Real-Time Analytics Engine for Contextual Multiparty Score Normalization
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Solution Overview
Problem
Existing multi-party interaction analysis systems struggle to provide comprehensive and accurate performance evaluation due to limited data capture and difficulty in normalizing performance metrics across varying conditions, leading to incomplete and misleading assessments.
Innovation Solution
An analytics engine system that includes a processor communicatively coupled to a memory, configured to receive and validate interaction data, retrieve contextual data, apply normalization rules and factors, and calculate aggregate scores for real-time performance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive data is collected from all interactions, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex data collection and processing task into multiple independent modules: data collection module, data validation module, contextual data retrieval module, normalization module, and score calculation module. Each module handles a specific aspect of the analysis process, making the overall system more manageable while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The patent introduces an intermediary normalization process that acts as a mediator between raw performance data and final evaluation scores. This normalization layer standardizes data from multiple sources and conditions, enabling accurate comparisons without requiring the entire system to handle every possible data variation directly.
2Productivity
If performance data is analyzed in real-time, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-defining normalization rules and contextual data structures before real-time analysis occurs. The normalization database and contextual data sources are prepared in advance, allowing the real-time analysis to simply retrieve and apply pre-established frameworks rather than creating complex calculations on the fly.
Solution Approach 2:
The real-time analysis process is segmented into distinct sequential operations: receiving interaction data, validating data, retrieving contextual information, applying normalization factors, and calculating scores. This segmentation allows each operation to be optimized independently and executed efficiently in real-time.
3Measurement precision
If multiple normalization factors are applied, then measurement precision is improved, but device complexity increases
Solution Approach 1:
Normalization factors and rules are determined and stored in advance in the normalization database before actual performance analysis occurs. This preliminary determination of normalization parameters eliminates the need for complex real-time calculations, as the system simply retrieves pre-computed factors and applies them to incoming data.
Solution Approach 2:
The system uses normalized historical data and pre-established normalization models as templates for analyzing current performance. By copying and adapting proven normalization approaches from historical contexts, the system achieves accurate normalization without requiring complex new calculations for each situation.
4Reliability
If comprehensive performance data is collected, then reliability is improved, but loss of information increases
Solution Approach 1:
The system incorporates feedback mechanisms where validated interaction data and contextual information are continuously fed back into the analysis process. This feedback loop ensures that all relevant information is considered in each analysis iteration, improving reliability while preventing information loss through systematic review and validation at each stage.
Data Source
AI summary
An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.