WO2018030672A1 - Procédé et système de consultation d'automatisation de robot pour la consultation avec un client selon un scénario prédéterminé en utilisant un apprentissage automatique - Google Patents

Procédé et système de consultation d'automatisation de robot pour la consultation avec un client selon un scénario prédéterminé en utilisant un apprentissage automatique Download PDF

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Publication number
WO2018030672A1
WO2018030672A1 PCT/KR2017/007954 KR2017007954W WO2018030672A1 WO 2018030672 A1 WO2018030672 A1 WO 2018030672A1 KR 2017007954 W KR2017007954 W KR 2017007954W WO 2018030672 A1 WO2018030672 A1 WO 2018030672A1
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Prior art keywords
counseling
consultation
scenario
node
chat
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PCT/KR2017/007954
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English (en)
Korean (ko)
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김우섭
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주식회사 피노텍
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/50—Business processes related to the communications industry
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/0003—Home robots, i.e. small robots for domestic use
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/008—Artificial life, i.e. computing arrangements simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

Definitions

  • the present invention relates to a robot automatic consultation method and system for consulting with a customer in a predetermined scenario utilizing machine learning.
  • a telephone call is generally used as a method for customer consultation.
  • a call center system has been established and operated for efficient counseling and customer management.
  • counseling is performed according to the working hours of the counselor, so that the counseling is not possible when the consultation center is not a designated counseling time.
  • a counseling system has been developed that uses a chat or a message transmission between mobile terminals to develop a counseling system.
  • a counselor still needs to respond to a customer question.
  • Korean Patent Registration No. 10-1339838 discloses a financial counseling system and method using a mobile terminal.
  • the machine in the case of automatic consultation with a customer based on the server-based robot chat method, if there is a predetermined scenario, the machine enters the scenario by using a machine learning technology so that the consultation is performed in a predetermined scenario. It is to provide a robot automatic consultation method and system for consulting with customers in a predetermined scenario using machine learning that enables robot consultation.
  • the present invention utilizes machine learning to make the current customer consultation more smoothly and efficiently by outputting a question-and-answer form on a separate display window for questions that may be of interest to the customer while proceeding with the automatic consultation in a predetermined scenario. It is to provide a robot automatic consultation method and system for consulting with a customer in a predetermined scenario.
  • a robot consultation system for consultation in the form of a chat with a customer terminal possessed by a customer subscribed to the robot consultation service, a chat server that exchanges the contents of the chat in the form of a chat with the customer terminal; And analyzing the conversation content received from the chat server using a machine learning technique, and if there is a corresponding intro node based on the analysis result, plays a prescribed counseling scenario to perform a scenario counseling with the customer terminal and a corresponding intro node exists. If not, a robot consultation system including a consultation server for performing general consultation is provided.
  • the consultation server may include a customer management module for managing a customer who subscribes to the robot consultation service; A counseling DB management module for managing learning data for entering one of the counseling scenarios for robotic counseling and one or more of the counseling scenarios in a database unit; It may include a chat management module for analyzing the contents of the conversation received from the chat server by a machine learning technique and selecting a predetermined consultation scenario from among the various consultation scenarios based on the analysis result.
  • the counseling DB management module manages a counseling scenario DB for storing one or more counseling scenarios, wherein the counseling scenario includes an intro node for initial entry, intro learning data used when entering the intro node, and an intro node. It can include one or more scenario nodes that make up the scenario as a child node of the connected hierarchy.
  • the intro node may be a mobile node designated in consideration of a node ID which is an ID for identifying the intro node, a query to be transmitted from the intro node to the customer terminal, and a number of cases that may occur in response to the query.
  • An ID and multiple choice items or subjective learning data matched with the mobile node ID may be included.
  • the intro learning data includes one or more query statements that are expected to be able to enter the counseling scenario, and the chat management module uses one or more of keywords, nouns, words, and morphemes for one or more of the query statements in a natural language processing manner.
  • the method may further include a machine learning unit for analyzing and matching a representative query having high similarity with a machine learning technique.
  • the scenario node is matched with a node ID for identifying a current node, a query statement for making a query, a mobile node ID for designating a number of cases that may occur in response to the query statement, and the mobile node ID. It may include multiple choice items or subjective learning data.
  • the counseling DB management module additionally manages a FAQ DB for storing frequently asked questions and answers (FAQ) data in the counseling process, and the chat management module includes a main chat window for playing the counseling scenario and the counseling scenario.
  • the sub chat window for reproducing the query and answer data retrieved from the FAQ DB can be managed together.
  • the chat server may include a messenger module configured to open a messenger type chat window between the customer terminal and the counseling server to communicate with each other, and generate a web chat page capable of two-way conversation to invite the customer terminal to be customized. It may include one or more of the web chat module to facilitate the consultation.
  • the chat management module may include: a feature extractor configured to extract a vocabulary having a relatively high value by calculating a value of the vocabulary from a customer input sentence; Machine learning may be performed based on the feature to infer the most similar sentence among the customer expected queries previously registered in the database, and may include a machine learning unit for determining the existence of the intro node corresponding to the inferred sentence.
  • a robot consultation method in the robot consultation system that performs a consultation in the form of chat with the customer terminal possessed by the customer to join the robot consultation service, activating a chat window in which the customer terminal participates step; Receiving an initial query from the customer terminal; Determining an existence of an intro node based on learning data learned by a machine learning method according to a result of analyzing the first query; When the intro node exists, the robot counseling method comprising the step of playing a consultation scenario corresponding to the intro node in the chat window, and performing a general consultation if the intro node does not exist.
  • the method may further include reproducing FAQ data related to the consultation scenario in a sub chat window.
  • the machine if there is a predetermined scenario in automatic consultation with a customer based on the server-based robot chat method, the machine enters the scenario using a machine learning technology so that the consultation is made in a predetermined scenario. It is effective to enable robot consultation with the doctor.
  • the automatic consultation in a predetermined scenario the questions that can be studied from the customer's point of view in a separate display window in the form of a query-response has the effect of making the current customer consultation more smooth and efficient.
  • FIG. 1 is a view showing a robot consultation system and linkage system according to an embodiment of the present invention
  • FIG. 2 is a block diagram showing a detailed configuration of a consultation DB management module
  • 3 is an exemplary diagram for explaining machine learning feature extraction
  • 5 is an exemplary diagram for explaining a feature distance measurement
  • 6 is an exemplary diagram for explaining a typo distance measurement
  • FIG. 7 is an exemplary diagram for explaining a spaced distance measurement
  • FIG. 8 is a view showing a structure of a consultation scenario
  • FIG. 9 is an exemplary diagram for a configuration of a scenario node
  • FIG. 10 is a flowchart of a robot counseling method according to an embodiment of the present invention.
  • 11 is a view showing the structure of the chat window and an example screen.
  • first and second may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.
  • FIG. 1 is a view showing a robot counseling system and the linkage system according to an embodiment of the present invention
  • Figure 2 is a block diagram showing the detailed configuration of the consultation DB management module
  • FIG. 5 is an exemplary diagram for explaining a characteristic distance measurement
  • FIG. 6 is an exemplary diagram for explaining a typo distance measurement
  • FIG. FIG. 8 is a diagram illustrating the structure of a counseling scenario
  • FIG. 9 is a diagram illustrating a configuration of a scenario node
  • FIG. 10 is a flowchart of a robot counseling method according to an embodiment of the present invention
  • 11 is a view showing the structure of the chat window and an example screen.
  • Robot counseling system (counseling system through a robo advisor (or robo counselor)) performs a consultation via a chat via a messenger or web chat method with a customer terminal, according to the question input from the customer terminal
  • General counseling will be conducted or robotic counseling based on robo chat will be performed through natural language processing based on a database on which case-by-case scenarios (conversation structures in which questions and answers are matched) are established.
  • necessary information may be obtained through API interworking with a financial institution's main system and provided to a customer terminal.
  • the robot consultation system 100 is connected to the customer terminal 10 through a network.
  • the customer terminal 10 is an electronic terminal on which the robot consultation application 12 having the consultation messenger library 14 can be installed.
  • the customer terminal 10 may be a smartphone, a mobile communication terminal, a PDA, a tablet PC, or a general PC equipped with an operating system. It can be one.
  • the customer terminal 10 installed with the robot counseling application 12 may separately designate a dedicated robot counselor (dedicated robo advisor) for counseling according to various fields, and may support a chat type counseling about a field to be consulted at any time. have.
  • a dedicated robot counselor dedicated robo advisor
  • the robot consultation system 100 analyzes the conversation contents input from the chat server 110 for conducting a consultation with the customer terminal 10 in a chat form, and the conversation contents input from the customer terminal 10 using a machine learning method, and plays back a predetermined consultation scenario. Or a counseling server 120 to proceed with general counseling so that appropriate counseling can be made.
  • the chat server 110 includes a messenger module 112 that opens a messenger type chat window between the customer terminal 10 and the counseling server 120 so as to communicate with each other.
  • the messenger module 112 opens a chat window in which the chat subject customer and the robot counselor (hereinafter referred to as 'robo advisor') participate, and the customer message input by the customer terminal 10 in the chat window is consulted by the server 120. ) And a consultation message provided by the consultation server 120 to the customer terminal 10.
  • the customer message may be a response to a question input by the customer or a request made by the consultation server 120.
  • the consultation message may be an answer to a question entered by the customer or a request for a matter to be provided by the customer.
  • the chat server 110 includes a web chat module 114 that generates a web page (web chat page) capable of two-way conversation without a chat window being opened and invites the customer terminal 10 to proceed with a customized consultation. You may.
  • the web chat module 114 when a character is transmitted to a predetermined number from the customer terminal 10, the text content transmitted when the customer is a customer who has subscribed to the robot consultation service is a question capable of robot consultation. If it is determined, a web chat page for the customer is generated, and a URL accessible to the web chat page is transmitted to the customer terminal 10. The customer terminal 10 can be connected to the received URL to receive the robot consultation service.
  • the web chat page may be implemented to require authentication of the user, and it may be possible to check the contents displayed on the web page only for the user who has successfully authenticated the user.
  • the web chat module 114 may allow the advertisement content to be displayed during the process of accessing the web chat page from the customer terminal 10. In this case, by providing access to the web chat page only for the customer terminal 10 confirming the advertisement content, it is possible to obtain advertising revenue in providing the robot consultation service.
  • the chat server 110 pushes various messages related to the outbound consultation even when a chat window is not opened between the client terminal 10 for outbound consultation provided by the consultation server 120. It may further include a push server (not shown) to push. Alternatively, if a chat window is established between the client terminal 10 and the push server, the push server pushes a message related to the outbound consultation through the corresponding chat window or as a separate push message even if the customer message from the client terminal 10 is not transmitted. You may.
  • the counseling server 120 frequently occurs in the customer management module 122 for managing a customer who subscribes to the robot counseling service, various counseling scenarios for robotic counseling, learning data for entering various counseling scenarios, and / or counseling processes.
  • Consulting DB management module 124 for managing the query and answer data (FAQ data) to be a database unit, and the customer message received from the chat server 110 to the general consultation or scenario consultation according to the results of the machine learning method
  • scenario counseling it includes a chat management module 126 to determine a suitable counseling scenario and to chat in accordance with a predetermined counseling scenario.
  • the information request module 128 may be further included to request and obtain information for generating a consultation message in association with a financial institution's main system. have.
  • the chat management module 126 analyzes a customer input sentence (customer message) transmitted from the chat server 110 through a messenger API and acquires it through natural language processing of analyzing keywords, nouns, words, and the like. It may include a sentence inference engine 210 for performing machine learning based on the results to find the nearest query stored in the database.
  • the sentence inference engine 210 performs inference on the customer input sentence using the machine learning result obtained by the machine learning tool 220.
  • the machine learning tool 220 may include a feature extractor 230 and a machine learning unit 240.
  • the feature extractor 230 extracts a feature from a customer input sentence transmitted from the customer terminal 10.
  • a feature may be a key keyword.
  • the feature extractor 230 may calculate a value of the vocabulary from the customer input sentence and extract a vocabulary having a relatively high value as a key keyword, that is, a feature.
  • the value of the vocabulary can be calculated by analyzing the effect of each vocabulary on the intention of the question in the relevant sentence, and can be automatically analyzed into meaningful vocabulary and meaningless vocabulary.
  • the vocabulary value is calculated in the example sentence, and two relatively high vocabulary are cards (83%) and lost (96.7%), and the words 'card' and 'lost' are inputted by the corresponding customer. It can be extracted as a feature from a sentence.
  • the vocabulary value can be determined by measuring the impact of the vocabulary on the question ID.
  • the question ID means an identification code prepared in advance to provide an appropriate answer to the customer. For example, if you prepared answers to 1000 kinds of customer input sentences, the number of question IDs would be 1000.
  • each vocabulary may be calculated based on a result of determining how each keyword or vocabulary influences the selection of the question ID in the machine learning process. If the same keyword or vocabulary is used for different question IDs, the value is relatively low, and if it affects only a specific question ID, the value can be relatively high.
  • the term 'lost' corresponds to a keyword having a high weight corresponding to question IDs such as 'card loss report', 'bankbook loss report', and 'wallet loss report'.
  • the vocabulary of 'what' corresponds to a keyword with a low weight corresponding to question IDs such as 'what is a card issuance document', 'what is a bank account loss report document', and 'what is a banquet fee'.
  • the feature extractor 230 includes a feature distance extractor 231, a synonym mapping unit 232, a keyword mapping unit 233, a noun mapping unit 234, a word mapping unit 235, and a typo distance measuring unit 236. It may include one or more of the spacing distance measurement unit 237.
  • the feature distance extractor 231 calculates a distance (error) between two features extracted from the customer input sentence.
  • a distance between a card and a loss which are two features extracted from the sentence of FIG. 3, may be extracted as a feature distance from a map generated according to similarities of a plurality of vocabularies.
  • Vocabulary appearing in the same question ID when the map is generated according to the similarity of the vocabulary may be located relatively close to the distance map, and vocabularies not used in the same question ID may be located relatively far from the distance map.
  • the synonym mapping unit 232 finds and maps synonyms or synonyms with respect to the vocabulary classified in the sentence through the thesaurus.
  • the keyword mapping unit 233, the noun mapping unit 234, and the word mapping unit 235 each find and map keywords, nouns, and words analyzed through the morpheme analyzer.
  • the typo distance measuring unit 236 infers a vocabulary (or sentence) originally intended by measuring a typo distance when a typo exists in a customer input sentence.
  • the analytical process can measure the distance of a typo. When the measured distance of the typo falls within a predetermined threshold distance, a lexical candidate ranking calculated based on a feature in which a typo frequently occurs may be generated. For example, when the word "accounting account” is input, the phrase “account inquiry" is most similar among the vocabulary registered in the database, and thus it may be determined that it corresponds to a typo of the phrase.
  • the spacing distance measurement unit 237 infers a word (or sentence) originally intended by measuring a spacing distance when a spacing error exists in a customer input sentence.
  • syllable unit analysis is performed on a customer input sentence, and if there is a spacing between syllables, it is determined whether proper spacing is present. If the spacing is not correct, stemming is impossible. Therefore, the optimal spacing is inferred while inputting and changing the number of various cases of spacing until morphological analysis is possible, and the spacing distance according to the deformation is obtained. For example, when input as "account_view_time", a spacing distance of "2" may be measured as compared with "account view”.
  • the feature extractor 230 may extract the feature by performing analysis on the customer input sentence.
  • the machine learning unit 240 performs machine learning based on the extracted feature and infers the most similar sentence among customer expected queries (statements identified by the question ID) registered in the database.
  • the counseling server Based on the inferred sentence, it is determined whether a corresponding intro node exists, and if there is a counseling scenario, if there is a counseling scenario, the counseling server asks questions based on the determined scenario and allows the customer terminal to answer. Allow counseling to take place. In the case of general counseling in which a counseling scenario does not exist, the counseling server may provide an appropriate answer to the question input through the customer terminal.
  • the customer management module 122 constructs a customer database for a customer who has subscribed to the robot consultation service to manage customer status and individual customer information.
  • the counseling DB management module 124 manages a counseling database including a counseling scenario DB 22 and an intronode learning DB 24.
  • the counseling scenario DB 22 stores a plurality of counseling scenarios having a structure in which various questions and answers are matched.
  • the structure of the consultation scenario is shown in FIG. 8, and the configuration of the scenario node is shown in FIG.
  • the counseling scenario includes an intro node for entering the scenario, an intro learning data utilized for entering the intro node, and a scenario node constituting the scenario with subnodes of a hierarchy connected to the intro node.
  • An intro node is a mobile node ID designated by considering a node ID, which is an ID for identifying the node, a query to be transmitted from the node to the customer terminal 10, and a number of cases that can be generated in response to the query. It includes multiple choice items or short answer learning data matched with the node ID.
  • Intro learning data is data for entering an intro node, and includes one or more query statements that are expected to enter the scenario.
  • a scenario node is a node that is connected to the intro node and constitutes a scenario.
  • a scenario node is a multiple choice item that matches a node ID for identifying the current node, a query statement for querying, a mobile node ID that specifies the number of cases that can occur in response to the query, and a mobile node ID. Or subjective learning data.
  • Sanirio nodes have a hierarchical structure in which intro nodes are arranged at the top level. As the customer progresses to the lower tier, the customer's requirements can be further subdivided to understand them in detail.
  • the counseling DB management module 124 may include a machine learning unit for learning intro learning data for entering an intro node for various counseling scenarios.
  • the machine learning unit performs machine learning to find the nearest query based on the result of analyzing keywords, nouns, words or morphemes for one or more queries expected to enter the intro node.
  • the result of machine learning is to find keywords, nouns, words or morphemes that are frequently used above the predetermined criteria for the intro node, and to study the correlations (eg word order, appearance rate, etc.) with intro learning data.
  • the machine learning unit may perform the learning through the machine learning on the subjective learning data in addition to the intro learning data.
  • the FAQ DB may store frequently asked question and answer data (FAQ data).
  • the customer provides answers to the questions provided in the scenario.
  • the customer manually chats in the direction specified in the scenario, and in this process, the customer may want to inquire.
  • the FAQ data associated with the counseling scenario currently being played in the main chat window can be played in a chat format, thereby resolving customer's curiosity.
  • the chat management module 126 activates the chat window when the robot consultation request is received from the customer terminal 10 (step S300).
  • the chat window may be implemented as a messenger type or a web chat type.
  • step S310 When receiving an initial query (customer input sentence) from the customer through the activated chat window (step S305), it is determined whether there is a corresponding intro node (step S310).
  • the determination of the existence of an intro node may be performed based on whether the intro node corresponding to the representative query is found based on finding the closest representative query based on the intro learning data described above.
  • step S315 general counseling is performed.
  • general consultation about customer query it is performed by searching and providing the answers registered in the database.
  • the chat management module 126 reproduces the counseling scenario corresponding to the selected intro node (step S320).
  • the counseling scenario is played, it can be played from the intro node.
  • the chat management module 126 may include a scenario player (not shown) that finds a suitable counseling scenario for a customer query and plays it in a chat window.
  • the scenario player understands the relationship between the nodes constituting the counseling scenario composed of many nodes, and moves to the next node determined according to the customer's response transmitted from the customer terminal 10 to the question transmitted from the counseling server 120. Play the player.
  • the scenario node may have a multiple choice item or a short answer item.
  • an item corresponding to each answer is determined.
  • the mobile node ID is determined.
  • one or more possible answers are arranged to determine which node to move next through machine learning.
  • the chat management module 126 may reproduce the FAQ data as well as the prescribed consultation scenario (step S330).
  • the chat management module 126 may further include a FAQ player (not shown) for reproducing the FAQ data.
  • the counseling may be started while asking about the annual income of the customer according to the prescribed consultation scenario.
  • the FAQ about credit loan qualification requirements which may be a question from the customer's point of view of the loan consultation, is expressed in a chat form on the sub chat window 420, so that the customer may miss related to the loan consultation. It is also possible to confirm the information, so that sufficient information can be obtained by one consultation.
  • the customer terminal 10 may receive feedback on a robot consultation currently in progress (step S340).
  • Feedback requested to the customer may be, for example, whether it was suitable as a consultation scenario for the customer's initial query or whether there was an additional item of consultation content that the user wanted to know.
  • the machine learning unit may perform machine learning on the intro learning data and the subjective learning data based on the customer feedback to update the learning data (step S345). For example, if the customer is satisfied with the current counseling scenario, the query sent by the client is regarded as having keywords, words, morphemes, etc. suitable for the counseling scenario, and additional learning about the intro learning data is performed. could be.
  • the robot consultation system 100 opens a chat window between the customer terminal 10 and the consultation server 120 through the chat server 110 so that free consultation is performed in a messenger or web chat format.
  • the customer message input from the customer terminal 10 automatically finds and reproduces the appropriate counseling scenario in the counseling DB through natural language processing (sentence recognition), so that even if the counseling personnel are not directly involved, smooth counseling This can be done to provide differentiated services to customers.
  • speech recognition natural language processing
  • the robot counseling method according to the present embodiment described above can be embodied as computer readable codes on a computer readable recording medium.
  • Computer-readable recording media include all kinds of recording media having data stored thereon that can be decrypted by a computer system. For example, there may be a read only memory (ROM), a random access memory (RAM), a magnetic tape, a magnetic disk, a flash memory, an optical data storage device, and the like.
  • the computer readable recording medium can also be distributed over computer systems connected over a computer network, stored and executed as readable code in a distributed fashion.

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Abstract

L'invention concerne un procédé et un système de consultation d'automatisation de robot pour la consultation avec un client selon un scénario prédéterminé à l'aide d'un apprentissage automatique. L'invention concerne également un système de consultation de robot qui effectue une consultation sous la forme d'une conversation en ligne avec un terminal client possédé par un client qui a rejoint un service de consultation de robot. L'invention comprend: un serveur de conversation en ligne pour envoyer ou recevoir un contenu de conversation sous la forme d'une conversation en ligne avec le terminal client; et un serveur de consultation qui analyse un contenu de conversation reçu en provenance du serveur de conversation en ligne par une technique d'apprentissage automatique et, sur la base d'un résultat d'analyse, reproduit un scénario de consultation prédéterminé pour effectuer une consultation de scénario avec le terminal de client lorsqu'un intra-noeuds correspondant existe et effectue une consultation normale lorsqu'un intra-noeuds correspondant n'existe pas.
PCT/KR2017/007954 2016-08-09 2017-07-24 Procédé et système de consultation d'automatisation de robot pour la consultation avec un client selon un scénario prédéterminé en utilisant un apprentissage automatique WO2018030672A1 (fr)

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KR1020160101041A KR101883185B1 (ko) 2016-08-09 2016-08-09 머신러닝을 활용한 정해진 시나리오로 고객과 상담하는 로봇 자동 상담 방법 및 시스템

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CN109212975A (zh) * 2018-11-13 2019-01-15 北方工业大学 一种具有发育机制的感知行动认知学习方法
CN110196862A (zh) * 2018-02-27 2019-09-03 财付通支付科技有限公司 一种数据场景构造方法、装置、服务器与系统
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CN109212975A (zh) * 2018-11-13 2019-01-15 北方工业大学 一种具有发育机制的感知行动认知学习方法
CN111444309A (zh) * 2019-01-16 2020-07-24 阿里巴巴集团控股有限公司 用于对图进行学习的系统
CN111444309B (zh) * 2019-01-16 2023-04-14 阿里巴巴集团控股有限公司 用于对图进行学习的系统
CN110321414A (zh) * 2019-04-19 2019-10-11 四川政资汇智能科技有限公司 一种基于深度学习的人工智能咨询服务方法及系统
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CN112394816B (zh) * 2020-11-26 2023-06-16 浙江连信科技有限公司 基于人机交互的心理服务方法和机器人
CN113762451A (zh) * 2021-08-27 2021-12-07 浙江康旭科技有限公司 基于场景和关键词规则的任务型问答机器人
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