Graph-Embedding Paragraph Vector Models for Document Analysis

The patent pre-computes and stores document embeddings and relational representations in advance using graph-embedding-based paragraph vector models. These pre-computed representations are stored for rapid retrieval during predictive analysis, eliminating the need to re-process the entire document hierarchy during each analysis operation. This preliminary action significantly reduces computational efficiency while maintaining analysis accuracy.

The patent creates vector representations (embeddings) as simplified copies of complex hierarchical document structures. Instead of processing the actual hierarchical documents during analysis, the system uses these compact vector copies that capture the essential semantic and relational information. This copying approach dramatically improves computational efficiency and reduces processing time while preserving the necessary analytical capabilities.

The patent replaces large hierarchical document structures with compact vector embeddings and relational representations. These vector copies occupy minimal storage space compared to the original documents while preserving the essential information needed for predictive analysis. The graph-embedding-based models create condensed representations that capture semantic meaning and relationships in a space-efficient manner.

The patent transforms hierarchical document data into fixed-dimensional vector representations with specific parameter constraints. By converting variable-size hierarchical structures into standardized vector formats with defined dimensions, the system achieves efficient storage utilization. The graph-embedding models optimize these parameter representations to minimize storage requirements while maintaining analytical fidelity.

The patent performs the complex graph-embedding computations and model training in advance as a preliminary step. Once the embeddings are computed, the actual predictive analysis operations become simple vector lookups and computations. This separates the complex model construction phase from the efficient inference phase, making the overall system computationally efficient despite the initial model complexity.

The patent introduces pre-computed document embeddings and relational representations as intermediary data structures between the complex graph-embedding model and the predictive analysis tasks. These intermediaries simplify subsequent operations by providing ready-to-use feature representations, reducing the computational burden during actual analysis while leveraging the power of the complex graph-embedding model.