Real-Time Contextual Document Retrieval for Call Center Agents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Call center agents face difficulties in accessing real-time information for resolving customer issues due to the inaccessibility of successful resolution approaches, which are often documented but not readily available during live interactions.
Innovation Solution
A system that utilizes real-time speech-to-text transcription, text segmentation, and machine learning processes to identify and rank contextually relevant documents from a database, providing agents with relevant past call segments and feedback mechanisms to improve response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If call recordings are stored for quality control purposes, then successful resolution approaches are documented, but these successful resolutions become inaccessible to agents in real time
Solution Approach 1:
The system pre-processes call recordings during non-real-time periods to extract successful resolution approaches and store them in a searchable database. This preliminary action ensures that when agents need information during live calls, the data is already prepared and immediately accessible, eliminating the time loss between storage and retrieval.
Solution Approach 2:
The patent introduces an intermediary system comprising speech-to-text transcription, text segmentation, and machine learning comparison components. This intermediary bridges the gap between stored call recordings and agent needs by automatically transcribing audio, segmenting text, comparing against current call context, and presenting relevant successful resolutions in real time.
2Productivity
If the system processes and compares vector representations of documents in real time, then relevant solutions are quickly identified, but the computational complexity and processing resources increase
Solution Approach 1:
The patent segments the call transcript into multiple sections and generates separate vector representations for each segment. This segmentation allows the system to process smaller, more manageable pieces of text independently, reducing the overall computational complexity while maintaining the ability to identify relevant solutions quickly through parallel processing.
Solution Approach 2:
The system performs partial processing by focusing computational resources only on segments of the call transcript that are most likely to contain relevant information. Rather than processing the entire transcript uniformly, the machine learning model identifies and prioritizes key segments for vector representation and comparison, reducing unnecessary computational overhead.
Data Source
AI summary
Methods, computer systems and computer program product are provided for retrieving contextually relevant documents in near real time. When text data it's received from an application, the text data is processed through a text segmentation model to generate a set of documents. Each document corresponds to a segment of the text data. A first vector representation is generated for a first document of the set of documents. A machine learning process compares the first vector representation and a set of vector representations for a set of documents within a data repository to determine a subset of the documents. A composite rank is generated for each respective document of the subset. The subset of documents is then presented through an interface, sorted according to the respective composite ranks.