CN111460112A - Online customer service consultation method, device, medium and electronic equipment - Google Patents
Online customer service consultation method, device, medium and electronic equipment Download PDFInfo
- Publication number
- CN111460112A CN111460112A CN202010136161.9A CN202010136161A CN111460112A CN 111460112 A CN111460112 A CN 111460112A CN 202010136161 A CN202010136161 A CN 202010136161A CN 111460112 A CN111460112 A CN 111460112A
- Authority
- CN
- China
- Prior art keywords
- consultation
- target user
- service
- robot
- channel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 68
- 238000009223 counseling Methods 0.000 claims description 15
- 230000001815 facial effect Effects 0.000 claims description 13
- 239000012634 fragment Substances 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 238000012545 processing Methods 0.000 description 6
- 238000013473 artificial intelligence Methods 0.000 description 5
- 238000012549 training Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000006854 communication Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000008451 emotion Effects 0.000 description 1
- 230000008921 facial expression Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
- G06F16/3329—Natural language query formulation or dialogue systems
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0281—Customer communication at a business location, e.g. providing product or service information, consulting
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Mathematical Physics (AREA)
- Strategic Management (AREA)
- Accounting & Taxation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Artificial Intelligence (AREA)
- Human Computer Interaction (AREA)
- Game Theory and Decision Science (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Manipulator (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The disclosure relates to the field of micro-expression recognition, and discloses an online customer service consultation method, device, medium and electronic equipment. The method comprises the following steps: when a consultation request of a target user is received, a robot consultation channel is started to provide robot consultation service for the target user through a customer service robot; acquiring a face image sequence of the target user who is receiving the robot consultation service; performing micro-expression recognition on the face image sequence of the target user to determine the satisfaction degree of the target user on the robot consultation service; and when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user. Under the method, the switching between the customer service robot and the manual mode is carried out based on the satisfaction degree of the user to the robot consultation service, and the balance between the customer service efficiency and the customer service quality is realized.
Description
Technical Field
The disclosure relates to the technical field of micro-expression recognition, in particular to an online customer service consultation method, an online customer service consultation device, a medium and electronic equipment.
Background
With the coming of the internet, especially the mobile internet age, the online purchasing of goods and shopping services is becoming more and more common in people's daily life. When purchasing goods and services online, in order to provide better service experience for the client, a robot or a manual customer service is generally arranged to provide consultation service for the client, so as to answer the client question. However, if the manual customer service is provided to provide the consultation service for the customer, the manual customer service often cannot provide the service for a large number of customers at the same time, and the customer may wait in a queue, so that the service efficiency of the manual customer service is low; if the robot is arranged to provide the consultation service for the client, although the service efficiency can be improved and the operation cost can be reduced, the capability of the customer service robot cannot be compared with that of a manual work at present, so that the customer service robot may not serve the client well, and the consultation experience of the client is reduced. Therefore, the implementation scheme of online customer service consultation cannot realize the compromise between customer service efficiency and customer service quality.
Disclosure of Invention
In the technical field of micro-expression recognition, the invention aims to solve the technical problem that the customer service efficiency and the customer service quality cannot be considered at the same time when online customer service consultation is carried out at present, and the invention aims to provide an online customer service consultation method, device, medium and electronic equipment.
According to an aspect of the present disclosure, there is provided an online customer service consultation method, including:
when a consultation request of a target user is received, a robot consultation channel is started to provide robot consultation service for the target user through a customer service robot;
acquiring a face image sequence of the target user who is receiving the robot consultation service;
performing micro-expression recognition on the face image sequence of the target user to determine the satisfaction degree of the target user on the robot consultation service;
and when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
According to another aspect of the present disclosure, there is provided an online customer service consultation apparatus, characterized in that the apparatus includes:
the system comprises a channel starting module, a service robot and a service robot, wherein the channel starting module is configured to start a robot consultation channel when a consultation request of a target user is received so as to provide robot consultation service for the target user through the service robot;
an acquisition module configured to acquire a sequence of facial images of the target user who is receiving the robotic advisory service;
a micro-expression recognition module configured to perform micro-expression recognition on the face image sequence of the target user to determine satisfaction of the target user on the robot consultation service;
and the switching module is configured to switch the robot consultation channel to a manual consultation channel when the satisfaction degree meets a preset condition so as to provide manual consultation service for the target user.
According to another aspect of the present disclosure, there is provided a computer readable program medium storing computer program instructions which, when executed by a computer, cause the computer to perform the method as previously described.
According to another aspect of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method as previously described.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the online customer service consultation method provided by the disclosure comprises the following steps: when a consultation request of a target user is received, a robot consultation channel is started to provide robot consultation service for the target user through a customer service robot; acquiring a face image sequence of the target user who is receiving the robot consultation service; performing micro-expression recognition on the face image sequence of the target user to determine the satisfaction degree of the target user on the robot consultation service; and when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
Under the method, the customer service robot and the manual customer service are simultaneously utilized to provide the consultation service for the user, wherein the manual customer service is used for providing the consultation service for the user under the condition that the satisfaction degree of the user to the robot consultation service in the process of providing the robot consultation service for the user meets the preset condition, so that the customer service efficiency and the customer service quality are considered at the same time. Specifically, on one hand, the customer service robot is used for providing the consultation service for the user when the consultation request of the user is received, and the robot can serve a large number of users, so that compared with the method of simply using manual customer service, the service efficiency is improved, the waiting time of the user is reduced, the user experience is ensured, and the operation cost is reduced by reducing the number of the manual customer service; on the other hand, after micro-expression recognition is carried out on the user, when the satisfaction degree of the user on the robot consultation service is judged to meet the preset condition, the manual consultation service is provided for the user, compared with the method of simply using the customer service robot, the service quality is guaranteed, and the loss of high-quality customers caused by poor user experience due to the robot consultation service is avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a system architecture diagram illustrating a method of online customer service consultation in accordance with an exemplary embodiment;
FIG. 2 is a flow diagram illustrating a method of online customer service consultation according to an exemplary embodiment;
FIG. 3 is a detailed flow diagram illustrating the application of the disclosed method for providing online customer service consultation in the financial product field in accordance with an exemplary embodiment;
FIG. 4 is a flowchart illustrating steps subsequent to step 240 according to one embodiment illustrated in a corresponding embodiment of FIG. 2;
FIG. 5 is a block diagram illustrating an online customer service advisory facility in accordance with an exemplary embodiment;
FIG. 6 is a block diagram illustrating an example electronic device implementing the above-described method for online customer service consultation, according to an example embodiment;
fig. 7 illustrates a computer-readable storage medium implementing the above-described online customer service consultation method according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
The present disclosure first provides an online customer service consultation method. The online customer service consultation refers to a process that a user communicates with a customer service through a network medium, questions are provided for the customer service, the customer service is requested to answer based on the questions, and data generated in the communication process is transmitted through the network medium. The online customer service consultation method can realize the adjustment of the online customer service consultation mode according to the satisfaction degree based on micro-expression recognition.
The implementation terminal of the present disclosure may be any device having an operation and processing function, which may be connected to an external device for receiving or sending data, and specifically may be a portable mobile device, such as a smart phone, a tablet computer, a notebook computer, a pda (personal Digital assistant), or the like, or may be a fixed device, such as a computer device, a field terminal, a desktop computer, a server, a workstation, or the like, or may be a set of multiple devices, such as a physical infrastructure of cloud computing or a server cluster.
Optionally, the implementation terminal of the present disclosure may be a server or a physical infrastructure of cloud computing.
FIG. 1 is a system architecture diagram illustrating a method of online customer service consultation in accordance with an exemplary embodiment. As shown in fig. 1, the system architecture includes an execution server 110, a customer service terminal 120, a robot server 130, and a smart phone 140, where the smart phone 140 is used by a user, the customer service terminal 120 is used by a customer service, and the smart phone 140, the customer service terminal 120, and the robot server 130 are respectively connected to the server 110 through communication links, and can be used to receive and send data. In this embodiment, the server 110 is an implementation terminal of the present disclosure, and the robot server 130 is fixedly provided with a customer service robot. When the online customer service consultation method provided by the present disclosure is applied to the system architecture shown in fig. 1, a user first sends a consultation request to the server 110 through the smartphone 140, and the robot server 130 connects the smartphone 140 through the server 110 to provide a robot consultation service for the user; in the process of performing the robot consultation service, the server 110 may obtain a face image of the user, and then perform micro-expression recognition on the face image, thereby obtaining the satisfaction degree of the user on the robot consultation service; when the server 110 judges that the satisfaction degree reaches the preset condition, the robot consultation service can be cut off, the customer service terminal 120 is connected with the smart phone 140, and the customer service using the customer service terminal 120 provides manual consultation service for the user.
It should be noted that fig. 1 is only one embodiment of the present disclosure. Although the implementation terminal in this embodiment is a server and the user terminal is a smartphone, in other embodiments, the implementation terminal and the user terminal in this disclosure may be various terminals or devices as described above; although in this embodiment, the implementation terminal, the user terminal, and the terminal where the customer service robot is located in the present disclosure are different terminals, in other embodiments or specific applications, any two or three terminals of the implementation terminal, the user terminal, and the terminal where the customer service robot is located in the present disclosure may be the same terminal. The present disclosure is not intended to be limited thereby, nor should the scope of the present disclosure be limited thereby.
FIG. 2 is a flow diagram illustrating a method for online customer service consultation according to an exemplary embodiment. The online customer service consultation method of the embodiment can be executed by a server, as shown in fig. 2, and includes the following steps:
The consultation request can be a consultation request of the user for various products and services. The product can be a physical commodity such as a computer, a mobile phone and the like, and can also be a virtual commodity such as a financial product and the like.
The robot consultation channel, that is, a data transmission channel between the target user and the customer service robot, may be, for example, a long connection channel under HTTP (HyperText Transfer Protocol).
The robot consultation service is a service for providing information or feeding back information for a user in an interactive mode, and the service generally lasts for a period of time.
The robot consultation service can be a text consultation service (feeding back information to the user in a text mode) or a voice consultation service (feeding back information to the user in a voice mode).
The customer service robot may be a robot that is established based on various models or algorithms, is capable of interacting with a target user, and answers questions consulted by the user. For example, the customer service robot may be built based on a machine learning model such as a language model and a voice model, for example, a bert (bidirectional Encoder retrieval from transforms) model.
In one embodiment, before the robot consultation channel is started when a consultation request of a target user is received to provide the robot consultation service for the target user through the customer service robot, the method further comprises the following steps:
and acquiring a preset data set, and training the customer service robot.
The data set for training the service robot may be text data for providing the text form of the counseling service, and the data set for training the service robot may be a combination of text data and voice data for providing the voice form of the counseling service.
In one embodiment, the robot advisory service is a text advisory service, and the customer service robot provides the robot advisory service to the target user by:
acquiring text information submitted by the target user;
extracting key information in the text information;
and inquiring answer information matched with the key information from a preset database, and sending the answer information to the target user.
When the consultation request of the target user is a consultation request for a financial product, the extracted key information may include "draw ahead", "earnings available", "what is represented by n + 1", and the like ".
The customer service robot can run at a terminal of a target user and can run at a server side opposite to the target user. When the customer service robot operates at a service end opposite to the target user, the consultation request of the target user is sent through a client end opposite to the service end on the user terminal. When the customer service robot runs at the terminal of the target user, the terminal where the customer service robot is located, the terminal of the target user and the implementation terminal of the disclosure can be the same terminal. For example, the terminal may be a vending machine installed in a public place, and may be connected to a network, and a customer service robot is fixedly installed inside the terminal, and the terminal may further include a camera and a recording device, so that a user may directly perform a conversation with the customer service robot inside the terminal through the terminal, or may perform a conversation with a remote customer service through the network.
The face image sequence is a sequence formed by a plurality of pieces of face image information of a target user detected by a terminal or equipment with a photographing or video recording function, and the sequence of the face image information is not necessarily absolutely continuous in time, and only shows that the face image information has a sequence in time. For example, the images may be multiple face images obtained by continuous photographing, or the video data may be composed of continuous multiple frames of images.
The face image sequence of the target user can be acquired in such a way that: the target user sends a consultation request to a service end for operating the customer service robot through a user terminal such as a mobile phone and a computer provided with a client, namely the terminal of the target user is different from the execution terminal of the application, and when the customer service robot provides robot consultation service for the target user, a camera of the user terminal is called to obtain a face image sequence of the target user.
The face image sequence of the target user can also be obtained in such a way that: the terminal of the target user and the execution terminal of the application are the same terminal, a customer service robot is preset in the terminal used by the target user, and when the customer service robot provides robot consultation service for the target user, a camera of the terminal is called to obtain a face image sequence of the target user.
In one embodiment, the acquiring of the sequence of facial images of the target user who is receiving the robot consultation service includes:
responding to the consultation request of the target user and starting to acquire the face image sequence of the target user when the consultation information is fed back to the target user for the first time.
Since consultation is a mutual interaction process, the facial expression of a user may change to the satisfaction degree of the service only after the consultation information is fed back to the user, that is, in general, the user first proposes a question, and the robot consultation service performs corresponding solution, that is, it may be worthless to acquire a facial image in a time period between the first question presentation by the user and the solution by the robot consultation service.
In one embodiment, the acquiring of the sequence of facial images of the target user who is receiving the robot consultation service includes:
and acquiring the recorded face video of the target user in a video streaming mode to be used as a face image sequence of the target user.
The latest face image of the target user can be quickly acquired through the video stream, so that the real-time performance of recognition can be improved.
In one embodiment, the acquiring of the sequence of facial images of the target user who is receiving the robot consultation service includes:
and acquiring the face video of the target user recorded in the preset time period at intervals of a preset time period as a face image sequence of the target user.
And step 230, performing micro-expression recognition on the face image sequence of the target user to determine the satisfaction degree of the target user on the robot consultation service.
The micro expression recognition can be realized based on various algorithms, models and systems, for example, the micro expression recognition can be performed by utilizing a deep neural network, a deep learning algorithm, a reinforcement learning algorithm and the like, and the micro expression recognition can also be realized by utilizing a Gamma intelligent micro expression system which is safe in China.
And 240, when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
In one embodiment, the robot advisory service and the human advisory service are text-form advisory services.
In one embodiment, the robot counseling service is a text-form counseling service, and the human counseling service is a voice-form counseling service.
In one embodiment, the robot advisory service and the human advisory service are both advisory services in voice form.
The preset condition is that the robot consultation channel is switched to the manual consultation channel so as to realize the process of providing manual consultation service for the target user through the manual consultation channel. In general, the predetermined condition may indicate that the target user has low satisfaction with the robot advisory service or is below a certain threshold.
In one embodiment, the switching the robot consultation channel to a manual consultation channel when the satisfaction degree meets a preset condition to provide manual consultation service for the target user comprises:
and when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel and determining a manual consultation request so as to send the manual consultation request to the manual consultation channel for manual customer service to provide manual consultation service for the target user, wherein the consultation request with the request time closest to the current time is used as the manual consultation request.
In this embodiment, when the robot consultation channel is switched to the manual consultation channel, the consultation request with the request time closest to the current time is fed back to the customer service, so that the customer service can timely grasp the question of the target user and perform corresponding answer, and smooth switching between the robot consultation channel and the manual consultation channel is realized.
In one embodiment, the performing micro-expression recognition on the facial image sequence of the target user to determine the satisfaction degree of the target user on the robot counseling service includes:
dividing the face image sequence into at least two micro-expression segments;
determining the satisfaction degree of each micro expression segment by utilizing a micro expression recognition model, wherein the satisfaction degree of the target user to the robot consultation service in the time period of the corresponding micro expression segment is used as the satisfaction degree of the target user;
when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user, wherein the method comprises the following steps:
and when the difference value between the maximum satisfaction degree and the minimum satisfaction degree in the satisfaction degrees of the micro-expression segments corresponding to the human face image sequence is greater than a preset satisfaction degree difference value threshold value, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
The micro-expression recognition model can be built based on various algorithms or neural networks as described above, and the model is usually trained by using face image data before being used.
When the difference between the maximum satisfaction degree and the minimum satisfaction degree in the satisfaction degrees of the micro-expression segments is large enough, the fact that the robot consultation service provided by the customer service robot for the target user causes large emotion fluctuation of the target user and poor experience of the robot consultation service cannot provide satisfactory service for the user is indicated, and at the moment, the artificial consultation service is provided for the target user, so that the service quality is guaranteed, and the user satisfaction degree is improved.
In one embodiment, the performing micro-expression recognition on the facial image sequence of the target user to determine the satisfaction degree of the target user on the robot counseling service includes:
dividing the face image sequence into at least two micro-expression segments;
determining the satisfaction degree of each micro expression segment by utilizing a micro expression recognition model, wherein the satisfaction degree of the target user to the robot consultation service in the time period of the corresponding micro expression segment is used as the satisfaction degree of the target user;
when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user, wherein the method comprises the following steps:
determining the average value of the satisfaction degrees of the micro-expression fragments;
and when the average value is smaller than a preset satisfaction average value threshold value, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
The average value of the satisfaction degree reflects the concentration degree of the satisfaction degree of the user to the robot consultation service in the time period formed by the micro expression segments as a whole, the moment of switching the robot consultation channel to the manual consultation channel is determined according to the comparison between the average value of the satisfaction degree of each micro expression segment and the corresponding threshold value, the robot consultation efficiency is improved by using the robot consultation service, the moment of switching the robot consultation channel to the manual consultation channel is more reasonable, the manual consultation mode is used for providing more professional consultation service for the user, and the user experience is guaranteed.
In one embodiment, the performing micro-expression recognition on the facial image sequence of the target user to determine the satisfaction degree of the target user on the robot counseling service includes:
dividing the face image sequence into at least two micro-expression segments;
determining the satisfaction degree of the micro expression fragment corresponding to the time period closest to the current time by using a micro expression recognition model, and taking the satisfaction degree of the target user to the robot consultation service;
when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user, wherein the method comprises the following steps:
and when the satisfaction is smaller than a preset satisfaction threshold, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
In the embodiment, the time when the robot consultation channel is switched to the manual consultation channel is determined by comparing the satisfaction degree of the micro-expression segment corresponding to the time period closest to the current time with the corresponding threshold value, so that the latest satisfaction degree of the target user on the robot consultation service can be conducted to determine whether the robot consultation channel is switched to the manual consultation channel, and both the consultation efficiency and the user experience are taken into consideration.
In summary, according to the online customer service consultation method provided in the embodiment of fig. 2, the consultation service is provided to the user by simultaneously using the customer service robot and the manual customer service, wherein the provision of the consultation service to the user by using the manual customer service is performed when the satisfaction degree of the user to the robot consultation service during the provision of the robot consultation service to the user meets the predetermined condition, so that the customer service efficiency and the customer service quality are both considered. Specifically, on one hand, the customer service robot is used for providing the consultation service for the user when the consultation request of the user is received, and the robot can serve a large number of users, so that compared with the method of simply using manual customer service, the service efficiency is improved, the waiting time of the user is reduced, the user experience is ensured, and the operation cost is reduced by reducing the number of the manual customer service; on the other hand, after micro-expression recognition is carried out on the user, when the satisfaction degree of the user on the robot consultation service is judged to meet the preset condition, the manual consultation service is provided for the user, compared with the method of simply using the customer service robot, the service quality is guaranteed, and the loss of high-quality customers caused by poor user experience due to the robot consultation service is avoided.
Fig. 3 is a specific flowchart illustrating the application of the method for providing online customer service consultation according to an exemplary embodiment to the field of financial products. In the embodiment of fig. 3, the customer service robot appears as an AI (Artificial Intelligence) assistant, and referring to fig. 3, the specific process of the online customer service consultation may be as follows: firstly, training an AI assistant, when a new financial product is to be sold, clicking a page of the financial product by a user through a terminal of the user for online consultation, answering a question by the AI assistant firstly, acquiring a face image of the user in the consultation process for micro-expression recognition, continuously answering the question for the user by the AI assistant when the user is found to be satisfied through the micro-expression recognition, and switching on an artificial customer service when the micro-expression recognition shows that the user is not satisfied any more, and answering the question for the user by the artificial customer service.
Fig. 4 is a flowchart illustrating steps subsequent to step 240 according to one embodiment illustrated in a corresponding embodiment of fig. 2.
As shown in fig. 4, the method comprises the following steps:
The token of "human service priority user" is an identification of the target user, who can initiate the consultation request, and generally has an account or identification, and after the token of the target user, the token can be stored in the database together with the account or identification of the user for inquiry.
When providing the consultation service for a user, if the satisfaction degree of the user to the robot consultation service is low and the manual consultation service is used for providing the service for the user, the robot consultation service cannot provide the consultation service for the user well, at the moment, the user is marked, when the consultation request sent by the user is received again, the manual consultation service is directly provided for the user according to the mark of the user, the experience of a target user which is not suitable for adopting the robot consultation service is ensured to the maximum degree, and the service quality is improved.
In one embodiment, the consultation request is a purchase consultation request for a product, and after the target user is marked as a human service priority user, the method further comprises:
and when the target user completes the purchase transaction of the product corresponding to the consultation request, the mark of the target user is removed.
The information of the purchase consultation request page and the purchase transaction information of the products can be maintained by a server side opposite to a client side of the user, if the server side is the implementation terminal disclosed by the disclosure, the server side can know which products the target user completes the purchase transaction to, and can know which products the target user aims at for the purchase consultation request, so that the server side can know whether the target user completes the purchase transaction of the products corresponding to the consultation request.
After the target user finishes purchasing the product, the target user does not need to continuously invest the resources of the manual customer service, and in the embodiment, the mark of the target user is removed after the target user finishes purchasing the product, so that the resources of the manual customer service are saved.
The disclosure also provides an online customer service consultation device, and the following device embodiment is disclosed.
FIG. 5 is a block diagram illustrating an online customer service advisory facility in accordance with one exemplary embodiment. As shown in fig. 5, the apparatus 500 includes:
a channel starting module 510 configured to start a robot consultation channel to provide a robot consultation service for a target user through a customer service robot when a consultation request of the target user is received;
an acquiring module 520 configured to acquire a sequence of facial images of the target user who is receiving the robot advisory service;
a micro-expression recognition module 530 configured to perform micro-expression recognition on the face image sequence of the target user to determine satisfaction of the target user with the robot advisory service;
a switching module 540 configured to switch the robot consultation channel to a manual consultation channel when the satisfaction degree meets a predetermined condition, so as to provide manual consultation service for the target user.
According to a third aspect of the present disclosure, there is also provided an electronic device capable of implementing the above method.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one memory unit 620, and a bus 630 that couples the various system components including the memory unit 620 and the processing unit 610.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention as described in the section "example methods" above in this specification.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)621 and/or a cache memory unit 622, and may further include a read only memory unit (ROM) 623.
The storage unit 620 may also include a program/utility 624 having a set (at least one) of program modules 625, such program modules 625 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
According to a fourth aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-mentioned method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 7, a program product 700 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. An online customer service consultation method, characterized by comprising:
when a consultation request of a target user is received, a robot consultation channel is started to provide robot consultation service for the target user through a customer service robot;
acquiring a face image sequence of the target user who is receiving the robot consultation service;
performing micro-expression recognition on the face image sequence of the target user to determine the satisfaction degree of the target user on the robot consultation service;
and when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
2. The method of claim 1, wherein the obtaining of the sequence of facial images of the target user who is receiving the robotic advisory service comprises:
responding to the consultation request of the target user and starting to acquire the face image sequence of the target user when the consultation information is fed back to the target user for the first time.
3. The method according to claim 1 or 2, wherein the performing micro-expression recognition on the facial image sequence of the target user to determine the satisfaction degree of the target user with the robot consultation service comprises:
dividing the face image sequence into at least two micro-expression segments;
determining the satisfaction degree of each micro expression segment by utilizing a micro expression recognition model, wherein the satisfaction degree of the target user to the robot consultation service in the time period of the corresponding micro expression segment is used as the satisfaction degree of the target user;
when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user, wherein the method comprises the following steps:
and when the difference value between the maximum satisfaction degree and the minimum satisfaction degree in the satisfaction degrees of the micro-expression segments corresponding to the human face image sequence is greater than a preset satisfaction degree difference value threshold value, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
4. The method according to claim 1 or 2, wherein the performing micro-expression recognition on the facial image sequence of the target user to determine the satisfaction degree of the target user with the robot consultation service comprises:
dividing the face image sequence into at least two micro-expression segments;
determining the satisfaction degree of the micro expression fragment corresponding to the time period closest to the current time by using a micro expression recognition model, and taking the satisfaction degree of the target user to the robot consultation service;
when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user, wherein the method comprises the following steps:
and when the satisfaction is smaller than a preset satisfaction threshold, switching the robot consultation channel to a manual consultation channel to provide manual consultation service for the target user.
5. The method as claimed in claim 1, wherein said switching the robot counseling channel to a manual counseling channel to provide manual counseling service for the target user when the satisfaction degree satisfies a predetermined condition comprises:
and when the satisfaction degree meets a preset condition, switching the robot consultation channel to a manual consultation channel and determining a manual consultation request so as to send the manual consultation request to the manual consultation channel for manual customer service to provide manual consultation service for the target user, wherein the consultation request with the request time closest to the current time is used as the manual consultation request.
6. The method as claimed in claim 1 or 2, wherein after switching the robot counseling channel to a manual counseling channel to provide manual counseling service for the target user when the satisfaction degree satisfies a predetermined condition, the method further comprises:
marking the target user as a manual service priority user;
and when the consultation request of the target user is received again after the artificial consultation service is finished, starting an artificial consultation channel according to the artificial service priority user mark of the target user so as to provide the artificial consultation service for the target user.
7. The method of claim 6, wherein the consultation request is a purchase consultation request for products, and after marking the target users as human service priority users, the method further comprises:
and when the target user completes the purchase transaction of the product corresponding to the consultation request, the mark of the target user is removed.
8. An online customer service advisory facility, the facility comprising:
the system comprises a channel starting module, a service robot and a service robot, wherein the channel starting module is configured to start a robot consultation channel when a consultation request of a target user is received so as to provide robot consultation service for the target user through the service robot;
an acquisition module configured to acquire a sequence of facial images of the target user who is receiving the robotic advisory service;
a micro-expression recognition module configured to perform micro-expression recognition on the face image sequence of the target user to determine satisfaction of the target user on the robot consultation service;
and the switching module is configured to switch the robot consultation channel to a manual consultation channel when the satisfaction degree meets a preset condition so as to provide manual consultation service for the target user.
9. A computer-readable program medium, characterized in that it stores computer program instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 7.
10. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any of claims 1 to 7.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010136161.9A CN111460112A (en) | 2020-03-02 | 2020-03-02 | Online customer service consultation method, device, medium and electronic equipment |
| PCT/CN2021/070750 WO2021175007A1 (en) | 2020-03-02 | 2021-01-08 | Online customer service consultation method and apparatus, medium, and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010136161.9A CN111460112A (en) | 2020-03-02 | 2020-03-02 | Online customer service consultation method, device, medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111460112A true CN111460112A (en) | 2020-07-28 |
Family
ID=71679990
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010136161.9A Pending CN111460112A (en) | 2020-03-02 | 2020-03-02 | Online customer service consultation method, device, medium and electronic equipment |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111460112A (en) |
| WO (1) | WO2021175007A1 (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112347236A (en) * | 2020-11-16 | 2021-02-09 | 友谊国际工程咨询股份有限公司 | Intelligent engineering consultation method and system based on AI (Artificial Intelligence) calculated quantity and computer equipment thereof |
| CN112543185A (en) * | 2020-11-23 | 2021-03-23 | 建信金融科技有限责任公司 | Customer service method, device and system |
| CN112929591A (en) * | 2021-01-28 | 2021-06-08 | 联通(辽宁)产业互联网有限公司 | 5G tax video customer service platform and operation method thereof |
| CN113065362A (en) * | 2021-03-19 | 2021-07-02 | 联想(北京)有限公司 | Intelligent reply method and device |
| CN113379229A (en) * | 2021-06-08 | 2021-09-10 | 北京沃东天骏信息技术有限公司 | Resource scheduling method and device |
| WO2021175007A1 (en) * | 2020-03-02 | 2021-09-10 | 深圳壹账通智能科技有限公司 | Online customer service consultation method and apparatus, medium, and electronic device |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114070937A (en) * | 2021-11-10 | 2022-02-18 | 京东科技信息技术有限公司 | Multi-modal client consultation method and related equipment |
| CN114117157B (en) * | 2021-11-19 | 2024-04-09 | 招联消费金融股份有限公司 | Session processing method, apparatus, computer device and storage medium |
| CN114047995A (en) * | 2021-11-25 | 2022-02-15 | 中国建设银行股份有限公司 | Method, device and equipment for determining label color and storage medium |
| CN114710471B (en) * | 2022-03-21 | 2024-07-16 | 京东科技信息技术有限公司 | Customer service voice communication method and device based on network, electronic equipment and medium |
| CN115297078B (en) * | 2022-07-06 | 2023-09-01 | 北京北信源软件股份有限公司 | Consultation response method, consultation response device, computer equipment and storage medium |
| CN115396389B (en) * | 2022-07-07 | 2023-09-22 | 深圳市华汇数据服务有限公司 | Emotion energy perception-based Internet of things information technology customer service system |
| CN116112630B (en) * | 2023-04-04 | 2023-06-23 | 成都新希望金融信息有限公司 | Intelligent video face tag switching method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150121305A (en) * | 2014-04-18 | 2015-10-29 | 현철우 | System for consultation service upon online and method for consultation service upon online therefor |
| CN108418981A (en) * | 2018-03-16 | 2018-08-17 | 苏州思必驰信息科技有限公司 | The method and device of dialogue service is provided for client |
| CN110705349A (en) * | 2019-08-26 | 2020-01-17 | 深圳壹账通智能科技有限公司 | Customer satisfaction recognition method, device, terminal and medium based on micro expression |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109015690B (en) * | 2018-08-31 | 2022-01-28 | 中通天鸿（北京）通信科技股份有限公司 | Active interactive dialogue robot system and method |
| CN110472023A (en) * | 2019-07-10 | 2019-11-19 | 深圳追一科技有限公司 | Customer service switching method, device, computer equipment and storage medium |
| CN111460112A (en) * | 2020-03-02 | 2020-07-28 | 深圳壹账通智能科技有限公司 | Online customer service consultation method, device, medium and electronic equipment |
-
2020
- 2020-03-02 CN CN202010136161.9A patent/CN111460112A/en active Pending
-
2021
- 2021-01-08 WO PCT/CN2021/070750 patent/WO2021175007A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150121305A (en) * | 2014-04-18 | 2015-10-29 | 현철우 | System for consultation service upon online and method for consultation service upon online therefor |
| CN108418981A (en) * | 2018-03-16 | 2018-08-17 | 苏州思必驰信息科技有限公司 | The method and device of dialogue service is provided for client |
| CN110705349A (en) * | 2019-08-26 | 2020-01-17 | 深圳壹账通智能科技有限公司 | Customer satisfaction recognition method, device, terminal and medium based on micro expression |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021175007A1 (en) * | 2020-03-02 | 2021-09-10 | 深圳壹账通智能科技有限公司 | Online customer service consultation method and apparatus, medium, and electronic device |
| CN112347236A (en) * | 2020-11-16 | 2021-02-09 | 友谊国际工程咨询股份有限公司 | Intelligent engineering consultation method and system based on AI (Artificial Intelligence) calculated quantity and computer equipment thereof |
| CN112543185A (en) * | 2020-11-23 | 2021-03-23 | 建信金融科技有限责任公司 | Customer service method, device and system |
| CN112929591A (en) * | 2021-01-28 | 2021-06-08 | 联通(辽宁)产业互联网有限公司 | 5G tax video customer service platform and operation method thereof |
| CN113065362A (en) * | 2021-03-19 | 2021-07-02 | 联想(北京)有限公司 | Intelligent reply method and device |
| CN113379229A (en) * | 2021-06-08 | 2021-09-10 | 北京沃东天骏信息技术有限公司 | Resource scheduling method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021175007A1 (en) | 2021-09-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111460112A (en) | Online customer service consultation method, device, medium and electronic equipment | |
| CN106685916B (en) | Intelligent device and method for electronic conference | |
| CN111641514B (en) | Conference intelligence system, method for conference intelligence, and storage medium | |
| CN107680019B (en) | Examination scheme implementation method, device, equipment and storage medium | |
| US11436863B2 (en) | Method and apparatus for outputting data | |
| CN107577807B (en) | Method and device for pushing information | |
| CN109993150B (en) | Method and device for identifying age | |
| CN107609506B (en) | Method and apparatus for generating image | |
| CN112733042B (en) | Recommendation information generation method, related device and computer program product | |
| WO2020000876A1 (en) | Model generating method and device | |
| US11228683B2 (en) | Supporting conversations between customers and customer service agents | |
| CN108268450B (en) | Method and apparatus for generating information | |
| CN112929253A (en) | Virtual image interaction method and device | |
| CN110288683B (en) | Method and device for generating information | |
| WO2022012107A1 (en) | Method and apparatus for displaying plug-in | |
| US11900067B1 (en) | Multi-modal machine learning architectures integrating language models and computer vision systems | |
| CN109710939B (en) | Method and device for determining theme | |
| CN111259698B (en) | Method and device for acquiring image | |
| CN111556096B (en) | Information pushing method, device, medium and electronic equipment | |
| CN110580648A (en) | financial information processing method and device based on artificial intelligence | |
| US10699127B1 (en) | Method and apparatus for adjusting parameter | |
| CN110263743B (en) | Method and device for recognizing images | |
| CN116057915A (en) | System and method for intent messaging | |
| CN112737919A (en) | Instant message sending method and device | |
| CN110166349A (en) | Sending method, device and the electronic equipment of message |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 40033033 Country of ref document: HK |
|
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200728 |