Automated Contact Center Quality Evaluation and Coaching System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current quality monitoring systems in contact centers rely heavily on manual evaluations, which are time-consuming and inefficient, leading to limited feedback for agents and poor performance trends analysis.
Innovation Solution
Automated systems that evaluate interactions using predefined quality criteria, provide real-time coaching, and generate customized coaching sessions based on performance metrics, enabling continuous improvement and trend analysis among agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation methods are used for quality monitoring, then evaluation accuracy can be maintained through human judgment, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent segments the quality monitoring process into distinct functional modules: speech recognition module for converting speech to text, topic detection module for identifying discussed topics, evaluation question module for generating assessment questions, and answer computation module for calculating responses. This segmentation enables parallel processing of different evaluation aspects, significantly reducing time consumption while maintaining comprehensive evaluation accuracy through specialized processing for each function.
Solution Approach 2:
The patent introduces an automatic speech recognition engine as an intermediary between the agent's speech and the evaluation system. This intermediary converts speech to text, enabling automated analysis while preserving the nuances of human communication. The system uses this intermediary to bridge the gap between manual evaluation accuracy and automated efficiency by processing speech through multiple analytical layers before generating evaluation results.
2Productivity
If automated evaluation systems are implemented, then processing speed and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent implements a universal evaluation platform that handles multiple evaluation dimensions (politeness, efficiency, solution accuracy, topic coverage) through a single integrated system. The same core modules—speech recognition, topic detection, and answer computation—serve multiple evaluation purposes simultaneously. This multi-functionality reduces overall system complexity compared to having separate systems for each evaluation aspect while maintaining high processing speed across all metrics.
Solution Approach 2:
The system incorporates feedback mechanisms where evaluation results are automatically fed back to agents in real-time or near-real-time. This feedback loop includes providing scores, identifying areas for improvement, and suggesting coaching topics. The automated feedback system simplifies the overall process by eliminating manual feedback generation while providing continuous performance guidance to agents, thereby improving productivity without proportionally increasing complexity.
3Reliability
If comprehensive evaluation criteria are applied, then quality assessment thoroughness is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial evaluation by focusing on specific, pre-defined evaluation questions and topics relevant to each interaction type rather than analyzing every aspect of every interaction equally. The system selectively processes speech content based on the evaluation form requirements, detecting only the topics and answering only the questions pertinent to the specific assessment. This partial action approach maintains thoroughness for critical quality aspects while significantly reducing overall computational resource consumption.
Solution Approach 2:
The system performs preliminary action by pre-defining evaluation forms, questions, and expected answer patterns before actual interactions occur. Topic dictionaries and evaluation criteria are established in advance, allowing the automated system to efficiently match detected topics against predefined categories during interaction analysis. This preliminary preparation reduces real-time computational requirements while ensuring comprehensive coverage of all necessary quality assessment dimensions.
Data Source
AI summary
A method includes: receiving, by a processor, an evaluation form including a plurality of evaluation questions; receiving, by the processor, an interaction to be evaluated by the evaluation form; selecting, by the processor, an evaluation question of the evaluation form, the evaluation question including a rule associated with one or more topics, each of the topics including one or more words or phrases; searching, by the processor, the interaction for the one or more topics of the rule in accordance with the presence of one or more words or phrases in the interaction to generate a search result; calculating, by the processor, an answer to the evaluation question in accordance with the rule and the search result; and outputting, by the processor, the calculated answer to the evaluation question of the evaluation form.