Machine Learning Call Annotation for Targeted Analytics Reports
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Solution Overview
Problem
Data analytics systems struggle with manual analysis of call data, leading to inefficiencies in generating nuanced insights, increased resource expenditure, and delayed decision-making in service-based entities with high call volumes.
Innovation Solution
Implement a targeted analytics report generation system using an annotation model and analytics model to autonomously process call transcripts, annotate them with relevant attributes, and generate tailored reports based on user inquiries, reducing manual effort and enhancing operational agility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analysis methods are used to analyze call data, then detailed and nuanced insights can be obtained, but the time required and resource expenditure increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated machine learning-based system. The annotation model uses natural language processing and pattern recognition algorithms to automatically identify, extract, and annotate relevant information from call transcripts, eliminating the need for manual review while maintaining high insight quality through sophisticated computational methods
Solution Approach 2:
The system enables self-service through automated annotation and analysis capabilities. The machine learning models continuously process call data independently, automatically generating insights and annotations without requiring human intervention. The system can operate autonomously to perform data processing, pattern identification, and insight generation, reducing dependency on manual resources
2Reliability
If manual analysis methods are used to process call transcripts, then comprehensive data review is possible, but computational and financial resources are excessively consumed
Solution Approach 1:
The patent substitutes manual computational efforts with optimized machine learning algorithms. The annotation model uses efficient pattern matching and natural language processing techniques to achieve comprehensive data review with reduced computational overhead. The system processes large volumes of call transcripts through scalable algorithms that maintain reliability while optimizing resource utilization
Solution Approach 2:
The system changes operational parameters by transitioning from manual processing to automated machine learning-based processing. This parameter change enables the system to handle larger datasets more efficiently, maintaining comprehensive review capability while reducing computational and financial resource consumption through optimized algorithmic approaches
3Productivity
If traditional data analysis techniques are used, then basic insights can be generated, but the ability to provide targeted and nuanced analytics is limited
Solution Approach 1:
The patent replaces traditional mechanical statistical analysis with sophisticated machine learning models. The annotation model uses natural language processing, pattern recognition, and contextual analysis algorithms to generate targeted and nuanced analytics insights. This substitution enables both rapid insight generation and high precision targeting by leveraging advanced computational methods
Solution Approach 2:
The system introduces machine learning models as intermediary components between raw call data and analytical insights. These intermediary models process and interpret complex patterns in the data, enabling the system to generate both fast and accurate targeted analytics. The machine learning layer acts as a mediator that transforms basic data processing into sophisticated analytical capabilities
4Measurement precision
If manual data processing is used for call transcripts, then thorough analysis is possible, but operational agility and decision-making speed are reduced
Solution Approach 1:
The patent substitutes manual analysis processes with automated machine learning-based processing that maintains thoroughness while dramatically increasing speed. The annotation model uses efficient algorithms to perform comprehensive analysis of call transcripts in real-time or near-real-time, enabling rapid decision-making without sacrificing analytical depth through optimized computational methods
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for providing a targeted analytics report. An example method includes receiving a call transcript, wherein the call transcript is associated with transcript metadata. The example method further includes determining one or more annotations for the call transcript. The example method further includes annotating the call transcript with the one or more annotations. The example method further includes storing the annotated call transcript in a history recorder repository. The example method further includes identifying an analytics inquiry comprising one or more attributes of interest, wherein an attribute of interest corresponds to one or more annotations. The example method further includes selecting one or more annotated call transcripts based on the one or more attributes of interest. The example method further includes generating a targeted analytics report based on the selected one or more annotated call transcripts, and providing the targeted analytics report.