AI Document Analysis System Using Knowledge Graph Embeddings
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Solution Overview
Problem
Attendees and potential attendees of events such as meetings, conferences, and presentations often face challenges in obtaining in-depth analysis of document content, as manual searching and analysis are time-consuming and inefficient.
Innovation Solution
A data processing system that utilizes machine learning models to analyze images of documents, generate textual representations, construct queries, and search a knowledge graph to provide automated content analysis and generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching and analysis methods are used to obtain in-depth analysis of document content, then comprehensive content understanding can be achieved, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical searching and analysis processes with automated machine learning models. The first ML model extracts text from document images, the second ML model generates embeddings for semantic understanding, and the knowledge graph system automatically retrieves and synthesizes relevant information, eliminating the need for manual content analysis while maintaining comprehensive understanding.
Solution Approach 2:
The patent introduces intermediate processing layers between the document image and final analysis results. The text extraction model serves as an intermediary to convert images to text, the embedding model acts as a mediator to transform text into semantic representations, and the knowledge graph system functions as an intermediary to connect query embeddings with relevant content items, enabling efficient automated analysis.
2Productivity
If automated machine learning models and knowledge graphs are used to analyze documents, then time efficiency is significantly improved, but system complexity increases
Solution Approach 1:
The patent divides the complex automated analysis system into distinct functional modules: a text extraction module using the first ML model to convert document images to text, an embedding generation module using the second ML model to create semantic representations, and a knowledge graph query module to retrieve and synthesize relevant information. This segmentation manages complexity by creating specialized, independent components that can be developed and maintained separately.
3Measurement precision
If multiple machine learning models and knowledge graph processing are implemented, then content analysis accuracy and relevance are improved, but computational resources and processing time are increased
Solution Approach 1:
The patent performs preliminary processing by pre-computing embeddings for content items and organizing them in a knowledge graph structure before actual queries are made. This allows the system to quickly retrieve and compare pre-processed embeddings against query embeddings without performing heavy computational tasks in real-time, reducing energy consumption during actual content recommendation operations.
Data Source
AI summary
A data processing system implements obtaining an image of a document and an indication of one or more content items to generate; analyzing the image of the document to generate a textual representation of contents of the document in the image; constructing a query based on the textual representation; analyzing the query using a second machine learning model to obtain embeddings representing one or more categories of information represented in the query; searching a knowledge graph based on the query embeddings to obtain results of the query; providing the query results to a content generation unit to generate the one or more content items based on the results of the query; and obtaining the one or more content items from the content generate unit.