The patent, titled “Techniques for Targeted Data Extraction from Unstructured Sets of Documents”, is the first of its kind in the sector. It covers ACTFORE’s dynamic, AI-driven interface that uses visual selectors and deep learning to identify and extract key data – such as names, account numbers, and health information – from large volumes of disorganized files, including scanned PDFs and images.
“In an industry where most data extraction still relies on manual workflows or basic automation, this patent sets a new standard,” said Christian Geyer, CEO of ACTFORE. “We’ve built a hybrid system that blends legal-grade human review with adaptive AI to create fast, accurate, and defensible breach response processes.”
The patented system uses a unique combination of visual box selection, deep learning, optical character recognition (OCR), and FAISS-based clustering to analyze document layouts—effectively acting like facial recognition, but for documents. According to Yumna Zaidi, Lead Inventor and Innovations Team Lead, the platform creates structural embedding vectors that enable rapid matching across large data sets.
“This technology lets us identify and process documents at scale, without sacrificing accuracy,” said Zaidi.
The innovation addresses one of the biggest pain points in breach response: extracting high-fidelity information from messy, inconsistent sources. By automating structural recognition and pairing it with targeted human oversight, ACTFORE significantly reduces review times while preserving legal defensibility.
“This patent reflects our commitment to solving complex problems with intelligent, scalable technology,” added Dhiraj Sharma, Senior Data Scientist and Co-Inventor.
ACTFORE’s patented solution supports a wide range of file types and formats and is already being deployed across multi-jurisdictional data breach engagements.
As regulatory pressure increases and breaches become more complex, ACTFORE’s latest patent positions the company as a frontrunner in the next generation of AI-driven breach response.
