Verizon Patent and Licensing Inc. (20240244135). SYSTEMS AND METHODS FOR UTILIZING A MACHINE LEARNING MODEL TO DETERMINE AN INTENT OF A VOICE CUSTOMER IN REAL TIME simplified abstract
SYSTEMS AND METHODS FOR UTILIZING A MACHINE LEARNING MODEL TO DETERMINE AN INTENT OF A VOICE CUSTOMER IN REAL TIME
Organization Name
Verizon Patent and Licensing Inc.
Inventor(s)
Srinivasa Kaniganti of Frisco TX (US)
Madhu Talupur of Princeton NJ (US)
Sankar Shanmugam of Dayton NJ (US)
Amol Chakradeo of Martinsville NJ (US)
RajeshKhanna Singa Ramalingam of Dayton NJ (US)
SYSTEMS AND METHODS FOR UTILIZING A MACHINE LEARNING MODEL TO DETERMINE AN INTENT OF A VOICE CUSTOMER IN REAL TIME - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240244135 titled 'SYSTEMS AND METHODS FOR UTILIZING A MACHINE LEARNING MODEL TO DETERMINE AN INTENT OF A VOICE CUSTOMER IN REAL TIME
The abstract describes a device that can analyze real-time audio data from a call between an agent and a customer, along with historical customer interactions, chat data, and interactive voice response data. The device uses machine learning to determine customer intent and suggests actions based on this intent.
- Device receives real-time audio data from calls
- Device analyzes historical customer interactions and other data
- Machine learning model determines customer intent
- Device suggests actions based on customer intent
- Enhances customer service and efficiency
Potential Applications: - Customer service call centers - Sales and marketing departments - Technical support teams
Problems Solved: - Improves customer service by understanding customer intent - Increases efficiency by suggesting appropriate actions - Enhances overall customer experience
Benefits: - Streamlines customer interactions - Increases agent productivity - Improves customer satisfaction
Commercial Applications: Title: Enhanced Customer Service Automation Technology This technology can be used in various industries such as telecommunications, e-commerce, and healthcare to improve customer service interactions and streamline processes. Companies can benefit from increased efficiency and customer satisfaction.
Prior Art: Researchers can explore existing technologies in the field of customer service automation, machine learning, and natural language processing to understand the evolution of similar systems.
Frequently Updated Research: Stay informed about advancements in machine learning algorithms, natural language processing techniques, and customer service automation tools to enhance the capabilities of this technology.
Questions about Enhanced Customer Service Automation Technology: 1. How does this technology impact customer satisfaction levels? - This technology improves customer satisfaction by understanding customer intent and providing appropriate solutions in real-time. 2. What are the potential challenges in implementing this technology in different industries? - The challenges may include data privacy concerns, integration with existing systems, and training employees to use the technology effectively.
Original Abstract Submitted
a device may receive real time audio data associated with a call between an agent and a customer, and may receive customer data identifying historical interactions with the customer. the device may receive chat data associated with the customer or interactive voice response (ivr) data associated with the customer, and may generate, based on the real time audio data, transcript data identifying a real time transcript of the call with the customer. the device may process the real time audio data, the customer data, the chat data or the ivr data, and the transcript data, with a machine learning model, to determine a customer intent and one or more actions to perform based on the customer intent; and may perform the one or more actions.
