KR20220165234A - Method and apparatus for providing chatbot service using condition expression generated interface - Google Patents

Abstract

A method and device for providing a chatbot service using a conditional expression generation interface are disclosed. According to one embodiment of the present invention, the method for providing a chatbot service may include the steps of: providing a conditional expression generation interface; and generating an execution block for processing an intent corresponding to a user dialogue through the conditional expression generation interface. Accordingly, it is possible for a user to effectively create conditional expressions necessary for driving a chatbot, and thus, a more improved chatbot service can be provided.

Description

Method and apparatus for providing chatbot service using condition expression generation interface {METHOD AND APPARATUS FOR PROVIDING CHATBOT SERVICE USING CONDITION EXPRESSION GENERATED INTERFACE}

The present invention relates to a method and apparatus for providing a chatbot service, and more particularly, to a technique for defining a bot driving process by providing an interface for a chatbot designer to create a conditional expression for driving a bot in a work area.

Unless otherwise indicated herein, material described in this section is not prior art to the claims in this application, and inclusion in this section is not an admission that it is prior art.

A chatbot refers to a computer program or artificial intelligence equipped with the ability to communicate by text or voice. It ranges from simple chatbots that use only word or phrase matching to complex and sophisticated natural language processing technology to conduct natural conversations like humans. There are various levels of chatbots, including chatbots that apply .

A conversational AI agent system using a chatbot identifies the service the user wants in response to the user's input and provides a response. In this case, when the user provides an input through a natural sentence, natural language processing technology may be applied to the input for processing. To this end, several utterance example sentences with the same user intent are collected and classified into one intent. It is possible to provide a chatbot service capable of handling various spontaneous speeches of users by mapping bot responses corresponding to each of the thus classified intents or defining operations such as service API calls.

When one bot response unit (execution block) is mapped for each user's utterance unit (intent), bot responses and corresponding intents increase as service functions increase. It is assigned to intents, and when intent is classified, a plurality of intents having a slight score difference are generated, which increases the possibility of error in intent classification.

Even if the number of intents does not increase, if various utterance patterns are registered in the intent to recognize the user's various natural utterances, similar patterns are registered in multiple intents, so that the natural language processor accurately identifies similar utterances between different intents. Difficult to process, prone to errors in intent classification.

That is, if utterances are classified according to the situation in which intents and bot response units are mapped 1:1, the performance of the natural language processor decreases as the number of intents increases or the number of example utterances registered in the intent increases. occurrence, and when utterances are classified to ensure the performance of the natural language processor, all similar utterance patterns are defined in one intent, which makes the implementation of blocks mapped to the intent very complicated, and many actually provided functions are included in one block. As it becomes a mapping situation, difficulties in management are inevitable.

In addition, most chatbots process bot actions by searching for all intents when the user utters them. In the case of searching for all intents in this way, the problem of processing results of natural language processors and bot responses and actions There is a problem that complicates the configuration.

Therefore, there is an urgent need for a new chatbot service providing technology that can more freely map intents and bot response units.

Korean Patent Publication No. 10-2019-0006403, published on January 18, 2019 (Name: Voice processing method and system supporting it)

An object of the present invention is to reduce errors in conditional expressions and improve readability by providing an interface to which conditional expression grammar based on keyword expressions optimized for slot recognition is applied when setting conditions necessary for driving a chatbot.

In addition, an object of the present invention is to write a conditional expression concisely by using parentheses limitedly only to set priorities, and to reduce the steps of input by recommending meaningful parentheses for parentheses created by a bot designer, The use of parentheses prevents conditional expressions from being complicated.

In addition, an object of the present invention is to enable N:N (N is a natural number) mapping between intents classified as suitable for a natural language processor and execution blocks processing the intents, so that a single It is to be able to execute a function, and conversely, to be able to perform a completely different function with similar utterances.

Also, an object of the present invention is to enable various intent-execution block mapping relationships by using an intent mapping list including an intent identifier and a slot condition for each one or more intents.

In addition, the object of the present invention is not limited to the objects as described above, and it is obvious that another object may be derived from the following description.

A method for providing a chatbot service according to an embodiment of the present invention for achieving the above object includes providing a conditional expression generation interface; and generating an execution block for processing an intent corresponding to a user dialogue through the conditional expression generation interface.

At this time, at least one condition included in the conditional expression generated through the conditional expression generation interface may be composed of a combination of a condition variable, a comparison operator, and a condition value.

In this case, the conditional expression generating interface may recommend at least one comparison operator in consideration of the input condition variable and recommend at least one condition value in consideration of the input comparison operator.

At this time, the conditional expression generating interface may recommend a pair of parentheses input at an arbitrary position among the conditional expressions.

In this case, the conditional expression generation interface may not use parentheses when calling a function.

At this time, the conditional expression generating interface includes a first type parenthesis capable of changing the result of the conditional expression, a second type parenthesis that does not change the result of the conditional expression but is determined to be unnecessary, and the first type parenthesis without changing the result of the conditional expression. At least one of a type parenthesis and a third type parenthesis that does not correspond to the second type parenthesis may be recommended as a pair of parentheses.

In this case, a method of searching for the first type parentheses may include a method of using a conditional expression result comparison table.

At this time, the conditional expression generation interface considers the type of the input parentheses before recommending a pair of input parentheses to determine whether the arbitrary position is a position where parentheses can be input, and the arbitrary position is parentheses input possible If it is not the position, it can be corrected by moving the arbitrary position based on the type of parentheses inputted above.

In this case, the conditional expression generation interface may display the first type parenthesis, the second type parenthesis, and the third type parenthesis to be visually distinguished.

In addition, a chatbot service providing apparatus according to an embodiment of the present invention includes one or more processors; and an execution memory storing one or more programs executed by the one or more processors. Including, the at least one program provides a conditional expression generation interface, and generates an execution block for processing an intent corresponding to a user dialogue through the conditional expression generation interface.

At this time, at least one condition included in the conditional expression generated through the conditional expression generation interface may be composed of a combination of a condition variable, a comparison operator, and a condition value.

In this case, the conditional expression generating interface may recommend at least one comparison operator in consideration of the input condition variable and recommend at least one condition value in consideration of the input comparison operator.

At this time, the conditional expression generating interface may recommend a pair of parentheses input at an arbitrary position among the conditional expressions.

In this case, the conditional expression generation interface may not use parentheses when calling a function.

At this time, the conditional expression generating interface includes a first type parenthesis capable of changing the result of the conditional expression, a second type parenthesis that does not change the result of the conditional expression but is determined to be unnecessary, and the first type parenthesis without changing the result of the conditional expression. At least one of a type parenthesis and a third type parenthesis that does not correspond to the second type parenthesis may be recommended as a pair of parentheses.

In this case, a method of searching for the first type parentheses may include a method of using a conditional expression result comparison table.

At this time, the conditional expression generation interface considers the type of the input parentheses before recommending a pair of input parentheses to determine whether the arbitrary position is a position where parentheses can be input, and the arbitrary position is parentheses input possible If it is not the position, it can be corrected by moving the arbitrary position based on the type of parentheses inputted above.

In this case, the conditional expression generation interface may display the first type parenthesis, the second type parenthesis, and the third type parenthesis to be visually distinguished.

According to the present invention, when setting conditions necessary for driving a chatbot, it is possible to reduce errors in conditional expressions and improve readability by providing an interface to which conditional expression grammar based on keyword expressions optimized for slot recognition is applied.

In addition, the present invention concisely writes conditional expressions by using parentheses limitedly only to set priorities, and recommends meaningful parentheses for parentheses created by bot designers to reduce input steps and use meaningless parentheses. This can prevent complex conditional expressions from being written.

In addition, the present invention enables N:N (N is a natural number) mapping between intents classified as suitable for a natural language processor and execution blocks processing the intents, so that one function can be executed with various different utterances. and conversely, similar utterances can perform completely different functions.

Also, according to the present invention, it is possible to define various intent-execution block mapping relationships by using an intent mapping list including an intent identifier and a slot condition for each one or more intents.

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

1 is an operational flowchart illustrating a method of providing a chatbot service according to an embodiment of the present invention.
2 is a diagram showing an embodiment of a conditional expression generation interface according to the present invention.
3 is a diagram showing an example of a conditional expression tokenized and analyzed according to the present invention.
4 to 5 are views showing an example of recommending a pair of parentheses according to the present invention.
6 is a diagram showing an example of a conditional expression result comparison table according to the present invention.
7 to 8 are diagrams illustrating an example in which position correction of input parentheses and pair recommendation of input parentheses are performed according to the present invention.
9 is a block diagram showing an apparatus for providing a chatbot service according to an embodiment of the present invention.
10 is a block diagram showing a chatbot service system according to an embodiment of the present invention.
11 is a block diagram of a 'coffee order' scenario according to an embodiment of the present invention.
FIG. 12 is a diagram illustrating an example of intents used in the scenario shown in FIG. 11 .
13 is a block diagram illustrating connections between a plurality of intents and a plurality of execution blocks.
FIG. 14 is a diagram illustrating an example of the execution block coffee selection shown in FIG. 13 .
FIG. 15 is a diagram illustrating an example of hot ice checking of the execution block shown in FIG. 13 .
FIG. 16 is a diagram illustrating an example of execution block takeout confirmation shown in FIG. 13 .
17 is a block diagram showing a connection relationship between execution blocks shown in FIGS. 13 to 16 .
18 is a diagram illustrating an example of connecting blocks by defining a waiting state for a user response in an execution block.
19 is a diagram illustrating an example of mapping intents defined for each speech pattern to blocks.
20 is a diagram illustrating a case of 1:1 mapping between intents and execution blocks.
21 is a diagram illustrating an example of intent classification and block mapping according to an embodiment of the present invention.
FIG. 22 is a diagram showing execution blocks corresponding to the responses shown in FIG. 21 .
FIG. 23 is a diagram illustrating an example of prioritization according to inclusion relationships of conditions corresponding to the coffee ordering scenarios illustrated in FIGS. 11 to 17 .
24 is a block diagram illustrating an example of modules necessary for executing a chatbot.
25 is a diagram illustrating a process of searching for intents and blocks while the chatbot is running.
26 is an operational flowchart illustrating a method of providing a chatbot service according to another embodiment of the present invention.
27 is a graph showing performance of a natural language processor according to an increase in the number of speech patterns.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, detailed descriptions of well-known functions or configurations that may obscure the gist of the present invention will be omitted in the following description and accompanying drawings. In addition, it should be noted that the same components are indicated by the same reference numerals throughout the drawings as much as possible.

The terms or words used in this specification and claims described below should not be construed as being limited to a conventional or dictionary meaning, and the inventors should appropriate the concept of terms to describe their invention in the best way. It should be interpreted as a meaning and concept consistent with the technical spirit of the present invention based on the principle that it can be defined in the following way. Therefore, the embodiments described in this specification and the configurations shown in the drawings are only one of the most preferred embodiments of the present invention, and do not represent all of the technical ideas of the present invention, so various alternatives can be made at the time of this application. It should be understood that there may be equivalents and variations. In addition, terms such as first and second are used to describe various components, and are used only for the purpose of distinguishing one component from another, and are not used to limit the components.

The present invention relates to a technique for setting various conditions necessary for driving a bot to provide a chatbot service and defining a chatbot driving process through the setting.

Hereinafter, an operation flow for defining a bot driving processor will be described in detail.

1 is an operational flowchart illustrating a method of providing a chatbot service according to an embodiment of the present invention.

Referring to FIG. 1 , the method for providing a chatbot service according to an embodiment of the present invention provides a conditional expression generating interface (S110).

At this time, the conditional expression generation interface according to an embodiment of the present invention may generate a conditional expression by applying a specialized conditional expression grammar to an expression considering readability or a task of comparing slots mainly used in a chatbot builder.

For example, if the conditional expression for the case where the "member number" slot is all numbers and the "alias" slot includes "member" or does not include "guest" is expressed in the existing JavaScript language grammar, it is as follows.

[Example of conditional expression expressed in JavaScript language grammar]

/^[0-9]+$/.test(slot_value("Nickname")) && (slot_value("Nickname").includes("member") || !slot_value("Nickname").includes("guest "))

Looking at conditional expressions expressed in JavaScript language grammar, parentheses used for function calls and parentheses used to indicate condition priority are mixed, resulting in poor readability. In addition, since the condition value is written before the condition variable, it is difficult to quickly determine which condition variable value the corresponding value is.

In the present invention, the above conditional expression can be expressed as follows by applying a specialized conditional expression grammar to improve readability in order to solve this problem.

[Example of conditional expression expressed in the conditional expression grammar proposed in the present invention]

$membernumber regex "^[0-9]+$" and ($nickname not contain "guest" or $nickname contain "member")

At this time, at least one condition included in the conditional expression generated through the conditional expression generation interface may be composed of a combination of a condition variable, a comparison operator, and a condition value.

To describe the condition included in the above conditional expression as an example, the combination of "$member number" corresponding to the condition variable, "regex" corresponding to the comparison operator, and "^[0-9]+$" corresponding to the condition value Through this, one condition, $member number regex "^[0-9]+$", can be configured.

At this time, the conditional expression generation interface may create conditions in an order corresponding to condition variables, comparison operators, and condition values.

To explain the condition included in the above conditional expression as an example, "$member number" corresponding to the condition variable is displayed first, then "regex" is displayed in place of the comparison operator, and finally " ^[0 corresponding to the condition value" -9]+$" is displayed.

In this way, the readability of the conditional expression can be improved by generating the conditions included in the conditional expression in the order corresponding to the condition variable, comparison operator, and condition value.

At this time, in the conditional expression grammar proposed in the present invention, the comparison function can be expressed in text like a comparison operator and used. Describing the above conditional expression as an example, the regular expression test method used in the JavaScript language grammar is denoted as "regex" according to the conditional expression grammar according to an embodiment of the present invention, and the includes function used in the JavaScript language grammar is one of the present invention. It can be seen that it is marked as "contain" according to the conditional expression grammar according to the embodiment. In addition, functions such as "empty" and "exist" may also be used.

In addition, in the conditional expression grammar according to an embodiment of the present invention, the "not" keyword is used to display negative conditions naturally. To explain the above conditional expression as an example, if the case where the "alias" slot does not include "guest" is expressed, it is equivalent to '!slot_value("alias").includes("guest"))' in the JavaScript language grammar. Alternatively, it can be expressed as '$ alias not contain "guest"' according to the conditional expression grammar according to an embodiment of the present invention.

At this time, in the conditional expression grammar according to an embodiment of the present invention, "not exist" and "not empty" are not used, but "empty" and "exist" are used to minimize the use of negative conditional display.

In more detail, the conditional expression notation rules in the conditional expression grammar according to an embodiment of the present invention are described in detail. The slot name is $slot, the slot value is "value", the conditional operation is ==, !=, <, >, <= It can be expressed as >=, contain, not contain, regex, empty, exist, etc., and logical operations can be used as shown in [Table 1].

comparison operator conditional expression sample matches $slot == "value" does not match $slot != "value" greater than $slot > "value greater than or equal to $slot >= "value" less than $slot < "value less than or equal to $slot <= "value" contains $slot contain "value" not include $slot　not contain　"value" regular expression $slot regex "[0-9]+" it does not exist $slot empty exists $slot =exist

In addition, the conditional expression created through the conditional expression writing interface according to an embodiment of the present invention can be expressed and used as, for example, the parser grammar rules shown in [Table 2].

Element Expression and usage example or_expression or_expression 'or' and_expression
| and_expression and_expression and_expression 'and' compare_expression
| compare_expression compare_expression parameter COMPARE_OPERATOR value
| parameter 'regex' value
| parameter 'contain' value
| parameter 'not''contain' value
| parameter 'exist'
| parameter 'empty'
| '(' expression ')'
| value value STRING
| NUMBER
| PARAMETER COMPARE_OPERATOR '==' | '!= ' | '<=' | '>=' | '<' | '>' STRING Plain string in quotes: /"([^\\\"]|\\.)*"/ NUMBER Normal numbers (including decimals): /[-+]? (\d*\.\d+|\d+)/ PARAMETER

\-_\.]+/Variables starting with $ (including Korean characters): /\$[a-zA-Z\d is-

\-_\.]+/

Hereinafter, for a clear understanding of the present invention, the conditional expression prepared according to an embodiment of the present invention will be interpreted based on the above description.

For example, in the conditional expression corresponding to ("$svc_cat == "ok cashback points" or "$svc_cat == "points") and "$action" == "accumulation", the condition value of the condition variable "svc_cat" is It may mean a case where the condition value of the condition variable "action" corresponds to "accumulation" while corresponding to "ok cashback point" or "point".

For another example, the conditional expression corresponding to ($svc_cat=="ok cashback point" or $svc_cat regex "[0-9]+") and $action=="earning" is the condition value of the condition variable "svc_cat" This "ok cashback point" or all numbers may mean a case in which the condition value of the condition variable "action" corresponds to "accumulation".

As another example, in the conditional expression corresponding to $svc_cat=="ok cashback point" and $location empty and $action =="accumulation", the condition value of the condition variable "svc_cat" is "ok cashback point" and the condition variable It may mean that the condition value of "location" does not exist and the condition value of the condition variable "action" corresponds to "accumulation".

In this case, the conditional expression generating interface may support a text-based direct input method and a button-based recommendation input method.

For example, in the text-based direct input method, the bot designer inputs a conditional expression by typing directly into the input window, and in the button-based recommendation input method, the bot designer selects the input button provided by the conditional expression creation interface with the mouse or other selection methods. and click to type.

In the existing UI (User Interface), which inputs conditional expressions based on text, function-type calls are mixed, so the location of variables is not constant, and parentheses used for functions and parentheses that group conditions for condition priority are mixed with each other to distinguish The downside is that it is difficult to do.

In addition, UI (User Interface), which allows input of conditional expressions without text errors through button input, often does not support nesting even if and/or conditions are possible, so there was inconvenience in writing conditional expressions.

In order to solve these problems, the present invention intends to provide a conditional expression generation interface that enables direct text input and input using buttons, so that conditional expressions are not incomplete or grammatical errors do not occur.

Accordingly, the conditional expression generation interface according to an embodiment of the present invention may correspond to an interface capable of direct input according to a predefined conditional expression grammar and simultaneously supporting conditional expression input through mouse control.

In this case, the conditional expression generating interface may recommend at least one comparison operator in consideration of the input condition variable and recommend at least one condition value in consideration of the input comparison operator.

For example, referring to the conditional expression generation interface according to one embodiment of the present invention shown in FIG. 2, when a bot designer inputs a slot name corresponding to a condition variable in the input window 220, the input slot name is considered. Therefore, at least one comparison operator that can be applied to this can be automatically recommended. In addition, if it is assumed that the bot designer selects and inputs a comparison operator through recommendation, at least one slot value corresponding to a condition value may be automatically recommended in consideration of the input slot name and comparison operator.

In addition, the conditional expression generation interface may automatically recommend at least one slot name applicable when the bot designer focuses the condition variable region of the input window 220 with a mouse.

In addition, the conditional expression generating interface may automatically recommend at least one comparison operator applicable when the bot designer focuses the comparison operator area of the input window 220 with a mouse.

In addition, the conditional expression generating interface may automatically recommend at least one slot value applicable when the bot designer focuses the conditional value area of the input window 220 with a mouse.

In this case, recommendation may be performed in consideration of whether a value is input to another area applied to the input window 220 according to which area is the focused area.

In addition, the conditional expression generation interface recommends at least one applicable slot name when the bot designer inputs "$" in the input window 220 in a text-based direct input method, and inputs the first letter of the comparison operator. At least one comparison operator starting with a letter is recommended, and at least one applicable slot value can be recommended by entering an opening double quotation mark (").

In addition, the conditional expression generation interface may recommend a pair of parentheses input at an arbitrary position in the conditional expression.

In this case, the conditional expression generation interface may not use parentheses when calling a function.

For example, in order to prevent confusion between parentheses used for function calls and parentheses indicating condition priority, the conditional expression generation interface according to an embodiment of the present invention uses parentheses only when indicating condition priority as in the example below. can be used To this end, the expression of the function that brings the slot value corresponding to the condition variable can be expressed in a different way, such as "$slot name".

[Example of conditional statement expressed in the conditional expression grammar proposed in the present invention]

$membernumber regex "^[0-9]+$" and ($nickname not contain "guest" or $nickname contain "member")

At this time, the conditional expression generation interface according to an embodiment of the present invention may first perform an analysis process on the conditional expression in order to recommend a pair of parentheses input at an arbitrary position in the conditional expression.

For example, assuming that a bot designer inputs an opening parenthesis to prioritize a condition after inputting a conditional expression, the conditional expression generating interface may analyze the conditional expression as shown in FIG. 3 . At this time, in order to perform analysis in units in which parentheses can be input, conditional expressions are classified into logical operators such as and and or and comparison syntax units, and items with the same comparison syntax are regarded as the same condition and tokenized.

For example, $type == "song" and $action == "play" or $type == "album" and $action == "play" is a conditional expression containing four comparison operation conditions, but A: $ If you define type == "song", B: $action == "play", C: $type == "album", you can simplify it to A and B or C and B.

When conditional expression analysis is completed in this way, all positions of parentheses that can be recommended according to the type of parentheses input can be searched as shown in FIGS. 4 and 5 .

For example, referring to FIG. 4, when the input parenthesis is an opening parenthesis, the position of the closing parenthesis (410, 420, 430) where the right side of each condition located after the conditional expression based on the input position of the opening parenthesis is recommended can be searched for.

For another example, referring to FIG. 5, when the input parenthesis is a closing parenthesis, the position of the opening parenthesis (510, 520, 530 ) can be searched.

At this time, the first type parenthesis that can change the result of the conditional expression, the second type parenthesis that does not change the result of the conditional expression but is judged unnecessary, and the first type parenthesis and the second type parenthesis without changing the result of the conditional expression At least one of the non-applicable third-type parentheses may be recommended as a pair of parentheses.

At this time, the first type parentheses may be meaningful parentheses that can change the result of the conditional expression, the second type parentheses may be unnecessary parentheses that do not matter even if there is no condition, and the third type parentheses correspond to parentheses for readability of the conditional expression can do.

For example, it may be determined that the parentheses are of the second type in the following cases.

- Entire parentheses enclosing the entire conditional expression

(A and B or C and B)

- unary parentheses surrounding only one condition

A and B or (C) and B

- redundant parentheses enclosing only parentheses

A and ((B or C)) and B

In this case, a method of searching for the first type parentheses may include a method of using a conditional expression result comparison table.

At this time, the conditional expression result comparison table is a True/False table created based on the conditional expression before the bot designer inputs the parentheses and a conditional expression in the state where the pair of parentheses recommended by the conditional expression creation interface is applied to the parentheses input by the bot designer. It may correspond to a True/False table created based on the table in a comparable form. If there is a difference between the results of the two True/False tables included in the conditional expression result comparison table, it may be determined that the parentheses recommended by the conditional expression generation interface are meaningful parentheses of the first type.

For example, referring to FIG. 6, the True/False table shown on the left corresponds to what was created based on the conditional expression 'A and B or C and B' before the bot designer inputs parentheses, and on the right The shown True/False table corresponds to 'A and (B or C) and B', a conditional expression in which the conditional expression creation interface applies a pair of parentheses recommended to the parentheses input by the bot designer. have. At this time, in the conditional expression result comparison table shown in FIG. 6, since there is a different value 610 among the result values between the two True / False tables, the pair of parentheses recommended by the conditional expression creation interface is the first type of parentheses. can judge

In this way, it is possible to search all first type parentheses by performing analysis on all pairs of parentheses recommended by the conditional expression generating interface.

In the present invention, the method of using the conditional expression result comparison table to search for the first type of parentheses has been described as an example, but the method of searching for the first type of parentheses is not limited thereto, and a pair of recommended parentheses can change the result of the conditional expression. It is possible to adopt and use various methods to determine whether or not a pair of meaningful parentheses is possible.

At this time, the conditional expression generating interface may determine whether an arbitrary position is a position where parentheses can be input in consideration of the type of input parentheses before recommending a pair of input parentheses.

In this case, the conditional expression generating interface may correct the position by moving the position based on the type of the input parenthesis when the position is not a position where parentheses can be input.

For example, referring to FIG. 7 , it may be assumed that the bot designer inputs an opening parenthesis at the indicated position 710 in the conditional expression. At this time, since the displayed position 710 corresponds to the right side of the condition variable constituting the condition or the left side of the comparison operator, it can be determined that the opening parenthesis is not an input position.

Therefore, as shown in FIG. 8 , the conditional expression generation interface may move the position of the opening parenthesis input by the bot designer so that the opening parenthesis is input at the corrected position 810 .

At this time, considering the type of parenthesis entered by the bot designer, the opening parenthesis "(" can be corrected by moving the position to the front of the conditional expression, and the closing parenthesis ")" can be corrected by moving to the position behind the conditional expression. .

In this case, the conditional expression generation interface may display the first type parenthesis, the second type parenthesis, and the third type parenthesis to be visually distinguished.

For example, you can prioritize parentheses of the first type as 1st priority, parentheses of the 3rd type as 2nd priority, and parentheses of the 2nd type as 3rd priority, and display the priority number corresponding to the pair of recommended parentheses. can

For another example, as shown in FIG. 8 , the first type parentheses 820 and 830 having high priority are most emphasized, and the third type parenthesis 840 corresponding to the next priority may be less emphasized. have. At this time, as shown in FIG. 8 , when several parentheses of the same type are recommended, they may be highlighted in order of proximity based on the positions of the parentheses input by the bot designer. Also, if parentheses of the 1st and 2nd priority types are recommended, parentheses of the 3rd priority type may not be recommended for readability.

Hereinafter, a process of recommending a pair of parentheses and generating a conditional expression by selecting a pair of recommended parentheses will be described in detail with reference to FIG. 8 .

First, the conditional expression generating interface may search all types of parentheses that can be recommended for the input opening parenthesis moved to the corrected position 810 through conditional expression analysis.

If it is assumed that the first type parentheses 820 and 830 and the third type parentheses 840 are searched as shown in FIG. 8 , first of the first type parentheses 820 and 830, a first type closest to the input parentheses is selected. Type parenthesis 820 can be recommended as the way to show the most emphasis.

At this time, when the bot designer selects the first type parenthesis 820 by performing touch and click by a point device or left and right direction keys and tap cycle input by a keyboard device, The pairing may be confirmed as the first type parenthesis 820 and the recommendation may end.

However, since the pair of parentheses that the bot designer intends to input may not be the first type parenthesis 820, the conditional expression interface converts the first type parenthesis 830 closest to the next entered parenthesis to the first type parenthesis 820. ), we can express that the choice is possible by showing less emphasis.

Similarly, when the bot designer selects the first type parenthesis 830, the pair of open parenthesis input at the corrected position 810 may be determined as the first type parenthesis 830, and the recommendation may end.

As such, the conditional expression generating interface according to an embodiment of the present invention may first recommend a parenthesis that is located close to the position of an input parenthesis when there are a plurality of parentheses of the same type.

In addition, referring to FIG. 8 , the conditional expression generation interface according to an embodiment of the present invention may indicate that selection is possible while also recommending the second type parenthesis 840 that is determined to be unnecessary, but the first type parenthesis or the third type parenthesis 840 may be displayed. It can be marked as de-emphasized compared to type brackets.

In this way, the conditional expression generation interface according to an embodiment of the present invention can verify whether the conditional expression input by the bot designer is properly entered in accordance with the grammar, and if the conditional expression is abnormally entered, the bot designer can input it again. can make it possible

For example, whether the parentheses included in the conditional expression are entered in pairs, whether the conditional expression is written according to the notation format for each operator defined in the grammar, whether the condition variable, comparison operator, and condition value are normally entered, and included in the conditional expression It is also possible to check whether the paired quotation marks are entered in pairs.

In addition, the chatbot service providing method according to an embodiment of the present invention generates an execution block for processing an intent corresponding to a user conversation through a conditional expression generation interface (S120).

For example, the conditional expression creation interface includes at least one of an intent mapping connection condition for mapping between an intent and an execution block, an execution condition for executing an action of a chatbot defined in an execution block, and a prompt message execution condition for filling slots. can be provided when creating a conditional expression for driving a bot.

Through such a chatbot service provision method, when setting conditions necessary for running a chatbot, errors in conditional expressions can be reduced and readability can be improved by providing an interface to which conditional expression grammar based on keyword expressions optimized for slot recognition is applied.

In addition, the use of parentheses is limited only to setting priorities to simplify the conditional expression, and by recommending meaningful parentheses for the parentheses created by the bot designer, the step of input is reduced and the use of meaningless parentheses reduces the conditional expression. It can avoid complicated writing.

9 is a block diagram showing an apparatus for providing a chatbot service according to an embodiment of the present invention.

Referring to FIG. 9 , an apparatus for providing a chatbot service according to an embodiment of the present invention includes a processor 920 and an execution memory 930. According to the embodiment, the chatbot service providing device may further include a communication unit 910 .

The communication unit 910 may serve to transmit information related to a chatbot service with a plurality of other devices through a communication network such as a network. At this time, the network provides a path for transferring data between devices, and is a concept encompassing both existing networks and networks that can be developed in the future.

For example, the network is an IP network that provides large data transmission and reception services and data services without disconnection through the Internet Protocol (IP), and All IP, an IP network structure that integrates different networks based on IP. ) network, and can be a wired network, Wibro (Wireless Broadband) network, 3G mobile communication network including WCDMA, 3.5G mobile communication network including HSDPA (High Speed Downlink Packet Access) network and LTE network, 4 including LTE advanced It may be achieved by combining one or more of a household mobile communication network, a satellite communication network, and a Wi-Fi network.

For example, the network includes a wired and wireless local area network that provides communication between various information devices within a limited area, a mobile communication network that provides communication between mobile bodies and between mobile bodies and the outside of the mobile body, and a satellite that provides communication between earth stations and earth stations. It may be a satellite communication network or any one of wired and wireless communication networks, or a combination of two or more. Meanwhile, the transmission method standards of the network are not limited to existing transmission method standards, and may include all transmission method standards to be developed in the future.

The execution memory 930 stores one or more programs executed by the processor 920 .

At this time, at least one program provides a conditional expression generation interface and generates an execution block for processing an intent corresponding to a user dialogue through the conditional expression generation interface.

At this time, at least one condition included in the conditional expression generated through the conditional expression generation interface may be composed of a combination of a condition variable, a comparison operator, and a condition value.

In this case, the conditional expression generating interface may recommend at least one comparison operator in consideration of the input condition variable and recommend at least one condition value in consideration of the input comparison operator.

At this time, the conditional expression generating interface may recommend a pair of parentheses input at an arbitrary position among the conditional expressions.

In this case, the conditional expression generation interface may not use parentheses when calling a function.

At this time, the conditional expression generating interface includes a first type parenthesis capable of changing the result of the conditional expression, a second type parenthesis that does not change the result of the conditional expression but is determined to be unnecessary, and the first type parenthesis without changing the result of the conditional expression. At least one of a type parenthesis and a third type parenthesis that does not correspond to the second type parenthesis may be recommended as a pair of parentheses.

In this case, a method of searching for the first type parentheses may include a method of using a conditional expression result comparison table.

At this time, the conditional expression generation interface considers the type of the input parentheses before recommending a pair of input parentheses to determine whether the arbitrary position is a position where parentheses can be input, and the arbitrary position is parentheses input possible If it is not the position, it can be corrected by moving the arbitrary position based on the type of parentheses inputted above.

In this case, the conditional expression generation interface may display the first type parenthesis, the second type parenthesis, and the third type parenthesis to be visually distinguished.

The device shown in FIG. 9 may receive various types of information, such as numbers and text information, and transmit input signals to the processor 920 through an input unit in relation to setting various functions and controlling functions of the device. In addition, the input unit may include at least one of a keypad and a touchpad that generate an input signal according to a user's touch or manipulation. At this time, the input unit of the device is configured in the form of a single touch panel (or touch screen) together with the display unit to simultaneously perform input and display functions. In addition, the input unit may use any type of input means that may be developed in the future, in addition to input devices such as a keyboard, keypad, mouse, joystick, and the like.

In addition, the display unit may display information about a series of operation states and operation results that occur while performing functions of the device. Also, the display unit may display menus of the device and user data input by the user. Here, the display unit includes a liquid crystal display (LCD), a thin film transistor LCD (TFT-LCD), a light emitting diode (LED), an organic light emitting diode (OLED), It can be composed of an active organic light emitting diode (AMOLED, Active Matrix OLED), a Retina Display, a flexible display, and a 3D display. At this time, when the display unit is configured in the form of a touch screen, the display unit may perform some or all of the functions of the input unit.

In addition, the storage unit of the device is a device for storing data, includes a main memory device and an auxiliary memory device, and may store application programs required for functional operation of the device. This storage unit may largely include a program area and a data area. Here, when the device activates each function in response to a user's request, the corresponding application programs are executed under the control of the processor 920 to provide each function.

In addition, the communication unit 910 may perform a function for transmitting and receiving data through a network. Here, the communication unit 910 may include an RF transmitting means for up-converting and amplifying the frequency of a transmitted signal, and an RF receiving means for low-noise amplifying a received signal and down-converting the frequency. The communication unit 1810 may include at least one of a wireless communication module and a wired communication module. And, the wireless communication module is a component for transmitting and receiving data according to a wireless communication method, and when the device uses wireless communication, data is transmitted using any one of a wireless network communication module, a wireless LAN communication module, and a wireless fan communication module. can be sent and received. In addition, the wired communication module is for transmitting and receiving data by wire. The wired communication module may access a network through a wire and transmit/receive data to and from the device. That is, the device accesses the network using a wireless communication module or a wired communication module, and can transmit/receive data with other devices through the network.

In addition, the processor 920 may be a process device that drives an operating system (OS) and each component.

According to the configuration described above, the chatbot service providing apparatus according to an embodiment of the present invention provides an interface to which a keyword expression-based conditional expression grammar optimized for slot recognition is applied when setting conditions necessary for driving a chatbot, so that errors in conditional expressions can be reduced and readability improved.

In addition, the chatbot service providing apparatus according to an embodiment of the present invention concisely writes a conditional expression by using parentheses limitedly only to set priorities, and recommends meaningful parentheses for parentheses created by a bot designer. It is possible to reduce input steps and prevent complex conditional expressions from being written using meaningless parentheses.

In addition, according to the present invention, according to natural language processing performed by a conversational AI agent system, intents corresponding to natural language inputs are classified according to similar properties, and a plurality of classified intents are mapped to a block, which is a processing unit of a bot. It's about technology.

For a useful chatbot service, it is necessary to continuously improve the ability to process natural language for terms by business or service domain and the processing ability for provided functions or tasks.

To this end, it is necessary to train the natural language processor so that various domain-specific terms and sentences can be processed by the natural language processor. At this time, the natural language processor needs to classify intents so that collisions between similar utterances do not occur. In other words, it is necessary to define intent from the perspective of Natural Language Understanding (NLU), rather than defining intent only by function or task.

On the other hand, in order to efficiently process functions or tasks, detailed processing steps or flows need to be defined as execution blocks, and a connection (order relationship) between the defined execution blocks needs to be defined. At this time, it is preferable that the connection relationship be defined in the form of a graph for freely connecting various execution blocks rather than in the form of a list or tree. At this time, it is preferable that the execution blocks are defined to suit the function provided by the service rather than from the perspective of natural language processing.

In this way, it is necessary to be able to freely (N:N) map intents and blocks defined in a form suitable for each purpose in order to efficiently provide a chatbot service that meets the user's intention. In other words, one intent can be written to several blocks, and one block must be able to handle multiple intents. To this end, intents and blocks must be mapped N:N rather than 1:1. .

When processing a large amount of data in which a large amount of utterance patterns are classified into various intents, collisions between similar utterances occur, which requires verification and processing.

At this time, similar utterance patterns can be grouped into the same intent, and conditions can be given based on slots to enable different bot responses.

In addition, if it is possible to map a plurality of user speech units to one bot response unit (execution block) so that they can be processed with the same response, even though they are not similar speech, it is possible to process them with the same response even if the speech type is different. This enables efficient intent management.

Hereinafter, a chatbot service is a concept covering not only a voice service based on a user's voice but also a messenger service based on text input from a user.

Chatbot design and implementation can be largely divided into three parts: chatbot builder, natural language understanding (NLU), and dialog management (DM). At this time, the chatbot builder structures and defines data for the dialog flow processing (DM) to operate and provides it, and the dialog flow processing (DM) processes the bot response based on this data.

A chatbot process may require two workspaces to run. One is a workspace where the bot designer defines the bot driving process, and the other is an execution area where the defined process is executed.

10 is a block diagram showing a chatbot service system according to an embodiment of the present invention.

Referring to FIG. 10 , the chatbot service system includes a chatbot 1010, a natural language processor 1020, a chatbot resource database 1030, and a chatbot builder 1040.

The chatbot 1010 receives a spoken sentence from the user, generates a response thereto, and provides a chatbot response to the user.

The natural language processor 1020 receives spoken sentences from the chatbot, receives intent definition information from the chatbot resource database 1030, generates intent classification results and slot extraction results, and provides them to the chatbot 1010.

The chatbot resource database 1030 receives intent definition information, block information, response information, and scenario information from the chatbot builder, provides block information, response information, and scenario information to the chatbot 1010, and the natural language processor 1020 provides intent definition information.

The chatbot builder 1040 receives input on chatbot resources from the bot designer, creates a chatbot 1010, and provides intent definition information, block information, response information, and scenario information to the chatbot resource database 1030. .

In the chatbot builder 1040, learning utterances and slots, which are important information for bot processing, are defined as intents, the defined intents are mapped to execution blocks, conditions are set in the execution blocks, and bot actions are performed accordingly. can be defined In addition, blocks that can be processed in the response waiting state are set in the chatbot builder 1040, and these blocks can be defined as graph-type scenarios.

At this time, the bot designer works to define the bot driving process through the chatbot builder 1040. First, a block diagram in which bot scenarios for major business processes are defined in a graph form may be provided.

11 is a block diagram of a 'coffee order' scenario according to an embodiment of the present invention.

Referring to FIG. 11 , in a block speech scenario of a coffee ordering chatbot, a user's speech "I will order coffee" is first received (1111).

In addition, in the block utterance scenario of the coffee ordering chatbot, the chatbot utters "What kind of coffee would you like?" (1121).

In response to the chatbot's speech, in the coffee ordering chatbot's block speech scenario, the user's desired coffee name is spoken (1112).

In response to the user's utterance, in the block utterance scenario of the coffee ordering chatbot, the chatbot executes the utterance "Would you like something warm or iced?" (1122).

In response to the conversation of the chatbot, the user selects hot or ice in a scenario where the coffee ordering chatbot triggers a block (1113).

In response to the user's speech, in the block speech scenario of the coffee ordering chatbot, the chatbot executes the speech "Are you going to eat?" (1123).

In response to the conversation of the chatbot, the user selects in-store dining or takeout in a scenario in which the coffee ordering chatbot triggers a block (1114).

When all three slots of $ (coffee name), $ (hot ice), and $ (takeout) are entered, the chatbot executes an utterance to confirm the order in a block firing scenario of the coffee ordering chatbot (1124).

In response to the chatbot's utterance, in the coffee ordering chatbot's block firing scenario, the user utters 'yes' (1115) or 'no' (1116), and if the user utters 'yes', the chatbot executes the coffee order executes a custom action (1125) and executes an utterance to confirm the order (1126), and if the user says 'No', the chatbot executes the utterance "Cancel the order" ( 1127), and then the operation ends.

In FIG. 11, "User" represents a user's utterance, "templete" represents a chatbot's utterance, and "Custom Action" represents a bot action process defined by an execution block.

In the example shown in FIG. 11, $ (coffee name), $ (hot ice), $ (takeout), and $ (order number) correspond to required slots, and although not explicitly shown in FIG. 11, coffee Option slots may be further used in the block firing scenario of the ordering chatbot. In this case, the required slot and/or the optional slot may be generated from the user's speech, or may be slots indicating the status of other chatbot systems or generated according to conditions in the block.

Based on the diagram shown in FIG. 11, all speech patterns that can be processed in each block can be collected, and the patterns can be grouped in a form that is easy for the natural language processor to classify. In addition, when an intent is classified based on grouped information and an intent is set by tagging slots for each utterance pattern, the utterance patterns based on the grouped and classified intents are matched to the learning intent of the natural language processor (NLU) learning data. can be used as

Scenarios can be viewed as connecting blocks in the form of a graph, and a list of intents that can be processed can be defined in each block.

FIG. 12 is a diagram illustrating an example of intents used in the scenario shown in FIG. 11 .

Referring to FIG. 12, the intents used in the coffee ordering scenario include the intent order_coffee (1210), the intent order_hotice (1220), the intent order_takeout (1230), the intent order_togo (1240), and the intent order_eatin (1250). can be found to contain.

At this time, slot $coffee, slot $hotice, and slot $takeout are defined for intent order_coffee(1210), slot $hotice is defined for intent order_hotice(1220), and slot $hotice is defined for intent order_takeout(1230). $takeout is defined, and slots may not be defined for intent order_togo (1240) and intent order_eatin (1250).

When the bot processes the intents shown in FIG. 12, they are processed in units called blocks (processing blocks). A plurality of intents can be mapped to and processed in one block. Also, one intent may be mapped to a plurality of blocks.

13 to 16 are diagrams illustrating examples of execution blocks used in the scenario shown in FIG. 11 .

13 is a block diagram illustrating connections between a plurality of intents and a plurality of execution blocks.

Referring to FIG. 13 , it can be seen that the intent order_coffee 1210 , the intent order_hotice 1220 , and the intent order_takeout 1230 are connected to the execution block select coffee 1310 . In this case, the intent mapping list of the execution block coffee selection 1310 may include intent identifiers corresponding to the intent order_coffee 1210, the intent order_hotice 1220, and the intent order_takeout 1230.

At this time, the intent order_hotice (1220) and the intent order_takeout (1230) are also connected to the execution block check hotice (1320).

At this time, the intent order_takeout (1230) is also connected to the execution block takeout confirmation (1330).

The intent order_togo (1240), the intent order_eatin (1250), the intent followup.yes (1260), and the intent followup.no (1270) are connected to the action block takeout confirmation (1330).

As shown in FIG. 13, intents are mapped to blocks for their execution, and a condition is defined as a grammar expression based on a variable called a slot for each mapped intent, so that the bot classifies utterances only by classifying intents for each user utterance. Appropriate detailed processing is possible through conditions without processing.

As shown in FIG. 13, a plurality of intents may be connected to one block (N:1) or a plurality of blocks may be connected to one intent (1:N), so that there is freedom between intents and blocks. Mapping (N:N) is possible.

Furthermore, one execution block may have a connection relationship with other execution blocks that can be executed later. For example, select runblock coffee 1310 can be linked with check runblock hotice 1320 and check runblock takeout 1330, where runblock hotice check 1320 confirms runblock takeout 1330 ) can be associated with

FIG. 14 is a diagram showing an example of the execution block coffee selection shown in FIG. 13 .

Referring to FIG. 14 , the execution block coffee selection 1310 may include a connected intent list, slot, slot filling, and action information.

In this case, the connected intent list may be included in the intent mapping list, and the intent mapping list may include an intent identifier and a slot condition for each intent. In this case, the slot condition may correspond to conditional statements related to one or more slots and may include a null condition.

The slots ($coffee, $hotice, and $takeout) shown in FIG. 14 may include the union of all slots corresponding to the intents (order_coffee, order_hotice, and order_takeout) to which the execution block coffee selection 1310 is connected.

The slot filling order information may include information on the filling order of the slots of the execution block coffee selection 1310 and ignition information necessary for filling the corresponding slot first.

Action information may include a condition and a response when the condition is satisfied. At this time, the condition may correspond to one or more conditional sentences using slots, and the response may include a link calling another block.

For example, the execution block coffee selection 1310 may connect the execution block hot ice check 1320 based on the condition determination using the slot $hotice.

For example, the execution block coffee selection 1310 may connect the execution block takeout check 1330 based on the condition determination using the slot $takeout.

FIG. 15 is a diagram illustrating an example of hot ice checking of the execution block shown in FIG. 13 .

Referring to FIG. 15 , the execution block hot ice check 1320 may include a connected intent list, slot, slot filling, and action information.

The slots ($hotice, $takeout) shown in FIG. 15 may include the union of all slots corresponding to intents (order_hotice, order_takeout) to which the execution block hotice confirmation 1320 is connected.

The slot filling order information may include information on the filling order of slots in the execution block hot ice check 1320 and ignition information necessary for filling the corresponding slot first.

Action information may include a condition and a response when the condition is satisfied. At this time, the condition may correspond to one or more conditional sentences using slots, and the response may include a link calling another block.

For example, the execution block hot ice check 1320 may connect the execution block takeout check 1330 based on condition determination using the slot $takeout.

FIG. 16 is a diagram illustrating an example of execution block takeout confirmation shown in FIG. 13 .

Referring to FIG. 16 , execution block takeout confirmation 1330 may include a connected intent list, slot, slot filling, and action information.

The slot ($takeout) shown in FIG. 16 may include the union of all slots corresponding to the intents (order_takeout, order_togo, order_eatin, followup.yes, followup.no) to which the execution block takeout confirmation 1330 is connected. have.

The slot filling order information may include information on the filling order of slots in the execution block takeout check 1330 and ignition information necessary for filling the corresponding slot first.

Action information may include a condition and a response when the condition is satisfied. At this time, the condition may correspond to one or more conditional sentences using slots, and the response may include a link calling another block.

17 is a block diagram showing a connection relationship between execution blocks shown in FIGS. 13 to 16 .

Referring to FIG. 17 , it can be seen that an execution block coffee selection 1310, an execution block hot ice confirmation 1320, and an execution block takeout confirmation 1330 are connected in a graph form.

In this case, the connection between the execution blocks may mean a connection with next execution blocks capable of processing the user's speech when entering the user's speech standby state in a specific current block. That is, since a situation that can occur when a user speech is received in a specific current block is execution blocks connected to the current block, only intents connected to these execution blocks can be searched as intents requiring user speech processing. In the state of waiting for user utterance, if you collect only the intents included in the sub-blocks connected based on the current block and search for the intents through the natural language processor, they correspond to utterances much more quickly and accurately than when all intents are searched. Intent can be identified.

In the example shown in FIG. 17, since the execution block coffee selection 1310 is connected to the execution block hot ice selection 1320 and the execution block takeout confirmation 1330, the user utterance in the state of the execution block coffee selection 1310 If input, the next state may be execution block hot ice selection 1320 or execution block takeout confirmation 1330.

In addition, when a user utterance is input in the state of execution block hot ice selection 1320, the next state may be execution block takeout confirmation 1330.

If a user intent is identified from the user's speech, the blocks may be searched by arranging them in the order of priority of intent conditions of blocks including the identified intent and/or a priority defined by a bot designer.

When a block that satisfies the conditions is searched for, it moves to the corresponding block, performs an operation defined according to the corresponding block setting, and returns to a state of waiting for a user response according to the condition of the action.

Connecting blocks to blocks may be defined as enumerating blocks to be processed next from the current block when a user response waiting state is entered, and several user response waiting states may be defined in one block.

18 is a diagram illustrating an example of connecting blocks by defining a waiting state for a user response in an execution block.

Referring to FIG. 18 , it can be seen that one block can be connected to a plurality of other blocks in the form of a graph.

Block 1 shown in FIG. 18 is connected to Block 2, Block 3, Block 4, and Block 5. At this time, Block 1 is connected to Block 2 and Block 3 by Condition 4, and by Condition 5, Block 3, It can be connected with Block 4 and Block 5. Accordingly, after Block 1, situations of Block 2, Block 3, Block 4, and Block 5 may occur.

Block 2 is connected to Block 6. Therefore, after Block 2, the situation of Block 6 may occur.

Block 3 is connected to Block 6 and Block 7. Accordingly, situations of Block 6 and Block 7 may occur after Block 3.

Block 5 is connected to Block 6 and Block 7. Accordingly, situations of Block 6 and Block 7 may occur after Block 5.

For example, if the current state corresponds to Block 2, it is sufficient to search for intents targeting only the intents corresponding to Block 6 for subsequent utterances, and the possibility of occurrence is high. There is no need to perform user utterance matching for intents corresponding to Block 7 that do not exist.

When one intent and one execution block are mapped, one intent may need to include a plurality of firing patterns.

For example, the intent (I1: intent to sign up) is the learning sentences "I want to write" (pattern classification P1), "How do I write" (pattern classification P1), "I'm curious about how to sign up" (pattern classification P3) and "Inquire about how to sign up" (pattern classification P3), and the intent (I2: withdrawal intention) includes the learning sentences "I want to stop writing" (pattern classification P2), "I don't want to write anymore" (pattern classification P2), It may include "I'm curious about how to cancel" (pattern classification P3) and "Inquire about how to cancel" (pattern classification P3).

At this time, one block may be connected to the intent (I1: intent to join) and one block may be connected to the intent (I2: intent to leave), but the performance of the natural language processor may deteriorate as the intents include a plurality of speech patterns may be lowered

Therefore, in order to prevent degradation of the performance of the natural language processor, it is desirable to avoid including a plurality of speech patterns in one intent. For example, the intent (I1: subscription intent) includes only the learning sentences "I want to write" (pattern classification P1) and "how to write" (pattern classification P1), and the intent (I2: withdrawal intent) It includes only the learning sentences "I want to stop writing" (pattern classification P2) and "I don't want to use anymore" (pattern classification P2), and the intent (I3: inquiry intent) is the learning sentences "I'm curious about the <sign up> method" ( Pattern classification P3), "Inquire about how to <register>" (pattern classification P3), "I'm curious about the <withdrawal> method" (pattern classification P3), and "Inquire about how to <subscribe>" (pattern classification P3). have.

In this way, when an intent is classified so that it can be effectively processed by a natural language processor, it is necessary to connect the intents and processing blocks in more diverse forms. To this end, each of the block's intents may have a condition, and this condition may be defined as statements comparing slot values. That is, block and intent mapping information may be defined in a block in the form of a list (intent mapping list) in which intents and slot conditions are paired.

19 is a diagram illustrating an example of mapping intents defined for each speech pattern to blocks.

Referring to FIG. 19 , it can be seen that three intents are connected to two blocks as a pair of intent and slot condition.

At this time, the slots used for the slot condition may include not only slots defined in the current intent, but also slots of other blocks set while executing a previous conversation, or slots provided by the system.

In the example shown in FIG. 19, Intent 1 corresponds to Speech Pattern 1, Intent 2 corresponds to Speech Pattern 2, and Intent 3 corresponds to Speech Pattern 2. It can be seen that 3) corresponds to the ignition pattern 3 (Pattern 3).

In the example shown in FIG. 19, the intent mapping list of Block 1 includes a pair of Intent 1 and Condition 1, and a pair of Intent 3 and Condition 2. ), there are two elements of the pair. Therefore, Block 1 is connected to two Intents (Intent 1 and Intent 3).

In the intent mapping list of Block 2, there are two elements: a pair of Intent 2 and Condition 3 and a pair of Intent 3 and Condition 4. exist. Therefore, Block 2 is connected with two intents (Intent 2 and Intent 3).

20 is a diagram illustrating a case of 1:1 mapping between intents and execution blocks.

Referring to FIG. 20 , it can be seen that each of the three intents I1, I2, and I3 includes four firing patterns.

That is, intent I1 (subscription intention) includes speech patterns "I want to try", "How do I write", "I'm curious about how to sign up", and "Inquire how to sign up".

Intent I2 (withdrawal intent) includes the utterance patterns "I want to stop using", "I don't want to use anymore", I'm curious about how to unsubscribe, and "Inquire about how to unsubscribe".

Intent I3 (intent to inquire about regular membership) includes speech patterns "I want to become a regular member", "I want to be promoted to a regular member", "I'm curious about how to become a regular member", and "How to become a regular member".

When intents (intents) and responses (execution blocks) are mapped 1:1, when a sentence like "*** I'm curious" appears in multiple intents, the natural language processor (NLU) can distinguish between these similar sentences. difficult. For example, a sentence such as “I am new, ask how to sign up” is processed by the natural language processor as a sentence similar to “inquire how to sign up” of the withdrawal intent, depending on the circumstances, resulting in false intention recognition.

That is, the user's intent classification may be different from the intent classification that is easy to be processed by a natural language processor (NLU). One actual user's intent may be divided into several intents that are easy to be processed by a natural language processor, and conversely, several The intentions of the user may be classified into one intention that is easy for the natural language processor to process.

21 is a diagram illustrating an example of intent classification and block mapping according to an embodiment of the present invention.

Referring to FIG. 21 , it can be seen that sentences are classified into a form that is easy to process by a natural language processor (NLU), and responses are mapped according to conditions by tagging necessary slots.

In the example shown in FIG. 21 , four intents (I1, I2, I3, and I4) are linked to three responses (R1, R2, and R3).

At this time, Intent 1 (I1) is connected to the subscription information response (R1). At this time, the slot condition may be a null condition.

At this time, Intent 4 (I4) is connected to a subscription guide response (R1), a withdrawal guide response (R2), and a member guide response (R3). At this time, intent 4 (I4) can be connected to the subscription information response (R1) by the slot condition $service == "subscription". At this time, intent 4 (I4) may be connected to the withdrawal guidance response (R2) by the slot condition $service == "withdrawal". At this time, intent 4 (I4) can be connected to regular member information response (R3) by slot conditions $level == "regular member" and $service == "subscription".

At this time, Intent 2 (I2) is connected to the withdrawal guidance response (R2). At this time, the slot condition may be a null condition.

At this time, Intent 3 (I3) is connected to the regular member information response (R3). At this time, the slot condition may be a null condition.

Each of the responses shown in FIG. 21 may correspond to one execution block.

At this time, the execution block may be defined including intent, slot conditions, and response processing of the chatbot.

FIG. 22 is a diagram showing execution blocks corresponding to the responses shown in FIG. 21 .

Referring to FIG. 22 , each execution block includes an intent mapping list including a pair of at least one intent and a slot condition and action information.

As shown in FIG. 22, the slot condition may include a null condition corresponding to "no condition" and may include conditional statements using one or more slots.

When the chatbot performs conversation processing, the natural language processor first classifies the intent from the sentence input by the user, extracts the slot, and then finds a matching block by examining the conditions listed in the block containing the classified intent. .

In the examples shown in FIGS. 21 and 22, when the intent is classified as "I4: Intent of inquiry", the blocks containing this intent are B1, B2, and B3, and the blocks matched according to the conditions are listed in the table below. Same as 3.

Condition block Custom condition check order $service == "subscribe" B1: Registration guide One $service == "Withdrawal" B2: Withdrawal Information 2 $service == "full member" and $service == "subscription" B3: Full member information 3

The order of checking conditions can be determined by the user designating the order of listing blocks. If, as shown in Table 3, processing is performed in the order listed by the user, a sentence such as regular membership registration satisfies the conditions of $level == "regular member" and $service == "registration", but at the same time logically $service = = "subscription" condition is also satisfied, and block B1 having a higher condition check order may be executed, which may not be a desirable result.

In order to solve this problem, if priority is given again in consideration of the inclusion relationship of conditions, it can be determined as shown in Table 4 below.

Condition block Custom condition check order Reordering the ranking considering inclusion $service == "full member" and $service == "subscription" B3: Full member information 3 One $service == "subscribe" B1: Registration guide One 2 $service == "Withdrawal" B2: Withdrawal Information 2 3

At this time, the inclusion relationship of the condition can be determined by the inclusion relationship of the set of slots used in the conditional expression. The inclusive side (large set) is processed with priority over the included side (small set), and if there is no inclusion relationship with each other, Priorities designated by the user may be maintained. In the example of Table 4, the condition of B3, which inspects both level and service, has a higher order of condition inclusion than B1 and B2, which inspect only service, so B3 can be processed with priority over B1 or B2.

FIG. 23 is a diagram illustrating an example of prioritization according to inclusion relationships of conditions corresponding to the coffee ordering scenarios illustrated in FIGS. 11 to 17 .

Referring to FIG. 23, it can be seen that the condition corresponding to two coffee slots and the takeout slot has higher priority than the condition corresponding to one coffee slot.

Therefore, when the user says "I want to take Americano takeout", the slot coffee is filled with "Americano" and the slot takeout is filled with "Takeout", so the execution block coffee order takes precedence over the execution block takeout confirmation.

When the chatbot process is executed, it collects intents from connected blocks in the current user response waiting state, and can search for an appropriate intent among the intents collected through the natural language processor.

When searching for a block by intent, a suitable block can be searched by arranging in the order of the priority of the intent conditions of the blocks that contain the intent and the priority defined by the user.

If a suitable block is not found because the condition is not met, the above method can be repeated using the next suitable intent.

When a block that satisfies the conditions is found, it moves to the corresponding block, performs a predetermined action in the chatbot's response process, and enters the state of waiting for the next user response according to the condition.

24 is a block diagram illustrating an example of modules necessary for executing a chatbot.

Referring to FIG. 24, if the modules required when the chatbot executes the resources defined by the bot designer are largely divided around the conversation flow processor, the part that controls message input and output, the part that manages block status, the part that processes natural language, and the block It can be seen as a part that processes conditions and a part that executes blocks.

In the example of FIG. 24, the modules required to run the chatbot include a dialog flow processor, a block state manager, a message input gateway, a message output gateway, a response message generator, a block process executor, an NLU searcher, a block searcher, an intent classifier, a slot extractor, and a natural language processor. , NLU priority manager, condition resolver and block condition priority manager.

25 is a diagram illustrating a process of searching for intents and blocks while the chatbot is running.

Referring to FIG. 25 , when a user's utterance is input while the chatbot is waiting for a user's input, blocks executable at the current location are first searched. At this time, sub-blocks connected to the current (execution) block may be searched first, and then main blocks of the scenario may be searched. At this time, the main block means a list of blocks to be operated in the initial state of the conversation, and becomes the target blocks that the bot searches for when the user first speaks. That is, if the intent is identified only for the subblocks that are executable after the current block and the main blocks that are newly executable, efficient dialog flow processing is possible.

When a block is searched, the processing priority is checked by enumerating intents mapped to the searched block. At this time, intents defined in the searched blocks are listed, and matching scores of intents corresponding to the user's speech are determined using a natural language processor. The defined intent pattern threshold score list is sequentially searched, and the names of intents and matching scores are compared, and the intent with the highest matching score among the intents that satisfy the pattern and threshold score is selected first. extract

If there is one or more blocks to which the matched intent belongs, the processing priorities are sorted according to the defined intent mapping condition, and if the conditions have the same priority, the scenario and block priorities defined by the user are followed. At this time, the priority of the condition may be prioritized according to the inclusion relationship of the condition comparison phrase. That is, when the condition is constructed based on the slots extracted from the user's utterance, the comparison syntax constituting the condition is composed mainly of the slots mentioned by the user and the slots not mentioned are omitted, so a condition containing many slots It can be seen as a better reflection of the user's intention. That is, when there are several conditions that are true in the conditional statement, the condition that includes more constraints or slots may be considered higher in priority.

The sorted conditions are inspected in order of priority, and the block to which the condition satisfied in the first order belongs is treated as an execution block. If a block that satisfies the condition is not found, the blocks to which the intent belongs are searched again with the intent of the next priority, and the block is found by repeating the process of determining the priority of the condition and checking the condition.

26 is an operational flowchart illustrating a method of providing a chatbot service according to another embodiment of the present invention.

Referring to FIG. 26 , a method for providing a chatbot service according to another embodiment of the present invention determines an intent corresponding to a user conversation (S2610).

In this case, an intent corresponding to the user dialogue may be selected from one or more candidate blocks connected to the current block through a graph or from among intent candidates connected to the main block.

In this case, the intent corresponding to the user dialogue may be determined by considering the first intent candidate linked to the candidate block with priority over the second intent candidate linked to the main block.

In addition, in the chatbot service providing method according to another embodiment of the present invention, an execution block corresponding to the intent is selected using an intent mapping list (S2620).

In this case, the intent mapping list is provided for each of the execution blocks and may correspond to one or more intents.

In this case, the intent mapping list may include an intent identifier and a slot condition for each intent.

In this case, the slot condition may correspond to conditional statements related to one or more slots.

At this time, the slot condition may include a null condition.

In this case, the intent mapping list may include intent identifiers and slot conditions corresponding to a plurality of intents (N:1), and the intent is that different execution blocks have the same intent identifier. In this case, if the slot conditions are different, they may be connected (1:N) to different execution blocks.

In this case, the slots corresponding to the execution block may include slots of intents connected to the execution block through the intent mapping list.

At this time, the execution block corresponding to the intent is a first execution block having an inclusion relationship of the slot condition corresponding to the large set when the intent corresponding to the user dialogue is connected to a plurality of execution blocks. It can be selected with priority over the second execution block having an inclusion relationship of slot conditions corresponding to a small set included in . In this case, the inclusion relationship of the slot condition may be the inclusion relationship of slots included in the slot condition. For example, an execution block corresponding to $level == "regular member" and $service == "subscription" may be selected with priority over an execution block corresponding to $service == "subscription".

In addition, the chatbot service providing method according to another embodiment of the present invention performs conversation processing corresponding to the intent based on the execution block (S2630).

According to the above configuration, the chatbot service providing method according to an embodiment of the present invention has N:N (N is a natural number) mapping between intents classified as suitable for a natural language processor and execution blocks processing the intents. This allows several different utterances to perform one function, and conversely, similar utterances to perform completely different functions.

In addition, in the chatbot service providing method according to an embodiment of the present invention, it is possible to define various intent-execution block mapping relationships by using an intent mapping list including an intent identifier and a slot condition for each intent.

In addition, in the method for providing a chatbot service according to an embodiment of the present invention, when an intent corresponding to a user conversation is identified, candidate blocks that can be executed after the current execution block are identified, and only intents connected to the candidate blocks are identified. Fast and accurate intent identification is possible by targeting the identification.

27 is a graph showing performance of a natural language processor according to an increase in the number of speech patterns.

Referring to FIG. 27, when intents are classified according to functions provided by a chatbot, when various spoken sentences are added, similar speech patterns often appear in several intents, and natural language processor (NLU) intents as shown in A It can be seen that the classification performance is rapidly degraded.

In order to solve this problem, by defining an intent for each similar speech pattern in a form suitable for processing by the natural language processor, even if the speech pattern increases, the performance of the natural language processor can be maintained high as in B.

In addition, by mapping different speech patterns to a unit called 'block' so that the bot can properly process them according to conditions, various speech patterns can be efficiently processed.

Blocks are connected in the form of a graph, and several response waiting states can be defined according to conditions, so that work processes with various processing flows or steps can be visually defined with a small number of blocks.

Implementations of the functional operations and subject matter described herein may be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or in a combination of one or more of the foregoing. can be implemented Implementations of the subject matter described herein are one or more computer program products, that is, one or more modules of computer program instructions encoded on a tangible program storage medium for execution by or for controlling the operation of a processing system. can be implemented

A computer readable medium may be a machine readable storage device, a machine readable storage substrate, a memory device, a composition of matter that affects a machine readable propagating signal, or a combination of one or more of these.

In this specification, the term 'system' or 'apparatus' encompasses all devices, devices, and machines for processing data, including, for example, a programmable processor, computer, or multiple processors or computers. A processing system may include, in addition to hardware, code that forms an execution environment for computer programs on demand, such as, for example, code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these. .

A computer program (also known as a program, software, software application, script or code) may be written in any form of programming language, including compiled or interpreted language, a priori or procedural language, and may be a stand-alone program or module; It may be deployed in any form including components, subroutines, or other units suitable for use in a computer environment. A computer program does not necessarily correspond to a file in a file system. A program may be in a single file provided to the requested program, or in multiple interacting files (e.g., one or more modules, subprograms, or files that store portions of code), or parts of files that hold other programs or data. (eg, one or more scripts stored within a markup language document). A computer program may be deployed to be executed on a single computer or multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

On the other hand, computer-readable media suitable for storing computer program instructions and data include, for example, semiconductor memory devices such as EPROM, EEPROM and flash memory devices, magnetic disks such as internal hard disks or external disks, magneto-optical disks and CDs. - may include all forms of non-volatile memory, media and memory devices, including ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated into, special purpose logic circuitry.

Implementations of the subject matter described herein may include back-end components, such as, for example, data servers, or may include middleware components, such as, for example, application servers, or may include, for example, web browsers or graphical users through which users may interact with implementations of the subject matter described herein. It may also be implemented in a computing system that includes a front-end component, such as a client computer having an interface, or any combination of one or more of such back-ends, middleware, or front-end components. The components of the system are interconnectable by any form or medium of digital data communication, such as, for example, a communication network.

Although this specification contains many specific implementation details, they should not be construed as limiting on the scope of any invention or what is claimed, but rather as a description of features that may be unique to a particular embodiment of a particular invention. It should be understood. Likewise, certain features that are described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable subcombination. Further, while features may operate in particular combinations and are initially depicted as such claimed, one or more features from a claimed combination may in some cases be excluded from that combination, and the claimed combination is a subcombination. or sub-combination variations.

In addition, although operations are depicted in the drawings in a specific order in this specification, it is not to be understood that such operations must be performed in the specific order shown or sequential order or that all illustrated operations must be performed in order to obtain desired results. Can not be done. In certain cases, multitasking and parallel processing can be advantageous. Further, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and the program components and systems described may generally be integrated together into a single software product or packaged into multiple software products. It should be understood that you can

As such, this specification is not intended to limit the invention to the specific terms presented. Therefore, although the present invention has been described in detail with reference to the above-described examples, those skilled in the art may make alterations, changes, and modifications to the present examples without departing from the scope of the present invention. The scope of the present invention is indicated by the following claims rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

As described above, the method and apparatus for providing a chatbot service using the conditional expression generation interface according to the present invention are not limited to the configuration and method of the embodiments described above, but various modifications can be made All or part of each embodiment may be configured by selectively combining so as to be.

According to the present invention, a method and apparatus for providing a chatbot service using a conditional expression generation interface are disclosed. A method for providing a chatbot service according to an embodiment of the present invention includes providing a conditional expression generation interface, and generating an execution block for processing an intent corresponding to a user conversation through the conditional expression generation interface. Accordingly, when a user sets a conditional expression for executing a chatbot using a chatbot builder or a chatbot service providing device, it is possible to effectively rescue the chatbot by reducing errors in the conditional expression and improving readability.

910: communication unit 920: processor
930: memory 1010: chatbot
1020: natural language processor 1030: chatbot resource DB
1040: Chatbot builder

Claims (5)

generating a conditional expression and providing a conditional expression generation interface for recommending a pair of parentheses input at an arbitrary position in the conditional expression; and
generating an execution block for processing an intent corresponding to a user dialogue through the conditional expression generation interface;
A method for providing a chatbot service using a conditional expression generating interface, comprising: The method of claim 1,
The conditional expression generation interface is
A method for providing a chatbot service using a conditional expression generation interface, characterized in that for correcting the arbitrary position in consideration of whether the arbitrary position is a parenthesis input possible position based on the type of the input parenthesis. The method of claim 2,
The conditional expression generation interface is
A method of providing a chatbot service using a conditional expression generation interface, characterized in that for recommending a pair of parentheses input to the corrected position after correcting by moving the arbitrary position. The method of claim 3,
The conditional expression generation interface is
If the type of the input parenthesis is an opening parenthesis, correction is performed while moving the position of the input parenthesis to the front of the conditional expression, and if the type of the input parenthesis is a closing parenthesis, the position of the input parenthesis is recalled A method of providing a chatbot service using a conditional expression generation interface, characterized in that performing correction while moving to the back of the conditional expression. The method of claim 1,
The above conditional expression is
A chatbot service method using a conditional expression generation interface, characterized in that it includes at least one condition consisting of a combination of a condition variable, a comparison operator, and a condition value.

Images