KR20190143792A - Apparatus and method for recommending learning contents - Google Patents

Abstract

The present invention relates to a learning content recommendation apparatus to recommend learning content appropriate to a user based on notification information of the user and a learning content recommendation method thereof. According to one embodiment of the present invention, the learning content recommendation apparatus comprises: an acquisition unit acquiring business card information which is provided to inform other persons of user′s identification; an extraction unit extracting one or more pieces of metadata including a preset attribute word from the business card information; a search unit searching for one or more pieces of learning content matched with the metadata; and a providing unit providing the user with the one or more pieces of learning content corresponding to a search result of the search unit.

Description

Device and method for recommending learning content {APPARATUS AND METHOD FOR RECOMMENDING LEARNING CONTENTS}

The present invention relates to an apparatus and method for recommending learning content.

In recent years, due to the rapid development of information and communication technology, smart phones, tablet PCs, netbooks, and the like are widely used. Accordingly, users can use various contents such as internet, video, and music at any time and place without restriction of time and space.

As various kinds of IT devices are spread to general households, services that allow a user to use a service that is used independently on a device in combination with a user or a content are attracting attention.

In addition, the dissemination of IT devices is changing the traditional method to a new framework in various fields. For example, the method of providing learning information includes video lectures using the Internet in traditional offline methods such as schools, private institutes, tutoring, and learning papers. The situation is gradually changing to an online method using a tablet PC.

The above-described background art is technical information possessed by the inventors for the derivation of the present invention or acquired in the derivation process of the present invention, and is not necessarily known technology disclosed to the general public before the application of the present invention.

Korean Unexamined Patent Publication No. 2011-0070625

In order to solve the above problems and / or limitations, the object of the present invention is to recommend learning contents suitable for a user based on notification information of a user connected to the learning content recommending apparatus.

Learning content recommendation apparatus according to an embodiment of the present invention, the acquisition unit for obtaining the card information to hand out to notify the other of the user of the user; From the business card information, one of the 26 large classification industry groups classified by the Ministry of Commerce and Industry and the industrial group information of the large classification industry groups classified into the 98 middle classification industry groups, and a plurality of industrially separated from the organizations, companies, and organizations to which the user belongs. The job names including the department name as the group belonging to the user belonging to the group, and the kinds of jobs, the difficulty, the authority and the degree of role responsibility are grouped together in a group, and the members of the organization are prescribed for personnel management and manpower operation. Extracting one or more primary metadata including meanings from attribute information including ranks divided into classes and arranged in a hierarchical system, wherein the industry group, the job name, An extraction unit for extracting secondary metadata including an attribute word including the rank; Receives the attribute information including the industry group, the job name, the position, the company address, the attribute word located below the attribute information, the learning content stored together with the attribute word as index information, and learning content for each user. Primary search for one or more learning content indexed to the attribute word included in the secondary metadata from a database in which a history is stored, and another pattern having a pattern similar to the secondary metadata from the results of the primary search Grouping users, collecting one or more learning content histories provided by matching the secondary metadata, arranging the learning contents from the learning content history in order of the highest number of replays, and arranging a predetermined number of learnings ranked at the top; A search unit which performs a second search for content; And a providing unit providing one or more learning contents to the user corresponding to the result of the second search of the searching unit, including at least one of a learning contents name, a lecturer name, a playing time, and a learning count, together with the attribute information to which the user belongs. Wherein the obtaining unit obtains one of the business card information including a voice file, the business card information including a video file, and an image business card information of a business card taken offline, and the business card information including the voice file. Converts the text from the audio information of the business card information including the video file, and converts the text from the image card information of the image of the offline business card to extract the text for the metadata extraction. It may further include a text conversion unit for generating.

The apparatus may further include a learner configured to learn the number of times of reproduction of the learning content and the order of providing the learning content matching the metadata and reflect the next learning content.

In accordance with an aspect of the present invention, there is provided a learning content recommendation method, including: acquiring, by an acquiring unit, business card information to be sent to notify another user of his / her identity; From the business card information, the extracting unit is used for the business of one of the 26 major classification industrial groups classified by the Ministry of Commerce and Industry and the industrial group information which classified the major classification industrial groups into 98 mid-class industrial groups, and the organizations, companies and organizations to which the user belongs. Organized for personnel management and manpower operation by grouping job titles including the department name as the group to which the user belongs, and similar job titles with similar types of job, difficulty, authority, and role responsibilities Extracting one or more primary metadata having meanings from attribute information including ranks divided into predetermined grades and arranged in a hierarchical system, and extracting one or more primary metadata having a meaning from the primary metadata; Extracts secondary metadata including the job name and an attribute word including the job title Doing; The search unit receives the industry group, the job name, the attribute information including the position, the attribute word located below the attribute information, the learning content in which the attribute word is stored as index information, and the learning content for each user. Primary search for one or more learning content indexed to the attribute word included in the secondary metadata from a database in which a history is stored, and another pattern having a pattern similar to the secondary metadata from the results of the primary search Grouping users, collecting one or more learning content histories provided by matching the secondary metadata, arranging the learning contents from the learning content history in order of the highest number of replays, and arranging a predetermined number of learnings ranked at the top; Secondary searching for content; And providing, by the providing unit, one or more learning contents including a learning content name, a teacher's name, a playing time, and a learning count together with the attribute information to which the user belongs, in response to a result of the secondary search of the searching unit. And providing, by the text converter, one of the business card information including a voice file, the business card information including a video file, and an image business card information of a business card taken offline. Obtains, performs text conversion from the business card information including the voice file, performs text conversion from audio information of the business card information including the video file, and texts from the image business card information of the business card on the offline image. Generating a text for extracting the metadata by performing a transformation; It may further include.

The method may further include learning, by the learner, the number of reproductions and the order of providing the learning content that matches the metadata to reflect the next learning content.

In addition, other methods, other systems, and computer programs for implementing the methods may be further provided to implement the present invention.

Other aspects, features, and advantages other than those described above will become apparent from the following drawings, claims, and detailed description of the invention.

According to embodiments, the user may conveniently learn by recommending the learning content suitable for the user based on the notification information of the user accessing the learning content recommending apparatus.

In addition, the user can improve the individual's competitiveness by receiving and learning the necessary learning content.

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

FIG. 1 is a diagram schematically illustrating a learning content recommendation system according to an exemplary embodiment.
FIG. 2 is a diagram schematically illustrating a detailed configuration of a learning content recommending device in the learning content recommending system of FIG. 1.
FIG. 3 is a diagram schematically illustrating a detailed configuration of a learning content recommending unit in the learning content recommending apparatus of FIG. 2.
FIG. 4 is a diagram schematically illustrating another embodiment of a searcher of the learning content recommender of FIG. 3.
5 and 6 are diagrams for explaining schematically the notification information to the user obtained by the learning content recommendation apparatus of FIG.
FIG. 7 is a diagram schematically illustrating a learning content provided to a user terminal by the learning content recommending apparatus of FIG. 2.
FIG. 8 is a diagram schematically illustrating a detailed configuration of a user terminal of the learning content recommendation system of FIG. 1.
9 is a flowchart illustrating a learning content recommendation method according to an exemplary embodiment.

Advantages and features of the present invention, and methods of achieving them will be apparent with reference to the embodiments described in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments set forth below, but may be embodied in many different forms and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. . The embodiments set forth below are provided to make the disclosure of the present invention complete, and to fully inform the scope of the invention to those skilled in the art. In the following description of the present invention, if it is determined that the detailed description of the related known technology may obscure the gist of the present invention, the detailed description thereof will be omitted.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "comprise" or "have" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof. Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

Throughout the specification, the term "user" may include one or more members who access the learning content recommendation application or the learning content recommendation site and receive the learning content service through an authentication process.

Throughout the specification, the term "user terminal (200 in FIG. 1)" refers to a desktop computer (201 in FIG. 1), a smartphone (202 in FIG. 1), a notebook (203 in FIG. 1), a tablet PC operated by a user. Smart TVs, mobile phones, personal digital assistants, laptops, media players, micro servers, global positioning system (GPS) devices, e-book readers, digital broadcasting terminals, navigation, kiosks, MP3 players, digital cameras, home appliances and Other mobile or non-mobile computing devices, but is not limited thereto. In addition, the user terminal 200 may be a wearable terminal such as a watch, glasses, a hair band, and a ring having a communication function and a data processing function. The user terminal 200 is not limited to the above description, and a terminal capable of web browsing may be borrowed without limitation.

Throughout the specification, "notification information" is unique to a user who can notify a user to another person, which is created by a user (member) accessing a learning content recommendation application or a learning content recommendation site by operating the user terminal 200. It can contain one piece of information. Such notification information may include first notification information to fourth notification information.

Throughout the specification, "first notification information" may include business card information handed to notify a person who has met for the first time. Such business card information may include name, company name, company address, job title, contact information (e-mail address, telephone number, etc.). General business card information is limited to office workers, but even a general person (for example, a student) who does not attend a company may have a business card including information representing himself.

Throughout the specification, the term "second notification information" includes resume information written to show the applicant's personal information, education, career, qualifications, education, other foreign languages, and computer skills for employment purposes. can do. If you are a man, you can list your medical history. Resumes can be written relatively honestly and sincerely, as the person in charge of the company can be used to determine the applicant's capabilities, abilities, and attitudes.

Throughout the specification, "third notification information" is intended to provide detailed information to others by listing their growth environment or advantages for the purpose of introducing themselves, together with or apart from the second notification information. You can include your cover letter information. Such self-introduction information may be prepared and submitted when joining a specific group, finding a job, or selecting a freshman.

Throughout the specification, "fourth notification information" may include SNS (eg, Facebook, Twitter, KakaoTalk, etc.) profile information, which is an online service that builds a network of people who share a particular interest or activity. Can be. The SNS information may include information such as an image of the user, an interest, a contact, a work place, and a place of residence.

The first to fourth notification information may be in the form of a post recorded on a paper off-line, in the form of a file stored in the user terminal 200, or may be link information included in a web page. It may be in the form of a file, the result of obtaining the first notification information to the fourth notification information may include text information as a text recognition result for the first notification information to the fourth notification information.

The term "property information" in the entire specification may include information indicating the nature of the first notification information to the fourth notification information when recommending the learning content. In the present embodiment, the attribute information on the first notification information may include at least one of an industry group, a job name, and a job title. An industry is an economically active organization that produces goods or services. It can classify industries according to the kind of goods they produce. There are many industries in the industry. Each of these sectors has industry specific characteristics. In Korea, the industry is classified into 20 categories. In the industrial classification of the Ministry of Commerce and Industry, it is divided into major, middle, and small categories. In general, what we refer to as an industry category refers to the middle classification, which is dozens of species. Each major class of industry can be subdivided into multiple middle class industries. For example, the industrial group may be classified into 26 major category industrial groups, and again, the major category industrial groups may be classified into a total of 98 mid-class industrial groups. The job name may include a department name as a group to which the user belongs among the various groups that are separated from the organization, company, organization, etc., for example, the management department, sales department, human resources department, quality department, purchasing department, accounting department, etc. It may include. Job titles are job classifications of jobs with similar job types, difficulty levels, levels of authority, roles, and responsibilities, and are hierarchically organized by dividing members of the organization into appropriate levels for personnel management and manpower operation. It can include the arrangement.

In the present embodiment, the attribute information of the second notification information may include at least one of a graduate school, education, career, certificate name, book, and thesis name. In the present embodiment, the attribute information of the third notification information may include one or more of a growth environment, personality advantages and disadvantages, hobbies, related activity experiences, support motivation, or aspirations. In the present embodiment, the attribute information of the fourth notification information may include one or more of community name, area of interest, place of residence, and place of work.

Throughout the specification, "content" or "content" shall include the concept of collectively referring to digital information or individual information elements consisting of letters, signs, voices, sounds, sound sources, images, video, etc., accessible through a communication network. Can be. Such content may include, for example, data such as text, images, videos, sound sources, links (eg, web links), or a combination of at least two of these data. In the present embodiment, the content may include learning content that allows the user to receive knowledge using the content.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings, and in the following description with reference to the accompanying drawings, the same or corresponding components will be given the same reference numerals and redundant description thereof will be omitted. Let's do it.

FIG. 1 is a diagram schematically illustrating a learning content recommendation system according to an exemplary embodiment. Referring to FIG. 1, the learning content recommendation system 1 may include a learning content recommendation apparatus 100, a user terminal 200, and a communication network 300.

The learning content recommendation apparatus 100 may provide learning contents suitable for a user based on the notification information obtained from the user.

The learning content recommendation apparatus 100 generates unique information (for example, a member number) of a user who has registered as a member through the user content 200 for accessing the learning content recommendation application and / or learning content recommendation site. The unique information may be stored in the database 150 of FIG. 2.

The learning content recommendation apparatus 100 may obtain one or more of the first notification information and the fourth notification information of the user through the user terminal 200 that has completed the authentication of the learning content recommendation application and / or the learning content recommendation site. .

The learning content recommendation apparatus 100 extracts one or more metadata including predetermined attribute information from one or more of the first notification information and the fourth notification information, and searches for one or more learning contents that match the metadata. Thus, one or more learning contents corresponding to the search result may be provided to the user.

The learning content recommendation apparatus 100 may learn the number of times of reproduction of the learning content and the order of providing the learning content matching the metadata and reflect the learning content in the next learning content.

The user terminal 200 may execute and / or display a learning content recommendation application and / or a learning content recommendation site provided by the learning content recommending apparatus 100, and access identification information of the user through the user terminal 200 ( The learning content recommendation device 100 having received the ID) and the password may perform user authentication for the learning content recommendation application and / or the learning content recommendation site. The user terminal 200 transmits one or more of the first notification information to the fourth notification information to the learning content recommendation device 100, receives the user-tailored learning content from the learning content recommendation device 100, and displays and / or plays the information. Can be.

The communication network 300 connects the user terminal 200 with the learning content recommendation apparatus 100. That is, the communication network 300 may refer to a communication network that provides an access path so that the user terminal 200 may transmit and receive predetermined information after connecting to the learning content recommending apparatus 100. The communication network 300 may be, for example, wired networks such as local area networks (LANs), wide area networks (WANs), metropolitan area networks (MANs), integrated service digital networks (ISDNs), wireless LANs, CDMA, Bluetooth, satellite communications, and the like. Although it may encompass a wireless network, the scope of the present invention is not limited thereto.

FIG. 2 is a diagram schematically illustrating a detailed configuration of the learning content recommending apparatus 100 of the learning content recommending system 1 of FIG. 1. Referring to FIG. 2, the learning content recommendation apparatus 100 may include a communication unit 110, a storage medium 120, a program storage unit 130, a control unit 140, a database 150, and a learning content recommendation unit 160. It may include.

The communication unit 110 may provide a communication interface necessary to provide a transmission / reception signal between the learning content recommendation apparatus 100 and the user terminal 200 in the form of packet data in cooperation with the communication network 300. Furthermore, the communication unit 110 may serve to receive a predetermined information request signal from the user terminal 200, and transmit the information processed by the learning content recommendation unit 160 to the user terminal 200. can do. Herein, the communication network refers to a medium that connects the learning content recommending apparatus 100 and the user terminal 200 and transmits and receives information after the user terminal 200 accesses the learning content recommending apparatus 100. It may include a path that provides a connection path to enable. In addition, the communication unit 110 may be a device including hardware and software necessary for transmitting and receiving a signal such as a control signal or a data signal through a wired or wireless connection with another network device.

The storage medium 120 performs a function of temporarily or permanently storing data processed by the controller 140. Here, the storage medium 120 may include a magnetic storage media or a flash storage media, but the scope of the present invention is not limited thereto. The storage medium 120 may include internal memory and / or external memory, and may include volatile memory such as DRAM, SRAM, or SDRAM, one time programmable ROM (OTPROM), PROM, EPROM, EEPROM, mask ROM, flash ROM, and the like. SSD, non-volatile memory, such as NAND flash memory, or NOR flash memory. It may include a flash drive such as a compact flash (CF) card, an SD card, a Micro-SD card, a Mini-SD card, an Xd card, or a memory stick, or a storage device such as an HDD.

The program storage unit 130 obtains at least one of the first notification information and the fourth notification information from the user terminal 200 and at least one of the obtained first notification information and the fourth notification information. Converting to at least one of the fourth text information, extracting at least one metadata including predetermined attribute information from at least one of the first to fourth text information, at least one learning to match the metadata Searching for content, providing one or more learning contents corresponding to the search result to the user terminal 200, clustering other users having a pattern similar to the metadata, matching the metadata of other users, Collecting one or more learning content histories, with the most playbacks from the learning content history Sorting the learning content in order and selecting a predetermined number of learning contents ranked at the top as the search results, learning the number of times of playing content matching the metadata, and providing a learning order to reflect the next learning content. Playing the learning content recommended by the user's content first play request signal, stopping the playing of the learning content being played by the user's content stop request signal, storing the content play time, and the user Playing after the learning content playback stop time by the content second playback request signal of the user, changing the playback speed of the learning content by the user's learning content playback speed conversion request signal, and playing the learning content recommendation application and / or Events and / or Updates on the Learning Content Referral Site The information and with the control software to perform tasks such as sending the user terminal 200.

The controller 140 may control an overall operating state of the learning content recommending apparatus 100. The control unit 140 may include at least one processor (not shown), may be implemented as an array of a plurality of logic gates, a general microprocessor and a memory in which a program that can be executed in the microprocessor is stored. It can also be implemented in combination. In addition, it can be understood by those skilled in the art that the present embodiment may be implemented in other forms of hardware.

The database 150 may include a user database that stores user information. Here, the user information includes basic information about the user such as the user's name, affiliation, personal information, gender, age, contact, email, address, and authentication (login) such as ID (or email) and password (password). It may include information about the connection, such as information about the country of connection, the location of the connection, the information on the device used for the connection, the connected network environment.

In addition, the user database includes user's unique information, information provided by the user who accesses the learning content recommendation application and / or learning content recommendation site, and / or history of receiving the learning content, environment setting information set by the user, and resource usage information used by the user. The billing and payment information corresponding to the resource usage of the user may be stored.

In addition, the database 150 may include an attribute information database that stores attribute information. The attribute information database includes one or more attribute information as information representing the nature of the first notification information through the fourth notification information or the first text information through the fourth text information when recommending the learning content, and one or more attribute words located under the attribution information. It may include.

The attribute information included in the first notification information may include at least one of an industry group, a job name, and a position as described above, and the attribute word included in the attribute information corresponding to the industry group may include, for example, a car, food and beverage, and clothing. , Steel, computer semiconductor, and the like, and the attribute word included in the attribute information corresponding to the job name may include, for example, management, sales, human resources, quality, purchasing, accounting, and the like. The attribute word included in the attribute information corresponding to may include, for example, an employee, agent / appointment, manager / responsibility, manager, director, and the like.

As described above, the attribution information included in the second notification information may include at least one of a graduation course, education, career, certificate name, book, and thesis name, and the attribute word included in the attribution information corresponding to the graduation course is For example, the Korean language, electronic engineering, business administration, and the like, and the attribute words included in the attribution information corresponding to the academic background may include doctoral, doctoral, master's, master's, bachelor, bachelor's degree, high school, etc. In addition, the attribute information corresponding to the career may include one or more of industrial classification, years of service, working department, and position as sub-attribute information, and the sub-attribute information may include one or more attribute words associated with it. . In addition, the attribute word included in the attribute information corresponding to the name of the certificate may include, for example, a driver's license, a professional engineer, a patent attorney, and the attribute information corresponding to the name of a book and a thesis includes one or more attribute words associated with it. It may include.

In addition, one or more attribute information included in the third notification information and / or the fourth notification information and one or more attribute words positioned under the attribute information may be inferred from the first notification information and the second notification information. Therefore, detailed description will be omitted.

In addition, the database 150 may include a content database that stores the learning content provided by the content management device 100. Here, the content database may store various learning contents collected by attribute information and / or attribute words, and the attribute information and / or attribute words may be stored together with index information.

The learning content recommending unit 160 may include the first notification information through the fourth notification information (or the first text information through the fourth text information) received from the learning content recommendation application and / or the user terminal 200 accessing the learning content recommendation site. ), Extract one or more metadata including predetermined attribute information and / or attribute words from one or more of the first to fourth notification information, and associate with the attribute word that matches the metadata. By searching one or more learning contents, one or more learning contents corresponding to the search result may be provided to the user terminal 200.

FIG. 3 is a diagram schematically illustrating a detailed configuration of the learning content recommending unit 160 of the learning content recommending apparatus 100 of FIG. 2. Referring to FIG. 3, the learning content recommender 160 may include an acquirer 161, an extractor 162, a searcher 163, a provider 164, and a learner 165.

The obtaining unit 161 may obtain one or more of the first notification information and the fourth notification information from the user terminal 200. 5 and 6 illustrate a method in which the user terminal 200 obtains the first notification information to the fourth notification information. Referring to FIG. 5, a user photographs an offline document in which a corresponding content is started using the user terminal 200, generates one or more of the first notification information and the fourth notification information, and transmits the generated content to the learning content recommendation device 100. Thus, the obtaining unit 161 may obtain one or more of the first notification information to the fourth notification information. Referring to FIG. 6A, the user transmits one or more of the first to fourth notification information stored on the user terminal 200 as a video file to the learning content recommending apparatus 100, thereby acquiring the unit 161. May obtain one or more of the first to fourth notification information. Referring to FIG. 6B, the user transmits one or more of the first notification information to the fourth notification information stored on the user terminal 200 as a voice file to the learning content recommending apparatus 100, thereby obtaining the acquisition unit 161. May obtain one or more of the first to fourth notification information. Furthermore, the user captures one or more of the first to fourth notification information displayed on the user terminal 200 and transmits the captured information to the learning content recommendation apparatus 100, so that the acquiring unit 161 receives the first notification information to the first notification information. 4 One or more of the notification information can be obtained.

In the present embodiment, the acquirer 161 may further include a text converter (not shown). The text converter may perform text recognition on the acquired first to fourth notification information to convert the first to fourth notification information into first to fourth text information. For example, with respect to the notification information acquired as an image file in the form of JPG, the text converter may convert the text information into text information by performing text recognition on the image. In addition, with respect to the notification information acquired as a video file of MPEG or the like, the text converter may convert the text information into text information by performing text recognition on the audio information of the video. The text converter may convert the text information into text information by performing text recognition on the audio information of the voice file.

The extractor 162 first extracts one or more metadata including meaning from one or more of the obtained first notification information to fourth notification information (or first text information to fourth text information), and extracts the extracted metadata. One or more metadata including predetermined attribute information and / or attribute words may be extracted secondarily from the data. The first to fourth notification information may be composed of metadata not including attribute information and metadata including attribute information. Accordingly, the extractor 162 may first extract metadata and secondarily extract metadata including or similar to attribute information and / or attribute words from the extracted metadata. For example, metadata corresponding to attribute information including at least one of an industry group, job name, and position and attribute words included in the attribute information from the first notification information, for example, XX patent corporation as an industry group as a job name. The XX electronic team can be extracted as secondary metadata as position manager. In addition, metadata corresponding to attribute information including at least one of a graduate course, education, career, certificate name, book, and thesis name from the second notification information, and an attribute word included in the attribute information, for example, the Graduate School of Electronic Engineering The XX thesis can be extracted as secondary metadata, the master's as an academic background, a new student as a career, a driver's license as a license name, a book and a paper name.

The searcher 163 may search for one or more learning contents that match the secondary metadata from the database 150. In this case, since the attribute information and / or the attribute word are stored together with the index information and the secondary metadata includes the attribute information and / or the attribute word, the at least one learning content matching the secondary metadata is searched. can do.

In the present embodiment, the search unit 163 may perform the first search and the second search. The primary search of the searcher 163 may include one or more learning content searches that match secondary metadata from the database 150 as described above. The second search of the search unit 163 may include searching the learning content that has been learned by other users among the one or more learning contents searched by the first search result, that is, the learning content having a high learning history as a priority. have.

4 is a diagram schematically illustrating another embodiment of the search unit 163. Referring to FIG. 4, the search unit 163 may include a cluster unit 163-1, a collector 163-2, and a selector 163-3. The database 150 stores the history of learning contents received by accessing the learning content recommending apparatus 100 for each user. The cluster unit 163-1 may cluster other users having a pattern similar to the secondary metadata. The collector 163-2 may collect one or more learning content histories provided by matching the metadata of other users. The selector 163-3 may sort the learning content in the order of the highest number of reproductions from the learning content history, and select a predetermined number of learning contents ranked at the top as a search result.

The provider 164 may provide the user terminal 200 with one or more learning contents corresponding to the search result of the searcher 163. The user terminal 200 may view the learning content provided by the providing unit 164 and may select and play one or more learning contents. FIG. 7 illustrates learning content provided by the provider 164 to the user terminal 200. Referring to FIG. 7, on the user terminal 200, user-tailored learning contents organized by priority along with attribute information to which the user belongs are illustrated. Each learning content may include content information including a lecturer's name, playing time, and learning replay count.

In some embodiments, the provider 164 may provide and play the learning content recommended by the learning content first playback request signal input from the user through the user terminal 200. When the user receives the content reproduction stop request signal during the reproduction of the learning content, the provider 164 may stop the reproduction of the learning content that is being played and store the reproduction priority time. Thereafter, when the user receives the second content request signal for learning content, the provider 164 may resume playback after the learning content time limit. As another example, the provider 164 may change and reproduce the playback speed of the learning content according to the learning content playback speed conversion request signal of the user. As an optional embodiment, the providing unit 164 may transmit the event and / or update information of the learning content recommendation application and / or the learning content recommendation site through a contact point of the user included in at least one of the first notification information and the fourth notification information. 200 may be provided.

The learner 165 learns the number of times of reproduction and the order of providing the learning content matching the metadata and reflects the next learning content. Here, the learning of the learning unit 165 may be performed by artificial intelligence technology. Artificial intelligence (AI) technology is a computer processing technology that realizes human-level intelligence, and unlike conventional rule-based smart technology, the machine can learn and judge itself and become smart. As the AI technology is used, the recognition rate is improved and the user taste can be understood more accurately, and the existing Rule-based smart system can be gradually replaced by the deep learning-based AI system.

Artificial intelligence technology may be composed of elementary technologies utilizing machine learning (deep learning) and machine learning. Machine learning is an algorithm technology that classifies / learns characteristics of input data by itself, and element technology is a technology that simulates the functions of human brain cognition and judgment by using machine learning algorithms such as deep learning. It may consist of technical fields such as understanding, reasoning / prediction, knowledge representation, motion control, and the like.

The various fields in which artificial intelligence technology is applied are as follows. Linguistic understanding is a technology for recognizing and applying / processing a human language / character, and may include natural language processing, machine translation, dialogue system, question and answer, speech recognition / synthesis, and the like. Visual understanding is a technology of recognizing and processing objects as human vision, and may include object identification, object tracking, image search, person recognition, scene understanding, spatial understanding, image enhancement, and the like. Inference prediction is a technique of determining, logically inferring, and predicting information, and may include knowledge / probability based inference, optimization prediction, preference based planning, and recommendation. Knowledge expression is a technology for automatically processing human experience information into knowledge data, and may include knowledge construction (data generation / classification), knowledge management (data utilization), and the like. Motion control is a technology for controlling autonomous driving of a vehicle and movement of a robot, and may include motion control (navigation, collision, driving), operation control (action control), and the like.

FIG. 8 is a diagram schematically illustrating a detailed configuration of a user terminal of the learning content recommendation system of FIG. 1. Referring to FIG. 8, the user terminal 200 may include a communication unit 210, a memory 220, an input / output unit 230, a program storage unit 240, a control unit 250, and a display unit 260.

The communication unit 210 may be a device including hardware and software necessary for transmitting and receiving a signal such as a control signal or a data signal through a wired or wireless connection with another network device such as the learning content recommending device 100. For example, the communication unit 210 may include a short range communication unit or a mobile communication unit. The short-range wireless communication unit includes a Bluetooth communication unit, a BLE (Bluetooth Low Energy) communication unit, a near field communication unit (Near Field Communication unit), a WLAN (Wi-Fi) communication unit, a Zigbee communication unit, an infrared ray (IrDA) Data Association (W Association) communication unit, WFD (Wi-Fi Direct) communication unit, UWB (ultra wideband) communication unit, Ant + communication unit and the like, but is not limited thereto. The mobile communication unit transmits and receives a radio signal with at least one of a base station, an external terminal, and a server on a mobile communication network. Here, the wireless signal may include various types of data according to transmission and reception of a voice call signal, a video call call signal, or a text / multimedia message.

The memory 220 may temporarily or permanently store data processed by the controller 250 or temporarily or permanently store data related to the learning content transmitted to the user terminal 200. Here, the memory 220 may include a magnetic storage media or a flash storage media, but the scope of the present invention is not limited thereto.

The input / output unit 230 may be configured as a touch-sensitive display controller or various input / output controllers. As an example, the touch-sensitive display controller may provide an output interface and an input interface between the device and the user. The touch-sensitive display controller may transmit and receive an electrical signal with the controller 250. In addition, the touch-sensitive display controller displays visual output to the user, and the visual output may include text, graphics, images, video, and combinations thereof. The input / output unit 230 may be, for example, a predetermined display member such as an organic light emitting display (OLED) or a liquid crystal display (LCD) capable of touch recognition.

The program storage unit 240 accesses the learning content recommendation device 100 to generate and transmit one or more of the first notification information to the fourth notification information, and the learning received from the learning content recommendation device 100 by request. It is possible to mount control software for performing the operation of playing / stopping / resuming playback of content.

The control unit 250 is a kind of central processing unit that provides various functions such as driving control software mounted in the program storage unit 240 and controlling the display unit 260 to display predetermined information on the user terminal 200. can do. Here, the controller 250 may include all kinds of devices capable of processing data, such as a processor. Here, the 'processor' may refer to a data processing apparatus embedded in hardware having, for example, a circuit physically structured to perform a function represented by code or instructions included in a program. As an example of a data processing device embedded in hardware, a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, and an application-specific integrated device (ASIC) It may include a processing device such as a circuit, a field programmable gate array (FPGA), etc., but the scope of the present invention is not limited thereto.

The display unit 260 may display various information received from the learning content recommending apparatus 100 under the control of the controller 250.

9 is a flowchart illustrating a learning content recommendation method according to an embodiment of the present invention. In the following description, portions overlapping with the description of FIGS. 1 to 8 will be omitted.

Referring to FIG. 9, in operation S910, the apparatus for recommending learning content 100 is unique information about a user as information unique to a user who may notify a user from another user terminal 200. 4 Acquire one or more of the notification information. In the present embodiment, the learning content recommendation apparatus 100 performs text recognition on the acquired first notification information to fourth notification information and converts the first notification information to the fourth notification information into the first text information to the fourth text information. Can be converted.

In operation S920, the learning content recommendation apparatus 100 extracts one or more metadata including predetermined attribute information from the first notification information or the fourth notification information. The learning content recommendation apparatus 100 first extracts one or more metadata including a meaning from one or more of the obtained first notification information or fourth notification information, and sets predetermined attribute information and / or attributes among the extracted metadata. One or more metadata containing words can be extracted secondarily.

In operation S920, the learning content recommendation apparatus 100 searches for one or more learning contents that match the metadata. In this case, since the attribute information and / or the attribute word are stored together with the index information and the secondary metadata includes the attribute information and / or the attribute word, the at least one learning content matching the secondary metadata is searched. can do. In the present embodiment, the learning content recommendation apparatus 100 may perform a first search and a second search. The primary search of the learning content recommendation apparatus 100 may include one or more learning content searches matching the secondary metadata from the database 150 as described above. The secondary search of the learning content recommendation apparatus 100 may include, among the one or more learning contents searched by the first search result, a learning content learned by other users, that is, a learning content having a high learning history as a priority. can do. In an exemplary embodiment, the learning content recommendation apparatus 100 clusters other users having a pattern similar to metadata, collects one or more learning content histories provided by matching the metadata of other users, and plays them from the learning content history. The learning contents may be sorted in the order of the highest number of times, and a predetermined number of learning contents ranked at the top may be selected as a search result.

In operation S940, one or more learning contents corresponding to the search result are provided to the user terminal 200. The user terminal 200 may view the learning content provided by the providing unit 164 and may select and play one or more learning contents. Optionally, the learning content recommendation apparatus 100 may provide and play the learning content recommended by the learning content first reproduction request signal input from the user through the user terminal 200. When the learning content recommendation device 100 receives a content reproduction stop request signal of the user during reproduction of the learning content, the learning content recommendation apparatus 100 may stop the reproduction of the learning content being played and store the reproduction focus time. When the learning content recommendation device 100 receives the user's second learning content playback signal, the learning content recommendation apparatus 100 may resume playback after the learning content playback stop time. As another embodiment, the learning content recommendation apparatus 100 may change the playback speed of the learning content by playing the learning content playback speed conversion request signal. In some embodiments, the learning content recommendation apparatus 100 may transmit the event and / or update information of the learning content recommendation application and / or the learning content recommendation site through a contact of the user included in at least one of the first notification information and the fourth notification information. It may be provided to the user terminal 200. In addition, the learning content recommendation apparatus 100 may learn the number of times of reproduction and the order of providing the learning content matching the metadata and reflect the next learning content.

Embodiments according to the present invention described above may be implemented in the form of a computer program that can be executed through various components on a computer, such a computer program may be recorded in a computer-readable medium. At this time, the media may be magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and ROMs. Hardware devices specifically configured to store and execute program instructions, such as memory, RAM, flash memory, and the like.

On the other hand, the computer program may be specially designed and configured for the present invention, or may be known and available to those skilled in the computer software field. Examples of computer programs may include not only machine code generated by a compiler, but also high-level language code executable by a computer using an interpreter or the like.

In the specification (particularly in the claims) of the present invention, the use of the term “above” and the similar indicating term may be used in the singular and the plural. In addition, in the present invention, when the range is described, it includes the invention to which the individual values belonging to the range are applied (if not stated to the contrary), and each individual value constituting the range is described in the detailed description of the invention. Same as

If the steps constituting the method according to the invention are not explicitly stated or contrary to the steps, the steps may be performed in a suitable order. The present invention is not necessarily limited to the description order of the above steps. The use of all examples or exemplary terms (eg, etc.) in the present invention is merely for the purpose of describing the present invention in detail, and the scope of the present invention is limited by the above examples or exemplary terms unless defined by the claims. It doesn't happen. In addition, one of ordinary skill in the art appreciates that various modifications, combinations and changes can be made depending on design conditions and factors within the scope of the appended claims or equivalents thereof.

Therefore, the spirit of the present invention should not be limited to the above-described embodiment, and all the scope equivalent to or equivalent to the scope of the claims as well as the claims to be described below are within the scope of the spirit of the present invention. Will belong to.

100: learning content recommendation device
200: user terminal
300: network

Claims (4)

An acquisition unit for acquiring business card information handed out to notify another user of his / her identity;
From the business card information, one of the 26 major classification industry groups classified by the Ministry of Commerce and Industry and the industrial group information of the major classification industry groups classified into the 98 middle classification industry groups, and a plurality of businessly separated from the organizations, companies and organizations to which the user belongs. The job names including the department name as the group to which the user belongs, and the job types, the difficulty, the authority and the degree of role responsibility are grouped together in a group, and the members of the organization are prescribed for personnel management and manpower operation. Extracting one or more primary metadata including meanings from attribute information including ranks divided into classes and arranged in a hierarchical system, wherein the industry group, the job name, An extraction unit for extracting secondary metadata including an attribute word including the rank;
Receives the attribute information including the industry group, the job name, the position, the company address, the attribute word located below the attribute information, the learning content stored together with the attribute word as index information, and learning content for each user. Primary search for one or more learning content indexed to the attribute word included in the secondary metadata from a database in which a history is stored, and another pattern having a pattern similar to the secondary metadata from the results of the primary search Grouping users, collecting one or more learning content histories provided by matching the secondary metadata, arranging the learning contents from the learning content history in order of the highest number of replays, and arranging a predetermined number of learnings ranked at the top; A search unit which performs a second search for content; And
A providing unit for providing the user with at least one learning content including a learning content name, an instructor name, a playing time, and a learning count, together with the attribute information to which the user belongs, corresponding to a result of the secondary search of the searching unit; Including,
The obtaining unit,
Obtains one of the business card information including a voice file, the business card information including a video file, and an image card information of a business card taken offline, and performs text conversion from the business card information including the voice file; A text converting unit for converting text from audio information of the card information including a video file and generating text for extracting the metadata by performing text conversion from the image card information of the photographed business card on the offline; Including, learning content recommendation device. The method of claim 1,
And a learning unit that learns the number of times of reproduction of the learning content and the order of providing the matching content and reflects the next learning content. Acquiring, by the acquiring unit, business card information to be handed out to notify another of the user's personality;
From the business card information, the extracting unit is configured to perform business in one of the industrial group information of 26 large classification industrial groups classified by the Ministry of Commerce and Industry and the industrial group information which classifies the large classification industrial group into 98 middle classification industrial groups, and the institutions, companies, and organizations to which the user belongs. Organized for personnel management and manpower operation by grouping job titles including the department name as the group to which the user belongs, and similar job titles with similar types of job, difficulty, authority and role responsibilities Extracting one or more primary metadata having meanings from attribute information including ranks divided into predetermined grades and arranged in a hierarchical system, and extracting one or more primary metadata having a meaning from the primary metadata; Extracts secondary metadata including the job name and the attribute word including the job title. Doing;
The search unit receives the industry group, the job name, the attribute information including the position, the attribute word located below the attribute information, the learning content in which the attribute word is stored as index information, and the learning content for each user. Primary search for one or more learning content indexed to the attribute word included in the secondary metadata from a database in which a history is stored, and another pattern having a pattern similar to the secondary metadata from the results of the primary search Grouping users, collecting one or more learning content histories provided by matching the secondary metadata, arranging the learning contents from the learning content history in order of the highest number of replays, and arranging a predetermined number of learnings ranked at the top; Secondary searching for content; And
Providing, by the providing unit, one or more learning contents including a learning content name, a lecturer name, a playing time, and a learning count, together with the attribute information to which the user belongs, in response to a result of the secondary search of the searching unit. Comprising;
The obtaining step,
A text conversion unit acquires one of the business card information including a voice file, the business card information including a video file, and the image business card information of a business card taken off-line and obtains text from the business card information including the voice file. Performing a conversion, performing a text conversion from the audio information of the business card information including the video file, and performing a text conversion from the image business card information of the image of the offline business card to generate text for the metadata extraction. Steps further comprising; learning content recommendation method. The method of claim 3, wherein
And learning by the learner to reflect the number of times of play and the order of providing the learning content that matches the metadata and to reflect the next learning content.

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