Calabrio, Inc. (20240211699). SYSTEMS AND METHODS FOR EVENT DRIVER DETECTION simplified abstract
Contents
SYSTEMS AND METHODS FOR EVENT DRIVER DETECTION
Organization Name
Inventor(s)
Catherine Bullock of Minneapolis MN (US)
Dylan Morgan of Minneapolis MN (US)
Laura Cattaneo of Rochester MN (US)
Kyle Smaagard of Forest Lake MN (US)
Boris Chaplin of Medina MN (US)
SYSTEMS AND METHODS FOR EVENT DRIVER DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211699 titled 'SYSTEMS AND METHODS FOR EVENT DRIVER DETECTION
The disclosed aspects relate to event driver detection from communication data (e.g., a stream of text, an image, and audio stream, and/or a video stream). In examples, an event is identified for a communication. For example, the event may be a system-driven event, a context-driven event, or a conversation-driven event. One or more segments of communication data (e.g., an utterance, a sentence, or a sentence fragment) relating to the event may be identified, such that a topic may be determined for each segment. The determined topic(s) may be associated with the event, thereby determining an event driver for the event that provides an indication as to why the event occurred. Multiple communications (e.g., having the same or a similar event type, agent, supervisor, time period, and/or queue) may be aggregated, such that patterns/trends for corresponding event drivers may be identified and further processed accordingly.
- Simplified Explanation:
- The technology focuses on detecting event drivers from communication data, such as text, images, audio, and video streams. - Events are identified within communications, and topics are determined for segments of data related to these events. - By associating topics with events, the technology determines event drivers that explain why the events occurred. - Patterns and trends for event drivers across multiple communications can be identified and analyzed.
- Key Features and Innovation:
- Detection of event drivers from various forms of communication data. - Association of topics with events to determine event drivers. - Aggregation and analysis of patterns and trends for event drivers across multiple communications.
- Potential Applications:
- Customer service analysis. - Marketing campaign effectiveness evaluation. - Fraud detection in financial transactions.
- Problems Solved:
- Understanding the reasons behind specific events in communications. - Identifying patterns and trends in event drivers for improved decision-making.
- Benefits:
- Enhanced insight into communication data. - Improved event analysis and response. - Better understanding of customer behavior and preferences.
- Commercial Applications:
- "Event Driver Detection Technology: Enhancing Communication Analysis and Decision-Making"
- Prior Art:
- Researchers in the field of natural language processing and data analytics may have explored similar techniques for event detection and analysis.
- Frequently Updated Research:
- Stay updated on advancements in natural language processing and communication data analysis for potential improvements in event driver detection technology.
Questions about Event Driver Detection: 1. How does the technology determine event drivers from communication data? 2. What are the potential real-world applications of event driver detection technology?
Original Abstract Submitted
the disclosed aspects relate to event driver detection from communication data (e.g., a stream of text, an image, and audio stream, and/or a video stream). in examples, an event is identified for a communication. for example, the event may be a system-driven event, a context-driven event, or a conversation-driven event. one or more segments of communication data (e.g., an utterance, a sentence, or a sentence fragment) relating to the event may be identified, such that a topic may be determined for each segment. the determined topic(s) may be associated with the event, thereby determining an event driver for the event that provides an indication as to why the event occurred. multiple communications (e.g., having the same or a similar event type, agent, supervisor, time period, and/or queue) may be aggregated, such that patterns/trends for corresponding event drivers may be identified and further processed accordingly.
