WO2022225285A1

WO2022225285A1 - Method and device for analyzing and recommending schedules by using artificial intelligence

Info

Publication number: WO2022225285A1
Application number: PCT/KR2022/005562
Authority: WO
Inventors: 한정훈
Original assignee: 주식회사 마일스톤삼육오
Priority date: 2021-04-20
Filing date: 2022-04-19
Publication date: 2022-10-27
Also published as: KR20220145944A

Abstract

According to one embodiment of the present specification, a server operating method for analyzing and recommending schedules by using artificial intelligence can be provided. Here, the server operating method may comprise the steps of: receiving schedule management information from each of a plurality of user devices; generating a group task corresponding to at least one device group from among the plurality of user devices; generating group task recommendation time information about a group task through artificial intelligence on the basis of the received schedule management information for each of the plurality of user devices; and transmitting group task recommendation time information to at least any one device in the group.

Description

Method and device for analyzing and recommending schedules using artificial intelligence

Embodiments relate to a method and apparatus for analyzing and recommending a schedule using artificial intelligence. More particularly, it relates to a method and apparatus for analyzing schedules for multiple users and recommending a common schedule.

In the past, it was common to manage personal or company schedules through calendars or memos. However, in the current environment where a large number of people create value through mutual exchange and conduct many discussions, there is a limit to the individual's ability to manage schedules through calendars and memos. In particular, when a large number of people hold a meeting or perform work together, many resources may be required to determine a meeting schedule, etc. in consideration of their respective schedules, and may not be efficient.

In addition, if individual circumstances are taken into consideration in group meetings and work, inefficiency may occur in setting a meeting schedule or conducting group work. In consideration of the above points, the following describes a method for efficiently managing schedules by analyzing multiple schedules using artificial intelligence and recommending schedules based on the analyzed information.

This specification relates to a method and apparatus for analyzing and recommending a schedule using artificial intelligence.

The present specification relates to a method of recommending a schedule for group work by analyzing schedules for each of a plurality of users.

The present specification relates to a method of recommending a schedule for a group task based on a user priority among a plurality of users.

This specification relates to a method of extracting a job candidate time using user information and other information, and determining the final time to perform a group job among the extracted candidate time.

The problem to be solved in the present specification is not limited to the above, but may be extended to various matters that can be derived by the embodiments of the invention described below.

According to an embodiment of the present specification, it is possible to provide a method of operating a server that analyzes and recommends a schedule using artificial intelligence. In this case, the method of operation of the server includes the steps of receiving schedule management information from each of a plurality of user devices, generating a group task corresponding to at least one device group among a plurality of user devices, and receiving a plurality of user devices. It may include generating group work recommendation time information for group work through artificial intelligence based on the schedule management information for each and transmitting the group work recommendation time information to at least any one or more devices in the group have.

In addition, according to an embodiment of the present specification, at least any one or more essential members and at least any one or more auxiliary members are included in the group, and when group work recommended time information is generated, the server sends the information to the device corresponding to the essential member. The schedule management information for the device may be reflected in the AI, and the schedule management information for the device corresponding to the auxiliary member may not be reflected in the AI.

In addition, according to an embodiment of the present specification, a priority is set for each of at least one or more essential members, and the server may generate group work recommendation time information by reflecting the priority.

In addition, according to an embodiment of the present specification, the group task recommendation time information includes candidate time information and specific time information, but the candidate time information includes schedule management information received from devices corresponding to at least one or more essential members. Based on the information on the available time for group work, the specific time information may be information about the time group work is performed among candidate time information.

In addition, according to an embodiment of the present specification, the artificial intelligence may derive specific time information by taking candidate time information as input and reflecting priority information, auxiliary member information, information included in a database, and feedback information.

In addition, according to an embodiment of the present specification, the server receives a group work availability confirmation request message to the device corresponding to the essential member having the highest priority among at least one essential member based on specific time information, Upon receiving a group work availability confirmation response message from the required member with the highest priority, the specific time information is determined, and the group work recommendation time information including the confirmed specific time information and candidate time information is transmitted to at least one device in the group. can be sent to

In addition, according to an embodiment of the present specification, schedule management information is set by a user based on a plurality of categories, and among the schedule management information, the first category information is transmitted to the server, and the second category information is not transmitted to the server. it may not be

The present specification may provide a method and apparatus for analyzing and recommending a schedule using artificial intelligence.

The present specification has the effect of setting the group task in consideration of the schedule of the plurality of users by analyzing the schedule for each of the plurality of users and recommending the schedule for the group task.

In the present specification, schedule recommendation in consideration of work importance may be possible by recommending a schedule for group work based on user priorities among a plurality of users.

The present specification has the effect of extracting a job candidate time using user information and other information, and determining the final time to perform a group job among the extracted candidate time to enable efficient job recommendation.

The problem to be solved in the specification is not limited to the above, but may be extended to various matters that can be derived by the embodiments of the invention described below.

1 is a diagram illustrating an example of an operating environment of a system according to an embodiment of the present specification.

2 is a block diagram for explaining the internal configuration of the computing device 200 according to an embodiment of the present specification.

3 is a diagram illustrating a method of analyzing a user's schedule according to an embodiment of the present specification.

4 is a diagram illustrating a method of analyzing schedules of multiple users according to an embodiment of the present specification.

5A is a diagram illustrating a method of recommending a task by analyzing a schedule of a plurality of users according to an embodiment of the present specification.

5B is a diagram illustrating a method of recommending a task by analyzing a schedule of a plurality of users according to an embodiment of the present specification.

5C is a diagram illustrating a method of recommending a task by analyzing a schedule of a plurality of users according to an embodiment of the present specification.

6A is a diagram illustrating a method of recommending a task based on artificial intelligence in an embodiment of the present specification.

6B is a diagram illustrating a method of recommending a task based on artificial intelligence in an embodiment of the present specification.

6C is a diagram illustrating a method of recommending a task based on artificial intelligence in an embodiment of the present specification.

6D is a diagram illustrating a method of recommending a task based on artificial intelligence in an embodiment of the present specification.

7 is a diagram illustrating group work recommendation time information according to an embodiment of the present specification.

8 is a flowchart illustrating a method of analyzing and recommending a schedule using artificial intelligence in an embodiment of the present specification.

In describing the embodiments of the present specification, if it is determined that a detailed description of a well-known configuration or function may obscure the gist of the embodiment of the present specification, a detailed description thereof will be omitted. And, in the drawings, parts not related to the description of the embodiments of the present specification are omitted, and similar reference numerals are attached to similar parts.

In the embodiments of the present specification, when it is said that a certain element is "connected", "coupled" or "connected" with another element, it is not only a direct connection relationship, but also an indirect relationship where another element exists in the middle. It can also include human connections. Also, when it is said that a component includes "includes" or "has" another component, it means that another component may be further included without excluding other components unless otherwise stated. .

In the embodiments of the present specification, terms such as first, second, etc. are used only for the purpose of distinguishing one component from other components, and unless otherwise specified, the order or importance between the components is limited. I never do that. Accordingly, within the scope of the embodiments of the present specification, a first component in an embodiment may be referred to as a second component in another embodiment, and similarly, a second component in an embodiment is referred to as a first component in another embodiment. may also be called

In the embodiment of the present specification, the components that are distinguished from each other are for clearly explaining each characteristic, and do not necessarily mean that the components are separated. That is, a plurality of components may be integrated into one hardware or software unit, or one component may be distributed and formed into a plurality of hardware or software units. Accordingly, even if not specifically mentioned, such integrated or dispersed embodiments are also included in the scope of the embodiments of the present specification.

In the present specification, the network may be a concept including both wired and wireless networks. In this case, the network may mean a communication network in which data exchange between devices and systems and devices can be performed, and is not limited to a specific network.

Embodiments described herein may have aspects that are entirely hardware, partly hardware and partly software, or entirely software. As used herein, “unit,” “device,” or “system,” or the like, refers to a computer-related entity such as hardware, a combination of hardware and software, or software. For example, as used herein, a part, module, device, or system is a running process, processor, object, executable, thread of execution, program, and/or computer. (computer), but is not limited thereto. For example, both an application running on a computer and a computer may correspond to a part, module, device, or system of the present specification.

In addition, in the present specification, the device may be a mobile device such as a smart phone, a tablet PC, a wearable device, and a head mounted display (HMD), as well as a fixed device such as a PC or a home appliance having a display function. Also, as an example, the device may be an in-vehicle cluster or an Internet of Things (IoT) device. That is, in the present specification, a device may refer to devices capable of an application operation, and is not limited to a specific type. Hereinafter, for convenience of description, a device in which an application operates is referred to as a device.

In the present specification, the communication method of the network is not limited, and the connection between each component may not be connected by the same network method. The network may include not only a communication method using a communication network (eg, a mobile communication network, a wired Internet, a wireless Internet, a broadcasting network, a satellite network, etc.) but also short-range wireless communication between devices. For example, the network may include all communication methods through which an object and an object can network, and is not limited to wired communication, wireless communication, 3G, 4G, 5G, or other methods. For example, a wired and/or network may be a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Global System for Mobile Network (GSM), an Enhanced Data GSM Environment (EDGE), a High Speed Downlink Packet Access (HSDPA), W-CDMA (Wideband Code Division Multiple Access), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), Bluetooth (Bluetooth), Zigbee, Wi-Fi, VoIP (Voice over) Internet Protocol), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and one or more communication methods selected from the group consisting of ultrasonic communication can refer to a communication network by means of a communication method. However, the present invention is not limited thereto.

Components described in various embodiments do not necessarily mean essential components, and some may be optional components. Accordingly, an embodiment composed of a subset of components described in the embodiment is also included in the scope of the embodiment of the present specification. In addition, embodiments including other components in addition to the components described in various embodiments are also included in the scope of the embodiments of the present specification.

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

1 is a diagram illustrating an example of an operating environment of a system according to an embodiment of the present specification. Referring to FIG. 1 , a user device 110 and one or

more servers

120 , 130 , 140 are connected through a network 1 . 1 is an example for the description of the invention, and the number of user devices or the number of servers is not limited as in FIG. 1 .

The user device 110 may be a fixed terminal implemented as a computer system or a mobile terminal. The user device 110 is, for example, a smart phone, a mobile phone, a navigation system, a computer, a laptop computer, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console (game). console), a wearable device, an Internet of things (IoT) device, a virtual reality (VR) device, an augmented reality (AR) device, and the like. In one example embodiments, the user device 110 is substantially one of a variety of physical computer systems capable of communicating with the other servers 120 - 140 over the network 1 using a wireless or wired communication scheme. can mean

Each server may be implemented as a computer device or a plurality of computer devices that communicate with the user device 110 and the network 1 to provide commands, codes, files, contents, services, and the like. For example, the server may be a system that provides each service to the user device 110 connected through the network 1 . As a more specific example, the server may provide a service (eg, information provision, etc.) targeted by the corresponding application to the user device 110 through an application as a computer program installed and driven in the user device 110 . As another example, the server may distribute the file for installation and operation of the above-described application to the user device 110 , and may provide a corresponding service by receiving user input information.

For example, in the following, each user device may operate based on any one of the user devices of FIG. 1 . For example, the user device may communicate with another user device or a server (or system) through a network and exchange information. Also, as an example, the user device described below may be a recording medium in which software or an application for managing a schedule is recorded. Also, as an example, each of the plurality of users may generate schedule information based on the above-described software or application, and may share the generated information with other users. As another example, the software or application for managing the schedule may operate based on one server (or system). Here, one server (or system) obtains schedule information for each of a plurality of users through the software or application, and uses artificial intelligence based on the acquired schedule information to derive group schedule information (or system) can be Here, the server (or system) may communicate with each of a plurality of users based on the network of FIG. 1 , and schedule management may be performed through this. Hereinafter, for convenience of description, a software or application for performing schedule management is referred to as a schedule management software, and a server or system for managing the schedule management software is referred to as a schedule management server. However, the schedule management software may be recorded and driven in the user device in the form of an application or other form, and is not limited to a specific form. In addition, as an example, the schedule management server is also a server that performs schedule management through communication with a plurality of devices, and it may be possible to operate based on a central server or operate through each node based on edge computing, in a specific form. It may not be limited.

2 is a block diagram for explaining the internal configuration of the computing device 200 according to an embodiment of the present specification. Such a computing device 200 may be applied to one or more user devices 110-1 and 110-2 or servers 120-140 described above with reference to FIG. 1, and each device and server may add some components or By being configured to exclude, it may have the same or similar internal configuration.

Referring to FIG. 2 , the computing device 200 may include a memory 210 , a processor 220 , a communication module 230 , and a transceiver 240 . The memory 210 is a non-transitory computer-readable recording medium, and is a non-volatile memory such as random access memory (RAM), read only memory (ROM), disk drive, solid state drive (SSD), flash memory, and the like. It may include a permanent mass storage device. Here, a non-volatile mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be included in the aforementioned device or server as a separate permanent storage device distinct from the memory 210 . In addition, the memory 210 stores an operating system and at least one program code (eg, a browser installed and driven on the user device 110 , or an application installed on the user device 110 to provide a specific service). can be These software components may be loaded from a computer-readable recording medium separate from the memory 210 . The separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD/CD-ROM drive, and a memory card.

In another embodiment, the software components may be loaded into the memory 210 through the communication module 230 instead of a computer-readable recording medium. For example, at least one program is a computer program (eg, a computer program installed by files provided through the network 1 by a file distribution system (eg, the above-described server) that distributes the installation files of developers or applications) It may be loaded into the memory 210 based on the above-described application).

The processor 220 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. The instructions may be provided to the processor 220 by the memory 210 or the communication module 230 . For example, the processor 220 may be configured to execute received instructions according to program code stored in a recording device such as the memory 210 .

The communication module 230 may provide a function for the user device 110 and the server 120-140 to communicate with each other through the network 1, and the device 110 and/or the server 120-140, respectively. It may provide a function for communicating with other electronic devices.

The transceiver 240 may be a means for interfacing with an external input/output device (not shown). For example, the external input device may include devices such as a keyboard, mouse, microphone, and camera, and the external output device may include devices such as a display, a speaker, and a haptic feedback device.

As another example, the transceiver 240 may be a means for an interface with a device in which functions for input and output are integrated into one, such as a touch screen.

Also, in other embodiments, the computing device 200 may include more components than those of FIG. 2 according to the nature of the applied device. For example, when the computing device 200 is applied to the user device 110, it is implemented to include at least some of the above-described input/output devices, or a transceiver, a global positioning system (GPS) module, a camera, various sensors, It may further include other components such as a database and the like. As a more specific example, when the user device is a smartphone, the acceleration sensor, gyro sensor, camera module, various physical buttons, buttons using a touch panel, input/output ports, vibrators for vibration, etc. Various components may be implemented to be further included.

As an example, in the following, a user device and a server (or system) may be an apparatus operating based on FIG. 2 . That is, in the following, the user device and the server may be a subject that is connected through a mutual network to perform communication.

Here, each subject is provided with a memory, a processor, a communication module and a transceiver and other configurations based on FIG. 2, and based on this, at least one of software, an application, and a program to provide a service based on schedule management may be a computing device operating as a recording medium, and may not be limited to a specific computing device.

Referring to FIG. 3 , the schedule management software may be recorded and driven in the user device. In this case, the schedule management software may provide calendar information and time information for each date in order to manage the schedule of the corresponding user. More specifically, the schedule management software may check the input schedule information based on the calendar information and time information for each date. Here, the schedule management software may further provide category information for schedule information input by the user. For example, the schedule management software may provide the category information 310 , 320 , and 330 as individual categories in consideration of each user's schedule type. For example, the category information 310 , 320 , and 330 may be provided in different forms. As an example, each category information 310 , 320 , and 330 in FIG. 3 may be provided to be distinguished by color. As a more specific example, in FIG. 3 , category information 310 for “annual leave” is displayed in blue, category information 320 for “personal schedule” is displayed in yellow, and category information for “meetings and meetings” in FIG. 3 . 330 may be indicated in green. However, this is only one example and is not limited to the above-described embodiment and may be displayed in other forms. Through this, users who use schedule management software can efficiently manage their own schedules.

In this case, as an example, the schedule management software may operate based on the schedule management server. Also, the schedule management server may perform schedule recommendation in consideration of schedule information for a plurality of users who manage schedules based on schedule management software, which will be described later. In consideration of the above, the schedule management server may provide a notification to input schedule information based on a predetermined period or a preset time through the schedule management software, and may provide a limit time for schedule information input.

More specifically, the schedule server may provide a notification for inputting schedule information on a specific date (e.g. 1st) of each month based on calendar information for each individual user, and may set a limit time to input schedule information. In addition, as an example, the notification information for inputting schedule information provides a notification to input schedule information on a specific date every week (e.g. Monday) or at a specific time every day (e.g. 9 o'clock), and sets a limit time to input schedule information. can do. As another example, the schedule management server may provide a notification for input of a plurality of schedule information at a plurality of time points (e.g., every 1st day of each month and every Monday), and set a limit time to perform schedule information input. For example, since the schedule management server should perform group schedule information recommendation using schedule information of a plurality of users, it may not be able to efficiently recommend when schedule information is missing. In consideration of the above, the schedule management server may set a notification and limit time for schedule information input for each of a plurality of users based on a predetermined period or a preset time point, and is not limited to the above-described embodiment.

4 is a diagram illustrating a method of analyzing schedules of multiple users according to an embodiment of the present specification. Referring to FIG. 4 , the schedule management server may receive schedule information from each of a plurality of

users

410 , 420 , and 430 . In this case, as an example, the schedule information received by the schedule management server may be schedule information to which category information is not reflected. That is, each user device may transmit all of the input schedule information to the schedule management server irrespective of the category. That is, the schedule management server may receive information on whether or not the schedule is managed irrespective of the category, and may perform group work recommendation in consideration of this.

As another example, some categories of the above-described category information may not be reported as schedule information to the schedule management server. More specifically, a specific category type (e.g. personal schedule) among category types set in individual devices may not be transmitted to the schedule management server.

As an example, the schedule management server may utilize all of each schedule information to recommend group tasks, and schedules with low priorities for individual devices need to be excluded so as not to become an obstacle in recommending group schedules. In consideration of the above, schedule information classified into a specific category among categories set in individual devices may not be transmitted to the schedule management server, and through this, effective group work recommendation may be performed. Thereafter, the schedule management server may perform group work recommendation using schedule management information received from a plurality of devices, which will be described later.

As an example, FIGS. 5A to 5C are diagrams illustrating a method of recommending tasks by analyzing schedules of a plurality of users according to an embodiment of the present specification.

5A to 5C , the schedule management server may recommend group work hours. Here, the group task may be a task commonly performed based on a preset user group. In addition, the schedule management server may set some users among a plurality of users who use the schedule management software for group work as members in the group. Here, the members in the group for group work may include essential members and auxiliary members. For example, the schedule management server may recommend group work hours by reflecting schedule management information transmitted from the device of the essential member. On the other hand, the schedule management server may recommend group work hours without reflecting the schedule management information transmitted from the auxiliary member's device, but may not be limited thereto.

As a specific example, the schedule management server may receive a request for group task creation and group work time recommendation from at least one of devices operating based on the schedule management software. In this case, when a request message for group work time recommendation is transmitted without group work creation, the request message may include information on essential members and auxiliary members as well as the number of group members. In this case, the schedule management server receiving the request message may receive the schedule management information by requesting the schedule management information from devices corresponding to the essential members included in the request message. After that, the schedule management server can recommend group work time using artificial intelligence, which will be described later. In this case, the schedule management server may transmit a response message including group work time recommendation information obtained through artificial intelligence to the device that transmitted the request message. In addition, as an example, the schedule management server may transmit group work time recommendation information to all devices corresponding to members in the group, and is not limited to the above-described embodiment.

As another example, a priority may be set for each essential member in the group. More specifically, as described above, the schedule management server may consider schedule management information from devices corresponding to essential members, and use this to generate group work time recommendation information. In this case, in consideration of a case in which a plurality of essential members in the group exist and each schedule information overlaps, priority may be determined for the essential members. In this case, the schedule management server may generate group work time recommendation information by reflecting priorities for essential members.

As another example, priority may not be set for essential members. At this time, the schedule management server may generate group work time recommendation information using the schedule management information of essential members. may generate a message indicating , and is not limited to the above-described embodiment.

As another example, the priority of the essential member and the essential member may be determined based on the type of group task and the group task content. More specifically, a simple participant can be set as the above-described auxiliary member based on the user role information in the group task based on the group task content. On the other hand, in consideration of the role information of the user performing the role of the host or speaker in the group, the corresponding users can be set as essential members. As another example, if all members are required to attend based on the group type, all members may be set as mandatory members. That is, the schedule management server may receive schedule management information of all members, generate group work recommendation time information in consideration of all schedules, and also generate a message about whether or not it is impossible, and is limited to the above-described embodiment. do not lose

As a specific example, referring to FIG. 5B , the schedule management server may create a group task based on a request from a device in which the schedule management software is recorded. In this case, the essential members of the group task may be user 1 510 , user 2 520 , and user 3 530 . In addition, as an example, other auxiliary members may be further set for group work, and the present invention is not limited to the above-described embodiment. Accordingly, the schedule management server may receive only schedule information for user 1 510 , user 2 520 , and user 3 530 and reflect it in the group work time recommendation information. Here, referring to FIG. 5C , the group work time recommendation information may include a plurality of candidate time information. For example, “meeting available time 1” and “meeting available time 2” and “meeting available time 3” may be candidate time information. In this case, as an example, the priority among the essential members may be set in the order of user 1, user 2, and user 3. However, this is only one example and is not limited to the above-described embodiment. In this case, the schedule management server may derive specific time information from among the candidate time information by reflecting the above-mentioned priority. As an example, by reflecting User 1's group role and User 1's other schedules, a time with the smallest schedule of User 1 may be selected as specific time information, and "meeting available time 1" may be derived in FIG. 5C . In this case, as an example, the schedule management server includes information of "meeting available time 1" and "meeting available time 2" and "meeting available time 3" as candidate time information and "meeting available time 1" as specific time information. You can create business hours recommendation information. Thereafter, the schedule management server may transmit group work time recommendation information from all members in the group, through which the group work time recommendation may be performed.

As another example, FIGS. 6A to 6D are diagrams illustrating a method of recommending a task based on artificial intelligence. For example, the above-described group work time recommendation information may be performed through artificial intelligence. As an example, the schedule management server may include an AI engine 610 based on artificial intelligence. For example, the AI engine 610 may be a learning model operating based on artificial intelligence, and may perform learning based on input information and output information. Also, as an example, the schedule management server may include a database 620 , and the database 620 may store information necessary for schedule management and other information. As another example, the database 620 may be located outside, and may be connected to the AI engine 610, and is not limited to the above-described embodiment.

As an example, when the schedule management server generates group work as described above and generates group work time recommendation information, group work time recommendation information may be derived based on the AI engine 610 . In this case, as an example, the AI engine 610 may acquire a plurality of pieces of information based on the database as well as the above-described priority information and auxiliary attendee information. As another example, the AI engine 610 may derive group work time recommendation information by reflecting information fed back from the result information, and is not limited to the above-described embodiment.

As a more specific example, the AI engine 610 may further acquire user role information within each schedule. For example, in the case of the above-described auxiliary member, other schedules may be overlapped, and in the case of an essential member who is a speaker, other schedules may be excluded at appropriate times before and after. Also, as an example, the AI engine 610 may further acquire big data information about essential members. As an example, the AI engine 610 may further acquire preferred time zone information for essential members through big data analysis. Also, as an example, the AI engine 610 may generate group work time recommendation information in consideration of members other than each group member. As a specific example, a user who can participate as an additional member or an alternate member may be recommended by further using the user's team member information and the project contents of the corresponding group. As another example, the AI engine 610 may analyze the meeting video (e.g. facial recognition) to generate meeting attention information and concentration information for each user, and reflect this to generate group work recommendation time information.

As a specific example, referring to FIG. 6A , the AI engine 610 may generate candidate time information for group work based on schedule management information of essential members. In this case, the AI engine 610 may extract specific time information in consideration of information obtained from the database 620 by inputting the candidate time information, feedback information, and other information. Here, the group work recommendation time information may include both the above-described candidate time information and specific time information. That is, the AI engine 610 may extract and recommend a specific time by using a plurality of pieces of information from the candidate time information, and is not limited to the above-described embodiment.

As another example, referring to 6b to 6d, the AI engine 610 may perform specific time extraction in consideration of a specific user. For example, the specific user may be a member having a high priority in attending group work as the above-mentioned essential member. For example, in FIGS. 6B to 6D , user 1 630 is a member having a higher priority than user 2 640 , and may be a member with the highest priority among essential members. In this case, the AI engine 610 may inquire whether the group work is possible based on the information about the specific time selected among the candidate times to the user 1 630 and the user 2 640 . At this time, if both user 1 (630) and user 2 (640) can participate in group work, the AI engine 610 determines a specific time, and collects all group work recommended time information including candidate time information and specific time information. It can be forwarded to group members.

On the other hand, referring to FIG. 6C , when the high priority user 1 630 indicates that the group task cannot be attended, the AI engine 610 newly selects a specific time from the candidate times regardless of the user 2 640 and , it is possible to determine a specific time based on the final agreement, and deliver group work recommendation time information including candidate time information and specific time information to all group members.

In addition, referring to FIG. 6D , when the low priority user 2 640 instructs not to attend the group work, the AI engine 610 considers the user 1 630's consent to change the group work time for a specific time can be newly selected from candidate times, a specific time can be determined based on the final agreement, and group work recommended time information including candidate time information and specific time information can be delivered to all group members. On the other hand, the AI engine 610 fixes a specific time based on the user 1 630 without the consent of the user 1 630, and transmits the group work recommendation time information including the candidate time information and the specific time information to all group members. can transmit That is, the AI engine 610 receives the consent of the essential members, but may consider the priority, and is not limited to the above-described embodiment.

Also, as an example, FIG. 7 is a diagram illustrating group work recommendation time information. As described above, the group work recommendation time information may include candidate time information as well as specific time information. As an example, the specific time information may be designated as a specific time for performing a group task, such as “Estimated time: Tuesday (11th), 15:00 to 17:00”. Also, as an example, the group work recommendation time information may include preliminary time information as candidate time information. In this case, the preliminary time information may further include information on whether other members are present as additional information. For example, candidate time information is "Preliminary meeting time 1: Wednesday (12th), 10:00-12 o'clock (all attendees are available)", "Preliminary meeting time 2: Friday (14th), 13:00-15:00 (user 3 It may include additional information such as “Cannot attend/Proceed”.

In addition, as an example, the group work recommendation time information may further include additional information such as "If the meeting of this week cannot be conducted, it will be postponed to the next week." That is, the group work recommendation time information may further include candidate time information and additional information, and through this, information on volatility and preliminary information in relation to group work time recommendation can be delivered. Through this, devices that have received group work recommendation time information from the schedule management server can efficiently manage schedules, and are not limited to the above-described embodiment.

Referring to FIG. 8 , an operation method of a server that analyzes and recommends a schedule using artificial intelligence may be considered. At this time, the server may receive schedule management information from each of the plurality of user devices. (S810) Thereafter, the server may create a group task corresponding to at least any one device group among the plurality of user devices. (S820) As an example, the server may be the above-described schedule management server, and when receiving a group task creation request message from a device in which schedule management software is recorded and driven, a group task may be created, which is as described above. same. Next, the server generates group work recommendation time information for group work through artificial intelligence based on the received schedule management information for each of the plurality of user devices (S830), and transmits the information to at least any one or more devices in the group (S840) In this case, as an example, at least any one or more essential members and at least any one or more auxiliary members may be included in the group. At this time, the server may generate the above-described group work recommendation time information by reflecting only schedule management information on devices corresponding to essential members among group members in the AI. On the other hand, the server may not reflect schedule management information on the device corresponding to the auxiliary member to the AI. That is, the server may reflect only the schedule management information for the device corresponding to the essential member.

Also, as an example, a priority may be set for each of at least one or more essential members. In this case, the server may generate and transmit group work recommendation time information by reflecting the priority, as described above. In this case, the group work recommendation time information may include candidate time information and specific time information. For example, the candidate time information may be information on a time during which group work is possible based on schedule management information received from devices corresponding to at least one or more essential members. That is, the candidate time information may be determined by reflecting the schedule management information of the device corresponding to the essential member. In this case, the artificial intelligence of the server may derive specific time information from the candidate time information. In this case, the specific time information may be time information at which a group task is performed.

At this time, the artificial intelligence of the server can derive specific time information by taking candidate time information as input and reflecting priority information, auxiliary member information, information included in the database, and feedback information, as described above.

Also, as an example, the server may transmit a group work availability confirmation request message to a device corresponding to a required member having the highest priority among at least one essential member based on specific time information. In this case, the server can determine specific time information when receiving a group work availability confirmation response message from the essential member with the highest priority. For example, in FIGS. 6B to 6D , user 1 may be an essential member having the highest priority, and the detailed operation is the same as described above. In addition, as an example, the server may transmit group work recommended time information including the determined specific time information and candidate time information to at least one device in the group, through which the group work may be performed.

As another example, schedule management information for each user device may be set by a user based on a plurality of categories. In this case, as shown in FIG. 3, specific category information among schedule management information may be transmitted to the server, and specific category information may not be transmitted to the server. That is, the specific type of category information may not be reflected in determining the group work recommendation time based on the group work, as described above.

The embodiments described above may be at least partially implemented as a computer program and recorded in a computer-readable recording medium. A computer-readable recording medium in which a program for implementing the embodiments is recorded includes all types of recording devices in which computer-readable data is stored. Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, and optical data storage device. In addition, the computer-readable recording medium is distributed in the computer system connected to the network, and the computer-readable code can be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present embodiment may be easily understood by those skilled in the art to which the present embodiment belongs.

Although the present specification reviewed above has been described with reference to the embodiments shown in the drawings, this is merely exemplary, and those skilled in the art will understand that various modifications and variations of the embodiments are possible therefrom. . However, such modifications should be considered to be within the technical protection scope of the present specification. Accordingly, the true technical protection scope of the present specification should be defined to include other implementations, other embodiments, and equivalents to the claims by the spirit of the appended claims.

Accordingly, the present invention can provide a method and apparatus for analyzing and recommending a schedule using artificial intelligence.

Claims

A method of operating a server that analyzes and recommends a schedule using artificial intelligence, the method comprising: receiving schedule management information from each of a plurality of user devices;

generating a group task corresponding to at least one device group among the plurality of user devices;

generating group work recommendation time information for the group work through artificial intelligence based on the received schedule management information for each of the plurality of user devices; and

Transmitting the group work recommendation time information to the at least any one or more devices in the group; Containing, the operating method of the server.
The method of claim 1,

At least any one or more essential members and at least any one or more auxiliary members are included in the group,

When the group work recommended time information is generated, the server reflects schedule management information on the device corresponding to the essential member to the artificial intelligence, and the schedule management information on the device corresponding to the auxiliary member is the How the server works, which is not reflected in artificial intelligence.
3. The method of claim 2,

A priority is set for each of the at least one or more essential members, and the server generates the group work recommendation time information by reflecting the priority.

Name, how the server works.
4. The method of claim 3,

The group work recommendation time information includes candidate time information and specific time information,

The candidate time information is information about a time during which the group work is possible based on the schedule management information received from devices corresponding to the at least one or more essential members,

The specific time information is time information during which the group task is performed among the candidate time information, the method of operating a server.
5. The method of claim 4,

The method of operation of a server, wherein the artificial intelligence derives the specific time information by reflecting the priority information, auxiliary member information, information included in the database, and feedback information by inputting the candidate time information.
5. The method of claim 4,

The server receives a group work availability confirmation request message to the device corresponding to the essential member with the highest priority among the at least one essential member based on the specific time information,

When receiving a group work possible confirmation response message from the essential member with the highest priority, the specific time information is confirmed,

The method of operating a server to transfer the group work recommendation time information including the determined specific time information and the candidate time information to at least one device in the group.
The method of claim 1,

The schedule management information is set by the user based on a plurality of categories,

Of the schedule management information, the first category information is transmitted to the server, and the second category information is not transmitted to the server.
A computer program stored in a computer-readable medium in combination with hardware to execute the method according to any one of claims 1 to 7.