Method for match job to new middle-aged generation, and computer program recorded on record-medium for executing method therefor

Abstract

The present invention proposes a job matching method for the elderly that expands employment opportunities for the elderly and provides customized talent to companies by recommending customized industries and companies tailored to the characteristics of the users of the elderly population. The method may include the steps of: the management server extracting characteristic information of an elderly user possessing a user terminal; the management server deriving an industry that matches the user based on the extracted characteristic information; the management server extracting job posting information for at least one pre-registered company related to the derived industry; and the management server providing the extracted job posting information to the user terminal.

Description

Technology Field

[0001] The present invention relates to a job matching system. More specifically, it relates to a job matching method for the elderly that recommends customized industries and companies suited to the characteristics of users targeting the elderly, thereby expanding employment opportunities for the elderly and providing customized talent to companies, and a computer program recorded on a recording medium for implementing the same. Background Technology

[0002] The "new middle-aged" refers to middle-aged people in their mid-40s to early 60s who are physically and mentally active and financially secure, breaking away from the rigid generational classification of the traditional middle-aged. In other words, the new middle-aged is a generational classification that reflects various factors such as lifestyle, consumption patterns, and social participation, rather than being defined by age.

[0003] Meanwhile, conventional job matching systems have primarily targeted young adults or general job seekers, adopting a method of matching job seekers with companies based on general conditions such as age, work experience, and educational background. Since these conventional systems do not consider the characteristics of the middle-aged demographic, middle-aged job seekers have faced difficulties in finding employment.

[0004] Specifically, unlike the younger generation, the range of jobs that the middle-aged and older adults can perform may vary depending on their individual physical capabilities. However, conventional job matching systems have had the problem of recommending unsuitable jobs because they do not take into account the physical characteristics of the middle-aged and older adults.

[0005] Furthermore, the middle-aged demographic possesses expertise built over many years of experience. However, conventional job matching systems were limited to simple classifications based on age, which failed to adequately reflect the experience and technical skills of this demographic.

[0006] Furthermore, the middle-aged demographic tends to prefer flexible work arrangements rather than simply permanent positions. However, conventional job matching systems have failed to provide detailed breakdowns of work arrangements for this demographic, leading to mismatches between job seekers and companies. Prior art literature

[0007] Korean Patent Publication No. 10-2617122, 'Job Matching Platform Server and Method for Controlling the Same', (Registered Dec. 19, 2023) The problem to be solved

[0008] One objective of the present invention is to provide a job matching method for the elderly that recommends customized industries and companies suited to the characteristics of the elderly, thereby expanding employment opportunities for the elderly and providing customized talent to companies.

[0009] Another objective of the present invention is to provide a computer program recorded on a recording medium to implement a job matching method for the elderly, which recommends customized industries and companies suited to the characteristics of the users targeting the elderly, thereby expanding employment opportunities for the elderly and providing customized talent to companies.

[0010] The technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0011] To achieve the technical objectives described above, the present invention proposes a job matching method for the elderly that recommends customized industries and companies suited to the characteristics of the elderly user, thereby expanding employment opportunities for the elderly and providing customized talent to companies. The method may include the steps of: the management server extracting characteristic information of an elderly user possessing a user terminal; the management server deriving an industry that matches the user based on the extracted characteristic information; the management server extracting job posting information for at least one pre-registered company related to the derived industry; and the management server providing the extracted job posting information to the user terminal.

[0012] Specifically, the step of extracting the feature information is characterized by providing a query text for deriving the feature information of the user to the user terminal, acquiring the feature information based on a response text input by the user corresponding to the query text, and additionally generating a query text corresponding to the response text input by the user through a pre-trained artificial intelligence (AI) model and providing it to the user terminal.

[0013] The step of extracting the above feature information is characterized by receiving acceleration information measured over a preset period through an acceleration sensor provided in the user terminal, analyzing an acceleration change pattern based on the received acceleration information, and evaluating the user's upper body ability based on the analyzed acceleration change pattern.

[0014] The step of extracting the above feature information is characterized by receiving location information measured over a preset period through a Global Positioning System (GPS) module equipped in the user terminal, analyzing a location change pattern based on the received location information, and evaluating the user's lower body ability based on the analyzed location change pattern.

[0015] The step of extracting the above feature information is characterized by hooking and receiving screen information of the user terminal for a preset period through an API (Application Programming Interface) installed on the user terminal, extracting text within the received screen information, identifying the number of words and font size included in the extracted text, evaluating visual ability based on the identified number of words and font size, and generating the above feature information based on the evaluated upper body ability, the lower body ability, and the above visual ability.

[0016] The step of deriving the above-mentioned industry is characterized by pre-defining a table of physical abilities required by industry in relation to upper body ability, lower body ability, and visual ability, and deriving an industry that matches the user by comparing the above-mentioned feature information with the table of physical abilities.

[0017] The step provided above is characterized by generating a resume based on the derived industry and extracted user characteristic information and providing it together with the job posting information.

[0018] The above-mentioned step is characterized by receiving voice related to the content of a resume from the user through the user terminal, converting the received voice into text, and generating a resume by combining the converted text with the derived industry and the extracted user characteristic information.

[0019] The step of providing the above is characterized by visualizing the feature information in the form of a graph, and listing the visualized feature information and the generated resume on a corporate terminal that has registered at least one of the extracted job postings.

[0020] The above computer program may be combined with a computing device comprising a memory and a processor that processes instructions residing in the memory. Furthermore, the computer program may be a computer program recorded on a recording medium to execute the steps of: the processor extracting characteristic information of a user of a certain age who possesses a user terminal; the processor deriving an industry that matches the user based on the received characteristic information; the processor extracting at least one job posting information regarding a pre-registered company related to the deriving industry; and the processor providing the extracted at least one job posting information to the user terminal.

[0021] Specific details of other embodiments are included in the detailed description and drawings. Effects of the invention

[0022] According to various embodiments of the present invention, by recommending customized industries and companies tailored to the characteristics of users to the older generation, employment opportunities for the older generation can be expanded and customized talent can be provided to companies.

[0023] The effects of the present invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art to which the present invention pertains from the description in the claims. Brief explanation of the drawing

[0024] Figure 1 is a configuration diagram of a job matching system according to one embodiment of the present invention. FIG. 2 is a logical configuration diagram of a management server according to one embodiment of the present invention. FIG. 3 is an example diagram illustrating an artificial intelligence model for receiving user information according to an embodiment of the present invention. FIG. 4 is an illustrative diagram for explaining a resume generated according to one embodiment of the present invention. FIG. 5 is an example diagram visualizing feature information according to one embodiment of the present invention. FIG. 6 is a hardware configuration diagram of a management server according to one embodiment of the present invention. FIG. 7 is a flowchart illustrating a job matching method according to one embodiment of the present invention. Specific details for implementing the invention

[0025] It should be noted that technical terms used in this specification are used merely to describe specific embodiments and are not intended to limit the invention. Furthermore, unless specifically defined otherwise in this specification, technical terms used in this specification should be interpreted in the sense generally understood by those skilled in the art to which the invention pertains, and should not be interpreted in an overly broad or overly narrow sense. Additionally, if a technical term used in this specification is an incorrect technical term that fails to accurately express the spirit of the invention, it should be understood as being replaced by a technical term that can be correctly understood by those skilled in the art. Moreover, general terms used in this invention should be interpreted according to their prior definitions or the context, and should not be interpreted in an overly narrow sense.

[0026] Additionally, singular expressions used in this specification include plural expressions unless the context clearly indicates otherwise. In this application, terms such as "composed of" or "have" should not be interpreted as necessarily including all of the various components or steps described in the specification, and should be interpreted as potentially including some of the components or steps, or including additional components or steps.

[0027] Additionally, terms including ordinal numbers, such as first, second, etc., used herein may be used to describe various components, but said components shall not be limited by said terms. Such terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component.

[0028] When it is stated that one component is "connected" or "connected" to another component, it may be directly connected or connected to that other component, or there may be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0029] Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings. Identical or similar components regardless of drawing symbols are given the same reference number, and redundant descriptions thereof will be omitted. Furthermore, in describing the present invention, if it is determined that a detailed description of related known technology may obscure the essence of the present invention, such detailed description will be omitted. Additionally, it should be noted that the attached drawings are intended only to facilitate an easy understanding of the concept of the present invention and should not be interpreted as limiting the concept of the present invention. The concept of the present invention should be interpreted as extending to all modifications, equivalents, and substitutions other than those shown in the attached drawings.

[0031] Meanwhile, conventional job matching systems have primarily targeted young adults or general job seekers, adopting a method of matching job seekers with companies based on general conditions such as age, work experience, and educational background. Since these conventional systems do not consider the characteristics of the middle-aged demographic, middle-aged job seekers have faced difficulties in finding employment.

[0032] To overcome these limitations, the present invention proposes various means to recommend customized industries and companies tailored to the characteristics of users targeting the new middle-aged demographic.

[0034] Figure 1 is a configuration diagram of a job matching system according to one embodiment of the present invention.

[0035] As illustrated in FIG. 1, a job matching system (400) according to one embodiment of the present invention may be configured to include a user terminal (100a, 100b, 100n; 100), a corporate terminal (200a, 200b, 200n; 200) and a management server (300).

[0036] As such, since the components of the job matching system (400) according to one embodiment of the present invention merely represent functionally distinct elements, two or more components may be implemented as integrated elements in an actual physical environment, or a single component may be implemented as separated elements in an actual physical environment.

[0037] To explain each component, the user terminal (100) can be a device possessed by an older user who wishes to find a job.

[0038] The user terminal (100) may have an application provided by the management server (300) installed, and the user may access the management server (300) through the application to receive job matching services. However, it is not limited to this, and the user terminal (100) may access a web page provided by the management server (300) to receive job matching services.

[0039] Specifically, the user terminal (100) can connect to the management server (300) and provide information for extracting feature information through a user interface (UI) provided by the management server (300). For example, the feature information may be the user's physical abilities, including upper body ability, lower body ability, and visual ability. Additionally, the user terminal (100) can provide the management server (300) with basic information such as age, gender, and region; career information such as previous occupation, training, and professional skills; and preference information such as preferred industry, work type, and working hours. Furthermore, the user terminal (100) can receive job posting information based on the information provided to the management server (300).

[0040] Additionally, the user terminal (100) may be configured to include an acceleration sensor and a GPS (Global Positioning System) module. The user terminal (100) can transmit acceleration information measured from the acceleration sensor and location information measured from the GPS module to the management server (300).

[0041] A user terminal (100) having the characteristics described above is not limited to a User Equipment (UE) defined by the 3GPP (3rd Generation Partnership Project), and any device capable of transmitting and receiving data with a corporate terminal (200) and a management server (300) and performing calculations based on the transmitted and received data may be allowed. For example, the user terminal (100) may be any one of a fixed computing device such as a desktop, workstation, or server, or a mobile computing device such as a smartphone, laptop, tablet, phablet, or Personal Digital Assistants (PDA).

[0042] With the following configuration, the corporate terminal (200) can be a device held by a manager of a company seeking to recruit.

[0043] The corporate terminal (200) may have an application provided by the management server (300) installed, and an administrator may access the management server (300) through the application to register job information related to corporate registration and recruitment. However, it is not limited to this, and the corporate terminal (200) may access a web page provided by the management server (300) to register job information related to corporate registration and recruitment.

[0044] The enterprise terminal (200), having the characteristics described above, is not limited to User Equipment (UE) defined by the 3GPP (3rd Generation Partnership Project), and any device capable of transmitting and receiving data with the user terminal (100) and the management server (300) and performing calculations based on the transmitted and received data may be allowed. For example, the enterprise terminal (200) may be any one of a fixed computing device such as a desktop, workstation, or server, or a mobile computing device such as a smartphone, laptop, tablet, phablet, or Personal Digital Assistants (PDA).

[0045] With the following configuration, the management server (300) can provide job matching services for users and companies through the user terminal (100) and the company terminal (200).

[0046] Specifically, the management server (300) can extract characteristic information of a user in the elderly group who possesses a user terminal (100), derive an industry that matches the user based on the extracted characteristic information, extract at least one job posting information for a pre-registered company related to the derived industry, and provide the extracted at least one job posting information to the user terminal (100).

[0047] Meanwhile, a detailed description of the management server (300) will be provided below with reference to the drawings.

[0048] The management server (300) can be any one of a fixed computing device such as a desktop, a workstation, or a server, but is not limited thereto.

[0049] The user terminal (100), corporate terminal (200), and management server (300) can transmit and receive data using a network that combines one or more of a secure line, a public wired communication network, or a mobile communication network that directly connects the devices.

[0050] For example, public wired communication networks may include Ethernet, Digital Subscriber Line (xDSL), Hybrid Fiber Coax (HFC), and Fiber To The Home (FTTH), but are not limited thereto. Additionally, mobile communication networks may include Code Division Multiple Access (CDMA), Wideband CDMA (WCDMA), High Speed ​​Packet Access (HSPA), Long Term Evolution (LTE), and 5th generation mobile telecommunication, but are not limited thereto.

[0052] Hereinafter, the logical configuration of a management server according to one embodiment of the present invention will be described in detail.

[0053] FIG. 2 is a logical configuration diagram of a management server according to one embodiment of the present invention.

[0054] As illustrated in FIG. 2, a management server (300) according to one embodiment of the present invention may be configured to include a communication unit (305), an input / output unit (310), a feature information extraction unit (315), a recruitment information extraction unit (320), a service provision unit (325), and a storage unit (330).

[0055] Since the components of the management server (300) are merely functionally distinct elements, two or more components may be implemented as a single integrated unit in an actual physical environment, or a single component may be implemented as a separate unit in an actual physical environment.

[0056] To explain each configuration, the communication unit (305) can transmit and receive data with the user terminal (100) and the corporate terminal (200).

[0057] Specifically, the communication unit (305) can receive acceleration information, location information, and screen information from the user terminal (100). In addition, the communication unit (305) can transmit recruitment information to the user terminal (100).

[0058] With the following configuration, the input / output unit (310) can receive various setting values ​​for matching jobs between users and companies.

[0059] Specifically, the input / output unit (310) can receive setting values ​​for extracting user feature information. Additionally, the input / output unit (310) can receive setting values ​​for extracting job information that matches the user.

[0060] With the following configuration, the feature information extraction unit (315) can extract feature information of an elderly user who possesses a user terminal (100).

[0061] In one embodiment, the feature information extraction unit (315) can receive user information for deriving feature information by performing a conversation with the user through the user terminal (100).

[0062] Here, user information may include basic information such as age, gender, and region; career information such as previous occupation, training, and professional skills; preference information such as preferred industry, work type, and working hours; and physical information such as the user's upper body ability, lower body ability, and visual ability.

[0063] Specifically, the feature information extraction unit (315) provides a query text to the user terminal (100) to derive the user's feature information, and can obtain feature information based on the response text input by the user corresponding to the query text.

[0064] Here, the feature information extraction unit (315) can additionally generate a query text corresponding to the response text input by the user through a pre-trained artificial intelligence (AI) model and provide it to the user terminal (100).

[0065] For example, the feature information extraction unit (315) can additionally generate a query text corresponding to the response text input by the user based on a transformer-based Large Language Model (LLM) and provide it to the user terminal (100).

[0066] For a moment, with reference to FIG. 3, an artificial intelligence model for receiving user information according to one embodiment of the present invention will be described.

[0067] FIG. 3 is an example diagram illustrating an artificial intelligence model for receiving user information according to an embodiment of the present invention.

[0068] As shown in Fig. 3, a transformer-based artificial intelligence model can be a set of neural networks consisting of an encoder and a decoder equipped with self-attention capabilities.

[0069] Specifically, the multi-head attention constituting each encoder block can output the result of calculating the attention h times using different weight matrices and concatenating them, as shown in Equation 1 below.

[0070] [Mathematical Formula 1]

[0071] MultiHead(Q, K, V) = [head1; … ; head h ]w O

[0072] Here, head i is Attention(QW i Q , KW i K , VW i V ) 4 Q is the hidden stage of the decoder, K is the hidden stage of the encoder, and V is the normalized weight to which attention is assigned to K. The scaled dot-product attention for Q, K, and V can be calculated through the following mathematical equation 2.

[0073] [Mathematical Formula 2]

[0074] Attention(Q, K, V) = softmax(QK T / root(d k ))V

[0075] And, the feed-forward network (FFN) that receives the attention result is composed of two linear transformations and can be implemented based on the following mathematical equation 3 with GELU (Gaussian Error Linear Units) applied.

[0076] [Mathematical Formula 3]

[0077] FFN(x) = max(0, xW1+ b1)W2+ b2

[0078] The masked multi-head self-attention, which is the first sublayer constituting each decoder block, performs the same operations as the multi-head attention, which is the sublayer of the aforementioned encoder, but differs in some ways in that it applies masking to the attention score matrix. That is, the masked multi-head self-attention sublayer can apply masking so that it can refer to attention scores only for words that are ahead of the word currently being processed.

[0079] Furthermore, the decoder receives the mixed feature vector, which is the output of the encoder, through the second sublayer, multi-head attention. It then passes the received mixed feature vector through multi-head attention and the third sublayer, the Feed Forward Network (FFN), and through a linear layer and a softmax layer to output the token with the highest relationship from the learned word database.

[0080] In this case, the linear layer is a fully-connected network that can project the vector last output by the decoder onto a logit vector, which is a vector of much larger size. Here, each cell of the logit vector can be a score for each word.

[0081] In another embodiment, the feature information extraction unit (315) can evaluate the user's physical ability based on acceleration information generated from the user terminal (100) and generate feature information based on the evaluated physical ability.

[0082] Specifically, the feature information extraction unit (315) can receive acceleration information measured over a preset period through an acceleration sensor provided in the user terminal (100). Here, the feature information extraction unit (315) can store the received acceleration information by listing it in chronological order. At this time, the feature information extraction unit (315) can instruct the user to perform a specific action through the user terminal (100) and receive acceleration information collected during the process of performing the instructed action. For example, the feature information extraction unit (315) can instruct the user to perform an action by outputting a phrase to repeat the action of raising and lowering the arm five times while holding the user terminal (100), and can receive acceleration information while performing the action.

[0083] For example, the feature information extraction unit (315) can store acceleration information by listing it as shown in the table below.

[0084] Time (ms) X-axis acceleration Y-axis acceleration Z-axis acceleration 0 0.1 9.8 0.2 100 1.5 8.7 0.4 200 2.0 7.9 0.5 ... ... ... ...

[0085] Next, the feature information extraction unit (315) can analyze the acceleration change pattern based on the received acceleration information and evaluate the user's upper body ability based on the analyzed acceleration change pattern. Here, the feature information extraction unit (315) can express the user's upper body ability by quantifying it into a score. Specifically, the feature information extraction unit (315) can evaluate the strength score, endurance score, agility score, muscle control ability score, stability score, and accuracy score for the upper body ability.

[0086] At this time, the feature information extraction unit (315) can evaluate the magnitude of the user's muscle strength based on the maximum acceleration information. Additionally, the feature information extraction unit (315) can evaluate the user's endurance based on the average acceleration information. Additionally, the feature information extraction unit (315) can evaluate the user's agility based on the time taken to reach maximum acceleration after the start of the movement. Additionally, the feature information extraction unit (315) can evaluate the user's muscle control ability based on the range of increase or decrease in acceleration. Additionally, the feature information extraction unit (315) can evaluate the stability of muscle strength based on the frequency of micro-vibrations. Furthermore, the feature information extraction unit (315) can evaluate the accuracy of the movement based on the presence or absence of a certain pattern.

[0087] Additionally, the feature information extraction unit (315) can evaluate the user's lower body ability based on the user's location information generated from the user terminal (100), and generate feature information based on the evaluated lower body ability.

[0088] Specifically, the feature information extraction unit (315) can receive location information measured over a preset period through a Global Positioning System (GPS) module equipped in the user terminal (100). Here, the feature information extraction unit (315) can store the received location information by listing it in chronological order. For example, the feature information extraction unit (315) can instruct the user to move by outputting a phrase through the user terminal (100) instructing the user to move a preset section while holding the user terminal (100), and can receive location information collected from the GPS module during the movement. Here, the location information may include time information, latitude, longitude, altitude, and speed information.

[0089] For example, the feature information extraction unit (315) can store location information by listing it as shown in the table below.

[0090] hour Latitude Longitude Altitude Speed ​​(m / s) 08:00 37.5678 126.9784 15.2 1.2 08:05 37.5685 126.9790 15.4 1.5 08:10 37.5690 126.9801 15.3 1.8 ... ... ... ... ...

[0091] Next, the feature information extraction unit (315) can analyze the position change pattern based on the received position information and evaluate the user's lower body ability based on the analyzed position change pattern. Specifically, the feature information extraction unit (315) can evaluate the endurance score, mobility score, muscle strength score, stability score, and lower body fatigue score for the lower body ability.

[0092] The feature information extraction unit (315) can evaluate the user's endurance based on the total distance the user has traveled within a certain period. Additionally, the feature information extraction unit (315) can evaluate the user's mobility based on the average speed calculated from the distance and time the user has traveled. Additionally, the feature information extraction unit (315) can evaluate the user's muscle strength based on the maximum speed during movement. Furthermore, the feature information extraction unit (315) can evaluate the user's lower body stability by analyzing the shaking pattern of the user's movement. Also, the feature information extraction unit (315) can evaluate the lower body fatigue by analyzing the number of times the user stopped during movement.

[0093] Additionally, the feature information extraction unit (315) can evaluate the user's visual ability based on screen information generated from the user terminal (100) and generate feature information based on the evaluated visual ability. Here, visual ability can serve as an indicator for evaluating document inspection ability, document utilization ability, or electronic device utilization ability. For example, the feature information extraction unit (315) can support recommending users with excellent visual ability for jobs requiring detailed work, such as data input and document processing, or for industries where visual information processing is important, such as monitoring and inspection tasks.

[0094] Specifically, the feature information extraction unit (315) can receive screen information of the user terminal (100) by hooking it during a pre-set period through an API (Application Programming Interface) installed on the user terminal (100). Here, the feature information extraction unit (315) can capture screen information when a set event occurs during the pre-set period. For example, the feature information extraction unit (315) can capture screen information when an event such as a screen switching event, a scroll operation event, or a touch operation event occurs on the user terminal (100). Subsequently, the feature information extraction unit (315) can extract text from the received screen information. For example, the feature information extraction unit (315) can extract text from the screen information through Optical Character Recognition (OCR).

[0095] The feature information extraction unit (315) can identify the number of words included in the extracted text. That is, the feature information extraction unit (315) can parse the extracted text in word units. Here, the feature information extraction unit (315) can separate words based on spaces, punctuation marks, special characters, etc.

[0096] Additionally, the feature information extraction unit (315) can identify the font size included in the extracted text. That is, the feature information extraction unit (315) can identify the font size based on the height of the bounding box generated during the optical character recognition process. Here, when text with various font sizes is mixed within the screen, the feature information extraction unit (315) can apply the average value of the font size identified from the identified multiple bounding boxes.

[0097] Next, the feature information extraction unit (315) can evaluate visual ability based on the number of identified words and font size. For example, the feature information extraction unit (315) can score visual ability based on the number of identified words and font size.

[0098] With the following configuration, the job information extraction unit (320) derives an industry that matches the user based on the extracted feature information and can extract job information for at least one pre-registered company related to the derived industry.

[0099] Specifically, the job information extraction unit (320) can predefine a table of physical abilities required by industry in relation to upper body ability, lower body ability, and visual ability, and derive an industry that matches the user by comparing feature information with the table of physical abilities.

[0100] For example, the recruitment information extraction unit (320) can define a table of physical abilities required by industry as shown in the table below.

[0101] Industry Upper body ability Lower body ability visual ability Major tasks Logistics / Delivery 9 8 5 Goods transportation, loading, delivery Office workers 4 3 9 Document work, data entry Security / Guard 7 7 7 Patrol, monitoring, access control Parking management 3 6 8 Vehicle guidance, parking lot surveillance service jobs 6 7 8 Customer service, store management Cleaning / Maintenance 8 8 6 Cleaning, organizing, facility management Data verification 3 3 10 Document review, error detection

[0102] In addition, the job information extraction unit (320) can calculate a matching score for each industry through the following mathematical formula and derive an industry that matches the user based on the calculated matching score.

[0103] [Mathematical Formula]

[0104]

[0105] (Here, is user ability score, is the industry requirement score, (industry-specific capability weighting)

[0106] That is, the job information extraction unit (320) calculates a matching score for each of the upper body ability, lower body ability, and visual ability through the above mathematical formula, and can derive the industry that matches the user with the highest sum of the matching scores for the upper body ability, lower body ability, and visual ability.

[0107] Next, the job information extraction unit (320) can extract at least one job information for a pre-registered company related to the derived industry. For example, the job information extraction unit (320) can extract job information for at least one company among the pre-registered companies that corresponds to the derived industry by using an industry code corresponding to the derived industry. For example, the job information may include information such as company name, job title, workplace, type of work, salary conditions, required physical ability, and application deadline.

[0108] Here, the job information extraction unit (320) can filter the extracted job information based on basic information such as the user's age, gender, and region, career information such as previous job, training, and professional skills, and preference information such as preferred industry, work type, and working hours, which are input from the user terminal (100).

[0109] With the following configuration, the service provider (325) can provide at least one extracted job information to the user terminal (100).

[0110] Specifically, the service provider (325) can generate a resume based on the derived industry and extracted user characteristic information and provide it to the user terminal (100) along with job information.

[0111] The service provider (325) can generate a resume based on user information received through the user terminal (100) described above, extracted user characteristic information, and derived industry.

[0112] For a moment, with reference to FIGS. 4 and FIGS. 5, the process of generating a resume according to one embodiment of the present invention will be described in detail.

[0113] FIG. 4 is an illustrative diagram for explaining a resume generated according to one embodiment of the present invention, and FIG. 5 is an illustrative diagram visualizing feature information according to one embodiment of the present invention.

[0114] As illustrated in FIG. 4, the service provider (325) can register user information received through the user terminal (100) described above into a pre-set resume template. For example, the service provider (325) can register basic information such as age, gender, and region; career information such as previous job, training, and professional skills; preference information such as preferred industry, work type, and working hours; and physical information such as the user's upper body ability, lower body ability, and visual ability.

[0115] At this time, the service provider (325) can receive voice input related to the content of the resume from the user through the user terminal (100), convert the received voice into text, and generate a resume by combining the converted text with the derived industry and extracted user characteristic information. That is, for users in the newer age group whose computer skills may be lower compared to older age groups, the service provider (325) can generate a resume by receiving the user's voice input through the user terminal (100). For example, the service provider (325) can generate a resume by inputting the text converted from voice input from the user terminal (100), the derived industry, and the extracted user characteristic information into a pre-trained artificial intelligence (AI) model. For example, the artificial intelligence model can be a transformer-based artificial intelligence model.

[0116] Additionally, as illustrated in FIG. 5, the service provider (325) can visualize the derived feature information in the form of a graph. For example, the service provider (325) can visualize the user's upper body ability, lower body ability, visual ability, age, and distance in the form of a radar chart.

[0117] In addition, the service provider (325) can list up and provide the visualized feature information and the generated resume to the corporate terminal (200) that has registered at least one extracted job information. Through this, the service provider (325) can not only promote the existence of suitable companies to the elderly, but also promote the existence of suitable elderly personnel to suitable companies based on the physical ability evaluation of the elderly.

[0119] Below, the hardware for implementing the logical components of the management server described above will be explained in more detail.

[0120] FIG. 6 is a hardware configuration diagram of a management server according to one embodiment of the present invention.

[0121] As illustrated in FIG. 6, the management server (300) may be configured to include a processor (350), memory (355), transceiver (360), input / output device (365), data bus (370) and storage (375).

[0122] Specifically, the processor (350) can implement the operation and function of the guide server (300) based on instructions according to the software (380a) that implements the job matching method residing in memory (355).

[0123] Software (380a) in which a job matching method according to embodiments of the present invention is implemented may be loaded in memory (355).

[0124] The transceiver (360) can transmit and receive data with the user terminal (100) and the corporate terminal (200).

[0125] The input / output device (365) can receive signals necessary for the operation of the management server (300) or output calculation results to the outside according to the command of the processor (350).

[0126] The data bus (370) is connected to the processor (350), memory (355), transceiver (360), input / output device (365), and storage (375), and can serve as a passage for transferring data between each component.

[0127] Storage (375) can store an Application Programming Interface (API), library files, resource files, etc., necessary for the execution of software (380a) in which a job matching method according to embodiments of the present invention is implemented. Additionally, storage (375) can store software (380b) in which a method according to embodiments of the present invention is implemented and a database (385). In this case, artificial intelligence (AI) implemented by an artificial neural network (ANN) can be stored in the database (385).

[0128] According to one embodiment of the present invention, software (380a, 380b) for implementing a job matching method that resides in memory (355) or is stored in storage (375) may be a computer program recorded on a recording medium to execute the steps of: a processor (350) extracting characteristic information of a user who possesses a user terminal (100); a processor (350) deriving an industry that matches the user based on the received characteristic information; a processor (350) extracting at least one job posting information for a pre-registered company related to the deriving industry; and a processor (350) providing the extracted at least one job posting information to the user terminal (100).

[0129] More specifically, the processor (350) may include an Application-Specific Integrated Circuit (ASIC), other chipsets, logic circuits, and / or data processing devices. The memory (355) may include Read-Only Memory (ROM), Random Access Memory (RAM), flash memory, memory cards, storage media, and / or other storage devices. The transceiver (360) may include a baseband circuit for processing wired and wireless signals. The input / output device (365) may include input devices such as a keyboard, mouse, and / or joystick, image output devices such as a Liquid Crystal Display (LCD), Organic Light Emitting Diode (OLED), and / or Active Matrix OLED (AMOLED), and printing devices such as a printer and plotter.

[0130] When the embodiments included in this specification are implemented in software, the above-described method may be implemented as a module (process, function, etc.) that performs the above-described function. The module may reside in memory (355) and be executed by a processor (350). Memory (355) may be located inside or outside the processor (350) and may be connected to the processor (450) by various widely known means.

[0131] Each component illustrated in FIG. 6 may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, one embodiment of the present invention may be implemented by one or more ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices), FPGAs (Field Programmable Gate Arrays), processors, controllers, microcontrollers, microprocessors, etc.

[0132] In addition, in the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc., that performs the functions or operations described above, and may be recorded on a recording medium readable through various computer means. Here, the recording medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the recording medium may be those specifically designed and configured for the present invention, or they may be those known and available to those skilled in the art of computer software. For example, the recording medium includes magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs (Compact Disk Read Only Memory) and DVDs (Digital Video Disks); magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions may include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. Such hardware devices may be configured to operate as one or more software to perform the operation of the present invention, and vice versa.

[0134] Hereinafter, a job matching method according to one embodiment of the present invention will be described in detail.

[0135] FIG. 7 is a flowchart illustrating a job matching method according to one embodiment of the present invention.

[0136] Referring to Fig. 7, in step S100, the management server can extract characteristic information of a user in the elderly group who possesses a user terminal.

[0137] In one embodiment, the management server may receive user information to derive feature information by performing a conversation with the user through a user terminal.

[0138] Specifically, the management server provides a query text to the user terminal to derive user feature information, and can obtain feature information based on the response text input by the user corresponding to the query text.

[0139] Here, the management server can additionally generate a query text corresponding to the response text entered by the user through a pre-trained artificial intelligence (AI) model and provide it to the user terminal.

[0140] In another embodiment, the management server may evaluate the user's physical ability based on acceleration information generated from the user terminal and generate feature information based on the evaluated physical ability.

[0141] Specifically, the management server can receive acceleration information measured over a preset period through an acceleration sensor equipped in the user terminal. Here, the management server can store the received acceleration information by listing it in chronological order. At this time, the management server can instruct the user to perform a specific action through the user terminal and receive acceleration information collected during the process of performing the instructed action.

[0142] Next, the management server can analyze the acceleration change pattern based on the received acceleration information and evaluate the user's upper body ability based on the analyzed acceleration change pattern. Here, the management server can express the user's upper body ability by quantifying it into a score. Specifically, the management server can evaluate the strength score, endurance score, agility score, muscle control ability score, stability score, and accuracy score for the upper body ability.

[0143] At this time, the management server can evaluate the magnitude of the user's muscle strength based on the maximum acceleration information. Additionally, the feature information extraction unit (315) can evaluate the user's endurance based on the average acceleration information. Additionally, the management server can evaluate the user's agility based on the time taken to reach maximum acceleration after the start of the movement. Additionally, the management server can evaluate the user's muscle control ability based on the range of increase or decrease in acceleration. Additionally, the management server can evaluate the stability of muscle strength based on the frequency of micro-vibrations. Furthermore, the management server can evaluate the accuracy of the movement based on the presence or absence of a certain pattern.

[0144] In addition, the management server can evaluate the user's lower body ability based on the user's location information generated from the user terminal, and generate feature information based on the evaluated lower body ability.

[0145] Specifically, the management server can receive location information measured over a preset period through a Global Positioning System (GPS) module equipped in the user terminal. Here, the management server can store the received location information by listing it in chronological order. At this time, the management server can instruct the user to move by outputting a message through the user terminal instructing the user to move along a preset section while holding the user terminal, and can receive location information collected from the GPS module during the movement. Here, the location information may include time information, latitude, longitude, altitude, and speed information.

[0146] Next, the management server can analyze the location change pattern based on the received location information and evaluate the user's lower body ability based on the analyzed location change pattern. Specifically, the management server can evaluate the endurance score, mobility score, muscle strength score, stability score, and lower body fatigue score for lower body ability.

[0147] The management server can evaluate the user's endurance based on the total distance traveled within a certain period. Additionally, the management server can evaluate the user's mobility based on the average speed calculated from the distance traveled and the time taken. Furthermore, the management server can evaluate the user's muscle strength based on the maximum speed during movement. Moreover, the management server can evaluate the user's lower body stability by analyzing sway patterns during movement. Finally, the management server can evaluate lower body fatigue by analyzing the number of times the user stopped during movement.

[0148] In addition, the management server can evaluate the user's visual ability based on screen information generated from the user terminal and generate feature information based on the evaluated visual ability. Here, visual ability can serve as an indicator to evaluate document inspection skills, document utilization skills, or electronic device utilization skills. For example, the management server can support the recommendation of users with excellent visual ability for jobs requiring detailed work, such as data entry and document processing, or for industries where visual information processing is critical, such as monitoring and inspection tasks.

[0149] Specifically, the management server can hook and receive screen information of the user terminal during a pre-set period through an API (Application Programming Interface) installed on the user terminal. Here, the management server can capture screen information when a set event occurs during the pre-set period. For example, the management server can capture screen information when an event such as a screen switching event, a scrolling event, or a touch event occurs on the user terminal. Subsequently, the management server can extract text from the received screen information. For example, the feature information extraction unit (315) can extract text from the screen information through Optical Character Recognition (OCR).

[0150] The management server can identify the number of words contained in the extracted text. In other words, the management server can parse the extracted text word by word. Here, the management server can separate words based on spaces, punctuation, special characters, etc.

[0151] In addition, the management server can identify the font size contained in the extracted text. That is, the management server can identify the font size based on the height of the bounding box generated during the optical character recognition process. Here, if text with various font sizes is mixed within the screen, the management server can apply the average value of the font sizes identified from multiple identified bounding boxes.

[0152] Next, the management server can evaluate visual ability based on the number of identified words and font size. For example, the management server can score visual ability based on the number of identified words and font size.

[0153] Next, in step S200, the management server derives an industry that matches the user based on the extracted feature information, and can extract job posting information for at least one pre-registered company related to the derived industry.

[0154] Specifically, the management server predefines a table of physical abilities required by industry in relation to upper body ability, lower body ability, and visual ability, and can derive an industry that matches the user by comparing feature information with the table of physical abilities.

[0155] Next, in step S300, the management server can extract at least one job posting for a pre-registered company related to the derived industry. For example, the management server can use an industry code corresponding to the derived industry to extract job postings for at least one company among the pre-registered companies that corresponds to the derived industry. For example, the job postings may include information such as the company name, job title, workplace, type of work, salary conditions, required physical ability, and application deadline.

[0156] Here, the management server can filter the extracted job information based on basic information such as the user's age, gender, and region entered from the user terminal, career information such as previous job, training, and professional skills, and preference information such as preferred industry, work type, and working hours.

[0157] And, in step S400, the management server can provide at least one extracted job posting to the user terminal.

[0158] Specifically, the management server can generate a resume based on the derived industry and extracted user characteristic information and provide it to the user terminal along with job posting information.

[0159] The management server can generate a resume based on user information received through the user terminal at step S100, extracted user characteristic information, and derived industry.

[0160] Specifically, the management server can register user information received through the user terminal described above into a pre-configured resume template.

[0161] At this time, the management server can receive voice input related to the content of the resume from the user through the user terminal, convert the received voice into text, and generate a resume by combining the converted text with the derived industry and extracted user characteristic information.

[0162] In addition, the management server can visualize the derived feature information in the form of a graph. For example, the management server can visualize the user's upper body ability, lower body ability, visual ability, age, and the distance between the company and residence in the form of a radar chart.

[0163] In addition, the management server can list and provide the visualized feature information and the generated resume to a corporate terminal that has registered at least one extracted job posting.

[0165] As described above, preferred embodiments of the present invention have been disclosed in this specification and drawings; however, it is obvious to those skilled in the art that other variations based on the technical spirit of the present invention are possible in addition to the embodiments disclosed herein. Furthermore, although specific terms have been used in this specification and drawings, they are used merely in a general sense to facilitate the explanation of the technical content of the present invention and to aid in understanding the invention, and are not intended to limit the scope of the present invention. Accordingly, the detailed description above should not be interpreted restrictively in any respect and should be considered illustrative. The scope of the present invention should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present invention are included within the scope of the present invention. Explanation of the symbols

[0166] 100 : User terminal 200 : Enterprise terminal 300: Management Server 305: Communications Unit 310: Input / Output Unit 315: Feature Information Extraction Unit 320 : Job Information Extraction Unit 325 : Service Provision Unit 330 : Storage unit

Claims

Claim 1 A method for matching jobs for older adults, comprising: a step of extracting characteristic information of an older adult user possessing a user terminal; a step of deriving an industry that matches the user based on the extracted characteristic information; a step of extracting job information for at least one pre-registered company related to the derived industry; and a step of providing the extracted at least one job information to the user terminal; wherein the step of extracting characteristic information is characterized by receiving acceleration information measured over a pre-set period through an acceleration sensor equipped in the user terminal, analyzing an acceleration change pattern based on the received acceleration information, and evaluating the user's upper body ability based on the analyzed acceleration change pattern. Claim 2 A method for matching jobs for new seniors according to claim 1, wherein the step of extracting feature information comprises providing a query text for deriving feature information of the user to the user terminal, acquiring feature information based on a response text input by the user corresponding to the query text, and additionally generating a query text corresponding to the response text input by the user through a pre-trained artificial intelligence (AI) model and providing it to the user terminal. Claim 3 delete Claim 4 A job matching method for seniors according to claim 1, wherein the step of extracting feature information comprises receiving location information measured over a preset period through a Global Positioning System (GPS) module provided in the user terminal, analyzing a location change pattern based on the received location information, and evaluating the user's lower body ability based on the analyzed location change pattern. Claim 5 In claim 4, the step of extracting the feature information comprises hooking and receiving screen information of the user terminal for a preset period through an API (Application Programming Interface) installed on the user terminal, extracting text within the received screen information, identifying the number of words and font size included in the extracted text, evaluating visual ability based on the identified number of words and font size, and generating the feature information based on the evaluated upper body ability, the lower body ability, and the visual ability. Claim 6 A job matching method for seniors according to claim 5, wherein the step of deriving the above-mentioned industry involves pre-defining a table of physical abilities required by industry in relation to upper body ability, lower body ability, and visual ability, and deriving an industry that matches the user by comparing the above-mentioned feature information with the table of physical abilities. Claim 7 A job matching method for older adults according to claim 1, wherein the step of providing the above is characterized by generating a resume based on the derived industry and the extracted user characteristic information and providing it together with the job information. Claim 8 A job matching method for new seniors according to claim 7, wherein the step provided is to receive voice related to the content of a resume from the user through the user terminal, convert the received voice into text, and generate a resume by combining the converted text with the derived industry and the extracted user characteristic information. Claim 9 A job matching method for older adults according to claim 7, wherein the step of providing the above visualizes the feature information in the form of a graph, and lists the visualized feature information and the generated resume to a corporate terminal that has registered at least one of the extracted job information. Claim 10 A computer program recorded on a recording medium, comprising a computing device configured to include a memory; a transceiver; and a processor that processes instructions residing in the memory, wherein the processor performs the steps of: extracting characteristic information of a user of a certain age who possesses a user terminal; deriving an industry that matches the user based on the extracted characteristic information; extracting at least one job posting information for a pre-registered company related to the derived industry; and providing the extracted at least one job posting information to the user terminal. The step of extracting characteristic information is performed to execute receiving acceleration information measured over a preset period through an acceleration sensor provided in the user terminal, analyzing an acceleration change pattern based on the received acceleration information, and evaluating the user's upper body ability based on the analyzed acceleration change pattern.

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