IP Library Granted Patent US 12,614,187
Granted Patent B2
US 12,614,187 · App. 17/130,125 · Granted Apr 28, 2026

Escalation management and journey mining

Inventor: Will Thiel (Malden, MA)
G06Q30/01G06F16/2455G06F16/248G06Q10/0635G06Q10/0637G06Q10/101G06Q10/103G06Q10/06393
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Quick Facts
Patent No.
US 12,614,187
App. No.
17/130,125
Granted
Apr 28, 2026
Kind
B2
Abstract

The journeys and/or timelines of multiple customers may be used in escalation management and/or journey mining. An event of interest, pertaining to an issue or an incident, on a timeline may be used in the escalation management and/or journey mining. Escalation management is directed to addressing and resolving incidents, problems, and customer situations which could result in a high level of customer dissatisfaction or damage to a service provider's reputation, using the appropriate response and/or resources. Journey mining is directed to using patterns across customers and their journeys to determine where things in the journey went differently than what was expected.

Claims (24)

1 . A method for identifying remedial action for a shared timeline event within a contact center environment among one or more customers, the method comprising:

accessing, by a computing device, a first customer timeline stored in a database, wherein the first customer timeline includes a first plurality of events, and wherein each event comprises a timestamp, a specified type, and at least one customer associated with it;

accessing, by the computing device, a second customer timeline stored in the database, wherein the second customer timeline includes a second plurality of events, and wherein each event comprises a timestamp, a specified type, and at least one customer associated with it;

displaying, by the computing device, the first customer timeline together with the second customer timeline on a graphical user interface;

comparing, by the computing device, the first customer timeline with the second customer timeline to identify a shared event that is common to the first customer timeline and the second customer timeline;

connecting, by the computing device, the shared event common to the first customer timeline and the second customer timeline by specifying shared customers and the relative position in time on the respective timelines via the timestamp associated with the event;

analyzing, by the computing device, the shared event to determine whether a pattern in the first customer timeline and the second customer timeline led to the shared event, wherein the analyzing further comprises:

investigating each customer history and transcripts, and

applying natural language processing to the transcripts to identify contextual data;

identifying, by an escalation management engine of the computing device, the shared event as a problematic shared event as a function of a machine learning model of a machine learning system, wherein the machine learning model continually learns statistical characteristics of events and event patterns denoted as problematic;

identifying and outputting, by the escalation management engine of the computing device, a remedial action configured to address a cause of a remedial issue associated with the problematic shared event;

applying, by the escalation management engine of the computing device, the remedial action configured to address the cause of the remedial issue associated with the problematic shared event, wherein the applying comprises identifying opportunities to guide an interaction towards a better outcome by creating deliverables from the shared timeline event; and

improving an accuracy of the machine learning model by incorporating into the machine learning model interactions provided via the graphical user interface with an output of the machine learning model, including a determination of an impact of the problematic shared event, wherein the impact is representative of a quantity of the customers that experienced the problematic shared event and an effect of the problematic shared event on one or more key performance indicators, to reinforce accurate identification, by the machine learning model, of problematic events and suppress future inaccuracies by the machine learning model.

2 . The method of claim 1 , wherein the first customer timeline is of a first customer, and the second customer timeline is of a second customer, wherein the first customer is different than the second customer.

3 . The method of claim 1 , further comprising receiving the first customer timeline and the second customer timeline from a database.

4 . The method of claim 1 , further comprising analyzing the shared event with respect to at least one additional customer timeline.

5 . The method of claim 4 , wherein the at least one additional customer timeline is selected from a third customer, wherein the third customer is different from both of the first and the second customers.

6 . The method of claim 1 , further comprising pinning a user-entered note about one or more of the first customer timeline or the second customer timeline to an event of the one or more of the first customer timeline or the second customer timeline displayed on the graphical user interface.

7 . The method of claim 6 , wherein the user-entered note denotes a root cause of a problematic event.

8 . The method of claim 6 , wherein the user-entered note assigns a task to another user in relation to one or more of the first customer timeline or the second customer timeline.

9 . The method of claim 6 , wherein the graphical user interface comprises a button that allows a user to toggle through user-entered notes about the one or more of the first customer timeline or the second customer timeline in sequence.

10 . The method of claim 1 , further comprising determining a scale of a problem associated with the shared event by comparing the pattern that led to the shared event to each other customer timeline of a plurality of other customer timelines stored for a contact center system.

11 . The method of claim 1 , wherein the first customer timeline and the second customer timeline are scaled to allow comparison of an order of events and respective relative positions in time of the events across the first customer timeline and the second customer timeline.

12 . The method of claim 1 , wherein the graphical user interface comprises an interface element that allows a user to modify the one or more of the first customer timeline or the second customer timeline to add an event selected from a library of stored events.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 12, 2025
From: POINTILLIST, INC.
To: GENESYS CLOUD SERVICES, INC.
Reel/Frame 073933/0158 →
NOTICE OF SUCCESSION OF SECURITY INTERESTS AT REEL/FRAME 058643/0545 Recorded Feb 3, 2025
From: BANK OF AMERICA, N.A., AS RESIGNING AGENT
To: GOLDMAN SACHS BANK USA, AS SUCCESSOR AGENT
Reel/Frame 070097/0075 →
SECURITY AGREEMENT SUPPLEMENT Recorded Jan 5, 2022
From: POINTILLIST, INC.
To: BANK OF AMERICA, N.A.
Reel/Frame 058643/0545 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 23, 2021
From: THIEL, WILL
To: POINTILLIST, INC.
Reel/Frame 057579/0675 →
Continuity (1)
Related Publication 20250069011A1 · Feb 27, 2025
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