Multiparty Interaction Analytics With Context-Based 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 variable conditions, leading to incomplete and misleading performance assessments.
Innovation Solution
An analytics engine system that includes a processor configured to receive and validate interaction data, retrieve contextual information, apply normalization rules, and calculate aggregate scores to normalize task scores, facilitating real-time performance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If only a portion of the data from the interaction is analyzed to generate performance statistics, then the analysis process is simpler and faster, but the evaluation becomes incomplete and may lead to misleading conclusions
Solution Approach 1:
The system segments the performance analysis into multiple independent components: data collection module, data validation module, task score calculation module, normalization module, and aggregate score calculation module. Each module processes specific portions of the data independently, allowing comprehensive analysis while maintaining processing efficiency through parallel operations.
Solution Approach 2:
The system performs preliminary data validation and filtering before comprehensive analysis. The data validation module pre-processes the interaction data to ensure quality and relevance, then the task score calculation module processes validated data through normalization before final aggregation, ensuring accurate evaluation without requiring reprocessing.
2Measurement precision
If comprehensive data from the entire interaction is analyzed, then the performance evaluation becomes more accurate and complete, but the analysis process becomes more complex and time-consuming
Solution Approach 1:
The comprehensive analysis system is divided into specialized modules, each handling specific tasks: data collection for capturing interaction data, validation for ensuring data quality, task score calculation for individual performance metrics, normalization for standardizing comparisons, and aggregate score calculation for overall performance. This segmentation reduces overall system complexity while enabling comprehensive analysis.
Solution Approach 2:
The system uses a universal normalization module that handles multiple types of performance data and comparison scenarios through a single standardized process. The normalization module applies different normalization rules based on the data type and interaction context, providing accurate evaluation across diverse multi-party interactions without requiring separate analysis procedures for each scenario.
3Ease of manufacture
If performance statistics are collected during only some plays, then the data collection process is simpler, but the statistics do not represent the player's performance throughout the entire game
Solution Approach 1:
The data collection module continuously captures interaction data throughout the entire multi-party interaction without interruption. The system maintains continuous monitoring and data accumulation from all participants throughout the complete interaction duration, ensuring that no performance aspects are omitted and that the final evaluation accurately represents overall performance.
Solution Approach 2:
The system performs preliminary data validation and quality assessment during the interaction to identify and filter out incomplete or inaccurate data points. This preliminary action ensures that only reliable data is included in the final performance evaluation, maintaining accuracy while managing data collection complexity.
4Device complexity
If variables associated with difficult plays are not normalized, then the analysis process is simpler, but comparing players from different teams playing different plays becomes extremely difficult
Solution Approach 1:
The normalization module transforms performance variables by applying different normalization rules based on play type, team, and other contextual parameters. The system changes the scale and characteristics of performance data through parameter transformations, converting diverse variables into standardized metrics that enable fair comparison across different teams and play scenarios while maintaining analytical simplicity.
Solution Approach 2:
The normalization module serves as a universal comparator that handles multiple player types, positions, and play scenarios through a single standardized process. It applies appropriate normalization rules automatically based on the data characteristics, providing comparable performance metrics across all players regardless of team or play type without requiring complex separate analysis procedures.
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.