KR20220088253A - Method for providing request management service based on message importance - Google Patents

Abstract

A method for providing a request management service according to message importance is provided. A method of providing a project management service based on analysis of a messenger conversation according to various embodiments of the present invention is a method performed by a computing device, comprising the steps of analyzing a message transmitted and received between project participants and extracting one or more summary sentences; and providing a project management service based on the extracted one or more summaries.

Description

Method of providing request management service according to message importance

Various embodiments of the present invention relate to a method for providing a request management service according to message importance.

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 being actively performed.

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

The conventional online collaboration tool provided various functions necessary to proceed with the project. For example, a conventional online collaboration tool provides a messaging service between participants who participate in the same project and progresses the project, or provides various functions related to the project and project progress, such as project progress management and schedule management.

However, the conventional online collaboration tool only provides each function one-dimensionally, and has a disadvantage that two or more functions cannot be linked for the convenience of the participant. Therefore, participants manually manipulate each function they want to use (e.g., directly register with the schedule management tool after confirming the schedule with messenger) or register in a limited way according to the set interface (e.g., when registering with the schedule management tool, the One-way registration that gives notifications) is inconvenient.

The problem to be solved by the present invention is to extract one or more summary sentences by analyzing a messenger conversation between project participants in order to improve the disadvantages of the conventional online collaboration tool as described above, and to extract one or more summary sentences based on the extracted one or more keywords. It relates to a method for providing a project management service, a server, and a computer program based on a messenger conversation analysis capable of providing a service (project management service).

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 following description.

A method for providing a project management service based on analysis of a messenger conversation according to an embodiment of the present invention for solving the above-described problems is a method performed by a computing device, by analyzing a message transmitted and received between project participants to one or more It may include extracting a summary and providing a project management service based on the extracted one or more abstracts.

In various embodiments, the extracting of the one or more summary sentences may include: when a schedule registration request for a specific message from the first participant is received, one or more summary sentences related to the schedule by analyzing the specific message - one or more summary related to the schedule The summary includes the step of extracting - including at least one of a schedule name, date and time, place and attendees, and providing the project management service includes the extracted schedule through a user interface (UI) providing one or more summaries related to the schedule 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 The method may include registering a schedule using one or more confirmed summaries, and providing a schedule management service that provides schedule guide information to the participants of the project according to the registered schedule.

In various embodiments, the extracting of the one or more summaries may include analyzing the transmitted and received message to one or more summaries related to a schedule, wherein the one or more summaries related to the schedule include an event name, date and time, place and attendees. and automatically extracting the project management service, wherein the step of providing the project management service includes automatically registering a schedule using one or more summary statements related to the extracted schedule, and according to the automatically registered schedule, participants of the project It may include providing a schedule management service for providing schedule guide 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 participants of the project to determine the importance of each of the plurality of messages, a criterion in which the determined importance is preset Step of providing guide information guiding to register the abnormal message as an important message, and when a request to register an important message is received from a participant in response to the provided guide information, extracting one or more summary sentences from the message requested to register the important message The step of providing the project management service includes registering a reminder for the important message using the extracted one or more abstracts, and setting the important message as a reminder according to the registered reminder. It may include the step of providing an important message management service to guide the user.

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

In various embodiments, the determining of the properties of the project may include extracting one or more summary texts from a plurality of messages transmitted and received between the participants of the project during a preset period, and analyzing the extracted one or more abstract texts of the project. Including the step of generating summary information related to progress, wherein the providing of the user interface includes providing a visualization of the generated summary information, wherein the visualized summary information includes one or more keywords related to the progress of the project. It may include a step, which is visualized in the form of a cloud (Word cloud).

In various embodiments, the step of determining the properties of the project includes at least one of the properties of a plurality of messages transmitted and received between the participants of the project, the progress of the project, and issues occurring within the project and whether the issues are resolved. and determining the mood of the project using Visualized in a graph form according to the weighted sum of a plurality of first points corresponding to each message, and displaying 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 is 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 attribute information providing service includes 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 The method may include providing a message including a list from the first participant to the first participant every preset time.

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

Project management service providing server based on messenger conversation analysis according to another embodiment of the present invention for solving the above-described problems, a processor, a network interface, a memory, and a computer loaded into the memory, and executed by the processor Including a program, wherein the computer program includes an instruction for extracting one or more summary sentences by analyzing a message transmitted and received between project participants, and an instruction for providing a project management service based on the extracted one or more summary sentences. can do.

A computer program according to another embodiment of the present invention for solving the above problems is coupled with a computing device, analyzing messages transmitted and received between project participants and extracting one or more summary sentences and the extracted one or more In order to execute the step of providing the project management service based on the summary, it may be stored in a computer-readable recording medium.

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

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

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 following description.

1 and 2 are diagrams illustrating a project management service providing system based on messenger conversation analysis according to an embodiment of the present invention.
3 is a hardware configuration diagram of a project management service providing server based on messenger conversation analysis according to another embodiment of the present invention.
4 is a flowchart of a method for providing a project management service based on messenger conversation analysis according to another embodiment of the present invention.
5 is a flowchart of a schedule management service providing method performed by a project management service providing server based on messenger conversation analysis, according to various embodiments.
6 and 7 are diagrams illustrating a schedule management service provided by a project management service providing server based on messenger conversation analysis, according to various embodiments.
8 is a flowchart of a method for providing an important message management service performed by a project management service providing server based on messenger conversation analysis, according to various embodiments.
9 is a diagram illustrating an important message management service provided by a project management service providing server based on messenger conversation analysis, according to various embodiments.
10 is a flowchart of a method of providing a project attribute information providing service performed by a project management service providing server based on a messenger conversation analysis according to various embodiments of the present disclosure.
11 to 14 are diagrams illustrating a project attribute information providing service provided by a project management service providing server based on messenger conversation analysis, according to various embodiments.
15 and 16 are diagrams illustrating a configuration of generating learning data of an artificial intelligence model for performing a method for providing a project management service based on analysis of a messenger conversation according to various embodiments of the present disclosure.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail 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, and only the present embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, 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 elements, these elements 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 component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

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

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe the correlation between a component and other components. Spatially relative terms should be understood as terms including different directions of components during use or operation in addition to the directions shown in the drawings. For example, when a component shown in the drawing is turned over, a component described as “beneath” or “beneath” of another component may be placed “above” of the other component. can Accordingly, the exemplary term “below” may include both directions 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 refers to all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

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

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

1 and 2 are diagrams illustrating a project management service providing system based on messenger conversation analysis according to an embodiment of the present invention.

1 and 2 , a system for providing a project management service based on messenger conversation analysis according to an embodiment of the present invention includes a project management service providing server 100 , a participant terminal 200 and an external server 300 . can do.

Here, the project management service providing system based on messenger conversation analysis shown in FIGS. 1 and 2 is according to an embodiment, and its components are not limited to the embodiments shown in FIGS. 1 and 2, and if necessary It may be added, changed, or deleted depending on the circumstances.

In one embodiment, the project management service providing server 100 may extract one or more summary sentences by analyzing messages transmitted and received between the participants of the project, and may provide a project management service using the extracted one or more summary sentences. . To this end, the project management service providing server 100 is 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. may include However, the present invention is not limited thereto.

Here, the summary sentence may be a single word (keyword), but is not limited thereto, and may be understood to encompass all words, phrases, or sentences composed of two or more words.

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

Here, the network 400 refers to a connection structure capable of exchanging information between each node, such as a plurality of terminals and servers, and an example of such a network includes a local area network (LAN), a wide area network ( WAN: Wide Area Network), Internet (WWW: World Wide Web), wired/wireless data communication network, telephone network, wired/wireless television communication network, and the like. 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 (Wi-Fi) , 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 ( Near-Field Communication) networks, satellite broadcast networks, analog broadcast networks, Digital Multimedia Broadcasting (DMB) networks, and the like are included, but are not limited thereto.

In various embodiments, the aggregation 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, E-mail recognition, etc. , phone number recognition and human name recognition functions, and through an untargeted message filtering module, specific messages based on message metadata (such as messages created automatically by the system or messages disabled by participants (such as messages not analyzed by participants) requested messages) can be filtered.

Here, the de-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 the message transmitted and received between the 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 use the schedule extraction module 103A to provide a schedule management service or An important message management service may 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 attribute information providing service using the project mood classification module 103C and the summary text extraction module 103D.

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

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

In addition, the interaction server 104 provides a result of providing a schedule management service (eg, registered schedule information) and a result of providing an important message management service (eg, an important message, a registered reminder) using the report interaction module 104B. 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 that allows the participant to access the project DB in the messenger based on information extracted from the message.

In one embodiment, the project management system 105 uses the project management service module 105A, based on the data stored in the database 105B, including a schedule management service, an important message management service, and a project attribute information providing service. We can provide various management services.

In addition, the project management system 105 may be connected to the external monitoring device 500 , and may 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 the 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 may receive various project management services from the project management service providing server 100 .

In various embodiments, the participant terminal 200 may be a smart-phone terminal including an operating system capable of driving an application provided by the project management service providing server 100 and including a display at least in part. , as the application provided by the project management service providing server 100 is downloaded, installed, and executed, UIs related to various project management services may be output through the display. However, the present invention is not limited thereto, and the participant terminal 200 is a wireless communication device that guarantees portability and mobility, and includes navigation, Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), and 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, and tablet PCs.

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 performs a project management service providing process based on messenger conversation analysis. Various types of information/data necessary to perform may be stored, or various types of information/data generated as the project management service providing server 100 performs a project management service providing process based on messenger conversation analysis may be stored. For example, the external server 300 may be a storage server separately provided outside the project management service providing server 100, but is not limited thereto. Hereinafter, a 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 a project management service providing server based on messenger conversation analysis according to another embodiment of the present invention.

Referring to FIG. 3 , the project management service providing server 100 (hereinafter, “computing device 100”) based on messenger conversation analysis according to another embodiment of the present invention includes one or more processors 110 and processors 110 . It may include a memory 120 for loading a computer program 151 executed by the , a storage 150 for storing a memory 120 , a bus 130 , a communication interface 140 , and a computer program 151 . Here, only the components related to the embodiment of the present invention are shown in FIG. 3 . Accordingly, those skilled in the art to which the present invention pertains can see that other general-purpose components other than the components shown in FIG. 3 may be further included.

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. can be

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

In various embodiments, the processor 110 temporarily and/or permanently stores a signal (or data) processed inside the processor 110 , a random access memory (RAM) and a read access memory (ROM). -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 graphic processing unit, a RAM, and a ROM.

The memory 120 stores various data, commands and/or information. The memory 120 may load the computer program 151 from the storage 150 to execute methods/operations according to various embodiments of the present disclosure. 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 as various types of buses, such as an address bus, a data bus, and a control bus.

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

The storage 150 may non-temporarily store the computer program 151 . When performing the project management service providing process based on the messenger conversation analysis through the computing device 100 , the storage 150 may store various types of information necessary to provide the project management service providing process based on the messenger conversation analysis.

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

The computer program 151 may include one or more instructions that, when loaded into the memory 120 , cause the processor 110 to perform methods/operations according to various embodiments of the present 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 is a messenger conversation comprising the steps of analyzing a message sent and received between the participants of the project to extract one or more summary texts, and providing a project management service based on the extracted one or more summary texts It may include one or more instructions for performing a method of providing a project management service based on analysis.

The steps of a method or algorithm described in relation to an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain 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 type of computer-readable recording medium well known in the art to which the present invention pertains.

The 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 components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , may be implemented in a programming or scripting language such as Java, assembler, or the like. Functional aspects may be implemented in an algorithm running on one or more processors. Hereinafter, a method of providing a project management service based on a messenger conversation analysis provided by the computing device 100 will be described with reference to FIGS. 4 to 14 .

4 is a flowchart of a method for providing a project management service based on messenger conversation analysis 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, the present invention 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, or special characters and emoticons.

In various embodiments, the computing device 100 may collect messages collected from the plurality of participant terminals 200 and classify the collected messages for each project, but is not limited thereto, and may be used in projects in which a plurality of participants participate. Accordingly, by first classifying a plurality of participants and collecting messages through the participant terminals 200 of the participants classified by project, messages classified by project can be collected. Here, the computing device 100 may be implemented in a form of collecting messages transmitted and received between the participants in real time through the message relay module of the participant terminal 200, but this is only an example, and the message 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) may be applied.

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

Here, the AI model may be a pre-trained 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 (eg, 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 that may be generally referred to as ‘nodes’. These ‘nodes’ may also be referred to as ‘neurons’. The one or more network functions are configured by including at least one or more nodes. Nodes (or neurons) constituting one or more network functions may be interconnected by one or more ‘links’.

In the AI model, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may be in an input node relationship in a relationship with another node, and vice versa. As described above, an input node to output node relationship may be created around a link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the 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 the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the AI model to perform a desired function. For example, when one or more input nodes are interconnected to one output node by respective links, the output node sets values input to input nodes connected to the output node and links corresponding to the 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 an output node relationship within the AI model. Characteristics of the AI model may be determined according to the number of nodes and links in the AI model, the correlation between nodes and links, and the value of a weight assigned to each of the links. For example, when the same number of nodes and links exist and there are two AI models having different weight values between the links, the two AI models may be recognized as different from each other.

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

The initial input node may mean one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the AI model. Alternatively, in the relationship between nodes based on the link in the artificial intelligence model network, 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. In addition, the hidden node may refer to nodes constituting an artificial intelligence model other than the first input node and the last output node. The artificial intelligence model according to an embodiment of the present disclosure may have more nodes in the input layer than nodes in the hidden layer close to the output layer, and is an artificial intelligence model in which the number of nodes decreases as the input layer progresses to the hidden layer. can

An AI model may include one or more hidden layers. The hidden node of the hidden layer may have the output of the previous layer and the output of the 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 calculated 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 be used to identify the latent structures of data. In other words, it can identify the potential structure of photos, texts, videos, voices, and music (e.g., what objects are in the photos, what the text and emotions are, what the texts and emotions are, etc.) . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, generative adversarial networks (GANs), and restricted Boltzmann machines (RBMs). boltzmann machine), a deep belief network (DBN), a Q network, a U network, a 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 auto-encoder. The auto-encoder may be a kind of artificial neural network for outputting output data similar to input data. The auto encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between the input/output layers. The number of nodes in 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 reduction from the bottleneck layer to the output layer (symmetrically with the input layer). The nodes of the dimensionality reduction layer and the dimension restoration layer may or may not be symmetrical.

The auto-encoder can perform non-linear dimensionality reduction. The number of input layers and output layers may correspond to the number of sensors remaining after pre-processing of input data. In the auto-encoder structure, the number of nodes of the hidden layer included in the encoder may have a structure that decreases 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 a certain number or more (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, the present invention 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 for the computing device 100 to generate learning data 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 specific data, and learning by using this, in which learning data is generated by labeling two data having a causal relationship, and generated learning How to learn from data.

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

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

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

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

The verification data may be composed of at least a portion 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 more than a predetermined epoch, the learning effect of the artificial intelligence model using the verification data is a predetermined level It can be judged whether it is abnormal or not. For example, if the computing device 100 performs learning with a target repetition learning number of 10 times using 100 pieces of training data, after performing repeated learning 10 times, which is a predetermined epoch, using 10 verification data By repeating learning three times, if the change in the output of the artificial intelligence model during the three iterative learning is below a predetermined level, it is determined that further learning is meaningless and learning can be terminated.

That is, the verification data may be used to determine the completion of learning based on whether the effect of learning for each epoch is greater than or less than a certain level in the iterative learning of the artificial intelligence model. The above-described number of training data and verification data and the number of repetitions are merely examples, and the present disclosure is not limited thereto.

The computing device 100 may generate an artificial intelligence model by testing the performance of one or more network functions using the test data and determining whether to activate the 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 portion of the training data. For example, 70% of the training data can be utilized for training of the AI model (that is, learning to adjust the weights to output results similar to labels), and 30% of the training data is used to verify the performance of the 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 it is above a predetermined performance by inputting test data into the trained artificial intelligence model and measuring an error.

The computing device 100 verifies the performance of the trained artificial intelligence model by using the test data on the trained artificial intelligence model, and when the performance of the trained artificial intelligence model is higher than or equal to a predetermined criterion, the AI model is transferred to another application. You can enable it for use.

Also, when the performance of the trained artificial intelligence model is less than or equal to a predetermined criterion, the computing device 100 may deactivate and discard the corresponding artificial intelligence model. 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 above-described performance evaluation criteria are merely examples, and the present disclosure is not limited thereto. According to an embodiment of the present disclosure, the computing device 100 may generate a plurality of artificial intelligence model models by independently learning each artificial intelligence model, and only use artificial intelligence models with a certain performance or higher by evaluating the performance. .

In various embodiments, the computing device 100 analyzes a plurality of messages transmitted and received between the participants of the project, and among the plurality of messages, messages set inactive by at least one participant among the participants of the project may be excluded from abstract extraction. have. For example, the participants may set (eg, enable or disable) the message they send in the process of transmitting the 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 the message 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 attribute information providing service by using one or more summary sentences 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 flowchart of a schedule management service providing method performed by a project management service providing server based on messenger conversation analysis in various embodiments, and FIGS. 6 and 7 are, in various embodiments, a project management service based on messenger conversation analysis It is a diagram showing the schedule management service provided by the providing 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, the computing device 100 may provide a message transmission/reception UI 10 that provides a message transmission/reception service between participants as shown in FIG. 6 .

Thereafter, the computing device 100 may obtain a request for providing a context menu for a specific message from a participant through the message sending/receiving UI 10 , and provides a context menu 11 for a specific message in response to the request for providing the 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 request for schedule registration with respect to a specific message. However, the present invention is not limited thereto.

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

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

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

First, the computing device 100 may analyze the properties of each of one or more summary sentences related to the schedule extracted from the message, and place each of the one or more summary sentences related to the schedule on a preset schedule registration form based on the attribute analysis result, , one or more summaries arranged 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 summary sentences related to the schedule according to a preset schedule registration form. For example, the computing device 100 uses a standard format conversion model (seq2seq model) to convert a summary related to the date and time among one or more summary statements in a standard format (eg, YYYY-MM-DD hh: mm) and can be placed on the schedule registration form. However, the present invention is not limited thereto.

In various embodiments, the computing device 100 arranges each of one or more summary sentences related to the schedule on a preset schedule registration form, but if there is no summary to be placed in a specific area on the schedule registration form, other than the message that is the schedule registration target By analyzing the messages, the corresponding summary can be extracted.

For example, the computing device 100 analyzes the specific message and, if there is no summary related to the attendee among one or more summary related to the schedule extracted by analyzing the specific message, the specific message is transmitted/received before a predetermined time and at a predetermined time By analyzing the messages sent and received during the subsequent time, it is possible to extract a summary related to the attendees.

At this time, the computing device 100 gradually increases the size of the predetermined time when the summary sentence related to the participant is not extracted from the message transmitted and received before or after a predetermined time based on the time when the specific message is transmitted and received, and one or more participants related A summary can be extracted, and when a summary related to an attendee is not extracted despite increasing the size of a predetermined time to a preset maximum, an area for arranging attendee information on the schedule registration form can be set to blank .

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

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

In various embodiments, the computing device 100 registers a schedule using one or more summary sentences determined through the schedule registration UI 12 , but based on a plurality of previously registered schedule information, the same time as the newly registered schedule And if there is another schedule to be held at the location, it is possible to provide guide information for guiding to change the time and place of the newly registered schedule.

In addition, the computing device 100 guides the change of the time of the newly registered schedule when a schedule that proceeds at the same time is newly registered for a first participant who has a different schedule at the first time, or registers a new one Guidance information guiding that the first participant cannot participate in the scheduled schedule may be provided.

Here, the method for providing the schedule management service according to FIGS. 5 to 7 describes that when a participant selects a specific message and requests schedule registration, extracts a summary included in the specific message and registers the schedule, but is not limited thereto. It may be implemented in various forms.

For example, the computing device 100 automatically analyzes messages sent and received between the participants even if a schedule registration for a specific message is not requested from the participants, and one or more summary sentences related to the schedule (eg, schedule name, date and time, When summary sentences related to place and attendees or summary sentences that can be identified as messages for setting a schedule such as schedule registration and schedule creation) are extracted, schedules may be automatically registered using one or more summary sentences related to the extracted schedule.

In addition, when one or more summary sentences related to a schedule are extracted by analyzing messages transmitted and received between participants, the computing device 100 outputs guide information guiding that schedules can be automatically registered using the corresponding messages (eg: By displaying an effect in a pop-up window or at least a part of the message), the participant may be induced to request a schedule registration for the message.

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

For example, if the computing device 100 shows a pattern in which participants participating in a specific project conduct a discussion related to the next schedule from about 10 to 20 minutes before the end of the meeting, a separate schedule registration request is not requested from the participants. Even if it does not, one or more summary sentences related to the schedule can be extracted by analyzing messages sent and received from about 10 to 20 minutes before the meeting the participants are conducting, and the next schedule can be automatically registered using the extracted summary. have.

Here, as shown in FIG. 7A, the project manager may arrange the following schedule and confirm the schedule in the form of announcing to the participants, but in some cases, the participants may modify the schedule organized and announced by the person in charge, or several participants In some cases, they discuss and confirm the schedule. For example, as shown in FIG. 7B, using the Thread function, when the project manager inputs a slack message related to the schedule, several participants input a message in the form of a comment to the corresponding slack message and set the schedule. In some cases, it is discussed and confirmed.

At this time, if the schedule is registered in consideration of only one message, the contents of the schedule that have been discussed and amended by the participants cannot be reflected, and in order to reflect this, it is inconvenient that the person in charge must manually modify the contents of the schedule automatically registered through message analysis. hamper may occur.

In consideration of this, when receiving a schedule registration request for a specific message, the computing device 100 analyzes a specific message as well as a message transmitted and received after the specific message is transmitted and received to extract a summary related to the schedule, and , the schedule can be registered using the extracted summary. For example, as shown in FIG. 7(B) , when multiple threads are input to a single Slack message related to a schedule, each thread is separated and a thread having the same subject, that is, including content related to the schedule A schedule may be registered by selecting a thread and extracting one or more summary sentences related to the schedule from the selected thread. Here, the computing device 100 selects threads having the same subject by using an artificial intelligence model, but the artificial intelligence model is a model trained using training data generated by sequential labeling (BIO tagging) of existing Slack messages. (BERT based fine tuning model).

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

8 is a flowchart of a method for providing an important message management service performed by a project management service providing server based on messenger conversation analysis in various embodiments, and FIG. 9 is a project management service providing server based on messenger conversation analysis in various embodiments. It is a diagram showing an important message management service provided by .

Referring to FIGS. 8 and 9 , in step S310 , the computing device 100 may determine the importance of each message by analyzing the messages transmitted and received between the participants of the project, and 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 to guide registration of a reminder for the selected important message.

In various embodiments, the computing device 100 analyzes the messages transmitted and received between the project participants and at least one or more abstracts extracted by analyzing the messages transmitted and received between the project participants and the important summary texts among the messages transmitted and received between the project participants based on pre-stored important summary data. A message including one may be determined to be an important message. For example, as shown in FIG. 8 , the computing device 100 considers a message including an important summary such as “correction”, “re-correction”, and “review” among messages transmitted and received between the participants of the project as an important message. can judge

In various embodiments, the computing device 100 sums up the importance scores for each of one or more abstract sentences extracted by analyzing messages transmitted and received between the participants of the project based on the previously stored importance scores for each summary sentence, and the summed importance score is A message equal to or greater than a preset reference score 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 preset importance level for each participant. However, the present invention is not limited thereto, and various methods for determining an important message among messages transmitted and received between project participants may be applied.

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

In step S320 , the computing device 100 may receive a request to register an important message from a participant with respect to the important message determined through step S310 . For example, when the participant wants to register the corresponding message as an important message, he or she may select the reminder registration button 20C output through the important message registration UI 20, and the computing device 100 allows the participant to register the reminder button Recognizing that selecting (20C) is an important message registration request, 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 a corresponding message as an important message in response to an important message registration request obtained from a participant, and may 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, may place the extracted one or more summaries in a preset reminder registration form 21A, and register a reminder A reminder registration form 21A in which one or more summaries are disposed 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, correction, addition, deletion, and confirmation of one or more summaries) input through the reminder registration UI 21 to generate a reminder. can register.

Here, the reminder registration method performed by the computing device 100 (eg, extracting a summary from a message, placing the extracted summary on a preset form, confirming and registering participants for the abstracts placed on the form) is illustrated in FIG. The schedule registration method performed in steps S220 and S230 of 5 may be implemented in the same or similar form, 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 is a reminder that outputs information about an important message (eg, content of an important message and a link connected to the location of the important message, etc.) to the reminder target according to the registered reminder. A UI 22 may be provided.

Here, the method of providing an important message management service according to FIGS. 8 and 9 provides guide information guiding a participant to register a specific message as an important message by determining the importance of the message, and in response to the guide information, a specific message from the participant is provided. When a request to register an important message for a message is received, it is described as setting the corresponding message as an important message and registering a reminder, but the present invention is not limited thereto, and may be implemented in various forms.

For example, the computing device 100 determines the importance of the message and, when it is determined that a specific message is an important message, sets the corresponding message as an important message and automatically sets a reminder without obtaining a separate important message registration request from the participant. can register. Hereinafter, a project attribute 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 for providing a project attribute information providing service performed by a project management service providing server based on messenger conversation analysis in various embodiments, and FIGS. 11 to 14 are, in various embodiments, a project based on messenger conversation analysis It is a diagram showing the project attribute information providing service provided by the management service providing server.

10 to 14 , in step S410 , the computing device 100 may analyze a message transmitted and received between the participants of the project to determine the properties of the project.

In various embodiments, the computing device 100 checks attributes (eg, positive, negative) of a plurality of messages transmitted and received between the participants of the project, issues occurring within the project and whether the issues are resolved, etc. can be used to judge the mood of the project (eg positive, negative, etc.). For example, if the number of messages having negative properties is greater than the number of messages having positive properties, the computing device 100 is based on the properties of a plurality of messages transmitted and received between the participants of the project, the atmosphere of the project can be considered negative.

In addition, the computing device 100 analyzes a plurality of messages transmitted and received between the participants of the project, and when an issue has occurred in the project, but the issue has not yet been processed, it may determine the atmosphere of the project as negative. However, it is not limited thereto, and when an issue has occurred within the project and the issue has not yet been processed, but the number of messages having positive attributes is greater than the number of messages having negative attributes by more than a preset number, the atmosphere of the project is positive. Various methods for judging the mood of the project, such as judging that the

In various embodiments, the computing device 100 extracts one or more summary sentences from a plurality of messages transmitted and received between the participants of the project for a preset period, and analyzes the extracted one or more summary sentences to generate 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 the 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, the present invention is not limited thereto.

In step S420 , the computing device 100 may provide a project property information providing service that provides 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 the properties of the project determined for each of the plurality of projects in which the first participant is participating, but as shown in FIG. 11 , a plurality of projects corresponding to each of the plurality of projects is provided. It can be implemented in a form in which the cards of are arranged in a grid 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 is displayed. The color of the corresponding card 30B may be displayed as the second color.

In addition, even if the atmosphere of the project is the same, it can be set to have different chroma for two cards having different degrees. 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 the first saturation, and when the atmosphere of the eighth project is very positive, the eighth The color of the card 30C corresponding to the project may be set to have a second chroma greater than the first chroma. Here, the first and second colors and the first and second saturation may be preset by the project manager or each participant, but are not limited thereto.

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

The computing device 100 defaults to outputting each of a plurality of cards corresponding to each of a plurality of projects to face the front, but when receiving a selection of a specific card from the user (eg, inputting a mouse click for a specific card) ), you can turn the card over so that the back 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 the project property information providing service from the participant. At this time, the cards of each of the plurality of projects included in the project property information providing UI 30 can face the front side. It can be printed out for viewing. However, the present invention 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 may be visualized in a graph form by using each of a plurality of transmitted and received messages as an individual first point (coordinate) and displayed as a line according to a weighted sum of the plurality of first points.

In addition, a plurality of second points corresponding to issues occurring within 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, when 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, the present invention 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 occurs and is resolved from a messenger conversation. For example, in the conversation, preset negative words such as "problem", "does not work", "error", "error" or If a similar summary is included, it can be determined that an issue has occurred. According to an embodiment, when the conversation volume increases after the issue occurrence message is recognized, it may be determined that the conversation for resolving the issue is in progress. If a summary sentence corresponding to the resolution of the issue (eg, "Resolution") is recognized after that, it can be determined that the issue has been resolved. can be judged as

The above method may be understood as an example, and various analysis methods without limitation thereto may be used. For example, based on natural language processing, the flow of issue occurrence and resolution can be determined without being limited to a specific summary or terminology.

In this way, it is possible to record information on issues that occur for each project and the history of messenger conversations up to resolution. Afterwards, when a messenger conversation or question determined to be a similar issue is recognized, it can help to quickly solve the same or similar issue by providing the corresponding conversation history.

In various embodiments, the computing device 100 corresponds to the corresponding point when any one point (eg, the first point and the second point) is selected from the participant in the atmosphere 31 of the project visualized in the form of a graph. content can be provided. For example, when the point selected by the participant is the first point, the computing device 100 may provide message content corresponding to the first point, and in the case of the second point, issue content corresponding to the second point and the history of the issue (eg, history information from issue occurrence to issue resolution) 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, the computing device 100 may provide visualization 32 of one or more keywords related to the progress of the project in the form of a word cloud, as shown in FIG. 13 . However, the present invention is not limited thereto.

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

The above-described method for providing a project management service based on the analysis of the messenger conversation has been described with reference to the flowchart shown in the drawings. For a brief description, the method for providing a project management service based on messenger conversation analysis has been described with a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks have a different order from that shown and operated in this specification may be performed or may be performed simultaneously. In addition, new blocks not described in the present specification and drawings may be added, or some blocks may be deleted or changed. Hereinafter, a process of generating learning data of an artificial intelligence model used by the computing device 100 to perform a method for providing a project management service based on messenger conversation analysis will be described with reference to FIGS. 15 and 16 .

15 and 16 are diagrams illustrating a configuration of generating learning data of an artificial intelligence model for performing a method for providing a project management service based on analysis of a messenger conversation according to various embodiments of the present disclosure.

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

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

Participants label one or more of when, where, who, and what for one or more summaries included in each of a 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 labeling target 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 whole phrase or sentence in which two or more words are combined. In addition, two or more labels may be labeled for one summary sentence.

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 the participants of the project. .

For example, the computing device 100 may provide the learning data generation UI 40 to the participant terminal 200 , and a plurality of second messages collected for learning data generation through the learning data generation UI 40 . Labeling can be performed on the summaries included in each. Here, the plurality of first messages collected for generating the learning data is a message including information on importance (eg, whether schedule, request are included) and signal (eg, whether or not an issue occurs and whether the generated issue is resolved). However, the present invention is not limited thereto.

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

As mentioned above, although 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 know that the present invention may be embodied in other specific forms without changing the technical spirit or essential features thereof. 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 providing server (computing device)
200: participant terminal
300 : external server
400: network

Claims (3)

A method performed by a computing device, comprising:
extracting one or more summary sentences by analyzing messages transmitted and received between the participants of the project; and
providing a project management service based on the extracted one or more summaries;
The step of extracting the one or more summary sentences comprises:
analyzing a plurality of messages transmitted and received between the participants of the project to determine the importance of each of the plurality of messages;
providing guide information for guiding to register a message having the determined importance greater than or equal to a preset criterion as an important message; and
When receiving a request to register an important message from a participant in response to the provided guide information, extracting one or more summary sentences from the message requested to register the important message;
The step of providing the project management service,
Registering a reminder for the important message by using the extracted one or more abstracts, and providing an important message management service for guiding the important message to a reminder target according to the registered reminder,
How to provide request management service according to message importance. The method of claim 1,
The step of providing the project management service,
analyzing the messages transmitted and received between the participants of the project to determine the properties of the project; and
Comprising the step of providing a project property information providing service that provides information on the determined properties of the project,
The step of providing the project attribute information providing service,
A user interface (UI) provides information on the properties of a project determined for each of a plurality of projects in which the first participant is participating, wherein the user interface provides a plurality of cards corresponding to each of the plurality of projects. Information on each of the plurality of projects is displayed on the front side of each of the plurality of cards, but information on each of the plurality of projects is a project name, an unconfirmed message among the messages transmitted and received between the participants of the project including at least one of the number and attribute information of the project - and outputting one or more keywords extracted from messages transmitted and received between the participants of each of the plurality of projects on the back side of each of the plurality of cards, comprising steps,
How to provide request management service according to message importance. 3. The method of claim 2,
The step of determining the properties of the project,
extracting one or more summary sentences from a plurality of messages transmitted and received between the participants of the project during a preset period, and analyzing the extracted one or more summary sentences to generate summary information related to the progress of the project;
Providing the user interface comprises:
Provided by visualizing the generated summary information, wherein the visualized summary information is a visualization of one or more keywords related to the progress of the project in the form of a word cloud, comprising a step,
How to provide request management service according to message importance.

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