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An Intelligence method for job transfer in mail system Disclosure Number: IPCOM000246523D
Publication Date: 2016-Jun-15
Document File: 4 page(s) / 69K

Publishing Venue

The Prior Art Database


It is always a problem that in big enterprise, employee may quit or be moved to other department so that his/her manager will have to transfer his/her work to others. Normally in this situation, the employee will be asked to transfer all related work materials to corresponding succeeders. And one might have multiple succeeders that cover different areas. Luckily major content of enterprise work have records in their mailbox, as it is the most common tool an enterprise will choose to use. So the common scenario of work transferring is to hand over related mails and involve the succeeders during the time. But it may lead to omissions as the mails are too many and each of them cover different areas. It would be very tiring to distinguish between mails one by one. Similar situation may also happen during the transferring process, as the leaving employee should copy all related in-coming mails to his/her succeeders. But the truth is this employee may missed some of them, and no one else may know this. For solving the stated problem above, a method and system is proposed to automatically identify the related mails and send to corresponding person.

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An Intelligence method for job transfer in mail system

Member A will quit team, and Member B and C under the same manager have been identified to take over A's task.

There's two roles of A need to be transferred - Role 1 and Role 2
* Group 1 is a group with emails
* G1 is a serials with keywords and contact list
* In below steps only take Backup member B as example
1. Manager input one keyword K10 for Role 1, define a duration of history email (system provide options like 3 months, 6 months or customize), and assign B as the backup for Role 1
2. Scan A's Send Box within the duration defined in step 1.

3. Rank with the frequency of keywords.
- Connect to an online Dictionary and only keep Noun keywords.
- Ignore those keywords that have higher frequency than K10.

- Group the emails which include keywords K10 in Group 1.

4. Extract contact list from emails with K10 to generate CL10. Add K10, CL10 to G1. G1 = {K10, CL10} 5. Enhance the Group 1 with more emails using keywords have high correlation with K10:
5.1 Repeat step 3 in Group 1, generate the top 2 keywords - K11 and K12.

5.2 Repeat step 4 from the emails with K11 and K12, combine the contact list as CL11.

5.3 Add K11, K12, CL11 to G1 => G1 = {K10, CL10, K11, K12, CL11}

6. Repeat step 5 two times G1 = {K10, CL10, K11, K12, CL11, K111, K112, CL111,...}

7. In future emails which send to A, map keyword with G1:
- if the email text includes the keyword K10, and contact belong to CL10, then copy email t...