Discrete Dataset Analysis via Latent Structure Segmentation
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Solution Overview
Problem
Current methods for document retrieval, content retrieval, and automatic translation often obscure the relationship between data and latent structures, fail to utilize contextual information effectively, and scale poorly with increasing dataset size, requiring significant memory and computational resources.
Innovation Solution
The method involves determining latent structures in discrete-valued datasets by calculating engineered features and weights vectors, which preserve contextual information and allow for iterative updates, enabling efficient processing and accurate retrieval by focusing on relevant subsets of the dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used for document retrieval and analysis, then complete dataset processing is achieved, but memory and computational resources are excessively consumed
Solution Approach 1:
The patent divides the dataset into multiple subsets based on latent structure identification. Each subset is processed separately to identify local patterns and relationships, reducing the memory footprint compared to processing the entire dataset at once while maintaining analytical accuracy through iterative refinement of the global model.
Solution Approach 2:
The system processes only the necessary portions of the dataset required to achieve convergence of the latent structure model. By iteratively updating the model with selected subsets rather than repeatedly processing the entire dataset, computational resources are optimized while still achieving reliable retrieval accuracy.
2Measurement precision
If traditional methods process entire datasets, then comprehensive analysis is achieved, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary identification of latent structures and key features before conducting the full retrieval analysis. This preliminary processing organizes the data into meaningful groups and identifies the most relevant subsets, so that subsequent analysis operates on pre-processed, organized data rather than raw unstructured data, significantly reducing processing time while preserving analytical precision.
3Loss of information
If contextual information is preserved in traditional methods, then interpretation accuracy improves, but data complexity and processing overhead increase
Solution Approach 1:
The system extracts and separates contextual information from the main data processing stream, storing it in dedicated structures that preserve the original relationships without interfering with the efficiency of the core retrieval operations. This extraction approach maintains interpretability by keeping contextual data accessible while allowing the main processing pipeline to operate efficiently on structured features.
Data Source
AI summary
Methods and systems disclosed herein may be used to determine the structure of a dataset comprising discrete-valued data corresponding to features and items. In some embodiments, a device may receive a discrete-valued matrix with a first dimension corresponding to items and a second dimension corresponding to features. The device may calculate an engineered features set and a weights set for the matrix. The device may update the engineered features set using the weights set, and update the weights set using the updated engineered features set based on the mutual information between the matrix and one of the updated engineered features set. The device may receive a request indicating at least one of the engineered features set, identify items based on the matrix and the indicated at least one of the engineered features set, and provide a response based on the identified items.