18189358. METHOD AND SYSTEM FOR AUTO SUMMARIZING CHAT CONVERSATION VIA MACHINE LEARNING AND APPLICATION THEREOF simplified abstract (Verizon Patent and Licensing Inc.)

METHOD AND SYSTEM FOR AUTO SUMMARIZING CHAT CONVERSATION VIA MACHINE LEARNING AND APPLICATION THEREOF

Organization Name

Verizon Patent and Licensing Inc.

Inventor(s)

RajeshKhanna Singa Ramalingam of Dayton NJ (US)

Kumaradevan Chandrabose of Basking Ridge NJ (US)

Srinivasa Kaniganti of Frisco TX (US)

Sankar Shanmugam of Dayton NJ (US)

METHOD AND SYSTEM FOR AUTO SUMMARIZING CHAT CONVERSATION VIA MACHINE LEARNING AND APPLICATION THEREOF - A simplified explanation of the abstract

This abstract first appeared for US patent application 18189358 titled 'METHOD AND SYSTEM FOR AUTO SUMMARIZING CHAT CONVERSATION VIA MACHINE LEARNING AND APPLICATION THEREOF

The present teaching involves auto-generated summaries of communications and enabled services. Summaries are created automatically using machine trained models for interactions between customers and service agents. A summary modification history is maintained for each summary, including the original machine-generated version and any updated versions. When a service request is received from a customer, the system retrieves the summary modification history of a prior communication with that customer to respond effectively. Feedback data is generated using these histories to update the models.

• Machine trained models are used to automatically generate summaries of communications between customers and service agents.
• A summary modification history is created for each summary, including the original machine-generated version and any updates.
• When a service request is received, the system retrieves the summary modification history of prior communications with the customer to provide a tailored response.
• Feedback data is generated from the summary modification histories to improve and update the machine trained models.
• This technology streamlines communication processes and enhances customer service efficiency.

Potential Applications: - Customer service industry - Call centers - Chatbot services

Problems Solved: - Improving communication efficiency - Enhancing customer service experience - Providing personalized responses to service requests

Benefits: - Increased customer satisfaction - Time-saving for service agents - Improved accuracy in responses

Commercial Applications: Title: Enhanced Customer Service Communication Technology This technology can be utilized in various industries such as call centers, customer service departments, and online chat services to streamline communication processes and improve overall customer satisfaction. The market implications include increased efficiency, cost savings, and enhanced customer loyalty.

Questions about Enhanced Customer Service Communication Technology:

1. How does this technology impact customer service efficiency? This technology improves customer service efficiency by automatically generating summaries of communications and providing personalized responses based on past interactions, leading to quicker and more accurate responses.

2. What are the potential applications of this technology beyond customer service? This technology can also be applied in various industries where communication summaries are essential, such as legal services, healthcare, and technical support.

Original Abstract Submitted

The present teaching relates to auto generated summaries of communications and enabled services. Summaries are automatically generated based on machine trained models for communications between customers and service agents. A summary modification history is created for each of the summaries including the machine generated summary and some updated versions of the summary. When a service request is received from a customer, a summary modification history of a prior communication associated with the customer is retrieved for responding to the request. Using the summary modification histories, feedback data is generated, which is then used for updating the models.