Root Document Trace Temporal Embedding
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Solution Overview
Problem
Current document embedding models in natural language processing neglect the temporal aspect of document processing, treating documents as lossy compressions rather than the objects themselves, which limits their analytical capabilities.
Innovation Solution
The method represents a document as a Root Document Trace, an explicit bijection between subsets of nonnegative integers and a semantic space, generated by parsing a text document into ordered tokens, using a frame object and increment generator to create increment objects with docID attributes, and then producing a vector representation that captures the document's temporal structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current document embedding models treat documents as aggregate compressions to simplify representation, then computational efficiency is improved, but temporal structure and analytical precision are lost
Solution Approach 1:
The patent segments a document into multiple overlapping frames (windows) of tokens, where each frame is processed independently to generate an embedding vector. This segmentation preserves temporal structure by maintaining the sequential order of frames while enabling efficient parallel processing. Each frame captures a local temporal context, and the collection of frame embeddings represents the document's temporal evolution without requiring processing of the entire document as a single aggregate unit.
2Device complexity
If document embeddings are created as single dense vectors to capture semantic essence, then representation simplicity is improved, but analytical capability for temporal patterns deteriorates
Solution Approach 1:
The patent transitions from representing a document as a single vector to representing it as a sequence of frame embedding vectors arranged in temporal order. This adds a temporal dimension to the representation, transforming a flat semantic space into a structured temporal sequence. The frame embeddings maintain simplicity as individual dense vectors while their sequential arrangement enables analysis of temporal patterns, narrative structures, and argument development that would be impossible with a single aggregate vector.
3Speed
If current methods perform computations on compressed document representations, then processing speed is improved, but measurement precision of document features deteriorates
Solution Approach 1:
The patent performs preliminary processing by dividing the document into frames and generating embedding vectors for each frame before any higher-level analysis. This preliminary action of creating frame-level representations preserves fine-grained feature information that would be lost in aggregate compression. The frame embeddings serve as detailed building blocks that can be subsequently aggregated or analyzed individually, allowing processing speed optimization through parallel computation while maintaining measurement precision through the preservation of local temporal features.
Data Source
AI summary
The present invention provides natural language processing methods and systems for representing a document temporally in an arbitrary well-defined semantic space. The disclosed methods and systems operate by creating a Root Document Trace, which is a novel document-representing computational object in high-dimensional semantic space. Among many uses, the Root Document Trace can be used, for example, to evaluate narrative and argument structures within and between documents.