KR20230084455A - Method, server and computer program for determining and managing important message about project through message analysis among project participants - Google Patents

Abstract

Provided are a method, server and computer program for determining and managing an important message about a project through message analysis among project participants. According to various embodiments of the present invention, a method for determining and managing an important message about a project through message analysis among project participants is performed by a computing device and includes steps of: extracting one or more summary sentences by analyzing the messages transmitted/received between the project participants; and providing a project management service on the basis of the one or more extracted summary sentences.

Description

Method for judging and managing important messages related to the project through message analysis between project participants, server and computer program

Various embodiments of the present invention relate to a method for determining and managing important messages related to a project through message analysis between project participants, a server, and a computer program.

With the spread of smart devices such as smart phones and smart pads and the expansion of communication infrastructure, collaboration using online and smart devices is actively being performed.

In other words, collaboration using the Internet and smart devices in different spaces as well as collaboration in offline spaces is becoming common with technological development, and accordingly, various collaboration tools using the Internet network are being developed and used.

Conventional online collaboration tools provided several functions necessary for conducting projects. For example, a conventional online collaboration tool provides a messaging service between project participants participating in the same project or provides various functions related to a project and project progress, such as project progress management and schedule management.

However, the conventional online collaboration tool only provides each function in one dimension, and has a disadvantage in that two or more functions cannot be linked for the convenience of participants. Therefore, participants can either manually manipulate the functions they want to use (e.g., directly register in the schedule management tool after confirming the schedule with the messenger) or register in a limited way according to the predetermined interface (e.g., registering in the schedule management tool to the messenger). There is an inconvenience of one-way registration that gives a notification).

The problem to be solved by the present invention is to extract one or more summaries by analyzing messenger conversations between project participants in order to improve the disadvantages of the conventional online collaboration tools as described above, and various various projects related to the project based on the extracted one or more keywords. It relates to a method for judging and managing important messages related to a project through message analysis between project participants who can provide services (project management services), a server, and a computer program.

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

A method for determining and managing important messages related to a project through message analysis between project participants according to an embodiment of the present invention for solving the above problems is a method performed by a computing device, which is transmitted and received between project participants It may include extracting one or more summaries by analyzing the message and providing a project management service based on the extracted one or more summaries.

In various embodiments, the step of extracting the one or more summaries may include, when a request for schedule registration is received from a first participant for a specific message, one or more summary sentences related to the schedule - one or more schedule related to the schedule by analyzing the specific message The summary includes extracting a schedule name, date, time, location, and at least one of attendees, and providing the project management service includes the extracted schedule through a user interface (UI). providing one or more summaries related to the first participant to the first participant, determining one or more summaries related to the extracted schedule based on a user input of the first participant input through the user interface; and Registering a schedule using one or more confirmed summaries and providing a schedule management service for providing schedule guidance information to participants of the project according to the registered schedule.

In various embodiments, the step of extracting one or more summaries may include one or more summaries related to a schedule by analyzing the transmitted/received message, wherein the one or more summaries related to the schedule include a schedule name, date, time, location, and attendees. Including the step of automatically extracting, wherein the step of providing the project management service automatically registers a schedule using one or more summaries related to the extracted schedule, and the participants of the project according to the automatically registered schedule. The method may include providing a schedule management service providing schedule guidance information to the user.

In various embodiments, the extracting of the one or more summaries may include analyzing a plurality of messages transmitted and received between the project participants to determine the importance of each of the plurality of messages, and the determined importance is based on a preset standard. Providing guide information for guiding registration of messages having the above values as important messages, and extracting one or more summaries from the message for which registration of important messages is requested when a request for registration of important messages is received from a participant in response to the provided guide information. The step of providing the project management service includes registering a reminder for the important message using the one or more extracted summaries, and sending the important message as a reminder target according to the registered reminder. It may include providing an important message management service for guiding the user.

In various embodiments, the providing of the project management service may include determining properties of the project by analyzing messages transmitted and received between project participants, and project properties providing information on the determined properties of the project. A step of providing an information providing service, wherein the step of providing the project property information providing service includes a project attribute determined for each of a plurality of projects in which a first participant is participating through a user interface (UI). The user interface arranges and outputs a plurality of cards corresponding to each of the plurality of projects in a grid form, and information on each of the plurality of projects is provided on the front side of each of the plurality of cards. Information on each project of includes at least one of a project name, the number of unconfirmed messages among messages transmitted and received between project participants, and project property information - is output, and on the back of each of the plurality of cards It may include a step of outputting one or more keywords extracted from messages transmitted and received between participants of each of the plurality of projects.

In various embodiments, the step of determining the attributes of the project may include extracting one or more summaries from a plurality of messages transmitted and received between project participants during a predetermined period of time, and analyzing the extracted one or more summaries to determine the quality of the project. The step of providing the user interface includes generating summary information related to progress, and the step of providing the user interface visualizes and provides the generated summary information, wherein the visualized summary information includes one or more keywords related to the progress of the project. It may include steps, which are visualized in the form of a word cloud.

In various embodiments, the step of determining the properties of the project may include at least one of properties of a plurality of messages transmitted and received between project participants, a progress of the project, an issue occurring in the project, and whether or not the issue is resolved. The step of providing the user interface is to visualize and provide the determined mood of the project, and the visualized mood of the project is It is visualized in a graph form according to the weighted sum of a plurality of first points corresponding to each message, and displays a plurality of second points corresponding to issues occurring in the project on the atmosphere of the project visualized in the graph form, The color of each of the plurality of second points may be determined according to whether an issue corresponding to each of the plurality of second points is resolved.

In various embodiments, the providing of the project property information providing service may include generating a summary information list including summary information about property information of each of a plurality of projects in which the first participant participates, and generating the generated summary information. The method may include providing a message including a list to the first participant at predetermined time intervals from the first participant.

In various embodiments, the extracting of one or more summaries may include analyzing a plurality of messages transmitted and received between the project participants, and selecting a message set to be deactivated from at least one of the project participants among the plurality of messages. It may include a step of excluding the summary statement from the extraction target.

An important message judgment and management server related to a project through message analysis between project participants according to another embodiment of the present invention for solving the above problems is loaded into a processor, a network interface, a memory, and the memory, and the It includes a computer program executed by a processor, wherein the computer program includes instructions for extracting one or more summaries by analyzing messages transmitted and received between project participants, and a project management service based on the one or more extracted summaries. It may include an instruction that provides.

A computer program according to another embodiment of the present invention for solving the above problems is combined with a computing device, analyzing messages transmitted and received between project participants, extracting one or more summaries, and extracting one or more summaries. It can be stored in a computer-readable recording medium in order to execute steps of providing project management services based on the summary.

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

According to various embodiments of the present invention, by analyzing messenger conversations between project participants, extracting one or more summaries, and providing various services (project management services) related to the project based on the extracted one or more summaries, It has the advantage of being able to overcome the problems of online collaboration tools.

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

1 and 2 are diagrams illustrating a system for judging and managing important messages related to a project through message analysis between project participants according to an embodiment of the present invention.
3 is a hardware configuration diagram of an important message determination and management server related to a project through message analysis between project participants according to another embodiment of the present invention.
4 is a flowchart of a method for judging and managing important messages related to a project through message analysis between project participants according to another embodiment of the present invention.
5 is a flowchart of a method of determining important messages related to a project through message analysis between project participants and providing a schedule management service performed by a management server, in various embodiments.
6 and 7 are diagrams illustrating a schedule management service provided by a management server and determining important messages related to a project through message analysis between project participants, in various embodiments.
8 is a flowchart of a method of determining important messages related to a project through message analysis between project participants and providing important message management services performed by a management server, in various embodiments.
9 is a diagram illustrating an important message management service provided by a management server and determining an important message related to a project through message analysis between project participants, in various embodiments.
10 is a flowchart of a method of determining an important message related to a project through message analysis between project participants and providing a project property information providing service performed by a management server, in various embodiments.
11 to 14 are diagrams illustrating a service of determining important messages related to a project through message analysis between project participants and providing project property information provided by a management server, in various embodiments.
15 and 16 are diagrams illustrating a configuration for generating learning data of an artificial intelligence model that performs a method of judging and managing important messages related to a project through message analysis between project participants, in various embodiments.

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

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

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

The term "unit" or "module" used in the specification means a hardware component such as software, FPGA or ASIC, and "unit" or "module" performs certain roles. However, "unit" or "module" is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside in an addressable storage medium and may be configured to reproduce one or more processors. Thus, as an example, a “unit” or “module” may refer to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Functions provided within components and "units" or "modules" may be combined into smaller numbers of components and "units" or "modules" or may be combined into additional components and "units" or "modules". can be further separated.

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

In this specification, a computer means any kind of hardware device including at least one processor, and may be understood as encompassing a software configuration operating in a corresponding hardware device according to an embodiment. For example, a computer may be understood as including a smartphone, a tablet PC, a desktop computer, a laptop computer, and user clients and applications running on each device, but is not limited thereto.

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

Although each step described in this specification is described as being performed by a computer, the subject of each step is not limited thereto, and at least a part of each step may be performed in different devices according to embodiments.

1 and 2 are diagrams illustrating a system for judging and managing important messages related to a project through message analysis between project participants according to an embodiment of the present invention.

1 and 2, a system for judging and managing important messages related to a project through message analysis between project participants according to an embodiment of the present invention includes a project management service providing server 100, a participant terminal 200 and an external The server 300 may be included.

Here, the important message judgment and management system related to the project through message analysis between project participants shown in FIGS. 1 and 2 is according to an embodiment, and its components are limited to the embodiment shown in FIGS. 1 and 2 It is not, and can be added, changed or deleted as needed.

In one embodiment, the project management service providing server 100 may extract one or more summaries by analyzing messages transmitted and received between project participants, and may provide project management services using the extracted one or more summaries. . To this end, the project management service providing server 100 includes a collection server 101, a storage server 102, an analysis server 103, an interaction server 104, and a project management system 105 as shown in FIG. can include However, it is not limited thereto.

Here, the summary sentence may be one word (keyword), but is not limited thereto, and may be understood as encompassing all words, phrases, or entire sentences composed of two or more words.

In one embodiment, the collection server 101 may be connected to the participant terminals 200 of the project participants through the network 400, and messages transmitted and received between the project participants through the message relay module of the participant terminal 200. can be collected.

Here, the network 400 refers to a connection structure capable of exchanging information between nodes such as a plurality of terminals and servers, and examples of such networks include a local area network (LAN) and a wide area network ( WAN: Wide Area Network), Internet (WWW: World Wide Web), wired and wireless data communication network, telephone network, wired and wireless television communication network, etc. Examples of wireless data communication networks include 3G, 4G, 5G, 3rd Generation Partnership Project (3GPP), 5th Generation Partnership Project (5GPP), Long Term Evolution (LTE), World Interoperability for Microwave Access (WIMAX), Wi-Fi , Internet (Internet), LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth (Bluetooth) network, NFC ( A Near-Field Communication (Near-Field Communication) network, a satellite broadcasting network, an analog broadcasting network, a Digital Multimedia Broadcasting (DMB) network, etc. are included, but not limited thereto.

In various embodiments, the collection server 101 may include a de-identification module 101A and a non-target message filtering module 101B, and through the de-identification module, regular expression pattern matching-based URL recognition and E-mail recognition , can perform phone number recognition and person recognition functions, and can perform certain messages based on message metadata through a non-target message filtering module (e.g., messages automatically created by the system or messages disabled by participants (e.g., prevented from parsing by participants). requested messages).

Here, the non-identification module 101A and the non-target message filtering module 101B may automatically collect work progress information for each of a plurality of non-face-to-face projects.

In one embodiment, the storage server 102 may store messages transmitted and received between project participants collected through the collection server 101 .

In one embodiment, the analysis server 103 may collect messages transmitted and received between project participants collected through the collection server 101 in real time, and provide a schedule management service using the schedule extraction module 103A, or An important message management service can be provided using the important message classification module 103B.

In addition, the analysis server 103 may collect messages stored in the storage server 102, and may provide a project property information providing service by using the project mood classification module 103C and the summary extract module 103D.

Here, the schedule extraction module 103A, the important message classification module 103B, the project mood classification module 103C, and the summary extract module 103D can recognize information related to the project included in the message, through which participants can Manual work can be minimized.

In one embodiment, the interaction server 104 provides a result of providing a schedule management service collected from the analysis server 103 (eg, registered schedule information) and a result of providing an important message management service (eg, important message, registration received reminder information, etc.) may be provided to the project management system 105 through the message interaction module 104A.

In addition, the interaction server 104 uses the report interaction module 104B to provide a result of providing a schedule management service (eg, registered schedule information) and a result of providing an important message management service (eg, important messages and registered reminders). information, etc.) may be generated and provided to the participant terminal 200 .

Here, the message interaction module 104A and the report interaction module 104B may generate and provide an interactive message allowing participants to access the project DB from a messenger based on information extracted from the message.

In one embodiment, the project management system 105 includes a schedule management service, an important message management service, and a project property information providing service based on previously stored data in the database 105B using the project management service module 105A. Various management services can be provided.

In addition, the project management system 105 can be connected to the external monitoring device 500 and can provide an integrated dashboard user interface (UI) that integrates and outputs various information related to the project.

Here, the integrated dashboard outputs progress information for each of a plurality of projects and information on issues occurring in each of a plurality of projects, so that participants can preemptively identify issues and manage risks.

In one embodiment, the participant terminal 200 may be connected to the project management service providing server 100 through the network 400 and receive various project management services from the project management service providing server 100 .

In various embodiments, the participant terminal 200 may be a smartphone (Smart-phone) terminal including a display at least in part and including an operating system capable of driving applications provided by the project management service providing server 100, and , As the application provided by the project management service providing server 100 is downloaded, installed, and executed, UI related to various project management services can be output through a display. However, it is not limited thereto, and the participant terminal 200 is a wireless communication device that ensures portability and mobility, and includes navigation, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) It may include all types of handheld-based wireless communication devices such as terminals, smart pads, tablet PCs, and the like.

In one embodiment, the external server 300 may be connected to the project management service providing server 100 through the network 400, and the project management service providing server 100 may analyze important information related to the project through message analysis between project participants. Various types of information/data required to perform the message determination and management process are stored or generated as the project management service providing server 100 performs the important message determination and management process related to the project through message analysis between project participants. Information/data can be stored. For example, the external server 300 may be a storage server provided separately outside the project management service providing server 100, but is not limited thereto. Hereinafter, the hardware configuration of the project management service providing server 100 will be described with reference to FIG. 3 .

3 is a hardware configuration diagram of an important message determination and management server related to a project through message analysis between project participants according to another embodiment of the present invention.

Referring to FIG. 3 , a server 100 (hereinafter referred to as “computing device 100”) determines important messages related to a project through message analysis between project participants according to another embodiment of the present invention, and one or more processors 110 ), the memory 120 for loading the computer program 151 executed by the processor 110, the bus 130, the communication interface 140, and the storage 150 for storing the computer program 151 can include Here, only the components related to the embodiment of the present invention are shown in FIG. 3 . Therefore, those skilled in the art to which the present invention pertains can know that other general-purpose components may be further included in addition to the components shown in FIG. 3 .

The processor 110 controls the overall operation of each component of the computing device 100 . The processor 110 includes a Central Processing Unit (CPU), a Micro Processor Unit (MPU), a Micro Controller Unit (MCU), a Graphic Processing Unit (GPU), or any type of processor well known in the art of the present invention. It can be.

Also, the processor 110 may perform an operation for at least one application or program for executing a method according to embodiments of the present invention, and the computing device 100 may include one or more processors.

In various embodiments, the processor 110 may temporarily and/or permanently store signals (or data) processed in the processor 110 (RAM: Random Access Memory, not shown) and ROM (ROM: Read -Only Memory, not shown) may be further included. In addition, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphics processing unit, RAM, and ROM.

Memory 120 stores various data, commands and/or information. Memory 120 may load computer program 151 from storage 150 to execute methods/operations according to various embodiments of the present invention. When the computer program 151 is loaded into the memory 120, the processor 110 may perform the method/operation by executing one or more instructions constituting the computer program 151. The memory 120 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

The bus 130 provides a communication function between components of the computing device 100 . The bus 130 may be implemented in various types of buses such as an address bus, a data bus, and a control bus.

The communication interface 140 supports wired and wireless Internet communication of the computing device 100 . Also, the communication interface 140 may support various communication methods other than internet communication. To this end, the communication interface 140 may include a communication module well known in the art. In some embodiments, communication interface 140 may be omitted.

The storage 150 may non-temporarily store the computer program 151 . When a process of judging and managing important messages related to a project through message analysis between project participants is performed through the computing device 100, the storage 150 determines and manages important messages related to the project through message analysis between project participants. Various types of information necessary to provide may be stored.

The storage 150 may be a non-volatile memory such as read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or the like, a hard disk, a removable disk, or a device well known in the art. It may be configured to include any known type of computer-readable recording medium.

Computer program 151 may include one or more instructions that when loaded into memory 120 cause processor 110 to perform methods/operations in accordance with various embodiments of the invention. That is, the processor 110 may perform the method/operation according to various embodiments of the present disclosure by executing the one or more instructions.

In one embodiment, the computer program 151 analyzes messages transmitted and received between project participants, extracts one or more summaries, and provides project management services based on the extracted one or more summaries. It may include one or more instructions for performing a method of judging and managing important messages related to a project through message analysis between messages.

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

Components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, such as C, C++ , Java (Java), can be implemented in a programming or scripting language such as assembler (assembler). Functional aspects may be implemented in an algorithm running on one or more processors. Hereinafter, with reference to FIGS. 4 to 14 , a method for determining and managing important messages related to a project through message analysis between project participants provided by the computing device 100 will be described.

4 is a flowchart of a method for judging and managing important messages related to a project through message analysis between project participants according to another embodiment of the present invention.

Referring to FIG. 4 , in step S110, the computing device 100 may be connected to a plurality of participant terminals 200 and collect messages (eg, message data transmitted and received between participants) from the plurality of participant terminals 200. can do. For example, the computing device 100 may collect messages transmitted and received by each of the plurality of participants from the message relay module of each of the plurality of participant terminals 200 . However, it is not limited thereto.

Here, the message may be a text message, but is not limited thereto, and may include a video (video, image) message, a voice message, special characters, and emoticons.

In various embodiments, the computing device 100 may collect messages collected from a plurality of participant terminals 200 and classify the collected messages by project, but is not limited thereto, and is not limited thereto, in projects in which a plurality of participants participate. It is possible to collect messages classified by project by first classifying a plurality of participants and collecting messages through the participant terminal 200 of the participants classified by project. Here, the computing device 100 may be implemented in a form of collecting messages transmitted and received between participants in real time through a message relay module of the participant terminal 200, but this is only one example and is not limited thereto. Other general-purpose methods of collection (eg, loading messages from a storage server that backs up and stores messages sent and received between participants) can be applied.

In step S120, the computing device 100 may extract one or more summaries by analyzing the messages collected in step S110. For example, the computing device 100 may extract one or more summaries included in the collected messages by analyzing the collected messages using an artificial intelligence model.

Here, the artificial intelligence model may be a pre-learned model (eg, entity name recognition model (NER model)) using a message labeled with one or more summaries as training data.

Here, an artificial intelligence model (e.g., a neural network) is composed of one or more network functions, and one or more network functions may be composed of a set of interconnected computational units generally referred to as 'nodes'. These 'nodes' may also be referred to as 'neurons'. One or more network functions include at least one or more nodes. Nodes (or neurons) that make up one or more network functions can be interconnected by one or more 'links'.

In an artificial intelligence model, one or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting an input node and an output node may have a weight. The weight may be variable, and may be variable by a user or an algorithm in order to perform a function desired by the artificial intelligence model. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in the AI model, one or more nodes are interconnected through one or more links to form an input node and output node relationship in the AI model. Characteristics of the artificial intelligence model may be determined according to the number of nodes and links in the artificial intelligence model, the relationship between the nodes and links, and the value of weight assigned to each link. For example, when there are two artificial intelligence models having the same number of nodes and links and different weight values between the links, the two artificial intelligence models may be recognized as different from each other.

Some of the nodes constituting the artificial intelligence model may constitute one layer based on distances from the first input node. For example, a set of nodes having a distance of n from the first input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed through to reach the corresponding node from the first input node. However, the definition of such a layer is arbitrary for explanation, and the order of a layer in an artificial intelligence model may be defined in a different way from the above. For example, a layer of nodes may be defined by a distance from a final output node.

An initial input node may refer to one or more nodes to which data is directly input without going through a link in relation to other nodes among nodes in the artificial intelligence model. Alternatively, in an artificial intelligence model network, in a relationship between nodes based on a link, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may refer to one or more nodes that do not have an output node in relation to other nodes among nodes in the artificial intelligence model. Also, the hidden node may refer to nodes constituting an artificial intelligence model other than the first input node and the last output node. An artificial intelligence model according to an embodiment of the present disclosure may have more nodes of an input layer than nodes of a hidden layer close to an output layer, and may be an artificial intelligence model in which the number of nodes decreases as the number of nodes increases from the input layer to the hidden layer. can

AI models can include one or more hidden layers. A hidden node of a hidden layer may use outputs of previous layers and outputs of neighboring hidden nodes as inputs. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes of the input layer may be determined based on the number of data fields of the input data and may be the same as or different from the number of hidden nodes. Input data input to the input layer may be operated by a hidden node of the hidden layer and may be output by a fully connected layer (FCL) that is an output layer.

In various embodiments, the artificial intelligence model may be a deep neural network (DNN). A deep neural network may refer to an artificial intelligence model including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted Boltzmann machines (RBMs). boltzmann machine), deep belief network (DBN), Q network, U network, Siamese network, and the like. The description of the deep neural network described above is only an example, and the present disclosure is not limited thereto.

In various embodiments, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network for outputting output data similar to input data. An auto-encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between input and output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically with the reduction from the bottleneck layer to the output layer (symmetrical to the input layer). Nodes of the dimensionality reduction layer and the dimensionality restoration layer may or may not be symmetrical.

Autoencoders can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to the number of remaining sensors after preprocessing of input data. In the auto-encoder structure, the number of hidden layer nodes included in the encoder may decrease as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, a sufficient amount of information may not be conveyed, so more than a certain number (e.g., more than half of the input layer, etc.) ) may be maintained.

In various embodiments, the computing device 100 may generate learning data for supervised learning (teacher learning) of the artificial intelligence model by labeling one or more summaries included in the message. However, it is not limited thereto, and the computing device 100 may train the artificial intelligence model through unsupervised learning and semi-supervised learning. Here, a method of generating learning data by the computing device 100 will be described later with reference to FIGS. 15 and 16 .

Here, supervised learning is a method of generating learning data by labeling specific data and information related to the specific data, and then learning using the labeling. It means learning from data.

More specifically, the computing device 100 may perform learning on one or more network functions constituting an artificial intelligence model using labeled training data. For example, the computing device 100 inputs each of the learning input data to one or more network functions, compares each of the output data calculated with the one or more network functions and each of the learning output data corresponding to a label of each of the learning input data, error can be derived. That is, in training of an artificial intelligence model, learning input data may be input to an input layer of one or more network functions, and learning output data may be compared with outputs of one or more network functions.

The computing device 100 may train an artificial intelligence model based on an error between an operation result of one or more network functions for learning input data and a learning output data (label).

Also, the computing device 100 may adjust weights of one or more network functions based on the error in a backpropagation method. That is, the computing device 100 may adjust the weight so that the output of one or more network functions is close to the learning output data based on an error between the calculation result of the one or more network functions for the learning input data and the learning output data.

When learning of one or more network functions is performed for a predetermined epoch or more, the computing device 100 may determine whether to stop learning by using verification data. The predetermined epochs may be part of an overall learning target epoch.

Verification data may consist of at least some of the labeled training data. That is, the computing device 100 performs learning of the artificial intelligence model through the learning data, and after the learning of the artificial intelligence model is repeated at least a predetermined epoch, the learning effect of the artificial intelligence model reaches a predetermined level using the verification data. It can be judged whether it is abnormal or not. For example, when the computing device 100 performs learning with a target number of learning iterations of 10 using 100 pieces of learning data, after performing iterative learning 10 times, which is a predetermined epoch, using 10 pieces of verification data By performing repeated learning three times, if the change in the output of the artificial intelligence model during the three iterative learning is less than a predetermined level, it is determined that further learning is meaningless and the learning may be terminated.

That is, the verification data can be used to determine the completion of learning based on whether the effect of learning per epoch is greater than or less than a certain level in repeated learning of the artificial intelligence model. The above-described number of learning data, verification data, and number of iterations are only examples, and the present disclosure is not limited thereto.

The computing device 100 may generate an artificial intelligence model by testing performance of one or more network functions using test data and determining whether to activate one or more network functions. The test data may be used to verify the performance of the artificial intelligence model and may be composed of at least a part of training data. For example, 70% of the training data can be used for training of an AI model (i.e., learning to adjust weights to output similar results to labels), and 30% of the training data can be used to verify the performance of an AI model. It can be used as test data for The computing device 100 may determine whether to activate the artificial intelligence model according to whether or not the performance exceeds a predetermined level by inputting test data to the artificial intelligence model for which learning has been completed and measuring an error.

The computing device 100 verifies the performance of the learned AI model by using test data for the AI model that has been learned, and if the performance of the AI model that has been learned is equal to or greater than a predetermined standard, the AI model is transferred to other applications. can be activated for use.

In addition, the computing device 100 may deactivate and discard the AI model when the performance of the trained AI model is below a predetermined standard. For example, the computing device 100 may determine the performance of the generated artificial intelligence model based on factors such as accuracy, precision, and recall. The aforementioned performance evaluation criteria are only examples and the present disclosure is not limited thereto. According to an embodiment of the present disclosure, the computing device 100 may independently train each artificial intelligence model to generate a plurality of artificial intelligence models, evaluate performance, and use only artificial intelligence models having a certain performance or higher. .

In various embodiments, the computing device 100 analyzes a plurality of messages transmitted and received between project participants, but excludes a message set to be deactivated from at least one participant among the project participants from a summary summary extraction target. there is. For example, participants may set (eg, activate or deactivate) a message they transmit in the process of sending a message, and the computing device 100 may set the message to be deactivated by the participant, that is, a message set not to be analyzed. By excluding from the summary text extraction target, the corresponding message may not be analyzed. In some cases, the computing device 100 may not collect messages set to be deactivated from the participant even through step S110.

In step S130, the computing device 100 may provide a project management service based on one or more summaries extracted in step S120. For example, the computing device 100 may provide a schedule management service, an important message management service, and a project property information providing service by using one or more summaries included in the message. Hereinafter, a schedule management service provided by the computing device 100 will be described with reference to FIGS. 5 to 7 .

5 is a flow chart of a method for determining important messages related to a project through message analysis between project participants and providing a schedule management service performed by a management server, in various embodiments, and FIGS. 6 and 7 are, in various embodiments, project participants It is a diagram showing the judgment of important messages related to the project through message analysis between the terminals and the schedule management service provided by the management server.

5 to 7 , in step S210, the computing device 100 may obtain a schedule registration request from a participant. For example, the computing device 100 may receive a schedule registration request for a specific message from a participant.

First, as shown in FIG. 6 , the computing device 100 may provide a message transmission/reception UI 10 providing a message transmission/reception service between participants.

Thereafter, the computing device 100 may obtain a request for providing a context menu for a specific message from a participant through the message transmission/reception UI 10, and provide a context menu 11 for a specific message in response to the request for providing a context menu. can do.

Thereafter, the computing device 100 may receive a selection of the schedule registration request item 11A included in the context menu 11 from the participant, and may recognize this as a schedule registration request targeting a specific message. However, it is not limited thereto.

In step S210, in response to the schedule registration request obtained in step S210, the computing device 100 analyzes a specific message and outputs one or more summaries related to the schedule (eg, at least one of the schedule name, date, time, location, and attendees). can be extracted.

For example, as shown in FIG. 7A, the computing device 100 uses a pre-learned entity name recognition model (NER model) to perform a "kick-off meeting" for a schedule name and "May 6 (Wednesday, May 6)" for a date and time. ) at 6:30 PM" and the "URL" for the place. However, it is not limited thereto.

In step S230, the computing device 100 may register a schedule using one or more summaries extracted in step S220.

First, the computing device 100 may analyze attributes of each of the one or more summaries related to the schedule extracted from the message, and arrange each of the one or more summaries related to the schedule on a predetermined schedule registration form based on the attribute analysis result. , One or more summaries disposed in the schedule registration form may be provided to the participant through the schedule registration UI 12 .

In this case, the computing device 100 may convert and arrange one or more summaries related to the schedule according to a preset schedule registration form. For example, as shown in FIG. 7A, the computing device 100 uses a standard format conversion model (seq2seq model) to convert one or more summaries related to date and time into a standard format (eg, YYYY-MM-DD hh: mm) to be placed on the schedule registration form. However, it is not limited thereto.

In various embodiments, the computing device 100 disposes each of one or more summaries related to the schedule on a predetermined schedule registration form, but when there is no summary to be placed in a specific area on the schedule registration form, other than messages subject to schedule registration A corresponding summary can be extracted by analyzing the messages.

For example, when the computing device 100 analyzes a specific message and there is no summary related to the attendee among one or more summaries related to the extracted schedule, a predetermined time before and a predetermined time based on the time when the specific message was transmitted and received. A summary related to the participant may be extracted by analyzing messages transmitted and received during the following time.

At this time, the computing device 100 gradually increases the size of the predetermined time when a summary related to the attendee is not extracted from the message transmitted and received before and after the predetermined time based on the time when the specific message is transmitted and received. The summary can be extracted, and if the summary related to the attendee is not extracted even though the size of the predetermined time is increased to a preset maximum, the area for disposing the information of the attendee on the schedule registration form can be set to blank. .

Thereafter, the computing device 100 may obtain user input (eg, an input for adding, modifying, deleting, and confirming one or more summaries disposed in the schedule registration form) through the schedule registration UI 12, and In this case, one or more summaries related to the schedule can be determined based on the participant's input.

Thereafter, the computing device 100 registers a schedule using one or more summaries determined through the schedule registration UI 12, and informs project participants (or attendees included in the registered schedule) according to the registered schedule. A schedule management service providing schedule guidance information (eg, information on registered schedules, schedule reminders based on registered schedules, etc.) may be provided.

In various embodiments, the computing device 100 registers a schedule using one or more summaries determined through the schedule registration UI 12, but the same time as the newly registered schedule based on a plurality of pre-registered schedule information. And, if there is another schedule in progress at the location, guidance information for guiding to change the time and location of the newly registered schedule may be provided.

In addition, when a schedule held at the same time is newly registered for a first participant having a different schedule at the first time, the computing device 100 guides the newly registered schedule to change the time or newly registers the schedule. Guidance information notifying that the first participant cannot participate in the scheduled schedule may be provided.

Here, the schedule management service providing method according to FIGS. 5 to 7 is described as registering a schedule by extracting a summary included in a specific message when a participant selects a specific message and requests schedule registration, but is not limited thereto. It can be implemented in various forms.

For example, the computing device 100 automatically analyzes messages transmitted and received between participants even if a participant does not request schedule registration targeting a specific message, and includes one or more summaries related to the schedule (eg, schedule name, date, time, When summaries related to venues and attendees or summaries that can be recognized as messages for scheduling, such as schedule registration and schedule creation) are extracted, the schedule can be automatically registered using one or more summaries related to the extracted schedule.

In addition, when one or more summaries related to the schedule are extracted by analyzing messages transmitted and received between the participants, the computing device 100 outputs guide information informing that the schedule can be automatically registered using the corresponding message (e.g., By displaying an effect on at least a part of a pop-up window or a message, etc.), it is possible to induce a participant to request schedule registration for the corresponding message.

In addition, the computing device 100 may automatically register a schedule by analyzing a pattern of messages transmitted and received between participants, even if a participant does not receive a request for registering a schedule targeting a specific message.

For example, the computing device 100 does not receive a separate schedule registration request from the participants when participants participating in a specific project show a pattern of discussing the next schedule from about 10 to 20 minutes before the end of the meeting. , it is possible to extract one or more summaries related to the schedule by analyzing messages sent and received from about 10 to 20 minutes before the end of the meeting held by the participants, and automatically register the next schedule using the extracted summary. there is.

Here, as shown in FIG. 7A, the schedule may be confirmed by the project manager organizing the next schedule and notifying the participants, but in some cases, the participants modify the schedule announced by the manager, or several participants In some cases, they discuss and finalize the schedule. For example, as shown in FIG. 7B , when a project manager inputs a slack message related to a schedule using a thread function, several participants input a message in the form of a comment to the slack message and schedule the schedule. Sometimes it is discussed and confirmed.

At this time, if the schedule is registered with only one message in mind, it is inconvenient that participants cannot reflect the modified schedule contents while discussing it, and the person in charge must manually modify the automatically registered schedule contents through message analysis to reflect this. may arise.

In consideration of this, the computing device 100, when receiving a request for schedule registration targeting a specific message, analyzes not only the specific message but also messages transmitted and received after the specific message is transmitted and received to extract a summary related to the schedule, , schedule can be registered using the extracted summary. For example, as shown in FIG. 7(B) , when multiple threads are input to one slack message related to a schedule, each thread is separated and a thread having the same subject, that is, one containing contents related to the schedule, is separated. A schedule may be registered by selecting a thread and extracting one or more summaries related to the schedule from the selected thread. Here, the computing device 100 selects threads having the same subject using an artificial intelligence model, but the artificial intelligence model is a model learned using training data generated by sequentially labeling existing slack messages (BIO tagging). (BERT-based fine tuning model).

Here, in the above situation (eg, when several people discuss and set a schedule), a plurality of summaries having the same properties (eg, schedule name, date, time, location, etc.) may be extracted, and the computing device 100 When a plurality of summaries having the same attribute are extracted, a schedule is registered using the summary extracted the most among the plurality of summaries, a schedule is registered using the summary extracted last, or information about the summary from the participant is registered. Once confirmed, you can register your schedule. However, it may be desirable to register the schedule using the final value, that is, the summary extracted at the end in consideration of the flow of conversation, but is not limited thereto. Also, the computing device 100 may register a new schedule by comparing a similarity between the schedule generated as described above and the existing schedule. Hereinafter, with reference to FIGS. 8 and 9 , an important message management service provided by the computing device 100 will be described.

8 is a flowchart of a method of determining important messages related to a project through message analysis between project participants and providing an important message management service performed by a management server, in various embodiments, and FIG. 9 is a flowchart showing messages between project participants in various embodiments. It is a diagram showing the important message management service provided by the important message determination and management server related to the project through analysis.

8 and 9, in step S310, the computing device 100 may determine the importance of each message by analyzing messages transmitted and received between project participants, and provide guide information based on the determined importance of each message. can provide For example, the computing device 100 may select an important message based on the importance of each message, and may provide guide information for guiding registration of a reminder for the selected important message.

In various embodiments, the computing device 100 analyzes messages transmitted and received between project participants and extracts at least one important summary text among messages transmitted and received between project participants based on one or more summaries extracted and pre-stored important summary data. A message containing one may be determined to be an important message. For example, as shown in FIG. 8 , the computing device 100 regards messages including important summaries, such as “modification”, “revision”, and “review” among messages transmitted and received between project participants as important messages. can judge

In various embodiments, the computing device 100 sums the importance scores for each of one or more summaries extracted by analyzing messages transmitted and received between project participants based on the pre-stored importance scores for each summary sentence, and the summed importance score is A message having a predetermined reference score or higher may be determined to be an important message.

In various embodiments, the computing device 100 may determine that a message transmitted by a specific participant is an important message based on a pre-set level of importance for each participant. However, it is not limited thereto, and various methods of determining important messages among messages transmitted and received between project participants may be applied.

Thereafter, the computing device 100 may provide guide information for guiding registration of a corresponding message as an important message with respect to a message determined to be an important message. For example, as shown in FIG. 9 , when an important message is identified among messages transmitted and received between project participants, the computing device 100 may provide an important message registration UI 20 to the participant, and may provide the important message to the participant. Through the registration UI 20, a message 20A determined to be an important message and guidance information 20B guiding to register the corresponding message as an important message (eg, registering a reminder) may be provided. However, it is not limited thereto.

In step S320, the computing device 100 may receive an important message registration request from a participant with respect to the important message determined through step S310. For example, when a participant wants to register a corresponding message as an important message, he or she can select a reminder registration button 20C output through the important message registration UI 20, and the computing device 100 allows the participant to register a reminder button. Selecting (20C) may be recognized as an important message registration request, and an operation of registering the corresponding message as an important message (step S330) may be performed.

In step S330, the computing device 100 may register the corresponding message as an important message in response to the important message registration request obtained from the participant and register a reminder for the registered important message. For example, the computing device 100 may provide the reminder registration UI 21 in response to obtaining an important message registration request from a participant.

In various embodiments, the computing device 100 may extract one or more summaries by analyzing a message set as an important message, place the extracted one or more summaries in a preset reminder registration form 21A, and register the reminder. A reminder registration form 21A in which one or more summaries are arranged may be provided to the participant through the UI 21 . In addition, the computing device 100 confirms one or more summaries according to the participant's input 21B (eg, modification, addition, deletion, and confirmation of one or more summaries) input through the reminder registration UI 21 to provide a reminder. can register.

Here, a reminder registration method performed by the computing device 100 (eg, extracting a summary from a message, arranging the extracted summary on a preset form, and confirming and registering participants for the summaries arranged on the form) is shown in FIG. It may be implemented in the same or similar form as the schedule registration method performed in steps S220 and S230 of 5, but is not limited thereto.

Thereafter, the computing device 100 may provide an important message management service for guiding an important message to a reminder target according to the registered reminder. For example, as shown in FIG. 9 , the computing device 100 outputs information on an important message (eg, the content of the important message, the location of the important message, a link, etc.) to the reminder target according to the registered reminder. A UI 22 may be provided.

Here, the method for providing an important message management service according to FIGS. 8 and 9 determines the importance of the message, provides guidance information for guiding participants to register a specific message as an important message, and responds to the guidance information by providing a specific message from the participant. When a message is requested to register an important message, it is explained that the corresponding message is set as an important message and a reminder is registered, but it is not limited thereto and may be implemented in various forms.

For example, when the computing device 100 determines the importance of a message and determines that a specific message is an important message, the computing device 100 sets the corresponding message as an important message and automatically provides a reminder even if a separate important message registration request is not obtained from a participant. can register. Hereinafter, a project property information providing service provided by the computing device 100 will be described with reference to FIGS. 10 to 14 .

10 is a flowchart of a method of providing project property information providing service performed by determining important messages related to a project through message analysis between project participants and providing a management server, in various embodiments. It is a diagram showing important message determination related to the project through message analysis between project participants and project property information providing service provided by the management server.

Referring to FIGS. 10 to 14 , in step S410 , the computing device 100 may determine properties of the project by analyzing messages transmitted and received between project participants.

In various embodiments, the computing device 100 detects properties (eg, positive and negative) of a plurality of messages transmitted and received between project participants, issues occurring in the project and whether or not the issues are resolved, and the like. It can be used to judge the mood (e.g. positive, negative, etc.) of a project. For example, when the number of messages having negative properties is greater than the number of messages having positive properties based on the properties of a plurality of messages transmitted and received between project participants, the computing device 100 determines the mood of the project. can be judged negative.

In addition, the computing device 100 analyzes a plurality of messages transmitted and received between project participants and determines that the mood of the project is negative when an issue has occurred in the project but the issue has not yet been dealt with. However, it is not limited to this, and if an issue has occurred in the project and the issue has not yet been dealt with, but the number of messages with positive attributes is greater than the number of messages with negative attributes, the atmosphere of the project is positive. Various methods of judging the atmosphere of the project, such as judging that it is , can be applied.

In various embodiments, the computing device 100 extracts one or more summaries from a plurality of messages transmitted and received between project participants during a predetermined period of time, analyzes the extracted one or more summaries, and generates summary information related to the progress of the project. can do. For example, the computing device 100 may extract one or more keywords related to the progress of the project by analyzing messages transmitted and received between participants within at least one of 3 hours, 6 hours, 12 hours, and 24 hours, Summary information may be generated using one or more extracted keywords. However, it is not limited thereto.

In step S420 , the computing device 100 may provide a project property information providing service providing information on the properties of the project determined in step S410 through the project property information providing UI 30 .

Here, the project property information providing UI 30 includes information on properties of the project determined for each of a plurality of projects in which the first participant participates, but as shown in FIG. 11, a plurality of corresponding to each of the plurality of projects. It can be implemented in a form in which the cards of are arranged in a lattice form and output.

In this case, the color of each card may be determined according to the atmosphere of the project corresponding to each card. For example, when the atmosphere of the third project is negative, the color of the card 30A corresponding to the third project may be displayed as the first color, and when the atmosphere of the fourth project is positive, the fourth project The color of the corresponding card 30B may be displayed as the second color.

In addition, even if the mood of the project is the same, two cards with different degrees of saturation may be set to have different saturation. For example, when the atmosphere of the fourth project is somewhat positive, the color of the card 30B corresponding to the fourth project may be set to have a first saturation, and when the atmosphere of the eighth project is very positive, the color of the card 30B corresponding to the fourth project may be set to have a first saturation. The color of the card 30C corresponding to the project may be set to have a second saturation greater than the first saturation. Here, the first color, the second color, the first saturation, and the second saturation may be set in advance by a project manager or each participant, but are not limited thereto.

In addition, on the front of the plurality of cards corresponding to each of the plurality of projects, information on each of the plurality of projects (eg, at least one of the project name, the number of unconfirmed messages among messages sent and received between project participants, and project attribute information) including one) can be output, and one or more keywords extracted from messages transmitted and received between participants of each of a plurality of projects can be output on the back of each of a plurality of cards.

The computing device 100 defaults to outputting each of a plurality of cards corresponding to a plurality of projects facing the front, but when a specific card is selected from the user (eg, a mouse click input for a specific card) ), the corresponding card can be turned over so that the back side is printed.

For example, the computing device 100 may provide the project property information providing UI 30 as shown in FIG. 11 in response to obtaining a request for a project property information providing service from a participant. At this time, each card of a plurality of projects included in the project property information providing UI 30 can look at the front side, and when an input for selecting a specific card 30D is obtained, the corresponding card 30D is turned over and the back side is displayed. You can print it out to see it. However, it is not limited thereto.

In various embodiments, the computing device 100 may visualize and provide the atmosphere of the project determined in step S410. For example, the computing device 100 may visualize and provide the atmosphere of the project in the form of a graph, as shown in FIG. 12 .

Here, the atmosphere of the project can be visualized in the form of a graph by taking each of a plurality of transmitted and received messages as an individual first point (coordinate) and displaying a line according to a weighted sum of the plurality of first points.

In addition, a plurality of second points corresponding to issues occurring in the project may be displayed on the atmosphere 31 of the project visualized in the form of a graph.

In this case, the color of the plurality of second points may be determined according to whether an issue corresponding to each of the plurality of second points is resolved. For example, if an issue corresponding to a specific second point is resolved, the second point corresponding to the issue may be displayed in blue, and if not resolved, the second point corresponding to the issue may be displayed in red. can However, it is not limited thereto.

In a non-limiting embodiment, an analysis method based on a summary sentence or sentence structure may be used as a method of determining whether an issue has occurred or been resolved from a messenger conversation. For example, in dialogue content, along with the name of a specific project or sub-component, program name, function or structure, or configuration of a project, preset negative words such as "problem", "no", "error", "error", or If a summary similar to this is included, it can be judged as an issue. Depending on the embodiment, when the amount of conversation increases after the issue generation message is recognized, it may be determined that a conversation to resolve the issue is in progress. If the summary corresponding to the resolution of the issue (e.g. "Resolved") is recognized afterwards, the issue can be determined to have been resolved. Similarly, if the amount of messages decreases after the summary corresponding to the resolution of the issue is recognized, the situation has ended. can be judged to be

The above method can be understood as an example, and various analysis methods that are not limited thereto may be used. For example, based on natural language processing, it is possible to determine the flow of issue generation and resolution without being limited to specific summaries or terms.

Through this method, it is possible to record information on issues occurring for each project and messenger conversation history until the resolution. Afterward, if a messenger conversation or question judged as a similar issue is recognized, the same or similar issue can be quickly resolved by providing the corresponding conversation history.

In various embodiments, the computing device 100 responds to a corresponding point when a participant selects one point (eg, a first point and a second point) on the project atmosphere 31 visualized in the form of a graph. content can be provided. For example, if the point selected by the participant is the first point, the computing device 100 may provide message content corresponding to the first point, and if it is the second point, issue content corresponding to the second point. and the history of the issue (e.g., history information from the occurrence of the issue to the resolution of the issue) can be provided.

In various embodiments, the computing device 100 may visualize and provide summary information related to the progress of the project generated in step S410. For example, as shown in FIG. 13 , the computing device 100 may visualize 32 and provide one or more keywords related to the progress of the project in the form of a word cloud. However, it is not limited thereto.

In various embodiments, the computing device 100 generates a summary information list 33 including summary information about attribute information of each of a plurality of projects in which the first participant participates, and a message including the generated summary information list. may be provided to the first participant at predetermined time intervals from the first participant. For example, as shown in FIG. 14 , the computing device 100 provides summary information on attribute information for each of a plurality of projects in which a first participant participates (eg, project name, project purpose, project participants). It is possible to create a summary information list 33 including the number of messages sent and received (or the number of unchecked messages among messages), the atmosphere of the project, etc.), and the time set by the first participant is 9 am, By providing a message including a summary information list 33 every 9:00 am, there is an advantage in that participants can quickly grasp status information on projects in which they are participating.

The method for determining and managing important messages related to the project through message analysis between project participants has been described with reference to the flowchart shown in the drawings. For a brief explanation, the method for judging and managing important messages related to the project through message analysis between project participants has been illustrated and described as a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks are shown in this specification. and may be performed in a different order or concurrently with the procedures performed. In addition, new blocks not described in the present specification and drawings may be added, or some blocks may be deleted or changed. Hereinafter, with reference to FIGS. 15 and 16, the computing device 100 performs a process of generating learning data of an artificial intelligence model used to perform a method of judging and managing important messages related to a project through message analysis between project participants. let me explain

15 and 16 are diagrams illustrating a configuration for generating learning data of an artificial intelligence model that performs a method of judging and managing important messages related to a project through message analysis between project participants, in various embodiments.

First, referring to FIG. 15 , the computing device 100 provides first learning data for generating an artificial intelligence model that analyzes messages transmitted and received between project participants and extracts one or more summary sentences related to schedules included in the messages. can create

For example, the computing device 100 may provide the learning data generating UI 40 to the participant terminal 200, and a plurality of first messages collected for generating learning data through the learning data generating UI 40. Labeling of summary statements included in each can be performed. Here, the plurality of first messages collected for generating learning data include schedule-related information (eg, information about when, where, who, and what) It may be a message, but is not limited thereto.

The participant labels any one of when, where, who, and what for one or more summaries included in each of the plurality of first messages collected for generating learning data. By doing so, it is possible to generate the first learning data.

In this case, the target of labeling is each summary sentence included in the message, and the summary sentence may be a single word, but is not limited thereto and may be a phrase or an entire sentence in which two or more words are combined. Also, two or more labels may be labeled for one summary statement.

Next, referring to FIG. 16 , the computing device 100 may generate second learning data for generating an artificial intelligence model that determines the importance of each message by analyzing messages transmitted and received between project participants. .

For example, the computing device 100 may provide the learning data generating UI 40 to the participant terminal 200, and a plurality of second messages collected for generating learning data through the learning data generating UI 40. Labeling of summary statements included in each can be performed. Here, the plurality of first messages collected for generating learning data may be messages that include information about importance (eg, schedule, whether or not requests are included) and signals (eg, whether an issue has occurred and whether or not the generated issue has been resolved). may, but is not limited thereto.

The participant includes a summary corresponding to the importance (eg, whether a schedule or request is included) and a signal (eg, whether an issue has occurred and whether or not the generated issue has been resolved) in each of the plurality of second messages collected for generating learning data. Second training data may be generated by labeling the corresponding message (eg, important, very important, very very important, (strong positive/negative, weak positive/negative)) according to whether the message exists. Here, the labeling target is the entire message. It may be, but is not limited thereto.

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

100: Project management service provider server (computing device)
200: participant terminal
300: external server
400: network

Claims (1)

In a method performed by a computing device,
extracting one or more summaries by analyzing messages transmitted and received between project participants; and
Providing a project management service based on the one or more extracted summaries,
The step of extracting the one or more summaries,
analyzing a plurality of messages transmitted and received between the project participants and determining an importance of each of the plurality of messages;
providing guide information for guiding registration of messages whose importance is greater than or equal to a predetermined standard as important messages; and
Extracting one or more summaries from the message requested to register the important message when a request to register an important message is received from a participant in response to the provided guide information,
The step of providing the project management service,
Registering a reminder for the important message using the extracted one or more summaries and providing an important message management service for guiding the important message to a reminder target according to the registered reminder,
Determining the importance of each of the plurality of messages,
Summing the importance scores of one or more summaries extracted from a specific message based on the pre-stored importance scores for each summary, and determining that the specific message is an important message when the summed importance scores are equal to or greater than a preset score contains,
The step of providing the project management service,
determining properties of the project by analyzing messages transmitted and received between project participants; and
Providing a project property information providing service providing information on the property of the determined project;
The step of determining the properties of the project,
Using at least one of the number of messages with positive attributes and messages with negative attributes among a plurality of messages transmitted and received between the project participants, the progress of the project, issues occurring in the project, and whether or not the issues are resolved. And determining the mood of the project by
The step of providing the project property information providing service,
The atmosphere of the determined project is visualized and provided, but the visualized atmosphere of the project is visualized in a graph form according to a weighted sum of a plurality of first points corresponding to each of the plurality of transmitted and received messages, in the form of a graph On the atmosphere of the visualized project, a plurality of second points corresponding to issues occurring in the project are displayed, and the color of each of the plurality of second points is determined according to whether the issue corresponding to each of the plurality of second points is resolved. Including the step, which is to be determined,
The step of providing the project property information providing service,
Generating a summary information list including summary information on attribute information of each of a plurality of projects in which the first participant is participating, and the generated summary information list - the generated summary information list includes a plurality of the plurality of projects in which the first participant is participating The project name of each project, the purpose of the project, the number of unconfirmed messages among messages sent and received between project participants, and the atmosphere of the project. 1 Including the step of providing participants,
The step of extracting the one or more summaries,
Analyzing a plurality of messages transmitted and received between the project participants, but excluding a message set to be deactivated from at least one participant among the project participants among the plurality of messages, from abstract extracting.
A method for judging and managing important messages related to the project through message analysis between project participants.

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