Predictive Classification via Identifier Distribution Analysis
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Solution Overview
Problem
Existing predictive data analysis techniques are time-consuming, resource-intensive, and lack accuracy and granularity, often relying on descriptive identifiers that are not automated or accessible, leading to inefficient and unreliable classifications.
Innovation Solution
A machine-learning classification model is trained to generate predictive classifications using preprocessed input data and new data structures, enabling the automatic processing of complex, potentially non-descriptive data to improve accuracy, availability, and granularity of classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing predictive data analysis techniques are used, then classifications can be generated, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent applies preliminary action by pre-processing historical interaction data to extract and store identifier distributions before actual classification is needed. The system pre-computes frequency distributions, mean values, and standard deviations of identifiers from historical data, so that when classification is required, the model can quickly reference these pre-computed statistics rather than processing raw data from scratch, thereby reducing processing time while maintaining classification quality
Solution Approach 2:
The patent replaces traditional mechanical data processing approaches with machine learning-based statistical modeling. Instead of using rule-based or deterministic classification methods that require extensive computational resources, the system uses trained machine learning models that leverage pre-computed identifier distributions to make rapid classifications, substituting heavy mechanical processing with more efficient statistical inference
2Measurement precision
If existing predictive data analysis techniques are used, then classifications can be generated, but they lack accuracy and granularity
Solution Approach 1:
The patent applies parameter changes by transforming raw identifier data into statistical parameters such as frequency distributions, mean values, and standard deviations. The machine learning model is trained on these transformed parameters rather than raw identifiers, enabling the system to capture subtle patterns and relationships in the data that improve classification accuracy and granularity while maintaining reliability through statistically robust features
Solution Approach 2:
The patent introduces identifier distributions as an intermediary between raw interaction data and classification outcomes. Instead of directly classifying raw data, the system first computes identifier distributions (frequency, mean, standard deviation) that serve as intermediate representations. These intermediary statistical features bridge the gap between raw data and final classifications, improving both accuracy and reliability by providing more informative input to the classification model
3Productivity
If automated techniques are used, then processing efficiency can be improved, but accuracy decreases due to reliance on non-descriptive identifiers
Solution Approach 1:
The patent transforms non-descriptive identifiers into meaningful statistical parameters through computation of frequency distributions, mean values, and standard deviations. This parameter transformation enables automated processing to work with quantitative features that capture the essence of identifier patterns, maintaining automation efficiency while improving the effective accuracy of the identifiers for classification purposes
Solution Approach 2:
The patent replaces direct use of non-descriptive identifiers with machine learning-based statistical modeling that processes identifier distributions. The system substitutes the limitation of non-descriptive identifiers by using trained models that interpret statistical patterns in identifier frequencies and distributions, thereby maintaining automation while achieving accuracy comparable to or exceeding manual descriptive approaches
Data Source
AI summary
Various embodiments of the present disclosure disclose a rules-based technique for automatically generating predictive insights using distributions of identifiers. The techniques include receiving a predictive identifier count data object for an entity based on a historical interaction dataset associated with a plurality of entities. The techniques include generating a distribution data object for the entity based on the first identifier count for the first predictive identifier and the second identifier count for the second predictive identifier. The techniques include generating, using a machine learning classification model, a predictive classification for the entity based on the distribution data object. The techniques include providing an indication of the predictive classification for the entity.