KR20220037217A - Method and apparatus for providing chatbot service using condition expression generated interface - Google Patents

Abstract

Disclosed are a method and device for providing a chatbot service using a conditional expression generation interface. The method of providing a chatbot service according to one embodiment of the present invention includes the steps of: 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, it is possible for a user to effectively write a conditional expression necessary for driving the chatbot, thereby providing an improved chatbot service.

Description

Method and device for providing chatbot service using conditional expression creation 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 technology for defining a bot driving process by providing an interface for a chatbot designer to write a conditional expression for bot driving in a work area.

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

A chatbot is a computer program or artificial intelligence equipped with the ability to communicate through text or voice. There are chatbots of various levels, including chatbots to which .

Conversational AI agent system using chatbot identifies which service the user wants in response to user 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 having the same user intent are collected and classified into one intent. It is possible to provide a chatbot service that can handle various spontaneous utterances of a user by mapping a bot response corresponding to each of the classified intents or defining an operation such as a service API call.

When one bot response unit (execution block) is mapped for each utterance unit (intent) of one user, as the service function increases, bot responses and corresponding intents also increase. Intents are allocated to intents, and when intent classification is performed, a plurality of intents having a slight score difference are generated, thereby increasing the possibility of an error in intention classification.

Even if the number of intents does not increase, if various utterance patterns are registered in an intent in order to recognize various natural utterances of the user, similar patterns are registered in several intents, so that the natural language processor accurately detects similar utterances between different intents. It is difficult to handle, so it is prone to errors in intent classification.

In other words, if utterances are classified according to the situation in which the intent and the bot response unit are mapped 1:1, the performance of the natural language processor decreases as the number of intents increases or the number of example utterance sentences registered in the intent increases. When utterances are classified to ensure the performance of the natural language processor, similar utterance patterns are all defined in one intent, which makes the implementation of the block mapped to the corresponding intent very complicated, and several functions actually provided are in one block. It becomes a mapping situation, and management difficulties are inevitable.

In addition, most chatbots process the bot's actions by searching for all intents when a user utters a utterance. There is a problem that the configuration becomes complicated.

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

Korean Patent Laid-Open Patent No. 10-2019-0006403, published on January 18, 2019 (Title: Voice processing method and system supporting the same)

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

In addition, an object of the present invention is to reduce the input step and reduce the level of input by concisely writing a conditional expression by using the use of parentheses limitedly only to set the priority, and recommending meaningful parentheses for the parentheses created by the bot designer. This is to prevent the conditional expression from being complicated by the use of parentheses.

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

Another 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 other objects may be derived from the following description.

In order to achieve the above object, a chatbot service providing method according to an embodiment of the present invention includes providing a conditional expression creation interface; and generating an execution block for processing an intent corresponding to a user conversation through the conditional expression generation interface.

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

In this case, the conditional expression generation 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.

In this case, the conditional expression generation 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 the 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 determines whether the arbitrary position is a parenthesis input possible position in consideration of the type of the input parenthesis before recommending the input parenthesis pair, and the arbitrary position is a parenthesis input possible If it is not a position, it can be corrected by moving the arbitrary position based on the type of the input parenthesis.

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

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

In this case, the conditional expression generation 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.

In this case, the conditional expression generation 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 the 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 determines whether the arbitrary position is a parenthesis input possible position in consideration of the type of the input parenthesis before recommending the input parenthesis pair, and the arbitrary position is a parenthesis input possible If it is not a position, it can be corrected by moving the arbitrary position based on the type of the input parenthesis.

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 a keyword expression-based conditional grammar optimized for slot recognition is applied.

In addition, the present invention uses the use of parentheses to limit the use of parentheses only to set priorities, writes conditional expressions concisely, and recommends meaningful parentheses for parentheses created by the bot designer to reduce the input step and use of meaningless parentheses This can prevent the conditional expression from being complicated.

In addition, the present invention enables N:N (N is a natural number) mapping between intents classified appropriately for a natural language processor and execution blocks processing the intents, thereby executing one function with various different utterances. Conversely, similar utterances may 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.

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

1 is an operation flowchart illustrating a method for providing a chatbot service according to an embodiment of the present invention.
2 is a diagram illustrating an embodiment of a conditional expression generation interface according to the present invention.
3 is a diagram illustrating an example of a conditional expression analyzed by tokenization 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 illustrating an example of a conditional expression result comparison table according to the present invention.
7 to 8 are views showing an example of performing correction of the position of the input parentheses and the pair recommendation of the input parentheses according to the present invention.
9 is a block diagram illustrating an apparatus for providing a chatbot service according to an embodiment of the present invention.
10 is a block diagram illustrating a chatbot service system according to an embodiment of the present invention.
11 is a diagram illustrating a block diagram of a 'coffee order' scenario according to an embodiment of the present invention.
12 is a diagram illustrating an example of intents used in the scenario shown in FIG. 11 .
13 is a block diagram illustrating a connection between a plurality of intents and a plurality of execution blocks.
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 executing block hot ice confirmation 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 illustrating a connection relationship between the execution blocks shown in FIGS. 13 to 16 .
18 is a diagram illustrating an example of connecting blocks by defining a user response standby state in an execution block.
19 is a diagram illustrating an example of mapping intents defined for each utterance pattern to a block.
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.
22 is a diagram illustrating execution blocks corresponding to the responses shown in FIG. 21 .
23 is a view showing an example of prioritization according to the inclusion of a condition corresponding to the coffee order scenario illustrated in FIGS. 11 to 17 .
24 is a block diagram illustrating an example of modules required to execute a chatbot.
25 is a diagram illustrating a process of searching for an intent and a block while the chatbot is running.
26 is an operation flowchart illustrating a method of providing a chatbot service according to another embodiment of the present invention.
27 is a graph showing the 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 in the following description and accompanying drawings will be omitted. Also, it should be noted that, throughout the drawings, the same components are denoted by the same reference numerals as much as possible.

The terms or words used in the present specification and claims described below should not be construed as being limited to their ordinary or dictionary meanings, and the inventor is appropriate as a concept of terms for describing his invention in the best way. It should be interpreted as meaning and concept consistent with the technical idea of the present invention based on the principle that it can be defined in Therefore, the embodiments described in this specification and the configurations shown in the drawings are only the most preferred embodiment of the present invention, and do not represent all the technical spirit of the present invention, so at the time of the present application, various It should be understood that there may be equivalents and variations. In addition, terms such as 1st, 2nd, etc. are used to describe various components, and are used only for the purpose of distinguishing one component from other components, and are not used to limit the components.

The present invention relates to a technology for setting various conditions necessary for driving a bot in order to provide a chatbot service, and defining a chatbot driving process through this.

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

1 is an operation flowchart illustrating a method for 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 an interface for generating a conditional expression ( S110 ).

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

For example, if the "member number" slot is all numbers and the "nickname" slot contains "member" or does not contain "guest", the conditional expression for the case is as follows in the existing JavaScript language grammar.

[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 the conditional expression expressed in the JavaScript language grammar, the parentheses used to call a function and the parentheses used to indicate the priority of the condition are mixed, so there is a problem of poor readability. In addition, since the condition value is written before the condition variable, it is difficult to quickly identify which condition variable the corresponding value is.

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

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

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

In this case, 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.

Taking 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 You can configure one condition, $member number regex "^[0-9]+$".

In this case, the conditional expression generation interface may create conditions in the order corresponding to the condition variable, the comparison operator, and the condition value.

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

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

In this case, in the conditional expression grammar proposed in the present invention, the comparison function may be expressed as text like a comparison operator and used. Taking the above conditional expression as an example, the regular expression test method used in the JavaScript language grammar is expressed 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 "contain" is displayed according to the conditional expression grammar according to the embodiment. In addition, functions such as "empty" and "exist" can be used.

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

In this case, in the conditional grammar according to an embodiment of the present invention, "not exist" and "not empty" are not used, and "empty" and "exist" are used in order to minimize the use of the negative conditional indication.

When describing the conditional expression notation rule in the conditional expression grammar according to an embodiment of the present invention in more 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 match with $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" included $slot contains "value" not included $slot　not contain　"value" regular expression $slot regex "[0-9]+" it does not exist $slot empty exists $slot　exist

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

Element Expression and usage examples 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 A regular string in quotes: /"([^\\\"]|\\.)*"/ NUMBER Regular numbers (including decimals): /[-+]? (\d*\.\d+|\d+)/ PARAMETER

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

\-_\.]+/

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

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

As another example, the conditional expression corresponding to $svc_cat=="ok cashback point" and $location empty and $action =="accumulation" is a condition variable while the condition value of the condition variable "svc_cat" is "ok cashback point" This may mean a case in which 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 generation 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 in the conditional expression creation interface with a mouse or other selection method. and clicking to enter.

In the existing text-based user interface (UI) for inputting conditional expressions, the location of variables is not constant because function calls are mixed. The downside is that it is difficult to do.

In addition, UI (User Interface) that allows conditional expression input without text error through button input often does not support overlapping 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 allows direct text input and input using buttons to prevent conditional expressions from being incomplete or grammatical errors from occurring.

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

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

For example, referring to the conditional expression generation interface according to an embodiment of the present invention shown in FIG. 2 , when the bot designer inputs a slot name corresponding to a condition variable in the input window 220 , the input slot name is considered. Thus, at least one comparison operator applicable to this can be automatically recommended. In addition, assuming 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 area of the input window 220 with a mouse.

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

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

In this case, the 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 “$” into the input window 220 in a text-based direct input method, and when the first letter of the comparison operator is input, the At least one comparison operator starting with a letter is recommended, and by entering an opening double quotation mark ("), at least one applicable slot value can be recommended.

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

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

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

In this case, 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, if it is assumed that the bot designer inputs the opening parenthesis to determine the priority of the condition after inputting the conditional expression, the conditional expression creation 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 and comparison syntax units such as and and or, and those with the same comparison syntax can be considered as the same condition and tokenized.

For example, $type == "song" and $action == "play" or $type == "album" and $action == "play" is a conditional expression containing 4 comparison operation conditions, but A: $ If we define type == "song", B: $action == "play", C: $type == "album", it can be simplified to A and B or C and B.

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

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

For another example, referring to FIG. 5 , when the input parenthesis is a closing parenthesis, the recommended opening parenthesis positions (510, 520, 530) are to the left of each condition located in front of the conditional expression based on the input position of the closing parenthesis. ) can be searched for.

At this time, the first type parentheses that can change the result of the conditional expression, the second type parentheses that do not change the result of the conditional expression but are judged to be unnecessary, and the first type parentheses and the second type parentheses without changing the result of the conditional expression At least one of the third type parentheses that do not correspond may be recommended as a pair of parentheses.

In this case, 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, and the third type parentheses correspond to parentheses for readability of the conditional expression can do.

For example, it may be determined that the second type of parenthesis corresponds to the following cases.

- Full parentheses surrounding the entire conditional expression

(A and B or C and B)

- unary parentheses surrounding only one condition

A and B or (C) and B

- Duplicate parentheses surrounding only parenthetical expressions

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

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

In this way, analysis is performed on all pairs of parentheses recommended by the conditional expression generation interface to retrieve all of the first type parentheses.

In the present invention, the method of using the conditional expression result comparison table to search for the first type parenthesis has been described as an example, but the method for searching the first type parenthesis is not limited thereto, and the recommended pair of parentheses can change the result of the conditional expression. Various methods that can be used to determine whether a pair of meaningful parentheses can be used can be employed.

In this case, the conditional expression generation interface may determine whether an arbitrary position is a position in which parentheses can be input in consideration of the type of the input parenthesis before recommending the input parenthesis pair.

In this case, the conditional expression generation interface may be corrected by moving an arbitrary position based on the type of the input parenthesis when the arbitrary position is not a parenthesis input possible position.

For example, referring to FIG. 7 , it may be assumed that the bot designer has input an opening parenthesis at a position 710 displayed 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 inputable position.

Accordingly, the conditional expression generation interface can be corrected so that the opening parenthesis is input to the corrected position 810 by moving the position of the opening parenthesis input by the bot designer as shown in FIG. 8 .

At this time, considering the type of parenthesis entered by the bot designer, the opening parenthesis "(" can be corrected while moving the front position of the conditional expression, and the closing parenthesis ")" can be corrected while moving it to the rear position of 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, to give priority to a first type parenthesis as 1st, a 3rd type parenthesis as 2nd priority, and a 2nd type type parenthesis as 3rd priority, and display the priority number corresponding to the recommended pair of parentheses. can

For another example, as shown in FIG. 8 , the first type parentheses 820 and 830 having a high priority are most emphasized, and the third type parenthesis 840 corresponding to the next priority may be expressed with less emphasis. there is. At this time, as shown in FIG. 8 , when multiple parentheses of the same type are recommended, the position of the parentheses input by the bot designer may be emphasized in order of proximity. In addition, if the types of parentheses corresponding to the 1st and 2nd positions are recommended, the type of parentheses corresponding to the 3rd priority may not be recommended for readability.

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

First, the conditional expression generating interface may search for 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 parenthesis 840 are searched as shown in FIG. 8 , first of all the first type parentheses 820 and 830 that are closest to the input parenthesis It can be recommended in a way that shows the type bracket 820 with the most emphasis.

At this time, when the bot designer selects the first type parenthesis 820 by performing a touch and click by a point device or a left and right direction key and a tap cycle input by a keyboard device, the opening parenthesis input to the corrected position 810 is A pair may be confirmed as the first type parentheses 820 and the recommendation may be terminated.

However, since the pair of parentheses intended to be entered by the bot designer may not be the first type parenthesis 820, the conditional expression interface then replaces the first type parenthesis 830 with the first type parenthesis 820 close to the next typed parenthesis. ) can be expressed with less emphasis than

Similarly, when the bot designer selects the first type parenthesis 830 , the pair of the opening parenthesis input in the corrected position 810 is confirmed as the first type parenthesis 830 , and the recommendation may be terminated.

As described above, when a plurality of parentheses of the same type exist, the conditional expression generation interface according to an embodiment of the present invention may preferentially recommend parentheses existing in a close position based on the position of the input parentheses.

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 determined to be unnecessary, but the first type parenthesis or the third type parenthesis It can be marked so that it is not emphasized compared to the type brackets.

As described above, the conditional expression generation interface according to an embodiment of the present invention can validate whether the conditional expression input by the bot designer is normally entered in accordance with the grammar, and when the conditional expression is input abnormally, the bot designer can input it again can make it happen

For example, whether the parentheses included in the conditional expression are matched 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 entered normally, and whether the conditional expression is included in the conditional expression It is also possible to check whether the double quotation marks are entered in the correct pair.

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 creation 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 the chatbot defined in the execution block, and a prompt message execution condition for slot filling It can be provided when creating a conditional expression for driving a bot.

Through this chatbot service provision method, when setting the conditions necessary for the operation of the chatbot, it is possible to reduce errors in the conditional expression and improve readability by providing an interface that applies the keyword expression-based conditional grammar optimized for slot recognition.

In addition, by limiting the use of parentheses to setting priorities, the conditional expression is written concisely, and meaningful parentheses are recommended for parentheses created by the bot designer, thereby reducing the input step and using meaningless parentheses. You can avoid complicating it.

9 is a block diagram illustrating 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 an embodiment, the chatbot service providing apparatus 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. In this case, the network provides a path for transferring data between devices, and is a concept that encompasses both an existing network and a network that can be developed in the future.

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

For example, a network is a wired/wireless local area network that provides communication of various information devices within a limited area, a mobile communication network that provides communication between mobile devices and with the outside of the mobile body, and a satellite that provides communication between an earth station and an earth station. It may be either a satellite communication network or a wired/wireless communication network, or a combination of two or more. Meanwhile, the transmission method standard of the network is not limited to the existing transmission method standard, and may include all transmission method standards to be developed in the future.

The execution memory 930 stores at least 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 conversation through the conditional expression generation interface.

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

In this case, the conditional expression generation 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.

In this case, the conditional expression generation 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 the 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 determines whether the arbitrary position is a parenthesis input possible position in consideration of the type of the input parenthesis before recommending the input parenthesis pair, and the arbitrary position is a parenthesis input possible If it is not a position, it can be corrected by moving the arbitrary position based on the type of the input parenthesis.

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 illustrated in FIG. 9 may receive various information such as number and character information, and may transmit signals input in relation to setting various functions and controlling functions of the device to the processor 920 through the input unit. In addition, the input unit may be configured to include at least one of a keypad and a touch pad that generate an input signal according to a user's touch or manipulation. In this case, the input unit of the device may be 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, as the input unit, all types of input means that may be developed in the future may be used in addition to input devices such as a keyboard, a keypad, a mouse, and a joystick.

In addition, the display unit may display information about a series of operation states and operation results that occur while the function of the device is performed. In addition, the display unit may display a menu of the device, user data input by a user, and the like. Here, the display unit is a liquid crystal display (LCD, Liquid Crystal Display), an ultra-thin liquid crystal display (TFT-LCD, Thin Film Transistor LCD), a light emitting diode (LED, Light Emitting Diode), an organic light emitting diode (OLED, Organic LED), It may be composed of an active organic light emitting diode (AMOLED, Active Matrix OLED), a retina display, a flexible display, and a three-dimensional display. In this case, 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, and includes a main storage device and an auxiliary storage device, and may store an application program necessary for a functional operation of the device. The storage unit may largely include a program area and a data area. Here, when each function is activated in response to a user's request, the device executes corresponding application programs under the control of the processor 920 to provide each function.

Also, 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 unit for up-converting and amplifying the frequency of the transmitted signal, and an RF receiving unit for low-noise amplifying the 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. In addition, the wireless communication module is a configuration for transmitting and receiving data according to a wireless communication method, and when the device uses wireless communication, the device transmits data 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 connect to a network through a wire and transmit/receive data to/from the device. That is, the device accesses the network using a wireless communication module or a wired communication module, and may transmit/receive data to and from other devices through the network.

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

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

In addition, the chatbot service providing apparatus according to an embodiment of the present invention uses the use of parentheses limitedly only to set the priority, writes the conditional expression concisely, and recommends meaningful parentheses for the parentheses created by the bot designer. It can reduce the input step and prevent the conditional expression from being complicated by the use of meaningless parentheses.

In addition, the present invention classifies intents corresponding to natural language input according to similar properties according to natural language processing performed by an interactive AI agent system, and maps a plurality of classified intents to blocks that are processing units of bots. It's about technology.

For a useful chatbot service, it is necessary to continuously improve the natural language processing ability for terms by business or service domain and the processing ability for the 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. In other words, it is necessary to define intents from the point of view of Natural Language Understanding (NLU) rather than defining intents only in units of functions or tasks.

On the other hand, in order to effectively process functions or tasks, detailed processing steps or flows need to be defined as execution blocks, and a connection relationship (precedence relationship) between the defined execution blocks needs to be defined. In this case, the connection relationship is preferably defined in the form of a graph for freely connecting various execution blocks rather than in the form of a list or tree. In this case, it is preferable that the execution blocks are defined appropriately for the function provided by the service rather than from the point of view 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. That is, one intent can be used for multiple blocks, and one block must be able to process multiple intents. For this, intents and blocks must be mapped N:N rather than 1:1 mapping .

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

In this case, similar utterance patterns can be grouped with the same intent, and conditions can be given based on slots to respond so that different bot responses are possible.

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

Hereinafter, the chatbot service is a concept that covers not only the voice service based on the user's voice but also the messenger service based on the text input by the user.

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

A chatbot process may need two work areas to run. One is the work area where the bot designer defines the bot driving process, and the other is the execution area where the defined process is executed.

10 is a block diagram illustrating 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 an utterance sentence from the user, generates a response thereto, and provides the chatbot response to the user.

The natural language processor 1020 receives an utterance sentence from the chatbot, receives intent definition information from the chatbot resource database 1030, generates an intent classification result and a slot extraction result, and provides it to the chatbot 1010 .

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

The chatbot builder 1040 receives an input about the chatbot resource from the bot designer, creates the 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, a slot, which is important information for learning speech sentences and bot processing, is defined as an intent, the defined intent is mapped to an execution block, and conditions are set in the execution block, and the action of the bot is performed accordingly. can be defined In addition, blocks that can be processed in the response waiting state in the chatbot builder 1040 are set, and these blocks may be defined as a graph-type scenario.

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 main business processing are defined in the form of a graph may be provided.

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

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

Also, in the block utterance scenario of the coffee ordering chatbot, the chatbot executes the utterance "What coffee would you like?" (1121).

In response to the chatbot's utterance, in the block utterance scenario of the coffee ordering chatbot, the user utters the desired coffee name ( 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 hot or iced?" (1122).

In response to the chatbot's utterance, in the block utterance scenario of the coffee ordering chatbot, the user selects hot or ice ( 1113 ).

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 to eat and go?" (1123).

In response to the chatbot's utterance, in the block utterance scenario of the coffee ordering chatbot, the user selects a store meal or takeout ( 1114 ).

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

In response to the chatbot's utterance, in the block utterance scenario of the coffee ordering chatbot, the user utters 'yes' (1115) or 'no' (1116), and when the user says 'yes', the chatbot executes a coffee order Executes a custom action (1125) that confirms the order details (1126), and when the user says 'no', the chatbot executes the utterance "Cancel the order" ( 1127), then the operation is terminated.

In FIG. 11 , “User” indicates a user’s utterance, “templete” indicates a chatbot utterance, and “Custom Action” indicates an action processing of the bot defined by an execution block.

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

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

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

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 order scenario are intent order_coffee ( 1210 ), intent order_hotice ( 1220 ), intent order_takeout ( 1230 ), intent order_togo ( 1240 ), and intent order_eatin ( 1250 ). It can be seen that it contains

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

When the bot processes the intents shown in FIG. 12, it is processed in a unit called a block (processing block), and a plurality of intents may be mapped to one block and processed. 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 a connection 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 coffee selection 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 hot ice check 1320 .

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

As shown in Fig. 13, intents are mapped to blocks for execution, and conditions are grammatically defined based on a variable called slot for each mapped intent, so that the bot can only classify the intent by 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), and a plurality of blocks may be connected to one intent (1:N), so that there is a free space between intents and blocks. Mapping (N:N) is possible.

Furthermore, one execution block may have a connection relationship with other executable blocks that can be executed later. For example, the execution block coffee selection 1310 may be connected to the execution block hot ice confirmation 1320 and the execution block takeout confirmation 1330, and the execution block hot ice confirmation 1320 is the execution block takeout confirmation 1330 ) can be associated with

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

Referring to FIG. 14 , the action block coffee selection 1310 may include a list of connected intents, slots, 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 a conditional statement related to one or more slots, and may include a null condition.

The slots ($coffee, $hotice, $takeout) shown in FIG. 14 may include the union of all slots corresponding to the intents (order_coffee, order_hotice, 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 utterance information required to fill the corresponding slot first.

The action information may consist of a condition and a response when the corresponding condition is satisfied. In this case, the condition may correspond to one or more conditional statements using slots, and the response may include a link that calls another block.

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

FIG. 15 is a diagram illustrating an example of executing block hot ice confirmation shown in FIG. 13 .

Referring to FIG. 15 , the execution block hot ice check 1320 may include 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 the intents (order_hotice, order_takeout) to which the execution block hot ice check 1320 is connected.

The slot filling order information may include information on the filling order of the slots of the execution block hot ice check 1320 and utterance information necessary to fill the corresponding slot first.

The action information may consist of a condition and a response when the corresponding condition is satisfied. In this case, the condition may correspond to one or more conditional statements using slots, and the response may include a link that calls another block.

For example, the execution block hot ice check 1320 may connect the execution block takeout check 1330 based on the 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 , the execution block takeout check 1330 may include a linked 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 check 1330 is connected. there is.

The slot filling order information may include information on the filling order of the slots of the execution block takeout check 1330 and utterance information required to fill the corresponding slot first.

The action information may consist of a condition and a response when the corresponding condition is satisfied. In this case, the condition may correspond to one or more conditional statements using slots, and the response may include a link that calls another block.

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

Referring to FIG. 17 , it can be seen that the execution block coffee selection 1310 , the execution block hot ice confirmation 1320 , and the 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 the next execution blocks capable of processing the user's utterance when entering the user utterance standby state in a specific current block. That is, when a user utterance is received from a specific current block, possible situations are execution blocks connected to the current block, so only intents connected to these execution blocks can be searched as intents requiring user utterance processing. In the waiting state for user utterance, if only the intents included in the sub-blocks connected based on the current block are collected and the intent is searched through the natural language processor, it corresponds to the utterance much more quickly and accurately than when all intents are searched. intent can be identified.

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

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

When the user intent is identified from the user utterance, 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 matching the condition is found, it can move to the block, perform an action defined according to the block setting, and return to the user response standby state according to the condition of the action.

Connecting a block to a block may be defined as listing blocks to be processed next in the current block when entering the user response waiting state, 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 user response standby state 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 a graph form.

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

Block 2 (Block 2) is connected to block 6 (Block 6). Accordingly, after block 2 (Block 2), the situation of block 6 (Block 6) may occur.

Block 3 (Block 3) is connected to block 6 (Block 6) and block 7 (Block 7). Accordingly, after block 3 (Block 3), the situation of block 6 (Block 6) and block 7 (Block 7) may occur.

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

For example, if the current state is a state corresponding to Block 2, it is sufficient to search for an intent for only the intents corresponding to Block 6 for subsequent utterance by the user, and the possibility of occurrence is high. There is no need to perform user utterance matching on intents corresponding to the absent Block 7 .

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

For example, the intent (I1: signup intention) is the learning sentences "I want to try" (pattern classification P1), "How to write" (pattern classification P1), "I am curious about how to join" (pattern classification P3) and "Inquiry on how to join" (pattern classification P3), and the intent (I2: intention to withdraw) is the learning sentences "I want to stop writing" (pattern classification P2), "I don't want to use it anymore" (pattern classification P2), It can include "I want to know how to withdraw" (Pattern classification P3) and "Inquire about withdrawal method" (Pattern classification P3).

In this case, one block may be connected to the intent (I1: join intention) and one block may be connected to the intent (I2: leave intention), but the intents include a plurality of utterance patterns, so that the performance of the natural language processor is reduced. can be lowered

Accordingly, in order to prevent degradation of the performance of the natural language processor, it is desirable to avoid including a plurality of utterance patterns in one intent. For example, the intent (I1: intention to join) includes only the learning sentences "I want to try" (pattern classification P1) and "how to write" (pattern classification P1), and the intent (I2: intention to withdraw) is Include only the learning sentences "I want to stop writing" (pattern classification P2) and "I don't want to write anymore" (pattern classification P2), and the intent (I3: Inquiry) is the learning sentence "I am curious about how to <join>" ( Pattern classification P3), "Inquire about how to <join>" (Pattern classification P3), "I want to know how to <withdraw>" (Pattern classification P3), and "Inquire about <withdrawal> method" (Pattern classification P3) there is.

In the case of classifying intents so that they can be effectively processed by the natural language processor as described above, intents and processing blocks need to be connected in more various forms. To this end, each intent of the block may have a condition, and this condition may be defined as statements comparing slot values. That is, the block and intent mapping information may be defined in the 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 utterance pattern to a block.

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

In this case, the slots used for the slot condition may include not only the 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 utterance pattern 1, intent 2 corresponds to utterance pattern 2, and intent 3 It can be seen that 3) corresponds to the firing pattern 3 (Pattern 3).

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

In the intent mapping list of block 2, two elements of a pair of intent 2 and condition 3 and a pair of intent 3 and condition 4 are exist. Accordingly, the block 2 (Block 2) is connected to the two intents (Intent 2, 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 utterance patterns.

That is, the intent I1 (join intent) includes the utterance patterns "I want to try", "How to write", "I want to know how to join", and "Inquire about how to join".

Intent I2 (withdrawal intention) includes utterance patterns "I want to stop using it", "I don't want to use it anymore", I want to know how to leave, and "I want to ask how to leave".

Intent I3 (intention to inquire about active members) includes the utterance patterns "I want to become a full member", "I want to be promoted to a full member", I want to know how to become a full member", and "How to become a full member".

If the intent (intent) and response (action block) are mapped 1:1, the natural language processor (NLU) is able to distinguish between these similar sentences when a sentence such as "*** I'm curious" appears in multiple intents. it's difficult. For example, a sentence such as "I am the first to ask how to join" may be processed by a natural language processor as a sentence similar to "I am asking how to unsubscribe" of the intention to leave, depending on the situation, resulting in incorrect intention recognition.

That is, the user's intention classification may be different from the intention classification that is easy to be processed by a natural language processor (NLU). The user's intentions may be classified as one intention that is easy to be processed by the natural language processor.

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 in a form that is easy for the natural language processor (NLU) to process, and responses are mapped according to conditions by tagging necessary slots.

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

At this time, the intent 1 (I1) is connected to the subscription guidance response (R1). In this case, the slot condition may be a null condition.

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

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

At this time, the intent 3 (I3) is connected to the regular member guidance response (R3). In this case, the slot condition may be a null condition.

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

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

22 is a diagram illustrating 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 operation information.

22 , the slot condition may include a null condition corresponding to “no condition” and may include a conditional statement using one or more slots.

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

In the example shown in FIGS. 21 and 22 , when an intent is classified as “I4: Intent of inquiry”, blocks including this intent are B1, B2, and B3, and blocks matched according to the condition are listed in the table below. Same as 3

condition block Custom condition check order $service == "Subscribe" B1: Registration Information One $service == "Withdraw" B2: Notice of withdrawal 2 $service == "Register" and $service == "Sign Up" B3: Regular member information 3

The order of checking the condition can be determined by the user designating the order in which blocks are listed. If, as shown in Table 3, only the order in which the user lists them is processed, a sentence such as regular membership registration satisfies the conditions of $level == "regular member" and $service == "join", but at the same time logically $service = = "join" is also satisfied, and the B1 block having a high condition check rank 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 ranking considering inclusion relationship $service == "Register" and $service == "Sign Up" B3: Regular member information 3 One $service == "Subscribe" B1: Registration Information One 2 $service == "Withdraw" B2: Notice of withdrawal 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, and the containing side (larger set) is treated with priority over the containing side (small set), and if there is no containment relation with each other, A user-specified priority may be maintained. In the example of Table 4, since the condition of B3 that checks both the level and the service has a higher condition inclusion relation order than B1 and B2 that checks only the service, B3 may be preferentially processed over B1 or B2.

23 is a view showing an example of prioritization according to the inclusion of a condition corresponding to the coffee order scenario illustrated in FIGS. 11 to 17 .

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

Therefore, when the user utters "I want to take out Americano", the slot coffee is filled with "Americano" and the slot takeout is filled with "Takeout", so the execution block coffee order is executed prior to the execution block takeout confirmation.

When the chatbot process is executed, intents may be collected from connected blocks in a current user response waiting state, and a suitable intent may be searched for among intents collected through a natural language processor.

When searching for a block by intent, a suitable block can be searched for by sorting in the order of the priority of intent conditions of the blocks including 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 meets the condition is found, it moves to the block, performs the action specified in the chatbot's response process, and enters the next user response waiting state according to the condition.

24 is a block diagram illustrating an example of modules required to execute a chatbot.

Referring to Figure 24, if the modules required for the chatbot to execute 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 the block state, 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 for running the chatbot are a dialog flow processor, block state manager, message input gateway, message output gateway, response message generator, block process executor, NLU searcher, block searcher, intent classifier, slot extractor, natural language processor , an NLU priority manager, a condition interpreter and a block condition priority manager.

25 is a diagram illustrating a process of searching for an intent and a block while the chatbot is running.

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

When a block is searched, the processing priority is checked by listing intents mapped to the searched block. In this case, intents defined in the searched blocks are listed, and a matching score of intents corresponding to the user's utterance is determined using a natural language processor. While sequentially searching the defined intent pattern threshold score list, comparing the names of intents and matching scores, the intent with the highest matching score among intents satisfying the pattern and threshold score is selected first. extract

If there is more than one block to which the matched intent belongs, processing priorities are sorted according to the defined intent mapping condition, and if the priorities of the conditions are the same, the priority of the scenario and block defined by the user is followed. In this case, the priority of the condition may be prioritized according to the inclusion relationship of the condition comparison phrase. That is, when a condition is configured based on a slot extracted from the user's utterance, the comparison syntax constituting the condition is mainly composed of the slots mentioned by the user and the unmentioned slots are omitted. It can be seen that the intention of the user is better reflected. That is, when there are multiple conditions that are true in the conditional statement, the condition including more constraints or slots may be considered as having higher priority.

The sorted conditions are checked in order of priority, and the block containing the condition that is satisfied in the first order is treated as an execution block. If a block that satisfies the condition cannot be found, the block to which the intent belongs is found again with the intent of the next priority, and the process of determining the priority of the condition and checking the condition is repeated to find the block.

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

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

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

In this case, the intent corresponding to the user conversation may be determined by considering a first intent candidate connected to the candidate block in preference to a second intent candidate connected to the main block.

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

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

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 a conditional statement related to one or more slots.

In this case, 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 indicates 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, when the intent corresponding to the user conversation is connected to a plurality of execution blocks, the execution block corresponding to the intent is the first execution block having the slot condition inclusion relationship corresponding to the large set. The second execution block having the inclusion relationship of the slot condition corresponding to the 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 considered and selected prior to an execution block corresponding to $service == "subscription".

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

With the above configuration, in the chatbot service providing method according to an embodiment of the present invention, N:N (N is a natural number) mapping between intents classified suitable for a natural language processor and execution blocks processing the intents. By making this possible, one function can be performed with several different utterances, and conversely, a completely different function can be performed with similar utterances.

Also, 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 using an intent mapping list including an intent identifier and a slot condition for each intent.

In addition, the chatbot service providing method according to an embodiment of the present invention identifies candidate blocks that can be executed after the current execution block when identifying an intent corresponding to a user conversation, and uses only intents connected to the candidate blocks. It is possible to quickly and accurately identify an intent by using it as an identification target.

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

Referring to FIG. 27 , when an intent is classified by a function provided by the chatbot, when various speech sentences are added, a similar speech pattern frequently appears in several intents, and as shown in A, the intent of the NLU It can be seen that the classification performance is rapidly degraded.

In order to solve this problem, by enabling the natural language processor to define an intent for each similar utterance pattern in a form suitable for processing, it is possible to maintain high performance of the natural language processor like B, even if the utterance patterns increase.

In addition, various utterance patterns can be efficiently processed by mapping different utterance patterns to units called 'blocks' so that the bot can properly process them according to conditions.

Blocks are connected in a graph form and multiple response waiting states can be defined depending on conditions, so that business processes with various processing flows or steps can be visually defined with a small number of blocks depending on conditions.

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

The 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 radio wave signal, or a combination of one or more thereof.

As used herein, the term 'system' or 'device' encompasses all devices, devices and machines for processing data, including, for example, a programmable processor, a computer, or multiple processors or computers. A processing system may include, in addition to hardware, code that upon request forms an execution environment for a computer program, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more thereof. .

A computer program (also known as a program, software, software application, script or code) may be written in any form of any programming language, including compiled or interpreted language or a priori or procedural language, and may be written as a stand-alone program or module; It can 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 (eg, files that store one or more modules, subprograms, or portions of code), or portions of files that hold other programs or data. (eg, one or more scripts stored within a markup language document). The computer program may be deployed to be executed on a single computer or multiple computers located at one site or distributed over a plurality of 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 EPROMs, EEPROMs and flash memory devices, such as magnetic disks such as internal hard disks or external disks, magneto-optical disks and CDs. -Can include all types of non-volatile memory, media and memory devices, including ROM and DVD-ROM disks. The processor and memory may be supplemented by, or integrated into, special purpose logic circuitry.

An implementation of the subject matter described herein may include a backend component, such as a data server, or a middleware component, such as an application server, or a web browser or graphical user, such as a user capable of interacting with an implementation of the subject matter described herein. It may be implemented in a front-end component, such as a client computer having an interface, or in a computing system including any combination of one or more of such back-end, middleware, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication, such as, for example, a communication network.

While this specification contains numerous specific implementation details, they should not be construed as limitations on the scope of any invention or claim, but rather as descriptions of features that may be specific to particular embodiments of particular inventions. should be understood Likewise, certain features that are described herein in the context of separate embodiments may be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented in multiple embodiments, either individually or in any suitable subcombination. Further, although features operate in a particular combination and may be initially depicted as claimed as such, one or more features from a claimed combination may in some cases be excluded from the combination, the claimed combination being a sub-combination. or a variant of a sub-combination.

Also, 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 or sequential order shown or that all illustrated operations must be performed in order to achieve desirable results. Can not be done. In certain cases, multitasking and parallel processing may be advantageous. Further, the separation of various system components of the above-described embodiments should not be construed 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

As such, this specification is not intended to limit the invention to the specific terms presented. Accordingly, although the present invention has been described in detail with reference to the above-described examples, those skilled in the art can make modifications, changes, and modifications to the 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 above detailed description, 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, in the method and apparatus for providing a chatbot service using the conditional expression creation interface according to the present invention, the configuration and method of the embodiments described above cannot be limitedly applied, but various modifications can be made to the embodiments. All or part of each embodiment may be selectively combined and configured.

According to the present invention, a method and apparatus for providing a chatbot service using a conditional expression generation interface are disclosed. A chatbot service providing method 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 reduce errors in the conditional expression and improve readability, thereby effectively rescuing the chatbot.

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

Claims (10)

providing a conditional expression generation interface;
Generating an execution block for processing an intent corresponding to a user conversation through the conditional expression generation interface
A method for providing a chatbot service using a conditional expression generation interface, comprising: The method according to claim 1,
At least one condition included in the conditional expression generated through the conditional expression generation interface is a method for providing a chatbot service using a conditional expression generation interface, characterized in that it consists of a combination of a condition variable, a comparison operator, and a condition value. 3. The method according to claim 2,
The conditional expression generation interface is
A method for providing a chatbot service using a conditional expression creation interface, characterized in that it recommends at least one comparison operator in consideration of an input condition variable, and recommends at least one condition value in consideration of the input comparison operator. 3. The method according to claim 2,
The conditional expression generation interface is
A method for providing a chatbot service using a conditional expression creation interface, characterized in that it recommends a pair of parentheses input at an arbitrary position among the conditional expressions. 5. The method according to claim 4,
The conditional expression generation interface is
A method of providing a chatbot service using a conditional expression creation interface, characterized in that parentheses are not used when calling a function. 5. The method according to claim 4,
The conditional expression generation interface is
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 and the second type without changing the result of the conditional expression A method for providing a chatbot service using a conditional expression creation interface, characterized in that it recommends at least one of the third type parentheses that do not correspond to the parentheses as a pair of the parentheses. 7. The method of claim 6,
The method of retrieving the first type parenthesis includes a method of using a conditional expression result comparison table. 5. The method of claim 4,
The conditional expression generation interface is
Before recommending the input pair of parentheses, it is determined whether the arbitrary position is a parenthesis input possible position in consideration of the type of the input parenthesis, and if the arbitrary position is not a parenthesis input possible position, the input A method for providing a chatbot service using a conditional expression creation interface, characterized in that the arbitrary position is moved and corrected based on the type of parenthesis. 7. The method of claim 6,
The conditional expression generation interface is
The method for providing a chatbot service using a conditional expression creation interface, characterized in that the first type parenthesis, the second type parenthesis, and the third type parenthesis are visually distinguished. one or more processors; and
an execution memory for storing at least one or more programs executed by the one or more processors; including,
the at least one program
A chatbot service providing apparatus using a conditional expression generation interface, characterized in that it provides a conditional expression generation interface and generates an execution block for processing an intent corresponding to a user conversation through the conditional expression generation interface.

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