Dynamic Risk Scoring for Audio NLP Systems
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Solution Overview
Problem
Corporate enterprises face challenges in identifying and treating customer service data that requires different handling due to its regulatory and proprietary nature, often resulting in misunderstandings and inadequate risk assessment, especially in high-velocity communication environments with various channels.
Innovation Solution
A computing system that processes natural language inputs from audio signals and textual communications using trained artificial intelligence models for automatic speech recognition and natural language understanding, dynamically interpreting and scoring risk elements to assign a risk score, which is adjusted based on additional risk elements and compared to a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing is used to understand customer communications, then the ability to interpret unstructured data improves, but the complexity of the system increases
Solution Approach 1:
The system segments the complex NLP task into distinct functional modules: automatic speech recognition module for converting audio to text, natural language understanding module for extracting meaning, and risk scoring module for assessment. This segmentation allows each module to specialize in a specific function, improving overall interpretation accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary components such as transcript data as an intermediate representation between raw audio input and risk assessment output. This intermediary layer allows the system to process and analyze language data in a structured format, bridging the gap between unstructured communication and structured risk evaluation without requiring the entire system to handle all complexities simultaneously.
2Speed
If real-time processing is implemented for high-velocity data, then the speed of risk identification improves, but the computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing audio signals into transcript data and pre-configuring risk assessment models before actual risk evaluation is needed. This allows the system to have processing pipelines ready in advance, enabling real-time risk scoring when communications arrive without requiring intensive computational resources at the moment of processing.
Solution Approach 2:
The risk scoring mechanism is designed to be dynamic, adjusting the level of processing intensity based on the characteristics of the incoming communication. For routine communications, the system uses streamlined processing paths, while for complex or high-risk communications, it allocates additional computational resources, thereby optimizing the balance between processing speed and resource consumption.
3Reliability
If dynamic risk scoring is applied to all communications, then the reliability of risk assessment improves, but the time required for processing increases
Solution Approach 1:
The system applies partial dynamic risk scoring by performing comprehensive risk assessment on communications that require it while using simplified scoring for routine communications. This selective approach maintains reliability for critical risk assessments while minimizing processing time for the majority of communications that do not require full analysis, thereby resolving the contradiction between thoroughness and speed.
Data Source
AI summary
Systems and methods receive, from a user device through a communication channel, and process, in real-time, a natural language input comprising unstructured data that is derived from an audio signal. The natural language input is dynamically interpreted, the interpreting including applying the unstructured data to trained AI models that (i) perform ASR to generate textual data and (ii) contextualize the textual data using a NLU model. Based thereon, a risk element from the natural language input is identified, and a risk score is assigned that ranks inherent risk of the natural language input. The risk score is dynamically adjusted based on identifying additional risk element(s) during the natural language input and is based on an aggregation of the risk element and the additional risk element(s). Risk analysis is performed on the natural language input and includes comparing the risk score to a threshold.