Federated Learning Document Compliance via Vector Embeddings
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Solution Overview
Problem
Users face challenges in determining whether a confidential policy document complies with governmental and organizational rules and regulations without sharing sensitive information, requiring extensive computing resources and time to compare terms and conditions with other documents.
Innovation Solution
A federated learning system that uses natural language processing and encoding models to generate and validate reference documents by comparing vectors from client devices without sharing confidential information, determining similarity scores and generating reference annotations to assess compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If confidential policy documents are shared for compliance comparison, then compliance assessment accuracy is improved, but data confidentiality is compromised
Solution Approach 1:
The patent creates vector representations (copies) of document content through encoding models, allowing compliance comparison to be performed on these abstract representations rather than the original confidential documents. The vectors capture semantic meaning while preserving confidentiality, enabling accurate compliance assessment without sharing actual policy documents.
Solution Approach 2:
The patent introduces an intermediary layer consisting of vector embeddings and similarity calculations. Instead of directly comparing original documents, the system compares vector representations generated from documents through encoding models. This intermediary transformation layer enables compliance assessment while maintaining data confidentiality.
2Measurement precision
If extensive computing resources are used for document comparison, then compliance assessment accuracy is improved, but resource consumption and time required increase
Solution Approach 1:
The patent extracts key semantic information from documents by generating vector representations through encoding models. Instead of processing entire documents through complex comparison algorithms, the system extracts essential meaning into compact vector forms and performs similarity calculations on these condensed representations, significantly reducing computational resource requirements while maintaining assessment accuracy.
Solution Approach 2:
The patent transforms documents from their original complex text format into vector parameter representations. This parameter transformation converts high-dimensional text data into a more efficient vector format that can be quickly compared using similarity metrics, reducing the computational complexity and resource consumption of compliance assessment.
Data Source
AI summary
In some implementations, a device may receive a first document from a first client device. The device may parse the first document into first sentences. The device may receive a second document from a second client device. The device may parse the second document into second sentences. The device may select a first sentence of the first document and a second sentence of the second document. The device may convert the first sentence into a first vector and the second sentence into a second vector. The device may determine a similarity score associated with the first vector and the second vector. The device may determine, based on the similarity score, that a first word set in the first sentence and a second word set in the second sentence correspond to a reference annotation. The device may perform an action associated with the reference annotation.