Inference Engine Vector Representation for Data Relationship Preservation
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Solution Overview
Problem
Conventional inference engines are inflexible and perform poorly due to their reliance on unordered training data, failing to accurately process slight variations in content and recognize similar meanings, leading to flawed inferences.
Innovation Solution
The use of vector representations that preserve relationships between objects in data, such as words in a sentence, by generating vector symbolic architecture (VSA) representations that group objects based on their relationships and apply binding operators to associate them, allowing for more accurate and flexible inference engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional inference engines use unordered training data, then the system is simple to implement, but the inference accuracy deteriorates due to inability to recognize relationships between objects
Solution Approach 1:
The patent segments data into hierarchical groups (e.g., words into phrases, phrases into sentences) and creates vector representations for each segment. This segmentation preserves relationships between objects while maintaining manageable complexity through systematic organization of data elements.
Solution Approach 2:
The patent transforms traditional unordered data into a multi-dimensional vector space where relationships between objects are encoded spatially. By mapping data elements to vectors with specific dimensions and applying binding operators, the system captures relational information that would be lost in flat unordered structures.
2Measurement precision
If inference engines process extensive training data to improve accuracy, then inference quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing by organizing training data into hierarchical groups and pre-computing vector representations during the training phase. This preliminary structuring allows the inference engine to work with pre-processed vector data during actual inference, reducing real-time processing requirements while maintaining accuracy.
Solution Approach 2:
The patent transforms data from traditional formats into vector representations with specific parameters (dimensions, magnitudes, orientations). This parameter transformation enables more efficient computation during inference, as vector operations can be performed more quickly than processing raw extensive training data, while preserving the relational information needed for accurate inference.
3Adaptability or versatility
If inference engines use traditional data representations, then the system is computationally efficient, but the ability to recognize similar meanings deteriorates
Solution Approach 1:
The patent changes the parameter representation of data from traditional formats to vector space representations. This parameter change enables the system to recognize similar meanings through vector similarity computations, where data with similar semantic content produces similar vector representations, allowing flexible and adaptive inference.
Solution Approach 2:
The patent creates composite vector representations by combining vectors from multiple data elements (words, phrases, sentences) using binding operators. These composite vectors capture the combined meaning and relationships of constituent elements, enabling the system to recognize similar meanings across different data compositions while managing computational complexity through systematic combination rules.
Data Source
AI summary
This disclosure describes, among other things, an apparatus for generating an inference engine about a document. The apparatus includes at least one processor and a memory with instructions. The memory including instructions that, when executed by the at least one processor, cause the at least one processor to perform a number of processes. The processor accesses a set of documents. Each document has a corresponding inference. The processor also generates a vector representation for each document in the set of documents. First, the processor parses text of the document into groups of words, and generates a vector representation for each group.