Call Data Model Management via Attribute Segmentation
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Solution Overview
Problem
Conventional systems for managing call data are inadequate in handling large volumes and complexity, lacking user-friendly tools for identifying and demarcating attributes in recorded calls between merchants and customers, leading to inefficient analysis.
Innovation Solution
A system and method that involve receiving input indicating a segment of a recorded call and an associated attribute, determining and updating a model parameter based on this input, and generating an updated model to analyze calls with similar attributes, utilizing a graphical user interface and electronic tagging for efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional systems are used to manage call data, then basic call recording is maintained, but the systems cannot handle large volumes and complexity of call data effectively
Solution Approach 1:
The patent segments call data into distinct components including audio segments, metadata, transcripts, and identified attributes. The system divides the analysis process into multiple stages: initial processing, attribute identification, model application, and result generation. This segmentation enables the system to handle large volumes of call data efficiently by processing different components through specialized pathways.
2Ease of operation
If conventional systems are used for call data management, then basic storage is provided, but user-friendly tools for identifying and demarcating attributes are lacking
Solution Approach 1:
The system automatically performs attribute identification and demarcation without requiring manual user intervention. The machine learning models autonomously analyze call segments, identify relevant attributes, and demarcate them with timestamps and descriptors. This self-service capability eliminates the need for users to manually navigate complex analysis tools while still providing comprehensive attribute identification.
Solution Approach 2:
The patent introduces an intermediary layer between the raw call data and the user interface. This intermediary consists of automated processing components that translate complex audio and metadata analysis into user-friendly results. The system acts as a mediator that handles the complexity of attribute identification internally while presenting simplified outputs to users through the graphical interface.
3Productivity
If sampling methods are used for call analysis, then some calls are reviewed, but the process is limited and time-consuming
Solution Approach 1:
The system transforms the analysis approach by changing key parameters: instead of reviewing calls sequentially or using random sampling, it processes multiple calls simultaneously using parallel computational pathways. The system adjusts parameters such as analysis depth, attribute priority weights, and model confidence thresholds to optimize both speed and completeness. This enables comprehensive review of all calls rather than limited sampling.
4Quantity of substance
If traditional processes are used for call data management, then basic monitoring is possible, but adequate handling of large amounts of data is not achieved
Solution Approach 1:
The patent implements a universal analysis framework that can process diverse types of call data through multiple functionally integrated components. The system uses multi-functional models that can identify various attributes (emotional state, compliance issues, key topics, outcomes) within the same processing pipeline. This universal approach maintains consistent reliability across different data types and volumes while scaling to handle large amounts of call data.
Solution Approach 2:
The system incorporates feedback mechanisms where analysis results are continuously refined. Models are trained on identified attributes and outcomes, with performance feedback used to improve future analyses. The system adjusts its analysis parameters based on feedback from processed calls, maintaining high reliability even as data volumes increase. This iterative feedback loop ensures accurate handling of large datasets.
Data Source
AI summary
Systems and methods for managing models for call data are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a user device, an input indicating a segment of a first recorded call and an attribute associated with the segment. The operations may include determining a parameter of a model, wherein the model is associated with the attribute. The operations may include changing the parameter based on the input. The operations may include generating an updated model based on the changed parameter, wherein the updated model may be configured to analyze recorded calls having one or more segments with the same attribute.