At Incognia, we’ve spent years developing technology that uses location behavior to help businesses recognize trusted users without adding unnecessary friction.
Our newly granted U.S. patent protects an important part of that work: the technology behind how we establish and recognize trusted locations using device sensor data.
On October 13, 2026, the United States Patent and Trademark Office (USPTO) issued Incognia U.S. Patent No. 12,764,491 B2, titled “Systems and methods for validating information about a user based on sensor information obtained from a user device.”
The patent protects a specific technical approach developed by Incognia to a question that has become increasingly important: how can we use real-world location signals to determine whether information about a user can be trusted?
The answer begins with a fundamental distinction. Knowing where a device is at a particular moment is not the same as a trustworthy location behavior over time.
Incognia’s Trusted Locations reflects the relationship between a user and the places associated with their location behavior over time. By recognizing these location patterns, Incognia can use location behavior as a signal to assess whether a digital interaction is legitimate.
For Incognia, the grant marks an important step in protecting years of research and engineering, and recognizes the work our teams have put into developing this technology.
Location intelligence is often associated with GPS coordinates or single-layer location determination. Incognia’s patented approach goes considerably further.
The invention combines data from multiple device sensors to create distinctive location IDs.
These IDs are tied to location-based behavior and identify patterns that are unique to a user, and can support authentication, risk assessment, address validation, and other forms of user-information verification.
Combining multiple signals makes the location intelligence more precise and resilient to spoofing and tampering.
Another important aspect of the invention is how it collects sensor information.
Its data-gathering logic considers factors such as device activity, location profiles, and predicted movement to gather data efficiently.
This helps to make an efficient use of device resources, a must for effective security technology, and also ensures the right amount of data is processed for trust determination purposes.
As AI can generate increasingly convincing identities, content, and interactions, it makes it harder to determine what and who can be trusted online.
Meeting that challenge requires a better way to establish trust. We believe signals grounded in the physical world, including Trusted Locations and behavioral patterns, will be an important part of it.
One example of how this technology is being applied is in payment scam detection.
Financial institutions and digital businesses face a difficult challenge: distinguishing legitimate users from suspicious activity without complicating every interaction.
Incognia Physical Social Graph helps address that challenge. Built on Incognia’s Trusted Locations intelligence, it evaluates potential real-world connections between payment senders and recipients based on places they repeatedly visit, including locations they may share.
For a bank assessing a potential scam, that history can provide context that a transaction record, or a simple distance calculation, cannot.
As digital interactions become more complex, recognizing activity through richer contextual signals will become increasingly valuable.
The grant is a meaningful milestone for Incognia and the people who developed this technology.
A point on a map tells us where a device is. Understanding the patterns behind that point can help us make better decisions about trust.
That distinction is at the heart of this invention, and it’s worth protecting.
Official patent record: U.S. Patent No. 12,764,491 B2 (USPTO, issued October 13, 2026).