Voice Interaction Metric Analysis With Real-Time Agent Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data-communication systems lack effective methods to analyze customer interaction metrics from digital voice data, such as tone and sentiment, to improve customer service outcomes and agent performance in real-time.
Innovation Solution
A data-communication server system that includes processing circuitry to analyze digital voice data from transcribed audio conversations, identifying keywords and speech characteristic parameters, and provides real-time feedback to agents to adjust their interactions based on threshold comparisons and historical associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital voice data analysis is implemented to identify customer interaction metrics, then customer service outcomes and agent performance are improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent introduces processing circuitry as an intermediary component that bridges the data-communication server and the analysis functions. This dedicated processing unit handles the complex tasks of identifying keywords, extracting speech characteristics, and comparing interactions against historical data, thereby improving service outcomes while isolating the complexity from the core communication system.
Solution Approach 2:
The system implements feedback mechanisms where analysis results from digital voice data are used to provide real-time or post-call feedback to agents. This feedback loop enables continuous improvement of agent performance by comparing actual interactions against historical associations and threshold values, thereby enhancing customer service outcomes through iterative learning.
2Measurement precision
If real-time analysis of digital voice data is performed to provide feedback to agents, then interaction quality is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical voice data associations, keywords, and speech characteristics in advance. This pre-computed historical data serves as a reference baseline that enables faster real-time comparisons during actual customer interactions, reducing the computational burden and processing time required for real-time analysis.
Solution Approach 2:
The patent applies partial action by selectively analyzing specific speech characteristics and keywords rather than processing entire voice datasets in full detail. The system identifies and focuses on relevant features such as tone, sentiment, and specific keywords that have historical associations with outcomes, thereby achieving sufficient measurement precision without the computational overhead of complete data processing.
3Measurement precision
If comprehensive speech characteristic parameters are analyzed including frequency, amplitude, and sentiment, then accuracy of outcome identification is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the most relevant speech characteristic parameters from the complete voice data spectrum. Instead of analyzing all possible acoustic features, the system selectively extracts keywords, sentiment indicators, and specific speech characteristics such as frequency and amplitude that have demonstrated historical associations with conversation outcomes. This extraction approach maintains outcome identification accuracy while reducing processing complexity.
Solution Approach 2:
The processing circuitry is designed with multi-functionality to handle diverse analysis tasks using a unified framework. The same processing components that identify keywords also extract speech characteristics and compare interactions against historical data, eliminating the need for separate specialized systems for each analysis function and thereby reducing overall data processing complexity.
Data Source
AI summary
Certain aspects of the disclosure are directed to apparatuses and methods for analyzing customer-interaction metrics from digital data in a data-communications system. An example method includes: accessing digital data from a computer-accessible database that is indicative of communication interactions between personnel of and at least one customer of a client entity and that includes parameters corresponding to at least one of words, phrases and speech parameters; identifying, based on the digital data and for the personnel of the client entity, at least one customer-interaction metric that has one or more associations with one or more outcomes of one or more of the communication interactions and that is outside a threshold value or rating established for or on behalf of the at least one agent of the client entity; and adjusting, via data-processing computer circuitry based on the digital data, at least one of the customer-interaction metrics and the one or more associations and providing feedback to the data-processing computer circuitry in response to said adjusting, for assessing an outcome of further instances of the one or more of the communication interactions involving the personnel of the client entity.