Encoded Uncertainty Data Structure for Faster Distribution Computation

Solution Overview

Problem

Conventional computing platforms are unable to directly handle uncertainty distributions, leading to inefficiencies and increased costs in processing uncertain data, particularly in safety-critical applications like autonomous vehicles.

Innovation Solution

A computer-accessible medium storing an encoded data structure that represents uncertainty distribution information, allowing for the encoding and decoding of uncertainty representations using specific algorithms and data structures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If Monte Carlo methods are used to handle uncertainty distributions, then accuracy of uncertainty analysis is improved, but processing time and computational resources increase significantly

Engineering Contradiction:

Improveaccuracy of uncertainty analysisVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the mechanical Monte Carlo simulation approach (re-running software millions of times) with a mathematical encoding system that represents uncertainty distributions directly as data structures. This substitution transforms an iterative computational process into a direct mathematical operation, enabling uncertainty calculations to be performed efficiently without repeated simulations while maintaining accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the parameter representation from discrete simulation runs to continuous mathematical encodings. By representing uncertainty distributions as encoded data structures with specific parameters (mean, variance, distribution type), the system enables direct mathematical manipulation of uncertainty parameters rather than relying on statistical sampling, thereby reducing computational time while preserving analytical accuracy.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If Monte Carlo methods are used to handle uncertainty distributions, then accuracy of uncertainty analysis is improved, but computational cost increases significantly

Engineering Contradiction:

Improveaccuracy of uncertainty analysisVSAvoidcomputational resources

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent replaces the mechanical Monte Carlo simulation approach (re-running software millions of times) with a mathematical encoding system that represents uncertainty distributions directly as data structures. This substitution transforms an iterative computational process into a direct mathematical operation, enabling uncertainty calculations to be performed efficiently without repeated simulations while maintaining accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the parameter representation from discrete simulation runs to continuous mathematical encodings. By representing uncertainty distributions as encoded data structures with specific parameters (mean, variance, distribution type), the system enables direct mathematical manipulation of uncertainty parameters rather than relying on statistical sampling, thereby reducing computational time while preserving analytical accuracy.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If conventional computing platforms average uncertainties into single numbers, then ease of computation is improved, but accuracy of processing results deteriorates

Engineering Contradiction:

Improveease of computationVSAvoidaccuracy of processing results

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary encoded data structure that bridges conventional computing platforms and uncertainty analysis. This intermediary representation allows standard computational operations to be performed on uncertainty distributions without eliminating the uncertainty information, thereby maintaining both computational ease and result accuracy by preserving the distributional characteristics throughout the computation process.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the parameter representation from discrete simulation runs to continuous mathematical encodings. By representing uncertainty distributions as encoded data structures with specific parameters (mean, variance, distribution type), the system enables direct mathematical manipulation of uncertainty parameters rather than relying on statistical sampling, thereby reducing computational time while preserving analytical accuracy.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentEP4539341A1Encoded data structure for uncertainty distributions

Publication Date: 2025.04.16 SIGNALOID LTD

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AI summary

The present disclosure relates to a computer-accessible medium having stored an encoded data structure, the encoded data structure comprising a plurality of data elements encoding a representation of uncertainty distribution information of a data set, and at least a part of decoding information for decoding the encoded representation.