KR20240143460A - Apparatus and method for providing matching service of bottleneck technology and highly experienced person based on ai - Google Patents

Abstract

The present invention relates to an apparatus and method for providing artificial intelligence-based, high-tech and highly experienced person matching services.
More specifically, an artificial intelligence-based challenging technology and highly experienced person matching service providing device according to one embodiment of the present invention includes a memory and a processor connected to the memory, wherein the processor receives challenging technology information regarding challenging technology and job capabilities required for the challenging technology from a first user terminal used by a person in charge of a company, receives a plurality of pieces of career information regarding careers of a plurality of highly experienced persons from a plurality of second user terminals used by the plurality of highly experienced persons, derives a matching degree between the challenging technology and the highly experienced persons based on the challenging technology information and the plurality of pieces of career information, extracts a highly experienced person whose matching degree exceeds a preset threshold matching degree, and transmits the highly experienced person and the career information of the highly experienced person to the first user terminal.

Description

{APPARATUS AND METHOD FOR PROVIDING MATCHING SERVICE OF BOTTLENECK TECHNOLOGY AND HIGHLY EXPERIENCED PERSON BASED ON AI}

The present invention relates to an apparatus and method for providing artificial intelligence-based, high-tech and highly experienced person matching services.

Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application and their inclusion in this section is not an admission that they are prior art.

General companies need human resources to solve difficult problems such as technical difficulties required for commercialization of technology. However, it is generally not easy for small and medium-sized companies to hire highly experienced professionals with the expertise to solve the difficult problems of the relevant technical difficulties.

Accordingly, we would like to propose a technology that can match a company's difficult technology with a highly experienced person with the expertise to solve the difficult technology.

Patent Registration No. 10-2196536 (Publicly Announced on December 29, 2020) Japanese Patent Publication No. 2022-188652 (published on December 21, 2022) Publication of Patent Publication No. 10-2001-0094213 (Published on October 31, 2001)

The disclosed embodiment provides an artificial intelligence-based device and method for providing a matching service for high-experienced people and a technology for solving complex problems.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

In order to achieve the above-described object, an artificial intelligence-based challenging technology and highly experienced person matching service providing device according to one embodiment of the present invention includes a memory and a processor connected to the memory, wherein the processor receives challenging technology information regarding a challenging technology and job competency required for the challenging technology from a first user terminal used by a person in charge of a company, receives a plurality of pieces of career information regarding careers of a plurality of highly experienced persons from a plurality of second user terminals used by the plurality of highly experienced persons, derives a matching degree between the challenging technology and the highly experienced persons based on the challenging technology information and the plurality of pieces of career information, extracts a highly experienced person whose matching degree exceeds a preset threshold matching degree, and transmits the highly experienced person and the career information of the highly experienced person to the first user terminal.

At this time, the processor can generate a first vector based on the above-mentioned technical information, generate a plurality of second vectors based on a plurality of pieces of the above-mentioned career information, and derive the matching degree based on the first vector and the second vector.

At this time, the processor derives a first job keyword from the difficulty technology and a job required for the difficulty technology based on the difficulty technology information, derives a first distance index between the first job keyword and keywords of a plurality of preset job competencies through word embedding, distinguishes a first job competency in which the first distance index is lower than or equal to a preset critical distance index and a second job competency in which the first distance index exceeds the critical distance index, and generates the first vector by listing the plurality of job competencies in a preset job competency order, wherein a component value of the first vector corresponding to the first job competency may be set to a preset first value, and a component value of the first vector corresponding to the second job competency may be set to 0.

At this time, the processor derives a second job keyword from the career history based on the career information, derives a second distance index between the second job keyword and keywords of the plurality of job competencies through word embedding, distinguishes a third job competency whose second distance index is lower than or equal to the critical distance index and a fourth job competency whose second distance index exceeds the critical distance index, and generates the second vector by listing the plurality of job competencies in the order of the job competencies, wherein a component value of the second vector corresponding to a job competency that is the same as the first job competency among the third job competencies is set to the first value, a component value of the second vector corresponding to the remaining job competencies is set to a second value that is preset lower than the first value, a component value of the second vector corresponding to the fourth job competency is set to 0, and a weight based on a career period can be added to a component value of the second vector linked to the career history.

At this time, the processor can derive the weight by normalizing the career period based on the total career period for the same career history of all high-career persons and the career period of the high-career person to a limit of 1 or a ratio of the first value and the second value.

At this time, the matching degree is derived by the mathematical formula below,

MS(Matching Score) refers to the matching degree, 1V(Vector)_i refers to the ith component value of the first vector value, 2V_i refers to the ith component value of the second vector value, and n may refer to the total number of job capabilities.

According to the disclosed embodiment, an artificial intelligence-based device and method for providing a matching service for high-experienced people and a technology for solving problems can be provided.

The effects obtainable from the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art to which the present invention belongs from the description below.

The above and other aspects, features and advantages of certain preferred embodiments of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings.
Figure 1 is a schematic diagram of an artificial intelligence-based device for providing a matching service for high-experienced people and a technology for difficult tasks according to one embodiment of the present invention.
FIG. 2 is a drawing showing an artificial intelligence-based device for providing a matching service for difficult technologies and highly experienced people according to one embodiment of the present invention.
FIG. 3 is a diagram showing a process of generating a first vector according to one embodiment of the present invention.
FIG. 4 is a diagram showing a process of generating a second vector according to one embodiment of the present invention.
FIG. 5 is a diagram illustrating a process of generating a matching degree of a first vector and a second vector according to one embodiment of the present invention.
Figure 6 is a flow chart of a method for providing artificial intelligence-based, difficult technology and highly experienced person matching services according to one embodiment of the present invention.
It should be noted that throughout the above drawings, like reference numerals are used to illustrate identical or similar elements, features and structures.

The advantages and features of the present invention, and the methods for achieving them, will become clear with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and the present embodiments are provided only to make the disclosure of the present invention complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

In describing embodiments of the present invention, if it is judged that a detailed description of a known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In addition, the terms described below are terms defined in consideration of functions in embodiments of the present invention, and these may vary depending on the intention or custom of the user or operator. Therefore, the definitions should be made based on the contents throughout this specification.

The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and the present embodiments are provided only to make the disclosure of the present invention complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

At this time, it will be understood that each block of the processing flow diagrams and combinations of the flow diagrams can be performed by computer program instructions. These computer program instructions can be loaded onto a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions executed by the processor of the computer or other programmable data processing equipment create a means for performing the functions described in the flow diagram block(s). These computer program instructions can also be stored in a computer-available or computer-readable memory that can be directed to a computer or other programmable data processing equipment to implement the functions in a specific manner, so that the instructions stored in the computer-available or computer-readable memory can also produce an article of manufacture that includes an instruction means for performing the functions described in the flow diagram block(s). Since the computer program instructions may be installed on a computer or other programmable data processing apparatus, a series of operational steps may be performed on the computer or other programmable data processing apparatus to produce a computer-executable process, so that the instructions executing the computer or other programmable data processing apparatus may also provide steps for executing the functions described in the flowchart block(s).

Additionally, each block may represent a module, segment, or portion of code that contains one or more executable instructions for performing a particular logical function(s). It should also be noted that in some alternative implementation examples, the functions mentioned in the blocks may occur out of order. For example, two blocks shown in succession may in fact be performed substantially concurrently, or the blocks may sometimes be performed in reverse order, depending on the functionality they perform.

Here, the term '~ part' used in the present embodiment means software or hardware components such as FPGA (field-programmable gate array) or ASIC (application specific integrated circuit), and the '~ part' performs certain roles. However, the '~ part' is not limited to software or hardware. The '~ part' may be configured to be in an addressable storage medium and may be configured to reproduce one or more processors. Accordingly, as an example, the '~ part' includes components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided in the components and '~ parts' may be combined into a smaller number of components and '~ parts' or further separated into additional components and '~ parts'. Additionally, the components and '~parts' may be implemented to regenerate one or more CPUs within the device or secure multimedia card.

In specifically describing embodiments of the present invention, examples of specific systems will be mainly targeted, but the main points claimed in this specification can be applied to other communication systems and services having similar technical backgrounds without significantly departing from the scope disclosed in this specification, and this can be done at the discretion of a person skilled in the relevant technical field.

Hereinafter, with reference to the attached drawings, an artificial intelligence-based troubleshooting technology and highly experienced person matching service providing device according to one embodiment of the present invention will be described in detail.

Figure 1 is a schematic diagram of an artificial intelligence-based device for providing a matching service for high-experienced people and a technology for difficult tasks according to one embodiment of the present invention.

Referring to FIG. 1, an AI-based problem-solving and high-experience matching service providing device according to one embodiment of the present invention receives problem-solving and career information from a first user terminal of a company and a second user terminal of a high-experienced person, matches them with each other, derives a high-experienced person who can solve the problem-solving and recommends the result to the company.

At this time, the first user terminal (200) and the second user terminal may include a desktop computer, a laptop computer, a notebook, a smart phone, a tablet PC, a mobile phone, a smart watch, a smart glass, an e-book reader, a portable multimedia player (PMP), a portable game console, a navigation device, a digital camera, a digital multimedia broadcasting (DMB) player, a digital audio recorder, a digital audio player, a digital video recorder, a digital video player, a PDA (Personal Digital Assistant), etc. capable of communication.

FIG. 2 is a drawing showing an artificial intelligence-based device for providing a matching service for difficult technologies and highly experienced people according to one embodiment of the present invention.

An artificial intelligence-based technology and highly experienced person matching service providing device according to one embodiment includes a processor and a memory. The processor can perform at least one of the above-described methods. The memory can store information related to the above-described method or store a program in which the above-described method is implemented. The memory can be a volatile memory or a nonvolatile memory. The memory can be called a 'database', a 'storage', etc.

At this time, the processor can receive technical information on the technical problem and job capabilities required for the technical problem from the first user terminal used by the person in charge of the company.

At this time, the above-mentioned difficult technology refers to a technology that is difficult to solve in a project that the company is currently working on, and the above-mentioned difficult technology information may refer to the above-mentioned difficult technology and the job skills required to solve the above-mentioned difficult technology. For example, when the above-mentioned difficult technology is developing a machine learning model in the Python language, the above-mentioned required job skills may be programming, Python, etc.

Additionally, the processor can receive multiple pieces of career information about the careers of multiple senior employees from multiple second user terminals used by the multiple senior employees.

At this time, for example, the above career information could be A developer, programming, Python, 10 years, etc., or B pet food manufacturer, food research and development, 5 years, etc.

In addition, the processor can derive a matching degree between the technical difficulty and the highly experienced person based on the technical difficulty information and a plurality of pieces of career information. This will be described later.

In addition, the processor can extract a high-experienced person whose matching degree exceeds a preset threshold matching degree, and transmit the high-experienced person and the career information of the high-experienced person to the first user terminal. Through this, the company's manager can select a high-experienced person who can solve their technical difficulties.

At this time, the critical matching degree can be set to the average value of the matching degrees of all highly educated people.

In addition, the processor can generate a first vector based on the above-mentioned technical information, generate a plurality of second vectors based on a plurality of pieces of the above-mentioned career information, and derive the matching degree based on the first vector and the second vector.

FIG. 3 is a diagram showing a process of generating a first vector according to one embodiment of the present invention.

Referring to FIG. 3, the processor can derive a first job keyword from the challenge technology and a job required for the challenge technology based on the challenge technology information.

At this time, the first job keyword can be derived by extracting keywords related to the job from the difficult technology and the job required for the difficult technology through an artificial intelligence module.

At this time, the artificial intelligence module can create a machine learning model that extracts keywords related to the job from the difficult technology and the job required for the difficult technology by using the deep learning technique, which is a field of machine learning.

In addition, the AI module can learn the weights of multiple inputs in the above function through deep learning. In addition, various models such as RNN (Recurrent Neural Network), DNN (Deep Neural Network), and DRNN (Dynamic Recurrent Neural Network) can be used as AI network models for such learning.

Here, RNN is a deep learning technique that considers current data and past data simultaneously, and a recurrent neural network (RNN) is a neural network in which the connections between the units constituting the artificial neural network form a directed cycle. Furthermore, various methods can be used for the structure that can configure a recurrent neural network (RNN), for example, a fully recurrent network, a Hopfield network, an Elman network, an Echo state network (ESN), a Long short term memory network (LSTM), a Bi-directional RNN, a Continuous-time RNN (CTRNN), a Hierarchical RNN, and a Second-order RNN are representative examples. In addition, methods such as gradient descent, Hessian Free Optimization, and Global Optimization Method can be used to train a recurrent neural network (RNN).

In addition, the processor derives a first distance index between the first job keyword and keywords of a plurality of preset job competencies through word embedding, distinguishes a first job competency in which the first distance index is lower than or equal to a preset critical distance index and a second job competency in which the first distance index exceeds the critical distance index, and generates the first vector by listing the plurality of job competencies in a preset order of job competencies, wherein a component value of the first vector corresponding to the first job competency may be set to a preset first value, and a component value of the first vector corresponding to the second job competency may be set to 0.

At this time, word embedding is a method of expressing a word in the form of a dense vector, and the dense vector is the result of the word embedding process, and is also called an embedding vector. In more detail, words are mapped to coordinates in a specific vector space, but the mapping can be done by changing the distance based on the similarity or relevance of each word. For example, the distance between words with high similarity or relevance can be short, and the distance between words with low similarity or relevance can be mapped far.

At this time, for example, the first vector can be set to (V1, V1, 0, 0, 0, V1).

At this time, the above job competencies can be arbitrarily set by the operator of the present invention, and can be set to, for example, programming, food research, Python, artificial intelligence, etc.

At this time, the above job competency order may mean an order arbitrarily set by the operator of the present invention to designate the above preset job competencies as a vector.

At this time, the critical distance index can be set to the average of the first distance indices.

In addition, the first value is an arbitrarily set value, and can be set by the operator of the present invention so as to satisfy only the relationship with the second value described below.

Through this, we can set criteria for recommending highly experienced candidates that companies require.

FIG. 4 is a diagram showing a process of generating a second vector according to one embodiment of the present invention.

Referring to FIG. 4, the processor derives a second job keyword from the career history based on the career information, derives a second distance index between the second job keyword and keywords of the plurality of job competencies through word embedding, distinguishes a third job competency whose second distance index is lower than or equal to the critical distance index and a fourth job competency whose second distance index exceeds the critical distance index, and generates the second vector by listing the plurality of job competencies in the order of the job competencies, wherein a component value of the second vector corresponding to a job competency that is the same as the first job competency among the third job competencies is set to the first value, a component value of the second vector corresponding to the remaining job competencies is set to a second value that is preset lower than the first value, and a component value of the second vector corresponding to the fourth job competency is set to 0.

At this time, the component value corresponding to the job competency that is the same as the first job competency among the third job competencies is set to the first value, and the component values corresponding to the remaining job competencies are set to the second value that is lower than the first value, so that when calculating the matching degree described later, the job competency that matches the job competency required by the company is judged to be higher than other job competencies.

Additionally, the processor may add a weight based on a career period to a component value of the second vector linked to the career history.

At this time, the processor can derive the weight by normalizing the career period based on the total career period for the same career history of all high-career persons and the career period of the high-career person to a limit of 1 or a ratio of the first value and the second value.

This is to calculate a higher matching degree by setting the component values of the vector higher as the career of the senior employee increases.

FIG. 5 is a diagram illustrating a process of generating a matching degree of a first vector and a second vector according to one embodiment of the present invention.

Referring to Fig. 5, the matching degree can be derived by the following mathematical expression 1.

[Mathematical Formula 1]

At this time, MS (Matching Score) may mean the matching degree, 1V (Vector)_i may mean the ith component value of the first vector value, 2V_i may mean the ith component value of the second vector value, and n may mean the total number of job capabilities.

Figure 6 is a flow chart of a method for providing artificial intelligence-based difficult technology and highly experienced person matching service according to one embodiment of the present invention.

Referring to FIG. 6, a method for providing a matching service for difficult technologies and highly experienced people based on artificial intelligence according to one embodiment of the present invention can receive difficult technology information regarding difficult technologies and job capabilities required for difficult technologies from a first user terminal used by a person in charge of a company (S101).

In addition, the method for providing an artificial intelligence-based pain point technology and a high-experience matching service according to one embodiment of the present invention can receive multiple pieces of career information about the careers of multiple high-experienced persons from multiple second user terminals used by the multiple high-experienced persons (S103).

In addition, the method for providing an artificial intelligence-based matching service for difficult technologies and highly experienced individuals according to one embodiment of the present invention can derive a matching degree between the difficult technologies and the highly experienced individuals based on the difficult technologies information and a plurality of pieces of career information (S105).

In addition, the method for providing an artificial intelligence-based, high-experienced person matching service and a high-experienced person matching service according to one embodiment of the present invention can extract high-experienced people whose matching degree exceeds a preset threshold matching degree (S107).

In addition, the method for providing an artificial intelligence-based, high-experienced person matching service according to one embodiment of the present invention can transmit the high-experienced person and the career information of the high-experienced person to the first user terminal (S109).

In addition, the method for providing an artificial intelligence-based difficult technology and highly experienced person matching service according to one embodiment of the present invention can be configured in the same manner as the artificial intelligence-based difficult technology and highly experienced person matching service providing device disclosed in FIGS. 1 to 5.

In addition, the embodiments of the present invention disclosed in this specification and drawings are only specific examples presented to easily explain the technical content of the present invention and help in understanding the present invention, and are not intended to limit the scope of the present invention. In other words, it is obvious to those skilled in the art that other modified examples based on the technical idea of the present invention are possible. In addition, each of the above embodiments can be combined and operated as needed.

In addition, the operation method of the augmented reality-based marketing system according to one embodiment of the present invention may be implemented in the form of program commands that can be executed through various computer means and recorded in a computer-readable medium.

Various embodiments of the present invention can be implemented as computer readable code on a computer readable recording medium, in certain aspects. A computer readable recording medium is any data storage device capable of storing data that can be read by a computer system. Examples of computer readable recording media can include read only memories (ROMs), random access memories (RAMs), compact disk-read only memories (CD-ROMs), magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission over the Internet). The computer readable recording medium can also be distributed over network-connected computer systems, such that the computer readable code is stored and executed in a distributed manner. Additionally, functional programs, codes, and code segments for achieving various embodiments of the present invention can be easily interpreted by programmers skilled in the field to which the present invention is applied.

It will also be appreciated that devices and methods according to various embodiments of the present invention can be realized in the form of hardware, software, or a combination of hardware and software. Such software can be stored in, for example, a volatile or nonvolatile storage device such as a ROM, regardless of whether it is erasable or rewritable, or a memory such as a RAM, a memory chip, a device, or an integrated circuit, or an optically or magnetically recordable and machine-readable storage medium such as a compact disk (CD), a DVD, a magnetic disk, or a magnetic tape. It will be appreciated that methods according to various embodiments of the present invention can be implemented by a computer or a portable terminal including a control unit and a memory, and such a memory is an example of a machine-readable storage medium suitable for storing a program or programs including commands for implementing embodiments of the present invention.

Accordingly, the present invention includes a program including a code for implementing the device or method described in the claims of this specification and a machine-readable storage medium (such as a computer) storing such a program. Furthermore, such a program may be electronically transmitted via any medium, such as a communication signal transmitted via a wired or wireless connection, and the present invention appropriately includes the equivalent thereof.

The embodiments of the present invention disclosed in this specification and drawings are merely specific examples to easily explain the technical content of the present invention and to help understand the present invention, and are not intended to limit the scope of the present invention. In addition, the embodiments according to the present invention described above are merely exemplary, and those skilled in the art will understand that various modifications and equivalent embodiments are possible from this. Therefore, the true technical protection scope of the present invention should be determined by the following patent claims.

Claims (6)

In the device providing artificial intelligence-based acupuncture technology and high-experience matching service,
comprising a memory and a processor connected to the memory;
The above processor,
Receive technical information on technical difficulties and job skills required for technical difficulties from the first user terminal used by the company's manager,
Receive multiple pieces of career information about the career history of multiple high-level employees from multiple second user terminals used by multiple high-level employees,
Based on the above-mentioned technical information and multiple pieces of above-mentioned career information, a matching degree between the above-mentioned technical information and the above-mentioned highly experienced person is derived.
An artificial intelligence-based, high-career technology and high-career matching service providing device, characterized in that the matching degree exceeds a preset threshold matching degree, extracts a high-career person and transmits the high-career person and the career information of the high-career person to the first user terminal.
In claim 1,
The above processor,
Generate a first vector based on the above technical information,
Generate multiple second vectors based on multiple pieces of above-mentioned career information,
An artificial intelligence-based device for providing a matching service for high-level technology and experienced workers, characterized in that it derives the matching degree based on the first vector and the second vector.
In claim 2,
The above processor,
Based on the above technical information, the first job keyword is derived from the technical information and the job required for the technical information.
Through word embedding, the first distance index between the first job keyword and keywords of multiple preset job abilities is derived,
Distinguish between a first job competency in which the first distance index is lower than or equal to a preset critical distance index and a second job competency in which the first distance index exceeds the preset critical distance index.
Generate the first vector by listing multiple of the above job competencies in the preset job competency order.
An artificial intelligence-based technology and highly experienced person matching service providing device, characterized in that the component value of the first vector corresponding to the first job ability is set to a preset first value, and the component value of the first vector corresponding to the second job ability is set to 0.
In claim 3,
The above processor,
Based on the above career information, the second job keyword is derived from the above career history,
Through word embedding, the second distance index between the second job keyword and multiple keywords of the job competency is derived,
Distinguish between a third job competency in which the second distance index is less than or equal to the critical distance index and a fourth job competency in which the second distance index exceeds the critical distance index.
Generate the second vector by listing multiple job competencies in the order of the job competencies above.
Among the above third job competencies, the component value of the second vector corresponding to the same job competency as the first job competency is set to the first value, and the component value of the second vector corresponding to the remaining job competencies is set to the second value that is preset lower than the first value.
The component value of the second vector corresponding to the fourth job competency is set to 0,
An artificial intelligence-based technology and high-experience matching service providing device characterized by adding a weight based on a career period to the component value of the second vector linked to the above career history.
In claim 4,
The above processor,
An artificial intelligence-based device for providing a matching service for high-career workers and a technology based on artificial intelligence, characterized in that the weight is derived by normalizing the overall career period for the same career history of all high-career workers and the career period of said high-career worker based on the above-mentioned career period to a limit of 1 or a ratio of the first value and the second value.
In claim 5,
The above matching is,
It is derived by the mathematical formula below,

An artificial intelligence-based, difficult technology and highly experienced person matching service providing device, characterized in that MS(Matching Score) denotes the matching degree, 1V(Vector)_i denotes the ith component value of the first vector value, 2V_i denotes the ith component value of the second vector value, and n denotes the total number of job capabilities.