Cohort-Based Event Prediction Using Bit Arrays
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Solution Overview
Problem
Current methods lack an effective way to predict future events in entities based on their characteristics and experiences, particularly using bit arrays generated from physical test devices, which fail to accurately correlate and predict occurrences across different events.
Innovation Solution
A processor-implemented method generates a first bit array describing characteristics of an entity experiencing a first event and a second bit array for an event cohort that did not experience the first event, then predicts if another entity with characteristics from both arrays will also experience the second event, using a combination of physical test devices and Bayesian analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods are used to predict future events in entities, then the prediction accuracy is insufficient, but the computational complexity and data processing requirements increase when using bit array combinations and Bayesian analysis
Solution Approach 1:
The patent segments the entity's characteristics into multiple bit arrays, where each bit array represents specific characteristics or conditions. This segmentation allows for efficient storage and comparison of entity states across different time points and cohorts, enabling accurate predictions without requiring complex monolithic data structures.
Solution Approach 2:
The patent creates bit array representations (copies) of entity characteristics from physical test devices. These bit array copies enable efficient computational operations and comparisons without manipulating the original complex data structures, reducing computational complexity while maintaining prediction accuracy.
2Reliability
If bit arrays from multiple sources are combined to improve prediction accuracy, then the predictive power increases, but the data processing and correlation analysis become more complex
Solution Approach 1:
The patent merges bit arrays from different sources (current state, future state, cohort data) into a unified prediction model. This combining approach integrates multiple data dimensions while leveraging the efficiency of bit array operations, achieving high predictive power without proportionally increasing processing complexity.
Solution Approach 2:
The patent transforms physical test device outputs into bit array parameters, enabling efficient logical operations and comparisons. This parameter transformation allows for complex correlation analysis between different bit arrays using simple bitwise operations, reducing the effective processing complexity.
3Measurement precision
If cohorts are used to predict future events by comparing characteristics, then the prediction reliability improves, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing bit array representations of cohort characteristics. When predictions are needed, these pre-processed bit arrays can be quickly compared with target entity bit arrays using efficient bitwise operations, significantly reducing the time required for prediction while maintaining high reliability.
Data Source
AI summary
A processor-implemented method, computer program product, and/or computer system predicts a future event. A first bit array, which describes characteristics of a single entity while experiencing a first event, is generated using values received from a set of physical test devices. After the first single entity experiences a different second event, a second bit array is generated from another set of physical test devices. The second bit array describes characteristics of an event cohort, which is made up of entities, other than the single entity, which also experience the second event, but which never experienced the first event. When another single entity experiences the first event, a determination is made as to whether that other single entity has characteristics from both the first bit array and the second bit array. If so, a prediction is made that the other single entity will also experience the second event.