Systems and methods for automatic generation of a relationship management system

• US 9,898,743 B2
• Filed: 10/09/2013
• Issued: 02/20/2018
• Est. Priority Date: 10/09/2013
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Bessler, Scott, Echague, Patricio, Evans, Adam, Hawksley, Andrea, Lerhaupt, Gary, Li, Jason, Loughlin, Steve, Punera, Kunal
• Primary Examiner(s): Jeanty, Romain
• Application Number:
  US14/050,125
• Publication Number:
  US 20150100356A1
• Time in Patent Office: 1,595 Days
• Field of Search: 705 712- 724
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 30/01 Customer relationship services

Abstract

A method and apparatus for the automatic creation of a relationship management system is described. The method may include receiving a request from a user to create a relationship management system, and receiving specification of one or more electronic communication systems and user access credentials that provide access to each of the corresponding accounts. Furthermore, the method may include obtaining past electronic communications using the received user access credentials and analyzing the past electronic communications to extract contact data. The method may also include creating the relationship management system for the user and adding the contact data as contacts associated with the user in the created relationship management system.

Claims

1. A computer-implemented method comprising:

receiving, with a computing system having one or more processors and at least one memory device coupled with the one or more processors, a request from a user to modify contents of a relationship management system having a relationship management interface and a relationship management database maintained in the at least one memory device;

receiving, with the one or more processors, specification of one or more electronic communication systems, for which the user has an account for sending and receiving electronic communications with the corresponding electronic communication system, and user access credentials that provide access to each of the corresponding accounts;

accessing, by providing user access credentials corresponding to the electronic communication system being accessed, electronic communications for the user within the electronic communication system being accessed;

obtaining, with the one or more processors, past electronic communications from each of the specified one or more electronic communication systems using the received user access credentials;

analyzing, with the one or more processors utilizing a machine learning model, the past electronic communications to extract contact data, to be added to the relationship management system, from the contents of the electronic communications, wherein the analyzing comprises at least interpreting the relationship management data, automatically culling contact information from the electronic communications, deduplicating contact information, merging contact information obtained from different sources, combining relationship analysis results obtained from different communication channels, and users, and generating process suggestions, follow-up suggestions, and team member collaboration suggestions; and

adding, with the one or more processors, the contact data extracted from the electronic communications as contacts associated to the relationship management system stored in the at least one memory device for the user.

2. The computer-implemented method of claim 1, wherein analyzing the past electronic communications to extract contact data further comprises:

analyzing an electronic communication, from the obtained one or more electronic communications, to extract one or more items of contact data from contents of the electronic communication by application of a pre-trained machine learning model to the contents of the electronic communication;

determining a confidence level that the one or more items of extracted contact data correspond to an existing contact in the relationship management system, wherein the confidence level is determined by the application of the pre-trained machine learning model to the contents of the electronic communication;

adding the extracted contact data to the existing contact data based on the determined confidence level.

3. The computer implemented method of claim 2, wherein the extracted contact data is automatically added to the existing contact when the confidence level exceeds a first threshold, and a suggestion is created for the user to decide whether to add the extracted contact data to the existing contact when the confidence level exceeds a second threshold and does not exceed the first threshold.

4. The computer-implemented method of claim 2, wherein the confidence level is determined based on a combined analysis of two or more different communications from two or more different electronic communication systems.

5. The computer-implemented method of claim 2, wherein the confidence level is determined based on a combined analysis of two or more different communications associated with two more users of the created relationship management system.

6. The computer-implemented method of claim 2, wherein the pre-trained machine learning model is a natural language analysis model for analyzing the electronic communication to locate a contact signature within the electronic communication, and to extract one or more contact details from the located contact signature.

7. The computer-implemented method of claim 1, further comprising:

analyzing the past electronic communications to determine a process, to be added to the relationship management system, from the contents of the electronic communications, wherein the process is a combination of one or more relationships, one or more collaborators, and a sequential series of stages in communication between the user, the relationships, and the collaborators; and

adding the determined process to the created relationship management system.

8. The computer-implemented method of claim 7, further comprising:

obtaining one or more electronic communications from the one or more electronic communication systems using a corresponding set of user access credentials;

analyzing the obtained one or more electronic communications; and

filtering the obtained one or more electronic communications to one or more relationships in the determined process.

9. The computer-implemented method of claim 7, wherein analyzing and filtering further comprises:

analyzing an electronic communication from the obtained one or more electronic communications, by application of a pre-trained machine learning model to the content of electronic communication, to determine a relationship in the process to which the electronic communication likely belongs;

determining a confidence level of the determination of the relationship in the process based on results of the pre-trained machine learning model application to the electronic communication; and

filtering the electronic communication to the relationship in the process based on the determined confidence level.

10. The computer-implemented method of claim 9, wherein the electronic communication is automatically filtered to the relationship in the process when the confidence level exceeds a first threshold, and a suggestion is created for the user to decide whether the electronic communication is to be filtered to the relationship in the process when the confidence level exceeds a second threshold and does not exceed the first threshold.

11. The computer-implemented method of claim 7, wherein the process is one of a sales process, a business development process, a fundraising process, or a recruitment process.

12. The computer-implemented method of claim 7, further comprising:

analyzing the past electronic communications to determine a relationship, to be added to the determined process, wherein the relationship is a contact that exchanges communication with the user within the determined process; and

adding the determined relationship to the relationship management process.

13. The computer-implemented method of claim 12, further comprising:

analyzing the past electronic communications to determine a collaborator to be added to the determined process, wherein the collaborator is a second user associated with the first user that has exchanged communications with the relationship in the determined process; and

adding the collaborator to the determined process.

14. The computer-implemented method of claim 1, further comprising:

receiving user specification of a process, wherein the process is a sequential series of stages in communication between the user and at least one contact; and

adding the user specified process to the created relationship management system.

15. The computer-implemented method of claim 1, wherein the one or more electronic communication systems include a plurality of different types of electronic communication systems that provide electronic communication services over a plurality of different communication channels.

16. The computer-implemented method of claim 1, wherein the one or more electronic communication systems include one or more web-based electronic mail systems, one or more social networking systems, one or more blogging systems, one or more multimedia communication systems, and a cellular telephone communication system.

17. The computer-implemented method of claim 1, further comprising:

obtaining new electronic communications after creation of the relationship management system from one or more of the specified electronic communication system using the user access credentials;

analyzing the new electronic communications based on the past electronic communications; and

updating the relationship management system based on analysis of the new electronic communications and the past electronic communications.

18. The computer-implemented method of claim 17, wherein the new electronic communications are obtained on a periodic basis, and the relationship management system is updated in near real time in response the new electronic communications being obtained.

19. The computer implemented method of claim 18, further comprising:

generating data for display of the relationship management system to the user; and

generating updated data for display to the user in near real time as the relationship management system is updated.

20. The computer-implemented method of claim 18, wherein updating the relationship management system further comprises:

refining existing contact data extracted from the past electronic communications with contact data obtained from the new electronic communications.

21. A non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to perform a method comprising:

receiving, with a computing system having one or more processors and at least one memory device coupled with the one or more processors, a request from a user to modify contents of a relationship management system having a relationship management interface and a relationship management database maintained in the at least one memory device;

receiving, with the one or more processors, specification of one or more electronic communication systems, for which the user has an account for sending and receiving electronic communications with the corresponding electronic communication system, and user access credentials that provide access to each of the corresponding accounts;

accessing, by providing user access credentials corresponding to the electronic communication system being accessed, electronic communications for the user within the electronic communication system being accessed;

obtaining, with the one or more processors, past electronic communications from each of the specified one or more electronic communication systems using the received user access credentials;

analyzing, with the one or more processors utilizing a machine learning model, the past electronic communications to extract contact data, to be added to the relationship management system, from the contents of the electronic communications, wherein the analyzing comprises at least interpreting the relationship management data, automatically culling contact information from the electronic communications, deduplicating contact information, merging contact information obtained from different sources, combining relationship analysis results obtained from different communication channels, and users, and generating process suggestions, follow-up suggestions, and team member collaboration suggestions; and

adding, with the one or more processors, the contact data extracted from the electronic communications as contacts associated to the relationship management system stored in the at least one memory device for the user.

22. The non-transitory computer readable storage medium of claim 21, wherein analyzing the past electronic communications to extract contact data further comprises:

analyzing an electronic communication, from the obtained one or more electronic communications, to extract one or more items of contact data from contents of the electronic communication by application of a pre-trained machine learning model to the contents of the electronic communication;

determining a confidence level that the one or more items of extracted contact data correspond to an existing contact in the relationship management system, wherein the confidence level is determined by the application of the pre-trained machine learning model to the contents of the electronic communication;

adding the extracted contact data to the existing contact data based on the determined confidence level.

23. The non-transitory computer readable storage medium of claim 22, wherein the extracted contact data is automatically added to the existing contact when the confidence level exceeds a first threshold, and a suggestion is created for the user to decide whether to add the extracted contact data to the existing contact when the confidence level exceeds a second threshold and does not exceed the first threshold.

24. The non-transitory computer readable storage medium of claim 22, wherein the confidence level is determined based on a combined analysis of two or more different communications from two or more different electronic communication systems.

25. The non-transitory computer readable storage medium of claim 22, wherein the confidence level is determined based on a combined analysis of two or more different communications associated with two more users of the created relationship management system.

26. The non-transitory computer readable storage medium of claim 22, wherein the pre-trained machine learning model is a natural language analysis model for analyzing the electronic communication to locate a contact signature within the electronic communication, and to extract one or more contact details from the located contact signature.

27. The non-transitory computer readable storage medium of claim 21, further comprising:

analyzing the past electronic communications to determine a process, to be added to the relationship management system, from the contents of the electronic communications, wherein the process is a combination of one or more relationships, one or more collaborators, and a sequential series of stages in communication between the user, the relationships, and the collaborators; and

adding the determined process to the created relationship management system.

28. The non-transitory computer readable storage medium of claim 27, further comprising:

obtaining one or more electronic communications from the one or more electronic communication systems using a corresponding set of user access credentials;

analyzing the obtained one or more electronic communications; and

filtering the obtained one or more electronic communications to one or more relationships in the determined process.

29. The non-transitory computer readable storage medium of claim 27, wherein analyzing and filtering further comprises:

analyzing an electronic communication from the obtained one or more electronic communications, by application of a pre-trained machine learning model to the content of electronic communication, to determine a relationship in the process to which the electronic communication likely belongs;

determining a confidence level of the determination of the relationship in the process based on results of the pre-trained machine learning model application to the electronic communication; and

filtering the electronic communication to the relationship in the process based on the determined confidence level.

30. The non-transitory computer readable storage medium of claim 29, wherein the electronic communication is automatically filtered to the relationship in the process when the confidence level exceeds a first threshold, and a suggestion is created for the user to decide whether the electronic communication is to be filtered to the relationship in the process when the confidence level exceeds a second threshold and does not exceed the first threshold.

31. The non-transitory computer readable storage medium of claim 27, wherein the process is one of a sales process, a business development process, a fundraising process, or a recruitment process.

32. The non-transitory computer readable storage medium of claim 27, further comprising:

analyzing the past electronic communications to determine a relationship, to be added to the determined process, wherein the relationship is a contact that exchanges communication with the user within the determined process; and

adding the determined relationship to the relationship management process.

33. The non-transitory computer readable storage medium of claim 32, further comprising:

analyzing the past electronic communications to determine a collaborator to be added to the determined process, wherein the collaborator is a second user associated with the first user that has exchanged communications with the relationship in the determined process; and

adding the collaborator to the determined process.

34. The non-transitory computer readable storage medium of claim 21, further comprising:

receiving user specification of a process, wherein the process is a sequential series of stages in communication between the user and at least one contact; and

adding the user specified process to the created relationship management system.

35. The non-transitory computer readable storage medium of claim 21, wherein the one or more electronic communication systems include a plurality of different types of electronic communication systems that provide electronic communication services over a plurality of different communication channels.

36. The non-transitory computer readable storage medium of claim 21, wherein the one or more electronic communication systems include one or more web-based electronic mail systems, one or more social networking systems, one or more blogging systems, one or more multimedia communication systems, and a cellular telephone communication system.

37. The non-transitory computer readable storage medium of claim 21, further comprising:

obtaining new electronic communications after creation of the relationship management system from one or more of the specified electronic communication system using the user access credentials;

analyzing the new electronic communications based on the past electronic communications; and

updating the relationship management system based on analysis of the new electronic communications and the past electronic communications.

38. The non-transitory computer readable storage medium of claim 37, wherein the new electronic communications are obtained on a periodic basis, and the relationship management system is updated in near real time in response the new electronic communications being obtained.

39. The non-transitory computer readable storage medium of claim 38, further comprising:

generating data for display of the relationship management system to the user; and

generating updated data for display to the user in near real time as the relationship management system is updated.

40. The non-transitory computer readable storage medium of claim 38, wherein updating the relationship management system further comprises:

refining existing contact data extracted from the past electronic communications with contact data obtained from the new electronic communications.

41. A system comprising:

a memory; and

a processor coupled with the memory to receive a request from a user to modify contents of a relationship management system having a relationship management interface and a relationship management database maintained in the memory;

receive, with the processor, specification of one or more electronic communication systems, for which the user has an account for sending and receiving electronic communications with the corresponding electronic communication system, and user access credentials that provide access to each of the corresponding accounts;

access, by providing user access credentials corresponding to the electronic communication system being accessed, electronic communications for the user within the electronic communication system being accessed;

obtain, with the processor, past electronic communications from each of the specified one or more electronic communication systems using the received user access credentials;

analyze, with the one or more processors utilizing a machine learning model, the past electronic communications to extract contact data, to be added to the relationship management system, from the contents of the electronic communications, wherein the analyzing comprises at least interpreting the relationship management data, automatically culling contact information from the electronic communications, deduplicating contact information, merging contact information obtained from different sources, combining relationship analysis results obtained from different communication channels, and users, and generating process suggestions, follow-up suggestions, and team member collaboration suggestions; and

add, with the processor, the contact data extracted from the electronic communications as contacts associated to the relationship management system stored in the memory for the user.