KR102219251B1 - Server providing expert recommend platform and operation method thereof - Google Patents

Abstract

The present invention relates to a server for providing an expert recommendation platform and an operation method thereof. According to one embodiment of the present invention, a method for providing an expert recommendation service comprises the steps of: executing, by a user terminal, a search service application; checking, by the user terminal, a search keyword inputted through the search service application; and outputting at least one piece of expert information corresponding to the search keyword. The expert information may include information on at least one of a specialized field, an expert, and a specialized company corresponding to the keyword.

Description

Server providing expert recommendation platform and its operation method {SERVER PROVIDING EXPERT RECOMMEND PLATFORM AND OPERATION METHOD THEREOF}

The present invention relates to a server providing an expert recommendation platform and a method of operating the same.

Korean Patent Publication No. 10-2019-0112334 relates to an expert recommendation method and expert recommendation system for providing a query response service based on a social network system.

In response to a request for a query from a first user terminal using a social network system, the document includes: extracting a query keyword from the query; Searching for a second user terminal that has registered a field of interest associated with the query keyword from among user terminals using the social network system; Configuring the second user terminal into an expert group capable of answering the query; And outputting an expert recommendation result according to the inquiry request to the first user terminal, including the expert group, to provide a query response service based on a social network system.

Accordingly, the present invention is to propose a server that provides an improved expert recommendation platform and a method of operating the same.

Unexamined Patent Publication No. 10-2019-0112334

An object of the present invention is to provide a server that provides an expert recommendation platform and a method of operating the same.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned will be clearly understood by those of ordinary skill in the technical field to which the present invention belongs from the following description. I will be able to.

According to an embodiment of the present invention, there is provided a method for providing an expert recommendation service, comprising: executing, by a user terminal, a search service app; Checking, by the user terminal, a search keyword input through a search service app; And outputting at least one expert information corresponding to the search keyword. Including, the expert information may include information on at least one of a specialized field corresponding to the keyword, an expert, and a specialized company.

The outputting comprises outputting the at least one expert information through a search service page of the search service app, wherein each of the at least one expert information is a full text corresponding to each of the at least one expert information. A professional mark object representing at least one of a field and a technical field may be further included.

The search keyword includes a first search keyword and a second search keyword, and the at least one expert information includes first expert information corresponding to the first search keyword and second expert information corresponding to the second search keyword. Can include.

The search keyword includes a first search keyword and a second search keyword, and the at least one expert information includes a first specialized field corresponding to the first search keyword and a second specialized field corresponding to the second search keyword. It may include third-party expert information about the experts serving all.

The method includes: learning an artificial neural network using the search keyword inputted through the search service app as learning data; Generating combined service recommendation information that recommends serving a plurality of specialized fields based on the learned artificial neural network; And transmitting the combined service recommendation information to a service providing terminal of a plurality of unspecified service providers providing services through the search service app. It may further include.

The method includes: checking information indicating a combined service uploaded by a specific service provider from among a plurality of unspecified service providers that have received the combined service recommendation information; Generating a reward corresponding to information indicating the combined service; And transmitting the generated reward to the specific service provider. It may further include.

In addition, an embodiment of the present invention is a server operating a search service app that provides an expert recommendation service, receiving a search keyword input through the search service app, and receiving at least one expert information corresponding to the search keyword. A control module to generate; Including, the expert information may include information on at least one of a specialized field corresponding to the keyword, an expert, and a specialized company.

The control module is characterized in that generating the at least one expert information through a search service page of the search service app, wherein each of the at least one expert information is a specialized field corresponding to each of the at least one expert information And it may further include a professional mark object representing at least one of the technical field (professional mark object).

The search keyword includes a first search keyword and a second search keyword, and the at least one expert information includes first expert information corresponding to the first search keyword and second expert information corresponding to the second search keyword. Or, the at least one expert information may include third expert information on experts who service both a first specialized field corresponding to the first search keyword and a second specialized field corresponding to the second search keyword. I can.

The control module is a combined service that trains an artificial neural network using the search keyword input through the search service app as learning data, and recommends serving a plurality of specialized fields based on the learned artificial neural network. It may be characterized by generating recommendation information.

The server includes: a communication module for transmitting the combined service recommendation information to service providing terminals of a plurality of unspecified service providers providing services through the search service app; It may further include.

The control module is characterized in that, among a plurality of unspecified service providers that have received the combined service recommendation information, check information indicating a combined service uploaded by a specific service provider, and generate a reward corresponding to the information indicating the combined service. I can.

As described above, an embodiment of the present invention has a technical effect in terms of proposing a server that provides an enhanced and smart expert recommendation platform and a method of operating the same.

In addition, an embodiment of the present invention has an advantage in that it induces to provide a service for a combined specialized field to service providers providing a service through a search service app.

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

Other aspects, features and benefits as described above of certain preferred embodiments of the present invention will become more apparent from the following description, which is handled in conjunction with the accompanying drawings.
1 is a diagram illustrating an operation example of a search service app according to an embodiment of the present invention.
2 is a diagram illustrating an operation example of a search service app according to another embodiment of the present invention.
3 is a diagram illustrating an expert recommendation system according to an embodiment of the present invention.
4 is a flowchart illustrating a method of providing an expert recommendation service according to an embodiment of the present invention.
5 is a flowchart illustrating a method of providing an expert recommendation service according to another embodiment of the present invention.
6 is a flowchart illustrating a method of providing an expert recommendation service according to another embodiment of the present invention.
7 is a block diagram showing a control module of an expert platform management server according to an embodiment of the present invention.
It should be noted that throughout the drawings, like reference numbers are used to show the same or similar elements, features, and structures.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

In describing the embodiments, descriptions of technical contents that are well known in the technical field to which the present invention pertains and are not directly related to the present invention will be omitted. This is to more clearly convey the gist of the present invention by omitting unnecessary description.

For the same reason, some components in the accompanying drawings are exaggerated, omitted, or schematically illustrated. In addition, the size of each component does not fully reflect the actual size. The same reference numerals are assigned to the same or corresponding components in each drawing.

Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, only the present embodiments are intended to complete the disclosure of the present invention, and the general knowledge in the technical field to which the present invention pertains. It is provided to completely inform the scope of the invention to those who have it, and the invention is only defined by the scope of the claims. The same reference numerals refer to the same components throughout the specification.

In this case, it will be appreciated that each block of the flowchart diagrams and combinations of the flowchart diagrams may be executed by computer program instructions. Since these computer program instructions can be mounted on the processor of a general purpose computer, special purpose computer or other programmable data processing equipment, the instructions executed by the processor of the computer or other programmable data processing equipment are described in the flowchart block(s). It creates a means to perform functions. These computer program instructions can also be stored in computer-usable or computer-readable memory that can be directed to a computer or other programmable data processing equipment to implement a function in a particular way, so that the computer-usable or computer-readable memory It is also possible to produce an article of manufacture containing instruction means for performing the functions described in the flowchart block(s). Computer program instructions can also be mounted on a computer or other programmable data processing equipment, so that a series of operating steps are performed on a computer or other programmable data processing equipment to create a computer-executable process to create a computer or other programmable data processing equipment. It is also possible for instructions to perform processing equipment to provide steps for executing the functions described in the flowchart block(s).

In addition, each block may represent a module, segment, or part of code that contains one or more executable instructions for executing the specified logical function(s). In addition, it should be noted that in some alternative execution examples, functions mentioned in blocks may occur out of order. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order depending on the corresponding function.

In this case, the term'~ unit' used in this embodiment refers to software or hardware components such as field-programmable gate array (FPGA) or application specific integrated circuit (ASIC), and'~ unit' is a certain role. Perform them. However,'~ part' is not limited to software or hardware. The'~ unit' may be configured to be in an addressable storage medium, or may be configured to reproduce one or more processors. Thus, as an example,'~ unit' refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, and procedures. , Subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays, and variables. The components and functions provided in the'~ units' may be combined into a smaller number of elements and'~ units', or may be further divided into additional elements and'~ units'. In addition, components and'~ units' may be implemented to play one or more CPUs in a device or a security multimedia card.

In describing the embodiments of the present invention in detail, examples of specific systems will be the main target, but the main subject matter to be claimed in this specification is the scope disclosed in the present specification to other communication systems and services having a similar technical background. It can be applied within a range that does not deviate greatly, and this will be possible at the judgment of a person skilled in the art.

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1 is a diagram illustrating an operation example of a search service app according to an embodiment of the present invention.

Referring to FIG. 1, the search service app of the present invention may output a screen, a web page, etc., as indicated by reference numeral 100 through a display of a user's terminal. For convenience of description, reference numeral 100 will be referred to as a search service page 100 hereinafter.

For example, the search service page 100 may include a keyword input window 110, a search button 120, a search result display window 130, and/or an expert information display window 140.

The search result display window 130 may display a search result corresponding to a search keyword input through the keyword input window 110. The search results here can be search results provided by general search sites, portal sites, etc. (e.g. websites, web page links, images, maps, blogs, shopping, language dictionaries, knowledge encyclopedias, posts, cafes, academic information, etc.) have.

The expert information display window 140 may display expert information corresponding to a search keyword input through the keyword input window 110.

Expert information includes, for example, photographs, images, certifications held, certifications held, careers and/or trademarks, trade names, and/or professional offices corresponding to the search keyword. Appearance, membership, location, contact information and/or information about professionals, advertisements of offices, events and/or specialized academies and/or specialized sites and/or professional certifications.

For example, when the search keyword is'invention', the specialized field included in the expert information may be'patent', the expert included in the expert information is'patent attorney', and the specialized office is'patent law firm' ','patent corporation', and the like, and the specialized site may be'patent search site','keypris (http://www.kipris.or.kr/)', and the like.

As an example, the specialized field corresponding to the search keyword may be determined as a field included with the search keyword in data recorded in a server operating a social network service (by a control module of the server).

For example, the specialized field may be related to at least one of financial consulting, tax, accounting, management, legal affairs, personnel, law, appraisal, economy, and entrepreneurship. As another example, the specialized field may be related to at least one of technology, counseling, medical care, training, fitness, massage, sports, lectures, and travel guides.

As an example, the expert is at least one of a doctorate in economics, a lawyer, a certified public accountant, a patent attorney, a customs officer, a tax accountant, a legal officer, an appraiser, a certified labor agent, an enterprise valuation agent, a certified broker, a translator, Could be a consultant.

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2 is a diagram illustrating an operation example of a search service app according to another embodiment of the present invention.

FIG. 2 can be seen as describing some modified embodiments from the example described with reference to FIG. 1.

For example, the search service page 100 may include a keyword input window 110, a search button 120, a first expert information display window 150, and/or a second expert information display window 160. have. In other words, unlike the case of FIG. 1, the search result display window 130 may be excluded.

For example, when a keyword is input into the keyword input window 110, the first expert information display window 150 and the second expert information display window 160 correspond to the keyword, but different expert information (for example, different Information about experts and/or different companies (and/or legal entities, offices)).

As another example, when a first keyword and a second keyword are input in the keyword input window 110, the first expert information display window 150 displays first expert information corresponding to the first keyword, and the second expert The information display window 160 may display second expert display information corresponding to the second keyword. For example, when the user enters the keyword'patent authentication' in the keyword input window 110, the server and/or the control module classifies'patent' as a first keyword and'authentication' as a second keyword. , The first expert information includes information on the expert (eg patent attorney) and/or the company (and/or corporation, office) related to the'patent', and the second expert information includes the expert (eg ; Created to include information about ISO (International Organization for Standardization) certification auditor, KS (Korean Industrial Standards) certification auditor, ISMS (Information Security Management System) certification auditor, etc.) and/or company (and/or corporation, office) can do.

Further, referring to FIG. 2, the first expert information display window 150 includes a first expert mark object 170, and the second expert information display window 160 is a second expert mark object 180 ) And a third expert mark object 190. In addition, unlike FIG. 2, the expert information display window may include/display three or more expert mark objects, and unlike this, the expert mark object may not be included/displayed in the expert information display window.

Here, the expert mark objects 170, 180, and 190 may include a mark related to expert information included in the corresponding expert information display window 150, 160. Referring to FIG. 2, for example, the first expert mark object 170 represents'legal advisory', and the second expert mark object 180 represents'specialist and/or professional of maintenance', The first expert mark object 170 may represent a'specialist and/or professional of vehicle'.

In addition, referring to FIG. 2, the first expert information display window 150 may be output in a different appearance and/or design from the second expert information display window 160. For example, in the case of the first expert information display window 150 including a service provider having a record of providing services more than a threshold number of times through the search service app of the present invention, a different appearance and/or design as shown in FIG. 2 Or a screen including a specific event may be displayed.

In addition, a first search keyword, a second search keyword, and a third search keyword may be input in the keyword input window 110, and the machine learning module 740 is configured to provide a first learning mode and a second The artificial intelligence network may be trained with at least one of a first search keyword, a second search keyword, and a third search keyword according to a learning mode, a third learning mode, or a fourth learning mode. For example, the first search keyword, the second search keyword, and the third search keyword may be classified based on spaces within the search keyword. For example, when the search keyword is'automobile invention startup', the first search keyword The first search keyword may be'car and/or vehicle', the second search keyword may be'invention', and the third search keyword may be'foundation and/or venture'.

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3 is a diagram illustrating an expert recommendation system according to an embodiment of the present invention.

3, the present invention proposes an expert recommendation system 300 that controls or monitors the expert recommendation system 300 of FIG. 3, and the expert recommendation system 300 is an expert platform management server 310, a user It includes a terminal 320, and/or an external server 330. Here, the external server 330 may be the expert recommendation system 300 of FIG. 3.

In addition, the expert platform management server 310 may include a first control module 311, a first communication module 312, a first input module 313, and/or a first output module 314, and The terminal 320 may include a second control module 321, a second communication module 322, and a second input module 323 (not shown).

The control modules 311 and 321 may directly/indirectly control the expert platform management server 310 and/or the user terminal 320 so as to implement the operation/step/process according to an embodiment of the present invention. In addition, the control modules 311 and 321 may include at least one processor, and the processor may include at least one central processing unit (CPU) and/or at least one graphic processing device (GPU).

The communication modules 312 and 322 may transmit and receive various data, signals, and information with the expert platform management server 310 and/or the user terminal 320. In addition, the communication modules 312 and 322 may be a wireless communication module (eg, a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (eg, a local area network (LAN) communication module). Module, or a power line communication module). In addition, the communication modules 312 and 322 may include a first network (eg, a short-range communication network such as Bluetooth, WiFi direct or IrDA (infrared data association)) or a second network (eg, a cellular network, the Internet, or a computer network (eg: LAN or WAN) to communicate with external electronic devices. These various types of communication modules may be integrated into one component (eg, a single chip), or may be implemented as a plurality of separate components (eg, multiple chips).

The input modules 313 and 323 transmit commands or data to be used for the expert platform management server 310 and/or the components of the user terminal 320 (eg, control modules 311 and 321). ) And/or external to the user terminal 320 (eg, a user (eg, a first user, a second user, etc.), an administrator of the expert platform management server 310, etc.). In addition, the input module (313, 323) is an expert platform management server 310, a user terminal 320, and / or a touch recognition display installed in the external server 330, a touch pad, a button type recognition module, a voice recognition sensor, It may include a microphone, mouse, or keyboard. Here, the touch-recognizable display, touch pad, and button-type recognition module may recognize a touch through a user's body (eg, a finger) through a pressure-sensitive and/or capacitive method.

The output modules 314 and 324 are generated by the expert platform management server 310 and/or the control modules 311 and 321 of the user terminal 320 or obtained through the communication modules 312 and 322 (eg; Audio signals), information, data, images, and/or various objects. For example, the output modules 314 and 324 may include a display, a screen, a display unit, a speaker, and/or a light emitting device (eg, an LED lamp).

In addition, the expert platform management server 310 and/or the user terminal 320 may further include a storage module, and for the operation of the expert platform management server 310 and/or the user terminal 320 It stores data such as basic program, application program, and setting information. In addition, the storage module includes a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg, SD or XD memory, etc.), Magnetic memory, magnetic disk, optical disk, RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), PROM (Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read) -Only Memory) may include at least one storage medium.

In addition, the storage module may include personal information of a first user who uses the expert platform management server 310 and/or the user terminal 320. Here, the personal information may include a name, an ID, a password, a resident registration number, a street name address, a phone number, a mobile phone number, and/or an email address. In addition, the first control modules 311 and 321 may perform various operations using various programs, contents, data, etc. stored in the storage module.

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4 is a flowchart illustrating a method of providing an expert recommendation service according to an embodiment of the present invention.

Referring to FIG. 4, a method according to an embodiment of the present invention may include a step of a user executing a search service app in a terminal (S410).

For example, the search service app may be implemented as shown in FIGS. 1 and 2, and the search service app may be operated by the server 310. In addition, the search service app may be executed on a user terminal of a user who wants to search for experts through the search service app, and/or may be executed on a service terminal of a service provider who wants to provide a professional service through the search service app. have.

In addition, the method may include inputting user's personal information through the terminal (S420).

The user may execute a search service app through a user terminal and input personal information through the search service app. The personal information may be transmitted from the terminal 320 to the server 310 and recorded in the server 310.

Here, the personal information may include a name, an ID, a password, a resident registration number, a street name address, a phone number, a mobile phone number, and/or an email address.

In addition, the search service app is only after performing a predetermined authentication procedure through the personal information i) the search service page 100 as shown in FIGS. 1 and 2 is displayed or ii) the expert information display window 140, the first The expert information display window 150 and/or the second expert information display window 160 may be displayed.

And, the method may include the step of the user inputting a keyword to search through the terminal (S430).

In addition, the method may include outputting search result information and expert information corresponding to the keyword (S440).

As described above, the search result display window 130 may display a search result corresponding to a search keyword input through the keyword input window 110. The search results here can be search results provided by general search sites, portal sites, etc. (e.g. websites, web page links, images, maps, blogs, shopping, language dictionaries, knowledge encyclopedias, posts, cafes, academic information, etc.) have. In addition, the expert information display window 140 may display expert information corresponding to a search keyword input through the keyword input window 110.

On the other hand, the S410 to S440 may be implemented sequentially, but the order may be changed, and only some of the S410 to S440 may be implemented in combination with other substrates of the present invention.

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5 is a flowchart illustrating a method of providing an expert recommendation service according to another embodiment of the present invention.

Referring to FIG. 5, a method according to an embodiment of the present invention may include the step of inputting a first keyword and a second keyword for a user to search through a terminal (S510).

For example, a user may input a search keyword including a plurality of words through a search service app, and the server 310 may divide the search keyword into a first search keyword and a second search keyword. Here, the operation of classifying the server 310 into the first search keyword and the second search keyword may be performed based on a space included in the search keyword input by the user.

In addition, the method may include outputting first search result information and first expert information corresponding to the first keyword (S520).

In addition, the method may include outputting second search result information and second expert information corresponding to the second keyword (S530).

For example, the second search result may be displayed separately from the first search result, and the second expert information may be displayed separately from the first expert information. For example, the second search result may be displayed at a lower portion than the first search result, and the second expert information may be displayed at a lower portion than the first expert information.

In addition, the method may include outputting third search result information and third expert information corresponding to the first keyword and the second keyword (S540).

For example, the search service page 100 provides a first specialized service for a first specialized field corresponding to the first search keyword and a second specialized service for a second specialized field corresponding to the second search keyword. When there is third expert information about the service provided together, i) is displayed above the first expert information and the second expert information, and/or ii) is not included in the first expert information and the second expert information. You can include certain events.

Meanwhile, the S510 to S530 may be implemented sequentially, but the order may be changed, and only some of the S510 to S530 may be implemented in combination with other substrates of the present invention.

6 is a flowchart illustrating a method of providing an expert recommendation service according to another embodiment of the present invention.

Referring to FIG. 6, the method according to an embodiment of the present invention may include the step of inputting a first keyword and a second keyword for a user to search through a terminal (S610).

In addition, the method may include learning an artificial neural network using the first keyword and the second keyword as training data (S620), and generating combined service recommendation information based on the learned artificial neural network. (S630).

The S620 and S630 may be described in relation to the machine learning module 740 and described later.

In addition, the method may include transmitting the combined service recommendation information to a plurality of unspecified service providers (S640).

For example, the communication module 312 of the server 310 receives the combined service recommendation information generated by the control module 311 of the server 310 and uses the search service app of the present invention or the server 310 ) Can be randomly transmitted to a number of unspecified service providers registered in ).

In addition, the method may include uploading information indicating the combined service in response to the combined service recommendation information (S650).

Here, the information indicating the combined service may be related to a service providing or supporting both the first recommended specialty field and the second recommended specialty field. This takes into account that different services can be more efficient if they are provided together through one company (and/or office).

In addition, the method may include generating a reward corresponding to the uploaded information (S660).

Here, the reward may include points that can be used in the search service app of the present invention, and the type and value of the points included in the reward may be set differently based on the number and/or content of the uploaded information. .

In addition, the reward is a specific service provider among the unspecified plurality of service providers within a specific period from the time when the combined service recommendation information is transmitted from the communication module 312 of the server 310 to a plurality of unspecified service providers. The server is limited to uploading information indicating a combined service indicating that both the first specialized service corresponding to the first specialized field indicated by and the second specialized service corresponding to the second specialized field are uploaded through a search service app. It may be generated by the control module 311 of 310.

Through this, the present invention has the advantage of providing a professional service that users expect to be provided together through a search service app. This is because, although it is necessary to provide services for one specialty field, in some cases, it may be necessary to receive services for other specialties as well.

On the other hand, the S610 to S660 may be implemented sequentially, but the order may be changed, and only some of the S610 to S660 may be implemented in combination with other substrates of the present invention.

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7 is a block diagram showing a control module of an expert platform management server according to an embodiment of the present invention.

Referring to FIG. 7, a search service app management unit 710, a personal information management unit 720, an expert management unit 730, a machine learning module 740, and/or a reward management unit 750 may be included.

The search service app management unit 710 may operate a search service app that provides a search service according to an embodiment of the present invention. Specific operations may implement features related to the search service app described with reference to FIGS. 1 to 7.

The personal information management unit 720 may receive or record personal information input through step S420 of FIG. 4, and when performing a predetermined authentication procedure, the received personal information and information recorded in the database of the server 310 You can perform the step of comparing with.

The expert manager 730 may generate, manage, or output expert information according to an embodiment of the present invention. A specific operation may implement features related to expert information described with reference to FIGS. 1 to 7.

The machine learning module 740 may generate or determine combined service recommendation information or the like based on the artificial intelligence network. In addition, the machine learning module 740 may generate or determine the combined service recommendation information or the like using machine learning on big data stored in a database. In addition, the machine learning module 740 learns the artificial intelligence network of the machine learning module 740 by using big data stored in the database of the server 310 as an input variable. Specifically, deep learning (deep learning) which is a field of machine learning Deep Learning) technique is used to learn to derive an accurate correlation.

In particular, in the case of the above-described correlation, the input is data about a search keyword, a first search keyword, and a second search keyword input by a plurality of unspecified users (from a search service app) through a plurality of terminals, and output (output) may be defined as the combined service recommendation information.

Further, the machine learning module 740 may calculate weights of a plurality of inputs in the function through learning through deep learning. In addition, various models, such as a recurrent neural network (RNN), a deep neural network (DNN), and/or a dynamic recurrent neural network (DRNN), may be used as an artificial intelligence network model used for such learning. Through this, the machine learning module 740 is technical in terms of being able to induce service providers to provide a combined service desired by the plurality of users by learning about specialized fields searched together by a plurality of users. Has a meaning.

Meanwhile, in the keyword input window 110, a first search keyword, a second search keyword, and a third search keyword may be input, and the machine learning module 740 provides a first learning mode and a third search keyword. The artificial intelligence network may be trained by at least one of a first search keyword, a second search keyword, and a third search keyword according to the second learning mode, the third learning mode, or the fourth learning mode. For example, the first search keyword, the second search keyword, and the third search keyword may be classified based on spaces within the search keyword. For example, when the search keyword is'automobile invention startup', the first search keyword The first search keyword may be'automobile', the second search keyword may be'invention', and the third search keyword may be'start-up'.

The control module 311 of the server 310 may control the machine learning module 740 to operate in a first learning mode, a second learning mode, a third learning mode, or a fourth learning mode according to a predetermined criterion.

When operating in the first learning mode, the machine learning module 740 may train the artificial intelligence network using a first search keyword, a second search keyword, and a third search keyword as learning data. Here, the first learning mode is i) within a predetermined first period after learning about the artificial neural network starts and/or ii) the number of training data input for learning the artificial neural network, a first search keyword, and a second search. The number of keywords and/or the third search keyword is lower than a predetermined number threshold and/or iii) the amount of data input for learning of the artificial neural network, a first search keyword, a second search keyword, and /Or the data amount of the third search keyword may be performed only when the data amount is lower than a predetermined data amount threshold.

When operating in the second learning mode, the machine learning module 740 may train the artificial intelligence network using the first search keyword and the second search keyword as learning data. Here, the second learning mode is i) until a predetermined second period has elapsed from the end of the first learning mode and/or ii) the number of training data input for learning of the artificial neural network, a first search keyword, The number of second search keywords and/or third search keywords is lower than a predetermined number threshold and/or iii) the amount of training data input for learning of the artificial neural network, the first search keyword, the second search It may be performed only when the data amount of the keyword and/or the third search keyword is lower than a predetermined data amount threshold.

For example, the machine learning module 740 may operate in the first learning mode or the second learning mode when the user's specialized field is not derived from the user's personal information.

When operating in the third learning mode, the machine learning module 740 may train the artificial intelligence network using the first search keyword as learning data. The third learning mode may be performed when the user's specialized field is derived from the user's personal information. This means that even though there is a user specialty field derived from the user's personal information, a search keyword for another specialty field different from the user specialty field is input, the user performing a task in the user specialty field is the other specialty field. It takes into account that there is a possibility that there is a possibility that you may need to receive services on the side. In other words, the machine learning module 740, when the user's specialized field is derived from the user's personal information, the user's specialized field and the first specialized field (or the specialized field derived from the first search keyword) Based on the combination service recommendation information may be generated or determined.

When operating in the fourth learning mode, the machine learning module 740 may train the artificial intelligence network using numerical values corresponding to each of the first search keyword, the second search keyword, and the third search keyword as learning data.

The reward management unit 750 may generate or output a reward according to an embodiment of the present invention. A specific operation may implement features related to the reward described with reference to FIGS. 1 to 7.

In addition, the present invention allows a plurality of users to use the search service app according to an embodiment of the present invention through a plurality of user terminals, and the same or different first search keyword and second search keyword through each of the plurality of user terminals. And/or a third search keyword (via the keyword input window 110) may be input.

At this time, at least two or more users (eg, the first user and the second user) who input two or more of the first search keyword, the second search keyword, and/or the third search keyword identically are the same business, sales, and business. ), and the like, and there is a possibility that a business connection between the at least two or more users (eg, a first user and a second user) is mutually beneficial.

In consideration of this point, the control module 311 and/or the communication module 312 of the server 310 of the present invention mutually exchange information for each of the at least two or more users (eg, a first user and a second user). It can perform or provide a business connection service that delivers or connects.

However, this means that only at least two users (eg, first and second users) inputting two or more of the same first search keyword, second search keyword, and/or third search keyword more than a predetermined threshold number of times Connection service is available.

As an example, even if the first user and the second user inputting two or more of the same first search keyword, second search keyword and/or third search keyword more than a predetermined threshold number of times are confirmed by the control module of the server (or Even if information indicating this is received by the communication module), i) information (or value) indicating a first distance difference between the terminal location information of the first user and the location information of the second user terminal is a predetermined first distance threshold When indicating a higher value and/or ii) the information (or value) indicating a second distance difference between the information indicating the company location of the first user and the information indicating the company location of the second user is a predetermined second When a value higher than the distance threshold is indicated, the business connection service may not be provided.

The embodiments of the present invention disclosed in the present specification and drawings are only provided for specific examples to easily explain the technical content of the present invention and to aid understanding of the present invention, and are not intended to limit the scope of the present invention. That is, it is apparent to those of ordinary skill in the art that other modifications based on the technical idea of the present invention can be implemented. In addition, each of the above embodiments can be operated in combination with each other as needed. For example, all the embodiments of the present invention may be implemented by the expert recommendation system 300, the expert platform management server 310, and/or the external server 330 by combining parts with each other.

In addition, the method of controlling the expert recommendation system 300, the expert platform management server 310, and/or the external server 330 according to the present invention is implemented in the form of a program command that can be executed through various computer means. It can be recorded on a readable medium.

As described above, various embodiments of the present invention may be implemented as computer readable code in a computer readable recording medium from a specific viewpoint. A computer-readable recording medium is any data storage device capable of storing data that can be read by a computer system. Examples of computer-readable recording media include read only memory (ROM), random access memory (RAM), and compact disk-read only memory (CD-ROM). ), magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission over the Internet). The computer readable recording medium can also be distributed through network-connected computer systems, so the computer readable code is stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for achieving various embodiments of the present invention can be easily interpreted by experienced programmers in the field to which the present invention is applied.

In addition, it will be appreciated that the apparatus and method according to various embodiments of the present invention can be realized in the form of hardware, software, or a combination of hardware and software. Such software may be, for example, a volatile or nonvolatile storage device such as a storage device such as a ROM, or a memory such as a RAM, memory chip, device or integrated circuit, or For example, it may be optically or magnetically recordable, such as a compact disk (CD), a DVD, a magnetic disk, or a magnetic tape, and stored in a storage medium that can be read by a machine (for example, a computer). The method according to various embodiments of the present invention may be implemented by a computer or a portable terminal including a control unit (control modules 311 and 321) and a memory, and such a memory can provide instructions for implementing the embodiments of the present invention. It will be appreciated that this is an example of a machine-readable storage medium suitable for storing the containing program or programs.

Accordingly, the present invention includes a program including a code for implementing the apparatus or method described in the claims of the present specification, and a storage medium readable by a machine (such as a computer) storing such a program. Further, such a program may be transferred electronically through any medium, such as a communication signal transmitted through a wired or wireless connection, and the present invention suitably includes equivalents thereto.

The embodiments of the present invention disclosed in the present specification and drawings are merely provided for specific examples to easily explain the technical content of the present invention and to aid understanding of the present invention, and are not intended to limit the scope of the present invention. In addition, the embodiments according to the present invention described above are merely exemplary, and those of ordinary skill in the art will understand that various modifications and equivalent ranges of embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the following claims.

Claims (10)

In the method of providing expert recommendation service,
The user terminal executing a search service app;
Checking, by the user terminal, a plurality of search keywords input through a search service app; And
Controlling at least one expert information corresponding to the plurality of search keywords to be displayed through a search service page of the search service app; Including,
The expert information includes information on at least one of a specialized field, an expert, and a specialized company corresponding to the keyword,
The plurality of search keywords include a first keyword, a second keyword, and a third keyword,
Each of the at least one expert information further includes a professional mark object expressed as an image representing at least one of a specialized field and a technical field corresponding to each of the at least one expert information,
The above method,
Based on the server operating the search service app being set to a first learning mode, a second learning mode, a third learning mode, or a fourth learning mode, the plurality of search keywords input through the search service app are used as learning data. Training an artificial neural network;
Generating combined service recommendation information that recommends serving a plurality of specialized fields based on the learned artificial neural network; And
Transmitting the combined service recommendation information to a service providing terminal of a plurality of unspecified service providers providing services through the search service app; Including more,
Learning the artificial neural network using the plurality of search keywords as training data,
When the server is set to the first learning mode under a condition within a predetermined period from the start of learning about the artificial neural network, the first keyword, the second keyword, and the third keyword are set as the learning data, and ,
When the server is set to the second learning mode under the condition that the predetermined period has elapsed from the start of learning about the artificial neural network, the first keyword and the second keyword are set as the learning data,
When the server is set to the third learning mode under a condition in which information on the specialized field is extracted from the user's personal information, the first keyword is set as the learning data,
When the server is set to the fourth learning mode, a numerical value corresponding to each of the first keyword, the second keyword, and the third keyword is set as the learning data,
Way. delete The method of claim 1,
The at least one expert information,
Including first expert information corresponding to the first search keyword and second expert information corresponding to the second search keyword,
Containing third expert information on experts who service both a first specialized field corresponding to the first search keyword and a second specialized field corresponding to the second search keyword,
Way. delete delete The method of claim 1,
The above method,
Checking information indicating a combined service uploaded by a specific service provider from among a plurality of unspecified service providers that have received the combined service recommendation information;
Generating a reward corresponding to information indicating the combined service; And
Transmitting the generated reward to the specific service provider; Further comprising,
Way. In the server operating a search service app that provides expert recommendation service,
A control module configured to receive a search keyword input through the search service app, generate at least one expert information corresponding to the search keyword, and control to be displayed through a search service page of the search service app; Including,
The expert information includes information on at least one of a specialized field, an expert, and a specialized company corresponding to the keyword,
The plurality of search keywords include a first keyword, a second keyword, and a third keyword,
Each of the at least one expert information further includes a professional mark object expressed as an image representing at least one of a specialized field and a technical field corresponding to each of the at least one expert information,
The control module,
An artificial neural network is trained using the search keyword input through the search service app as learning data,
Generates combined service recommendation information recommending serving a plurality of specialized fields based on the learned artificial neural network,
Control to transmit the combined service recommendation information to service providing terminals of a plurality of unspecified service providers providing services through the search service app,
Checking information indicating a combined service uploaded by a specific service provider among a plurality of unspecified service providers receiving the combined service recommendation information, and generating a reward corresponding to the information indicating the combined service,
The control module,
When the server is set to the first learning mode under a condition within a predetermined period from the start of learning about the artificial neural network, the first keyword, the second keyword, and the third keyword are set as the learning data, and ,
When the server is set to the second learning mode under the condition that the predetermined period has elapsed from the start of learning about the artificial neural network, the first keyword and the second keyword are set as the learning data,
When the server is set to the third learning mode under a condition in which information on the specialized field is extracted from the user's personal information, the first keyword is set as the learning data,
When the server is set to the fourth learning mode, a numerical value corresponding to each of the first keyword, the second keyword, and the third keyword is set as the learning data,
server. delete The method of claim 7,
The at least one expert information includes first expert information corresponding to the first search keyword and second expert information corresponding to the second search keyword, or
The at least one expert information includes third expert information on experts who service both a first specialized field corresponding to the first search keyword and a second specialized field corresponding to the second search keyword,
server. delete

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