Probability Mapping Model for Natural Resource Location
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Solution Overview
Problem
Current methods for locating natural resources are inefficient and costly due to reliance on limited sources of information, lacking effective integration of historical, scientific, and real-time data for precise targeting, which hampers the accuracy and productivity of resource discovery.
Innovation Solution
A method that processes and integrates large datasets from various sources, including historical and scientific data, using analytical engines to generate topic-based clusters, apply similarity heuristics, and determine probabilities through supervised learning models, resulting in a real-time probability heat map for informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to locate natural resources, then the process is simpler and less complex, but the accuracy and precision of location determination deteriorates
Solution Approach 1:
The patent segments the natural resource location problem into multiple independent data sources (historical data, scientific data, real-time data) and processes each through separate analytical engines. Each data source is clustered independently into topic-based groups, and probabilities are calculated for each cluster separately before being combined. This segmentation allows the system to manage complexity by breaking down the overall task into manageable, modular components while maintaining high accuracy through comprehensive data integration.
2Measurement precision
If comprehensive data from multiple sources is integrated, then the accuracy of resource location improves, but the time and cost required for processing deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing and clustering data from multiple sources before the actual resource location query. Historical data andscientific data are organized into topic-based clusters in advance, creating a structured knowledge base. When a location query is made, the system only needs to calculate probabilities for relevant pre-clustered data groups rather than processing all raw data from scratch, significantly reducing processing time while maintaining comprehensive data integration.
3Reliability
If multiple data sources are processed, then the reliability of resource location determination improves, but the quantity of data to be managed increases
Solution Approach 1:
The patent merges multiple data sources (historical data,scientific data, real-time data) into a unified probability assessment framework. Instead of managing separate analysis streams for each data source, the system combines all data into topic-based clusters and calculates a single integrated probability score for each potential resource location. This merging approach maintains high reliability by incorporating diverse data sources while reducing the management burden by consolidating results into a unified probability map.
Data Source
AI summary
A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.