Contact center system and method for advanced outbound communications to a contact group

• US 10,554,595 B2
• Filed: 09/27/2017
• Issued: 02/04/2020
• Est. Priority Date: 05/22/2017
• Status: Active Grant

• Current Assignee: Genesys Telecommunications Laboratories Incorporated (Genesys Cloud Services Incorporated)
• Original Assignee: Genesys Telecommunications Laboratories Incorporated (Genesys Cloud Services Incorporated)
• Inventors: Friio, Andrea, Eidsvik, Bruce
• Primary Examiner(s): Wasel, Mohamed A.
• Application Number:
  US15/717,509
• Publication Number:
  US 20180337875A1
• Time in Patent Office: 860 Days
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/045 Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
• CPC Class Codes: G06Q 10/1093 Calendar-based scheduling for persons or groups
• CPC Class Codes: G06Q 50/14 Travel agencies
• CPC Class Codes: H04L 12/1895 for short real-time information, e.g. alarms, notifications, alerts, updates
• CPC Class Codes: H04L 51/02 using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• CPC Class Codes: H04L 51/046 Interoperability with other network applications or services
• CPC Class Codes: H04L 51/214 using selective forwarding
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1097 for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
• CPC Class Codes: H04L 67/133 Protocols for remote procedure calls [RPC]
• CPC Class Codes: H04L 67/141 Setup of application sessions admission control or resource allocation in data switching networks H04L47/70
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/56 Provisioning of proxy services store-and-forward switching systems in data switching networks H04L12/54

Abstract

A method of establishing communication across contact center systems of unaffiliated entities includes receiving a first signal from a first server of a first contact center system, the first signal being indicative of an event associated with a first entity of the unaffiliated entities, identifying a user based on an identifier embedded within the first signal, identifying one or more second entities from among the unaffiliated entities, wherein the second entities are identified as being trusted by the user, predicting impact of the event on the one or more second entities, identifying a particular entity from among the one or more second entities predicted to be impacted by the event, and generating a second signal for transmission to a second server of a second contact center system associated with the identified particular entity, the second signal including data corresponding to the user and the event.

Claims

1. A method of establishing communication across contact center systems of unaffiliated entities based on machine learning, the method comprising:

receiving, by the processor, identification of first and second entities of the unaffiliated entities selected by a user via a graphical user interface, the selection of the first and second entities being indicative of trust of the first and second entities by the user;

storing, by the processor, identification of the first and second entities in a list in association with the user;

receiving, by the processor, a first signal from a first server of a first one of the contact center systems associated with the first entity, the first signal being received from the first server over a wide area network, the first signal including data of a first interaction between the user and the first entity, wherein the first interaction is conducted over a first interface provided by the first entity;

generating, by the processor, a first record in response to the first signal;

receiving, by the processor, a second signal from a second server of a second one of the contact center systems associated with the second entity, the second signal being received from the second server over the wide area network, the second signal including data of a second interaction between the user and the second entity, wherein the second interaction is conducted over a second interface provided by the second entity;

generating, by the processor, a second record in response to the second signal;

receiving, by the processor, a third signal from the first server over the wide area network, the third signal being indicative of an event associated with the first interaction between the user and the first entity;

in response to receipt of the third signal;

identifying, by the processor, the user based on an identifier embedded within the third signal;

updating, by the processor, the first record based on the event;

retrieving, by the processor, the list of the plurality of entities stored in association with the user;

identifying, by the processor, the second entity from the list;

predicting, by the processor, based on machine learning, impact of the event on the second entity;

determining, by the processor, based on the machine learning, that the second entity is predicted to be impacted by the event;

identifying, by the processor, the second record generated for the second entity; and

in response to the determining of the impact and the identification of the second record, generating, by the processor, a fourth signal and transmitting the fourth signal to the second server over the wide area network, the fourth signal comprising data corresponding to the user and the event.

2. The method of claim 1, wherein the method further comprises:

identifying the event as an update event, wherein the updating of the first record is based on the identifying of the event as an update event.

3. The method of claim 1, wherein the identifying the second entity further comprises:

identifying, by the processor, the second record as an open service record associating the second entity and the user with the second interaction.

4. The method of claim 1, wherein the second entity is predicted to be impacted within a threshold level of confidence.

5. The method of claim 1, wherein the event is one among a plurality of predetermined events associated with the first one of the contact center systems.

6. The method of claim 1, wherein in transmitting the fourth signal to the second entity, identity of the first entity is kept anonymous to the second entity.

7. The method of claim 1, wherein the first and second signals each include an open event, wherein the first and second records are generated respectively in response to the first and second signals including the open event.

8. A method of establishing communication across contact center systems of unaffiliated entities based on machine learning, the method comprising:

receiving, by the processor, a first signal from a first server of a first one of the contact center systems, the first signal being indicative of an event associated with a first entity of the unaffiliated entities;

identifying, by the processor, a user based on an identifier embedded within the first signal;

identifying, by the processor, one or more second entities from among the unaffiliated entities, wherein the second entities are identified as being trusted by the user;

predicting, by the processor, impact of the event on the one or more second entities, wherein the predicting of the impact includes;

maintaining, by the processor, a model for each particular industry vertical of a plurality of industry verticals, each of the models modeling a correlation of a plurality of first events associated with the particular industry vertical to other ones of the plurality of industry verticals;

identifying, by the processor, the particular industry vertical associated with the first entity associated with the event;

retrieving, by the processor, the model for the identified particular industry vertical;

providing, by the processor, information on the event to the retrieved model;

receiving, by the processor, identification of one or more second industry verticals correlated to the event; and

determining, by the processor, whether the one or more second entities are associated with the one or more second industry verticals;

identifying, by the processor, a particular entity from among the one or more second entities predicted to be impacted by the event; and

generating, by the processor, a second signal for transmission to a second server of a second one of the contact center systems associated with the identified particular entity, the second signal comprising data corresponding to the user and the event.

9. The method of claim 8, further comprising:

monitoring, by the processor, a second event generated by the identified particular entity, the identified particular entity being associated with the one or more second industry verticals; and

updating, by the processor, a corresponding model for the identified particular industry vertical based on the second event.

10. A cross-entity communication system for establishing communication across contact center systems of unaffiliated entities, the cross-entity communication system being external to the contact center systems of the unaffiliated entities, the system comprising:

a processor; and

memory storing instructions that, when executed on the processor, cause the processor to perform;

receiving identification of first and second entities of the unaffiliated entities selected by a user via a graphical user interface, the selection of the first and second entities being indicative of trust of the first and second entities by the user;

storing identification of the first and second entities in a list in association with the user;

receiving a first signal from a first server of a first one of the contact center systems associated with the first entity, the first signal being configured to be received from the first server over a wide area network, the first signal including data of a first interaction between the user and the first entity, wherein the first interaction is configured to be conducted over a first interface provided by the first entity;

generating a first record in response to the first signal;

receiving a second signal from a second server of a second one of the contact center systems associated with the second entity, the second signal being configured to be received from the second server over the wide area network, the second signal including data of a second interaction between the user and the second entity, wherein the second interaction is configured to be conducted over a second interface provided by the second entity;

generating a second record in response to the second signal;

receiving a third signal from the first server over the wide area network, the third signal being indicative of an event associated with the first interaction between the user and the first entity;

in response to receipt of the third signal;

identifying the user based on an identifier embedded within the third signal;

updating the first record based on the event;

retrieving the list of the plurality of entities stored in association with the user;

identifying the second entity from the list;

predicting, based on machine learning, impact of the event on the second entity;

determining, based on the machine learning, that the second entity is predicted to be impacted by the event;

identifying the second record generated for the second entity; and

in response to the determining of the impact and the identification of the second record, generating a fourth signal and transmitting the fourth signal to the second server over the wide area network, the fourth signal comprising data corresponding to the user and the event.

11. The system of claim 10, wherein the identifying of the particular entity further comprises:

identifying the second record as an open service record associating the second entity and the user with the second interaction.

12. The system of claim 10, wherein the second entity is predicted to be impacted within a threshold level of confidence.

13. The system of claim 10, wherein the event is one among a plurality of predetermined events associated with the first one of the contact center systems.

14. The system of claim 10, wherein the instructions that cause the processor to transmit the fourth signal to the second entity, include instructions that cause the processor to keep identity of the first entity anonymous to the second entity.

15. The system of claim 10, wherein the first and second signals each include an open event, wherein the first and second records are configured to be generated respectively in response to the first and second signals including the open event.

16. A cross-entity communication system for establishing communication across contact center systems of unaffiliated entities, the cross-entity communication system being external to the contact center systems of the unaffiliated entities, the system comprising:

a processor; and

memory storing instructions that, when executed on the processor, cause the processor to perform;

receiving a first signal from a first server of a first one of the contact center systems, the first signal being indicative of an event associated with a first entity of the unaffiliated entities;

identifying a user based on an identifier embedded within the first signal;

identifying one or more second entities from among the unaffiliated entities, wherein the second entities are identified as being trusted by the user;

predicting impact of the event on the one or more second entities, wherein the instructions that cause the processor to predict include instructions that cause the processor to perform;

maintaining a model for each particular industry vertical of a plurality of industry verticals, each of the models modeling a correlation of a plurality of first events associated with the particular industry vertical to other ones of the plurality of industry verticals;

identifying the particular industry vertical associated with the first entity associated with the event;

retrieving the model for the identified particular industry vertical;

providing information on the event to the retrieved model;

receiving identification of one or more second industry verticals correlated to the event; and

determining whether the one or more second entities are associated with the one or more second industry verticals;

identifying a particular entity from among the one or more second entities predicted to be impacted by the event; and

generating a second signal for transmission to a second server of a second one of the contact center systems associated with the identified particular entity, the second signal comprising data corresponding to the user and the event.

17. The system of claim 16, wherein the instructions further cause the processor to perform:

monitoring a second event generated by the identified particular entity, the identified particular entity being associated with the one or more second industry verticals; and

updating a corresponding model for the identified particular industry vertical based on the second event.