KR102432747B1 - Method, device and system for managing performance indicator of organizational member - Google Patents

Abstract

According to one embodiment, in a method for managing performance indicators of organizational members performed by a device, an organizational member performance indicator management method comprises the steps of: obtaining first user information corresponding to a first user account when receiving a log-in request through the first user account from a first user terminal; identifying a first user registered as a member of a first company based on the first user information and identifying a first team to which the first user belongs; checking a progress rate for each goal set for the first user and calculating an average progress rate of the first user using the progress rate for each goal of the first user; checking an average progress rate for each user belonging to the first team and calculating an average progress rate of the first team using the average progress rate for each user belonging to the first team; checking an average progress rate for each team divided into the organization of the first company and calculating an average progress rate of the first company using the average progress rate for each team divided into the organization of the first company; and generating a first area configured to enable the check of the average progress rate of the first user, the average progress rate of the first team, and the average progress rate of the first company and providing a main page including the first area to the first user terminal. Accordingly, the responsibilities of the team and members can be improved.

Description

Method, device and system for managing organizational member performance indicators {METHOD, DEVICE AND SYSTEM FOR MANAGING PERFORMANCE INDICATOR OF ORGANIZATIONAL MEMBER}

The following embodiments relate to a technique for managing a performance indicator of an organization member.

Recently, interest in organizational performance management is increasing, and an Objective and Key Results (OKR) technique is widely used for performance management.

OKR is a performance management technique that started at Intel and spread to all of Silicon Valley via Google.

Intel introduced a system that evaluates performance every three months, breaking away from the method of other companies that had a one-year performance management cycle before, and this led to performance improvement. Later, John Doerr, a venture capitalist, introduced this performance management system to Google based on his experience working at Intel, and Google developed the 3-3-3 principle. Every three months, teams and individuals set three goals and three key outcomes and evaluate their performance. As a result, the duration and goals of performance management were reduced, and performance improved.

In particular, Google disclosed each OKR in-house so that anyone can see each other's OKRs. This is to create a healthy tension by giving feedback on each other's OKRs. It also encourages managers to check on OKR progress on a weekly or monthly basis so that they can share each other's progress and keep them reminded. In addition, the OKR goal was set high, but the achievement of the goal was separated from employee compensation such as personnel appraisals and bonuses.

Accordingly, there is an increasing demand to easily manage the performance indicators of organizational members, and research on related technologies is required.

Korean Patent Registration No. 10-2223531 Korean Patent Registration No. 10-1748399 Korean Patent Registration No. 10-1729283 Korean Patent No. 10-0842231

According to an embodiment, upon receiving a login request through the first user account from the first user terminal, the first user information corresponding to the first user account is obtained, and based on the first user information, the Identify the first user registered as a member, check the first team to which the first user belongs, check the progress rate for each goal set for the first user, and use the progress rate for each goal of the first user. Calculating the average progress rate of the first user, checking the average progress rate for each user belonging to the first team, calculating the average progress rate of the first team using the average progress rate for each user belonging to the first team, Check the average progress rate for each team divided into the organization of the first company, calculate the average progress rate of the first company by using the average progress rate for each team divided into the organization of the first company, and the average progress rate of the first user; An organization member performance indicator management method, comprising: creating a first area configured to enable confirmation of the average progress rate of the first team and the average progress rate of the first company, and providing a main page including the first area to the first user terminal; The purpose is to provide an apparatus and a system.

Objects of the present invention are not limited to the objects mentioned above, and other objects not mentioned will be clearly understood from the description below.

According to an embodiment, in a method for managing a performance index of an organization member, performed by a device, when a login request through a first user account is received from a first user terminal, a first user account corresponding to the first user account is received. 1 obtaining user information; identifying a first user registered as a member of a first company based on the first user information, and confirming a first team to which the first user belongs; checking a progress rate for each goal set for the first user, and calculating an average progress rate of the first user by using the progress rate for each goal of the first user; checking an average progress rate for each user belonging to the first team, and calculating an average progress rate for the first team by using the average progress rate for each user belonging to the first team; checking an average progress rate for each team divided by the organization of the first company, and calculating the average progress rate of the first company by using the average progress rate for each team divided by the organization of the first company; and a first area configured to check the average progress rate of the first user, the average progress rate of the first team, and the average progress rate of the first company, and display a main page including the first area as the first A method for managing organizational member performance indicators is provided, including providing to a user terminal.

In the organizational member performance indicator management method, upon receiving an OKR generation request through the expert mode from the first user terminal, a quick mode OKR generation page configured to enable basic setting of goals and key results to the first user terminal providing; providing, to the first user terminal, an OKR generation page in a general mode configured to enable basic setting and detailed setting of goals and key results when receiving a request for generating OKR through the intermediate mode from the first user terminal; and when receiving the OKR generation request through the beginner mode from the first user terminal, a step-by-step question required for OKR generation is displayed, and an OKR generation page in easy mode configured to input answers to the step-by-step question is displayed to the first user It may further include the step of providing to the terminal.

The method for managing the organizational member performance indicator may include: when receiving a template recommendation request for the main page from the first user terminal, confirming a job of the first user based on the first user information; confirming that the first user is included in a first group among a plurality of groups grouped based on the job through the job of the first user; obtaining template information for each main page of users belonging to the first group; generating template information of the first group by collecting the obtained template information; applying the template information of the first group to a first artificial neural network and setting a template suitable for the job of the first user based on the output of the first artificial neural network; and providing recommendation information on the set template to the first user terminal.

According to an embodiment, upon receiving a login request through the first user account from the first user terminal, the first user information corresponding to the first user account is obtained, and based on the first user information, the Identify the first user registered as a member, check the first team to which the first user belongs, check the progress rate for each goal set for the first user, and use the progress rate for each goal of the first user. Calculating the average progress rate of the first user, checking the average progress rate for each user belonging to the first team, calculating the average progress rate of the first team using the average progress rate for each user belonging to the first team, Check the average progress rate for each team divided into the organization of the first company, calculate the average progress rate of the first company by using the average progress rate for each team divided into the organization of the first company, and the average progress rate of the first user; By creating a first area configured to be able to check the average progress rate of the first team and the average progress rate of the first company, and providing a main page including the first area to the first user terminal, the goals of the team and members of the company It can be connected and aligned according to the priorities of the organization, and through this, the responsibility of the team and members is improved, and it has the effect of allowing all members to focus on the key results for achieving the goals.

On the other hand, the effects according to the embodiments are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

1 is a diagram schematically showing the configuration of a system according to an embodiment.
2 is a flowchart illustrating a process of managing a performance index of an organization member according to an exemplary embodiment.
3 is a diagram illustrating a screen on which a main page is displayed according to an exemplary embodiment.
4 is a flowchart illustrating a process of providing an OKR generation page according to a user level according to an embodiment.
5 is a diagram illustrating a screen on which an OKR generation page is displayed according to an exemplary embodiment.
6 is a flowchart illustrating a process of recommending a template for each job according to an exemplary embodiment.
7 is a flowchart illustrating a process of recruiting a team and distributing tasks according to an exemplary embodiment.
8 is a diagram for explaining learning of an artificial neural network according to an embodiment.
9 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It should be understood that all modifications, equivalents and substitutes for the embodiments are included in the scope of the rights.

Specific structural or functional descriptions of the embodiments are disclosed for purposes of illustration only, and may be changed and implemented in various forms. Accordingly, the embodiments are not limited to a specific disclosure form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Although terms such as first or second may be used to describe various elements, these terms should be interpreted only for the purpose of distinguishing one element from another. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component.

When a component is referred to as being “connected to” another component, it may be directly connected or connected to the other component, but it should be understood that another component may exist in between.

Terms used in the examples are used for the purpose of description only, and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present specification, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It is to be understood that this does not preclude the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

In addition, in the description with reference to the accompanying drawings, the same components are assigned the same reference numerals regardless of the reference numerals, and the overlapping description thereof will be omitted. In the description of the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

The embodiments may be implemented in various types of products, such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent cars, kiosks, wearable devices, and the like.

1 is a diagram schematically showing the configuration of a system according to an embodiment.

Referring to FIG. 1 , a system according to an embodiment may include a plurality of user terminals 100 and devices 200 capable of communicating with each other through a communication network.

First, the communication network may be configured regardless of its communication mode, such as wired and wireless, and may be implemented in various forms so that communication between a server and a server and communication between a server and a terminal are performed.

The plurality of user terminals 100 are terminals used by users who are registered as members of the company, and include a first user terminal 110 used by a first user, a second user terminal 120 used by a second user, The third user terminal 130 used by the third user may be included.

Each of the plurality of user terminals 100 may be a desktop computer, a notebook computer, a tablet, a smart phone, or the like. For example, as shown in FIG. 1 , the first user terminal 110 , the second user terminal 120 , the third user terminal 130 , etc. may be smartphones, and may be employed differently depending on the embodiment. have.

Each of the plurality of user terminals 100 may be configured to perform all or part of an arithmetic function, a storage/referencing function, an input/output function, and a control function of a typical computer. The plurality of user terminals 100 may be configured to communicate with the device 200 by wire or wireless.

Each of the plurality of user terminals 100 is connected to a website operated by a person or organization that provides a service using the device 200 , or is developed and distributed by a person or organization that provides a service using the device 200 . One application can be installed. Each of the plurality of user terminals 100 may be linked with the device 200 through a website or an application.

Each of the plurality of user terminals 100 may access the device 200 through a website or an application provided by the device 200 .

Hereinafter, for convenience of description, the operation of the first user terminal 110 will be mainly described, but other user terminals such as the second user terminal 120 may perform the operation of the first user terminal 110 instead. to be.

The device 200 may be a self-server owned by a person or organization that provides services using the device 200, a cloud server, or a peer-to-peer (p2p) set of distributed nodes. may be The device 200 may be configured to perform all or part of an arithmetic function, a storage/referencing function, an input/output function, and a control function of a typical computer. The device 200 may be configured to communicate with the plurality of user terminals 100 by wire or wireless, and controls the operation of each of the plurality of user terminals 100 , and displays which information on the screen of each of the plurality of user terminals 100 . You can control what is displayed.

Meanwhile, for convenience of explanation, only the first user terminal 110 , the second user terminal 120 , and the third user terminal 130 are shown in FIG. 1 among the plurality of user terminals 100 , but the number of terminals is not It may vary depending on the example. As long as the processing capacity of the apparatus 200 allows, the number of terminals is not particularly limited.

The device 200 may provide a platform to manage performance indicators of organization members, and may provide various services related to performance indicators through the platform. Here, the performance index may be set through Objective and Key Results (OKR).

That is, the device 200 may provide a service for managing performance indicators for members within the company organization, and may manage the performance indicators of members based on OKR.

The device 200 may provide an easily accessible method for beginners in utilizing the OKR, and for this purpose, the OKR may be divided into three types and provided in a quick mode, a normal mode and an easy mode. have. Here, the easy mode can increase user accessibility through a method of answering questions in stages. A detailed description related thereto will be described later with reference to FIG. 4 .

The device 200 may provide a template for each job. Specifically, when data on the templates of users is accumulated, using artificial intelligence, big data, etc., recommendation information about the template for each job field (marketing, design, etc.) can provide A detailed description related thereto will be described later with reference to FIG. 6 .

2 is a flowchart illustrating a process of managing a performance index of an organization member according to an exemplary embodiment.

Referring to FIG. 2 , first, in step S201 , the device 200 may receive a login request through the first user account from the first user terminal 110 .

That is, when the ID and password of the first user account are input to the first user terminal 110 , the device 200 may receive a login request through the first user account from the first user terminal 110 .

In step S202 , the device 200 may obtain first user information corresponding to the first user account. To this end, user information for each user account is stored in the database of the device 200 , and the device 200 may obtain the first user information from the database. Here, the first user information may include information about an identifier of the first user, a team to which the first user belongs, and a job of the first user.

In step S203 , the device 200 may identify the first user registered as a member of the first company based on the first user information, and identify the first team to which the first user belongs.

That is, when a login request through the first user account is received, the device 200 identifies whether the first user is registered as a member of which company, and identifies the first user registered as a member of the first company. It is possible to check which team of the first company the first user belongs to, thereby confirming the first team to which the first user belongs.

In step S204 , the device 200 may check a progress rate for each goal set for the first user. Here, the goals set for the first user may be registered in advance at the request of the first user, and the progress of each of the goals may be calculated as a result of tracking goal achievement.

For example, when the first goal, the second goal, and the third goal are set for the first user, the device 200 may check the progress rate of the first goal, the progress rate of the second goal, and the progress rate of the third goal, respectively. have.

In operation S205 , the device 200 may calculate the average progress rate of the first user by using the progress rate for each goal of the first user.

For example, a first goal, a second goal, and a third goal are set for the first user, and the progress rate of the first goal is 60%, the progress rate of the second goal is 50%, and the progress rate of the third goal is 40%. If confirmed, the device 200 may calculate the average progress rate of the first user as 50%.

In step S206 , the device 200 may check an average progress rate for each user belonging to the first team.

For example, if the first user, the second user, and the third user belong to the first team, the device 200 may display an average progress rate of the first user, an average progress rate of the second user, and an average progress rate of the third user. can be checked for each.

In step S207 , the device 200 may calculate the average progress rate of the first team by using the average progress rate for each user belonging to the first team.

For example, a first user, a second user, and a third user belong to the first team, the average progress of the first user is 50%, the average progress of the second user is 60%, and the average progress of the third user is When this 70% is confirmed, the device 200 may calculate the average progress rate of the first team as 60%.

In step S208 , the device 200 may check the average progress rate for each team divided into the organization of the first company.

For example, if the organization of the first company is divided into a first team, a second team, and a third team, the device 200 may determine the average progress of the first team, the average progress of the second team, and the third team. You can check the average progress of each.

In step S209 , the device 200 may calculate the average progress rate of the first company by using the average progress rate for each team divided by the organization of the first company.

For example, the organization of the first company is divided into a first team, a second team, and a third team, and the average progress of the first team is 60%, the average progress of the second team is 70%, and the average progress of the third team is When it is confirmed that the average progress rate is 80%, the device 200 may calculate the average progress rate of the first company as 70%.

In operation S210 , the device 200 may generate a first area configured to enable confirmation of the average progress rate of the first user, the average progress rate of the first team, and the average progress rate of the first company.

In step S211 , the device 200 may provide the main page including the first area to the first user terminal 110 .

That is, when receiving a login request through the first user account from the first user terminal 110 , the device 200 may provide a main page including the first area to the first user terminal 110 .

3 is a diagram illustrating a screen on which a main page is displayed according to an exemplary embodiment.

Referring to FIG. 3 , the main page may include a first area 301 , a second area 302 , a third area 303 , a fourth area 304 , and a fifth area 305 .

According to an embodiment, the main page may be set differently according to the user's job, for example, the first area 301 , the second area 302 , the third area 303 , and the fourth area ( 304 ) and a part of the fifth region 305 , or each of the first region 301 , the second region 302 , the third region 303 , the fourth region 304 , and the fifth region 305 . Position and size can be adjusted.

The first area 301 is an area in which information on OKR is displayed, and the average progress rate of the user, the average progress rate of the team to which the user belongs, and the average progress rate of the company to which the team belongs are displayed in the first area 301, respectively. can be displayed.

In addition, in the first area 301 , a predetermined important goal among the goals set for the user is displayed along with the average progress rate of the user, and a predetermined important goal among the goals set in the team is displayed together with the average progress rate of the team. , a predetermined important goal among the goals set in the company may be displayed along with the average progress of the company.

In other words, a dashboard function can be provided so that the OKR progress of the entire organization, which is shared transparently, can be grasped at a glance, and the progress of the company, team, and individual OKR is reflected and displayed in real time. issues can be identified, reviewed, and improved.

The second area 302 is an area in which TO-DO information is displayed, and a task to be processed by a user, a task processing deadline, etc. may be displayed in the second area 302 . In this case, the tasks to be processed by the user may be sorted and displayed in the order of the earliest task processing deadline.

The third area 303 is an area in which information about a 1:1 meeting is displayed, and a person who has decided to meet, a meeting time, a place, a remaining period until the scheduled date of the meeting, etc. may be displayed in the third area 303 . In this case, the meeting schedules may be displayed in order of the shortest remaining period until the scheduled meeting date.

In other words, 1:1 meeting function can be provided to build mutual trust through 1:1 meetings, and through this, regular one-on-one meetings are held to help members achieve effective results Leaders create a relationship of mutual trust and can increase the participation rate of members.

The fourth area 304 is an area in which feedback information is displayed, and a person who gave feedback, content of the feedback, etc. may be displayed in the fourth area 304 . In this case, the feedback contents may be displayed in an arrangement according to the time at which the feedback is given.

In other words, the feedback function can be provided to foster a positive communication culture through feedback and praise. A positive communication culture can be created along with growth.

In addition, a performance review function can be provided to support the growth and development of members through performance review. It can provide insight on how to improve and vision for growth of the organization.

The fifth area 305 is an area in which calendar information is displayed, and information on important tasks set for each date may be displayed in the fifth area 305 .

4 is a flowchart illustrating a process of providing an OKR generation page according to a user level according to an embodiment.

Referring to FIG. 4 , first, in step S401 , when the device 200 receives a login request through the first user account from the first user terminal 110 , the main page including the first area is displayed on the first user terminal ( 110) can be provided.

Meanwhile, if the OKR generation request menu through the expert mode is selected on the main page, in step S402 , the device 200 may receive the OKR generation request through the expert mode from the first user terminal 110 . In this case, the OKR generation request through the expert mode may mean requesting a quick mode in which only a minimal interface is provided in utilizing the OKR.

In step S403 , the device 200 may provide the OKR generation page in the quick mode to the first user terminal 110 . Here, the OKR generation page in the quick mode may be configured to enable only basic setting of goals and key results.

Meanwhile, if the OKR generation request menu through the intermediate mode is selected on the main page, in step S404 , the device 200 may receive the OKR generation request through the intermediate mode from the first user terminal 110 . In this case, the request for generating the OKR through the intermediate mode may mean requesting a general mode in which a basic interface is provided when using the OKR.

In step S405 , the device 200 may provide the OKR generation page in the normal mode to the first user terminal 110 . Here, the OKR generation page in the general mode may be configured to enable basic setting and detailed setting of goals and key results.

Meanwhile, if the OKR generation request menu through the beginner mode is selected on the main page, in step S406 , the device 200 may receive the OKR generation request through the beginner mode from the first user terminal 110 . In this case, the request for generating OKR through the beginner mode may mean requesting an easy mode in which an interface is provided through a method of answering questions step by step in utilizing the OKR.

In step S407 , the device 200 may provide the OKR generation page in the easy mode to the first user terminal 110 . Here, the OKR generation page in the easy mode may be configured to enable input of answers to step-by-step questions.

5 is a diagram illustrating a screen on which an OKR generation page is displayed according to an exemplary embodiment.

Referring to FIG. 5 , the OKR generation page includes a sixth region 501 , a seventh region 502 , an eighth region 503 , a ninth region 504 , a tenth region 505 , and an eleventh region 506 . ) may be included.

The sixth area 501 is an area in which a step-by-step question and an answer input box for a step-by-step question are displayed. For example, in the sixth area 501 , a question may be displayed for each TO-BE, AS-IS, HOW TO, and RESULT step, and an answer input box may be displayed for each step.

The seventh area 502 is an area in which recommended questions that can be used for each step are displayed. For example, in the seventh area 502 , a recommended question that can be used in the TO-BE step may be displayed.

The eighth area 503 is an area in which an interface for basic setting of a goal is displayed. For example, an interface for setting basic details such as target content, user, and target period may be displayed in the eighth area 503 .

The ninth area 504 is an area in which an interface for detailed setting of a target is displayed. For example, in the ninth area 504 , an interface for setting details such as various indicators related to a goal, an upper goal, and a disclosure range may be displayed.

The tenth area 505 is an area in which an interface for basic setting of key results is displayed. For example, in the tenth area 505 , an interface for setting basic details such as content of core results, users, and duration of core results may be displayed.

The eleventh area 506 is an area in which an interface for detailed setting of key results is displayed. For example, in the eleventh area 506 , an interface for setting detailed details such as various indicators related to key results and disclosure ranges may be displayed.

When providing the OKR generation page in the quick mode, the device 200 may provide the OKR generation page including the eighth area 503 and the tenth area 505 .

When the device 200 provides the OKR generation page in the normal mode, the device 200 displays the OKR generation page including the eighth region 503 , the ninth region 504 , the tenth region 505 , and the eleventh region 506 . can provide

When the device 200 provides the OKR generation page in the easy mode, the sixth region 501 , the seventh region 502 , the eighth region 503 , the ninth region 504 , and the tenth region 505 . , an OKR generation page including the eleventh region 506 may be provided.

6 is a flowchart illustrating a process of recommending a template for each job according to an exemplary embodiment.

Referring to FIG. 6 , first, in step S601 , the device 200 may receive a template recommendation request for the main page from the first user terminal 110 . For example, when a template recommendation request menu is selected on the main page, the device 200 may receive a template recommendation request for the main page from the first user terminal 110 .

In step S602 , the device 200 may identify the job of the first user based on the first user information.

In step S603 , the device 200 may check that the first user is included in the first group among a plurality of groups grouped based on the job through the job of the first user.

For example, the first group is a user group divided and classified designer users, the second group is a user group divided and classified users in charge of marketing, and the third group is a user group divided and classified by users in charge of human resources. In this case, when the job of the first user is identified as the designer, the device 200 may confirm that the first user is included in the first group.

In step S604 , the device 200 may obtain template information for each main page of the users belonging to the first group. To this end, template information on the main page for each user is stored in the database of the device 200 , and the device 200 may obtain template information of users belonging to the first group from the database. Here, the template information may include information on the type, location, size, etc. of the interface included in the main page.

In step S605 , when the device 200 obtains template information for each main page of users belonging to the first group, the obtained template information may be collected to generate template information of the first group.

For example, when the first group includes the first user, the second user, and the third user, the device 200 may provide template information for the first user, template information for the second user, and template information for the third user. may be combined to generate template information of the first group.

In step S606 , the device 200 may apply the template information of the first group to the pre-trained first artificial neural network. Here, the first artificial neural network may be an algorithm that receives template information of a group and then analyzes and outputs a template suitable for a job. The first artificial neural network may be learned through a method described later with reference to FIG. 8 .

In step S607 , the device 200 may set a template suitable for the job of the first user based on the output of the first artificial neural network.

According to an embodiment, the first artificial neural network may be trained to analyze a template suitable for group users through template information of the group. Through this, the first artificial neural network may analyze and output a template suitable for each job in consideration of the template characteristics of group users.

In step S608 , when a template suitable for the job of the first user is set, the device 200 may provide recommendation information on the set template to the first user terminal 110 . Here, the template recommendation information is information for recommending a template suitable for the job of the first user, and a template suitable for the job of the first user may be recommended through a template frequently used by users having the same job as the first user. have. In this case, the recommendation information on the template may include information indicating a difference between the existing template of the first user and a template suitable for the first user's job.

For example, when the job of the first user is a designer, and a majority of designers use a main page including the first area, the second area, and the third area, the device 200 may display the first area and the second area. The main page including the area and the third area may be set as a template suitable for the job of the first user.

7 is a flowchart illustrating a process of recruiting a team and distributing tasks according to an exemplary embodiment.

Referring to FIG. 7 , first, in step S701 , when the first project is assigned to the task of the first team, the device 200 may check the average progress rate of the first team.

Specifically, when the first team is set as the team in charge of the first project by the request of the manager terminal logged into the manager account, the device 200 checks the first project newly assigned to the task of the first team, You can check the current average progress of the first team.

In step S702 , the device 200 may determine whether the average progress rate of the first team is lower than the first reference value. Here, the first reference value may be set differently depending on the amount of work of the project, and may be set to a higher value as the amount of work of the project increases.

If it is determined in step S702 that the average progress rate of the first team is lower than the first reference value, in step S703 , the device 200 may determine that the first team needs to be filled.

In step S704 , if it is determined that the first team needs to be filled, the device 200 may transmit the first team's recruitment required notification message to the manager terminal.

On the other hand, if it is confirmed in step S702 that the average progress rate of the first team is higher than the first reference value, in step S705 , the device 200 is a user with the highest average progress among users belonging to the first team. can be checked.

In step S706 , if it is determined that the average progress rate of the first user is the highest among users belonging to the first team, the device 200 may determine whether the average progress rate of the first user is higher than the second reference value. Here, the second reference value may be set differently depending on the amount of work of the project, and may be set to a higher value as the amount of work of the project increases.

If it is determined in step S706 that the average progress rate of the first user is higher than the second reference value, in step S707 , the device 200 assigns the task of the first project to the first user, and assigns the task of the first user to the first project can be added.

If it is determined in step S706 that the average progress rate of the first user is lower than the second reference value, in step S708 , the device 200 performs the second highest average progress rate after the first user among users belonging to the first team. You can check the user.

In step S709 , the device 200 may distribute and allocate the task of the first project to the first user and the second user, and add the first project to each of the task of the first user and the task of the second user.

According to an embodiment, the device 200 may receive a plan view of a first space requiring design of a communication infrastructure from the first user terminal 110 . Here, the first space is a space that requires the design of communication infrastructure, and the communication infrastructure may include computing equipment, communication equipment, and the like, and the computing equipment is a computing equipment with a communication function, which communicates with an external device through the communication equipment. It is the equipment that performs the communication, and the communication equipment is the equipment that handles the connection between the computer equipment and the external device through wired/wireless communication.

The device 200 may design a structure for how to arrange the equipment in the first space before actually placing the computing equipment, communication equipment, etc. in the first space, and the location of the equipment arranged according to the designed structure may be displayed to provide a service recommended for equipment arrangement to the user.

According to an embodiment, in the plan view of the first space received from the first user terminal 110, the computing equipment to be arranged in the first space is displayed. For example, the first space is divided into a first zone and a second zone, the locations of the first and second computing equipment are marked in the first zone, and the locations of the third computing equipment are displayed in the third zone may be displayed.

Specifically, the device 200 may provide a plurality of space lists to the first user terminal 110 , and when a first space is selected from the plurality of space lists, a plan view of the first space is displayed on the first user terminal 110 . ) can be processed to be displayed on the screen. To this end, the device 200 may store and manage each floor plan in a database for a plurality of spaces.

Thereafter, when the positions of the computer equipment to be disposed in the first space are respectively set on the plan view of the first space displayed on the screen of the first user terminal 110 , the device 200 performs the first operation from the first user terminal 110 . A plan view of the first space in which computing equipment to be placed in the space is displayed may be received.

The device 200 may check whether the computing equipment to be disposed in the first space is displayed on the plan view of the first space.

If it is confirmed that the computing equipment is not displayed in the plan view of the first space, the device 200 may transmit a notification message informing that the location setting of the computing equipment to be disposed in the first space is required, and the device 200 A plan view of the first space may be received again.

When it is confirmed that the computing equipment to be disposed in the first space is marked on the plan view, the apparatus 200 may apply the plan view of the first space to the second artificial neural network learned in advance.

According to an embodiment, the second artificial neural network may be an algorithm for receiving a plan view in which the arrangement positions of the computing devices are displayed, and then outputting the arrangement positions of the communication devices according to the arrangement positions of the computing devices. The second artificial neural network may be learned through a method described later with reference to FIG. 8 .

The apparatus 200 may select a location where communication devices are to be disposed in the first space based on the output of the second artificial neural network.

According to an embodiment, the second artificial neural network may be trained to output the positions of the communication equipments arranged on the communication wiring by arranging the communication equipment to be arranged in the first space to be located on the communication wiring. To this end, when the communication wiring is marked on the plan view of the first space, the second artificial neural network may output the arrangement positions of the communication devices so that the communication devices are disposed on the communication wiring.

Also, the second artificial neural network may be trained to arrange communication devices to be disposed in the first space for each zone of the first space, and to output positions of the communication devices disposed for each zone. To this end, when the first space is divided into a plurality of zones, the second artificial neural network may output the arrangement positions of the communication devices so that the communication devices are disposed for each zone.

In addition, the second artificial neural network may be trained to output the positions of the communication equipment to be disposed in the first space by disposing the communication equipment to be disposed in the first space adjacent to the computing equipment to be disposed in the first space. . To this end, the second artificial neural network may output the arrangement positions of the communication devices so that the communication devices are disposed adjacent to the computing devices when the positions of the plurality of computing devices are marked in the plan view of the first space.

In addition, the second artificial neural network is arranged to minimize overlapping areas by comparing the area occupied by the communication devices to be disposed in the first space with the area occupied by the user movement line, so that the communication equipment and the user movement line do not overlap. It can be learned to output the location of communication equipment. To this end, when the user movement line is displayed on the plan view of the first space, the second artificial neural network may output the arrangement positions of the communication devices so that the communication equipments do not overlap the user movement line.

Thereafter, the device 200 may modify the plan view by displaying the communication devices in the plan view based on the positions where the communication devices are to be arranged in the first space.

For example, when the positions of the first computing equipment, the second computing equipment, and the third computing equipment are indicated in the plan view of the first space, the device 200 may display the first computing equipment, the second computing equipment, and the third computing equipment. Based on the location of the equipment, it is possible to select a location where the first communication equipment and the second communication equipment are to be arranged, and by displaying the first communication equipment and the second communication equipment on the plan view of the first space, the plan view of the first space can be modified.

Thereafter, the device 200 may transmit the modified plan view to the first user terminal 110 .

That is, the device 200 receives from the first user terminal 110 a plan view of the first space in which only the positions of the computer equipment are displayed, and then displays the positions of the communication equipments to obtain a modified plan view of the first space. may be transmitted to the first user terminal 110 .

According to an embodiment, when the device 200 checks the plan view of the first space received from the first user terminal 110 , when the first space is divided into a first area and a second area, the first space Based on the floor plan of , it is possible to check the computer equipment arranged in the first area. In this case, the computer equipment disposed in the first zone may be set by user selection.

Thereafter, the device 200 may calculate the number of computing devices disposed in the first area as the first number of devices.

As a result of checking the plan view of the first space received from the first user terminal 110, the device 200 determines that the first space is divided into a first area and a second area, based on the plan view of the first space, You can check the computer equipment placed in the second area. In this case, the computer equipment disposed in the second zone may be set by user selection.

Thereafter, the device 200 may calculate the number of computing devices disposed in the second area as the second number of devices.

Thereafter, the device 200 may calculate the third number of devices by summing the first number of devices and the second number of devices. That is, when the first space is divided into a first area and a second area, the device 200 may calculate the number of computer equipment disposed in the first space as the third equipment number.

Thereafter, the device 200 may check whether the third number of devices is greater than the reference value. Here, the reference value may be set differently depending on the embodiment.

If it is confirmed that the third number of equipment is greater than the reference value, the device 200 displays the first communication equipment in the first zone, and the second communication equipment in the second zone, based on the location where the communication equipments are to be placed in the first space. By displaying them, the plan view of the first space can be modified.

That is, when the number of third devices is greater than the reference value, on the plan view of the first space, the first communication equipment is displayed in the first area and the second communication equipment is displayed in the second area, so that the communication equipments can be arranged for each area. have.

Meanwhile, when it is confirmed that the third number of devices is smaller than the reference value, the device 200 may check whether the first number of devices is greater than the second number of devices.

If it is confirmed that the first number of equipment is greater than the second number of equipment, the device 200 displays the first communication equipment only in the first area based on the location where the communication equipment is to be arranged in the first space, and the second area in the second area. 2 By not displaying the communication equipment, the floor plan of the first space can be modified.

If it is confirmed that the second number of equipment is greater than the first number of equipment, the device 200 displays the second communication equipment only in the second area based on the location where the communication equipment is to be placed in the first space, and the second communication equipment is displayed in the first area. 1 By not displaying the communication equipment, the floor plan of the first space can be modified.

That is, when the number of third devices is smaller than the reference value, communication devices are displayed in only one of the first and second zones on the plan view of the first space, and only one communication device can be disposed in the first space. .

8 is a diagram for explaining learning of an artificial neural network according to an embodiment.

According to an embodiment, the artificial neural network may be any one of the first artificial neural network and the second artificial neural network. Here, the first artificial neural network is an algorithm that analyzes and outputs a template suitable for a job after receiving template information of the group, and the second artificial neural network receives a plan view showing the arrangement positions of the computational equipment, and then the computational equipment It may be an algorithm for outputting the arrangement positions of communication devices according to their arrangement positions.

The learning device in which the artificial neural network is trained may be the same device as the device 200 for managing the performance index of an organization member, or may be a separate device. Hereinafter, a process in which an artificial neural network is trained will be described.

First, in step S801 , the device 200 may generate an input to be input to the artificial neural network. In this case, the device 200 may generate an input based on the template information of the group in order to input it to the first artificial neural network, and to input to the second artificial neural network, the first artificial neural network in which the arrangement positions of the computational equipment are indicated. An input can be generated based on data about the floor plan of the space.

Specifically, the device 200 performs pre-processing on the template information of the group, and uses the template information of the group on which the pre-processing is performed as it is as an input to the first artificial neural network, or after a normal process of removing unnecessary information, 1 It is possible to generate the input of an artificial neural network.

In addition, the device 200 performs pre-processing such as unifying the extension format of the floor plan data, matching the scale, unifying the resolution, and leaving only the minimum color required in the color information, and providing the pre-processed floor plan data 2 The input of the second artificial neural network may be generated as it is as it is as an input of the artificial neural network, or through a normal process of removing unnecessary information.

In step S802 , the device 200 may apply an input to the artificial neural network. In this case, the device 200 may apply the input to the first artificial neural network when the input of the first artificial neural network is generated, and apply the input to the second artificial neural network when the input of the second artificial neural network is generated. .

The artificial neural network may be an artificial neural network trained according to reinforcement learning. The artificial neural network may be a Q-Network, DQN (Depp Q-Network), or relational network (RL) structure suitable for outputting abstract reasoning through reinforcement learning.

An artificial neural network trained according to reinforcement learning can be updated and optimized by reflecting the evaluation in various rewards. For example, the first artificial neural network may be updated and optimized through the first and second rewards, and the second artificial neural network may be updated and optimized through the third, fourth, fifth, and sixth rewards. have.

The first reward may be higher as the work area frequently used by users is disposed in the same position in the template, and the second reward may be higher as the work area not frequently used by users is not placed in the template.

The third compensation may increase as the communication devices to be disposed in the first space are positioned on the communication wiring, and the fourth compensation may increase as the communication devices to be disposed in the first space are positioned for each zone of the first space, and the fifth compensation The compensation may increase as the communication devices to be disposed in the first space are positioned closer to the computing devices to be disposed in the first space, and the sixth compensation is the area occupied by the communication devices to be disposed in the first space and the area occupied by the user movement line. In comparison, the overlapping area may be increased as the area overlaps with each other is minimized.

In step S803, the device 200 may obtain an output from the artificial neural network.

The output of the first artificial neural network may be a template suitable for group users. In this case, the first artificial neural network arranges the work area frequently used by group users at the same location in the template, and prevents the work area that group users frequently use from being placed in the template, so that the work area optimized for group users is The deployed template can be output.

The output of the second artificial neural network may be a location where communication devices are to be disposed in the first space. At this time, the artificial neural network ensures that communication equipment is located on the communication wiring, communication equipment is located in each zone, communication equipment is located adjacent to the computing equipment, and the area occupied by the communication equipment does not overlap with the area occupied by the user movement line, so that the communication is optimized It is possible to output the arrangement position of the equipment.

In step S804 , the device 200 may evaluate the output of the artificial neural network and provide a reward.

The evaluation of the output of the first artificial neural network may be divided into a first reward and a second reward. The device 200 grants a greater amount of the first reward as the work area frequently used by users is placed in the same position in the template, and the second reward is greater as the work area that users do not use much is not placed in the template. can

The evaluation of the output of the second artificial neural network may be divided into a third reward, a fourth reward, a fifth reward, and a sixth reward. The device 200 grants a greater third reward as the communication equipment to be placed in the first space is located on the communication wiring, and the fourth reward is greater as the communication equipment to be placed in the first space is located in each zone of the first space The fifth reward is awarded as the communication equipment to be arranged in the first space is located closer to the computer equipment to be arranged in the first space, and the area occupied by the communication equipment to be arranged in the first space and the area occupied by the user movement line By comparing , as the overlapping area with each other is minimized, the sixth reward may be increased.

In step S805 , the device 200 may update the artificial neural network based on the evaluation.

The device 200 is an environment in which the first artificial neural network sets the template so that the work area frequently used by users is disposed at the same location in the template, and the work area that users do not use frequently is not placed in the template , to update the first artificial neural network through the process of optimizing a policy that determines actions to be taken in specific states so that the expectation of the sum of rewards is maximized. can

For example, if there is a problem with including the first region in the template, the device 200 generates first learning data including information indicating that there is a problem with including the first region in the template, By applying the first learning data to the first artificial neural network, the first artificial neural network may be updated through the process of learning the first artificial neural network so that a work area having characteristics similar to the first area is not included in the template. .

The device 200 is configured such that the second artificial neural network is configured such that communication equipment is placed on a communication wiring, communication equipment is arranged by zone, communication equipment is placed adjacent to computing equipment, and communication equipment is placed so that communication equipment does not overlap with the user movement line. In an environment that sets the placement location of communication devices, a policy that determines the actions to be taken in specific states so that the expectation of the sum of rewards is maximized The second artificial neural network may be updated through the optimization process.

For example, the device 200 indicates that there is no problem with the first communication equipment placed on the communication wiring when there is no problem with the first communication equipment to be disposed in the first space placed on the communication wiring. Generates second learning data including information, and applies the second learning data to the second artificial neural network to learn the second artificial neural network so that communications equipment having characteristics similar to those of the first communications equipment are placed on the communication wiring Through this process, the second artificial neural network can be updated.

Meanwhile, the process of optimizing the policy may be performed through the process of estimating the maximum value of the expected value of the sum of rewards or the maximum value of the Q-function, or estimating the minimum value of the loss function of the Q-function. Estimation of the minimum value of the loss function may be performed through stochastic gradient descent (SGD) or the like. The process of optimizing the policy is not limited thereto, and various optimization algorithms used in reinforcement learning may be used.

The apparatus 200 may gradually update the artificial neural network by repeating the learning process of the artificial neural network as described above.

Specifically, in setting a template suitable for each job of the group users, the device 200 outputs a template suitable for each job of the group users in accordance with the purpose of optimizing the arrangement, size, inclusion, etc. of the work area. 1 Can train artificial neural networks.

That is, when the device 200 analyzes a template suitable for group users through the template information of the group, by reflecting reinforcement learning through the first reward, the second reward, etc., and adjusting the analysis criteria, the first artificial Neural networks can be trained.

In addition, the device 200, in designing a structure to perform information and communication infrastructure construction, in accordance with the purpose of optimizing and determining the arrangement position of the communication equipment, the second artificial outputting the arrangement position of the communication equipment in the first space Neural networks can be trained.

That is, the device 200 reflects reinforcement learning through the third reward, the fourth reward, the fifth reward, the sixth reward, etc. when optimizing and setting the arrangement positions of the communication devices, and adjusts the analysis criteria, so that the second Artificial neural networks can be trained.

9 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

The device 200 according to an embodiment includes a processor 210 and a memory 220 . The processor 210 may include at least one of the devices described above with reference to FIGS. 1 to 8 , or perform at least one method described above with reference to FIGS. 1 to 8 . A person or organization using the apparatus 200 may provide a service related to some or all of the methods described above with reference to FIGS. 1 to 8 .

The memory 220 may store information related to the above-described methods or a program in which methods to be described below are implemented. The memory 220 may be a volatile memory or a non-volatile memory.

The processor 210 may execute a program and control the device 200 . The code of the program executed by the processor 210 may be stored in the memory 220 . The device 200 may be connected to an external device (eg, a personal computer or a network) through an input/output device (not shown), and may exchange data through wired/wireless communication.

The device 200 may be used to train an artificial neural network or to use a learned artificial neural network. The memory 220 may include a learning or learned artificial neural network. The processor 210 may learn or execute an artificial neural network algorithm stored in the memory 220 . The apparatus 200 for learning an artificial neural network and the apparatus 200 for using the learned artificial neural network may be the same or may be separate.

The embodiments described above may be implemented by a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the apparatus, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA) array), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that may include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

The software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (3)

A method for managing a performance indicator of an organization member, performed by a device, the method comprising:
when receiving a login request through a first user account from a first user terminal, obtaining first user information corresponding to the first user account;
identifying a first user registered as a member of a first company based on the first user information, and confirming a first team to which the first user belongs;
checking a progress rate for each goal set for the first user, and calculating an average progress rate of the first user by using the progress rate for each goal of the first user;
checking an average progress rate for each user belonging to the first team, and calculating an average progress rate for the first team by using the average progress rate for each user belonging to the first team;
checking an average progress rate for each team divided by the organization of the first company, and calculating an average progress rate of the first company by using the average progress rate for each team divided by the organization of the first company;
Create a first area configured to check the average progress rate of the first user, the average progress rate of the first team, and the average progress rate of the first company, and display a main page including the first area to the first user providing to a terminal;
when the first project is assigned to the task of the first team, checking whether an average progress rate of the first team is lower than a preset first reference value;
determining that the first team needs to be filled when it is confirmed that the average progress rate of the first team is lower than the first reference value;
when it is confirmed that the average progress rate of the first team is higher than the first reference value, identifying a user having the highest average progress rate among users belonging to the first team;
checking whether the average progress rate of the first user is higher than a preset second reference value when it is determined that the average progress rate of the first user is the highest among users belonging to the first team;
allocating the task of the first project to the first user when it is confirmed that the average progress rate of the first user is higher than the second reference value; and
When it is confirmed that the average progress rate of the first user is lower than the second reference value, a second user with the highest average progress rate after the first user is identified among users belonging to the first team, and the first user Comprising the step of distributing and allocating the task of the project to the first user and the second user,
How to manage organizational member performance indicators. According to claim 1,
providing, to the first user terminal, an OKR generation page in a quick mode configured to enable basic setting of goals and key results when receiving a request for generating OKR through the expert mode from the first user terminal;
providing, to the first user terminal, an OKR generation page in a general mode configured to enable basic setting and detailed setting of goals and key results when receiving a request for generating OKR through the intermediate mode from the first user terminal; and
Upon receiving the OKR generation request through the beginner mode from the first user terminal, a step-by-step question required for OKR generation is displayed, and an OKR generation page in easy mode configured to input answers to the step-by-step question is displayed on the first user terminal Further comprising the step of providing as
How to manage organizational member performance indicators. According to claim 1,
when receiving a template recommendation request for the main page from the first user terminal, confirming the job of the first user based on the first user information;
confirming that the first user is included in a first group among a plurality of groups grouped based on the job through the job of the first user;
obtaining template information for each main page of users belonging to the first group;
generating template information of the first group by collecting the obtained template information;
applying the template information of the first group to a first artificial neural network and setting a template suitable for the job of the first user based on the output of the first artificial neural network; and
Further comprising the step of providing recommendation information for the set template to the first user terminal,
How to manage organizational member performance indicators.

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