CN110060083A - The personalization method, apparatus and equipment to be serviced such as busy based on machine learning - Google Patents

The personalization method, apparatus and equipment to be serviced such as busy based on machine learning Download PDF

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CN110060083A
CN110060083A CN201910054510.XA CN201910054510A CN110060083A CN 110060083 A CN110060083 A CN 110060083A CN 201910054510 A CN201910054510 A CN 201910054510A CN 110060083 A CN110060083 A CN 110060083A
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busy
waiting
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demand
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王雅芳
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Advanced New Technologies Co Ltd
Advantageous New Technologies Co Ltd
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Abstract

This disclosure relates to a kind of personalization method to be serviced such as busy based on machine learning, including user demand is analyzed by human-computer dialogue；In the case where can determine that the user demand or the human-computer dialogue beyond limitation based on the analysis, user being assigned to corresponding manual service and enters busy waiting；And in the busy waiting time, using personalized busy etc. to be serviced to provide for user based on the model of machine learning, wherein personalization is busy, to wait service include providing to be adapted to the waiting of the user and divert oneself or further analyze the user demand based on the model of machine learning using described.Present disclosure also relates to corresponding device, equipment and computer-readable mediums.

Description

The personalization method, apparatus and equipment to be serviced such as busy based on machine learning
Technical field
This application involves artificial intelligence, more particularly to the intelligent customer service based on machine learning.
Background technique
Internet+epoch, technology and economic form constantly develop progress.So-called " internet+" is exactly " internet+each A traditional industries ".It is added however, this does not imply that the two is simple, but utilizes Information and Communication Technology and internet platform, allowed Internet and traditional industries carry out depth integration, create new developing ecology.Internet+represent new social pattern, and And expedite the emergence of out it is a series of it is new be related to the internet platform of various services, such as consumption, finance, supply chain, credit, crowd raise, are silver-colored Row, traffic, communication, government affairs, tourism, medical treatment, education, information etc..The platform to emerge one after another needs a large amount of customer service to help Solve the problems, such as that user proposes various.The scale of platform is bigger, function/service is more complicated, user propose the problem of and need To the local just more diversified of help and complicate.This brings great pressure to platform operation quotient and attendant.People The training cost of work customer service is very high, and to turn out that cope with user's artificial customer service difficulty of all the problems very big.
A kind of method of industry is that artificial customer service is divided into several technical ability groups according to specific business to be trained respectively at present Instruction, wherein the problem of each technical ability group only answers certain kinds.In service request (for example, phone, the chat etc.) for being connected to user, First pass through customer service robot and user carry out it is one or many exchange, the information provided according to user classify to user/point Client is pointed to suitable artificial customer service technical ability group to be exchanged with user by artificial customer service and to be answered/solve by stream The problem of user.For example, some customer service robot requirement users first describe problem, then by crawl user's description language Keyword guesses user demand and client is required to confirm.After client provides ack/nack/supplemental information, customer service robot Inquire client etc., further optionally so that user is finally assigned to suitable artificial customer service technical ability group.
However, since demand for services amount is huge, seat/artificial customer service the is no-trunk feelings for needing user to wait often are encountered Shape.For example, in phone scene fixed music may be played for user's iterative cycles, or even can also hang up electricity because of time-out Words, cause user because not only to waste the time again final do not obtain experiencing due to service it is very poor.And in chat scenario, it is also possible to meet The problems such as reacting slow to customer service, go offline.
Therefore, there is a need in the art for the technologies that can improve busy waiting.
Summary of the invention
The disclosure relates in one aspect to a kind of personalization method to be serviced such as busy based on machine learning, including passes through User demand is analyzed in human-computer dialogue；It can determine that the case where user demand or the human-computer dialogue are beyond limitation based on the analysis Under, user is assigned to corresponding manual service and enters busy waiting；And in the busy waiting time, using based on engineering The model of habit is personalized busy etc. to be serviced to provide for user, and the wherein busy waiting service of the personalization includes being based on using this The model of machine learning is adapted to the waiting pastime of the user or further analyzes the user demand to provide.
And nonlimiting examples exemplary according to one, provide the individual character using based on the model of machine learning for user Change the busy confidence level for waiting service including determining the user demand；If the confidence level of identified user demand is equal to or height In threshold value, then the waiting pastime for being adapted to user is provided based on the model of machine learning using this；Else if identified The confidence level of user demand is lower than threshold value, then further analyzes the user demand by human-computer dialogue.
The nonlimiting examples according to another exemplary, if the confidence level of identified user demand is lower than threshold value, This method further comprises that corresponding manual service is reassigned for user based on identified user demand.
The nonlimiting examples according to another exemplary determine that the confidence level of the user demand includes with the next item down or more Or any combination thereof: determine user to the descriptions of problems/requirements whether sufficiently clear；It determines and carries out accurate manual service Whether required information is abundant enough；Determine whether assigned manual service is accurate enough；And/or determine that this refers to user It sends to corresponding manual service and enters busy whether wait because the human-computer dialogue is beyond limitation.
The nonlimiting examples according to another exemplary, are provided based on the model of machine learning using this and are adapted to use The waiting pastime at family further comprises carrying out portrait modeling for the user and/or the demand；And based on the portrait model come The waiting pastime for being adapted to the user is provided.
The nonlimiting examples according to further exemplary, are modeled based on the portrait and are adapted to the user's to provide Pastime is waited to include: recommendation or play the music adaptable with the mood of the user；Recommend or play the hobby phase with the user The music of adaptation；And/or chatting service is provided for the user by chat robots.
The nonlimiting examples according to further exemplary, this method further comprise based on being adapted to the user's Pastime is waited further to improve the portrait modeling to the user.
The nonlimiting examples according to another exemplary, if the confidence level of identified user demand is equal to or higher than Threshold value, this method further comprise determining whether the user demand is urgent；And if the user demand is urgent, further extremely Lack the mood based on the user to improve the busy waiting priority of user.
According to another exemplary, nonlimiting examples use the mould based on machine learning in the busy waiting time Type come for user provide the personalized scheduled time to be serviced for further comprising determining the busy waiting such as busy whether long enough； And if the scheduled time long enough of the busy waiting, use the model based on machine learning to provide the personalization for user It is busy etc. to be serviced.
The nonlimiting examples according to another exemplary, this method further comprise being guessed according to the action trail of user User demand；And guessed user demand is based at least partially on to carry out the human-computer dialogue.
The other aspects of the disclosure further relate to corresponding device, equipment and computer-readable medium.
Detailed description of the invention
Fig. 1 shows the flow chart of the customer service worksheet processing method of the prior art.
Figure 1A shows the example scenario during the customer service worksheet processing of the prior art.
Figure 1B shows the example scenario during the customer service worksheet processing of the prior art.
Fig. 2 shows the flow charts of customer service intelligence worksheet processing method according to the one side of the disclosure.
Fig. 2A is shown according to the disclosure on the one hand exemplary realization to the busy waiting of personalization of Fig. 2.
On the one hand Fig. 3 is shown to be realized the another exemplary of the busy waiting of personalization of Fig. 2 according to the disclosure.
Fig. 3 A, Fig. 3 B and Fig. 3 C show the busy waiting scene of personalization accoding to exemplary embodiment.
Fig. 4 shows the busy frame for waiting recommendation apparatus of the personalization based on machine learning according to the one side of the disclosure Figure.
Specific embodiment
Technical solution for a better understanding of the present invention with reference to the accompanying drawing retouches embodiments herein in detail It states.
It will be appreciated that described embodiment is only a part of the embodiment of the application, instead of all the embodiments Enumerate.Based on embodiment described in the disclosure, those of ordinary skill in the art create the feelings of habit labour not paying Every other change case obtained belongs to the protection scope of the application under condition.
In existing customer service system, fixed music or busy is often played in the situation for staying in phone such as busy repeatedly Sound, or the often simple waiting in the situation of chat.System does not make full use of this period to obtain useful letter It ceases and/or takes strategy to improve user satisfaction.
For example, Fig. 1 shows the flow chart of the customer service worksheet processing method 100 of the prior art.In frame 102, from user receive to The request that customer service system is initiated.For example, user can dial service calls, or initiate the chat etc. with online customer service.In frame 104, customer service robot guides user's exposition need.For example, customer service robot can be putd question to directly to user in the situation of phone To obtain the answer of user's Yes/No, or client can be guided to select to go for taking in services menu by key etc. The problem of business major class.And in the situation of chat, customer service robot can be putd question to directly to user to obtain returning for user's Yes/No It answers, or user can be guided directly to describe problem, or options for user is provided and clicks the ready-made problem major class etc. of selection.Example Such as, in an exemplary and non-limiting shopping online scene, customer service robot can inquire that user is intended to it is to be understood that commodity are believed It ceases, requires goods return and replacement, is still other.In another exemplary and non-limiting handling bank business scene, customer service robot Can inquire that user is individual client or corporate client, be intended to open an account, handle the deposit or the withdrawal, purchase finance product or its He.The above is only the example of customer service robot guidance user's exposition need, the disclosure is not limited fixed in this regard.
Figure 1A shows the example scenario during the customer service worksheet processing of the prior art.(A) of Figure 1A shows such as net Shop shopping scene, wherein customer service robot inquiry user wants to know about merchandise news or goods return and replacement etc., and then user proposes The hope of goods return and replacement.(B) of Figure 1A, which is shown, for example handles telephone banking scene, and wherein customer service robot inquiry user thinks It opens an account, handle the deposit or the withdrawal, purchase finance product or other, then user indicates oneself by way of key Demand.(C) of Figure 1A shows such as APP scene, and wherein customer service robot guidance user expresses the demand of oneself, then user Inform the problem of oneself cannot log in original account because changing cell-phone number.
According to roughly determining customer problem major class, in frame 106, it is further right that customer service robot is carried out with user Words, for example to refine user demand and/or obtain further useful information.For example, in the situation of phone, customer service robot It can provide common several problems to select for user.
Figure 1B shows the example scenario during the customer service worksheet processing of the prior art.For example, (A) with Figure 1A is corresponding Ground, (A) of Figure 1B are shown for example in aforementioned shopping online scene, when determining that user has selected goods return and replacement to service, can be ask Ask whether user is service below needing, such as: (i) is only intended to understand goods return and replacement policy；(ii) it needs to return goods；(iii) it needs to change Goods；Or (iv) is other, then user expresses the intention to be returned goods.For another example, accordingly with (B) of Figure 1A, (B) of Figure 1B shows Go out and handled in telephone banking scene aforementioned, when determining that user has selected individual to open an account, can ask the user whether to need It to service below, such as (i) open up RMB current account；(ii) foreign currency current account is opened up；(iii) it is regular to open up RMB Account；(iv) foreign currency fixed account is opened up；Or it is (v) other.In such scene, bootable client continues through key etc. and exists Required specific service content etc. is further selected in next stage services menu.For example, in this instance, user's selection opens up the people Coin current account.In the non-limiting scene of another exemplary, for example, accordingly with (C) of Figure 1A, (C) of Figure 1B shows Gone out customer service robot can continue guide user problem is described, such as " you are that can not log in original account now? " and User may directly make voice or typewriting is replied.Customer service robot can for example, by crawl user reply in keyword, Such as cell-phone number, login, loss etc. are come details the problem of further determining that user.The above is only customer service robots and user to carry out The example further talked with, the disclosure are not limited fixed in this regard.
In frame 108, system determines whether that user demand or customer service can be analyzed according to collected user information Whether robot and user session have exceeded limitation.Determining whether can be based on scheduled time threshold, Huo Zheke beyond limitation With the wheel number (for example, at most three-wheel) etc. based on customer service robot and user session, or any combination thereof.If according to frame The information obtained in 104 and 106 has been able to analyze user demand or customer service robot is alreadyd exceed with user session Such as threshold time but still not can determine that the specific requirements of user, then method 100 goes to frame 110.For example, Figure 1A (A) and In the scene of (B) of (A) of Figure 1B and (B) of Figure 1A and Figure 1B, system may be had been able to from the reply that user provides Accurately determine user demand.Then method 100 may be advanced to frame 110.
On the other hand, if not can determine that user is intended to and customer service robot and user session not yet exceed limitation still, Such as in aforementioned on-line shop's shopping scene, user has selected (iv) other, or in aforementioned handling bank business scene, Yong Huxuan Select that (v) is other, this shows intelligent robot not yet and can be recognized accurately user's intention, then method 100 returns to frame 106, herein Customer service robot and user carry out further dialogue to attempt to determine that user is intended to.For another example, in (C) of Figure 1A and Figure 1B (C) in APP scene, user still without sufficiently clear gives expression to pair of his/her intention but customer service robot and user Words are not yet beyond limitation, then method 100 returns to frame 106.
In frame 110, it is intended to based on identified user and user is assigned to properly by other associated information, system Manual service technical ability group further to be talked with by artificial customer service with user and User Demand.For example, when in frame 108 It determines in the case where having analyzed user demand, can be assigned to user suitably based on the user demand analyzed Manual service technical ability group.For example, user can be assigned to by system to be responsible for moving back in the scene of (A) of (A) and Figure 1B of Figure 1A The artificial customer service technical ability group of goods.For another example, in the scene of (B) of Figure 1A and (B) of Figure 1B, user can be assigned to open by system If the artificial customer service technical ability group of RMB current account.According to another example, when in frame 108 because of customer service robot and user couple Words have exceeded limitation (for example, customer service robot and user session have been more than scheduled time threshold or customer service robot and have used The wheel number of family dialogue has been more than scheduled wheel number threshold value etc., or any combination thereof) and in the case where entering frame 110, system User can be assigned to and be responsible for difficult miscellaneous artificial customer service technical ability group.
In frame 114, service is provided for user by the artificial customer service in the suitable manual service technical ability group assigned.So And the customer service of nobody work is idle in manual service technical ability group if appropriate, then between frame 110 and 114, it is also possible to deposit In busy waiting frame 112.In busy waiting frame 112, user can only passively wait the service of artificial customer service to be obtained.System is extremely Mostly user plays music or busy tone, without making full use of obtain useful information this period, or takes tactful increase User satisfaction.Even in the system having, in the case where the busy waiting time is overtime, system can also terminate automatically and user Session.For example, being more than scheduled time threshold value when the busy waiting time in the scene of phone customer service, system will be automatically hung up Phone.It is excessively poor experience for a user by on-hook in the case where having answered many problems and having waited for a good while again.? In another exemplary situation, user is assigned to the artificial of inaccuracy since customer service robot has exceeded limitation with user session Customer service technical ability group often finds that user should be assigned in waiting and after the artificial customer service inquiry in artificial customer service technical ability group Another artificial customer service technical ability group.To again user needs the waiting into next round.This is inefficiency and user experience is bad 's.
Fig. 2 shows the flow charts of customer service intelligence worksheet processing method 200 according to the one side of the disclosure.With 100 class of method As, in frame 202, the request initiated to customer service system is received from user.With the frame 102 of method 100 the difference is that, frame 202 can also guess user demand according to the characterization factor (for example, action trail etc. of user) of user.For example, user can dial Service calls are beaten, or initiate the chat etc. with online customer service.According to an exemplary and non-limiting embodiment, for example, user Action trail show that user's other day has purchased some article, and according to logistics information, which is not yet sent to.In this feelings In shape, system can guess that user may want to inquire when the article can be sent to.For another example, the action trail of user shows user Other day has purchased some article, and according to logistics information, which has been sent to.In this case, system can guess that user can It can want goods return and replacement.For another example, the action trail of user shows user and repeatedly browses certain class article within the next few days but not yet place an order.? In this situation, system can guess that user may wish to inquire details of the article etc..It is non-limiting according to another exemplary Embodiment, frame 202 can be omitted.
In frame 204, customer service robot guides user's exposition need.For example, customer service robot can be straight in the situation of phone It connects to user and puts question to obtain the answer of user's Yes/No, or user can be guided to select in services menu by key etc. The problem of going for service major class.And in the situation of chat, customer service robot can be putd question to directly to user to obtain use The answer of family Yes/No, or user can be guided directly to describe problem, or provide options for user and click the ready-made problem of selection Major class etc..For example, customer service robot can inquire that user is intended in an exemplary and non-limiting shopping online scene Solution merchandise news requires goods return and replacement, is still other.In another exemplary and non-limiting handling bank business scene, visitor Taking robot can inquire that user is individual client or corporate client, be intended to open an account, handle the deposit or the withdrawal, purchase financing production Product or other.In the case where frame 202 guesses user demand, also user can be guided based on the user demand guessed Exposition need.For example, being possible in the situation for wanting to inquire certain special article guessing user, it can preferentially inquire that client is The no favor information etc. for wanting to know about the particular commodity and/or the particular commodity.For another example, it is possible to want to move back guessing user In the situation exchanged goods, it can preferentially ask the user whether to have the cargo dissatisfied etc..The above is only customer service robots to draw The example of user's exposition need is led, the disclosure is not limited fixed in this regard.
According to roughly determining customer problem major class, in frame 206, it is further right that customer service robot is carried out with user Words, for example to refine user demand and/or obtain further useful information.For example, in the situation of phone, customer service robot It can provide common several problems to select for user.For example, in aforementioned shopping online scene, when determining that user selected to replace When goods services, it can ask the user whether to be service below needing, such as: (i) is only intended to understand goods return and replacement policy；(ii) it needs to move back Goods；(iii) it needs to exchange goods；Or (iv) is other.For another example, in aforementioned handling bank business scene, when determining that user selects When individual opens an account, following service can be inquired whether the user needs to, such as (i) opens up RMB current account；(ii) foreign currency is opened up Current account；(iii) RMB fixed account is opened up；(iv) foreign currency fixed account is opened up；Or it is (v) other.In such scene In, bootable client continues through key etc. and further selects required specific service content etc. in next stage services menu. In the non-limiting scene of another exemplary, customer service robot can directly guide user to describe problem, such as " you are good, What, which be may I ask, can help you? " and user can directly voice or typewriting reply: " cell-phone number is lost, and is then mended again Do, then log in again APP just with it is pervious what if be not an account? ".Customer service robot can be used for example, by crawl Keyword, such as account, login, loss in the reply of family etc. are come details the problem of determining user.The above is only customer service robots The example further talked with user, the disclosure are not limited fixed in this regard.It is non-limiting according to another exemplary Embodiment, frame 204 and 206 can also be merged, as long as being able to carry out the dialogue of customer service robot and user to guide user's table Up to demand or provide relevant information.
In frame 208, system determines whether that user demand or customer service can be analyzed according to collected user information Whether robot and user session have exceeded limitation.Determining whether can be based on scheduled time threshold, Huo Zheke beyond limitation With the wheel number etc. based on customer service robot and user session, or any combination thereof.If according to what is obtained in frame 204 and 206 Information, have been able to analyze user demand or customer service robot and user session alreadys exceed such as threshold time but still The specific requirements of user are not can determine that, then method 200 goes to frame 210.If cannot still analyze user demand and service machine Device people and user session not yet exceed limitation, then method 200 returns to frame 206, carry out in this customer service robot and user further Dialogue with attempt determine user be intended to.It is clicked in the situation for selecting ready-made problem major class for example providing options for user, If user continuously selects " other " option, this, which shows intelligent robot not yet, can be recognized accurately user's intention.For another example, in visitor It takes in the situation that robot can be putd question to directly to user to obtain the answer of user's Yes/No, user continuously provides negative acknowledge, then User's intention can be recognized accurately by showing intelligent robot not yet.For another example, in the situation that guidance user directly describes problem, such as Fruit extracts effective keyword in cannot describing from user, then shows that intelligent robot fails that user's intention is recognized accurately.
In frame 210, it is intended to based on identified user and user is assigned to by other associated information, system automatically Suitable manual service technical ability group with user further talk with simultaneously User Demand by artificial customer service.For example, working as In the case that the determination of frame 108 has analyzed user demand, user can be assigned to based on the user demand analyzed Suitable manual service technical ability group.According to another example, when in frame 108 because customer service robot and user session have exceeded limitation (for example, customer service robot and user session have been more than the wheel number of scheduled time threshold or customer service robot and user session It has been more than scheduled wheel number threshold value etc., or any combination thereof) and in the case where entering frame 110, system can refer to user It sends to and is responsible for difficult miscellaneous artificial customer service technical ability group.
In frame 214, service is provided for user by the artificial customer service in the suitable manual service technical ability group assigned.Method 200 be in place of another difference of method 100, the customer service of nobody work is empty in manual service technical ability group if appropriate Spare time, then the busy waiting frame between frame 210 and 214 is personalized busy waiting frame 212.In personalized busy waiting frame 212 In, system, which can make full use of, to be continued to obtain useful information this period, or strategy is taken to increase user satisfaction.According to one Exemplary embodiment, the customer service of nobody work is idle in manual service technical ability group if appropriate, and system prediction user The service of artificial customer service cannot be obtained within threshold time (for example, 2 minutes), then system enters personalized busy waiting frame 212；Otherwise, if prediction user can obtain the service of artificial customer service within threshold time, directly user is allowed to continue waiting for Until obtaining artificial customer service.The nonlimiting examples according to another exemplary, system can also be directly entered personalized numerous Busy waiting frame 212.The personalized busy specific embodiment for waiting frame 212 is described below in association with Fig. 2A and 3 etc..
Fig. 2A is shown according to the disclosure on the one hand to the exemplary realization 200A of the busy waiting frame 212 of the personalization of Fig. 2. And nonlimiting examples exemplary according to one, method 200A may include in frame 202A, can for user and/or customer problem/ Demand carries out portrait modeling (profiling).For example, to user carry out portrait modeling may include to user carry out age bracket, The classification of gender, occupation, residence, educational background, business history etc..For another example, portrait modeling can be carried out to user demand.Such as to It may include carrying out the classification of urgency level or business demand that family demand, which carries out portrait modeling,.For example, urgency level classification can be divided into It is high, medium and low or be divided into finer 0-10 grade etc..The disclosure is not limited fixed in this regard.The classification of business demand can be by tool Body business is divided into such as pre-sales, mid-sales, after sale.The disclosure is also not limited in this regard.
After frame 202A carries out portrait modeling for user and/or customer problem/demand, method 200A proceeds to frame 204A.In frame 204A, system determines whether the confidence level of user demand is sufficiently high.For example, determining the confidence level of user demand is It is no it is sufficiently high may include determining user to the descriptions of problems/requirements whether sufficiently clear and/or information whether enough, really Whether the artificial customer service technical ability group classification of the worksheet processing model output before determining accurately waits enough and/or any combination thereof.Determination is set Reliability can be based at least partially on the portrait modeling carried out for user and/or customer problem/demand.If confidence level is enough It is high, then it is assumed that information needed for description sufficiently clear and/or the accurate manual service of progress of the user to problems/requirements is Through enough.Otherwise it is assumed that user is not clear enough to the description of problems/requirements and/or information is not enough.It is exemplary according to one and Nonlimiting examples, for example, when in the frame 208 of Fig. 2 be because customer service robot and user session have exceeded limitation and in In the situation that only customer service robot further talks with and assigned user with user, it may be determined that artificial customer service technical ability group classification Confidence level it is not high enough.For example, in this case, it is believed that user is not clear enough to the descriptions of problems/requirements and/or information not It is enough abundant.
If system judges that user is sufficient to the description sufficiently clear of problems/requirements and/or information in frame 204A When enough, then method 200A may be advanced to frame 206A.According to an alternative, frame 202A can also be in the "Yes" branch of frame 204A It is executed before frame 206A later.In the case, determine that confidence level can be not based on for user and/or customer problem/need The portrait of progress is asked to model.For example, in the case, determine that confidence level can be based at least partially on is in the frame 208 of Fig. 2 It is no to go to frame 210 because customer service robot and user session are beyond limitation and assigned automatically.
In frame 206A, system can recommend the waiting pastime for being adapted to user in busy waiting time based on the portrait of user. For example, system can provide intelligent sound box service.Intelligent sound box service for example may include playing recommend music, provide music for user Program request provides and chats the service such as robot.Music recommends to draw a portrait based on user and the Music on Demand of the user is believed in history Breath etc., is realized using above-described proposed algorithm and model etc..When there is the artificial customer service free time, the waiting of end block 206A Pastime (for example, chat of music and/or chat robot), then goes to the frame 214 of the method 200 of Fig. 2.
However, when in frame 204A, when system judges that user is not clear enough to the description of problems/requirements and/or information is inadequate, Method 200A may be advanced to frame 208A.Needs further inquiry user can be determined in frame 208A, method 200A.Show according to one Example property and nonlimiting examples, when stopping customer service robot and user due to customer service robot and user session have exceeded limitation Further dialogue and user is assigned and is determined in not high enough etc. the situation of the confidence level of artificial customer service technical ability group classification, it can Think that user is not clear enough to the description of problems/requirements and/or information is not enough.In the case, for example, although will use Family is assigned to specific artificial customer service technical ability group (for example, being responsible for difficult miscellaneous artificial customer service technical ability group or another inaccuracy Artificial customer service technical ability group), but if there is the more time to user proposes that further problem and/or request user are provided into one The information of step then probably enables a customer to be assigned to more suitable artificial customer service technical ability group, to obtain more acurrate Service and/or shorten needed for the manual service time.Therefore, method 200A then returnes to the frame 206 of the method 200 of Fig. 2, from And customer service robot is further talked with user, for example to obtain further useful information.To which system can be into And turn again to frame 208, with determined whether to analyze according to the user information being further collected into user demand or Whether customer service robot and user session have exceeded the automaticmanual customer service worksheet processing for limiting and correspondingly carrying out again.According to another Alternative embodiment, can also further talk in customer service robot and user with obtain after further useful information without Worksheet processing again, but subsequent manual service is carried out using useful information obtained.It, can according to another replacement new embodiment Further talk in customer service robot and user and carries out selectively worksheet processing after further useful information to obtain.For example, System can determine whether the accuracy of worksheet processing for the first time, and and if only if just worksheet processing again when worksheet processing departure degree is excessive for the first time.For example, if User is assigned to close manual service technical ability group by worksheet processing for the first time, then can not carry out worksheet processing again；And if sent for the first time Singly fail for user to be assigned to close manual service technical ability group, then can carry out worksheet processing again.
Further inquiry user is needed determining, the case where confidence level for example to improve the classification of artificial customer service technical ability group Under, by proposing that further problem and/or request user provide further information to user using busy waiting period, just It can be effectively by customers assignment to more suitable artificial customer service technical ability group, to more accurately be serviced and/or be shortened institute Need the manual service time.
On the one hand Fig. 3 is shown to be realized the busy another exemplary for waiting frame 212 of the personalization of Fig. 2 according to the disclosure 300.And nonlimiting examples exemplary according to one, method 300 may include that can ask for user and/or user in frame 302 Topic/demand carries out portrait modeling (profiling).For example, carrying out portrait modeling to user may include carrying out the age to user The classification of section, gender, occupation, residence, educational background, business history etc..For another example, portrait modeling can be carried out to user demand.Such as Carrying out portrait modeling to user demand may include carrying out the classification of urgency level or business demand.For example, urgency level classification can It is divided into high, medium and low or is divided into finer 0-10 grade etc..The disclosure is not limited fixed in this regard.The classification of business demand can Be divided by specific business such as pre-sales, mid-sales, after sale.The disclosure is also not limited in this regard.
After frame 302 carries out portrait modeling for user and/or customer problem/demand, method 300 proceeds to frame 304.In frame 304, system determines whether the confidence level of user demand is sufficiently high.For example, determine user demand confidence level whether It is sufficiently high to may include determining user whether enough whether sufficiently clear and/or information, determine to the descriptions of problems/requirements Whether the artificial customer service technical ability group classification of worksheet processing model output before accurately waits enough and/or any combination thereof.Determine confidence Degree can be based at least partially on the portrait modeling carried out for user and/or customer problem/demand.If confidence level is enough It is high, then it is assumed that information needed for description sufficiently clear and/or the accurate manual service of progress of the user to problems/requirements is Through enough.Otherwise it is assumed that user is not clear enough to the description of problems/requirements and/or information is not enough.It is exemplary according to one and Nonlimiting examples, for example, when in the frame 208 of Fig. 2 be because customer service robot and user session have exceeded limitation and in In the situation that only customer service robot further talks with and assigned user with user, it may be determined that artificial customer service technical ability group classification Confidence level it is not high enough.For example, in this case, it is believed that user is not clear enough to the descriptions of problems/requirements and/or information not It is enough abundant.
If system judges that user is sufficient to the description sufficiently clear of problems/requirements and/or information in frame 304 When enough, then method 300 may be advanced to frame 306.According to an alternative, frame 302 can also be after the "Yes" branch of frame 304 It is executed before frame 306.In the case, it determines that confidence level can be not based on to carry out for user and/or customer problem/demand Portrait modeling.For example, in the case, determining that whether confidence level can be based at least partially in the frame 208 of Fig. 2 because of visitor It takes robot and goes to frame 210 beyond limitation with user session and assigned automatically.
In frame 306, whether the problem of determining user is urgent.If urgent the problem of frame 306 determines user, enter frame 308.In frame 308, the portrait carried out for user and/or customer problem/demand can be based at least partially on and modeled to analyze use The mood at family.It is exemplary according to one and nonlimiting examples, the mood for analyzing user can be realized by machine learning.Example Such as, it can establish mood analysis model, which can be based on such as deep learning.The training of the mood analysis model can be Have supervision, it is unsupervised or semi-supervised.And nonlimiting examples exemplary according to one, can be used supervised learning To train the mood analysis model.This can be realized by providing the affection data collection through marking.It is exemplary according to one rather than Limited embodiment, frame 308 can be incorporated into frame 302 or frame 302 can be incorporated into frame 308.Shown according to another Example property and nonlimiting examples, frame 308 can also execute after frame 304 and before frame 306.The disclosure in this regard not by It limits.
According to an exemplary embodiment, which can word based on such as user, punctuate, word speed, language It adjusts etc. and the mood of user is analyzed.For example, there is more fierce word, exclamation mark, faster word speed, higher Intonation etc. can be shown that user emotion excitement.According to a further exemplary embodiment, which can be for example based on the use The mood of the user is analyzed in the history mood of the system at family.For example, if according to the user going through in the system History mood, the general mood of the user is tranquil, has a friendly attitude, then can be shown that a possibility that user emotion is tranquil is higher.According to another Exemplary embodiment, the mood analysis model can for example based on the user the system historical behavior come the mood to the user It is analyzed.For example, if the user can be shown that user is emotional discontented once because of similar situation complaint or complaint etc..Feelings Thread analysis model can also carry out mood analysis based on any combination of above-mentioned items.After trained, which for example can be with Identify the various moods of user, such as whether eager user is, nervous, angry, irritated, discontented etc..
After analyzing the mood of user in frame 308, method 300 determines whether user emotion is negative in frame 310.Example Such as, negative emotions may include but be not limited to angry, nervous, irritated, discontented, eager etc..When frame 310 determine user emotion be negative Face, method 300 correspondingly improves the busy waiting priority of user in mood of the frame 312 based on user and portrait etc..According to One is exemplary and nonlimiting examples, improve user busy waiting priority can for example including by user in waiting list (for example, predetermined ranking or in advance predetermined percentage etc. in advance) in advance.The nonlimiting examples according to another exemplary, It is higher that the busy waiting priority of raising user may also include experience level in the corresponding artificial customer service technical ability group of for example preferential appointment Artificial customer service service the user.The nonlimiting examples according to another exemplary, the busy waiting for improving user are preferential Grade may also include such as providing discount coupon or small gift to attempt to alleviate user's negative emotions.The disclosure is unrestricted in this regard It is fixed.Then method 300 goes to frame 314.
When determining that user emotion is not negative (for example, being positive or neutral) in frame 310, method 300 goes to frame 314.In frame 314, the portrait based on user, system correspondingly recommends the waiting pastime for being adapted to user.It is exemplary according to one and Nonlimiting examples, system can also recommend the waiting pastime for being adapted to user based on the mood of the user analyzed.Example Such as, system can play the recommendation music being adapted with the mood of user automatically.The adaptable recommendation music with the mood of user It can be determined based on the Music on Demand of the user in history, user's portrait, user's current emotional.For example, proposed algorithm can be used Collaborative filtering.For example, the collaborative filtering based on user can find user to sound by the historical behavior data of user The attitude (for example, preference) of happy (or other contents), and these attitudes can be measured and be given a mark.According to different user to phase The attitude of same music (or other contents) calculates the relationship between user.Between the user for having identical attitude (for example, preference) Carry out the recommendation of music (or other contents).For another example, the collaborative filtering based on content then can be by calculating different user pair The scoring of different content (for example, music) is to obtain the relationship between content.User is carried out in similar based on the relationship between content Hold the recommendation of (for example, music).Scoring can represent user to the attitude (for example, preference) of content.Based on collaborative filtering Recommendation may include the collaborative filtering for example based on memory and the collaborative filtering based on model.Collaborative filtering based on model can be adopted With matrix decomposition (Matrix Factorization) technology.For example, matrix decomposition technology can have m user, n content with And sparse rating matrix R (R ∈ Rm*n) recommendation task in learn user-feature matrix U out and characteristic-article matrix V so that R=UV (that is, the UV of matrix R is decomposed), so as to predict the scoring lacked in sparse rating matrix R.Collaboration based on model Deep learning model, such as the Wide&Deep model of Google etc. can also be used in filtering, which passes through Wide model and Deep mould The joint training of type can be remembered (that is, from the correlation found between interior perhaps feature in data is lasted) and extensive simultaneously The ability of (i.e. the transmitting of correlation, to find that seldom in lasting data or even without appearance new feature combines).? System played recommendation music automatically before/after/period, user can also voluntarily request music at any time.System continues to remember in turn It records and the program request for accumulating the user is recorded with the training and update for recommended models.According to another exemplary embodiment, may be used Chatting service etc. is provided based on the mood of the portrait of user and/or user by chat robots.When there is the artificial customer service free time, The recommendation of end block 314 is diverted oneself, and the frame 214 of the method 200 of Fig. 2 is then gone to.
On the other hand, if it is not urgent the problem of frame 306 determines user, enter frame 312.In frame 312, system can be Busy waiting time recommends the waiting pastime for being adapted to user.For example, system can provide intelligent sound box service.Intelligent sound box service It such as may include playing recommend music, Music on Demand is provided, provides and chats the service such as robot for user.Music recommendation can be based on User's portrait and in history the Music on Demand information etc. of the user, using above-described proposed algorithm and model etc. come real It is existing.When there is the artificial customer service free time, the music of end block 312 and/or the chat for chatting robot then go to Fig. 2's The frame 214 of method 200.
However, when in frame 304, when system judges that user is not clear enough to the description of problems/requirements and/or information is inadequate, Method 300 may be advanced to frame 316.In frame 316, method 300 can determine needs further inquiry user.It is exemplary according to one And nonlimiting examples, when stopping customer service robot and user into one due to customer service robot and user session have exceeded limitation Step dialogue is simultaneously assigned user and is determined in not high enough etc. the situation of the confidence level of artificial customer service technical ability group classification, it is believed that User is not clear enough to the description of problems/requirements and/or information is not enough.In the case, for example, although referring to user Specific artificial customer service technical ability group is sent to (for example, being responsible for the artificial of difficult miscellaneous artificial customer service technical ability group or another inaccuracy Customer service technical ability group), but if there is the more time is further to user's proposition further problem and/or request user offer Information then probably enables a customer to be assigned to more suitable artificial customer service technical ability group, to more accurately be taken The manual service time needed for business and/or shortening.Therefore, method 300 then returnes to the frame 206 of the method 200 of Fig. 2, thus objective It takes robot and is further talked with user, for example to obtain further useful information.To which system can in turn again It is secondary to return to frame 208, to be determined whether that user demand or customer service can be analyzed according to the user information being further collected into Whether robot and user session have exceeded the automaticmanual customer service worksheet processing for limiting and correspondingly carrying out again.According to another replacement Property embodiment, can also further talk in customer service robot and user to obtain after further useful information without again Worksheet processing, but subsequent manual service is carried out using useful information obtained.It, can be in visitor according to another replacement new embodiment It takes robot and user further talks with to obtain further useful information and carry out selectively worksheet processing later.For example, system It can determine whether the accuracy of worksheet processing for the first time, and and if only if just worksheet processing again when worksheet processing departure degree is excessive for the first time.For example, if for the first time User is assigned to close manual service technical ability group by worksheet processing, then can not carry out worksheet processing again；And if worksheet processing is not for the first time User can be assigned to close manual service technical ability group, then can carry out worksheet processing again.
Further inquiry user is needed determining, the case where confidence level for example to improve the classification of artificial customer service technical ability group Under, by proposing that further problem and/or request user provide further information to user using busy waiting period, just It can be effectively by customers assignment to more suitable artificial customer service technical ability group, to more accurately be serviced and/or be shortened institute Need the manual service time.
Fig. 3 A, Fig. 3 B and Fig. 3 C show the busy waiting scene of personalization accoding to exemplary embodiment.
The scene of Fig. 3 A can be the scene of such as on-line shop's shopping.Information, history, behavior based on user etc., system is true The elderly men that user A is one's mid-60s is determined, has lived in certain county-level city, received elementary education, it is determined that user A is to purchase The very dissatisfied desired return of goods of commodity are simultaneously complained.System can determine that the urgency level of the business is for example based on the scheme of the disclosure Height, and having intense feelings for user is analyzed, it is very discontented.At this point, system can mood and portrait based on user A, correspondingly mention The busy waiting priority of high user.For example, improving the busy waiting priority of user can include: by user in waiting list (for example, predetermined ranking or in advance predetermined percentage etc. in advance) in advance, it is preferential to assign experience in corresponding artificial customer service technical ability group Higher ranked artificial customer service come service the user and/or provide discount coupon or small gift etc. with attempt alleviate user's negative emotions Deng or any combination thereof.Later, system can mood and portrait based on user A, facilitate use of releiving to recommend or directly play The classical old song of the mood of family A.
The scene of Fig. 3 B can be the scene for for example handling telephone banking.Information, history based on user, behavior Deng, system has determined that user B is 30 years old or so unmarried female foreign enterprise white collar, lives in Guangzhou, it is well-educated etc., it determines The demand of user B is to open up foreign currency current account, and be determined that the time is lunch break etc. at that time.System can be based on this public affairs The scheme opened determines that the urgency level of the business is for example as low as medium.Due to exchanging by customer service robot and user B, The demand of user can be determined clearly and without the information for more needing to obtain from user by client machine people, then system It can be modeled based on the portrait to user B, to recommend some suitable music, such as recent hot music etc. for user B, for User's B program request.
The scene of Fig. 3 C can be such as APP scene.Information, history, behavior based on user etc., system has determined user The problem of C is 16 years old or so male student, and user C has substantially been determined is can not to log in original account.System can be based on this public affairs The scheme opened determine the business urgency level be for example in height.In the frame 208 of such as Fig. 2, because of customer service robot and user C Talk time or wheel number exceeded limitation, therefore system is automatically into the stage that user is assigned to manual service technical ability group (for example, frame 210), and the user is assigned to the manual service technical ability group for handling login problem.System enters busy etc. at this time To the stage.However, user C is not very clear to the description of problem, therefore is due in the dialogue of customer service robot and user C System can determine needs further inquiry client.At this time system can go to such as Fig. 2 frame 206 and by customer service robot and user into One step is talked with to obtain more information.
In this example scenario, by the further dialogue of customer service robot and user, more information are got simultaneously More accurate manual service technical ability group has been reassigned for user.Hereafter, optionally, if artificial customer service is still no-trunk, System can be for example based on the portrait to user C, and determination will provide chatting service etc. by chat robots for user C.
Fig. 3 A -3C provides the busy example for waiting scene of personalization accoding to exemplary embodiment.By to user's Music on Demand, the collection and analysis for cutting song and chat information etc., enrich the description that system draws a portrait to user again.These are only The exemplary and non-limiting example for the scene that the disclosure is applicable in, sole purpose are so that those of ordinary skill in the art There can be the visual understanding of perception to the technology of the disclosure, and any restriction not constituted to the disclosure.
Such as institute as it can be seen that the present invention can be extensive although being to describe the present invention in conjunction with customer service worksheet processing scene above User is needed to wait to obtain the scene of service suitable for other.As it is found that be directed to different scenes, can correspondingly design Different intelligent policy recommendations.
Fig. 4 shows the busy waiting recommendation apparatus 400 of personalization based on machine learning according to the one side of the disclosure Block diagram.Equipment 400 may include such as human-computer dialogue module 402, user requirements analysis and intention assessment module 404, intelligence group Single module 406, user and demand portrait modeling module 408, worksheet processing confidence calculations module 410, user emotion analysis module 412, one or more modules in intelligent Service module 414 and intelligent sound box service module 416 etc..
The nonlimiting examples according to exemplary, the executable above frame 202 for combining Fig. 2 of human-computer dialogue module 402, 204, the function of the descriptions such as 206.User requirements analysis and executable above frame 208, the Fig. 3 for combining Fig. 2 of intention assessment module 404 The equal descriptions of frame 306 function.The function of the descriptions such as the executable above frame 210 in conjunction with Fig. 2 of intelligent worksheet processing module 406.User With the executable above function in conjunction with descriptions such as the frames 302 of frame 202A, Fig. 3 of Fig. 2A of demand portrait modeling module 408.Worksheet processing is set The executable above function in conjunction with descriptions such as the frames 304,316 of frame 204A, 208A and Fig. 3 of Fig. 2A of reliability computing module 410. The executable above function in conjunction with descriptions such as the frames 306,308,310 of Fig. 3 of user emotion analysis module 412.Intelligent Service module The 414 executable above functions in conjunction with descriptions such as the frames 312 and 314 of frame 206A, Fig. 3 of Fig. 2A.Intelligent sound box service module 416 The function of the descriptions such as the executable above frame 314 in conjunction with Fig. 3.
As it is found that above-mentioned function division and appointment is merely exemplary and non-limiting.Implement in other replacements In example, the function of at least some frames can be merged and be executed by a module and/or the function of at least some frames can be split To be executed by multiple modules, and can also add/reduce module.
Human-computer dialogue module 402, user requirements analysis and intention assessment module 404, intelligent worksheet processing module 406, Yong Huhe Demand portrait modeling module 408, worksheet processing confidence calculations module 410, user emotion analysis module 412, intelligent Service module One or more modules in 414 and intelligent sound box service module 416 etc. can be realized by various modes.According to One exemplary and non-limiting embodiment, above-mentioned module can be realized by software.In such embodiments, above-mentioned module can quilt It stores in memory and is executed by a processor to realize its corresponding function.
According to other embodiments, above-mentioned module also may be implemented as hardware, such as ASIC, field programmable gate array (FPGA), discrete logic unit etc..Above-mentioned module also may be implemented as software, be such as stored in memory 404 and by Processor 402 is executed to realize corresponding function.Above-mentioned module can also be implemented as the various combinations of hardware and software.These are Within the scope of the present disclosure.
The new busy waiting strategy that the scheme of the disclosure is proposed can both have been enriched to the understanding of customer problem/demand, Worksheet processing accuracy rate can be promoted again.Moreover, the scheme of the disclosure can also promote user satisfaction.
It will be recognized by one of ordinary skill in the art that the beneficial effect of the disclosure is not by any single embodiment Lai all real It is existing.Various combinations, modification and replacement are that those of ordinary skill in the art are illustrated on the basis of the disclosure.
In addition, term "or" is intended to indicate that inclusive "or" and nonexcludability "or".That is, unless otherwise specified or from upper and lower Text can be clearly seen, otherwise phrase " X " use " A " or " B " be intended to indicate that it is any naturally can and arrangement.That is, phrase " X " is adopted Met with " A " or " B " by example any in following instance: X uses A；X uses B；Or X uses both A and B.Belong to " connection " can indicate identical meanings with " coupling ", indicate the electrical connection of two devices.In addition, disclosure and the accompanying claims book Used in the article " one " and " certain " be generally to be understood as indicating " one or more ", unless stated otherwise or can be from upper It is hereinafter apparent from and refers to singular.
Various aspects or feature by by may include several equipment, component, module, and the like system in the form of be in It is existing.It should be understood that and understand, various systems may include optional equipment, component, module etc., and/or can not include in conjunction with attached drawing Armamentarium, component, module for being discussed etc..Also the combination of these methods can be used.
It can be with general in conjunction with various illustrative logicals, logical block, module and the circuit that presently disclosed embodiment describes Processor, digital signal processor (DSP), specific integrated circuit (ASIC), field programmable gate array (FPGA) or it is other can Programmed logic device, discrete door or transistor logic, discrete hardware component or its be designed to carry out function described herein Any combination realize or execute.General processor can be microprocessor, but in alternative, and processor, which can be, appoints What conventional processor, controller, microcontroller or state machine.Processor is also implemented as calculating the combination of equipment, example As DSP and the combination of microprocessor, multi-microprocessor, the one or more microprocessors cooperateed with DSP core or it is any its Its such configuration.In addition, at least one processor may include may act on execute one or more steps described above and/or One or more modules of movement.For example, above can be by processor and being coupled in conjunction with the embodiment of each method description The memory of processor realizes, wherein the processor can be configured to execute any step of aforementioned any method or its is any Combination.
In addition, the method that describes in conjunction with aspect disclosed herein or the step of algorithm and/or movement can be directly hard Implement in part, in the software module executed by processor or in combination of the two.For example, combining each method above The embodiment of description can realize by being stored with the computer-readable medium of computer program code, wherein the computer journey Sequence code executes any step or any combination thereof of aforementioned any method when being executed by processor/computer.
The element of the various aspects described in the whole text in the disclosure is that those of ordinary skill in the art are currently or hereafter known Equivalent scheme in all structures and functionally is clearly included in this by citation, and is intended to be intended to be encompassed by the claims. In addition, any content disclosed herein is all not intended to contribute to the public --- it is no matter such open whether in claim It is explicitly recited in book.

Claims (22)

1. a kind of personalization method to be serviced such as busy based on machine learning characterized by comprising
User demand is analyzed by human-computer dialogue；
In the case where can determine that the user demand or the human-computer dialogue beyond limitation based on the analysis, user is assigned To corresponding manual service and enter busy waiting；And
In the busy waiting time, using personalized busy etc. to be serviced to be provided for user based on the model of machine learning, The wherein busy waiting service of the personalization includes being adapted to the user using described provide based on the model of machine learning Waiting pastime or further analyze the user demand.
2. the method as described in claim 1, which is characterized in that using described to be provided for user based on the model of machine learning Personalized busy waiting service includes:
Determine the confidence level of the user demand；
If the confidence level of identified user demand be equal to or higher than threshold value, using described in based on the model of machine learning come The waiting pastime for being adapted to user is provided；Otherwise
If the confidence level of identified user demand is lower than threshold value,
The user demand is further analyzed by human-computer dialogue.
3. method according to claim 2, which is characterized in that if the confidence level of identified user demand is lower than threshold value, The method further includes:
Corresponding manual service is reassigned for user based on identified user demand.
4. method according to claim 2, which is characterized in that determine the user demand confidence level include with the next item down or It is multinomial or any combination thereof:
Determine user to the descriptions of problems/requirements whether sufficiently clear；
Determine whether information needed for carrying out accurate manual service is abundant enough；
Determine whether assigned manual service is accurate enough；And/or
Determine it is described by user be assigned to corresponding manual service and enter it is busy wait whether because the human-computer dialogue beyond limitation.
5. the method as described in claim 1, which is characterized in that be adapted to using described based on the model of machine learning to provide The waiting pastime of user further comprises:
Portrait modeling is carried out for the user and/or the demand；And
The waiting pastime for being adapted to the user is provided based on the portrait modeling.
6. method as claimed in claim 5, which is characterized in that modeled based on the portrait and be adapted to the user's to provide It waits diverting oneself and includes:
Recommend or play the music adaptable with the mood of the user；
Recommend or play the music adaptable with the hobby of the user；And/or
Chatting service is provided by chat robots for the user.
7. method as claimed in claim 6, which is characterized in that further comprise being diverted oneself based on the waiting for being adapted to the user Further to improve the portrait modeling to the user.
8. method according to claim 2, which is characterized in that if the confidence level of identified user demand is equal to or higher than Threshold value, the method further includes:
Determine whether the user demand is urgent；And
If the user demand is urgent, the busy waiting that user is further at least improved based on the mood of the user is excellent First grade.
9. the method as described in claim 1, which is characterized in that in the busy waiting time, using based on machine learning Model personalized busy etc. to be serviced further comprises to provide for user:
Determine the busy waiting scheduled time whether long enough；And
If the scheduled time long enough of the busy waiting, the model based on machine learning is used to provide described for user Propertyization is busy etc. to be serviced.
10. the method as described in claim 1, which is characterized in that further comprise:
User demand is guessed according to the action trail of user；And
Guessed user demand is based at least partially on to carry out the human-computer dialogue.
11. a kind of personalization device to be serviced such as busy based on machine learning characterized by comprising
For analyzing the module of user demand by human-computer dialogue；
For in the case where can determine that the user demand or the human-computer dialogue beyond limitation based on the analysis, by user It is assigned to corresponding manual service and enters the module of busy waiting；And
For being taken using based on the model of machine learning to provide personalized busy waiting for user in the busy waiting time The module of business, wherein the busy waiting service of the personalization includes being adapted to using described based on the model of machine learning to provide The waiting pastime of the user further analyzes the user demand.
12. device as claimed in claim 11, which is characterized in that mentioned for using the model based on machine learning for user For the personalization is busy etc., module to be serviced includes:
For determining the module of the confidence level of the user demand；
If the confidence level for identified user demand is equal to or higher than threshold value, the mould based on machine learning is used Type is adapted to the module for waiting pastime of user to provide；Otherwise
If the confidence level for identified user demand is lower than threshold value,
The module of the user demand is further analyzed by human-computer dialogue.
13. device as claimed in claim 12, which is characterized in that if further comprising for identified user demand Confidence level is lower than threshold value, then the module of corresponding manual service is reassigned for user based on identified user demand.
14. device as claimed in claim 12, which is characterized in that for determining the module packet of the confidence level of the user demand It includes for module one or more or any combination thereof below determining:
Determine user to the descriptions of problems/requirements whether sufficiently clear；
Determine whether information needed for carrying out accurate manual service is abundant enough；
Determine whether assigned manual service is accurate enough；And/or
Determine it is described by user be assigned to corresponding manual service and enter it is busy wait whether because the human-computer dialogue beyond limitation.
15. device as claimed in claim 11, which is characterized in that described provided based on the model of machine learning for using The module for being adapted to the waiting pastime of user further comprises:
For carrying out the module of portrait modeling for the user and/or the demand；And
For providing the module for being adapted to the waiting pastime of the user based on the portrait modeling.
16. device as claimed in claim 15, which is characterized in that provided for being modeled based on the portrait described in being adapted to User waiting pastime module include:
For recommending or playing the module for the music being adapted with the mood of the user；
For recommending or playing the module of the music adaptable with the hobby of the user；And/or
For providing the module of chatting service by chat robots for the user.
17. device as claimed in claim 16, which is characterized in that further comprise for based on be adapted to the user etc. The module of the portrait modeling to the user is further improved wait divert oneself.
18. device as claimed in claim 12, which is characterized in that if further comprising for identified user demand Confidence level is equal to or higher than threshold value, determines the whether urgent module of the user demand；And
If urgent for the user demand, the busy etc. of user is further at least improved based on the mood of the user Module to priority.
19. device as claimed in claim 11, which is characterized in that be used in the busy waiting time, using based on machine The model of study further comprises to provide the personalized device to be serviced such as busy for user:
Scheduled time whether sufficiently long module for determining the busy waiting；And
If using the model based on machine learning for the scheduled time long enough of the busy waiting to provide institute for user State the personalized module to be serviced such as busy.
20. device as claimed in claim 11, which is characterized in that further comprise:
For guessing the module of user demand according to the action trail of user；And
The interactive module is carried out for being based at least partially on guessed user demand.
21. a kind of personalization equipment to be serviced such as busy based on machine learning characterized by comprising
Memory；And
The processor coupled with the memory, is configured to:
User demand is analyzed by human-computer dialogue；
In the case where can determine that the user demand or the human-computer dialogue beyond limitation based on the analysis, user is assigned To corresponding manual service and enter busy waiting；And
In the busy waiting time, using personalized busy etc. to be serviced to be provided for user based on the model of machine learning, The wherein busy waiting service of the personalization includes being adapted to the user using described provide based on the model of machine learning Waiting pastime or further analyze the user demand.
22. a kind of computer-readable medium of storage processor executable instruction, the processor-executable instruction is processed When device executes, the processor is made to execute the methods to be serviced such as the personalization based on machine learning is busy, comprising:
User demand is analyzed by human-computer dialogue；
In the case where can determine that the user demand or the human-computer dialogue beyond limitation based on the analysis, user is assigned To corresponding manual service and enter busy waiting；And
In the busy waiting time, using personalized busy etc. to be serviced to be provided for user based on the model of machine learning, The wherein busy waiting service of the personalization includes being adapted to the user using described provide based on the model of machine learning Waiting pastime or further analyze the user demand.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112182185A (en) * 2020-09-30 2021-01-05 深圳供电局有限公司 Intelligent client auxiliary processing method and system for power supply field
CN113128763A (en) * 2021-04-19 2021-07-16 天津大学 Enterprise supply chain demand optimization method based on dynamic planning

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104010282A (en) * 2014-06-10 2014-08-27 百度在线网络技术（北京）有限公司 Implementation method and device for call-waiting prompt tones
CN108712583A (en) * 2018-06-05 2018-10-26 上海木木机器人技术有限公司 A kind of manual service method and system based on robot
CN108769437A (en) * 2018-05-28 2018-11-06 中国联合网络通信集团有限公司 Call-waiting processing method, Call Waiting server and Call Waiting processing system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104010282A (en) * 2014-06-10 2014-08-27 百度在线网络技术（北京）有限公司 Implementation method and device for call-waiting prompt tones
CN108769437A (en) * 2018-05-28 2018-11-06 中国联合网络通信集团有限公司 Call-waiting processing method, Call Waiting server and Call Waiting processing system
CN108712583A (en) * 2018-06-05 2018-10-26 上海木木机器人技术有限公司 A kind of manual service method and system based on robot

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112182185A (en) * 2020-09-30 2021-01-05 深圳供电局有限公司 Intelligent client auxiliary processing method and system for power supply field
CN113128763A (en) * 2021-04-19 2021-07-16 天津大学 Enterprise supply chain demand optimization method based on dynamic planning

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