experience, and English proficiency.

[0843] When a user inputs a skill set, the terminal sends this data to the server. The terminal is a device such as the user's PC or smartphone, and sends the input data to the server as an HTTP request. The data is sent in JSON format.

[0844] The server receives the data sent from the device and stores it in a database. The database uses a relational database management system (RDBMS) such as MySQL or PostgreSQL. For example, it executes the following SQL query:

[0845] sql

[0846] INSERT INTO skill_sets (user_id, skills) VALUES (1, 'Python, Java, Project Management 3 years, TOEIC 850 points');

[0847] The server then analyzes the stored skill set and categorizes each skill into categories, using a programming language such as Python or Java and natural language processing libraries (e.g., NLTK or spaCy).

[0848] Based on the analyzed skill set, the server generates a prompt sentence and inputs it into a generative AI model, such as OpenAI's GPT-3 or GPT-4. An example of a prompt sentence is as follows:

[0849] The user's skill set is as follows:

[0850] Programming languages: Python, Java

[0851] Project Management: 3 years of experience

[0852] English proficiency: TOEIC 850 points

[0853] Suggest the next skills or career path that would be suitable for this user.

[0854] The generative AI model generates suggestions based on the input prompt and returns them to the server. Examples of generated suggestions are as follows:

[0855] Based on your skill set, your next skillset could include knowledge of data science and machine learning, and your career path could be to move from project manager to data scientist.

[0856] Finally, the server displays the suggestions returned by the generative AI model to the user, who can review them in their web browser.

[0857] In this way, users receive specific suggestions based on their skill set, allowing them to make the most of their skillset and plan appropriate career paths and skill acquisition.

[0858] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0859] Step 1:

[0860] The user enters a skillset.

[0861] Users input their skill set through the system's user interface. For example, they enter information such as "Python, Java, 3 years of project management, TOEIC score 850" into a form on a web browser. The input data is text information about the skill set.

[0862] Step 2:

[0863] The terminal sends the input data to the server.

[0864] The terminal (user's PC or smartphone) sends the entered skill set to the server as an HTTP request. At this time, the data is sent in JSON format. The input is the text information of the skill set entered by the user, and the output is the JSON data sent to the server.

[0865] Step 3:

[0866] The server receives the data and stores it in a database.

[0867] The server receives the data sent from the device and stores it in a database. The database uses an RDBMS such as MySQL or PostgreSQL. For example, it executes the following SQL query:

[0868] sql

[0869] INSERT INTO skill_sets (user_id, skills) VALUES (1, 'Python, Java, Project Management 3 years, TOEIC 850 points');

[0870] The input is JSON data sent from the device, and the output is a skillset record stored in a database.

[0871] Step 4:

[0872] The server analyzes the skill set and categorizes it.

[0873] The server analyzes the stored skill set and classifies each skill into a category. This process is performed using a programming language such as Python or Java, and a natural language processing library (e.g., NLTK or spaCy). The input is the skill set records stored in the database, and the output is the skill information classified by category.

[0874] Step 5:

[0875] The server inputs a prompt sentence into the generative AI model.

[0876] The server generates a prompt sentence based on the analyzed skill set and inputs it into the generative AI model. OpenAI's GPT-3 or GPT-4 is used as the generative AI model. An example of a prompt sentence is as follows:

[0877] The user's skill set is as follows:

[0878] Programming languages: Python, Java

[0879] Project Management: 3 years of experience

[0880] English proficiency: TOEIC 850 points

[0881] Suggest the next skills or career path that would be suitable for this user.

[0882] The input is skill information categorized by category, and the output is a prompt sentence that is input into the generative AI model.

[0883] Step 6:

[0884] The generative AI model generates suggestions and returns them to the server.

[0885] A generative AI model (e.g., OpenAI's GPT-4) generates suggestions based on the input prompt and returns them to the server. An example of a generated suggestion is as follows:

[0886] Based on your skill set, your next skillset could include knowledge of data science and machine learning, and your career path could be to move from project manager to data scientist.

[0887] The input is the prompt sentence and the output is the generated suggestion.

[0888] Step 7:

[0889] The server displays the suggestions to the user.

[0890] The server displays the suggestions returned by the generative AI model to the user, who can view them in a web browser. The input is the generated suggestions, and the output is the suggestions displayed to the user.

[0891] (Application example 1)

[0892] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0893] With conventional systems, it was difficult to properly utilize users' skill sets, optimize work, and monitor progress. Furthermore, tasks were not reassigned in real time based on user skills, making efficient work management impossible. This resulted in problems such as reduced work efficiency and difficulty in properly allocating personnel.

[0894] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0895] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, means for optimizing work based on the user's skill set, and means for monitoring work progress and reallocating tasks as necessary. This makes it possible to make the most of the user's skill set, enabling efficient work management and appropriate personnel allocation.

[0896] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[0897] "Commonalities with industries and occupations" refers to the points of agreement between the user's skill set and the required skills and conditions of existing industries and occupations.

[0898] "Generative AI methods" are artificial intelligence methods that generate suitable jobs based on the intersection of a user's skill set with industries and occupations.

[0899] "Means for recommending" refers to means for suggesting the generated suitable jobs to users.

[0900] A "means for optimizing work" is a means for maximizing the efficiency of work based on the user's skill set.

[0901] "Means for monitoring work progress" refers to means for monitoring the progress of work in real time.

[0902] The "means for reallocating tasks" refers to a means for reallocating tasks according to the progress of work.

[0903] To implement this invention, a system is required that provides an interface for users to input their own skill sets. This system is implemented as an application that runs on a terminal such as a smartphone or tablet. A specific embodiment of this system will be described below.

[0904] System configuration

[0905] The system consists of the following main components:

[0906] 1. User interface: Provides a screen for users to input their skill sets, such as knowledge of programming languages, project management experience, and English proficiency.

[0907] 2. Generative AI methods: Analyze the skillset entered by the user and find commonalities with existing industries and occupations. This is done using generative AI models.

[0908] 3. Recommendation method: The generative AI method recommends suitable jobs to the user. The recommendations are based on the user's interests and preferences.

[0909] 4. Work Optimizer: Optimize work based on the user's skill set. For example, generate instructions to optimize the operation of a factory robot.

[0910] 5. Progress monitoring measures: Monitor work progress in real time and reassign tasks as needed.

[0911] Hardware and Software

[0912] Hardware: smartphones, tablets, servers

[0913] Software: Python, JSON, generative AI models

[0914] Data processing and calculation

[0915] 1. User Interface: When a user enters a skill set, the data is sent to the server in JSON format.

[0916] 2. Generative AI method: The server analyzes the received skill set and generates suitable jobs using a generative AI model. At this time, the prompt sentence is "Please enter the user's skill set. For example, welding technology, machine operation experience, quality control knowledge, etc."

[0917] 3. Recommendation method: The generated suitable jobs are recommended based on the user's interests and preferences. The recommendation results are displayed in the user interface.

[0918] 4. Work optimization: Instructions are generated to optimize the operation of factory robots based on the user's skill set.

[0919] 5. Progress Monitoring Measures: Work progress is monitored in real time and tasks are reassigned as necessary.

[0920] Specific examples

[0921] For example, if a user inputs "welding skills" and "quality control knowledge," the generative AI method generates suitable jobs based on these skill sets. The generated suitable jobs are recommended based on the user's interests and preferences. In addition, the operation of factory robots is optimized based on the user's skill set. Work progress is monitored in real time, and tasks are reallocated as necessary.

[0922] An example of a prompt sentence is "Please enter the user's skill set. For example, welding technology, machine operation experience, knowledge of quality control, etc."

[0923] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0924] Step 1:

[0925] The user enters the skillset using the terminal.

[0926] Input: User skill set (e.g. welding techniques, machine operation experience, quality control knowledge)

[0927] How it works: The user is presented with a screen to input their skillset through the user interface. The user inputs their skillset and presses the submit button.

[0928] Output: The input skillset is sent to the server in JSON format.

[0929] Step 2:

[0930] The server analyzes the received skillset.

[0931] Input: Skillset data in JSON format

[0932] Operation: The server parses the received skill set data and extracts each skill individually.

[0933] Output: Parsed skillset data (e.g. {'welding': True, 'machine_operation': False, 'quality_control': True})

[0934] Step 3:

[0935] The server generates suitable jobs using a generative AI model.

[0936] Input: Parsed skillset data

[0937] How it works: The server inputs the analyzed skill set data into the generative AI model along with the prompt, "Please enter the user's skill set. For example, welding techniques, machine operation experience, quality control knowledge, etc." The generative AI model then generates suitable jobs based on this.

[0938] Output: Generated job data (e.g., {'job_title': 'Welding technician', 'industry': 'Manufacturing'})

[0939] Step 4:

[0940] The server recommends the generated suitable jobs to the user.

[0941] Input: Generated job aptitude data

[0942] How it works: The server filters the generated job data based on the user's interests and preferences to generate recommendation results.

[0943] Output: Recommendation results (e.g., {'recommended_jobs': ['Welding engineer', 'Quality control']})

[0944] Step 5:

[0945] The user receives the recommendations.

[0946] Input: Recommendation results

[0947] Operation: The device displays the recommended job results in the user interface. The user can then confirm the recommended job.

[0948] Output: User confirmation result (e.g., user selects the recommended suitable job)

[0949] Step 6:

[0950] The server optimizes work based on the user's skill set.

[0951] Input: User's skill set, selected suitable job

[0952] Operation: The server generates instructions to optimize the factory robot's operation based on the user's skill set and selected vocation.

[0953] Output: Optimized work instructions (e.g., {'robot_task': 'welding', 'parameters': {'speed': 'medium', 'precision': 'high'}})

[0954] Step 7:

[0955] The server monitors the progress of work and reassigns tasks as needed.

[0956] Input: Optimized work instructions, real-time work progress data

[0957] How it works: The server monitors the progress of work in real time and reallocates tasks as needed. For example, if progress is slow, it will reallocate the task to another robot or worker.

[0958] Output: Updated task assignment (e.g., {'robot_task': 'Welding', 'assigned_to': 'robot_2'})

[0959] Example 2

[0960] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0961] Conventional career recommendation systems have difficulty efficiently comparing a user's skill set with the skill sets of industries and occupations to recommend appropriate careers. Furthermore, they have not adequately generated suitable careers that take into account the user's skill set and job information, nor have they made recommendations based on the user's interests and preferences. This has made it difficult for users to find the career that best suits them, resulting in the problem of career mismatches.

[0962] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0963] In this invention, the server includes means for receiving a user's skill set as input, accessing a database to compare the skill set of each industry or occupation with the user's skill set, means for extracting industries or occupations with matching or similar skill sets, and means for recommending the extracted industries and occupations to the user, thereby enabling efficient recommendation of optimal occupations based on the user's skill set.

[0964] A "user's skill set" is the collection of knowledge, skills, experience, etc. that a user possesses.

[0965] A "database" is a collection of information that stores skill sets for each industry or occupation.

[0966] A "comparison means" is a method for comparing a user's skill set with skill sets in a database and evaluating the degree of match or similarity.

[0967] A "matching or similar skill set" is a skill set in an industry or occupation that has a high degree of match or similarity with the user's skill set.

[0968] The "extraction means" is a method for selecting industries or occupations that match or are similar to the user's skill set based on the comparison results.

[0969] "Recommendation methods" are methods of presenting extracted industries and occupations to users and recommending them as suitable jobs.

[0970] This invention is a system that receives a user's skill set as input, accesses a database to compare the skill sets of each industry or occupation with the user's skill set, extracts industries or occupations with matching or similar skill sets, and recommends the extracted industries and occupations to the user.

[0971] System configuration

[0972] Hardware

[0973] Server: Accesses the database to compare and extract skill sets.

[0974] Terminal: The device on which the user inputs the skillset and displays the results, such as a computer or smartphone.

[0975] software

[0976] Database management systems: Software for storing industry- or occupation-specific skill sets, such as MySQL or PostgreSQL.

[0977] Programming language: A language for manipulating and analyzing data, specifically Python.

[0978] Libraries: Libraries for efficient data manipulation and analysis, specifically Pandas and Scikit-learn.

[0979] Program processing

[0980] The server receives the skill set entered by the user from the terminal. Then, it accesses the database and compares the skill sets for each industry and occupation with the user's skill set. This comparison uses the Python Pandas library to manipulate and analyze the data. Specifically, it calculates the cosine similarity to evaluate the degree of match between the skill sets.

[0981] The server uses Scikit-learn to extract industries and occupations with skill sets that match or are similar to the user's skill set. For example, if a user has skills in "Python programming" and "data analysis," IT and software development occupations will be extracted.

[0982] The server sends the extracted list of industries and occupations to the device, which then analyzes the results and displays them to the user. For example, it might display information such as "Matching industries: IT industry" or "Matching occupations: software developer, data scientist."

[0983] Specific examples

[0984] For example, consider the following skill set entered by a user:

[0985] User Skill Set: Python Programming, Data Analysis

[0986] The server receives this skill set and compares it with the skill sets in its database. Based on the comparison, industries and occupations are extracted, such as:

[0987] Matching Industry: IT Industry

[0988] Matching occupations: Software developer, data scientist

[0989] The results are sent to the device and displayed to the user, allowing them to find the perfect job based on their skill set.

[0990] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0991] Step 1:

[0992] The user enters a skillset.

[0993] The user inputs their skill set into an input form on the device. For example, they input skills such as "Python programming" or "data analysis." The input skill set is temporarily stored in the device's memory.

[0994] Step 2:

[0995] The terminal transmits the input skill set to the server.

[0996] The terminal converts the skillset entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is sent to the server as a JSON-formatted string.

[0997] Step 3:

[0998] The server receives the skillset and accesses the database.

[0999] The server parses the received JSON data and extracts the user's skill set. Then, the server connects to a database to retrieve skill sets by industry and occupation. The data retrieved from the database is loaded into memory as a Pandas dataframe.

[1000] Step 4:

[1001] The server compares the user's skill set with the skill sets for each industry or occupation in the database.

[1002] The server uses the Pandas library to compare the skillsets in the database with the user's skillsets. Specifically, it calculates the cosine similarity to evaluate the match between the skillsets. The input data is the user's skillsets and the skillsets in the database, and the output data is the match score.

[1003] Step 5:

[1004] The server extracts industries and occupations with matching or similar skill sets.

[1005] The server uses Scikit-learn to extract industries and occupations with skill sets that match or are close to the user's skill set. Specifically, it selects industries and occupations with high match scores. The input data is the match score, and the output data is a list of matching industries and occupations.

[1006] Step 6:

[1007] The server sends the results to the terminal.

[1008] The server converts the extracted list of industries and occupations into JSON format and sends it to the terminal as an HTTP response. The input data is a list of matching industries and occupations, and the output data is a JSON-formatted string.

[1009] Step 7:

[1010] The terminal displays the results to the user.

[1011] The device parses the received JSON data and displays it to the user. Specifically, it displays information such as "Matching industry: IT industry" and "Matching occupation: software developer, data scientist." The input data is a string in JSON format, and the output data is text information displayed to the user.

[1012] (Application example 2)

[1013] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1014] Conventional career advice systems have difficulty effectively matching a user's skill set with the required skill sets of industries and occupations, and therefore have the problem of being unable to recommend the most suitable occupation for the user. Furthermore, they do not adequately consider the user's interests and preferences when recommending suitable occupations, which makes it difficult to increase user satisfaction.

[1015] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's ability set as input and searching for commonalities with existing fields and occupations, generative artificial intelligence means for generating suitable occupations based on the commonalities, means for recommending the generated suitable occupations to the user, and means for sending the user's ability set as a prompt to the generative artificial intelligence model to suggest optimal occupations. This makes it possible to effectively match the user's skill set with the required skill sets of industries and occupations and suggest optimal occupations to the user. It also makes it possible to recommend suitable occupations taking into account the user's interests and preferences, thereby increasing user satisfaction.

[1016] "User's competency set" refers to the knowledge, skills, experience, etc. possessed by the user, including the skills and qualifications required for a particular occupation or industry.

[1017] "Fields and occupations" are a general term for the various industries and occupations that exist in society, and refer to jobs and roles that require specific skill sets.

[1018] "Intersection tools" refer to methods and techniques for comparing a user's ability set with the skill sets required by a field or occupation to identify matches or similarities.

[1019] "Generative AI means" refers to AI technology that generates suitable jobs based on the intersection of a user's skill set with fields or occupations.

[1020] "Recommendation means" refers to methods and technologies for presenting the generated suitable jobs to the user and suggesting the most suitable occupation based on the user's interests and preferences.

[1021] "Generative AI model" refers to an AI model that takes a user's skill set as input and is used to suggest the most suitable occupation.

[1022] A "prompt" is a form of instruction or question input to a generative artificial intelligence model, and includes specific data such as a user's skill set or job information.

[1023] The system for implementing this invention receives a user's skill set as input, searches for commonalities with existing fields and occupations, and suggests the most suitable occupation. A specific embodiment of this system will be described below.

[1024] 1. System Configuration

[1025] The system consists of the following major components:

[1026] User devices (smartphones, smart glasses, head-mounted displays, etc.)

[1027] server

[1028] Generative AI models (OpenAI API, etc.)

[1029] 2. Program Processing

[1030] The server receives the user's ability set sent from the user's device and sends it as a prompt to the generative AI model. The generative AI model compares the user's ability set with the required skill set for the field or occupation and suggests the most suitable occupation. The suggested occupation is sent to the user's device and displayed to the user.

[1031] 3. Hardware and Software Used

[1032] Hardware: User devices (smartphones, smart glasses, head-mounted displays)

[1033] Software: OpenAI API, Python

[1034] 4. Data processing and calculation

[1035] The server receives the user's ability set in text format and sends it as a prompt to the generative AI model. The generative AI model performs data calculations based on the received prompt and generates the optimal occupation. The generated occupation is returned to the server in text format and sent to the user's device.

[1036] 5. Specific Examples

[1037] For example, if a user has the capability set "Programming languages: Python, JavaScript; Data analysis; Machine learning," the system will send the following prompt to the generative artificial intelligence model:

[1038] Example prompt sentence:

[1039] User Skill Set: Programming languages: Python, JavaScript; Data Analysis; Machine Learning

[1040] Suggest suitable industries and occupations.

[1041] The generative AI model performs data calculations based on this prompt and generates a suggestion such as, "Data scientists and software engineers in the IT industry are suitable." This suggestion is sent to the user's device and displayed to the user.

[1042] In this way, it is possible to effectively match the user's ability set with the required skill set of a field or occupation, and suggest the most suitable occupation for the user.

[1043] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1044] Step 1:

[1045] The user inputs their own skill set using a terminal, which is then sent to the server in text format. The input data includes specific skills such as programming languages, data analysis, and machine learning.

[1046] Step 2:

[1047] The server then sends the received user skill set to the generative AI model as a prompt. The prompt is generated based on the user's skill set. For example, it could be in the format "User's skill set: Programming languages: Python, JavaScript; Data analysis; Machine learning. Please suggest suitable industries and occupations."

[1048] Step 3:

[1049] The generative AI model receives a prompt, compares the user's ability set with the required skill set for the field or occupation, performs data calculations, and generates the most suitable occupation. During this process, the generative AI model uses its internal database and pre-trained models to identify suitable occupations.

[1050] Step 4:

[1051] The generative AI model returns the generated suitable jobs in text format to the server. The output data includes specific job titles, such as "Data scientist or software engineer in the IT industry would be suitable."

[1052] Step 5:

[1053] The server sends the suitable jobs received from the generative AI model to the user's terminal. The user's terminal displays the suitable jobs received to the user. The user can check the displayed suitable jobs and use them as a reference for their own career path.

[1054] Step 6:

[1055] If the user requests more detailed information based on their interests and preferences, the server sends additional prompts to the generative artificial intelligence model, providing more specific career and job information. This process is repeated until the user is satisfied.

[1056] Example 3

[1057] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1058] Conventional career recommendation systems suggest suitable jobs based on the user's skill set, but because they cannot take into account the latest job information, it is difficult to suggest the most suitable job for the user.In addition, since recommendations do not reflect the user's interests and preferences, there is an issue of low user satisfaction.

[1059] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1060] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, means for preprocessing data provided by the user, means for generating prompt sentences based on the preprocessed data, means for inputting the generated prompt sentences into the generative AI model, means for collecting job information on the Internet, means for generating new prompt sentences based on the collected job information, and means for generating suitable jobs taking the job information into consideration. This makes it possible to suggest suitable jobs taking the user's skill set and the latest job information into consideration.

[1061] "User skill set" refers to the knowledge, abilities, specific skills and experience possessed by the user.

[1062] "Intersections with industries and occupations" refers to the similarities between a user's skill set and the skills and knowledge required for a particular industry or occupation.

[1063] "Generative AI tools" refers to artificial intelligence functions that generate new job combinations and suitable jobs based on a user's skill set and other input data.

[1064] "Recommendation means" refers to the functions and methods for suggesting generated suitable jobs to users.

[1065] "Data preprocessing" refers to the process of converting data provided by users into a format that is easy for generative AI models to understand.

[1066] A "prompt" refers to an instruction or question that is input into a generative AI model.

[1067] A "generative AI model" refers to an artificial intelligence model that generates new information or suggestions based on an input prompt.

[1068] "Job information" refers to information published on the Internet or other media about the personnel sought by companies and organizations.

[1069] "New prompts" refer to new instructions or questions that are generated based on collected job information and are input into the generative AI model.

[1070] This invention is a system that generates suitable jobs based on a user's skill set and makes recommendations taking into account the latest job information. Specific embodiments of this system are described below.

[1071] System Overview

[1072] The system takes a user's skill set as input, uses a generative AI model to generate suitable jobs, and recommends them to the user. It also has the ability to collect the latest job information and generate suitable jobs based on that information.

[1073] Hardware and software used

[1074] Server: Processes data, runs generative AI models, and collects job information.

[1075] Terminal: Used by users to input their skill sets and view recommended suitable jobs.

[1076] Generative AI models: For example, using advanced natural language processing models such as OpenAI's GPT-4.

[1077] Data processing and calculation

[1078] 1. User data input: The user inputs their skill set (e.g., knowledge of programming languages ​​such as Python and Java, business-level English ability) through the terminal.

[1079] 2. Data preprocessing: The server receives the data provided by the user and converts it into a format that the generative AI model can understand, for example, JSON format.

[1080] 3. Prompt generation: The server generates a prompt based on the preprocessed data. For example, it generates the following prompt:

[1081] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[1082] 4. Input to the generative AI model: The server inputs the generated prompt sentence into the generative AI model, which then generates a suitable job based on the prompt sentence.

[1083] 5. Generating suitable jobs: The generative AI model generates suitable jobs for users based on the prompt, such as "IT consultant overseas."

[1084] 6. Job information collection: The server collects job information from the Internet, for example, scraping the latest job information from IT industry job sites.

[1085] 7. Generating suitable jobs based on job information: The server generates a new prompt based on the collected job information. For example, it generates the following prompt:

[1086] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[1087] The server then inputs this prompt sentence back into the generative AI model to generate a suitable job taking into account the job information.

[1088] 8. Result recommendation: The server recommends the generated suitable jobs to the user. The user can check the recommended jobs through the terminal.

[1089] Specific examples

[1090] For example, if a user has the following skill set:

[1091] Programming languages: Python, Java

[1092] Language ability: English (business level)

[1093] The server inputs the following prompt into the generative AI model:

[1094] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[1095] The generative AI model generates suitable jobs such as "IT consultant overseas." The server then collects the latest job information and generates prompts such as the following:

[1096] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[1097] The generative AI model generates "suitable jobs based on the latest IT consultant job information" based on this prompt sentence, and the server recommends the result to the user. The flow of the identification process in Example 3 will be described with reference to Fig. 15.

[1098] Step 1:

[1099] The user inputs their skill set through the terminal. For example, the user inputs "Knowledge of programming languages: Python, Java" and "Business-level English ability." The input data is sent to the server.

[1100] Step 2:

[1101] The server receives the data provided by the user and converts it into the required format. This preprocessing converts the input skill set into JSON format, making it understandable to the generative AI model. The input is the user's skill set, and the output is the preprocessed data.

[1102] Step 3:

[1103] The server generates a prompt based on the preprocessed data, for example:

[1104] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[1105] The input is the preprocessed data and the output is the generated prompt sentence.

[1106] Step 4:

[1107] The server inputs the generated prompt sentence into a generative AI model, such as OpenAI's GPT-4. The input is the generated prompt sentence, and the output is the suitable job generated by the generative AI model.

[1108] Step 5:

[1109] The generative AI model generates a suitable occupation for the user based on the prompt sentence. For example, it generates an occupation such as "IT consultant overseas." The input is the prompt sentence, and the output is the generated suitable occupation.

[1110] Step 6:

[1111] The server collects job information from the Internet. For example, it scrapes the latest job information from a job site in the IT industry. The input is a request to collect job information, and the output is the collected job information.

[1112] Step 7:

[1113] The server generates a new prompt based on the collected job information, for example:

[1114] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[1115] The input is the collected job information, and the output is the newly generated prompt.

[1116] Step 8:

[1117] The server inputs this new prompt sentence into the generative AI model again to generate a suitable job that takes the job information into account. The input is the newly generated prompt sentence, and the output is a suitable job that takes the job information into account.

[1118] Step 9:

[1119] The server recommends the generated suitable jobs to the user. The user can check the recommended jobs through their terminal. The input is the generated suitable jobs, and the output is the recommended results for the user.

[1120] (Application example 3)

[1121] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1122] While conventional career matching systems can recommend suitable jobs based on a user's skill set and interests, they lack the functionality to recommend products and services related to the generated suitable jobs, making it difficult for users to efficiently acquire the knowledge and tools necessary for a new career.

[1123] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1124] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a generative AI means for generating suitable jobs based on the commonalities, a means for recommending the generated suitable jobs to the user, and a means for recommending products related to the generated suitable jobs. This allows the user to efficiently acquire the knowledge and tools necessary for a new occupation.

[1125] A "user's skill set" is the collection of knowledge, skills, experience, etc. that a user possesses.

[1126] An "industry" is a group of businesses or organizations that conduct a particular type of economic activity.

[1127] An "occupation" is a job performed by people who have the skills and knowledge required to perform a particular task or role.

[1128] "Commonalities" are similarities or coincidences that exist between different elements or phenomena.

[1129] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[1130] A "job of choice" is a job that best suits a user's skill set and interests.

[1131] A "recommendation" is the act of suggesting specific information or options to a user.

[1132] A "product" is a good or service that a consumer can purchase.

[1133] "Job information" refers to information about job content and conditions that companies and organizations publish for new employment.

[1134] A "prompt" is an instruction or question input to a generative AI.

[1135] A system for carrying out this invention includes a generative AI means for receiving a user's skill set as input, searching for commonalities with existing industries and occupations, and generating suitable jobs based on those commonalities. It also includes a means for recommending the generated suitable jobs to the user and for recommending products related to the generated suitable jobs.

[1136] System Program

[1137] The system is implemented using the Python programming language and OpenAI's API. The server receives the user's skill set and the latest job information as input, and generates suitable jobs using a generative AI model. The generated suitable jobs are then recommended to the user based on their interests and preferences. Products related to the generated suitable jobs (books, tools, online courses, etc.) are also recommended.

[1138] Processing Description

[1139] The server receives the user's skill set and the latest job information as input. This data is then fed into a generative AI model using OpenAI's API as prompts. The generative AI model generates suitable jobs based on the input data. The generated suitable jobs are then recommended based on the user's interests and preferences. In addition, products related to the generated suitable jobs are also recommended.

[1140] Hardware and software used

[1141] Hardware: Servers, user devices (smartphones, PCs, etc.)

[1142] Software: Python, OpenAI API

[1143] Specific examples

[1144] For example, if a user has "knowledge of programming languages" and "English proficiency," the server inputs these skill sets and the latest job information as prompts into the generative AI model. The generative AI model generates a suitable job, such as "IT consultant overseas." Furthermore, it recommends products related to this suitable job, such as "books for IT consultants," "project management tools," and "online courses."

[1145] Example prompt sentence:

[1146] User skill set: Knowledge of programming languages, English proficiency

[1147] Latest Jobs: Overseas IT Consultant, Data Scientist

[1148] Generate suitable jobs based on this.

[1149] In this way, users can efficiently acquire the knowledge and tools necessary for a new profession.

[1150] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1151] Step 1:

[1152] The server receives the user's skill set and the latest job information as input. The user inputs their skill set (e.g., knowledge of programming languages, English ability) through a terminal and sends it to the server. The server also retrieves the latest job information from the database.

[1153] Input: User's skill set, latest job postings

[1154] Output: User skill sets and job listings

[1155] Step 2:

[1156] The server inputs the user's skill set and job information as prompts into the generative AI model, which then generates suitable jobs based on this data.

[1157] Input: User skill set and job posting combination

[1158] Output: Generated suitable jobs

[1159] How it works: The server uses the OpenAI API to send prompts to the generative AI model to generate suitable jobs.

[1160] Step 3:

[1161] The server recommends the generated suitable jobs to the user, who can then confirm the recommended suitable jobs through their device.

[1162] Input: Generated job titles

[1163] Output: Recommended jobs for the user

[1164] Specific operation: The server sends the generated suitable job to the user's device so that the user can check it.

[1165] Step 4:

[1166] The server recommends products related to the generated suitable job. Based on the generated suitable job, it searches a database for related products such as books, tools, and online courses and recommends them to the user.

[1167] Input: Generated job titles

[1168] Output: Recommended products

[1169] Specific operation: The server retrieves product information related to the generated suitable job from the database and sends it to the user's terminal.

[1170] Step 5:

[1171] Users can check the recommended suitable jobs and related products and purchase the necessary items. Users select the products and complete the purchase process via their device.

[1172] Input: Recommended products

[1173] Output: Purchased items

[1174] Specific actions: The user selects the product through the terminal and completes the purchase procedure.

[1175] In this way, users can efficiently acquire the knowledge and tools necessary for a new profession.

[1176] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1177] "Example 1"

[1178] One embodiment of the present invention is a system that combines an emotion engine that recognizes a user's emotions. This system receives a user's skill set as input and searches for commonalities with existing industries and occupations. The search for commonalities is performed using the emotion engine that recognizes the user's emotions. For example, if a user expresses the emotion "I like programming," the emotion engine analyzes this information and searches for commonalities with industries and occupations related to programming.

[1179] "Example 2"

[1180] Next, there is a generative AI method that generates suitable jobs based on commonalities. This generative AI method also uses an emotion engine that recognizes the user's emotions to generate suitable jobs that take the user's emotions into consideration. For example, if the user expresses the emotion "I like working cooperatively with others," the emotion engine analyzes this information and generates suitable jobs that require teamwork. "Example 3"

[1181] Finally, there is a way to recommend suitable jobs to users. This recommendation method also takes the user's emotions into consideration using an emotion engine that recognizes their feelings. For example, if a user expresses the emotion "I like trying new things," the emotion engine will analyze this information and recommend new industries or new occupations.

[1182] The processing flow of each embodiment will be described below.

[1183] "Example 1"

[1184] Step 1: Receive skillset input from the user.

[1185] Step 2: Use the emotion engine to recognize the user's emotions.

[1186] Step 3: Use the emotions you've identified to find commonalities between your skill set and your industry or occupation.

[1187] "Example 2"

[1188] Step 1: Receive skillset input from the user.

[1189] Step 2: Use the emotion engine to recognize the user's emotions.

[1190] Step 3: Generate suitable jobs based on the identified emotions and skill sets.

[1191] "Example 3"

[1192] Step 1: Receive skillset input from the user.

[1193] Step 2: Use the emotion engine to recognize the user's emotions.

[1194] Step 3: Generate suitable jobs based on the identified emotions and skill sets.

[1195] Step 4: Recommend the generated suitable jobs to the user.

[1196] Example 1

[1197] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1198] Conventional career recommendation systems recommend suitable jobs based on the user's skill set, but because they are unable to consider the user's emotions and interests, they have the problem of making it difficult to find the best job for the user. In addition, because they are unable to consider currently available job information, they are unable to recommend suitable jobs that are in line with the actual job market.

[1199] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1200] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a means for analyzing the user's emotions, a generative AI means for generating suitable jobs based on the commonalities and the analyzed emotions, and a means for recommending the generated suitable jobs to the user. This makes it possible to recommend suitable jobs that take into account not only the user's skill set but also their emotions and interests, and by taking into account currently available job information, it is possible to recommend suitable jobs that are in line with the actual job market.

[1201] "User skill set" refers to information that specifically describes the skills, experience, qualifications, etc. possessed by the user.

[1202] "Means for finding commonalities with industries and occupations" refers to methods and techniques for finding connections with existing industries and occupations based on the user's skill set.

[1203] "Means for analyzing user emotions" refers to methods or techniques for extracting emotions from user input and analyzing those emotions.

[1204] "Generative AI methods" are artificial intelligence technologies that generate suitable jobs based on the user's skill set and analyzed emotions.

[1205] "Recommendation means" refers to the methods and technologies used to present and recommend the generated suitable jobs to users.

[1206] "Job information" refers to information about currently available jobs, including details such as job content, required skills, and work location.

[1207] A "prompt" is input information that the generative AI means uses as a reference when generating suitable jobs.

[1208] A "suitable job" is a job that is best suited to the user, generated based on the user's skill set and emotions.

[1209] The present invention provides a system that generates suitable jobs based on the user's skill set and emotions and recommends them to the user. Specific embodiments of this system are described below.

[1210] First, a user uses a terminal to access the system's interface, which includes text boxes and drop-down menus where the user can input their skill set, which can include knowledge of programming languages, project management experience, qualifications, and even emotions (e.g., "I like programming").

[1211] The server then receives the skill set entered by the user and stores it in a database, where the skill sets for each user are organized and stored.

[1212] The server calls the emotion engine based on the stored skill set. The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to analyze emotions from the user's input. For example, it recognizes the emotion "I like programming."

[1213] The server then uses the emotion engine's analysis to present a list of industries and occupations that fit the user's skill set and emotions. For example, if a user likes programming, it will present occupations such as software development and IT consulting.

[1214] The server generates a list of industries and occupations in HTML format and displays it in the user's browser, allowing the user to consider their own career path based on the information presented.

[1215] As a concrete example, if a user enters the following skillset:

[1216] Programming languages: Python, Java

[1217] Experience: Project Management (5 years)

[1218] Qualification: PMP (Project Management Professional)

[1219] Emotion: I like programming

[1220] In this case, the server proceeds as follows:

[1221] 1. The user enters the above skill set.

[1222] 2. The server stores the entered skillset in a database.

[1223] 3. The emotion engine analyzes the emotion "I love programming."

[1224] 4. Based on the analysis results of the emotion engine, the server lists industries and occupations such as software development and IT consulting.

[1225] 5. The server presents the listed industries and occupations to the user.

[1226] Example prompts to input to a generative AI model:

[1227] The user entered the following skillset:

[1228] Programming languages: Python, Java

[1229] Experience: Project Management (5 years)

[1230] Qualification: PMP (Project Management Professional)

[1231] Emotion: I like programming

[1232] Based on this skill set and emotion, make a list of suitable industries and occupations.

[1233] This system makes it possible to recommend suitable jobs that take into account not only the user's skill set but also their emotions and interests.Furthermore, by taking into account currently available job information, it is possible to recommend suitable jobs that are in line with the actual job market.

[1234] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1235] Step 1:

[1236] The user enters a skillset.

[1237] Users access the system's interface using a terminal and enter their skill set using text boxes and drop-down menus. The input can include programming language knowledge, project management experience, qualifications, and even emotions (e.g., "I like programming"). The input data is sent to the server as an HTTP request.

[1238] Step 2:

[1239] The server receives the skillset and stores it in a database.

[1240] The server receives the HTTP request sent by the user and parses the skill set data. The parsed data is stored in a database along with the user ID. Storing the input data prepares the foundation for use in subsequent processing.

[1241] Step 3:

[1242] The server calls the emotion engine and analyzes the user's emotions.

[1243] The server passes the skillset stored in the database to the emotion engine. The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to extract and analyze emotions from the user's input. For example, it recognizes the emotion "I like programming." The analysis results are returned to the server.

[1244] Step 4:

[1245] The server lists appropriate industries and occupations based on the analysis results of the emotion engine.

[1246] The server uses a generative AI means to generate suitable jobs based on the analysis results returned by the emotion engine and the user's skill set. The generative AI means lists industries and occupations that are suitable for the user's skill set and emotions. For example, for a user who likes programming, occupations such as software development and IT consulting will be listed. The listed industries and occupations are stored on the server.

[1247] Step 5:

[1248] The server presents the user with a list of industries and occupations.

[1249] The server generates a list of industries and occupations in HTML format and displays it in the user's browser. The user can then use the information presented to consider their own career path. The displayed information is organized in a way that is easy for the user to understand.

[1250] (Application example 1)

[1251] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1252] Conventional skill matching systems suggest suitable jobs based on a user's skill set, but they are unable to consider the user's emotions and interests. This makes it difficult for users to find jobs that truly interest and satisfy them. Furthermore, for autonomous vehicles, there has been no system that suggests suitable driving tasks based on the driver's skill set and emotions. This has led to problems such as reduced driver satisfaction and efficiency.

[1253] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1254] In this invention, the server includes: means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations; generative AI means for generating suitable jobs based on the commonalities; means for generating suitable jobs based on the user's emotions using an emotion engine that recognizes the user's emotions; means for recommending the generated suitable jobs to the user; and means that is installed in the autonomous vehicle and suggests suitable driving tasks based on the user's skill set and emotions. This makes it possible to suggest suitable jobs and driving tasks that take the user's skill set and emotions into comprehensive consideration.

[1255] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[1256] "Means for finding commonalities with industries and occupations" refers to methods and techniques for finding connections with existing industries and occupations based on the user's skill set.

[1257] "Generative AI means" is an artificial intelligence technology that generates suitable jobs and driving tasks based on the user's skill set and emotional information.

[1258] An "emotion engine" is a technology that recognizes a user's emotions and analyzes information based on those emotions.

[1259] "Recommendation means" refers to methods and technologies for suggesting the generated suitable jobs or driving tasks to users.

[1260] An "autonomous vehicle" is a vehicle that drives autonomously using artificial intelligence and sensor technology.

[1261] A "driving task" refers to the specific driving operations and route selection that an autonomous vehicle performs.

[1262] The system for implementing this invention suggests suitable jobs and driving tasks based on the user's skill set and emotions. Specific embodiments of the system are described below.

[1263] System Configuration

[1264] The system consists of the following main components:

[1265] 1. User device: The device through which the user inputs their skillset and emotions. This can be a smartphone, tablet, or PC.

[1266] 2. Server: A central processing unit that receives the user's skill set and emotions and generates suitable jobs and driving tasks. It uses Python, the EmotionEngine library, and the SkillMatcher library.

[1267] 3. Self-driving vehicles: Vehicles equipped with on-board computers that perform driving tasks based on the skillset and emotions of the user.

[1268] Program processing

[1269] The server performs the process in the following steps.

[1270] 1. Skillset input: Receive the user's skillset from the user's device. The skillset is sent to the server in JSON format.

[1271] 2. Emotion Recognition: Using the EmotionEngine library, we recognize the user's emotions from text. For example, we analyze the text "I like programming" and recognize positive emotions.

[1272] 3. Job Generation: Using the SkillMatcher library, we generate suitable jobs based on the user's skill set and emotions. The generated suitable jobs are recommended based on the user's interests and preferences.

[1273] 4. Driving task suggestion: The system installed in the autonomous vehicle suggests suitable driving tasks based on the user's skill set and emotions.

[1274] Specific examples

[1275] For example, if a user types "I love programming," the emotion engine will recognize this "positive emotion" and suggest programming-related tasks (e.g., updating or debugging a vehicle's software).

[1276] Prompt Sentence Examples

[1277] Below are some example prompts for a generative AI model:

[1278] Enter the user's skill set, for example, knowledge of programming languages, project management experience, English proficiency, etc.

[1279] Next, enter your feelings in text, such as "I love programming."

[1280] Based on this information, the system suggests suitable driving tasks and routes.

[1281] In this way, it becomes possible to suggest suitable jobs or driving tasks that take into account the user's skill set and emotions comprehensively.

[1282] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1283] Step 1:

[1284] The user's device receives the user's skill set as input. The user then specifically describes their own skills, experience, qualifications, etc. For example, "knowledge of programming languages," "experience in project management," "English proficiency," etc. The input skill set is sent to the server in JSON format.

[1285] Step 2:

[1286] The server receives the skillset sent from the user terminal, analyzes the received skillset, and stores it in a database. The analyzed skillset is used in subsequent processing.

[1287] Step 3:

[1288] The user inputs the user's emotion in text form, such as "I like programming." The input emotion text is then sent to the server.

[1289] Step 4:

[1290] The server analyzes the user's emotion text using the EmotionEngine library. The emotion engine recognizes the user's emotion from the input text and classifies it into emotion categories such as positive, negative, and neutral. The analysis results are used in subsequent processing.

[1291] Step 5:

[1292] The server uses the SkillMatcher library to generate suitable jobs based on the user's skill set and emotions. It receives skill set and emotion information as input and generates suitable jobs using a generative AI model. The generated suitable jobs are recommended based on the user's interests and preferences.

[1293] Step 6:

[1294] The server sends the generated suitable jobs to the user's device, which then displays the recommended suitable jobs to the user. The user can review the suggested suitable jobs and select the one that interests them.

[1295] Step 7:

[1296] The autonomous vehicle proposes suitable driving tasks based on the user's skill set and emotions. The vehicle's on-board computer generates driving tasks based on the skill set and emotion information sent from the server. For example, tasks such as updating vehicle software or debugging are proposed.

[1297] Step 8:

[1298] The autonomous vehicle executes the proposed driving task, which is optimized based on the user's skill set and emotions, improving user satisfaction and efficiency.

[1299] Example 2

[1300] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1301] Conventional job recommendation systems can suggest suitable jobs based on a user's skill set, but they cannot consider the user's emotions or work style preferences, making it difficult to find the best job for the user. Furthermore, the generation of suitable jobs that take into account currently available job information is also insufficient.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1303] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a generative AI means for generating suitable jobs based on the commonalities, an emotion engine means for recognizing the user's emotions and generating suitable jobs taking those emotions into consideration, and a means for recommending the generated suitable jobs to the user. This makes it possible to suggest optimal jobs that take into consideration not only the user's skill set but also their emotions and work style preferences.

[1304] A "user's skill set" is the collection of knowledge, skills, and experience that a user possesses.

[1305] A "means for finding commonalities with industries or occupations" is a method or device for comparing a user's skill set with the skill sets required by each industry or occupation and finding matches or similarities.

[1306] "Generative AI tools" are methods or devices that use artificial intelligence technology to generate suitable jobs based on the intersection of a user's skill set with industries or occupations.

[1307] The "emotion engine means" is a method or device for recognizing a user's emotions and generating suitable jobs by taking those emotions into consideration.

[1308] The "means for recommending" refers to a method or device for presenting and suggesting the generated suitable jobs to the user.

[1309] "Job Information" means information about current employment opportunities, including details such as job type, location, and salary.

[1310] A "prompt" is input information that the generative AI means uses as a reference when generating suitable jobs.

[1311] The present invention is a system that suggests the most suitable occupation for a user by taking into consideration the user's skill set and emotions. Specific embodiments of this system will be described below.

[1312] Hardware and software used

[1313] Hardware: Servers, devices (users' PCs and smartphones)

[1314] Software: Generative AI model, emotion engine

[1315] System configuration

[1316] 1. Entering the user's skill set

[1317] Users use a terminal to input their skill set, such as knowledge of programming languages ​​(Python, Java) and project management experience (3 years).

[1318] 2. Submit and analyze your skillset

[1319] The device sends the input skill set to the server, which then passes the received data to the generative AI model and begins analysis.

[1320] 3. Extracting commonalities with industries and occupations

[1321] The server uses a generative AI model to compare the user's skill set with the skill sets required for each industry or occupation, extracting commonalities and similarities.

[1322] 4. User Emotion Input

[1323] Users use the device to input their feelings and work preferences, such as "I like working collaboratively with others."

[1324] 5. Generating suitable jobs

[1325] The server receives the user's emotional data and analyzes it using an emotion engine. The generative AI model combines the skill set and emotional data to generate a suitable occupation for the user.

[1326] 6. Displaying the results

[1327] The server sends the generated job candidates to the terminal and displays them to the user, who can then review the results and consider their next steps.

[1328] Specific examples

[1329] Example of user skill set input

[1330] Programming languages: Python, Java

[1331] Project Management: 3 years of experience

[1332] Communication skills: High

[1333] Example prompts for generative AI models

[1334] User skill set:

[1335] Programming languages: Python, Java

[1336] Project Management: 3 years of experience

[1337] Communication skills: High

[1338] User sentiment:

[1339] I like working collaboratively with people

[1340] Use this information to generate suitable occupations for your users.

[1341] Example of suitable job generation

[1342] IT Project Manager

[1343] Software Development Team Leader

[1344] The system helps users find the career that best suits them by taking into account their skill set and emotions and suggesting the most suitable careers.

[1345] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1346] Step 1:

[1347] Entering user skill sets

[1348] How it works: A user uses a terminal to enter their skill set.

[1349] Input: Knowledge of programming languages ​​(Python, Java), project management experience (3 years), etc.

[1350] Output: The input skillset data.

[1351] Specific behavior: The user enters "Python, Java, 3 years of project management" into a dedicated web form and clicks the "Submit" button.

[1352] Step 2:

[1353] Skillset submission and analysis

[1354] How it works: The device sends the input skillset to the server, which then passes the received data to the generative AI model and begins analysis.

[1355] Input: Skillset data entered by the user.

[1356] Output: Skillset feature data as the analysis result.

[1357] Specific operation: The device sends the data "Python, Java, 3 years of project management" to the server, and the server inputs this data into the generative AI model.

[1358] Step 3:

[1359] Extracting commonalities with industries and occupations

[1360] How it works: The server uses a generative AI model to compare the user's skill set with the skill sets required for each industry or occupation, extracting commonalities and similarities.

[1361] Input: Parsed skillset feature data.

[1362] Output: Industry and occupation intersection data.

[1363] How it works: The server analyzes the skill set "Python, Java, 3 years of project management" and extracts occupations in the IT industry and software development as commonalities.

[1364] Step 4:

[1365] User emotion input

[1366] How it works: The user uses the device to input their emotions and work preferences.

[1367] Input: User sentiment data (e.g., "I like working collaboratively with others").

[1368] Output: The input emotion data.

[1369] Specific behavior: User types "I enjoy working with people" and clicks the "Submit" button.

[1370] Step 5:

[1371] Generating suitable jobs

[1372] How it works: The server receives the user's emotional data and analyzes it using the emotion engine. The generative AI model combines the skill set and emotional data to generate a suitable occupation for the user.

[1373] Input: Industry and occupation commonality data, user sentiment data.

[1374] Output: Job aptitude data.

[1375] Specific operation: The server uses an emotion engine to analyze emotional data such as "I like working collaboratively with others," and the generative AI model generates suitable jobs such as "IT project manager" and "software development team leader."

[1376] Step 6:

[1377] Displaying the results

[1378] How it works: The server sends the generated job listings to the device and displays them to the user, who can then review the results and consider their next steps.

[1379] Input: Job aptitude data.

[1380] Output: The job listing displayed to the user.

[1381] Specific operation: The server sends the result, such as "IT project manager" or "software development team leader," to the device, and the user confirms it on the screen.

[1382] (Application example 2)

[1383] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1384] While conventional career consulting systems can suggest suitable jobs based on a user's skill set, they face the challenge of being unable to suggest suitable jobs that take into account the user's emotions and interests. Furthermore, when users seek career advice in a virtual space, there is a lack of systems that can suggest suitable jobs in real time. This has led to problems such as reduced user satisfaction and reduced accuracy of suitable jobs.

[1385] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1386] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a generative AI means for generating suitable jobs based on the commonalities, a means for recommending the generated suitable jobs to the user, and an emotion engine for recognizing the user's emotions and generating suitable jobs taking those emotions into consideration. This makes it possible to suggest suitable jobs that take into consideration both the user's skill set and emotions.

[1387] A user's skill set is the collection of knowledge, skills, and experience that a user possesses.

[1388] An "industry or occupation intersection search" is a method or device for comparing a user's skill set with the skill sets of existing industries or occupations to find matches or similarities.

[1389] "Generative AI tools" are methods or devices that use artificial intelligence technology to generate suitable jobs based on the intersection of a user's skill set with industries or occupations.

[1390] The "means for recommending" refers to a method or device for suggesting the generated suitable jobs to the user.

[1391] An "emotion engine" is a technology or device that recognizes a user's emotions, analyzes them, and reflects them in generating suitable jobs.

[1392] "Job information" refers to information about currently available jobs, including details such as job content, required skills, and work location.

[1393] "Interests and preferences" refer to areas in which a user is particularly interested or has preferences, and are important factors in choosing a career.

[1394] A system for implementing this invention analyzes a user's skill set and emotions and suggests suitable jobs based on the analysis. The system includes: a means for receiving the user's skill set as input and searching for commonalities with industries or occupations; a generative AI means for generating suitable jobs based on the commonalities; a means for recommending the generated suitable jobs to the user; and an emotion engine for recognizing the user's emotions and generating suitable jobs taking those emotions into consideration.

[1395] Hardware and Software Configuration

[1396] Hardware:

[1397] Smartphone

[1398] Smart Glasses

[1399] head-mounted display

[1400] software:

[1401] OpenAI API

[1402] emotion_recognition library

[1403] skill_matching library

[1404] System Operation

[1405] 1. User Skillset and Emotion Input:

[1406] Users input their skills and emotions using a smartphone or head-mounted display. For example, a user can input "I have knowledge of programming languages" and express their emotion by saying "I like working collaboratively with others."

[1407] 2. Industry and occupational commonality analysis:

[1408] The server uses the skill_matching library to compare the user's skill set with existing industry and occupation skill sets and find commonalities.

[1409] 3. Job Creation:

[1410] The server uses generative AI methods to generate suitable jobs based on commonalities, using the OpenAI API to generate suitable jobs using the user's skill set and emotions as prompts.

[1411] 4. Use of Emotion Engine:

[1412] The server uses the emotion_recognition library to analyze the user's emotions and generate suitable jobs taking those emotions into consideration.

[1413] 5. Job Recommendations:

[1414] The server then recommends the generated suitable jobs to the user, based on the user's interests and preferences.

[1415] Specific examples

[1416] If a user inputs "I have knowledge of programming languages" and indicates the emotion "I like working collaboratively with others," the system will suggest occupations such as "Software development team leader."

[1417] Prompt Sentence Examples

[1418] User skill set: Knowledge of programming languages

[1419] User sentiment: I like working collaboratively with others

[1420] Please suggest suitable jobs.

[1421] In this way, the system can suggest suitable jobs that take into account the user's skill set and emotions simultaneously.

[1422] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1423] Step 1:

[1424] The user inputs their skill set and emotions using a smartphone or head-mounted display. The input skill set and emotions are sent to the server. Examples of input data include "knowledge of programming languages" and "I like working collaboratively with others."

[1425] Step 2:

[1426] The server uses the skill_matching library to compare the user's skill set with existing industry and occupation skill sets. Specifically, it matches the user's skill set with industry and occupation skill sets in the database to find matches or similarities. The input data is the user's skill set, and the output data is the intersection with the industry or occupation.

[1427] Step 3:

[1428] The server uses generative AI to generate suitable jobs based on the commonalities. It uses the OpenAI API to generate suitable jobs using the user's skill set and emotions as prompts. Specifically, it generates a prompt and sends it to the OpenAI API to receive suitable job suggestions. The input data are the commonalities and emotions, and the output data are suitable job suggestions.

[1429] Step 4:

[1430] The server analyzes the user's emotions using the emotion_recognition library. Specifically, it analyzes the emotional data entered by the user and takes those emotions into consideration when generating suitable jobs. The input data is the user's emotions, and the output data is a suitable job proposal that takes those emotions into consideration.

[1431] Step 5:

[1432] The server recommends the generated suitable jobs to the user. The recommendations are based on the user's interests and preferences. Specifically, the suitable job suggestions are sent to the user's device and displayed to the user. The input data are the suitable job suggestions, and the output data are the recommended information for the user.

[1433] In this way, the system can suggest suitable jobs that take into account the user's skill set and emotions simultaneously.

[1434] Example 3

[1435] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1436] Conventional career recommendation systems only consider the user's skill set and interests, making it difficult to provide recommendations that reflect the latest job information and the user's emotions. Furthermore, recommendations that do not consider the user's emotions can reduce user satisfaction. This makes it difficult to provide the most suitable career for the user.

[1437] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1438] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, emotion analysis means for analyzing the user's emotions and making recommendations taking those emotions into consideration, and means for acquiring the latest job information and generating suitable jobs based on that information. This makes it possible to recommend suitable jobs that take into consideration not only the user's skill set and interests but also the latest job information and the user's emotions.

[1439] A "user's skill set" is the specific set of skills, knowledge, and abilities that a user possesses.

[1440] "Methods for finding commonalities with industries and occupations" are methods and techniques for comparing a user's skill set with the required skills of existing industries and occupations to find common elements.

[1441] "Generative AI tools" are artificial intelligence technologies that generate suitable jobs based on a user's skill set, interests, and the latest job information.

[1442] "Recommendation means" refers to the methods and technologies used to present and suggest the generated suitable jobs to users.

[1443] "Emotion analysis means" refers to methods and technologies for analyzing emotions from user input and behavior and making recommendations that take those emotions into consideration.

[1444] "Means for obtaining the latest job information" refers to methods and technologies for collecting the latest job information from the Internet or databases.

[1445] A "prompt" is an instruction or question input to the generative AI, and serves as the basic data for generating suitable jobs.

[1446] This invention is a system that recommends suitable jobs by taking into account a user's skill set, interests, the latest job information, and the user's emotions. A specific embodiment of this system will be described below.

[1447] First, a user uses a terminal to input their skills and interests. For example, they can input their skills in "Python programming" and "data analysis" and their interest in "remote work." This input data is then sent to the server.

[1448] The server receives the skill set and interest data submitted by the user and generates a prompt to send to a generative AI model, such as OpenAI's GPT-4. An example of a generated prompt is as follows:

[1449] User skills: Python programming, data analysis

[1450] User Interest: Remote Work

[1451] The server then sends the generated prompt to a generative AI model to generate a suitable job. The generative AI model analyzes the prompt and generates a suitable job based on the user's skill set and interests. For example, a suitable job such as "Data Scientist" or "AI Engineer" is generated.

[1452] Furthermore, the server retrieves the latest job information via the internet. For example, it retrieves job information for "data scientists who can work remotely." Then, it generates an additional prompt sentence based on the retrieved job information to send to the generative AI model. An example of the additional prompt sentence is as follows:

[1453] Latest Job Opening: Remote Data Scientist

[1454] The server sends this additional prompt to the generative AI model, which then regenerates suitable jobs that take into account the latest job information, enabling recommendations based on the latest job information to be provided to users.

[1455] Finally, the server uses an emotion engine to analyze the user's emotions. For example, if the user expresses an emotion such as "I like trying new things," the server analyzes that information. The emotion engine analyzes emotions from the user's input and past behavior, and makes recommendations taking those emotions into account.

[1456] The server generates recommendations that take the user's emotions into account based on the analyzed emotional information. For example, it may recommend new industries or new occupations. The generated suitable jobs are then displayed on the user's device, where the user can check the recommended suitable jobs.

[1457] In this way, suitable job recommendations are realized that comprehensively take into account the user's skill set and interests, the latest job information, and the user's emotions. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1458] Step 1:

[1459] Users enter their skills and interests.

[1460] Users use the terminal to input their skillset (e.g., "Python programming," "data analysis") and interests (e.g., "remote work"), which is then sent to the server.

[1461] Input: User's skill set and interests

[1462] Output: User skill set and interest data sent to the server

[1463] Step 2:

[1464] The server receives the input data and generates a prompt for the generative AI model.

[1465] The server receives the skill set and interest data submitted by the user and generates prompts based on this data to send to the generative AI model.

[1466] Input: User skill set and interest data

[1467] Output: The prompt to send to the generative AI model

[1468] Specific operation: The server generates prompt sentences of the form "User skills: Python programming, data analysis" and "User interests: remote work".

[1469] Step 3:

[1470] The server sends prompts to the generative AI model to generate suitable jobs.

[1471] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-4), which analyzes the prompt and generates suitable jobs based on the user's skill set and interests.

[1472] Input: A prompt to send to the generative AI model

[1473] Output: A list of suitable jobs returned by the generative AI model

[1474] Specific operation: The generative AI model generates suitable jobs such as "data scientist" or "AI engineer."

[1475] Step 4:

[1476] The server retrieves the latest job information and generates additional prompts for the generative AI model.

[1477] The server retrieves the latest job information from the internet, then generates additional prompts based on the information to send to the generative AI model.

[1478] Input: Latest Jobs

[1479] Output: Additional prompts to send to the generative AI model

[1480] What it does: The server generates an additional prompt of the form "Latest job opening: Data scientist with remote work options."

[1481] Step 5:

[1482] The server sends additional prompts to the generative AI model to generate suitable jobs taking into account the latest job information.

[1483] The server sends additional prompts to the generative AI model, which then takes into account the latest job postings and regenerates suitable jobs based on the user's skill set and interests.

[1484] Input: Additional prompts to send to the generative AI model

[1485] Output: A list of suitable jobs, taking into account the latest job postings, returned by the generative AI model

[1486] Specific behavior: The generative AI model generates suitable jobs such as "data scientist with remote work capabilities."

[1487] Step 6:

[1488] The server analyzes the user's emotions using an emotion engine.

[1489] The server uses an emotion engine to analyze emotions from user input and past behavior. For example, if a user expresses an emotion such as "I like trying new things," the server analyzes that information.

[1490] Input: User input and past behavior data

[1491] Output: Analyzed user emotion information

[1492] Specific operation: The emotion engine analyzes the user's text input and behavioral history to identify emotions such as "I like challenges."

[1493] Step 7:

[1494] The server generates recommendations that take into account the user's emotions.

[1495] The server generates recommendations based on the analyzed emotional information, taking into account the user's emotions, such as recommending new industries or new job types.

[1496] Input: Analyzed user emotion information

[1497] Output: A list of recommendations that takes user sentiment into account

[1498] Specific operation: The server recommends suitable jobs that match the user's emotions, such as "data scientist in a new industry."

[1499] Step 8:

[1500] The server recommends the generated suitable jobs to the user.

[1501] The server recommends the generated suitable jobs to the user, who can then check the recommended suitable jobs through their device.

[1502] Input: Generated list of suitable jobs

[1503] Output: Information on suitable jobs recommended to the user

[1504] Specific operation: The server sends suitable job information to the terminal, and the user checks the information on the terminal.

[1505] (Application example 3)

[1506] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1507] Conventional career recommendation systems generate suitable jobs based on a user's skill set, interests, and preferences, but because they do not take into account behavioral data such as the user's purchase history, it is difficult to make more accurate recommendations. Furthermore, if the system does not generate suitable jobs that take into account currently available job information, it is unable to provide users with suitable jobs based on the latest job information. Furthermore, because recommendations do not take into account the user's emotions and interests, it is difficult to increase user satisfaction.

[1508] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1509] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, means for acquiring the user's purchase history, and means for generating suitable jobs based on the acquired purchase history. This enables highly accurate suitable job recommendations that take into account the user's skill set and purchase history.

[1510] A "user's skill set" is the collection of knowledge, skills, and experience that a user possesses.

[1511] An "industry" is a group of businesses or organizations that conduct a particular type of economic activity.

[1512] An "occupation" is a type of work with specific tasks or roles.

[1513] A "commonality" is a characteristic or property that exists in common among multiple elements.

[1514] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[1515] A "recommendation" is the act of recommending specific information or options to a user.

[1516] "Purchase history" is a record of products and services a user has purchased in the past.

[1517] "Job information" is information about the personnel that companies and organizations are looking for.

[1518] "Interests and preferences" are the things that a user is particularly interested in and the tendencies they like.

[1519] The system for implementing this invention generates and recommends suitable jobs based on a user's skill set and purchase history. A specific embodiment of this system is described below.

[1520] System Configuration

[1521] The system consists of the following main components:

[1522] 1. User device: The device used by the user, such as a smartphone or computer.

[1523] 2. Server: A central server that processes data and runs generative AI.

[1524] 3. Database: A database that stores user skill sets, purchase history, job information, etc.

[1525] Hardware and software used

[1526] Hardware: smartphones, PCs, servers

[1527] Software: Python, OpenAI API, database management system (e.g., MySQL)

[1528] Data processing and calculation

[1529] 1. Obtaining the user's skill set: The skill set entered from the user's terminal is sent to the server.

[1530] 2. Get purchase history: Get the user's purchase history from the database.

[1531] 3. Prompt generation: Based on the acquired skill set and purchase history, prompts are created to allow the generative AI to generate suitable jobs.

[1532] 4. Job Generation: Uses OpenAI's API to generate job prompts.

[1533] 5. Displaying recommendations: The generated suitable jobs are displayed on the user's device.

[1534] Specific examples

[1535] For example, if a user has purchased "programming books" and "English learning materials," the generative AI would generate suitable jobs using prompts like the following:

[1536] Prompt Sentence Examples

[1537] User purchase history: Programming books, English learning materials. Generate a suitable occupation for this user.

[1538] Based on this prompt, the generative AI generates suitable jobs such as "IT consultant overseas" and recommends them to the user.

[1539] In this way, it becomes possible to make highly accurate job recommendations that take into account the user's skill set and purchasing history.

[1540] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1541] Step 1:

[1542] A user terminal receives the user's skill set as input.

[1543] Input: The skill set the user entered into the terminal (e.g., programming, English)

[1544] Output: Skillset data is sent to the server.

[1545] Specific operation: The user enters their skillset into an input form on their smartphone or computer and presses the submit button.

[1546] Step 2:

[1547] The server retrieves the user's purchase history from the database.

[1548] Input: User ID

[1549] Output: Purchase history data (e.g. programming books, English learning materials)

[1550] What happens: The server queries the database to retrieve the purchase history associated with the user ID.

[1551] Step 3:

[1552] The server generates a prompt sentence based on the acquired skill set and purchase history.

[1553] Input: Skill set data, purchase history data

[1554] Output: Prompt statement (e.g., User's purchase history: programming books, English learning materials. Please generate a suitable occupation for this user.)

[1555] Specific operation: The server combines the skill set and purchase history to create a prompt for the generative AI to generate a suitable job.

[1556] Step 4:

[1557] The server uses generative AI to generate suitable jobs based on the prompt.

[1558] Input: prompt statement

[1559] Output: Generated suitable job (e.g. IT consultant overseas)

[1560] Specific operation: The server sends the prompt to the OpenAI API and receives the generated suitable job.

[1561] Step 5:

[1562] The server transmits the generated suitable jobs to the user terminal.

[1563] Input: Generated job titles

[1564] Output: Job listings displayed on the user's device

[1565] Specific operation: The server sends the generated suitable job to the user's device, which displays it.

[1566] Step 6:

[1567] The user terminal displays the generated suitable jobs to the user.

[1568] Input: Job information sent from the server

[1569] Output: Job listings displayed to the user

[1570] Specific operation: The user's device displays suitable job information on the screen, and the user confirms it.

[1571] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1572] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1573] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1574] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1575] [Third embodiment]

[1576] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1577] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1578] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1579] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1580] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1581] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1582] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1583] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1584] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1585] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1586] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1587] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1588] "Example 1"

[1589] In one embodiment of the present invention, a user inputs their own skill set into the system. This skill set specifically describes the user's skills, experience, and qualifications. For example, it may include knowledge of programming languages, project management experience, English proficiency, etc.

[1590] "Example 2"

[1591] Next, the system looks for commonalities with existing industries and occupations. This is done by comparing the user's skill set with the skill sets required for each industry or occupation and looking for matches or similarities. For example, if the user has knowledge of programming languages, the IT industry and software development occupations will be extracted as commonalities.

[1592] "Example 3"

[1593] The system also uses generative AI to generate suitable jobs. This AI generates new job combinations based on commonalities and recommends them to users. For example, if a user has knowledge of programming languages ​​and English skills, the AI ​​will generate a suitable job such as "overseas IT consultant."

[1594] "Example 4"

[1595] Generative AI also receives prompts from currently available job postings and generates suitable jobs based on those job postings. This allows it to recommend jobs based on the latest job postings to users. For example, for a user with a specific skill set currently in demand in the IT industry, it can generate suitable jobs based on the latest job postings that utilize those skill sets.

[1596] The processing flow of each embodiment will be described below.

[1597] "Example 1"

[1598] Step 1: The user enters their own skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, they might enter "3 years of Java development experience, PMP certification, TOEIC score 850."

[1599] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[1600] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[1601] "Example 2"

[1602] Step 1: The user enters their own skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, a user might enter "two years of experience in data analysis using Python, knowledge of database design, and presentation skills in English."

[1603] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[1604] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[1605] "Example 3"

[1606] Step 1: The user enters their skill set into the system. This skill set is a detailed description of the user's skills, experience, qualifications, etc. For example, they might enter "5 years of development experience in C++, knowledge of network design, and ability to negotiate in Chinese."

[1607] Step 2: The system analyzes the input skill set and searches for commonalities with existing industries and occupations. This commonality search is carried out by comparing the skill sets required by each industry and occupation with the user's skill set and looking for matches or similarities.

[1608] Step 3: The system generates suitable jobs based on the commonalities and recommends them to the user. This suitable job is generated by a generative AI based on the commonalities and job information.

[1609] Example 1

[1610] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1611] Previous systems struggled to effectively analyze users' skill sets and suggest appropriate career paths and skill acquisition. Furthermore, they lacked recommendations based on users' interests and preferences, making it difficult for users to make optimal suggestions. This made it difficult for users to make the most of their skill sets.

[1612] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1613] In this invention, the server includes means for receiving a user's skill set as input and saving the input skill set in a database, means for analyzing the saved skill set and classifying it by category, means for generating prompt sentences based on the analyzed skill set and inputting them into a generative AI model, and means for displaying suggestions generated by the generative AI model to the user. This makes it possible to effectively analyze the user's skill set and provide suggestions for appropriate career paths and skill acquisition.

[1614] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[1615] A "database" is a system for efficiently storing, managing, and retrieving data.

[1616] "Analysis" is the process of examining input data in detail to understand its meaning and structure.

[1617] A "category" is a group for classifying data based on specific criteria.

[1618] A "prompt" is text that is input to a generative AI model and contains instructions for the model to generate appropriate output.

[1619] A "generative AI model" is an artificial intelligence model that generates appropriate output based on input data.

[1620] A "Suggestion" is specific advice or recommendation provided to a user.

[1621] "Display" is the act of showing information to a user through a user interface.

[1622] This invention is a system that effectively analyzes a user's skill set and proposes appropriate career paths and skill acquisition. A specific embodiment of this system is described below.

[1623] First, users input their skill set into the system. This skill set specifically describes the user's skills, experience, and qualifications. For example, it may include knowledge of programming languages, project management experience, and English proficiency.

[1624] When a user inputs a skill set, the terminal sends this data to the server. The terminal is a device such as the user's PC or smartphone, and sends the input data to the server as an HTTP request. The data is sent in JSON format.

[1625] The server receives the data sent from the device and stores it in a database. The database uses a relational database management system (RDBMS) such as MySQL or PostgreSQL. For example, it executes the following SQL query:

[1626] sql

[1627] INSERT INTO skill_sets (user_id, skills) VALUES (1, 'Python, Java, Project Management 3 years, TOEIC 850 points');

[1628] The server then analyzes the stored skill set and categorizes each skill into categories, using a programming language such as Python or Java and natural language processing libraries (e.g., NLTK or spaCy).

[1629] Based on the analyzed skill set, the server generates a prompt sentence and inputs it into a generative AI model, such as OpenAI's GPT-3 or GPT-4. An example of a prompt sentence is as follows:

[1630] The user's skill set is as follows:

[1631] Programming languages: Python, Java

[1632] Project Management: 3 years of experience

[1633] English proficiency: TOEIC 850 points

[1634] Suggest the next skills or career path that would be suitable for this user.

[1635] The generative AI model generates suggestions based on the input prompt and returns them to the server. Examples of generated suggestions are as follows:

[1636] Based on your skill set, your next skillset could include knowledge of data science and machine learning, and your career path could be to move from project manager to data scientist.

[1637] Finally, the server displays the suggestions returned by the generative AI model to the user, who can review them in their web browser.

[1638] In this way, users receive specific suggestions based on their skill set, allowing them to make the most of their skillset and plan appropriate career paths and skill acquisition.

[1639] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1640] Step 1:

[1641] The user enters a skillset.

[1642] Users input their skill set through the system's user interface. For example, they enter information such as "Python, Java, 3 years of project management, TOEIC score 850" into a form on a web browser. The input data is text information about the skill set.

[1643] Step 2:

[1644] The terminal sends the input data to the server.

[1645] The terminal (user's PC or smartphone) sends the entered skill set to the server as an HTTP request. At this time, the data is sent in JSON format. The input is the text information of the skill set entered by the user, and the output is the JSON data sent to the server.

[1646] Step 3:

[1647] The server receives the data and stores it in a database.

[1648] The server receives the data sent from the device and stores it in a database. The database uses an RDBMS such as MySQL or PostgreSQL. For example, it executes the following SQL query:

[1649] sql

[1650] INSERT INTO skill_sets (user_id, skills) VALUES (1, 'Python, Java, Project Management 3 years, TOEIC 850 points');

[1651] The input is JSON data sent from the device, and the output is a skillset record stored in a database.

[1652] Step 4:

[1653] The server analyzes the skill set and categorizes it.

[1654] The server analyzes the stored skill set and classifies each skill into a category. This process is performed using a programming language such as Python or Java, and a natural language processing library (e.g., NLTK or spaCy). The input is the skill set records stored in the database, and the output is the skill information classified by category.

[1655] Step 5:

[1656] The server inputs a prompt sentence into the generative AI model.

[1657] The server generates a prompt sentence based on the analyzed skill set and inputs it into the generative AI model. OpenAI's GPT-3 or GPT-4 is used as the generative AI model. An example of a prompt sentence is as follows:

[1658] The user's skill set is as follows:

[1659] Programming languages: Python, Java

[1660] Project Management: 3 years of experience

[1661] English proficiency: TOEIC 850 points

[1662] Suggest the next skills or career path that would be suitable for this user.

[1663] The input is skill information categorized by category, and the output is a prompt sentence that is input into the generative AI model.

[1664] Step 6:

[1665] The generative AI model generates suggestions and returns them to the server.

[1666] A generative AI model (e.g., OpenAI's GPT-4) generates suggestions based on the input prompt and returns them to the server. An example of a generated suggestion is as follows:

[1667] Based on your skill set, your next skillset could include knowledge of data science and machine learning, and your career path could be to move from project manager to data scientist.

[1668] The input is the prompt sentence and the output is the generated suggestion.

[1669] Step 7:

[1670] The server displays the suggestions to the user.

[1671] The server displays the suggestions returned by the generative AI model to the user, who can view them in a web browser. The input is the generated suggestions, and the output is the suggestions displayed to the user.

[1672] (Application example 1)

[1673] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1674] With conventional systems, it was difficult to properly utilize users' skill sets, optimize work, and monitor progress. Furthermore, tasks were not reassigned in real time based on user skills, making efficient work management impossible. This resulted in problems such as reduced work efficiency and difficulty in properly allocating personnel.

[1675] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1676] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, means for optimizing work based on the user's skill set, and means for monitoring work progress and reallocating tasks as necessary. This makes it possible to make the most of the user's skill set, enabling efficient work management and appropriate personnel allocation.

[1677] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[1678] "Commonalities with industries and occupations" refers to the points of agreement between the user's skill set and the required skills and conditions of existing industries and occupations.

[1679] "Generative AI methods" are artificial intelligence methods that generate suitable jobs based on the intersection of a user's skill set with industries and occupations.

[1680] "Means for recommending" refers to means for suggesting the generated suitable jobs to users.

[1681] A "means for optimizing work" is a means for maximizing the efficiency of work based on the user's skill set.

[1682] "Means for monitoring work progress" refers to means for monitoring the progress of work in real time.

[1683] The "means for reallocating tasks" refers to a means for reallocating tasks according to the progress of work.

[1684] To implement this invention, a system is required that provides an interface for users to input their own skill sets. This system is implemented as an application that runs on a terminal such as a smartphone or tablet. A specific embodiment of this system will be described below.

[1685] System configuration

[1686] The system consists of the following main components:

[1687] 1. User interface: Provides a screen for users to input their skill sets, such as knowledge of programming languages, project management experience, and English proficiency.

[1688] 2. Generative AI methods: Analyze the skillset entered by the user and find commonalities with existing industries and occupations. This is done using generative AI models.

[1689] 3. Recommendation method: The generative AI method recommends suitable jobs to the user. The recommendations are based on the user's interests and preferences.

[1690] 4. Work Optimizer: Optimize work based on the user's skill set. For example, generate instructions to optimize the operation of a factory robot.

[1691] 5. Progress monitoring measures: Monitor work progress in real time and reassign tasks as needed.

[1692] Hardware and Software

[1693] Hardware: smartphones, tablets, servers

[1694] Software: Python, JSON, generative AI models

[1695] Data processing and calculation

[1696] 1. User Interface: When a user enters a skill set, the data is sent to the server in JSON format.

[1697] 2. Generative AI method: The server analyzes the received skill set and generates suitable jobs using a generative AI model. At this time, the prompt sentence is "Please enter the user's skill set. For example, welding technology, machine operation experience, quality control knowledge, etc."

[1698] 3. Recommendation method: The generated suitable jobs are recommended based on the user's interests and preferences. The recommendation results are displayed in the user interface.

[1699] 4. Work optimization: Instructions are generated to optimize the operation of factory robots based on the user's skill set.

[1700] 5. Progress Monitoring Measures: Work progress is monitored in real time and tasks are reassigned as necessary.

[1701] Specific examples

[1702] For example, if a user inputs "welding skills" and "quality control knowledge," the generative AI method generates suitable jobs based on these skill sets. The generated suitable jobs are recommended based on the user's interests and preferences. In addition, the operation of factory robots is optimized based on the user's skill set. Work progress is monitored in real time, and tasks are reallocated as necessary.

[1703] An example of a prompt sentence is "Please enter the user's skill set. For example, welding technology, machine operation experience, knowledge of quality control, etc."

[1704] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1705] Step 1:

[1706] The user enters the skillset using the terminal.

[1707] Input: User skill set (e.g. welding techniques, machine operation experience, quality control knowledge)

[1708] How it works: A screen for entering a skillset is displayed through the user interface. The user enters their skillset and presses the submit button.

[1709] Output: The input skillset is sent to the server in JSON format.

[1710] Step 2:

[1711] The server analyzes the received skillset.

[1712] Input: Skillset data in JSON format

[1713] Operation: The server parses the received skill set data and extracts each skill individually.

[1714] Output: Parsed skillset data (e.g. {'welding': True, 'machine_operation': False, 'quality_control': True})

[1715] Step 3:

[1716] The server generates suitable jobs using a generative AI model.

[1717] Input: Parsed skillset data

[1718] How it works: The server inputs the analyzed skill set data into the generative AI model along with the prompt, "Please enter the user's skill set. For example, welding techniques, machine operation experience, quality control knowledge, etc." The generative AI model then generates suitable jobs based on this.

[1719] Output: Generated job data (e.g., {'job_title': 'Welding technician', 'industry': 'Manufacturing'})

[1720] Step 4:

[1721] The server recommends the generated suitable jobs to the user.

[1722] Input: Generated job aptitude data

[1723] How it works: The server filters the generated job data based on the user's interests and preferences to generate recommendation results.

[1724] Output: Recommendation results (e.g., {'recommended_jobs': ['Welding engineer', 'Quality control']})

[1725] Step 5:

[1726] The user receives the recommendations.

[1727] Input: Recommendation results

[1728] Operation: The device displays the recommended job results in the user interface. The user can then confirm the recommended job.

[1729] Output: User confirmation result (e.g., user selects the recommended suitable job)

[1730] Step 6:

[1731] The server optimizes work based on the user's skill set.

[1732] Input: User's skill set, selected suitable job

[1733] Operation: The server generates instructions to optimize the factory robot's operation based on the user's skill set and selected vocation.

[1734] Output: Optimized work instructions (e.g., {'robot_task': 'welding', 'parameters': {'speed': 'medium', 'precision': 'high'}})

[1735] Step 7:

[1736] The server monitors the progress of work and reassigns tasks as needed.

[1737] Input: Optimized work instructions, real-time work progress data

[1738] How it works: The server monitors the progress of work in real time and reallocates tasks as needed. For example, if progress is slow, it will reallocate the task to another robot or worker.

[1739] Output: Updated task assignment (e.g., {'robot_task': 'Welding', 'assigned_to': 'robot_2'})

[1740] Example 2

[1741] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1742] Conventional career recommendation systems have difficulty efficiently comparing a user's skill set with the skill sets of industries and occupations to recommend appropriate careers. Furthermore, they have not adequately generated suitable careers that take into account the user's skill set and job information, nor have they made recommendations based on the user's interests and preferences. This has made it difficult for users to find the career that best suits them, resulting in the problem of career mismatches.

[1743] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1744] In this invention, the server includes means for receiving a user's skill set as input, accessing a database to compare the skill set of each industry or occupation with the user's skill set, means for extracting industries or occupations with matching or similar skill sets, and means for recommending the extracted industries and occupations to the user, thereby enabling efficient recommendation of optimal occupations based on the user's skill set.

[1745] A "user's skill set" is the collection of knowledge, skills, experience, etc. that a user possesses.

[1746] A "database" is a collection of information that stores skill sets for each industry or occupation.

[1747] A "comparison means" is a method for comparing a user's skill set with skill sets in a database and evaluating the degree of match or similarity.

[1748] A "matching or similar skill set" is a skill set in an industry or occupation that has a high degree of match or similarity with the user's skill set.

[1749] The "extraction means" is a method for selecting industries or occupations that match or are similar to the user's skill set based on the comparison results.

[1750] "Recommendation methods" are methods of presenting extracted industries and occupations to users and recommending them as suitable jobs.

[1751] This invention is a system that receives a user's skill set as input, accesses a database to compare the skill sets of each industry or occupation with the user's skill set, extracts industries or occupations with matching or similar skill sets, and recommends the extracted industries and occupations to the user.

[1752] System configuration

[1753] Hardware

[1754] Server: Accesses the database to compare and extract skill sets.

[1755] Terminal: The device on which the user inputs the skillset and displays the results, such as a computer or smartphone.

[1756] software

[1757] Database management systems: Software for storing industry- or occupation-specific skill sets, such as MySQL or PostgreSQL.

[1758] Programming language: A language for manipulating and analyzing data, specifically Python.

[1759] Libraries: Libraries for efficient data manipulation and analysis, specifically Pandas and Scikit-learn.

[1760] Program processing

[1761] The server receives the skill set entered by the user from the terminal. Then, it accesses the database and compares the skill sets for each industry and occupation with the user's skill set. This comparison uses the Python Pandas library to manipulate and analyze the data. Specifically, it calculates the cosine similarity to evaluate the degree of match between the skill sets.

[1762] The server uses Scikit-learn to extract industries and occupations with skill sets that match or are similar to the user's skill set. For example, if a user has skills in "Python programming" and "data analysis," IT and software development occupations will be extracted.

[1763] The server sends the extracted list of industries and occupations to the device, which then analyzes the results and displays them to the user. For example, it might display information such as "Matching industries: IT industry" or "Matching occupations: software developer, data scientist."

[1764] Specific examples

[1765] For example, consider the following skill set entered by a user:

[1766] User Skill Set: Python Programming, Data Analysis

[1767] The server receives this skill set and compares it with the skill sets in its database. Based on the comparison, industries and occupations are extracted, such as:

[1768] Matching Industry: IT Industry

[1769] Matching occupations: Software developer, data scientist

[1770] The results are sent to the device and displayed to the user, allowing them to find the perfect job based on their skill set.

[1771] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1772] Step 1:

[1773] The user enters a skillset.

[1774] The user inputs their skill set into an input form on the device. For example, they input skills such as "Python programming" or "data analysis." The input skill set is temporarily stored in the device's memory.

[1775] Step 2:

[1776] The terminal transmits the input skill set to the server.

[1777] The terminal converts the skillset entered by the user into JSON format and sends it to the server using an HTTP POST request. The input data is sent to the server as a JSON-formatted string.

[1778] Step 3:

[1779] The server receives the skillset and accesses the database.

[1780] The server parses the received JSON data and extracts the user's skill set. Then, the server connects to a database to retrieve skill sets by industry and occupation. The data retrieved from the database is loaded into memory as a Pandas dataframe.

[1781] Step 4:

[1782] The server compares the user's skill set with the skill sets for each industry or occupation in the database.

[1783] The server uses the Pandas library to compare the skillsets in the database with the user's skillsets. Specifically, it calculates the cosine similarity to evaluate the match between the skillsets. The input data is the user's skillsets and the skillsets in the database, and the output data is the match score.

[1784] Step 5:

[1785] The server extracts industries and occupations with matching or similar skill sets.

[1786] The server uses Scikit-learn to extract industries and occupations with skill sets that match or are close to the user's skill set. Specifically, it selects industries and occupations with high match scores. The input data is the match score, and the output data is a list of matching industries and occupations.

[1787] Step 6:

[1788] The server sends the results to the terminal.

[1789] The server converts the extracted list of industries and occupations into JSON format and sends it to the terminal as an HTTP response. The input data is a list of matching industries and occupations, and the output data is a JSON-formatted string.

[1790] Step 7:

[1791] The terminal displays the results to the user.

[1792] The device parses the received JSON data and displays it to the user. Specifically, it displays information such as "Matching industry: IT industry" and "Matching occupation: software developer, data scientist." The input data is a string in JSON format, and the output data is text information displayed to the user.

[1793] (Application example 2)

[1794] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1795] Conventional career advice systems have difficulty effectively matching a user's skill set with the required skill sets of industries and occupations, and therefore have the problem of being unable to recommend the most suitable occupation for the user. Furthermore, they do not adequately consider the user's interests and preferences when recommending suitable occupations, which makes it difficult to increase user satisfaction.

[1796] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving the user's ability set as input and searching for commonalities with existing fields and occupations, generative artificial intelligence means for generating suitable occupations based on the commonalities, means for recommending the generated suitable occupations to the user, and means for sending the user's ability set as a prompt to the generative artificial intelligence model to suggest optimal occupations. This makes it possible to effectively match the user's skill set with the required skill sets of industries and occupations and suggest optimal occupations to the user. It also makes it possible to recommend suitable occupations taking into account the user's interests and preferences, thereby increasing user satisfaction.

[1797] "User's competency set" refers to the knowledge, skills, experience, etc. possessed by the user, including the skills and qualifications required for a particular occupation or industry.

[1798] "Fields and occupations" are a general term for the various industries and occupations that exist in society, and refer to jobs and roles that require specific skill sets.

[1799] "Intersection tools" refer to methods and techniques for comparing a user's ability set with the skill sets required by a field or occupation to identify matches or similarities.

[1800] "Generative AI means" refers to AI technology that generates suitable jobs based on the intersection of a user's skill set with fields or occupations.

[1801] "Recommendation means" refers to methods and technologies for presenting the generated suitable jobs to the user and suggesting the most suitable occupation based on the user's interests and preferences.

[1802] "Generative AI model" refers to an AI model that takes a user's skill set as input and is used to suggest the most suitable occupation.

[1803] A "prompt" is a form of instruction or question input to a generative artificial intelligence model, and includes specific data such as a user's skill set or job information.

[1804] The system for implementing this invention receives a user's skill set as input, searches for commonalities with existing fields and occupations, and suggests the most suitable occupation. A specific embodiment of this system will be described below.

[1805] 1. System Configuration

[1806] The system consists of the following major components:

[1807] User devices (smartphones, smart glasses, head-mounted displays, etc.)

[1808] server

[1809] Generative AI models (OpenAI API, etc.)

[1810] 2. Program Processing

[1811] The server receives the user's ability set sent from the user's device and sends it as a prompt to the generative AI model. The generative AI model compares the user's ability set with the required skill set for the field or occupation and suggests the most suitable occupation. The suggested occupation is sent to the user's device and displayed to the user.

[1812] 3. Hardware and Software Used

[1813] Hardware: User devices (smartphones, smart glasses, head-mounted displays)

[1814] Software: OpenAI API, Python

[1815] 4. Data processing and calculation

[1816] The server receives the user's ability set in text format and sends it as a prompt to the generative AI model. The generative AI model performs data calculations based on the received prompt and generates the optimal occupation. The generated occupation is returned to the server in text format and sent to the user's device.

[1817] 5. Specific Examples

[1818] For example, if a user has the capability set "Programming languages: Python, JavaScript; Data analysis; Machine learning," the system will send the following prompt to the generative artificial intelligence model:

[1819] Example prompt sentence:

[1820] User Skill Set: Programming languages: Python, JavaScript; Data Analysis; Machine Learning

[1821] Suggest suitable industries and occupations.

[1822] The generative AI model performs data calculations based on this prompt and generates a suggestion such as, "Data scientists and software engineers in the IT industry are suitable." This suggestion is sent to the user's device and displayed to the user.

[1823] In this way, it is possible to effectively match the user's ability set with the required skill set of a field or occupation, and suggest the most suitable occupation for the user.

[1824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1825] Step 1:

[1826] The user inputs their own skill set using a terminal, which is then sent to the server in text format. The input data includes specific skills such as programming languages, data analysis, and machine learning.

[1827] Step 2:

[1828] The server then sends the received user skill set to the generative AI model as a prompt. The prompt is generated based on the user's skill set. For example, it could be in the format "User's skill set: Programming languages: Python, JavaScript; Data analysis; Machine learning. Please suggest suitable industries and occupations."

[1829] Step 3:

[1830] The generative AI model receives a prompt, compares the user's ability set with the required skill set for the field or occupation, performs data calculations, and generates the most suitable occupation. During this process, the generative AI model uses its internal database and pre-trained models to identify suitable occupations.

[1831] Step 4:

[1832] The generative AI model returns the generated suitable jobs in text format to the server. The output data includes specific job titles, such as "Data scientist or software engineer in the IT industry would be suitable."

[1833] Step 5:

[1834] The server sends the suitable jobs received from the generative AI model to the user's terminal. The user's terminal displays the suitable jobs received to the user. The user can check the displayed suitable jobs and use them as a reference for their own career path.

[1835] Step 6:

[1836] If the user requests more detailed information based on their interests and preferences, the server sends additional prompts to the generative artificial intelligence model, providing more specific career and job information. This process is repeated until the user is satisfied.

[1837] Example 3

[1838] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1839] Conventional career recommendation systems suggest suitable jobs based on the user's skill set, but because they cannot take into account the latest job information, it is difficult to suggest the most suitable job for the user.In addition, since recommendations do not reflect the user's interests and preferences, there is an issue of low user satisfaction.

[1840] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1841] In this invention, the server includes means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, generative AI means for generating suitable jobs based on the commonalities, means for recommending the generated suitable jobs to the user, means for preprocessing data provided by the user, means for generating prompt sentences based on the preprocessed data, means for inputting the generated prompt sentences into the generative AI model, means for collecting job information on the Internet, means for generating new prompt sentences based on the collected job information, and means for generating suitable jobs taking the job information into consideration. This makes it possible to suggest suitable jobs taking the user's skill set and the latest job information into consideration.

[1842] "User skill set" refers to the knowledge, abilities, specific skills and experience possessed by the user.

[1843] "Intersections with industries and occupations" refers to the similarities between a user's skill set and the skills and knowledge required for a particular industry or occupation.

[1844] "Generative AI tools" refers to artificial intelligence functions that generate new job combinations and suitable jobs based on a user's skill set and other input data.

[1845] "Recommendation means" refers to the functions and methods for suggesting generated suitable jobs to users.

[1846] "Data preprocessing" refers to the process of converting data provided by users into a format that is easy for generative AI models to understand.

[1847] A "prompt" refers to an instruction or question that is input into a generative AI model.

[1848] A "generative AI model" refers to an artificial intelligence model that generates new information or suggestions based on an input prompt.

[1849] "Job information" refers to information published on the Internet or other media about the personnel sought by companies and organizations.

[1850] "New prompts" refer to new instructions or questions that are generated based on collected job information and are input into the generative AI model.

[1851] This invention is a system that generates suitable jobs based on a user's skill set and makes recommendations taking into account the latest job information. Specific embodiments of this system are described below.

[1852] System Overview

[1853] The system takes a user's skill set as input, uses a generative AI model to generate suitable jobs, and recommends them to the user. It also has the ability to collect the latest job information and generate suitable jobs based on that information.

[1854] Hardware and software used

[1855] Server: Processes data, runs generative AI models, and collects job information.

[1856] Terminal: Used by users to input their skill sets and view recommended suitable jobs.

[1857] Generative AI models: For example, using advanced natural language processing models such as OpenAI's GPT-4.

[1858] Data processing and calculation

[1859] 1. User data input: The user inputs their skill set (e.g., knowledge of programming languages ​​such as Python and Java, business-level English ability) through the terminal.

[1860] 2. Data preprocessing: The server receives the data provided by the user and converts it into a format that the generative AI model can understand, for example, JSON format.

[1861] 3. Prompt generation: The server generates a prompt based on the preprocessed data. For example, it generates the following prompt:

[1862] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[1863] 4. Input to the generative AI model: The server inputs the generated prompt sentence into the generative AI model, which then generates a suitable job based on the prompt sentence.

[1864] 5. Generating suitable jobs: The generative AI model generates suitable jobs for users based on the prompt, such as "IT consultant overseas."

[1865] 6. Job information collection: The server collects job information from the Internet, for example, scraping the latest job information from IT industry job sites.

[1866] 7. Generating suitable jobs based on job information: The server generates a new prompt based on the collected job information. For example, it generates the following prompt:

[1867] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[1868] The server then inputs this prompt sentence back into the generative AI model to generate a suitable job taking into account the job information.

[1869] 8. Result recommendation: The server recommends the generated suitable jobs to the user. The user can check the recommended jobs through the terminal.

[1870] Specific examples

[1871] For example, if a user has the following skill set:

[1872] Programming languages: Python, Java

[1873] Language ability: English (business level)

[1874] The server inputs the following prompt into the generative AI model:

[1875] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[1876] The generative AI model generates suitable jobs such as "IT consultant overseas." The server then collects the latest job information and generates prompts such as the following:

[1877] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[1878] The generative AI model generates "suitable jobs based on the latest IT consultant job information" based on this prompt sentence, and the server recommends the result to the user. The flow of the identification process in Example 3 will be described with reference to Fig. 15.

[1879] Step 1:

[1880] The user inputs their skill set through the terminal. For example, the user inputs "Knowledge of programming languages: Python, Java" and "Business-level English ability." The input data is sent to the server.

[1881] Step 2:

[1882] The server receives the data provided by the user and converts it into the required format. This preprocessing converts the input skill set into JSON format, making it understandable to the generative AI model. The input is the user's skill set, and the output is the preprocessed data.

[1883] Step 3:

[1884] The server generates a prompt based on the preprocessed data, for example:

[1885] The user has knowledge of the Python and Java programming languages ​​and business-level English proficiency. Please suggest suitable jobs for this user.

[1886] The input is the preprocessed data and the output is the generated prompt sentence.

[1887] Step 4:

[1888] The server inputs the generated prompt sentence into a generative AI model, such as OpenAI's GPT-4. The input is the generated prompt sentence, and the output is the suitable job generated by the generative AI model.

[1889] Step 5:

[1890] The generative AI model generates a suitable occupation for the user based on the prompt sentence. For example, it generates an occupation such as "IT consultant overseas." The input is the prompt sentence, and the output is the generated suitable occupation.

[1891] Step 6:

[1892] The server collects job information from the Internet. For example, it scrapes the latest job information from a job site in the IT industry. The input is a request to collect job information, and the output is the collected job information.

[1893] Step 7:

[1894] The server generates a new prompt based on the collected job information, for example:

[1895] Current job listings show many IT consultant positions with Python and Java skills. Use this information to suggest suitable careers for your users.

[1896] The input is the collected job information, and the output is the newly generated prompt.

[1897] Step 8:

[1898] The server inputs this new prompt sentence into the generative AI model again to generate a suitable job that takes the job information into account. The input is the newly generated prompt sentence, and the output is a suitable job that takes the job information into account.

[1899] Step 9:

[1900] The server recommends the generated suitable jobs to the user. The user can check the recommended jobs through their terminal. The input is the generated suitable jobs, and the output is the recommended results for the user.

[1901] (Application example 3)

[1902] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1903] While conventional career matching systems can recommend suitable jobs based on a user's skill set and interests, they lack the functionality to recommend products and services related to the generated suitable jobs, making it difficult for users to efficiently acquire the knowledge and tools necessary for a new career.

[1904] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1905] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a generative AI means for generating suitable jobs based on the commonalities, a means for recommending the generated suitable jobs to the user, and a means for recommending products related to the generated suitable jobs. This allows the user to efficiently acquire the knowledge and tools necessary for a new occupation.

[1906] A "user's skill set" is the collection of knowledge, skills, experience, etc. that a user possesses.

[1907] An "industry" is a group of businesses or organizations that conduct a particular type of economic activity.

[1908] An "occupation" is a job performed by people who have the skills and knowledge required to perform a particular task or role.

[1909] "Commonalities" are similarities or coincidences that exist between different elements or phenomena.

[1910] "Generative AI" is a system that uses artificial intelligence technology to generate new information and data.

[1911] A "job of choice" is a job that best suits a user's skill set and interests.

[1912] A "recommendation" is the act of suggesting specific information or options to a user.

[1913] A "product" is a good or service that a consumer can purchase.

[1914] "Job information" refers to information about job content and conditions that companies and organizations publish for new employment.

[1915] A "prompt" is an instruction or question input to a generative AI.

[1916] A system for carrying out this invention includes a generative AI means for receiving a user's skill set as input, searching for commonalities with existing industries and occupations, and generating suitable jobs based on those commonalities. It also includes a means for recommending the generated suitable jobs to the user and for recommending products related to the generated suitable jobs.

[1917] System Program

[1918] The system is implemented using the Python programming language and OpenAI's API. The server receives the user's skill set and the latest job information as input, and generates suitable jobs using a generative AI model. The generated suitable jobs are then recommended to the user based on their interests and preferences. Products related to the generated suitable jobs (books, tools, online courses, etc.) are also recommended.

[1919] Processing Description

[1920] The server receives the user's skill set and the latest job information as input. This data is then fed into a generative AI model using OpenAI's API as prompts. The generative AI model generates suitable jobs based on the input data. The generated suitable jobs are then recommended based on the user's interests and preferences. In addition, products related to the generated suitable jobs are also recommended.

[1921] Hardware and software used

[1922] Hardware: Servers, user devices (smartphones, PCs, etc.)

[1923] Software: Python, OpenAI API

[1924] Specific examples

[1925] For example, if a user has "knowledge of programming languages" and "English proficiency," the server inputs these skill sets and the latest job information as prompts into the generative AI model. The generative AI model generates a suitable job, such as "IT consultant overseas." Furthermore, it recommends products related to this suitable job, such as "books for IT consultants," "project management tools," and "online courses."

[1926] Example prompt sentence:

[1927] User skill set: Knowledge of programming languages, English proficiency

[1928] Latest Jobs: Overseas IT Consultant, Data Scientist

[1929] Generate suitable jobs based on this.

[1930] In this way, users can efficiently acquire the knowledge and tools necessary for a new profession.

[1931] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1932] Step 1:

[1933] The server receives the user's skill set and the latest job information as input. The user inputs their skill set (e.g., knowledge of programming languages, English ability) through a terminal and sends it to the server. The server also retrieves the latest job information from the database.

[1934] Input: User's skill set, latest job postings

[1935] Output: User skill sets and job listings

[1936] Step 2:

[1937] The server inputs the user's skill set and job information as prompts into the generative AI model, which then generates suitable jobs based on this data.

[1938] Input: User skill set and job posting combination

[1939] Output: Generated suitable jobs

[1940] How it works: The server uses the OpenAI API to send prompts to the generative AI model to generate suitable jobs.

[1941] Step 3:

[1942] The server recommends the generated suitable jobs to the user, who can then confirm the recommended suitable jobs through their device.

[1943] Input: Generated job titles

[1944] Output: Recommended jobs for the user

[1945] Specific operation: The server sends the generated suitable job to the user's device so that the user can check it.

[1946] Step 4:

[1947] The server recommends products related to the generated suitable job. Based on the generated suitable job, it searches a database for related products such as books, tools, and online courses and recommends them to the user.

[1948] Input: Generated job titles

[1949] Output: Recommended products

[1950] Specific operation: The server retrieves product information related to the generated suitable job from the database and sends it to the user's terminal.

[1951] Step 5:

[1952] Users can check the recommended suitable jobs and related products and purchase the necessary items. Users select the products and complete the purchase process via their device.

[1953] Input: Recommended products

[1954] Output: Purchased items

[1955] Specific actions: The user selects the product through the terminal and completes the purchase procedure.

[1956] In this way, users can efficiently acquire the knowledge and tools necessary for a new profession.

[1957] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1958] "Example 1"

[1959] One embodiment of the present invention is a system that combines an emotion engine that recognizes a user's emotions. This system receives a user's skill set as input and searches for commonalities with existing industries and occupations. The search for commonalities is performed using the emotion engine that recognizes the user's emotions. For example, if a user expresses the emotion "I like programming," the emotion engine analyzes this information and searches for commonalities with industries and occupations related to programming.

[1960] "Example 2"

[1961] Next, there is a generative AI method that generates suitable jobs based on commonalities. This generative AI method also uses an emotion engine that recognizes the user's emotions to generate suitable jobs that take the user's emotions into consideration. For example, if the user expresses the emotion "I like working cooperatively with others," the emotion engine analyzes this information and generates suitable jobs that require teamwork. "Example 3"

[1962] Finally, there is a way to recommend suitable jobs to users. This recommendation method also takes the user's emotions into consideration using an emotion engine that recognizes their feelings. For example, if a user expresses the emotion "I like trying new things," the emotion engine will analyze this information and recommend new industries or new occupations.

[1963] The processing flow of each embodiment will be described below.

[1964] "Example 1"

[1965] Step 1: Receive skillset input from the user.

[1966] Step 2: Use the emotion engine to recognize the user's emotions.

[1967] Step 3: Use the emotions you've identified to find commonalities between your skill set and your industry or occupation.

[1968] "Example 2"

[1969] Step 1: Receive skillset input from the user.

[1970] Step 2: Use the emotion engine to recognize the user's emotions.

[1971] Step 3: Generate suitable jobs based on the identified emotions and skill sets.

[1972] "Example 3"

[1973] Step 1: Receive skillset input from the user.

[1974] Step 2: Use the emotion engine to recognize the user's emotions.

[1975] Step 3: Generate suitable jobs based on the identified emotions and skill sets.

[1976] Step 4: Recommend the generated suitable jobs to the user.

[1977] Example 1

[1978] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1979] Conventional career recommendation systems recommend suitable jobs based on the user's skill set, but because they are unable to consider the user's emotions and interests, they have the problem of making it difficult to find the best job for the user. In addition, because they are unable to consider currently available job information, they are unable to recommend suitable jobs that are in line with the actual job market.

[1980] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1981] In this invention, the server includes a means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations, a means for analyzing the user's emotions, a generative AI means for generating suitable jobs based on the commonalities and the analyzed emotions, and a means for recommending the generated suitable jobs to the user. This makes it possible to recommend suitable jobs that take into account not only the user's skill set but also their emotions and interests, and by taking into account currently available job information, it is possible to recommend suitable jobs that are in line with the actual job market.

[1982] "User skill set" refers to information that specifically describes the skills, experience, qualifications, etc. possessed by the user.

[1983] "Means for finding commonalities with industries and occupations" refers to methods and techniques for finding connections with existing industries and occupations based on the user's skill set.

[1984] "Means for analyzing user emotions" refers to methods or techniques for extracting emotions from user input and analyzing those emotions.

[1985] "Generative AI methods" are artificial intelligence technologies that generate suitable jobs based on the user's skill set and analyzed emotions.

[1986] "Recommendation means" refers to the methods and technologies used to present and recommend the generated suitable jobs to users.

[1987] "Job information" refers to information about currently available jobs, including details such as job content, required skills, and work location.

[1988] A "prompt" is input information that the generative AI means uses as a reference when generating suitable jobs.

[1989] A "suitable job" is a job that is best suited to the user, generated based on the user's skill set and emotions.

[1990] The present invention provides a system that generates suitable jobs based on the user's skill set and emotions and recommends them to the user. Specific embodiments of this system are described below.

[1991] First, a user uses a terminal to access the system's interface, which includes text boxes and drop-down menus where the user can input their skill set, which can include knowledge of programming languages, project management experience, qualifications, and even emotions (e.g., "I like programming").

[1992] The server then receives the skill set entered by the user and stores it in a database, where the skill sets for each user are organized and stored.

[1993] The server calls the emotion engine based on the stored skill set. The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to analyze emotions from the user's input. For example, it recognizes the emotion "I like programming."

[1994] The server then uses the emotion engine's analysis to present a list of industries and occupations that fit the user's skill set and emotions. For example, if a user likes programming, it will present occupations such as software development and IT consulting.

[1995] The server generates a list of industries and occupations in HTML format and displays it in the user's browser, allowing the user to consider their own career path based on the information presented.

[1996] As a concrete example, if a user enters the following skillset:

[1997] Programming languages: Python, Java

[1998] Experience: Project Management (5 years)

[1999] Qualification: PMP (Project Management Professional)

[2000] Emotion: I like programming

[2001] In this case, the server proceeds as follows:

[2002] 1. The user enters the above skillset.

[2003] 2. The server stores the entered skillset in a database.

[2004] 3. The emotion engine analyzes the emotion "I love programming."

[2005] 4. Based on the analysis results of the emotion engine, the server lists industries and occupations such as software development and IT consulting.

[2006] 5. The server presents the listed industries and occupations to the user.

[2007] Example prompts to input to a generative AI model:

[2008] The user entered the following skillset:

[2009] Programming languages: Python, Java

[2010] Experience: Project Management (5 years)

[2011] Qualification: PMP (Project Management Professional)

[2012] Emotion: I like programming

[2013] Based on this skill set and emotion, make a list of suitable industries and occupations.

[2014] This system makes it possible to recommend suitable jobs that take into account not only the user's skill set but also their emotions and interests.Furthermore, by taking into account currently available job information, it is possible to recommend suitable jobs that are in line with the actual job market.

[2015] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2016] Step 1:

[2017] The user enters a skillset.

[2018] Users access the system's interface using a terminal and enter their skill set using text boxes and drop-down menus. The input can include programming language knowledge, project management experience, qualifications, and even emotions (e.g., "I like programming"). The input data is sent to the server as an HTTP request.

[2019] Step 2:

[2020] The server receives the skillset and stores it in a database.

[2021] The server receives the HTTP request sent by the user and parses the skill set data. The parsed data is stored in a database along with the user ID. Storing the input data prepares the foundation for use in subsequent processing.

[2022] Step 3:

[2023] The server calls the emotion engine and analyzes the user's emotions.

[2024] The server passes the skillset stored in the database to the emotion engine. The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to extract and analyze emotions from the user's input. For example, it recognizes the emotion "I like programming." The analysis results are returned to the server.

[2025] Step 4:

[2026] The server lists appropriate industries and occupations based on the analysis results of the emotion engine.

[2027] The server uses a generative AI means to generate suitable jobs based on the analysis results returned by the emotion engine and the user's skill set. The generative AI means lists industries and occupations that are suitable for the user's skill set and emotions. For example, for a user who likes programming, occupations such as software development and IT consulting will be listed. The listed industries and occupations are stored on the server.

[2028] Step 5:

[2029] The server presents the user with a list of industries and occupations.

[2030] The server generates a list of industries and occupations in HTML format and displays it in the user's browser. The user can then use the information presented to consider their own career path. The displayed information is organized in a way that is easy for the user to understand.

[2031] (Application example 1)

[2032] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[2033] Conventional skill matching systems suggest suitable jobs based on a user's skill set, but they are unable to consider the user's emotions and interests. This makes it difficult for users to find jobs that truly interest and satisfy them. Furthermore, for autonomous vehicles, there has been no system that suggests suitable driving tasks based on the driver's skill set and emotions. This has led to problems such as reduced driver satisfaction and efficiency.

[2034] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2035] In this invention, the server includes: means for receiving a user's skill set as input and searching for commonalities with existing industries and occupations; generative AI means for generating suitable jobs based on the commonalities; means for generating suitable jobs based on the user's emotions using an emotion engine that recognizes the user's emotions; means for recommending the generated suitable jobs to the user; and means that is installed in the autonomous vehicle and suggests suitable driving tasks based on the user's skill set and emotions. This makes it possible to suggest suitable jobs and driving tasks that take the user's skill set and emotions into comprehensive consideration.

[2036] "User skill set" is information that specifically describes the skills, experience, qualifications, etc. that a user possesses.

[2037] "Means for finding commonalities with industries and occupations" refers to methods and techniques for finding connections with existing industries and occupations based on the user's skill set.

[2038] "Generative AI means" is an artificial intelligence technology that generates suitable jobs and driving tasks based on the user's skill set and emotional information.

[2039] An "emotion engine" is a technology that recognizes a user's emotions and analyzes information based on those emotions.

[2040] "Recommendation means" refers to methods and technologies for suggesting the generated suitable jobs or driving tasks to users.

[2041] An "autonomous vehicle" is a vehicle that drives autonomously using artificial intelligence and sensor technology.

[2042] A "driving task" refers to the specific driving operations and route selection that an autonomous vehicle performs.

[2043] The system for implementing this invention suggests suitable jobs and driving tasks based on the user's skill set and emotions. Specific embodiments of the system are described below.

[2044] System Configuration

[2045] The system consists of the following main components:

[2046] 1. User device: The device through which the user inputs their skillset and emotions. This can be a smartphone, tablet, or PC.

[2047] 2. Server: A central processing unit that receives the user's skill set and emotions and generates suitable jobs and driving tasks. It uses Python, the EmotionEngine library, and the SkillMatcher library.

[2048] 3. Self-driving vehicles: Vehicles eq...

Claims

1. An emotion engine that recognizes the user's emotions; A means for finding commonalities between existing industries and occupations based on the user's skill set; A suitable job generating means for generating a suitable job based on the common points in consideration of the user's emotions by inputting a prompt sentence for suggesting a suitable job based on the skill set into a generating AI model; A recommendation means for recommending the generated suitable jobs to the user; A system including:

2. The system according to claim 1 , wherein the suitable job generating means receives currently available job information as the prompt text, and generates a suitable job taking into account the job information.

3. The system according to claim 1 , wherein the recommendation means recommends the generated suitable jobs based on the user's interests and preferences.

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