AI Inbox Prioritization Using Traffic Signals for Critical Queries
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Solution Overview
Problem
Contact centers face challenges in prioritizing real-time customer interactions efficiently, leading to increased average handling time and long waiting times for critical queries due to manual analysis by agents, who struggle to process interaction transcripts effectively.
Innovation Solution
An AI-driven system using Amazon Web Services (AWS) Comprehend and natural language processing (NLP) to automatically prioritize interactions based on keywords, historical relevance, and context, with visual indicators in the agent inbox to highlight high-priority items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agents manually search and read entire interaction contents to identify urgency, then they can accurately assess priority, but average contact handling time increases
Solution Approach 1:
The patent replaces the mechanical manual reading and analysis process with an automated natural language processing system. The NLP model automatically analyzes interaction transcripts, extracts key information, and assigns priority scores, eliminating the need for agents to manually read entire interaction contents while maintaining accurate priority assessment.
Solution Approach 2:
The patent introduces an intermediary NLP-based priority scoring system that acts as a mediator between the raw interaction data and the agent inbox. This intermediary automatically processes interactions, generates priority scores based on predefined criteria, and presents prioritized lists to agents, reducing their cognitive load and time investment.
2Reliability
If agents manually process real-time information in message threads and interaction transcripts, then they can identify critical queries, but waiting times in queues increase
Solution Approach 1:
The patent applies preliminary action by automatically analyzing and scoring interactions before they reach the agent inbox. The NLP system pre-processes message threads and transcripts, identifies critical information, and assigns priority scores in advance, so that when agents receive the prioritized list, critical queries are already identified and ready for immediate attention.
Solution Approach 2:
The patent replaces the manual real-time information processing that agents would otherwise perform with an automated NLP system. This substitution enables continuous automatic analysis of incoming interactions, extracting key information and assigning priorities without human intervention, thereby reducing queue waiting times for critical queries.
3Productivity
If no automated prioritization system is used, then system complexity remains low, but agent productivity decreases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically prioritize interactions without requiring manual agent intervention. The NLP-based priority scoring system autonomously analyzes interactions, applies predefined criteria, and generates priority assignments, allowing the system to serve itself in the prioritization task and freeing agents to focus on high-value activities.
Solution Approach 2:
The patent utilizes parameter changes by transforming unstructured interaction text into structured priority scores through NLP processing. The system changes the parameter state from raw textual data to quantified priority metrics, enabling automated sorting and prioritization while maintaining manageable system complexity through well-defined transformation rules.
Data Source
AI summary
Interaction prioritization systems and methods, and non-transitory computer readable media, include receiving a transcript of a first customer interaction in an agent inbox; extracting, in real-time, keywords from the transcript; comparing, in real-time by an artificial intelligence (AI) model, the extracted keywords to keywords in a customized historical database; calculating, in real-time by the AI model, a priority score of the first customer interaction based on the comparison; assigning, in real-time by the AI model, a priority to the first customer interaction based on the calculated priority score; and applying, in real-time, a visual indicator on the first customer interaction in the agent inbox, wherein the visual indicator corresponds to the assigned priority of the first customer interaction.