KR20220155065A - System and method for providing automatic response call service based on ai chatbot - Google Patents

Abstract

According to the present invention, disclosed is an automatic call answering service and, more specifically, to an automatic call answering service system based on an artificial intelligence chatbot which automatically answers to an inquiry call of a customer by using a chatbot. According to an embodiment of the present invention, an SIP terminal operated by a corresponding company processes a VOIP call and records a received voice if a customer makes a call to a customer center through a terminal such as his or her smartphone or the like, thereby having an effect of providing customer answering and information providing services.

Description

AI chatbot-based automatic call response service system {SYSTEM AND METHOD FOR PROVIDING AUTOMATIC RESPONSE CALL SERVICE BASED ON AI CHATBOT}

The present invention relates to an automatic call response service, and more particularly, to an artificial intelligence chatbot-based automatic call response service system that automatically responds to customer inquiries using a chatbot.

Based on neural network theory, artificial intelligence technology is rapidly approaching human-level intelligence. Unlike conventional rule-based systems, recent artificial intelligence systems are characterized in that machines learn, judge, and develop on their own.

As these artificial intelligence systems are used, their recognition rates improve and users' tastes can be more accurately understood, and conventional rule-based systems are gradually being replaced by deep learning-based artificial intelligence systems.

According to the notice, artificial intelligence technology consists of machine learning such as deep learning and element technologies using machine learning. Machine learning is an algorithm technology that classifies and learns the characteristics of input data by itself, and element technology is a technology that uses machine learning algorithms such as deep learning to mimic the functions of the human brain, such as cognition and judgment. It consists of technical fields such as understanding, inference/prediction, knowledge expression, and motion control.

Artificial intelligence systems are being applied in various fields. Linguistic understanding of artificial intelligence systems is a technology that recognizes, applies, and processes human language and text, and is used for natural language processing, machine translation, dialogue systems, question and answering, voice recognition and synthesis, etc.

In addition, visual understanding is a technology for recognizing and processing objects like human vision, and is used for object recognition, object tracking, image search, person recognition, scene understanding, space understanding, and image improvement. In addition, artificial intelligence systems can be applied to fields requiring inference prediction and knowledge expression.

As one of the services that materialize the application fields of the above-mentioned artificial intelligence system, a chatbot is a virtual robot that chats like a human being applied to a messenger, and can implement an automatic conversation with a customer for a specific purpose. For example, a customer can communicate with a chatbot through text or voice over the phone and provide chatting services for shopping, customer consultation, and reservations.

Registered Patent Publication No. 10-2050244 (Public date: 2019.11.29.)

The present invention relates to a service using an artificial intelligence chatbot, and the task is to provide a service that automatically responds to an incoming call without human intervention by configuring a SIP (Session Initiation Protocol) communication phone in a software method. there is

In addition, the present invention uses artificial intelligence technology to make direct calls with customers in response to calls such as situations where it is difficult to make phone calls at the reception desk, call center, or repetitive simple tasks such as ordering and reservations, There is a task to provide a service that allows the call log and key matters to be delivered and checked to the person in charge at any time.

In order to solve the above problems, an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention makes a call with a customer terminal by SIP communication method, analyzes the customer voice received during the call, and extracts the intent A SIP terminal that infers and transmits an answer according to the inferred intent to the customer terminal through a chatbot, and is connected to the SIP terminal to receive and store a call log including conversation contents between the chatbot and the customer in a database , a call management server that updates machine learning content if a log of inference failure exists in the call log and provides a machine learning result using the machine learning content to the SIP terminal.

The SIP terminal includes a SIP client that transmits and receives VoIP calls according to the SIP communication method, a voice recognition unit that converts a customer's voice included in a VoIP call from the SIP client into a text format, and text corresponding to content to be included in the VoIP call. A voice synthesis unit that converts into chatbot voice and provides it to the SIP client, a response database that stores a plurality of various answers corresponding to intents, searches for and provides answers according to requests, and analyzes customer voice text to provide intent and an inference unit that infers and extracts an answer corresponding to the intent inferred from the answer database and provides the extracted answer to the voice synthesizer.

The SIP terminal may further include a result storage for receiving and storing a newly updated machine learning result from the call management server, and the reasoning unit may infer the intent of the customer voice text by referring to the machine learning result. have.

The SIP terminal receives the call log from the reasoning unit and analyzes the content including the entity to generate main entity information and inference failure text information for the customer voice processed as inference failure from the entire call log. and a call log providing unit for transmitting the call log including the main entity information and inference failure text generated by the call log analyzer to the call management server.

The call management server includes a call log receiver for receiving the call log from the SIP terminal, a call log manager for managing call logs for all calls generated in the SIP terminal, a call log database for storing the received call logs, and , According to a request, a call log transmission unit for extracting a call log of a corresponding customer stored in the call log database and transmitting the same to the customer terminal.

The call management server updates machine learning content when learning data is additionally input or inference failure text information exists in the received call log, performs machine learning, outputs a machine learning result, and transmits the machine to the SIP terminal. A running unit may be included.

The call management server is further connected to a manager terminal used by a manager, and the manager terminal requests and receives phone-related information including the call log from the call log transmitter according to an input of the manager. It may include a call log utilization unit and a call log output unit that extracts entire call history, entity information, and statistical information related to a call based on the received call log and displays the extracted statistical information on a screen.

According to an embodiment of the present invention, when a customer makes a call to a customer center through a terminal such as his or her smartphone, the SIP terminal operated by the company handles the VOIP call and records the received voice, thereby providing customer response and information There is an effect of providing the provided service.

In addition, according to an embodiment of the present invention, the manager can easily check the call log and important information processed in response to the call with a smartphone, and the SIP phone using AI technology can answer the call by using the call forwarding function when necessary. It has the effect of allowing the person in charge who has left his/her seat to perform the task more conveniently.

1 is a diagram schematically showing the overall structure of an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention.
2 is a diagram showing the structure of a SIP terminal in an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention.
3 is a diagram showing the structure of a call management server and a customer terminal in an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention.
4 is a diagram illustrating an artificial intelligence chatbot-based automatic call response service method according to an embodiment of the present invention.

Prior to the description, when a part in the entire specification "comprises" or "includes" a certain component, this may further include other components, not excluding other components, unless otherwise stated. means there is In addition, terms such as "...unit", "...server", and "...system" described in the specification mean a unit that processes at least one function or operation. and may be implemented in hardware, software, or a combination of hardware and software.

In addition, the term "embodiment" in this specification means to serve as an illustration, example, or illustration, but the subject matter of the invention is not limited by such an example. Also, while "comprising", "comprising", "having" and other similar terms are used, when used in the claims as an open transition word that does not exclude any additional or different elements, " Used generically in a similar way to the term "comprising".

The various techniques described herein may be implemented with hardware or software, or a combination of both, where appropriate. As used herein, the terms "...Unit", "...Server", "...System", etc. are similarly referred to as computer-related entities, that is, hardware , a combination of hardware and software, software, or software in execution. In addition, each function implemented in the system of the present invention may be composed of a module unit program, and may be recorded in one physical memory or distributed between two or more memories and recording media.

In the following description, the "automatic call response service system based on artificial intelligence chatbot" of the present invention may be abbreviated as "call response service system" or "system" for convenience of description.

Hereinafter, an automatic call response service system based on an artificial intelligence chatbot according to an embodiment of the present invention will be described with reference to the drawings.

1 is a diagram schematically showing the overall structure of an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention.

Referring to FIG. 1, an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention makes a call with a customer terminal 100 in a SIP communication method, analyzes the customer's voice received during the call, and The SIP terminal 200 that infers and transmits an answer according to the inferred intent to the customer terminal through the chatbot, and is connected to the SIP terminal 200 to receive a call log including the conversation between the chatbot and the customer, and It may include a call management server 300 that stores in a database, updates machine learning content if a log of inference failure exists in the call log, and provides a machine learning result using the machine learning content to the SIP terminal.

The system of the present invention is introduced into a call center, etc. to provide a chatbot service corresponding to a customer's request for telephone consultation, and the SIP terminal responds to the VoIP call attempt of the customer terminal 100 so that the chatbot can respond to the customer's utterance. It is possible to provide a service that extracts an answer from a database by grasping meaning, that is, intents, and automatically responds after voice conversion. In addition, after the call is completed, the system may provide a call log check service according to the request of the manager terminal 400 .

As a means for using this service, the customer terminal 100 may be installed with a web browser or a dedicated application for accessing the system through the Internet network, in addition to a call request using the telephone network.

In detail, the customer terminal 100 is a terminal device possessed by a customer who wants to receive consultation from a company where the system has been introduced, and a smartphone and a tablet PC capable of making phone calls can be used, and according to the customer's input A call can be generated and connected to the SIP terminal 200 through a VoIP method through an information communication network.

The SIP terminal 200 may create a session for a call in response to a call attempt of the customer terminal 100, and perform a call with a customer based on Session Initiation Protocol (SIP) through a prepared chatbot. To this end, the SIP terminal 200 includes AI for converting voice transmitted from the customer terminal 100 into text and inferring the intent included in the text, technical means for converting an answer corresponding to the customer's utterance into voice, A database for storing answers corresponding to multiple intents can be installed. In addition, the SIP terminal 200 may generate a call log for call contents. In addition, although not shown, the person in charge of management of the company, not the customer, may, if necessary, use VoIP of the SIP client of the SIP terminal 200 in response to the call request of the customer terminal 100 for his or her general phone terminal or smart phone terminal. You can use the function of forwarding calls to a phone number, and accordingly, the manager can handle customer consultation through the chatbot even when he or she is absent or cannot answer the phone.

The call management server 300 is connected to the SIP terminal 200 to store and manage a call log for call details generated in the SIP terminal 200, and provides a service allowing the manager to view the call log upon request. can do.

To this end, the call management server 300 receives a call log including call details generated by the SIP terminal 200 after the call between the customer terminal 100 and the SIP terminal 200 is completed, and stores the call log in the database. can be stored and managed. Thereafter, when a call log including call details is requested from the manager terminal 400 , the database may be searched to extract the customer's call log and provide it to the manager terminal 400 .

The aforementioned information communication network may be a wired telephone network such as a public switched telephone network (PSTN), as well as a closed network such as a local area network (LAN) and a wide area network (WAN) as an example of a data communication network, An open network such as the Internet may be used. The Internet is based on the TCP/IP protocol and several services that exist on its upper layer, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP ( Simple Network Management Protocol), NFS (Network File Service), NIS (Network Information Service) may mean a worldwide open computer network structure. In addition, as an example of a wireless communication network, technical standards or communication methods for mobile communication (eg, Global System for Mobile communication (GSM), long-term evolution (LTE), LTE Advance (LTE-A), CDMA) (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications), 5G, etc., any one may be used, but is limited to this it is not going to be

In addition, the call management server 300 according to an embodiment of the present invention performs deep learning and machine learning techniques using the inference failure information included in the call log as learning data for each call. By deriving a machine learning result and providing it to the SIP terminal 200, there is a feature of improving accuracy in a conversation by a chatbot afterwards.

According to the above structure, the artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention analyzes the contents of a call based on text based on a machine learning technique according to a customer's call request, and the chatbot automatically responds To respond to customers, collect and manage call logs through the call management server, provide call contents upon customer request, and in particular, learn the details of failure to infer intent during a call through machine learning and analyze the intent later The accuracy of the chatbot can be improved by using

Hereinafter, the SIP terminal of the artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention will be described in detail with reference to the drawings.

2 is a diagram showing the structure of a SIP terminal in an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention.

Referring to FIG. 2, the SIP terminal 200 according to an embodiment of the present invention includes a SIP client 210 that transmits and receives a VoIP call according to the SIP communication method, and the customer's voice included in the VoIP call from the SIP client in text format. The speech recognition unit 220 that converts, the inference unit that analyzes the customer voice text to infer the intent, extracts an answer corresponding to the inferred intent from the answer database 240, and provides it to the voice synthesis unit 270. 230, an answer database 240 that stores a plurality of various answers corresponding to intents, retrieves and provides answers according to requests, and receives and stores newly updated machine learning results from the call management server 300 After receiving the call log from the result storage 245 and the reasoning unit 230, the content including the entity is analyzed to generate main entity information and inference failure text information for the customer voice processed as inference failure from the entire call log. The call log analyzer 250 transmits the call log including the main entity information and inference failure text generated by the call log analyzer 250 to the call management server 300, and the call log provider 260 and VoIP It may include a voice synthesis unit 270 that converts text corresponding to content to be included in a call into chatbot voice and provides it to the SIP client 210 .

The SIP client 210 processes a VoIP call using the SIP communication protocol and provides a chatbot, and can deliver the voice received from the customer terminal 100 to the voice recognition unit 220, and the It is possible to reply with an answer corresponding to the uttered content.

To this end, the SIP client 210 provides the received voice to the voice recognition unit 220 to convert it into text, and receives the voice to be uttered by the chatbot from the voice synthesizer 270 to the customer terminal 100. can be sent

The voice recognition unit 220 converts the customer's voice signal transmitted by the SIP client 210 into a text form based on SST technology (speech to text), can be forwarded to

The inference unit 230 may receive the converted text from the voice recognition unit 220 and infer the customer's intention, that is, the intent, using a machine learning result for which learning has been completed in advance.

In addition, the inference unit 230 may search the answer database 240 using the inferred intent, receive an answer corresponding thereto, and transmit the returned answer to the voice synthesizer 270 to the customer. to be converted into an audio signal.

In addition, the reasoning unit 230 may generate a call log including text generated and transmitted text in a conversation with a customer during a call when the call is terminated, and transmit it to the call log analyzer 250 .

The answer database 240 may store answers corresponding to various patterns of intent, and may reply appropriate answers according to requests.

In detail, the answer database 240 may extract an answer corresponding to the intent according to the search by the reasoning unit 230 and return it to the reasoning unit 230 . In addition, the answer database 240 prepares answers in advance for cases in which there is no inferred intent, inference rate is low, or inference such as "I'm sorry. I didn't understand, could you explain again?" It is characterized by replying.

The result storage 245 may store a machine learning result that is a basis when the inference unit 230 infers the intent. The call management server 300 can generate machine learning results according to continuously accumulated learning data and update existing results, and the result storage 245 provides the machine learning results learned from the call management server 300 at any time can be received and stored. Thereafter, the result storage 245 may provide the stored machine learning result when the reasoning unit analyzes the intent.

The call log analysis unit 250 receives the call log from the reasoning unit 230 and analyzes contents including entities to obtain important entity information such as orders, reservations, and delivery matters, and the other party processed as reasoning failure. Text information that failed inference by converting the voice of the voice in the voice recognition unit 220 may be generated from the entire call log.

The call log providing unit 260 transmits various call log information analyzed by the call log analysis unit 250 to the call log receiving unit 310 of the call management server 300, so that each customer is provided by the call management server 300. It allows you to store and manage the call history performed by each person.

The voice synthesis unit 270 converts the answer in text form derived by the reasoning unit 230 into a voice signal based on TTS (Test To Speech) technology, and delivers it to the SIP client 210 so that the chatbot can By having them ignite replies to questions, they implement automated calls with customers.

Hereinafter, a call management server of an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention will be described in detail with reference to the drawings.

3 is a diagram showing the structure of a call management server and a customer terminal in an artificial intelligence chatbot-based automatic call response service system according to an embodiment of the present invention.

Referring to FIG. 3 , the call management server 300 according to an embodiment of the present invention includes a call log receiving unit 310 that receives call logs from the SIP terminal 200 and information on all calls generated by the SIP terminal 200. A call log management unit 320 that manages call logs, a call log database 330 that stores received call logs, and, upon request, a call log of a corresponding customer stored in the call log database 330 is extracted and managed by a manager The call log transmission unit 340 transmitted to the terminal 400 updates the machine learning content, performs machine learning, and outputs a machine learning result when learning data is additionally input or inference failure text information exists in the received call log. and a machine learning unit 360 transmitting to the SIP terminal.

The call log receiving unit 310 may receive a call log including details of a call with a corresponding customer, entity information, and deduction failure information from the SIP terminal 200 after a call with the customer has been completed.

The call log management unit 320 may store the call log for each customer received by the call log receiver 310 in the call log database 330, and if there is a customer's request or other needs, the call log database 330 may make a call. Related information including logs may be extracted and provided to the management manager terminal 400 through the call log transmission unit 340 . In addition, the call log management unit 320 may provide functions related to information management, such as creation, update, copy, move, and deletion of call logs stored in the call log database 330 .

The call log database 330 may store various information for operating the call management server, including call logs collected from the SIP terminal 200, and extract call logs for each customer at the request of the call log management unit 320. And it can be delivered to the call log management unit 320.

The call log transmission unit 340 may receive the call log of the customer from the call log management unit 320 described above according to the request of the manager terminal 400 and provide it to the manager terminal 400 through an information communication network. .

In addition, when new machine learning content is added to the database 330 or inference failure information is included in the updated call log, the machine learning unit 360 receives the new machine learning content and inputs the new machine learning content to machine learning as learning data. The machine learning result may be derived by performing machine learning, or by reflecting inference failure information to the machine learning content and performing machine learning using the inference failure information. In addition, the machine learning unit 360 may automatically update the machine learning result by providing the derived machine learning result to the SIP terminal 200 .

On the other hand, the call management server 300 according to an embodiment of the present invention works with a web browser or a dedicated application mounted on the manager terminal 400 to record records related to previous calls at the request of the manager terminal 400. As a configuration for this, the manager terminal 400 may include a call log use unit 410 and a call log output unit 120.

The call log utilization unit 410 may request and receive phone-related information including the call log from the call log transmission unit 340 of the call management server 300 according to a customer's input. For example, when a customer who has been consulted through a chatbot wants to check the contents of previous consultations again, and selects a call history check function in the manager terminal 400, the call log use unit 410 sends the call log sender ( 340) may request and receive a call log.

The call log output unit 420 may extract the entire call history, entity information, and statistical information of the customer based on the received call log and output the extracted information on the screen. In the above example, when the call log utilization unit 410 receives the call log according to the execution of the call history check function, it can be displayed on the screen of the manager terminal 400 . At this time, the call log output unit 420 may display the entire call history and entity information included in the received call log, and various statistical information related to the call.

Hereinafter, an automatic call response service method based on an artificial intelligence chatbot according to an embodiment of the present invention will be described with reference to the drawings.

4 is a diagram illustrating an artificial intelligence chatbot-based automatic call response service method according to an embodiment of the present invention. In the following description, the execution subject for each step becomes each component constituting the SIP terminal 200 and the call management server 300 of the present invention, even if there is no specific description.

Referring to FIG. 4, according to the artificial intelligence chatbot-based automatic call response service method according to an embodiment of the present invention, first, when a customer terminal 100 tries to make a phone call to a company that introduced the system of the present invention, the SIP terminal (200) runs the chatbot and connects the phone to each other (S100).

Accordingly, when the customer's voice is delivered to the SIP terminal 200 by the customer's speech, the SIP terminal 200 converts the voice signal into text form through STT technology and infers the entity and intent from the text (S120). ).

According to the inference result by the above step S120 (S130), if the inference of the intent is successful, the SIP terminal 200 extracts an answer corresponding to the intent from the database and transmits it to the customer terminal 100 through the chatbot And, if the reasoning fails, the reasoning failure information is recorded in the log (S140), and the call continues. Accordingly, the chatbot may transmit a prepared answer to the customer terminal 100 when reasoning fails.

Next, when the call is completed by repeating steps S120 to S140 described above (S160), the SIP terminal 200 creates a call log including call details and transmits it to the call management server 300 (S170). ).

Accordingly, the call management server 300 stores the received call logs in a database for each customer (S180).

Thereafter, when the machine learning content of the call management server 300 is updated or the updated call log includes inference failure information, the call management server 300 updates the machine learning content and uses it as learning data to A machine learning result is derived by performing learning (S210), and the generated machine learning result is provided to the SIP terminal 200 (S220). Thereafter, the SIP terminal 200 uses the stored machine learning result when updating and reasoning.

Afterwards, although not shown, when the manager wants to check the contents and status of the consultation conducted by the chatbot, the call log is requested from the call management server 300 as the call log check function of the manager's terminal is executed. , The call management server 300 further performs the step of extracting the customer's call log from the database and transmitting the message to the terminal in charge of management, so that the manager can handle the management of the customer's call.

Although many details are specifically described in the above description, this should be interpreted as an example of a preferred embodiment rather than limiting the scope of the invention. Therefore, the invention should not be defined by the described examples, but should be defined by what is equivalent to the claims and claims.

100: customer terminal 200: SIP terminal
210: SIP client 220: voice recognition unit
230: reasoning unit 240: answer database
245: result storage 250: call log analysis unit
260: call log providing unit 300: call management server
310: call log reception unit 320: call log management unit
330: call log database 340: call log transmitter
400: Management manager terminal 410: Call log usage unit
420: call log output unit

Claims (7)

A SIP terminal that performs a call with a customer terminal by SIP communication method, analyzes the customer's voice received during the call, infers intent, and transmits an answer according to the inferred intent to the customer terminal through a chatbot; and
Connected to the SIP terminal, receiving and storing a call log including the contents of a call between the chatbot and a customer in a database, updating machine learning content if a log of inference failure exists in the call log, and using the machine learning content A call management server providing machine learning results to the SIP terminal
Artificial intelligence chatbot-based automatic call response service system that includes. According to claim 1,
The SIP terminal,
A SIP client that transmits and receives VoIP calls according to the SIP communication method;
a voice recognition unit that converts the customer's voice included in the VoIP call from the SIP client into a text format;
a voice synthesis unit that converts text corresponding to content to be included in the VoIP call into chatbot voice and provides the text to the SIP client;
An answer database for storing a plurality of various answers corresponding to intents and searching for and providing answers according to requests; and
Inference unit that analyzes customer voice text to infer intent, extracts an answer corresponding to the inferred intent from the answer database, and provides the extracted answer to the voice synthesizer.
Artificial intelligence chatbot-based automatic call response service system that includes. According to claim 2,
The SIP terminal further includes a result storage for receiving and storing a newly updated machine learning result from the call management server,
The reasoning unit,
An artificial intelligence chatbot-based automatic call response service system for inferring the intent of the customer voice text by referring to the machine learning result. According to claim 2,
The SIP terminal,
a call log analysis unit that receives the call log from the reasoning unit and analyzes contents including entities to generate main entity information and inference failure text information about the customer's voice processed as inference failure from the entire call log; and
Call log providing unit for transmitting the call log including the main entity information and inference failure text generated by the call log analyzer to the call management server
Artificial intelligence chatbot-based automatic call response service system that includes. According to claim 1,
The call management server,
a call log receiving unit receiving the call log from the SIP terminal;
a call log management unit managing call logs for all calls made in the SIP terminal;
Call log database for storing the received call log; and
Upon request, a call log transmission unit extracting the call log of the corresponding customer stored in the call log database and transmitting the same to the customer terminal.
Artificial intelligence chatbot-based automatic call response service system that includes. According to claim 5,
The call management server,
A machine learning unit for updating machine learning content, performing machine learning, outputting a machine learning result, and transmitting the machine learning result to the SIP terminal if learning data is additionally input or inference failure text information exists in the received call log.
Artificial intelligence chatbot-based automatic call response service system that includes. According to claim 5,
The call management server is further connected to a management manager terminal used by a management manager,
The management manager terminal,
a call log utilization unit requesting and receiving phone-related information including the call log from the call log transmission unit according to an input of a manager; and
Call log output unit that extracts the entire call history, entity information, and statistical information related to the call based on the received call log and displays it on the screen
Artificial intelligence chatbot-based automatic call response service system that includes.

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