View source for 20240031217. Agent Instance Live-Monitoring By A Management Network for Burnout and Attrition Prediction And Response simplified abstract (Intradiem, Inc.)

=Agent Instance Live-Monitoring By A Management Network for Burnout and Attrition Prediction And Response=


==Organization Name==

[[:Category:Intradiem, Inc.|Intradiem, Inc.]]


[[Category:Intradiem, Inc.]]

==Inventor(s)==

[[:Category:Tristan Pahud of London (GB)|Tristan Pahud of London (GB)]][[Category:Tristan Pahud of London (GB)]]

[[:Category:Christopher Powell Busbee of Marietta GA (US)|Christopher Powell Busbee of Marietta GA (US)]][[Category:Christopher Powell Busbee of Marietta GA (US)]]

[[:Category:Omar Shetta of Southampton (GB)|Omar Shetta of Southampton (GB)]][[Category:Omar Shetta of Southampton (GB)]]

[[:Category:Akul Dewan of Sugar Hill GA (US)|Akul Dewan of Sugar Hill GA (US)]][[Category:Akul Dewan of Sugar Hill GA (US)]]

[[:Category:Asmita Jiva of Cumming GA (US)|Asmita Jiva of Cumming GA (US)]][[Category:Asmita Jiva of Cumming GA (US)]]

[[:Category:Michael Carl Jarus of Suwanee GA (US)|Michael Carl Jarus of Suwanee GA (US)]][[Category:Michael Carl Jarus of Suwanee GA (US)]]

[[:Category:Eric Victor Drucker of Roswell GA (US)|Eric Victor Drucker of Roswell GA (US)]][[Category:Eric Victor Drucker of Roswell GA (US)]]

[[:Category:Kevin Wilson of Bradenton FL (US)|Kevin Wilson of Bradenton FL (US)]][[Category:Kevin Wilson of Bradenton FL (US)]]

[[:Category:Jennifer Lee of Hays KS (US)|Jennifer Lee of Hays KS (US)]][[Category:Jennifer Lee of Hays KS (US)]]

[[:Category:Jennifer Christine East of Milton GA (US)|Jennifer Christine East of Milton GA (US)]][[Category:Jennifer Christine East of Milton GA (US)]]

[[:Category:Kayla Heflin of Marietta GA (US)|Kayla Heflin of Marietta GA (US)]][[Category:Kayla Heflin of Marietta GA (US)]]

[[:Category:Xiangyu Meng of Decatur GA (US)|Xiangyu Meng of Decatur GA (US)]][[Category:Xiangyu Meng of Decatur GA (US)]]

==Agent Instance Live-Monitoring By A Management Network for Burnout and Attrition Prediction And Response - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240031217 titled 'Agent Instance Live-Monitoring By A Management Network for Burnout and Attrition Prediction And Response

==Simplified Explanation==

The patent application describes a computer-implemented method for managing a contact center with multiple agents. Here is a simplified explanation of the abstract:

* The method involves receiving real-time data associated with each agent servicing incoming communications in the contact center.
* The data is processed using a trained supervised machine learning model to categorize each agent into a burnout risk category.
* Based on a specified logical directive, an operation is determined to be performed in relation to at least one agent.
* The specification includes conditions related to the categorization of agents into burnout risk categories.
* When the conditions are satisfied, the specified operation is performed on the agent.
* The method is implemented using a computing system, and an article of manufacture is provided.

Potential applications of this technology:

* Contact center management: This technology can be used to effectively manage and monitor the well-being of agents in a contact center, ensuring their workload is balanced and preventing burnout.
* Employee performance optimization: By categorizing agents into burnout risk categories, this technology can help identify areas where additional support or training may be needed to improve agent performance and job satisfaction.

Problems solved by this technology:

* Burnout prevention: The technology helps identify agents at risk of burnout, allowing proactive measures to be taken to prevent it.
* Workload management: By categorizing agents based on their workload and performance, the technology enables better distribution of tasks and resources, ensuring a more balanced workload for each agent.

Benefits of this technology:

* Improved agent well-being: By identifying and addressing burnout risks, the technology promotes a healthier work environment for agents, leading to increased job satisfaction and reduced turnover.
* Enhanced customer experience: By optimizing agent performance and workload, the technology can improve the quality and efficiency of customer interactions, resulting in a better overall customer experience.


==Original Abstract Submitted==

a computer-implemented method for managing a contact center having a plurality of agents includes (a) receiving real-time data associated with each of a plurality of agents servicing incoming communications for the contact center, (b) categorizing each of the plurality of agents into a respective one of a plurality of burnout risk categories by processing the real-time data associated with each of the plurality of agents using a trained supervised machine learning model having a plurality of input features, (c) determining, based on a specification of a logical directive, an operation to be performed in relation to at least one agent in the plurality of agents, wherein the specification has at least one condition relating to the categorizing of each of the plurality of agents into the respective one of the plurality of burnout risk categories, and wherein the specification defines the operation to be performed in relation to the at least one agent in the plurality of agents upon the condition being satisfied, and (d) causing the operation to be performed in relation to the at least one agent instance in the plurality of agents. a computing system and article of manufacture are also provided.



[[Category:H04L41/046]]
[[Category:G06F9/54]]
[[Category:H04L9/40]]