CN110348009A - Email processing method, device, medium and electronic equipment based on machine learning - Google Patents
Email processing method, device, medium and electronic equipment based on machine learning Download PDFInfo
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
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Abstract
The embodiment of the invention provides a kind of email processing method based on machine learning, device, medium and electronic equipments, are related to technical field of data processing, this method comprises: extracting priority aspects information from the fisrt feature region of unread mail；By preset first machine learning model of priority aspects information input, the priority number of unread mail is obtained；It is greater than in the second feature region of the unread mail of preset threshold from priority number and extracts template characteristic information；By the second machine learning model of template characteristic information input, e-mail response template corresponding with unread mail is generated；E-mail response instruction is obtained, shows corresponding e-mail response template.The mail that the technical solution of the embodiment of the present invention keeps addressee's priority processing important improves the mail treatment efficiency of addressee, avoids addressee that from can not reading important email in time and miss best turnaround time.
Description
Technical field
The present invention relates to technical field of data processing, in particular to a kind of mail treatment side based on machine learning
Method, device, medium and electronic equipment.
Background technique
Currently, the mail transmission/reception of mailbox and prompting etc. service in, generally can in recipient mailbox first to sender into
Row grouping, according to the different type of grouping, the mail that the sender in different grouping sends is arranged to only to receive not remind or
Person does not read to remind.Addressee is needed to pay special attention to and the content of preference return when existing in the mail that sender sends, but outbox
People is in when only receiving the grouping that do not remind, addressee may can not in time reading mail and miss best turnaround time.
It should be noted that information is only used for reinforcing the reason to background of the invention disclosed in above-mentioned background technology part
Solution, therefore may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
The embodiment of the present invention be designed to provide a kind of email processing method based on machine learning, device, medium and
Electronic equipment, and then the mail for enabling addressee's priority processing important at least to a certain extent, improve addressee
Mail treatment efficiency, avoid addressee that from can not reading important email in time and miss best turnaround time, improve addressee's
E-mail response efficiency.
Other characteristics and advantages of the invention will be apparent from by the following detailed description, or partially by the present invention
Practice and acquistion.
According to a first aspect of the embodiments of the present invention, a kind of email processing method based on machine learning is provided, it is described
Method includes:
Priority aspects information is extracted from the fisrt feature region of unread mail；
By preset first machine learning model of the priority aspects information input, the preferential of the unread mail is obtained
Series；
It is greater than in the second feature region of the unread mail of preset threshold from priority number and extracts template characteristic letter
Breath；
By second machine learning model of template characteristic information input, generates mail corresponding with the unread mail and return
Multiple template；
E-mail response instruction is obtained, shows the corresponding e-mail response template.
In some embodiments of the invention, aforementioned schemes are based on, the fisrt feature region includes the unread mail
Mail matter topics, sender's email address, sending time and recipient mailbox address；The fisrt feature area from unread mail
Priority aspects information is extracted in domain includes:
It obtains in mail matter topics, sender's email address, sending time and the recipient mailbox address of the unread mail
The first text information；
The first keyword is extracted from first text information；
The priority aspects information of the unread mail is determined according to first keyword.
In some embodiments of the invention, aforementioned schemes are based on, are mentioned in the fisrt feature region from unread mail
Taking out priority aspects information includes:
Obtain the characteristic area selection instruction of addressee's input；
It is determined in the unread mail according to preset characteristic area selection instruction and the fisrt feature regional location table of comparisons
Fisrt feature region position；
Obtain the second text information at the corresponding fisrt feature region of the characteristic area selection instruction；
The second keyword is extracted from second text information；
The priority aspects information of the unread mail is determined according to second keyword.
In some embodiments of the invention, aforementioned schemes are based on, the fisrt feature region includes the unread mail
Mail matter topics, sender's email address, sending time and recipient mailbox address；
First machine learning model obtains in the following manner:
It obtains existing with reference to the mail matter topics of mail, sender's email address, sending time and recipient mailbox address；
According to the mail matter topics with reference in mail, sender's email address, sending time and recipient mailbox address
In keyword determine the existing priority aspects information with reference to mail, and generate the first training sample data；
Machine learning model is trained by the first training sample data of generation, obtains first machine
Learning model.
In some embodiments of the invention, aforementioned schemes are based on, the second feature region includes the unread mail
Mail matter topics, sender's email address, recipient mailbox address and message body；
Second machine learning model obtains by the following method:
It obtains existing with reference to the mail matter topics of mail, sender's email address, recipient mailbox address and message body；
According to the mail matter topics with reference in mail, sender's email address and recipient mailbox address and message body
In keyword determine the existing template characteristic information with reference to mail, and generate the second training sample data；
Machine learning model is trained by the second training sample data of generation, obtains second machine
Learning model.
In some embodiments of the invention, aforementioned schemes, the training sample data pair by generation are based on
Machine learning model is trained, after obtaining second machine learning model further include:
Obtain modification of the addressee to e-mail response template；
Second machine learning model is trained using modified content as training sample data.
In some embodiments of the invention, aforementioned schemes are based on, the method also includes:
Multiple unread mails are arranged successively according to the descending of the priority number of the unread mail.
According to a second aspect of the embodiments of the present invention, a kind of mail treatment device based on machine learning is provided, comprising:
First extraction unit, for extracting priority aspects information from the fisrt feature region of unread mail；
Priority determining unit, for obtaining preset first machine learning model of the priority aspects information input
To the priority number of the unread mail；
Second extraction unit, in the fisrt feature region from priority number greater than the unread mail of preset threshold
Extract template characteristic information；
Template generation unit, for by second machine learning model of template characteristic information input, generate with it is described not
Read the corresponding e-mail response template of mail；
Processing unit shows the corresponding e-mail response template for obtaining e-mail response instruction.
In some embodiments of the invention, aforementioned schemes are based on, the fisrt feature region includes the unread mail
Mail matter topics, sender's email address, sending time and recipient mailbox address；
First extraction unit is configured that the mail matter topics for obtaining the unread mail, sender's email address, sends
The first text information in time and recipient mailbox address；The first keyword is extracted from first text information；Root
The priority aspects information of the unread mail is determined according to first keyword.
In some embodiments of the invention, aforementioned schemes are based on, first extraction unit, which is configured that, obtains addressee
The characteristic area selection instruction of input；It is determined according to preset characteristic area selection instruction and the fisrt feature regional location table of comparisons
The position in the fisrt feature region in the unread mail；Obtain the corresponding fisrt feature region of the characteristic area selection instruction
Second text information at place；The second keyword is extracted from second text information；It is determined according to second keyword
The priority aspects information of the unread mail.
In some embodiments of the invention, aforementioned schemes are based on, the fisrt feature region includes the unread mail
Mail matter topics, sender's email address, sending time and recipient mailbox address；
Mail treatment device based on machine learning further include:
First acquisition unit, for obtaining the existing mail matter topics with reference to mail, sender's email address, sending time
With recipient mailbox address；
First generation unit, for according to the mail matter topics with reference in mail, sender's email address, sending time
The existing priority aspects information with reference to mail is determined with the keyword in recipient mailbox address, and generates the first instruction
Practice sample data；
First training unit instructs machine learning model for the first training sample data by generating
Practice, obtains first machine learning model.
In some embodiments of the invention, aforementioned schemes are based on, the second feature region includes the unread mail
Mail matter topics, sender's email address, recipient mailbox address and message body；
The mail treatment device based on machine learning further include:
Second acquisition unit, for obtaining existing mail matter topics, sender's email address, addressee's postal with reference to mail
Case address and message body；
Second generation unit, for according to the mail matter topics with reference in mail, sender's email address and addressee
Keyword in email address and message body determines the existing template characteristic information with reference to mail, and generates the second instruction
Practice sample data；
Second training unit instructs machine learning model for the second training sample data by generating
Practice, obtains second machine learning model.
In some embodiments of the invention, aforementioned schemes are based on, the mail treatment device based on machine learning is also
Include:
Third acquiring unit, the modification of the e-mail response template for obtaining addressee couple；
Third training unit is used for using modified content as training sample data to second machine learning model
It is trained.
In some embodiments of the invention, aforementioned schemes are based on, the mail treatment device based on machine learning is also
Include:
Sequencing unit, the descending for the priority number according to the unread mail, which is arranged successively, multiple described does not read postal
Part.
According to a third aspect of the embodiments of the present invention, a kind of computer program medium is provided, computer is stored thereon with
Readable instruction makes computer execute such as above-described embodiment when the computer-readable instruction is executed by the processor of computer
Based on the email processing method of machine learning described in middle first aspect.
According to a fourth aspect of the embodiments of the present invention, a kind of electronic equipment is provided, comprising:
Memory is stored with computer-readable instruction；
Processor, for reading the computer-readable instruction of memory storage, to execute such as first party in above-described embodiment
Based on the email processing method of machine learning described in face.
Technical solution provided in an embodiment of the present invention can include the following benefits:
In the technical solution provided by some embodiments of the present invention, by from the fisrt feature region of unread mail
Priority aspects information is extracted, and by preset first machine learning model of the priority aspects information input, is not read
The priority number of mail avoids addressee can not so that addressee be enable preferentially to check the higher unread mail of priority number
Important mail is read in time and misses best turnaround time.In addition, by not reading postal from priority number greater than preset threshold
Template characteristic information is extracted in the fisrt feature region of part, and by the second machine learning model of template characteristic information input, is given birth to
At e-mail response template corresponding with unread mail, when addressee needs to reply the higher mail of priority number, lead to
The instruction of acquisition e-mail response is crossed, shows corresponding e-mail response template, addressee is enable to rapidly input reply content, improves and receives
The e-mail response efficiency of part people.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
It can the limitation present invention.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows and meets implementation of the invention
Example, and be used to explain the principle of the present invention together with specification.It should be evident that the accompanying drawings in the following description is only the present invention
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.In the accompanying drawings:
Fig. 1 diagrammatically illustrates the stream of the email processing method according to an embodiment of the invention based on machine learning
Cheng Tu；
Fig. 2 diagrammatically illustrates the process according to an embodiment of the invention for obtaining priority aspects information process
Figure；
Fig. 3 diagrammatically illustrates the process of acquisition priority aspects information process according to another embodiment of the invention
Figure；
Fig. 4 diagrammatically illustrates the stream according to an embodiment of the invention being trained to the first machine learning model
Cheng Tu；
Fig. 5 diagrammatically illustrates the stream according to an embodiment of the invention being trained to the second machine learning model
Cheng Tu；
Fig. 6 diagrammatically illustrates the frame of the mail treatment device according to an embodiment of the invention based on machine learning
Figure；
Fig. 7 shows the structural schematic diagram for being suitable for the computer system for the electronic equipment for being used to realize the embodiment of the present invention；
Fig. 8 shows the computer-readable of the mail treatment based on machine learning according to an embodiment of the invention and deposits
Storage media figure.
Specific embodiment
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be with a variety of shapes
Formula is implemented, and is not understood as limited to example set forth herein；On the contrary, thesing embodiments are provided so that the present invention will more
Fully and completely, and by the design of example embodiment comprehensively it is communicated to those skilled in the art.
In addition, described feature, structure or characteristic can be incorporated in one or more implementations in any suitable manner
In example.In the following description, many details are provided to provide and fully understand to the embodiment of the present invention.However,
It will be appreciated by persons skilled in the art that technical solution of the present invention can be practiced without one or more in specific detail,
Or it can be using other methods, constituent element, device, step etc..In other cases, it is not shown in detail or describes known side
Method, device, realization or operation are to avoid fuzzy each aspect of the present invention.
Block diagram shown in the drawings is only functional entity, not necessarily must be corresponding with physically separate entity.
I.e., it is possible to realize these functional entitys using software form, or realized in one or more hardware modules or integrated circuit
These functional entitys, or these functional entitys are realized in heterogeneous networks and/or processor device and/or microcontroller device.
Flow chart shown in the drawings is merely illustrative, it is not necessary to including all content and operation/step,
It is not required to execute by described sequence.For example, some operation/steps can also decompose, and some operation/steps can close
And or part merge, therefore the sequence actually executed is possible to change according to the actual situation.
Fig. 1 diagrammatically illustrates the stream of the email processing method according to an embodiment of the invention based on machine learning
Cheng Tu.
Shown in referring to Fig.1, the email processing method according to an embodiment of the invention based on machine learning be can wrap
Following steps S110 is included to step S150, detailed description are as follows:
In step s 110, priority aspects information is extracted from the fisrt feature region of unread mail.
In one embodiment of the invention, above-mentioned unread mail can be the unread mail in webpage mailbox, can also be with
It is the unread mail in client mailbox.Priority aspects information can be in unread mail for indicating the important journey of unread mail
The information of degree.
Wherein, the fisrt feature region on unread mail is the region that record has priority aspects information, the fisrt feature
Region can have one or more, when the fisrt feature region of unread mail has many places, in different fisrt feature regions
Priority aspects information type may not also be identical.Optionally, the fisrt feature region of unread mail may include unread mail
Mail matter topics, sender's email address, sending time and recipient mailbox address etc..
In one embodiment of the invention, as shown in Fig. 2, being extracted from the fisrt feature region of unread mail preferential
Grade characteristic information may include steps of S210 to step S230, and detailed description are as follows:
In step S210, mail matter topics, sender's email address, sending time and the addressee of the unread mail are obtained
The first text information in people's email address.
In one embodiment of the invention, the first text information obtained in the mail matter topics of unread mail can be
Particular content in mail matter topics；The first text information obtained in sender's email address and recipient mailbox address can be with
For the name of sender and the name of addressee；Available sender sends the specific time of mail in the outbox time.
In step S220, the first keyword is extracted from first text information.
In one embodiment of the invention, since the acquisition region of the first text information is different, from different the
The first keyword extracted in one text information is also different.Such as: it can be by the entry name in the mail matter topics of unread mail
Referred to as the first keyword extraction comes out；Alternatively, when sender's remarks in the mail matter topics of unread mail have " urgent ", " weight
Want ", the vocabulary such as " as early as possible reply " when, can be come out these vocabulary as the first keyword extraction；In addition, when outbox artificially compares
More important people, alternatively, there are when important people in addressee, it can be by people important in addressee or sender
Name come out as the first keyword extraction；Furthermore the outbox time of unread mail is more early, more addressee is needed to locate as early as possible
Reason, thus using specific sending time as the first keyword extraction in the first text information that can also be obtained from the outbox time
Out.
In step S230, the priority aspects information of the unread mail is determined according to first keyword.
It in one embodiment of the invention, can will be from the mail matter topics of unread mail, outbox time, sender's mailbox
The first keyword extracted in address and recipient mailbox address is as priority aspects information.
Specifically, can from " project name " extracted in mail matter topics or in mail matter topics " urgent " of remarks,
" important ", " replying as early as possible "；The specific time that the name of people important in sender and addressee and mail are sent
As priority aspects information.
In another embodiment of the present invention, as shown in figure 3, the fisrt feature extracted region from unread mail is preferential out
Grade characteristic information also may include steps of S310 to step S350, and detailed description are as follows:
In step s310, the characteristic area selection instruction of addressee's input is obtained.
In one embodiment of the invention, the fisrt feature region of unread mail is in addition to mail matter topics, outbox time, hair
Outside part people email address and recipient mailbox address, addressee can also voluntarily specify the fisrt feature region of unread mail.
Wherein, different characteristic area selection instructions corresponds to the different fisrt feature regions of unread mail, and addressee passes through
Inputting corresponding characteristic area selection instruction specifies some position in unread mail as fisrt feature region.
In step s 320, institute is determined according to preset characteristic area selection instruction and the fisrt feature regional location table of comparisons
State the position in the fisrt feature region in unread mail.
It in one embodiment of the invention, can be by after the characteristic area selection instruction for getting addressee's input
Unread mail specified by addressee is determined according to preset characteristic area selection instruction and the fisrt feature regional location table of comparisons
The position in fisrt feature region.
Wherein, the region that addressee specifies can be mail matter topics, outbox time, the sender's email address of unread mail
With recipient mailbox address, or a certain region of the text of unread mail, it specifically can be depending on addressee's demand.
In step S330, the second text envelope at the corresponding fisrt feature region of the characteristic area selection instruction is obtained
Breath.
It in one embodiment of the invention, may be text at the corresponding fisrt feature region of characteristic area selection instruction
Word, it is also possible to picture.When being text at the corresponding fisrt feature region of characteristic area selection instruction, the can be directly obtained
Two text informations；When being picture at the corresponding fisrt feature region of characteristic area selection instruction, image recognition skill can be used
Art is by the text conversion in picture at the second text information.
In step S340, the second keyword is extracted from second text information.
In one embodiment of the invention, since the fisrt feature region for obtaining the second text information is different, it mentions
The second keyword taken out is not also identical.Such as: it can specify the text of unread mail as predeterminated position, do not read by obtaining
The text of mail can be carried out when referring to addressee's name in unread mail using the name of addressee as the second keyword
It extracts.
In step S350, the priority aspects information of the unread mail is determined according to second keyword.
It in one embodiment of the invention, can will be from the of the predetermined position in the fisrt feature region of unread mail
The second keyword extracted in two text informations is as priority aspects information.Alternatively, second keyword can also be carried out
Priority aspects information is formed after pretreatment.
It, in the step s 120, can be by preset first engineering of the priority aspects information input with continued reference to Fig. 1
Model is practised, the priority number of the unread mail is obtained.
It in one embodiment of the invention, can will be from the mail matter topics of unread mail, outbox time, sender's mailbox
The fisrt feature extracted region such as address and recipient mailbox address go out priority aspects information input in advance trained machine
In device learning model, the priority number of the unread mail is obtained by machine learning model.
In one embodiment of the invention, the first machine learning model is by sample training data to machine learning mould
What type was trained.Wherein, machine learning model can be CNN (Convolutional Neural Network, convolution
Neural network) model, or be also possible to deep neural network model etc..
As shown in figure 4, the process according to an embodiment of the invention being trained to the first machine learning model, packet
Include following steps:
Step S410 is obtained existing with reference to the mail matter topics of mail, sender's email address, sending time and addressee
Email address.
It in one embodiment of the invention, i.e., can be in the embodiment of the present invention with reference to mail, that is, existing mail
There is mail to be analyzed, the recipient mailbox for having the theme of mail, the outbox time of existing mail, existing mail with determination
Location and sender's email address.
Step S420, according to the mail matter topics with reference in mail, sender's email address, sending time and addressee
Keyword in email address determines the existing priority aspects information with reference to mail, and generates the first number of training
According to.
It in one embodiment of the invention, can be according to mail matter topics, sender's email address, the hair from reference mail
Send the keyword that extracts in time and recipient mailbox address as first eigenvector, using the priority number of reference mail as
Second feature vector generates number of training evidence.
Step S430 is trained machine learning model by the first training sample data of generation, obtains institute
State the first machine learning model.
In one embodiment of the invention, first eigenvector can be inputted into the first machine learning model, obtains the
The reference mail priority number of one machine learning model output, when the priority number and second feature vector with reference to mail is different
When cause, the first machine learning model is adjusted, until the priority number of the reference mail of output is consistent with second feature vector.
As a result, after the completion of the training of the first machine learning model, postal can not read by the first machine learning model
The priority number of part.
With continued reference to Fig. 1, in step s 130, the second spy of the unread mail of preset threshold is greater than from priority number
Sign extracts template characteristic information in region.
In one embodiment of the invention, the priority of unread mail can obtained by the first machine learning model
After number, the priority number of unread mail is compared with preset threshold, when the priority number of unread mail is greater than preset threshold
When, then illustrate that the significance level of unread mail is higher, addressee is needed to handle as early as possible.At this point it is possible to be greater than from priority number pre-
If extracting template characteristic information in the second feature region of the unread mail of threshold value.
It optionally, may include unread mail for extracting the second feature region of template characteristic information in unread mail
Mail matter topics, sender's email address, recipient mailbox address and message body etc..
Wherein, template characteristic information can be project name, sender and addressee's name in mail matter topics, mail just
Content in text etc..
In step S140, by second machine learning model of template characteristic information input, generates and do not read postal with described
The corresponding e-mail response template of part.
It is understood that automatically generating priority number by the second machine learning model does not read postal greater than preset threshold
After the e-mail response template of part, addressee can directly pass through the e-mail response template Quick-return mail after reading mail,
Improve the mail treatment efficiency of addressee.
As shown in figure 5, the process according to an embodiment of the invention being trained to the second machine learning model, packet
Include following steps:
Step S510, obtain it is existing with reference to the mail matter topics of mail, sender's email address, recipient mailbox address and
Message body.
It in one embodiment of the invention, i.e., can be in the embodiment of the present invention with reference to mail, that is, existing mail
There is mail to be analyzed, to determine the sender of the theme of existing mail, the recipient mailbox address of existing mail, existing mail
The message body of email address and existing mail.
Step S520, according to the mail matter topics with reference in mail, sender's email address and recipient mailbox address,
And the keyword in message body determines the existing template characteristic information with reference to mail, and generates the second training sample
Data.
It in one embodiment of the invention, can be according to the mail matter topics from reference mail, sender's email address, receipts
The keyword extracted in part people email address and message body is as third feature vector, by the e-mail response template of reference mail
The second training sample data are generated as fourth feature vector.
Step S530 is trained machine learning model by the second training sample data of generation, obtains institute
State the second machine learning model.
In one embodiment of the invention, third feature vector can be inputted into the second machine learning model, obtains the
The e-mail response template of the reference mail of two machine learning models output, when the e-mail response template with reference to mail and the 4th spy
When sign vector is inconsistent, the second machine learning model is adjusted, until the e-mail response template of the reference mail of output and the 4th spy
It is consistent to levy vector.
As a result, after the completion of the training of the second machine learning model, postal can not read by the second machine learning model
The e-mail response template of part.
With continued reference to Fig. 1, in step S150, e-mail response instruction is obtained, shows the corresponding e-mail response template.
In one embodiment of the invention, when e-mail response instruction can be the setting after addressee reads unread mail
Between automatically generate, be also possible to addressee by click mail in some function button after generate.
In one embodiment of the invention, machine learning model is trained by the training sample data of generation,
After obtaining the second machine learning model further include:
Modification of the addressee to e-mail response template is obtained, and using modified content as training sample data to second
Machine learning model is trained.
In one embodiment of the invention, the e-mail response template that the second machine learning model generates may and not met
The requirement of addressee, at this point it is possible to by addressee (namely user) to the modification content of e-mail response template, and will be in modification
Hold and continue to be trained the second machine learning model as feature vector, so that the mail that the second machine learning model generates returns
Multiple template meets the individual requirement of addressee.
In one embodiment of the invention, can be arranged successively according to the descending of the priority number of unread mail it is multiple not
Read mail.
As a result, after addressee opens mailbox, it can make more important according to the significance level successively reading mail of mail
Mail gets over advanced processing completion.
In the technical solution provided by some embodiments of the present invention, by from the fisrt feature region of unread mail
Priority aspects information is extracted, and by preset first machine learning model of the priority aspects information input, is not read
The priority number of mail avoids addressee can not so that addressee be enable preferentially to check the higher unread mail of priority number
Important mail is read in time and misses best turnaround time.In addition, by not reading postal from priority number greater than preset threshold
Template characteristic information is extracted in the second feature region of part, and by the second machine learning model of template characteristic information input, is given birth to
At e-mail response template corresponding with unread mail, when addressee needs to reply the higher mail of priority number, lead to
The instruction of acquisition e-mail response is crossed, shows corresponding e-mail response template, addressee is enable to rapidly input reply content, improves and receives
The e-mail response efficiency of part people.
The device of the invention embodiment introduced below can be used for executing the above-mentioned mail based on machine learning of the present invention
Processing method.
Fig. 6 diagrammatically illustrates the frame of the mail treatment device according to an embodiment of the invention based on machine learning
Figure.
Referring to shown in Fig. 6, the mail treatment device 100 according to an embodiment of the invention based on machine learning, packet
Include: the first extraction unit 110, priority determining unit 120, the second extraction unit 130, template generation unit 140 and processing are single
Member 150.
Wherein, the first extraction unit 110 from the fisrt feature region of unread mail for extracting priority aspects letter
Breath；Priority determining unit 120 is used to preset first machine learning model of the priority aspects information input obtaining institute
State the priority number of unread mail；Second extraction unit 130 is used to be greater than from priority number the unread mail of preset threshold
Second feature region in extract template characteristic information；Template generation unit 140 is used for the template characteristic information input
Second machine learning model generates e-mail response template corresponding with the unread mail；Processing unit 150 is for obtaining mail
Replying instruction shows the corresponding e-mail response template.
In one embodiment of the invention, the fisrt feature region includes the mail matter topics of the unread mail, hair
Part people email address, sending time and recipient mailbox address；First extraction unit 110, which is configured that, obtains the unread mail
Mail matter topics, sender's email address, the first text information in sending time and recipient mailbox address；From described first
The first keyword is extracted in text information；The priority aspects letter of the unread mail is determined according to first keyword
Breath.
In another embodiment of the present invention, the first extraction unit 110 is configured that the characteristic area for obtaining addressee's input
Domain selection instruction；The unread mail is determined according to preset characteristic area selection instruction and the fisrt feature regional location table of comparisons
In fisrt feature region position；Obtain the second text at the corresponding fisrt feature region of the characteristic area selection instruction
Information；The second keyword is extracted from second text information；The unread mail is determined according to second keyword
Priority aspects information.
In one embodiment of the invention, the fisrt feature region includes the mail matter topics of the unread mail, hair
Part people email address, sending time and recipient mailbox address；The mail treatment device 100 based on machine learning also wraps
It includes: first acquisition unit, it is existing with reference to the mail matter topics of mail, sender's email address, sending time and receipts for obtaining
Part people's email address；First generation unit, for according to the mail matter topics with reference in mail, sender's email address, hair
It send the keyword in time and recipient mailbox address to determine the existing priority aspects information with reference to mail, and generates
First training sample data；First training unit, for the first training sample data by generating to machine learning mould
Type is trained, and obtains first machine learning model.
In one embodiment of the invention, the second feature region includes the mail matter topics of the unread mail, hair
Part people email address, recipient mailbox address and message body；The mail treatment device 100 based on machine learning also wraps
It includes: second acquisition unit, for obtaining existing mail matter topics, sender's email address, recipient mailbox with reference to mail
Location and message body；Second generation unit, for according to the mail matter topics with reference in mail, sender's email address and receipts
Keyword in part people email address and message body determines the existing template characteristic information with reference to mail, and generates the
Two training sample data；Second training unit, for the second training sample data by generating to machine learning model
It is trained, obtains second machine learning model.
In one embodiment of the invention, the mail treatment device 100 based on machine learning further include: third
Acquiring unit, for obtaining modification of the addressee to e-mail response template；Third training unit, for making modified content
Second machine learning model is trained for training sample data.
In one embodiment of the invention, the mail treatment device 100 based on machine learning further include: sequence
Unit, the descending for the priority number according to the unread mail are arranged successively multiple unread mails.
Due to example embodiments of the present invention the mail treatment device based on machine learning each functional module with it is upper
The step of stating the example embodiment of the email processing method based on machine learning is corresponding, therefore in apparatus of the present invention embodiment
Undisclosed details please refers to the embodiment of the processing method of the above-mentioned page resource data of the present invention.
In an exemplary embodiment of the present invention, a kind of electronic equipment that can be realized the above method is additionally provided.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or
Program product.Therefore, various aspects of the invention can be embodied in the following forms, it may be assumed that complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here
Referred to as circuit, " module " or " system ".
The electronic equipment 600 of this embodiment according to the present invention is described referring to Fig. 7.The electronics that Fig. 7 is shown
Equipment 600 is only an example, should not function to the embodiment of the present invention and use scope bring any restrictions.
As shown in fig. 7, electronic equipment 600 is showed in the form of universal computing device.The component of electronic equipment 600 can wrap
It includes but is not limited to: at least one above-mentioned processing unit 610, at least one above-mentioned storage unit 620, the different system components of connection
The bus 630 of (including storage unit 620 and processing unit 610).
Wherein, the storage unit is stored with program code, and said program code can be held by the processing unit 610
Row, so that various according to the present invention described in the execution of the processing unit 610 above-mentioned " illustrative methods " part of this specification
The step of illustrative embodiments.For example, the processing unit 610 can execute step S110 as shown in fig. 1, never reading postal
Priority aspects information is extracted in the fisrt feature region of part；Step S210 presets the priority aspects information input
The first machine learning model, obtain the priority number of the unread mail；Step S310 is greater than preset threshold from priority number
The unread mail second feature region in extract template characteristic information；Step S410, by the template characteristic information
The second machine learning model is inputted, e-mail response template corresponding with the unread mail is generated；Step S510 obtains mail and returns
Multiple instruction, shows the corresponding e-mail response template.
Storage unit 620 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit
(RAM) 6201 and/or cache memory unit 6202, it can further include read-only memory unit (ROM) 6203.
Storage unit 620 can also include program/utility with one group of (at least one) program module 6205
6204, such program module 6205 includes but is not limited to: operating system, one or more application program, other program moulds
It may include the realization of network environment in block and program data, each of these examples or certain combination.
Bus 630 can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in a variety of bus structures
Local bus.
Electronic equipment 600 can also be with one or more external equipments 700 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 600 communicate, and/or with make
Any equipment (such as the router, modulation /demodulation that the electronic equipment 600 can be communicated with one or more of the other calculating equipment
Device etc.) communication.This communication can be carried out by input/output (I/O) interface 650.Also, electronic equipment 600 can be with
By network adapter 660 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network,
Such as internet) communication.As shown, network adapter 660 is communicated by bus 630 with other modules of electronic equipment 600.
It should be understood that although not shown in the drawings, other hardware and/or software module can not used in conjunction with electronic equipment 600, including but not
Be limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and
Data backup storage system etc..
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the present invention
The technical solution of embodiment can be embodied in the form of software products, which can store non-volatile at one
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating
Equipment (can be personal computer, server, terminal installation or network equipment etc.) executes embodiment according to the present invention
Method.
In an exemplary embodiment of the present invention, a kind of computer readable storage medium is additionally provided, energy is stored thereon with
Enough realize the program product of this specification above method.In some possible embodiments, various aspects of the invention may be used also
In the form of being embodied as a kind of program product comprising program code, when described program product is run on the terminal device, institute
Program code is stated for executing the terminal device described in above-mentioned " illustrative methods " part of this specification according to this hair
The step of bright various illustrative embodiments.
Refering to what is shown in Fig. 8, describing the program product for realizing the above method of embodiment according to the present invention
800, can using portable compact disc read only memory (CD-ROM) and including program code, and can in terminal device,
Such as it is run on PC.However, program product of the invention is without being limited thereto, in this document, readable storage medium storing program for executing can be with
To be any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or
It is in connection.
Described program product can be using any combination of one or more readable mediums.Readable medium can be readable letter
Number medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or
System, device or the device of semiconductor, or any above combination.The more specific example of readable storage medium storing program for executing is (non exhaustive
List) include: electrical connection with one or more conducting wires, portable disc, hard disk, random access memory (RAM), read-only
Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory
(CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
In carry readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Readable signal medium can also be any readable Jie other than readable storage medium storing program for executing
Matter, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or and its
The program of combined use.
The program code for including on readable medium can transmit with any suitable medium, including but not limited to wirelessly, have
Line, optical cable, RF etc. or above-mentioned any appropriate combination.
The program for executing operation of the present invention can be write with any combination of one or more programming languages
Code, described program design language include object oriented program language-Java, C++ etc., further include conventional
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user
It calculates and executes in equipment, partly executes on a user device, being executed as an independent software package, partially in user's calculating
Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far
Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
In addition, above-mentioned attached drawing is only the schematic theory of processing included by method according to an exemplary embodiment of the present invention
It is bright, rather than limit purpose.It can be readily appreciated that the time that above-mentioned processing shown in the drawings did not indicated or limited these processing is suitable
Sequence.In addition, be also easy to understand, these processing, which can be, for example either synchronously or asynchronously to be executed in multiple modules.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to of the invention its
His embodiment.This application is intended to cover any variations, uses, or adaptations of the invention, these modifications, purposes or
Adaptive change follow general principle of the invention and including the undocumented common knowledge in the art of the present invention or
Conventional techniques.The description and examples are only to be considered as illustrative, and true scope and spirit of the invention are by claim
It points out.
Claims (10)
1. a kind of email processing method based on machine learning, which is characterized in that the described method includes:
Priority aspects information is extracted from the fisrt feature region of unread mail；
By preset first machine learning model of the priority aspects information input, the priority of the unread mail is obtained
Number；
It is greater than in the second feature region of the unread mail of preset threshold from priority number and extracts template characteristic information；
By second machine learning model of template characteristic information input, e-mail response mould corresponding with the unread mail is generated
Plate；
E-mail response instruction is obtained, shows the corresponding e-mail response template.
2. the method according to claim 1, wherein the fisrt feature region includes the postal of the unread mail
Part theme, sender's email address, sending time and recipient mailbox address；In the fisrt feature region from unread mail
Extracting priority aspects information includes:
Obtain in mail matter topics, sender's email address, sending time and the recipient mailbox address of the unread mail
One text information；
The first keyword is extracted from first text information；
The priority aspects information of the unread mail is determined according to first keyword.
3. the method according to claim 1, wherein being extracted in the fisrt feature region from unread mail
Priority aspects information includes:
Obtain the characteristic area selection instruction of addressee's input；
The in the unread mail is determined according to preset characteristic area selection instruction and the fisrt feature regional location table of comparisons
The position of one characteristic area；
Obtain the second text information at the corresponding fisrt feature region of the characteristic area selection instruction；
The second keyword is extracted from second text information；
The priority aspects information of the unread mail is determined according to second keyword.
4. the method according to claim 1, wherein the fisrt feature region includes the postal of the unread mail
Part theme, sender's email address, sending time and recipient mailbox address；
First machine learning model obtains in the following manner:
It obtains existing with reference to the mail matter topics of mail, sender's email address, sending time and recipient mailbox address；
According in the mail matter topics with reference in mail, sender's email address, sending time and recipient mailbox address
Keyword determines the existing priority aspects information with reference to mail, and generates the first training sample data；
Machine learning model is trained by the first training sample data of generation, obtains first machine learning
Model.
5. the method according to claim 1, wherein the second feature region includes the postal of the unread mail
Part theme, sender's email address, recipient mailbox address and message body；
Second machine learning model obtains by the following method:
It obtains existing with reference to the mail matter topics of mail, sender's email address, recipient mailbox address and message body；
According in the mail matter topics with reference in mail, sender's email address and recipient mailbox address and message body
Keyword determines the existing template characteristic information with reference to mail, and generates the second training sample data；
Machine learning model is trained by the second training sample data of generation, obtains second machine learning
Model.
6. according to the method described in claim 5, it is characterized in that, the training sample data by generation are to machine
Learning model is trained, after obtaining second machine learning model further include:
Obtain modification of the addressee to e-mail response template；
Second machine learning model is trained using modified content as training sample data.
7. the method according to claim 1, wherein the method also includes:
Multiple unread mails are arranged successively according to the descending of the priority number of the unread mail.
8. a kind of mail treatment device based on machine learning characterized by comprising
First extraction unit extracts priority aspects information from the fisrt feature region of unread mail；
Priority determining unit, for obtaining institute for preset first machine learning model of the priority aspects information input
State the priority number of unread mail；
Second extraction unit, for being extracted in the second feature region from priority number greater than the unread mail of preset threshold
Template characteristic information out；
Template generation unit, for generating second machine learning model of template characteristic information input and not reading postal with described
The corresponding e-mail response template of part；
Processing unit shows the corresponding e-mail response template for obtaining e-mail response instruction.
9. a kind of computer program medium, is stored thereon with computer-readable instruction, when the computer-readable instruction is calculated
When the processor of machine executes, computer perform claim is made to require the mail treatment based on machine learning described in any one of 1-7
Method.
10. a kind of electronic equipment characterized by comprising
Memory is stored with computer-readable instruction；
Processor, for reading the computer-readable instruction of memory storage, to execute as described in any one of claim 1-7
The email processing method based on machine learning.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910532896.0A CN110348009A (en) | 2019-06-19 | 2019-06-19 | Email processing method, device, medium and electronic equipment based on machine learning |
| PCT/CN2020/087470 WO2020253388A1 (en) | 2019-06-19 | 2020-04-28 | Machine learning-based e-mail message processing method, apparatus, medium, and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910532896.0A CN110348009A (en) | 2019-06-19 | 2019-06-19 | Email processing method, device, medium and electronic equipment based on machine learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110348009A true CN110348009A (en) | 2019-10-18 |
Family
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910532896.0A Pending CN110348009A (en) | 2019-06-19 | 2019-06-19 | Email processing method, device, medium and electronic equipment based on machine learning |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN110348009A (en) |
| WO (1) | WO2020253388A1 (en) |
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| WO2020253388A1 (en) * | 2019-06-19 | 2020-12-24 | 深圳壹账通智能科技有限公司 | Machine learning-based e-mail message processing method, apparatus, medium, and electronic device |
| CN112257814A (en) * | 2020-11-26 | 2021-01-22 | 携程计算机技术（上海）有限公司 | Mail labeling method, system, equipment and storage medium based on deep learning |
| US20210142150A1 (en) * | 2019-11-07 | 2021-05-13 | Fujitsu Limited | Information processing device and method, and device for classifying with model |
| CN112818109A (en) * | 2021-02-25 | 2021-05-18 | 网易（杭州）网络有限公司 | Intelligent reply method, medium, device and computing equipment for mail |
| CN113343682A (en) * | 2021-06-07 | 2021-09-03 | 中国工商银行股份有限公司 | Mail processing method, mail processing device, electronic device, and storage medium |
| CN113592461A (en) * | 2021-08-10 | 2021-11-02 | 平安普惠企业管理有限公司 | Mail processing method, device and storage medium |
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| WO2020253388A1 (en) | 2020-12-24 |
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