CN111798193A - Intelligent collaboration support system based on analysis of message intent - Google Patents
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Abstract
An intelligent collaboration support system based on analysis of message intent is provided. The intelligent collaboration support system intelligently integrates and manages various scattered and sporadic messages input from various devices, analyzes the messages according to the intentions of the messages, and integrally supports internal and external collaboration services of various organizations, so that the convenience of users can be improved.
Description
Cross Reference to Related Applications
This application claims priority from korean patent application No. 10-2019-0039351 filed by the korean intellectual property office at 4/2019, the entire contents of which are incorporated herein by reference.
Technical Field
The following description relates to groupware, and more particularly, to an intelligent collaboration support system based on analysis of message intent.
Background
There is an increasing demand for an intelligent collaboration support system that can improve user convenience by intelligently integrating and managing various scattered and sporadic messages input from various devices, analyzing the messages according to the intentions of the messages, and integrally supporting internal and external collaboration services of various organizations.
Korean unexamined patent publication (7/13/2018) No. 10-2018-0081021 discloses a task strategy graph management server that supports collaboration when many business organizations are associated with interoperation between couriers and Enterprise Resource Planning (ERP). In order to manage task items, the task strategy diagram management server includes a technical means, and a project message thread of the technical means is 'responsibility evaluation item'.
Korean patent No. 10-1881114 (7/17/2018) proposes a technique for automatically populating a relevant user interface through Natural Language Processing (NLP) of a message. With respect to the candidate user tasks and the rule paths, relevant tasks are determined based on the scores, and a user interface for the tasks is selected and proposed. The data entry fields of the user interface are then automatically populated based on the message analysis.
Us patent No. 9,928,229 (2018, 3/27) discloses a technique for pre-populating a table with information relating to an operation command found in a message. According to this technique, information extracted by analyzing a natural language message is compared with input fields required in a candidate target form, and the target form is determined by scoring.
However, according to these related arts, the task must be performed by the user. In addition, the task is determined by a single message. Therefore, it is difficult to apply these techniques to the complex tasks of modern companies.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
The following description relates to an intelligent collaboration support system based on analysis of message intentions, which is capable of improving user convenience by intelligently integrating and managing various scattered and sporadic messages input from various devices, analyzing the messages according to the intentions of the messages, and integrally supporting internal and external collaboration services of various organizations.
The following description also relates to an improved intelligent collaboration support system based on analysis of message intent, which is capable of intelligently determining a collaboration task through analysis of message intent.
The following description relates to a reliable intelligent collaboration support system based on analysis of message intentions, which not only fills individual task slots (individual task slots) constituting a task story in units of collaborative tasks, but also performs collaborative task actions when the individual task slots are filled.
In one general aspect, an intelligent collaboration support system based on analysis of message intent includes: an intelligent collaboration support platform configured to manage asynchronous sessions for collaboration tasks and interact with collaboration tasks corresponding to intentions of messages exchanged between terminals of a number of collaboration participants by analyzing intentions of the messages based on context; and an intelligent collaboration system configured to execute task processing steps included in individual task slots constituting a task story in units of collaboration tasks, according to a collaboration task call from the intelligent collaboration support platform.
Drawings
Fig. 1 is a block diagram illustrating a configuration of an intelligent collaboration support system based on analysis of message intention according to an exemplary embodiment of the present invention.
Fig. 2 is a diagram illustrating a framework-slot based architecture for system-to-system (S2S) communication.
Fig. 3 is a diagram illustrating the concept of context-based asynchronous session management.
FIG. 4 is a diagram illustrating extracting multiple intents based on ranking.
FIG. 5 is a diagram illustrating a task story for coordinating and registering schedules of collaboration participants in units of collaboration tasks related to the schedules.
Throughout the drawings and detailed description, unless otherwise indicated, like reference numerals will be understood to refer to like elements, features and structures. The relative sizes and depictions of these elements may be exaggerated for clarity, illustration, and convenience.
Detailed Description
Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily understand and implement the present invention.
Fig. 1 is a block diagram illustrating a configuration of an intelligent collaboration support system based on analysis of message intention according to an exemplary embodiment of the present invention. As shown in fig. 1, the intelligent collaboration support system based on analysis of message intention according to the exemplary embodiment of the present invention includes an intelligent collaboration support platform 100 and an intelligent collaboration system 200. The intelligent collaboration support platform 100 and the intelligent collaboration system 200 may be implemented in the form of Software (SW) and installed on the same device or separate devices.
The intelligent collaboration support platform 100 manages an asynchronous session for a collaboration task corresponding to the intention of messages exchanged between terminals of many collaboration participants (not shown in the drawings) and interacts with the collaboration task by analyzing the intention of the messages based on the context.
For example, the intelligent collaboration support platform 100 may be implemented to intercept messages exchanged between terminals of many collaboration participants based on context and analyze the intent of the intercepted messages.
Meanwhile, asynchronous session management for a collaborative task is an operation of: the collaborative tasks are separated from each other, details of processing each collaborative task are stored in the session memory, and since many different collaborative tasks are randomly executed, the stored details are separately managed to ensure the continuity of each collaborative task.
Moreover, the interaction with the collaborative task is such an operation: a task process for executing the cooperation task is called from the intelligent cooperation system 200 and executed.
The intelligent collaboration system 200 executes task processing steps included in individual task slots constituting a task story in units of collaboration tasks, according to a collaboration task call from the intelligent collaboration support platform 100.
To this end, the intelligent collaboration system 200 defines a task story in units of collaboration tasks, defines individual task slots constituting the task story defined in units of collaboration tasks, and defines task processing steps for the individual task slots, thereby creating a task scene.
The intelligent collaboration system 200 may include a number of task processes for processing task processing steps defined for individual task slots that make up a task story in units of collaboration tasks.
For example, the task processes may be a payment collaboration task process 210 for payment sharing, a mail collaboration task process 220 for mail sharing, a calendar collaboration task process 230 for calendar sharing, a collaboration task process 240 related to a meeting for a meeting, an Intelligent Personal Assistant (IPA) collaboration task process 250 for IPA functionality, etc., but the task processes are not limited thereto.
Meanwhile, when the intelligent cooperative system 200 separates the authentication protocol and the authentication apparatus from each other, the problem of password use can be solved by performing authentication without a password. Accordingly, the task process may further include an online fast identity authentication (FIDO) security process 260 adapted for a mobile environment and a logger process 270 for logging into the big data analysis platform 300.
According to another aspect of the present invention, the intelligent collaboration support platform 100 may include an interactor 110 and a SW robot 120.
The interactor 110 includes a communicator 111, and the communicator 111 performs frame-slot based system-to-system (S2S) communication with an internal or external system including the intelligent collaboration system 200.
Fig. 2 is a diagram illustrating a framework-slot based architecture for S2S communication.
The intelligent collaboration system 200 defines a task story in units of collaboration tasks, defines individual task slots constituting the task story defined in units of collaboration tasks, and defines task processing steps for the individual task slots, thereby creating a task scene.
With the frame-slot based structure shown in fig. 2, the intelligent collaboration support platform 100 requests a task process for invoking a collaboration task for executing a collaboration task of collaboration task units corresponding to a task scenario defined by the intelligent collaboration system 200 from the intelligent collaboration system 200.
The framework-slot based structure is a knowledge representation technique for representing typical knowledge about a particular object or concept as a set of slots. In the present invention, the frame is described in units of stories, and the frame includes one or more slots and actions. A "slot" describes a task processing step and intent. An "action" describes a task process that is invoked to perform a collaborative task action that is performed after all slots are filled. The action type may be described as an executable application, an Application Program Interface (API), a question, a suggestion, a message template, and the like, but is not limited thereto.
Specifically, the "slot" describes details to be processed in each task processing step. Details to be processed in each task processing step are defined by characterizing the state transfer (REST) API and registered as a Uniform Resource Locator (URL) in the slot. And managing a group of REST API parameters and an execution result as meta-information, and binding at an execution time point.
For example, applications executed by the SW robot 120 may include: classification applications 121 for data classification (including task classification), response applications 122 for query response (including task response), recommendation applications 123 for data recommendation (including task recommendation), processing applications 124 for command processing (including task process invocation), and analysis applications 125 for data analysis (including context analysis), but are not limited to such.
The interactor 110 requests the SW robot 120 to execute the application requested by the frame-slot based S2S interaction, and the SW robot 120 executes the application requested by the interactor 110 and reports the application execution result to the interactor 110.
According to another aspect of the invention, the interactor 110 may further comprise a context analyzer 112. As shown in fig. 3, the context analyzer 112 may include a text analyzer 112a, a conversation analyzer 112b, and an intent analyzer 112 c.
The text analyzer 112a derives from the text analysis of the message the collaboration participants related to the messages exchanged between the terminals of many collaboration participants. At this time, time information may also be derived by the text analyzer 112 a.
The session analyzer 112b derives a cooperation task unit related to messages exchanged between the terminals of many cooperation participants from the session analysis of the messages, and manages the task continuity of the derived cooperation task unit.
Fig. 3 is a diagram illustrating the concept of context-based asynchronous session management. As shown in fig. 3, the session analyzer 112b may manage the details of the ongoing collaborative task process to determine whether to start, continue, or end the session, and may store and manage the details of the collaborative task process in the session memory to manage the continuity of the collaborative task.
The intention analyzer 112c extracts the intention of the collaboration participant derived by the text analyzer 112a from the analysis of the intention of the messages exchanged between the terminals of many collaboration participants.
As a method of analyzing the intention of a message, there is a technique of scoring based on morphological analysis and a decision tree and determining an expected pattern by scoring.
Meanwhile, the intent analyzer 112c may be implemented to extract a plurality of intents based on the ranking. When the collaboration participant has multiple intentions, the intent analyzer 112c may be implemented to determine the intentions in a priority order.
FIG. 4 is a diagram illustrating extracting multiple intents based on ranking. Referring to fig. 4, the message content is analyzed and all intents included in the content are derived. The derived intents are then prioritized according to predefined rules and determined according to predefined rankings.
Meanwhile, according to another aspect of the present invention, the SW robot 120 may be implemented as: among the task processing steps included in the individual task slots constituting the task story in units of the collaboration task determined by the session analyzer 112b, a task process for executing the task processing step corresponding to the intention of the collaboration participant is invoked from the intelligent collaboration system 200.
In this case, the SW robot 120 may be implemented to define creation, reading, updating, and deletion (CRUD) according to a Uniform Resource Identifier (URI) and a basic hypertext transfer protocol (HTTP) method (release, acquire, place, and delete), and to call a REST (representational state transfer) -based task process for accessing a objectified task processing step when a corresponding method is called.
FIG. 5 is a diagram illustrating a task story in units of schedule-related collaboration tasks for coordinating and registering schedules of collaboration participants. The SW robot 120 calls a task process for executing a task processing step corresponding to the intention of a collaboration participant (mail or message recipient) from the intelligent collaboration system 200 among task processing steps S1 (checking schedule of addressees), S2 (sending mail regarding available time), and S3 (sending confirmation mail) included in individual task slots constituting schedule coordination of task stories in units of collaboration tasks related to schedules.
Meanwhile, according to another aspect of the present invention, the SW robot 120 may be implemented to call a task process for performing Named Entity Recognition (NER) from the intelligent collaboration system 200 when all individual task slots are filled, for extracting intents required to fill the individual task slots constituting a task story in units of collaboration tasks, for filling with the extracted intents, and for performing a collaboration task action from messages exchanged between terminals of collaboration participants.
For example, when many collaboration participants are participating in a collaboration task related to a meeting, the intentions required by the NER may be location, time, list of participants.
The conference-related message is transmitted to the terminals of the cooperative participants, and the intentions of the cooperative participants are analyzed by the intention analyzer 112 c. The SW robot 120 executes the NER to extract a place, time, and participant list, which are intentions required to fill individual task slots constituting a task story in units of collaborative tasks related to the conference, and fills the individual task slots with the extracted intentions.
When the place, time, and participant list, which are the intentions required for the individual task slot, are filled up by this process, the SW robot 120 calls a task process for performing conference schedule registration, which is a cooperative task action in units of a cooperative task related to a conference, from the intelligent cooperative system 200, and registers the conference schedule.
Meanwhile, according to another aspect of the present invention, the interactor 110 may further include a learner 113. The learner 113 analyzes the non-standard message transmitted from the external collaboration system 400 through learning, converts the non-standard message into a standardized intention usable in the intelligent collaboration system, and learns the intention.
Since the message transmitted from the external collaboration system 400 is a non-standard message different in format or the like from the standard message transmitted from the internal intelligent collaboration system 200, it is necessary to analyze and learn a non-standard intention included in the non-standard message transmitted from the external collaboration system 400 through the learner 113, convert the non-standard intention into a standard intention, and accumulate the standard intention for use in the intelligent collaboration support platform 100.
Meanwhile, according to another aspect of the present invention, the intelligent collaboration support platform 100 may further include an external collaboration service adaptor 130. The external cooperation interoperation adapter 130 is an element for interoperating with the external cooperation system 400.
When the external cooperation system 400 interoperates through the external cooperation interoperation adaptor 130, an external message transmitted from the external cooperation system 400 is analyzed, and an intention is extracted from the external message by using information learned by the learner 113.
Meanwhile, according to another aspect of the present invention, the intelligent collaboration support platform 100 may further include a Natural Language Processing (NLP) platform adapter 140. NLP platform adapter 140 is an element for interoperating with NLP platform 500.
The NLP platform 500, which performs NLP including morpheme analysis, entity name analysis, speech behavior analysis, and intent analysis required for message analysis, interoperates through the NLP platform adapter 140.
Meanwhile, according to another aspect of the present invention, the intelligent collaboration support platform 100 may further include a controller 150. The controller 150 controls the entire intelligent cooperative support platform 100 including the intention analysis control.
According to the present invention, when a message is analyzed through the intelligent collaboration support platform 100, first, session continuity is determined based on priority, and then whether to perform a collaboration task action is determined through intention analysis and NER extraction.
At this time, since a new session may be generated or an existing session may be ended while exchanging messages, the session is asynchronously managed to ensure continuity. Meanwhile, one message may be related to a plurality of sessions.
According to the present invention, it is possible to improve user convenience by intelligently integrating and managing various scattered and sporadic messages input from various devices, analyzing the messages according to the intentions of the messages, and integrally supporting internal and external collaboration services of various organizations.
Further, according to the present invention, since a collaboration task can be intelligently determined and a task progress can be performed through analysis of message intention, user selection for collaboration can be minimized.
Further, according to the present invention, it is possible to support reliable collaboration not only by filling individual task slots constituting a task story in units of collaborative tasks, but also by performing a collaborative task action when the individual task slots are filled.
The embodiments disclosed herein and shown in the drawings are set forth as specific examples merely to aid understanding and are not intended to limit the scope of the various embodiments of the invention.
Claims (6)
1. An intelligent collaboration support system based on analysis of message intent, the system comprising:
an intelligent collaboration support platform configured to manage asynchronous sessions for collaboration tasks and interact with collaboration tasks corresponding to intentions of messages exchanged between terminals of a number of collaboration participants by analyzing intentions of messages based on context; and
an intelligent collaboration system configured to execute task processing steps included in individual task slots constituting a task story in units of collaboration tasks, according to a collaboration task call from the intelligent collaboration support platform.
2. The intelligent collaboration support system according to claim 1, wherein the intelligent collaboration support platform comprises:
an interactor comprising a communicator configured to perform a framework-slot based system-to-system (S2S) communication with an internal or external system comprising an intelligent collaboration system;
a Software (SW) robot configured to execute an application corresponding to a frame-slot based system-to-system interaction executed by the interactor.
3. The intelligent collaboration support system according to claim 1, wherein the intelligent collaboration system defines the task story in units of collaboration tasks, defines individual task slots constituting the task story defined in units of collaboration tasks, defines task processing steps for the individual task slots, thereby creating a collaboration scene.
4. The intelligent collaboration support system as claimed in claim 2, wherein the interactor further comprises a context analyzer comprising:
a text analyzer configured to derive collaboration participants related to messages exchanged between terminals of a number of collaboration participants from a text analysis of the messages;
a session analyzer configured to derive a unit of a cooperation task related to the message exchanged between the terminals of a number of cooperation participants from session analysis of the message, and manage task continuity in the unit of the derived cooperation task; and
an intent analyzer configured to extract an intent of the collaboration participant derived by the text analyzer from an intent analysis of the messages exchanged between the terminals of a number of collaboration participants.
5. The intelligent collaboration support system according to claim 4, wherein the software robot invokes a task process for executing a task processing step corresponding to the intention of the collaboration participant from the intelligent collaboration system among task processing steps included in individual task slots constituting a task story in units of collaboration tasks determined by the session analyzer.
6. The intelligent collaboration support system as claimed in claim 2, wherein the interactor further comprises a learner configured to analyze non-standard messages received from an external collaboration system by learning, convert the non-standard messages into standardized intentions available in the intelligent collaboration system, and learn the intentions.
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| Application Number | Priority Date | Filing Date | Title |
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| KR1020190039351A KR102233235B1 (en) | 2019-04-04 | 2019-04-04 | Intelligence collaboration assistant system based on analyzing user intent from message |
| KR10-2019-0039351 | 2019-04-04 |
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- 2019-04-04 KR KR1020190039351A patent/KR102233235B1/en active IP Right Grant
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