KR102466623B1 - Method, apparatus and system of recommending and managing meeting data using member information based on artificial intelligence - Google Patents

Abstract

Disclosed are a method, a device and a system for providing a meeting data recommendation and management service based on member information. The device for providing a meeting data recommendation and management service based on member evaluation information according to an aspect of the present disclosure comprises: a memory; and a processor communicating with the memory, wherein the processor receives member information, extracts meeting participant information on a first meeting participant and a second meeting participant, which is determined based on the evaluation information extracted from the received member information, and relation information between the first meeting participant and the second meeting participant, and generates and provides first meeting data and second meeting data related to the first meeting data based on the extracted meeting participant information and meeting participant relation information based on artificial intelligence. Therefore, provided are a method, a device and a system for providing a member evaluation and management service, wherein members can be evaluated properly.

Description

Method, apparatus and system for providing meeting data recommendation and management service using artificial intelligence-based member information

The present disclosure relates to a meeting data recommendation and management service, and more particularly, to a method, apparatus, and system for providing an optimal meeting data recommendation and management service to a user based on artificial intelligence in consideration of member information.

In organizations with a large number of members, such as public institutions or companies, evaluation of members is conducted on a regular or irregular basis.

Regarding the evaluation of these members, a performance measurement system for evaluating the performance or ability of members has been developed.

However, the conventional performance measurement system is mainly focused on measuring the performance or evaluating the ability of the current member's work, and there is a problem in that the member evaluation is limited because the diversity of the organization or members is not considered.

Therefore, there is a demand for the development of a system capable of properly evaluating members by considering various general matters including organization or member information.

Korean Patent Publication No. 10-2009-0012944 (2007.07.31)

The problem to be solved by the present disclosure is to provide a method, device, and system for providing member evaluation and management services that can properly evaluate members based on artificial intelligence in consideration of various matters including information of organizations and members. is to provide

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

An apparatus for providing meeting data recommendation and management service based on artificial intelligence-based member evaluation information according to an aspect of the present disclosure for solving the above problems includes a memory; and a processor communicating with the memory, wherein the processor receives member information and conference participant information about a first conference participant and a second conference participant determined based on evaluation information extracted from the received member information. and extracting relationship information between the first conference participant and the second conference participant, and based on the artificial intelligence-based extracted conference participant information and the conference participant relationship information, first conference data and a first conference data related to the first conference participant. Two times data can be generated and provided.

According to the artificial intelligence-based meeting data recommendation and management service providing device according to the present disclosure, the first meeting data is meeting data on a large topic related to meeting purposes related to growth, immersion, collaboration, and performance, and the second meeting data is It may be conference data on a specific sub-topic according to the first conference data.

According to the artificial intelligence-based conference data recommendation and management service providing apparatus according to the present disclosure, the conference participant information includes user input information input by at least one of the first conference participant and the second conference participant, and user behavior. It may include information accumulated according to the use of the service platform provided by the device, including information and user context information.

According to the artificial intelligence-based conference data recommendation and management service providing apparatus according to the present disclosure, the user behavior information may include actions generated by at least one of the first conference participant and the second conference participant using the service platform. and information extracted based on behavioral log data.

According to the artificial intelligence-based conference data recommendation and management service providing apparatus according to the present disclosure, the behavior log data is composed of the type of the meeting participant's behavior, the time difference between each behavior, and the target of the behavior, and the processor stores each log The behavior log data may be generated by collecting and extracting the type and additional information of the meeting participant through machine learning.

According to the artificial intelligence-based conference data recommendation and management service providing apparatus according to the present disclosure, the user context information includes information on the current situation or state of the conference participant, and the processor affects the performance of the conference participant. The current situation or state may be defined in relation to a predefined event.

According to the artificial intelligence-based conference data recommendation and management service providing apparatus according to the present disclosure, the relationship information includes information representing the difference or homogeneity between affiliation, position, year, and evaluation experience of each conference participant, and the processor Based on the relationship information, a relationship suitability between the conference participants may be calculated.

According to the artificial intelligence-based conference data recommendation and management service providing apparatus according to the present disclosure, the processor determines an information sharing range for the conference participant based on the relationship information, and the third information sharing range based on the determined information sharing range. 1 and 2 data can be controlled to be generated.

A method of providing an artificial intelligence-based conference data recommendation and management service, performed by a computing device according to an embodiment of the present disclosure, includes receiving member information; extracting evaluation information from the received member information; determining a first meeting participant and a second meeting participant based on the extracted evaluation information; extracting conference participant information about the determined first and second conference participants and relationship information between the first and second conference participants; generating first conference data and second conference data related to the first conference data based on the extracted conference participant information and the conference participant relationship information based on artificial intelligence; and providing the generated first and second meeting data.

A system for providing meeting data recommendation and management service based on artificial intelligence-based member evaluation information according to an embodiment of the present disclosure includes: a terminal requesting conference data recommendation and management; and a server, wherein the server receives member information, conference participant information about a first conference participant and a second conference participant determined based on evaluation information extracted from the received member information, and the first conference participant and Extracts the relationship information of the second conference participant, and generates and provides first conference data and second conference data related to the first conference data based on the extracted conference participant information and the conference participant relationship information based on artificial intelligence. It may include a processor that

Other specific details of the disclosure are included in the detailed description and drawings.

According to the present disclosure, the following effects may be obtained.

According to the present disclosure, it is possible to provide an optimal configuration evaluation and management service to a user through a member evaluation and management service that considers various aspects of organizations and members.

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

1 is a diagram schematically illustrating a member evaluation and management service system according to an embodiment of the present disclosure.
FIG. 2 is a configuration block diagram of the computing device shown in FIG. 1 .
3 is a configuration block diagram of a processing unit according to an embodiment of the present disclosure.
4 is a configuration block diagram of a processing unit according to another embodiment of the present disclosure.
5 is a configuration block diagram of a processing unit according to another embodiment of the present disclosure.
6 is a block diagram illustrating a detailed configuration of a processing unit according to an embodiment of the present disclosure.
7 is a flowchart illustrating a member evaluation and management service providing method according to an embodiment of the present disclosure.
8 to 10 are diagrams illustrating user interfaces for providing member evaluation and management services according to an embodiment of the present disclosure.
11 is a flowchart illustrating a member evaluation and management service providing method according to another embodiment of the present disclosure.
12 is a diagram illustrating a user interface for providing member evaluation and management services according to another embodiment of the present disclosure.
13 is a flowchart illustrating a member evaluation and management service providing method according to another embodiment of the present disclosure.
14 is a diagram illustrating a user interface for providing member evaluation and management services according to an embodiment of the present disclosure.

Advantages and features of the present disclosure, and methods of achieving them, will become clear with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms, but only the present embodiments make the disclosure of the present disclosure complete, and are common in the art to which the present disclosure belongs. It is provided to fully inform the person skilled in the art of the scope of the present disclosure, which is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present disclosure. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present disclosure.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which this disclosure belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

The spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe a component's correlation with other components. Spatially relative terms should be understood as including different orientations of elements in use or operation in addition to the orientations shown in the drawings. For example, if you flip a component that is shown in a drawing, a component described as "below" or "beneath" another component will be placed "above" the other component. can Thus, the exemplary term “below” may include directions of both below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

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

In this specification, the member evaluation and management service providing device according to the present disclosure includes all of various devices capable of providing results by performing calculation processing. For example, an apparatus for providing member evaluation and management services according to the present disclosure may include at least one computer, computing device, server device, terminal, or the like, or may take any one form.

In the above, the computer may include, for example, a laptop computer, a desktop computer, a laptop computer, a tablet PC, a slate PC, and the like equipped with a web browser.

In the above, the server device is a server that processes information by communicating with an external device, and includes an application server, a computing server, a database (DB) server, a file server, a game server, a mail server, a proxy server, a web server, and the like. can be included

In the above, the portable terminal, for example, as a wireless communication device that ensures portability and mobility, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System) , PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, smart phone ( All kinds of handheld-based wireless communication devices such as smart phones, watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-devices (HMDs). A wearable device may be included.

In this specification, an information provision control model may be defined or a related service platform may be built in relation to the member evaluation and management service providing device according to the present disclosure, which includes big data and artificial intelligence. ) can be generated and provided by technology-based computers, and virtual convergence technology (XR, eXtended), which collectively refers to virtual reality (VR), augmented reality (AR), and mixed reality Reality), ICT (Information and Communication Technology) technology such as block-chain technology may be used or referred to and implemented for the security of personal information of a user using an information providing device. However, in this specification, a detailed description of such ICT technology is omitted with reference to known technologies, and a separate description thereof is omitted.

For convenience, the term “member” described in this specification may refer to a member belonging to a company, organization, group, etc. using a service platform provided by a service system or a service providing device according to the present disclosure. Such a member may be an evaluator or an evaluator who is a subject of evaluation.

In this disclosure, the following information can be referred to or used for member evaluation and management services.

1 is a diagram schematically illustrating a member evaluation and management service system according to an embodiment of the present disclosure.

A member evaluation and management service system according to the present disclosure may include a first terminal 100 , a second terminal 150 , and a computing device 200 .

The member evaluation and management service system according to the present disclosure may provide services related to evaluation methods, evaluation relationships, evaluation questions, evaluation feedback, meeting data recommendation and management, etc. to service platform users by providing a service platform. At this time, the system may determine and use the suitability of each service item in order to provide an optimal member evaluation and management service.

A member evaluation and management service system according to the present disclosure includes software for a member evaluation creation service based on member information, an evaluation feedback service based on the analysis of the evaluator's propensity for members and evaluation contents, and a meeting agenda recommendation and management service based on member evaluation records. , hardware, etc.

Depending on embodiments, the member evaluation and management service system may be configured by adding one or more components in relation to performing operations according to the present disclosure, in addition to the components shown in FIG. 1 .

The member evaluation and management service according to the present disclosure may be provided in the form of a web service through an application or web.

In the above, when the application is provided by, for example, the computing device 200, downloaded and installed in the terminals 100 and 150, and then executed, user information is input through a user interface (UI) or member evaluation and management is performed. Service information (eg, member evaluation results, etc.) may be provided. At this time, when user information is input according to the execution of the application, the terminals 100 and 150 may directly process it and output result data, that is, member evaluation and management service result information. According to an embodiment, the terminals 100 and 150 transmit the user information received through the application to the computing device 200 without directly processing it, and receive and output result data generated by the computing device 200. You may. Meanwhile, in order to directly process user information, the computing device 200 uses an algorithm or logic for providing or processing a member evaluation and management service according to the present disclosure, or/and an application programming interface (API) related thereto. ) or a plug-in may be provided to the terminals 100 and 150.

However, for convenience of explanation, the terminals 100 and 150 do not directly process the input user information obtained through the executed application, but pass it to the computing device 200, and process it in the computing device 200 so that members An example of generating evaluation and management service information, returning it to the terminals 100 and 150, and providing the terminals 100 and 150 to users will be described as an example, but the present disclosure is not limited thereto. Meanwhile, in the above, at least some of the operation(s) according to the present disclosure performed by the computing device 200 may be performed through resources of the terminals 100 and 150 .

As described above, the terminals 100 and 150 download and install applications for member evaluation and management services, and after installation, they can perform various operations, such as user information and various search requests, through the applications.

Referring to FIG. 1 , a first terminal 100 is a user involved in evaluation, for example, a terminal of an evaluator or an evaluation target (ie, an evaluation subject) or a customer who performs evaluation management (eg, performance management activity). terminal can be indicated.

The computing device 200 may collect a variety of information about the user's personal details, work to be performed, past evaluation history, etc. through the first terminal 100 or an internal database.

The second terminal 150 may collect external data about the user of the first terminal 100, that is, the evaluator/evaluator or the company to which the user belongs.

At this time, the external data for the company may include the size (for example, number of full-time employees), duration, type of business, industry, and the like of the company.

In the above, external data for the user of the first terminal may refer to all data except data accumulated using the first terminal 100 , for example. Such data could include, for example, social media history, whether or not a job search platform was used. As the job search platform, a typical job search site, for example, LinkedIn, and the like may be included.

The computing device 200 may recommend or suggest an evaluation method for evaluating members according to the present disclosure based on information collected through the first terminal 100 and/or the second terminal 150, and the evaluator may perform evaluation according to a recommended or suggested evaluation method through the first terminal 100 .

According to an embodiment, the computing device 200 may diagnose the propensity of the evaluator based on the evaluation data, and provide feedback on evaluation contents performed by the evaluator based on the diagnosis.

According to an embodiment, the computing device 200 automatically recommends and proposes an evaluation method by referring to the information collected through the first and second terminals 100 and 150, and based on the proposed result, the meeting participant is informed of the purpose of the meeting. can be set, progressed and recorded.

Even if it is merely referred to as the computing device 200 in this specification, since it is an entity that provides and manages a service platform according to the present disclosure, it may indicate that a service based on such a service platform is provided.

Meanwhile, although the first terminal 100 and the second terminal 150 are separately described in FIG. 1 , the two terminals may be implemented as one terminal. Depending on the embodiment, conversely, more terminals may be used for the member evaluation and management service according to the present disclosure.

In addition, even if it is only the first terminal 100, it is used in a symbolic meaning and may be used to indicate a plurality of terminals without necessarily representing only one terminal.

Meanwhile, the computing device 200 may be called various names such as a server, a computer, and a control unit, and is not limited thereto.

FIG. 2 is a block diagram of the computing device 200 shown in FIG. 1 .

Referring to FIG. 2 , the computing device 200 according to an embodiment of the present disclosure can be largely divided into a memory and a processor 210, the memory corresponding to or including a storage unit 260, and a processor 210 may include a communication unit 220, a collection unit 230, a processing unit 240, a control unit 250, and the like.

The communication unit 220 may provide an environment for communication with the first terminal 100, the second terminal 150, and an external source.

The communication unit 220 may include the following modules.

The communication module supports data communication with the terminals 100 and 150 and can receive user information from the terminals 100 and 150 . The communication module may be connected to various external sources to receive data related to member evaluation and management services according to the present disclosure. In the above, some data may be received directly from the terminals 100 and 150 of the user rather than from an external source, and redundant data is from a highly reliable source (in this case, the source also includes the terminals 100 and 150), the latest It may be filtered by referring to information or the like.

Such a communication module may include at least one or more components enabling communication with the terminals 100 and 150 or at least one external source (not shown). For example, a wired communication module, a wireless communication module, and a short distance It may include at least one of a communication module, a location information module, and the like.

In the above, the wired communication module includes not only various wired communication modules such as a Local Area Network (LAN) module, a Wide Area Network (WAN) module, or a Value Added Network (VAN) module, but also a USB ( Universal Serial Bus), High Definition Multimedia Interface (HDMI), Digital Visual Interface (DVI), recommended standard-232 (RS-232), powerline communications, or plain old telephone service (POTS). have.

The wireless communication module includes, in addition to a Wi-Fi module and a wireless broadband module, GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G (eneration), 5G, 6G, and may include a wireless communication module supporting various wireless communication schemes.

The short-range communication module is for short-range communication, and includes Bluetooth™, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and Near NFC (Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and wireless USB (Wireless Universal Serial Bus) technology may be used to support short-distance communication.

The location information module is, for example, a module for obtaining the location (or current location) of the terminals 100 and 150, and a representative example thereof is a Global Positioning System (GPS) module or a Wi-Fi module. For example, if a GPS module is utilized, the location of the terminals 100 and 150 may be acquired using signals transmitted from GPS satellites. As another example, if the Wi-Fi module is used, the location of the terminals 100 and 150 may be obtained based on information of a wireless access point (AP) that transmits or receives a wireless signal with the Wi-Fi module. If necessary, the location information module may perform any function among other modules of the communication module in order to obtain data on the location of the terminals 100 and 150 in substitution or addition. The location information module is a module used to acquire the location (or current location) of the terminal 100 or 150, and is not limited to a module that directly calculates or obtains the location of the terminal 100 or 150. Such a location information module may be embedded in the terminals 100 and 150 to provide location information of the terminals 100 and 150 to the computing device 200 .

The collection unit 230 may collect information about users of the first terminal 100 and the second terminal 150 and information about organizations to which the terminal users belong, through the communication unit 220 .

Processing unit 240 may, for example, process evaluation and management related data related to various embodiments of the present disclosure.

The control unit 250 is involved in overall control of the computing device 200 and may transmit necessary control signals for each component.

The control unit 250 controls the operation of all components of the computing device 200, but the storage unit 260 stores data for various algorithms or programs that reproduce the algorithms available in the process. , and various operations for member evaluation and provision of management service information according to the present disclosure may be performed using data stored in the storage unit 260 .

The storage unit 260 may store data collected, calculated, processed, etc. within the computing device 200 .

A more detailed description of a member evaluation and management method based on member information, a member evaluation content analysis and feedback method, and a member evaluation-based meeting recommendation and management method according to the present disclosure are as follows.

FIG. 3 is a block diagram of the processing unit 240 according to an embodiment of the present disclosure, showing components related to evaluation and management based on member information, but is not limited thereto.

The “evaluation method” described in this specification refers to a method of evaluating members through a service platform for member evaluation and management services, and these evaluation methods include, for example, evaluation direction, evaluation order, and job evaluation data. It can be determined with reference to the like. In this case, the evaluation direction data may indicate whether the evaluation of members is bottom-up, top-down, parallel, or a combination of bottom-up and top-down, for example. The parallel expression may indicate an evaluation between members having the same or similar job title or position. The evaluation order data indicates the number of evaluations used for member evaluation. For example, if the number of evaluations is 1, the evaluation by the direct supervisor of the target member is usually included, and in the case of 2, evaluation by the next highest direct supervisor is also included. can indicate Further, the job evaluation data may indicate data for evaluation of a member's job, for example, competency, performance, skill, and the like. However, the present disclosure is not necessarily limited to the above definitions or numerical values.

The term "evaluation relationship" described in this specification may indicate an entity performing evaluation on members through the service platform, for example, a relationship between an evaluator and an evaluator among members. This evaluation relationship can be referenced for determining the optimal evaluator for the evaluation target and setting the evaluation authority for the determined evaluator. In this case, the evaluator may be determined based on the degree of fitness calculated in consideration of the relationship with the evaluator, that is, the evaluation target. That is, an evaluator with the highest degree of fitness calculated as an evaluator for an evaluator may be determined as an optimal evaluator, and such an optimal evaluator may be, for example, a colleague who has collaborated on multiple tasks, a predecessor in the relevant job, or a teammate in the same team. etc. In determining the optimal evaluator, various circumstances may be further considered in addition to the suitability. On the other hand, the setting of the evaluation authority means whether or not the previous year's evaluation results and evaluation results of others can be referred to, etc., and is calculated in an optimal evaluation relationship and used as auxiliary data in the evaluation process according to the hierarchy between the designated evaluation target and the evaluator and related predetermined policies. It is possible to indicate whether various types of information about possible subjects can be viewed. For example, if colleague A is selected as the best evaluator according to the calculation of the evaluation relationship, authority may be set so that past evaluation results and self-evaluation results cannot be viewed because A is a colleague. Depending on embodiments, the evaluation authority may be determined according to the suitability of the evaluator determined to be the best evaluator. For example, even if an evaluator having the highest fitness among a plurality of evaluation candidates is determined to be an optimal evaluator, the evaluation authority of the corresponding evaluator may be set differently according to the fitness level of the first critical fitness interval, the second critical fitness interval, and the like. In another embodiment, if the evaluation suitability calculated for all target evaluators above is less than the reference value (eg, 50%), the optimal evaluator is not selected from the target evaluators, and the target evaluation candidate is reselected, but In this case, it may be excluded from the re-selected evaluator candidate list. On the other hand, the above evaluation authority may be the contents of data or information access range or authority for evaluation target, evaluation method, evaluation questions, etc., according to an embodiment.

The term "evaluation item" described in this specification may indicate an evaluation item used in the evaluation of the subject according to the evaluation method selected by the evaluator in the evaluation relationship. In this case, the evaluation items may include information for defining and determining diversity of organizations and members as well as work problems.

Referring to FIG. 3 , the processing unit 240 may include an evaluation method calculation unit 310, an evaluation relationship calculation unit 320, and an evaluation item classification unit 330, but is not limited thereto.

The evaluation method calculation unit 310 may calculate an evaluation method suitable for member evaluation based on the information collected through the collection unit 230 of FIG. 2 .

The evaluation relationship calculation unit 320 provides relationship data for selecting an evaluator optimal for the evaluation of the evaluation target, that is, the person to be evaluated, among the evaluation candidates selected based on the evaluation method calculated by the evaluation method calculation unit 310. can As described above, the evaluation relation calculation unit 320 may grant the evaluation authority to the corresponding evaluator differently based on the relation data.

The evaluation item classification unit 330 classifies the evaluation items to be used for evaluation based on the evaluation method calculated by the evaluation method calculation unit 310 and the relationship data between the evaluator and the evaluated person provided by the evaluation relationship calculation unit 320, Among the classified evaluation items, an optimal evaluation item may be determined and provided.

The member evaluation and management service system (or device) according to the present disclosure may provide evaluation method data determined by referring to evaluation subjects and organization data.

In the above, evaluation subjects, organization data, etc. referred to to define the evaluation method may be classified into external data and internal data.

In the above, internal data may be defined as data input by a user of a service platform provided by a device or system according to the present disclosure, or data generated, stored, and processed during use, and external data refers to the service platform. It can be defined as data that is difficult or impossible to obtain through For example, the external data may be data that is collected, obtained, and processed through an external source.

The internal data of the evaluation target information can be divided into user input information and user behavior information.

In the above, the user input information may include personnel information such as a job, position, job title, year of joining, department, department, and the like input by the user in the process of using the service platform.

In the above, the user behavior information may represent information extracted based on a behavior occurring as a user uses the service platform and log data indicating the behavior. In the above, the action may indicate, for example, page inquiry, evaluation writing, interview request, etc. in the process of using the service platform.

In the above, the behavioral log data may be composed of the type of behavior of the service platform user, the time difference between each behavior, and the log of the target of the behavior, and the user's behavioral type is collected through machine learning by collecting each log. It can be used as a reference for forecasting, such as or additional information. In this regard, the computing device 200 vectorizes a sequence of various information including various behavior logs of members on the service platform and a time difference between the behavior logs, and a sequence after vectorizing various information based on the respective behavior logs. User behavior information may be defined by generating at least one of the behavior log vector sequences to be transformed, and calculating the type, time, etc. of a user's behavior related to member evaluation based on the behavior log sequence vector and/or the behavior log vector sequence. can

In general, vectorization refers to digitizing a concept so that a machine can learn it. In this regard, vectorization in the present disclosure may indicate, for example, that an evaluator digitizes and records behavioral log data of a user, such as browsing past records of an evaluation subject, browsing interview records, starting evaluation writing, and ending evaluation. Meanwhile, in the present disclosure, sequencing may indicate that a behavior log or the vectorized behavior log is sequentially arranged and set as one object. For example, the evaluator's sequence may be expressed in a time-sequential order, such as reading past evaluation records of evaluation subjects, reading previous interview records, starting writing evaluations, and ending writing evaluations. In this way, performing sequencing can identify the evaluation process of each member (evaluator), for example, the pattern before and after the evaluation, by sequencing the behavior vectors of the members, for example, and classify the evaluators in various ways based on this. Because you can.

In Table 1 below, evaluator types are classified according to an embodiment, but the present disclosure is not limited thereto.

example type 1 View past evaluation 　 -> 　 Type of writing evaluation type 2 Reading past evaluations　->　Reading interview records　->　Type of writing an evaluation type 3 Reading interview records　->　Viewing past evaluations　->　Type of writing an evaluation type 4 Reading interview records　->　Type of writing an evaluation type 5 A type of evaluation without reviewing records type 6 A type of writing multiple member evaluations in a short period of time

In the present disclosure, as described above, by defining and classifying evaluation types in various ways based on behavior log vector sequences or behavior log sequence vectors, quantified and more detailed information on members (evaluators) for each evaluation type can be obtained. In addition, the computing device 200 may generate or obtain statistical data on an evaluation writing method of each evaluation type or evaluation type group through the quantified classification. In addition, the computing device 200 may generate an evaluation guide suitable for each evaluation type based on artificial intelligence by learning the acquired quantification data with artificial intelligence (computer), and provide the service.

In addition, for example, in preparing for a meeting (1:1) between members through a service platform, by accessing the service platform in advance, whether to register an agenda, that is, to register an agenda and to add opinions to the agenda of the other party From the point of view or the like, the computing device 200 may classify the type of user. Here, the additional time point may indicate, for example, addition right before the interview, addition 1 hour before the interview, and the like.

Next, in the above, external data refers to data acquired through an external source, such as social media (SNS) data of a user other than the service platform, through which information on total career, social media activity, etc. is extracted. and can be referred to user type classification, etc.

According to the present disclosure, the member evaluation and management service system (or device) extracts or calculates a list of evaluation methods suitable for a subject and the suitability of each evaluation method based on the aforementioned internal and/or external data to optimize evaluation A method can be suggested.

On the other hand, organization information mentioned in the present disclosure can also be divided into internal data and external data. Internal data represents the number of organizations entered by the user, organizational structure such as relationship between organizations, size, etc., and external data represents the organization's It can indicate the existence period, industry, industry group information, and the degree of media exposure.

According to the present disclosure, the member evaluation and management service system (or device) identifies the inclination and philosophy of the organization based on the aforementioned various internal and/or external data, and the identified organization's inclination and philosophy are combined with a list of suitable evaluation methods and The suitability of each evaluation method can be referred to for extraction or calculation.

The member evaluation and management service system (or device) according to the present disclosure may extract or calculate a calculation degree of an appropriate evaluation time and evaluation method by referring to past evaluation records.

The member evaluation and management service system (or device) according to the present disclosure may calculate overall fitness including fitness for an organization and target person, and recommend and provide an appropriate evaluation method by referring to past evaluation records.

The member evaluation and management service system (or device) according to the present disclosure may calculate the evaluation relationship using the same data as the above evaluation method.

The evaluation suitability for each evaluator candidate selected based on the evaluation method for each evaluation subject can be calculated. That is, the evaluation suitability indicates the suitability of the evaluation target for the evaluation target of the corresponding evaluator candidate according to the evaluation method, and eventually objectively identifies whether the evaluation target is suitable as an evaluator of an individual evaluator candidate and, if appropriate, how appropriate it is. It can be defined as data that is calculated and provided to make it possible.

In this case, selection of evaluator candidates based on the above evaluation method may mean selection of a leader of each evaluator in the case of a top-down method, and selection of subordinates of each subject in the case of a bottom-up method.

On the other hand, the suitability for evaluation may be selected in consideration of the length of service between the subject and the evaluator candidate, joint work performance, the quality and quantity of interviews, the consistency of behavioral patterns, and evaluation tendencies. This , the evaluation tendency may indicate, for example, remuneration, generosity, and the like.

After calculating the suitability for each evaluation candidate, the evaluation burden of the evaluator can be minimized by considering evaluations that are already in progress or concurrently.

That is, the computing device 200 may recommend and provide an evaluation relationship in which the fitness of each evaluator-to-be-evaluated (ie, to-be-evaluated) pair is greater than or equal to a threshold value or a maximum value while minimizing the evaluation burden of each evaluator.

The computing device 200 may automatically grant appropriate evaluation authority according to the relationship between each evaluator-subject pair and preset evaluation guidelines. The relationship between each evaluator-subject pair may be expressed as a superior/lower relationship, a colleague relationship, and the like.

The member evaluation and management service system (or device) according to the present disclosure may present evaluation items according to a proposed evaluation method.

The member evaluation and management service system (or device) classifies and databases the evaluation history of users using the service platform and the evaluation items they input, and based on the evaluation method presented and the value and evaluation guideline of the organization The suitability of each evaluation item can be calculated.

Therefore, according to the present disclosure, it is possible to present a set of evaluation items capable of calculating a degree of fitness above a threshold while avoiding evaluation of unnecessarily overlapping items through evaluation items. In this case, the degree of fitness equal to or greater than the threshold value may indicate the maximum degree of fitness, but is not limited thereto.

6 is a block diagram illustrating a detailed configuration of the processing unit 240 according to an embodiment of the present disclosure.

Referring to FIG. 6, the evaluation method calculating unit 310 of FIG. 3 will be described as follows.

The evaluation method calculation unit 310 may include a data classification unit 610, a data extraction unit 620, a data conversion unit 630, a learning unit 640, and a calculation unit 650. However, the present disclosure is not limited thereto.

The data classification unit 610 may classify the external data collected through the collection unit 230 of FIG. 2 according to predetermined criteria.

The data extractor 620 may extract user behavior information indicating user characteristics.

The data conversion unit 630 may convert the data extracted through the data extraction unit 620 into a learnable form.

The learning unit 640 may learn by combining the personal information input by the user through the first terminal 100, the result of the data conversion unit 630, and the result of the first data classification unit 610.

The calculation unit 650 may derive suitable evaluation methods based on the result of the learning unit 640 and transmit them to the communication unit 220 .

Accordingly, the evaluation method calculation unit 310 may provide an appropriate type of evaluation method in consideration of the characteristics of the organization and the characteristics of the evaluation target.

As described above, the characteristics of the organization may be classified by collecting external data such as duration, type/industry group, and location.

In addition, the target person's characteristics are used behavior information accumulated through the service platform using the first terminal 100 and previously input personal information (eg, rank, position, task to be performed, past evaluation results, etc.) can be identified on the basis of

Meanwhile, the usage behavior information may indicate a behavior sequence vector for sequencing time lags between various behavior logs and behaviors on the service platform, tasks performed on the service platform, and various behaviors. This may be referred to, for example, for determining the sincerity or degree of participation in the evaluation of evaluation candidates.

Accordingly, the evaluation method calculation unit 310 may recommend and suggest an optimal evaluation method based on information about the specified organization and target person.

That is, the evaluation method may mean the direction of evaluation, the order of evaluation, the cycle and timing of evaluation, evaluation items (questions), and the like.

In the above, the evaluation period and time point may be indicated as regular or non-regular, for example. A predefined periodic evaluation period may be adjusted according to an evaluation result (eg, an average) for the immediately preceding or previous predetermined round. For example, if the evaluation result is less than the threshold (or judged to be bad), the evaluation result is adjusted to be shorter than a preset regular evaluation cycle for the corresponding member, and in the opposite case, that is, the evaluation result is greater than the threshold (or judged to be good). ), it can be adjusted to be longer than the regular evaluation cycle preset for the member. In the above, the degree of adjustment of the regular evaluation period may be differentially adjusted and controlled depending on how far the evaluation result is from the threshold value. Alternatively, after the current evaluation is performed, feedback data for the corresponding evaluation may be generated within a predetermined time so as to be reflected in a preset evaluation period and timing adjustment. Similarly, the reliability of the previous evaluation may be calculated, and if the reliability value is less than a threshold according to the calculated reliability value, the evaluation of the corresponding member through another evaluator may be performed at an arbitrary evaluation time point. In this case, the evaluation of the corresponding member by the other evaluators may be performed according to the same evaluation method as the previous evaluator or a different evaluation method. Another evaluation method may be document evaluation through evaluation data, such as conversations exchanged between previous evaluators and members in the evaluation process, as well as the aforementioned evaluation direction, evaluation order, or evaluation items.

Non-regular evaluation with the evaluation period may be arbitrarily performed according to occurrence of a predetermined event. As the above event, for example, if the evaluation results of the regular evaluation cycle fall by more than a threshold compared to the previous one, the evaluation cycle can be set irregularly, and the member's work performance, promotion, qualification change such as acquisition of other qualifications, position or job change Occasional evaluation may be performed immediately or at any time irrespective of the preset periodic evaluation period.

A weight is assigned to each of the above-described evaluation period and timing and related contents, and based on this, an evaluation method may be influenced.

It is possible to present an evaluation type suitable for the user by continuously learning and improving using the satisfaction level and adoption of the evaluation method recommended for similar types or the same organization and subject in the past.

The evaluation relationship calculator 320 of FIG. 3 will be described with reference to FIG. 6 as follows.

Referring to FIG. 4 , the evaluation relationship calculation unit 320 includes a data classification unit 610, a data extraction unit 620, a data conversion unit 630, a learning unit 640, and a calculation unit 650. It can be. However, the present disclosure is not limited thereto.

The data classification unit 610 may classify the external data collected through the collection unit 230 of FIG. 2 according to predefined criteria.

The data extractor 620 may extract relationship information representing characteristics between a user and other users. In this case, the user and other users may indicate, for example, an evaluator and an evaluator.

The data conversion unit 630 may convert the data extracted through the data extraction unit 620 into a form that is a network analysis index and learnable form, such as proximity and mediation.

The learning unit 640 includes the personal information input by the user through the first terminal 100, the result of the data conversion unit 630, the result of the data classification unit 610 that collects and classifies tissue characteristic information, and Based on the relationship and results of evaluations performed in the past, it is possible to learn to present the evaluation suitability for potential evaluators (for example, the optimal evaluator for the proposed subject).

The calculation unit 650 may calculate and present evaluation suitability of potential evaluators based on the learned result.

Such evaluation suitability may be used as a weight for each evaluator. That is, having a high evaluation fit may mean having a high weight.

Calculation of the evaluation relationship can be seen as a process for selecting the optimal evaluator among evaluator candidates.

In determining the evaluator candidates for the purpose of calculating this evaluation relationship, if each evaluator candidate has a history of participating in evaluation in the past, the post-evaluation content of the previous evaluation may be referred to. For example, a weight may be assigned according to whether or not a corresponding evaluator candidate made a mistake as a result of post-evaluation in the previous evaluation. The post-evaluation result may include, for example, a general mistake and a mistake according to a post-evaluation state of the subject of the evaluation. First, simple mistakes, repetition of mistakes, intentional mistakes, etc. can be classified as general mistakes, and different weights can be assigned to each case. In addition, as a result, the evaluation of the subject made by the candidate evaluator at the time of the evaluation and the current state of the subject may be compared, and the degree of mistake may be quantified and weighted differently. For example, if a candidate evaluator gave a negative evaluation opinion to an evaluator, but the current evaluation of the evaluator is the best, a negative weight may be assigned to the qualification or ability as an evaluator of the candidate evaluator. On the other hand, in the above case, reference can be made to ex post consideration of such evaluation mistakes, for example, that the evaluation of the candidate for evaluation at the time was not a mistake, and whether there was any motive or motivation of the subject after that. However, it is desirable that evaluation results made too long ago be given a lower weight than recent evaluations in this post-evaluation. In addition, since the value of evaluation may change depending on the head or goal of an organization or group, it is desirable to reflect such circumstances in determining the weight.

In determining the evaluator candidates to calculate the evaluation relationship, relationship information between each evaluator candidate and the evaluated person may be considered. At this time, the relationship information is determined not only by the above-mentioned position or position, but also whether or not the same or similar job has been performed, whether or not the same department exists, gender, number of years worked together in the same department, current or recent working status in the same department, whether or not there is experience in carrying out a project together, etc. Different weights may be assigned.

In determining the evaluator candidates for the purpose of calculating this evaluation relationship, group information to which each evaluator candidate or the evaluator belongs may be further considered. For example, different weights may be assigned to a case where the atmosphere of the group to which the candidate evaluator or the target evaluator belongs is bad and good. In addition, when the candidate evaluator belongs to a group different from the target evaluator, the relationship with the leader of the group to which the evaluator belongs may also be considered. In the above case, the weight may be determined by further considering the relationship between the group to which the candidate evaluator belongs and the group to which the target evaluator belongs.

In addition, in determining the evaluator candidate for calculating this evaluation relationship, the internal reputation of the evaluator candidate, gender, the number of evaluator participations, and whether or not the target evaluator has been previously evaluated may be assigned as individual weights.

Referring to FIG. 6 , it is a block diagram of the evaluation item classification unit 330 of FIG. 3 .

In FIG. 6 , the evaluation item classification unit 330 may include a data classification unit 610 , a learning unit 620 , and a calculation unit 630 . However, the present disclosure is not limited thereto.

The data classification unit 610 may classify the accumulated item data collected through the collection unit 230 of FIG. 2 according to predetermined criteria.

The learning unit 620 may train a classification model that automatically classifies, for example, when a new item is input, based on the item data classified by the data classification unit 610 .

The calculation unit 630 may automatically classify a new item when it is input using the learned model.

In addition, the calculation unit 630 may present an optimal item set by calculating the suitability of each item and the suitability of the set of items based on the set evaluation method and relationship.

The evaluation item classification unit 330 may provide order information of evaluation items. This is to derive the evaluation result logically.

The evaluation item classification unit 330 may provide different evaluation items depending on whether the evaluation method determined according to the present disclosure is non-face-to-face evaluation or face-to-face evaluation. For example, if the evaluation item classification unit 330 is a non-face-to-face evaluation method and the evaluation order is 3rd, the evaluation item classification unit 330 may classify and provide evaluation items according to each order.

The evaluation item classification unit 330 may generate and provide evaluation items themselves by dividing them into common questions and evaluation target-specific questions. The subject-specific questions may be derived from common questions.

The evaluation item classification unit 330 may generate and store a set of each evaluation item and correct answers in the form of a look-up table, and may provide additional questions by comparing actual responses of the subject with the look-up table. That is, the evaluation item classification unit 330 may include information on additional questions or related questions according to predictable responses instead of a single correct answer in the lookup table. The additional questions or related questions may be created in the form of individual lookup tables, and link information may be created so that each lookup table in the hierarchical structure is correlated with each other so that additional evaluation item data may be immediately extracted and provided. If a response not defined in the lookup table is received, the evaluation item classification unit 330 may induce a question about an evaluation item other than the evaluation items defined in the previous lookup table set to be moved.

The evaluation item classification unit 330 may assign weights to the evaluation items based on evaluation items adopted and used in previous evaluations and post-evaluation, ie, feedback. At this time, the evaluation item classification unit 330 may adjust the weight by referring to a recent trend. In the above, the trend may include evaluation question-response between other evaluators and evaluateees, recently issued in-company information, or information that is an issue on recruitment/job sites.

The evaluation item classification unit 330 may reclassify a predefined set of items by referring to the above-described information. For example, the evaluation item classification unit 330 selects and provides some individual evaluation items or a set of evaluation items to an arbitrary member at an arbitrary time before evaluation, performs a test, and based on the result, each evaluation item or evaluation It is possible to set the adoption or non-acceptance, weight, etc. for the item set. In this case, some of the individual evaluation items or sets of evaluation items in the test may be related to a common evaluation or may include those specific to a job. Subjects of the test may include the general public, job seekers, evaluation coaching experts, evaluation experts such as psychiatrists, and the like, in addition to the members. Different weights may be assigned to the result of the corresponding test according to the test subject.

The evaluation item classification unit 330 generates and tests a plurality of evaluation items or item sets in consideration of nuance or gender for the same question or category, and selects a specific item or set according to the result to use for evaluation. .

7 is a flowchart illustrating a member evaluation and management service providing method according to an embodiment of the present disclosure. 8 to 10 are diagrams illustrating user interfaces for providing member evaluation and management services according to an embodiment of the present disclosure.

Referring to FIG. 7 , in operation 11, the computing device 200 may receive member information.

In operation 12, the computing device 200 may generate evaluation method data by extracting evaluation target and organization information from the received member information.

In operation 13, the computing device 200 may generate evaluation relationship data including an evaluator candidate and evaluation suitability for the extracted evaluation target based on the generated evaluation method data.

In operation 14, the computing device 200 may generate evaluation item data for a candidate evaluator selected from the evaluation relationship data based on the generated evaluation method data.

In operation 15, the computing device 200 may provide the generated evaluation method data, evaluation relationship data, and evaluation item data.

In (a) of FIG. 8, information on members who are evaluated is provided through the user interface provided by the service platform. Through the user interface shown in (a) of FIG. 8, evaluation subjects and their information can be known.

In (b) of FIG. 8, it is shown that evaluator candidates and their suitability are provided through the user interface provided by the service platform.

Through the user interface shown in (a) of FIG. 8 , for example, an evaluator may obtain information about a member who is an evaluation target and information about a recommendation evaluation method for a selected member.

Through the user interface shown in (b) of FIG. 8 , for example, the evaluator can know information about the evaluator candidate and the degree of suitability, and can obtain information about the most suitable evaluator candidate.

In the user interface 900 shown in FIG. 9, evaluation method and evaluation relationship information 910, item sets and their suitability 920, individual items and their suitability 930, and recommended item set and individual item information 940 is being provided. When the item set is selected in the above, information on individual items belonging to or constituting the corresponding item set and their suitability may be provided. Also, the recommended item set and individual item may be completely new item sets and individual items selected from among the item set information 920 and individual item information 930 or extracted and processed therefrom.

In the user interface 1000 shown in FIG. 10 , evaluationee information 1010, recommendation evaluator candidate information 1020 suitable for the evaluationee, recommendation evaluation method information 1030, evaluation relationship information 1040, and recommended item sets and individual Item information 1050 may be provided.

4 is a configuration block diagram of a processing unit 240 according to another embodiment of the present disclosure.

Referring to FIG. 4 , the processing unit 240 may include an evaluation propensity diagnosis unit 410, an evaluation content determination unit 420, an evaluation content correction unit 430, an evaluation guide processing unit 440, and the like.

According to an embodiment of the present disclosure, the tendency of the evaluator is diagnosed based on the evaluation data, the evaluation contents of the evaluator are evaluated, an appropriate evaluation guide is recommended from the database (DB), or word usage characteristic information of the company is calculated. Based on this, evaluation sentence improvement and feedback can be output.

To this end, in the present disclosure, evaluation of members may be performed in advance, recorded, and stored.

In this regard, the present disclosure discloses diagnosing the propensity of an evaluator and providing feedback on evaluation contents based on evaluation data, securing accuracy based on an existing evaluation propensity when preparing a quantitative evaluation of an evaluator, and an automatic sentence conversion system based on evaluation feedback.

Evaluation of evaluation contents may consist of sincerity and suitability. Conscientiousness indicates whether the evaluator wrote the evaluation with sincerity and sincerity, and suitability can indicate the suitability and appropriateness of the evaluation contents.

The degree of sincerity indicating whether the computing device 200 faithfully participated in the evaluation may be largely calculated based on the evaluation preparation and the similarity of the prepared evaluation.

The computing device 200 may determine conscientiousness based on an action log indicating a time lag between actions performed by an evaluator until completion of the evaluation in relation to preparation for evaluation. The computing device 200 may sequence the behavioral log to create a sequence vector representing rater behavioral characteristics. The computing device 200 may calculate the types of preliminary tasks performed for preparing the evaluation and the amount of time devoted thereto based on the action sequence vector.

The evaluation similarity may indicate a similarity between past evaluation contents and evaluation contents created in the past. The computing device 200 may calculate the degree of conscientiousness by integrating the time devoted to evaluation preparation, the type of actions performed prior to evaluation, and the evaluation similarity.

The suitability/evaluation type can be calculated, for example, it can be divided into general suitability and suitability for the evaluation field and purpose.

Through the general fitness, it is possible to identify slang, negative/critical tone and expression, and whether or not a specific person is referred to through morphological analysis of the evaluation content. The computing device 200 estimates the subject through a machine learning technique such as morphological analysis and topic modeling of the evaluation contents with respect to the fitness for the evaluation field and purpose, and calculates the similarity between the evaluation subject and purpose and the calculated subject. can

In addition, the computing device 200 may calculate the suitability of the corresponding evaluation by integrating the general suitability and the suitability for the evaluation field/purpose.

The diagnosis of evaluator propensity can be diagnosed through comparison with internal/external evaluators by synthesizing information about the evaluator, information on the relationship between the evaluator and the person being evaluated, and information about the organization to which the evaluator/evaluation belongs.

The computing device 200 may classify each type of evaluator information, relationship information, and organization information. That is, the type of evaluator, the type of relationship, and the type of organization can be calculated. The computing device 200 may extract an internal evaluation history matching the calculated type and an external (other customer) evaluation history existing on the service platform. The computing device 200 may diagnose the evaluation propensity of the evaluator based on the extracted internal/external evaluation history. The computing device 200 may determine whether the evaluator's propensity is lenient or conservative through comparison of the distribution of evaluation results.

In relation to the evaluation sentence correction, the computing device 200 learns the company's sentence patterns recorded through 1:1, review, and target products of the service platform, calculates word usage characteristic information and tone of the company, and calculates the The evaluation sentence can be modified through the backfire of the sentence using the characteristic.

When the computing device 200 cannot calculate word usage characteristics and tone due to lack of necessary data, the computing device 200 uses information about companies (business history, industry group, media exposure, organization chart, etc.) The tone of voice can be calculated by extracting sentence data of companies or individuals.

The computing device 200 may collect all sentences written by company members, extract contextual information through keyword extraction and topic extraction, and specify the meaning of a word used based on the extracted contextual information. In addition to specifying general dictionary meanings and synonyms, you can specify the meaning of specific words in your business. Through this, it is possible to group companies with similar propensity of usage sentences as well as information about companies.

In addition, the computing device 200 decomposes sentences into token units and builds data that can be learned by a natural language processing model through stopword processing, etc., through which a specific company or corresponding company can be applied to an existing natural language processing model such as BERT. It is possible to build a model that reflects the characteristics by learning data that reflects the characteristics of similar types of companies.

The computing device 200 performs a single language translation using the built model to convert a sentence through co-language translation/paraphrase, thereby guaranteeing anonymity. That is, the computing device 200 may convert the evaluation written by all individuals into a representative tone of the corresponding company or a representative tone of companies similar to the corresponding company.

After the conversion, the computing device 200 may be substituted as needed according to the use case of a specific word in the company specified above.

The computing device 200 may identify and remove information included in the evaluation contents that may identify the evaluation target and the evaluation creator, such as “Mrs. As shown in FIG. 12 , the computing device 200 may compare and present before/after modification of the evaluation sentence.

The evaluation propensity diagnosis unit 410 may diagnose the evaluation propensity of a target evaluator.

The evaluation content determination unit 420 may determine the evaluation content of the target evaluator.

The evaluation content correction unit 430 may perform correction on the evaluation content determined by the evaluation content determination unit 420 .

The evaluation guide providing unit 440 may provide an evaluation guide according to the evaluation contents determined by the evaluation contents determination unit 420 .

The computing device 200 may recommend items of a related evaluation guide based on the evaluation result of the evaluator and the evaluation contents. For example, the computing device 200 presents content that conveys the advantages/disadvantages of the tolerant tendency to an evaluator showing a strong tendency to be tolerant, and evaluates the same as the content that informs the importance of evaluation in accordance with the subject when the degree of fitness is low. Evaluation contents of other organizations in the field can be presented as examples.

First, an embodiment in which the computing device 200 according to an embodiment of the present disclosure diagnoses an evaluator's propensity based on evaluation data and provides feedback on evaluation contents based on the diagnosis will be described.

The evaluation of the evaluator may be made by, for example, identifying the evaluator's propensity. The computing device 200 may compare, for example, one or more other evaluators in order to identify a propensity for the target evaluator. That is, the computing device 200 may define the propensity of the subject evaluator based on the evaluations of other evaluators. In this case, the at least one other evaluator may include, for example, a member of the same rank or a member of the same organization. Meanwhile, the disposition in the above may be defined as directness, generosity, and the like.

The computing device 200 , for example, may compare the evaluation scores of at least one or more other evaluators for the same member to determine the propensity of the evaluation score of the target evaluator. At this time, if the evaluation scores of a plurality of other evaluators are used, the reference data for determining the propensity of the evaluation scores of the target evaluator is the average evaluation score of all other evaluators and the evaluation scores of all other evaluators. The average evaluation score of the evaluation scores excluding the highest and lowest evaluation scores, and the distribution of evaluation scores of all other evaluators are calculated, and based on the calculated distribution, for example, the propensity is classified and defined, and the distribution of the target evaluation score is determined. If the distribution is greater than the threshold value (eg, very wide), the other evaluators are classified again based on common items (eg, the same position, etc.) among the plurality of other evaluators, It can be re-evaluated by calculating the distribution of evaluation scores.

If the foregoing content is related to a kind of quantitative evaluation, the computing device 200 may also perform qualitative evaluation by evaluating evaluation contents of an evaluator. That is, the computing device 200 may calculate and provide qualitative evaluation results for evaluation contents, such as whether the evaluator's evaluation is of good quality, eg, the suitability of the evaluator's evaluation, the reliability of the evaluation, and the level of diligence in the evaluation.

The computing device 200 may evaluate the evaluation content of the evaluator based on, for example, keywords.

For example, the computing device 200 may calculate the suitability of the evaluation contents by evaluating whether or not the contents are suitable for the evaluation field based on keywords of the evaluation contents of the evaluator. In this case, the evaluation field may include, for example, competency, achievement, interpersonal, peer evaluation, and the like.

The computing device 200 may calculate the suitability of the evaluation contents by learning the main keywords mainly used for each evaluation field using artificial intelligence technology.

For example, when inputting the evaluation content, that is, the evaluation sentence into the AI model, if the evaluation sentence contains keywords mainly used in the evaluation field above a threshold value, the suitability of the evaluation contents of the evaluation sentence can be judged as positive or suitable. .

On the other hand, the computing device 200 extracts keywords (words) unrelated to the evaluation field from the corresponding evaluation sentence, and when the number of extracted keywords is greater than or equal to a threshold value, the suitability of the evaluation content of the corresponding evaluator is negative or negative. It can be judged as non-conformity, etc.

The computing device 200 may predefine main keywords, individually and arbitrarily assign preset weights to each keyword, and use the keywords to determine suitability for evaluation contents based on the keywords. In this case, the main keywords may be defined separately for each category, for example, positive and negative. Meanwhile, the computing device 200 may additionally assign a weight according to how many times a defined main keyword is repeatedly used in a corresponding evaluation sentence.

In defining the main keyword, the computing device 200 is used repeatedly in the evaluation sentences of at least one or more other evaluators or evaluators having the same or opposite evaluation propensity when the propensity of the evaluator is judged, or as a main keyword. Keywords that have been previously defined or whose number of times of exposure is greater than or equal to a threshold can be referred to in the evaluation statements of a plurality of other evaluators.

The computing device 200 may provide data on reliability, conscientiousness, etc. of the evaluation of the target evaluator through analysis of the evaluation statement writing pattern of the evaluator.

At this time, the evaluation text writing pattern may include, for example, the time required to write the evaluation text and the timing of writing the evaluation text.

The computing device 200 compares the time required to write the evaluation statement of the target evaluator with the time required to write the evaluation statement by other evaluators for the same member, stores the data required for the writing time, and based on this, calculates the reliability score of the corresponding evaluator in writing the evaluation statement. can be calculated The score calculated in this way may be provided together with results of trust, suspicion, distrust, and the like.

According to another embodiment, the computing device 200 does not determine the reliability of the evaluation statement based on only the time required to write the evaluation statement, but compares the average time required for evaluation by other evaluators and the distribution of the time required for evaluation by other evaluators. can be considered. In addition, the computing device 200 may apply a weight in consideration of the determined propensity of the evaluator so that the propensity of the evaluator may be reflected in the time required to prepare the evaluation statement.

With regard to the writing time, the computing device 200 may compare the writing time with respect to evaluations of the same member or other members of the corresponding evaluator with respect to other evaluators. For example, as a result of analyzing the evaluation time and evaluation time, the computing device 200 may recognize a reliability deterioration factor when evaluation data of a short time required is concentrated and distributed right before the evaluation deadline.

The computing device 200 may evaluate the evaluation statement of the target evaluator through a combination of the two, not just the time required or the time of writing the evaluation statement of the target evaluator.

The computing device 200 may extract and recommend an appropriate evaluation guide from a database (eg, an in-house DB) based on the evaluation of the target evaluator and the evaluation of evaluation contents.

The computing device 200 compares and analyzes the evaluation propensity based on the accumulated evaluation writing data of the target evaluation writer with other evaluation writers, and provides insights on the evaluation propensity and guides or recommended articles for quality feedback. .

For example, the computing device 200 may extract and present as a guide some of the evaluations of members whose evaluation scores are previously set and distributed among members of the same rank.

The computing device 200 may extract data containing content corresponding to a factor in which evaluation scores are low, among in-house columns or guides prepared in-house, and provide the extracted material to the target evaluator. For example, the computing device 200 may recommend a column titled “How to evaluate a target member suitable for a job?” among internal columns.

The computing device 200 may provide a guide by collecting and recommending articles from external sources. That is, the computing device 200 may analyze a factor resulting in a low evaluation score from an external source, bring an article containing content for improving the corresponding factor, and provide it to a target evaluator. For example, the computing device 200 may recommend a 'radical candor' article when the subject evaluation author's determined propensity is tolerant.

The computing device 200 may provide a quantitative evaluation correction system so as to secure accuracy based on existing evaluation tendencies when creating a quantitative evaluation of a subject evaluator.

For this purpose, the computing device 200 may, for example, first identify the target evaluator's propensity, and correct the quantitative evaluation result for the evaluation contents according to the identified target evaluator's evaluation propensity.

For example, when an evaluator with a lenient evaluation propensity gives a lenient evaluation score compared to other evaluators in the same way as the existing evaluation, the computing device 200 provides components corresponding to the lenient propensity (e.g., evaluation items, lenient Quantitative scores for propensity, etc.) may be removed and evaluation scores for the remaining evaluation items may be recommended. For example, the computing device 200 determines that, when the first evaluation score of the target rater is '5-4-5-5-4', the score for each evaluation item excluding the generosity component is '4-2-4-'. As shown in 4-3`, a result window suggesting application of corrections can be provided.

The computing device 200 may provide an automatic conversion system for at least one or more sentences included in the evaluation sentence based on evaluation feedback in relation to qualitative evaluation correction following the quantitative evaluation correction described above.

To this end, the computing device 200 may calculate word usage characteristic information within an organization (company) including a target evaluation writer. In this case, the word usage characteristic information may be extracted not only for words used in evaluation within the organization but also for words used in various documents and texts of the organization. This may be to ensure anonymity by referring to objectivity and lowering the risk of exposure to the subject evaluator.

The computing device 200 may extract frequently used words and sentence structures for each type of business of the company and use them for qualitative evaluation. Meanwhile, data to be extracted may be databased. These data to be extracted may include internal reports, external reports, announcements, and bulletin boards.

The computing device 200, for example, collects language or sentence data mainly used in the above extraction target data within the company, and among words included in the evaluation sentence of the target evaluator, is a synonym for a word frequently used in the organization. In this case, it can be automatically replaced or recommended to be replaced.

The computing device 200 may replace a specific word included in the inscription correction and the written evaluation sentence with a representative word in which the company's characteristics are reflected or may provide replacement recommendation.

The computing device 200 may provide a service so that it is difficult to easily infer who the target evaluator is by substituting sentence structures, sentence endings, words, etc. with frequently used sentence structures, sentence endings, words, etc. in a company.

The computing device 200 may learn words or sentence structures mainly used for evaluation based on the evaluation database (DB) and use them for sentence substitution.

For example, the computing device 200 qualitatively corrects the evaluation data with more uniformity and consistency by substituting a word that is not frequently used in the evaluation field or when there is a word that is mainly used in the evaluation field among synonyms. can be performed or recommended.

In the foregoing, for example, the language mainly used in the field of evaluation is not necessarily words used inside the target company, but data crawled from external sources such as external evaluation sites and evaluation columns may also be used or referred to.

The computing device 200 may provide overall sentence improvement and feedback data in consideration of the qualitative feedback propensity.

The computing device 200 may, for example, learn words or sentence structures mainly used for evaluation and substitute evaluation sentences based on an evaluation database (DB) to ensure anonymity of a target evaluation writer. Overall sentence improvement and feedback data may be provided in consideration of sexual feedback tendencies (eg, accent, dialect, specific words, etc.).

For example, the computing device 200 may output result feedback that can lead to correction and flexible evaluation by improving the end of a sentence to a flexible tone that is not stiff in an evaluation sentence of a subject evaluator determined to have a strict evaluation propensity. . At this time, as the result feedback, for example, a phrase such as “a flexible and soft tone can help improve a relationship with an evaluated person!” may be provided.

For example, the computing device 200 extracts a word with relatively low work relevance for an evaluation statement of a target evaluator whose reliability or sincerity is low as a result of determining the evaluation propensity, and notifies the word to improve the service. can provide

The computing device 200 may provide feedback data based on keywords and evaluation relevance when a target evaluator frequently exposes words with relatively low relevance to evaluation as described above. At this time, by recognizing keywords such as “health, alcohol, and tobacco” as the feedback data, guide data such as 'How about focusing more on work-related feedback?' can be provided to the target evaluator. have.

The identification of the propensity of the target evaluator, the qualitative correction, and the quantitative correction may be performed, for example, on an evaluation statement submitted after the target evaluator evaluates the person to be evaluated, or may be provided in advance prior to submission of the evaluation statement. In addition, the correction may be performed with respect to simulations or previous evaluation statements (for the same or different persons to be evaluated) prior to the evaluation of the subject to be evaluated, and may be provided for prior learning.

The above contents may be applied or recommended for evaluation by a target evaluator individually or in a form of combining at least two or more contents.

11 is a flowchart illustrating a member evaluation and management service providing method according to another embodiment of the present disclosure. 12 is a diagram illustrating a user interface 1200 for providing member evaluation and management services according to another embodiment of the present disclosure.

Referring to FIG. 11 , in operation 21 , the computing device 200 may receive evaluation contents of the evaluator for the member.

In operations 22 and 23, the computing device 200 may determine the propensity of the evaluator and suitability for the evaluation contents from the received evaluation contents of the evaluator, and generate evaluation contents feedback including the determined suitability.

In operation 24, the computing device 200 may correct the received evaluation contents of the evaluator step by step according to the generated evaluation contents feedback.

In operation 25, the computing device 200 may provide the generated evaluation content feedback and corrected evaluation content.

In the above, the step-by-step correction for the evaluation contents may be performed through quantitative correction and qualitative correction.

In the above, the computing device 200 may store previously received evaluation contents of the evaluator for the member.

The computing device 200 may compare the evaluation score of the evaluator by comparing the propensity of the evaluator with the evaluation score of at least one or more other evaluators for the same member.

The computing device 200 may determine suitability for the evaluation contents based on the artificial intelligence-based keywords included in the evaluation contents.

The computing device 200 may determine the suitability of the evaluation contents based on evaluation reliability and sincerity through analysis of evaluation writing patterns included in the evaluation contents.

The computing device 200 may extract evaluation guide data from a database based on the generated evaluation content feedback and provide the extracted evaluation guide data.

The computing device 200 may perform quantitative correction of the evaluation contents of the evaluator by modifying the evaluation score for each evaluation item included in the evaluation contents based on the propensity of the evaluator.

The computing device 200 may perform qualitative correction of the evaluation contents of the evaluator by converting at least one of words and sentences included in the evaluation contents of the evaluator based on the generated evaluation feedback.

The user interface 1200 shown in FIG. 12 includes evaluator information 1210, subject information 1220, evaluation content feedback information 1230, evaluator propensity information 1240, evaluation content relevance information 1250, and original evaluation content 1260, a correction copy of evaluation contents 1270, and other information may be included and provided.

Next, a method for recommending a meeting agenda for an individual interview based on the evaluation contents or evaluation records of the above-mentioned evaluators is described.

Referring back to FIG. 1 , in relation to the present disclosure, the first terminal 100 in the member evaluation and management system may represent terminals of all customers attending a meeting, and the computing device 200 may represent the first terminal ( 100), the user's personal information, tasks to be performed, information on past evaluation history, etc. may be collected, and the second terminal 150 provides external information about the company to which the user of the first terminal 100, that is, the conference participant belongs. data can be collected.

5 is a configuration block diagram of a processing unit 240 according to another embodiment of the present disclosure.

Referring to FIG. 5 , the processing unit 240 may include a meeting topic calculating unit 510 and a related information calculating unit 520 .

The meeting topic calculating unit 510 may calculate an appropriate meeting topic and agenda based on the information collected through the collecting unit.

The related information calculation unit 520 may present information about the information used for calculating the meeting topic in the meeting topic calculation unit and information on the subject/agenda of the meeting participant.

The computing device 200 analyzes member information to determine the main subject of the interview (eg, first meeting data) such as growth, immersion, collaboration, and performance, and a specific talking point (agenda) (eg, second meeting data). By presenting meeting data) and related information, it can help efficient and effective interviews between members.

The computing device 200 according to the present disclosure may provide information including a major topic, an agenda, an interview time point, and related information based on information on each of at least two interview participants, information on a relationship between participants, and organizational data. have.

In the above, the interview participant and organization data can be further divided into external data and internal data. At this time, as described above, internal data means information input by a user to the service platform and data generated and stored according to the use of the service platform, and external data may mean data that cannot be obtained through the service platform. .

In the above, the internal data among participant information may be divided into user input data, user behavior information, and user context information. The user input data refers to data accumulated by using the services of the service platform, such as personnel information such as job, position, title, year of employment, department, performance, etc. entered by the user for use of the service platform, and records of past evaluations/meetings. can do. The user behavior information may refer to information extracted based on log data indicating behaviors (page inquiry, evaluation writing, interview application, etc.) occurring as the user uses the service platform. Records of past meetings/interviews and evaluations may also fall under this category. User context information refers to a recent situation of a user, and this situation may mean a major event that affects performance, such as whether a person has recently been evaluated and whether or not a promotion has occurred. In the above, the action log data is composed of the type of action, the lag between each action, and the target of the action, and the processor may collect each log and extract the user type and additional information through machine learning. For example, in preparing for a meeting (1:1) between members, which is part of the service platform service, access the service platform in advance to register an agenda (agenda) for discussion and whether or not to add opinions on the other party's agenda and when to add it (eg For example, the type of user can be classified as adding right before the interview, adding 1 hour before the interview, etc.).

External data refers to SNS data of users other than the service platform, and through this, information such as total career and SNS activity can be extracted and applied to user type classification.

On the other hand, the relationship information may refer to information indicating the difference or homogeneity between the affiliation, position, year, evaluation experience (subject and evaluator) of each interview participant. That is, it may mean a relationship between information about each participant specified above. For example, in the case of an interview between A and B, a general manager and a deputy manager, the manager/deputy manager is participant information about A and B, and the fact that A is superior to B is relationship information.

In addition, in relation to organization information, internal data may refer to the structure of an organization (number of organizations, relationships, etc.), size, and the like input by a user. In addition, information on evaluation/reviews that are in progress or have been performed may also mean information. External data may mean the existence period of the organization, industry, industry group information, and the degree of media exposure. The computing device 200 may assess the tendency and philosophy of the organization by integrating various internal/external data, and present an appropriate major topic or agenda accordingly.

In addition, in relation to related information, the computing device 200 may provide information corresponding to the presented main topic and agenda. At this time, the range of information sharing with respect to the other party may be limited in consideration of relational information. For example, when an agenda for recent evaluation results is presented for the major topic of evaluation, information on recent and past evaluation results and trends is provided to the other party's direct supervisor (whose evaluation results are disclosed), but otherwise, the evaluation results Or, in some cases, the fact that you have been assessed recently may not be provided.

The computing device 200 may calculate a similarity/relevance between behaviors and histories of recent meeting attendees with respect to the presented topic and agenda, and provide relevant information showing a high similarity to all attendees among them.

Referring to FIG. 6, the configuration of the meeting topic calculation unit 510 of FIG. 5 will be described as follows.

The meeting topic calculation unit 510 may include a data classification unit 610, a data extraction unit 620, a data conversion unit 630, a learning unit 640, and a calculation unit 650.

The data classification unit 610 may classify the external data collected through the collection unit 230 of FIG. 2 according to predetermined criteria.

The data extractor 620 may extract user behavior information representing the characteristics of the user. In addition, the data extraction unit 620 may extract relationship information indicating a relationship between conference participants (senior, motive, etc.).

The data conversion unit 630 may convert the data extracted through the data extraction unit 620 into a learnable form.

The learning unit 640 may learn by combining the personal information input by the user through the first terminal 100 , the result of the data conversion unit 630 , and the result of the data classification unit 610 .

Based on the result of the learning unit 640, the calculation unit 650 presents the optimal topic among the selected conference participants, and the referenced information and information synthesis method for this purpose are as follows, but are not limited to the following elements. don't

By querying the past meeting history of meeting participants, it is possible to check whether the meeting is repeated and the topic/agenda that could not be resolved in the previous meeting or is to be discussed.

By integrating various information such as personal information of meeting participants, other meeting information, evaluation/review in progress, etc., the status/circumstances of current meeting participants such as waiting for evaluation results and viewing evaluation results can be checked.

It is possible to identify and synthesize topics/agendas utilized by users with similar inclinations, situations, and personal inclinations.

Appropriate topics and agendas can be presented by integrating the above aggregated information, personal information of each attendee, and information on the relationship between attendees.

Agenda can generate and present a pool of recommended agendas by considering the similarity between the list of recommended agendas developed by the existing service platform and the types of agendas used by other users, including the viewpoint, agenda, and type of meeting attendees.

Referring to FIG. 6, the configuration of the related information calculating unit 520 of FIG. 5 will be described as follows.

The related information calculation unit 520 may also include a data classification unit 610, a data extraction unit 620, a data conversion unit 630, a learning unit 640, and a calculation unit 650.

The data classification unit 610 may classify the external data collected through the collection unit 230 of FIG. 2 according to predetermined criteria.

The data extractor 620 may extract information about attendees of a meeting related to the presented topic/agenda.

The data conversion unit 630 may convert the similarity and relevance between the data extracted through the data extraction unit 620 and the selected topic/agenda into a form that can be calculated using a method such as natural language processing.

The learning and calculating units 640 and 650 may learn and calculate information with a high degree of relevance based on the converted data.

The computing device 200 controls the calculated information to be exposed to the attendees by selecting the degree of disclosure/private disclosure and masking based on the relationship information between the attendees calculated in the above-described topic/agenda calculation process, and applying the calculated information. can

The computing device 200 may perform an evaluation of a member, that is, an evaluator, in advance, store a corresponding evaluation record, and assist a meeting between the evaluator and the evaluator based on the stored evaluation record. In this case, the evaluator is not necessarily limited to the evaluator who has performed the evaluation on the subject of evaluation.

When an input for an evaluation target is received, the computing device 200 may extract and provide evaluation information on a corresponding member, that is, an evaluation record.

For example, the computing device 200 may quantify and provide evaluation target evaluation information, compare it with an evaluation average of the same occupational group/position, and separately classify and display an item in which the evaluation score of the person to be evaluated is lower than the average value.

At this time, the computing device 200 may include evaluation grounds including reference information in the member's evaluation information. For example, the reference information may include goal achievement progress, promotion records, past careers, and the like.

The computing device 200 may provide a meeting agenda recommendation service based on the evaluation information of the evaluation target.

The computing device 200 may recommend an agenda suitable for a meeting, such as a 1:1 interview, based on the evaluation information of the evaluator. At this time, the computing device 200 may recommend a related item as a meeting agenda, for example, when a low evaluation score is confirmed for a specific item based on the evaluation information of the evaluation subject.

The computing device 200 may automatically recommend a topic for a meeting with a review subject having a low evaluation of the corresponding item through machine learning.

For example, the computing device 200 uses “how to avoid stress in the organization”, “maintaining amicable relationships with colleagues” as an agenda after the meeting for an evaluation target with low ratings on interpersonal related items, such as friendliness felt by colleagues. Guidelines for action”, “Discussing programs/clubs to promote friendship among company members”, “Effective countermeasures when dealing with unfair things from superiors”, and “How to be friendly when dealing with subordinates” As a 1:1 meeting agenda, it can be automatically recommended as an agenda or question related to interpersonal relations in the company.

As described above, the computing device 200 may provide evaluation information and a question (survey) list based on the conference agenda.

The computing device 200 may provide a list of questions for collecting related information when the evaluator selects a specific agenda from among the recommended agendas.

The computing device 200, for example, when the “How to not be stressed within the organization” agenda is selected, “Q: Are there any behaviors of your colleagues that make you feel difficult?”, “Q: Persons who are difficult to approach in the department Do you have this?”, “Q: Do you want to move to a department?”, “Q: Is working alone more efficient than working with a group of people?” A list of questions may be provided as a recommendation service.

The computing device 200 may calculate items requiring additional information collection based on the evaluation information, and may provide a list of questions for collecting related information.

For example, in terms of evaluation information, if the progress toward achieving a recent goal has decreased and is below the average, the computing device 200 may ask, “Q: How do you evaluate your recent work efficiency?”, “Q: Do you think you need a break?” Have you ever?”, “Q: Is there anything you would like to improve on in terms of work handling?”, “Q: What, if any, factors hinder work handling?”, “Q: Is there anything you want from your work-related colleagues? ?”, “Q: Are you less interested in your job or are you interested in other fields?” Based on the evaluation information, etc., a list of questions suitable for grasping the member's status may be extracted and an automatic recommendation service may be provided.

The computing device 200 may provide an automatic processing output service of evaluation information in consideration of the aforementioned conference agenda.

The computing device 200 may directly set the purpose of the meeting when an evaluator establishes a meeting, or may receive and set a meeting agenda according to the above-described method.

The computing device 200 may process and output evaluation information of members according to the purpose set by the evaluator's selection.

The computing device 200 may process evaluation information according to the purpose of opening a meeting and/or a meeting agenda and output the evaluation information instead of providing a simple output service.

For example, when the purpose of the meeting is “salary negotiation,” the computing device 200 determines the degree of achievement of a goal set by agreement between the organization and the individual within the salary contract period, rather than the degree of simple goal achievement of the member, based on the evaluation information of the member. information can be printed.

The computing device 200 may output information about how much the member contributed to the organization's goal within the annual salary contract period, the organization's contribution, and the company's contribution, based on the member's evaluation information.

The computing device 200 references or outputs information on whether or not the goal agreed upon at the time of the annual salary contract was achieved by referring to the member's next year's goal and expected OKR (Object Key Result) and KPI at the time of the annual salary contract of the member last year. can serve

The computing device 200 may output annual salary information of another member having a similar evaluation score as the corresponding member or present it as comparison data.

13 is a flowchart illustrating a member evaluation and management service providing method according to another embodiment of the present disclosure. 14 is a diagram illustrating a user interface for providing member evaluation and management services according to an embodiment of the present disclosure.

Referring to FIG. 13 , in operation 31 , the computing device 200 may receive member information.

In operation 32, the computing device 200 may extract evaluation information from the received member information.

In operation 33, the computing device 200 may determine a first conference participant and a second conference participant based on the extracted evaluation information.

In operation 34, the computing device 200 extracts conference participant information about the determined first conference participant and second conference participant and relationship information between the first conference participant and the second conference participant, and retrieves the information based on artificial intelligence. Based on the extracted conference participant information and the conference participant relationship information, first conference data and second conference data related to the first conference data may be generated.

In operation 35, the computing device 200 may provide the generated first and second meeting data.

The user interface 1400 shown in FIG. 14 includes evaluator information 1410, evaluated information 1420, evaluation content feedback information 1430, recommendation agenda and suitability information 1440, and meeting agenda-based question list information 1460. ), evaluation contents 1470 processed according to the agenda of the meeting, and other information may be included and provided.

According to various embodiments of the present disclosure described above, an optimal configuration evaluation and management service can be provided to users through member evaluation and management services that consider various matters regarding organizations and members.

Steps of a method or algorithm described in connection with an embodiment of the present disclosure may be implemented directly in hardware, implemented in a software module executed by hardware, or a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which this disclosure pertains.

In the above, the embodiments of the present disclosure have been described with reference to the accompanying drawings, but those skilled in the art to which the present disclosure pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present disclosure. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100: first terminal 150: second terminal
200: computing device 210: processor
220: communication unit 230: collection unit
240: processing unit 250: control unit
260: storage unit

Claims (10)

An apparatus for providing meeting data recommendation and management service using artificial intelligence-based member evaluation information,
Memory; and
Including a processor that performs communication with the memory,
the processor,
By receiving member information,
Extracting conference participant information about a first meeting participant and a second meeting participant determined based on the evaluation information extracted from the received member information and relationship information between the first meeting participant and the second meeting participant;
Based on artificial intelligence, based on the extracted conference participant information and the conference participant relationship information, first conference data on the main topic of the conference and second conference data on the sub-topic according to the first conference data are generated and provided,
The conference participant information includes user input information input by at least one of the first conference participant and the second conference participant, user behavior information, and user context information according to the use of the service platform provided by the device. contains information that accumulates;
The user behavior information includes information extracted based on a behavior performed by at least one of the first meeting participant and the second meeting participant while using the service platform and log data for the behavior;
The log data for the behavior is composed of the type of the meeting participant's behavior, time lag between each behavior, and the target of the behavior, and the processor collects the log data for each behavior and uses machine learning to determine the type and behavior of the meeting participant. to extract additional information;
AI-based meeting data recommendation and management service provision device. delete delete delete delete According to claim 1,
The user context information includes information on the current situation or state of the conference participant,
wherein the processor defines the current situation or state in relation to a predefined event that affects the performance of the meeting participant;
AI-based meeting data recommendation and management service provision device. According to claim 6,
The relationship information includes information indicating the difference or homogeneity between the affiliation, position, year, and evaluation experience of each meeting participant,
The processor calculates the degree of fitness of the relationship between the conference participants based on the relationship information.
AI-based meeting data recommendation and management service provision device. According to claim 7,
the processor,
Determining an information sharing range for the conference participants based on the relationship information, and controlling the first and second conference data to be generated based on the determined information sharing range.
AI-based meeting data recommendation and management service provision device. A method performed by a computing device to provide artificial intelligence-based meeting data recommendation and management services,
receiving member information;
extracting evaluation information from the received member information;
determining a first meeting participant and a second meeting participant based on the extracted evaluation information;
extracting conference participant information about the determined first and second conference participants and relationship information between the first and second conference participants;
Generating first conference data on a major topic of the conference and second conference data on a sub-topic according to the first conference data based on the extracted conference participant information and the conference participant relationship information based on artificial intelligence; and
Providing the generated first and second meeting data;
The conference participant information includes user input information, user behavior information, and user context information input by at least one of the first conference participant and the second conference participant, and is related to use of the service platform provided by the computing device. Including information that accumulates along
The user behavior information includes information extracted based on a behavior performed by at least one of the first meeting participant and the second meeting participant while using the service platform and log data for the behavior;
The log data for the behavior is composed of the type of the meeting participant's behavior, time lag between each behavior, and the subject of the behavior, and the computing device collects the log data for each behavior and uses machine learning to perform the type of the meeting participant. and to extract additional information;
A method for providing artificial intelligence-based meeting data recommendation and management services. In the artificial intelligence-based member evaluation information-based meeting data recommendation and management service providing system,
a terminal requesting conference data recommendation and management; and
Including a server, wherein the server,
Member information is received, and conference participant information about the first meeting participant and the second meeting participant determined based on the evaluation information extracted from the received member information and the relationship information between the first meeting participant and the second meeting participant are extracted. And based on artificial intelligence, based on the extracted conference participant information and the conference participant relationship information, the first conference data on the main topic of the conference and the second conference data on the sub-topic according to the first conference data are generated and provided contains a processor;
The conference participant information includes user input information input by at least one of the first conference participant and the second conference participant, user behavior information, and user context information according to the use of the service platform provided by the server. contains information that accumulates;
The user behavior information includes information extracted based on a behavior performed by at least one of the first meeting participant and the second meeting participant while using the service platform and log data for the behavior;
The log data for the behavior is composed of the type of the meeting participant's behavior, time lag between each behavior, and the target of the behavior, and the processor collects the log data for each behavior and uses machine learning to determine the type and behavior of the meeting participant. to extract additional information;
AI-based meeting data recommendation and management service provision system.

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