Computing Network Architecture for Reducing Operation Time
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Solution Overview
Problem
Current computing systems face inefficiencies in reducing time and memory usage when determining data subsets in complex computing networks, particularly in identifying target data elements associated with specific computing operation results.
Innovation Solution
A method involving the use of computing device processors to receive and process data elements, generate mapped data elements based on target operation results, determine equivalence, and utilize memory to store and delete intermediate data sets efficiently, optimizing the computation of data subsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional computing methods are used to determine data subsets in complex computing networks, then computing operation time and memory usage increase, but data element selection accuracy is maintained
Solution Approach 1:
The patent segments the computing network into multiple processing stages: generating mapped data elements from the first set, determining equivalence relationships among mapped elements, identifying target data elements based on equivalence, and deleting intermediate mapped data. This segmentation reduces computing operation time by processing data in discrete, optimized steps rather than traditional exhaustive search methods.
Solution Approach 2:
The patent introduces mapped data elements as an intermediary representation between the original first set of data elements and the final second set. These mapped elements serve as a temporary computational layer that simplifies equivalence determination and target identification, reducing both computing time and memory usage compared to direct comparison of original data elements.
2Quantity of substance
If traditional computing methods are used to determine data subsets, then memory usage increases, but data element selection accuracy is maintained
Solution Approach 1:
The patent performs preliminary actions by generating mapped data elements with simplified equivalence relationships before the actual target selection process. This preliminary mapping reduces the computational complexity of subsequent operations, allowing faster processing with reduced memory requirements compared to traditional methods that process all data elements simultaneously.
Solution Approach 2:
The patent discards the intermediate mapped data elements after they have served their purpose in identifying target data elements. The mapped data set is deleted from memory after the second set is determined, recovering memory resources. This temporary use and subsequent disposal of intermediate data structures optimizes memory usage while maintaining computing efficiency.
3Speed
If mapped data elements are generated and stored in memory, then computing operation time is reduced, but memory usage temporarily increases
Solution Approach 1:
The patent implements dynamic memory management where the mapped data elements are created, used, and then deleted in a fluid sequence. The memory allocation for mapped data is not static but dynamically adjusted: allocated when generating mapped elements, utilized during equivalence determination and target identification, and released by deletion after the second set is determined. This dynamic approach optimizes both speed and memory usage.
Data Source
AI summary
This disclosure is directed to reducing a computing operation time and reducing memory usage associated with determining at least two target data elements, associated with a target computing operation result, from a set of data elements. This disclosure can be extended to determining more than two target data elements, associated with a target computing operation result, from a set of data elements, as well.