AI Pipeline Pre-Filtering by Token Predictability
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Solution Overview
Problem
Artificial intelligence pipelines inefficiently process data with little or no predictive value, leading to significant computational resource waste.
Innovation Solution
Pre-filtering incoming data using a trained convolutional neural network to assign predictability scores to tokens, passing only data with a maximum score above a threshold to downstream pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all incoming information is processed by the artificial intelligence pipeline, then prediction coverage is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent applies preliminary action by implementing a pre-filtering step before the main AI pipeline processing. The pre-filter evaluates incoming information using a trained model to predict whether it contains sufficient predictive value, and only passes qualifying information to the downstream AI pipeline. This preliminary assessment prevents wasteful processing of low-value data while ensuring that potentially valuable information is not missed, thus resolving the contradiction between prediction coverage and computational resource consumption.
2Reliability
If data with little or no predictive value is processed, then data completeness is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent applies the extraction principle by removing low-value data from the processing stream before it enters the main AI pipeline. The pre-filter identifies and extracts only those data points that meet a minimum predictive value threshold, effectively separating useful information from noise. This extraction approach maintains data completeness for valuable information while eliminating the processing burden of irrelevant data, thereby improving processing efficiency without sacrificing meaningful data coverage.
Data Source
AI summary
Systems and methods that pre-filter incoming data records for an artificial intelligence pipeline. The filtering is based on predictability: data records with a desired level of predictability are passed on to the pipeline while the remaining data records are filtered out. The level of predictability is determined by tokenizing the data records, retrieving a dictionary generated by training a neural network where the dictionary includes predictability scores of individual tokens, picking a maximum value for the tokens for each data record, and filtering out the data records with maximum values below a threshold. The computational efficiency of the artificial intelligence pipeline is significantly improved because the pipeline does not have to process data records with lower predictability.