In late 2021, a strange claim began circulating on Instagram and anti-vaccine forums. Pfizer had patented a contact tracing system to monitor vaccinated individuals remotely using 5G frequencies and graphene oxide.

The supposed evidence? US patent No. 11,107,588 B2 granted on August 2021, titled Methods and Systems of Prioritizing Treatments, Vaccination, Testing and/or Activities While Protecting the Privacy of Individuals.

The post went viral. But there was one major problem: none of it was true.

Fact-checkers were quick to dismantle the misinformation. The patent wasn’t filed by Pfizer. It makes no reference to vaccines, 5G, or tracking implants. Instead, it was filed by two independent inventors – Gal Ehrlich and Maier Fenster – during the early stages of the COVID-19 pandemic. 

And far from being a dystopian surveillance system, the patent actually proposes a privacy-first solution to a very real public health challenge:

How do you prioritize limited vaccine or treatment resources without collecting sensitive health or location data?

These are questions worth asking and innovating on.

That’s why at PQAI, we did what we always do:

We translated the idea into a plain-English query and ran it through our AI-powered search tool. We wanted to uncover whether this was a true one-off innovation, or part of a much broader movement shaping the future of epidemic tech.

Let’s start with the patent’s core logic, and how we turned it into a query.

From Bluetooth Signals to Prioritization Logic: Translating the Patent Into a Query

US11107588B2 describes a system for prioritizing vaccination or treatment during an epidemic. This is not based on age or health records, but on how likely someone is to infect others if they become sick. The system works like this:

• Every person carries a smart device (like a phone) that generates a rotating, anonymous ID.
• When two such devices come into proximity, say, on a train or in a store, they exchange IDs.
• Over time, each device builds up a log of these proximity events.
• The system then generates a “propensity score” for each person. This is a measure of how socially connected or mobile they are.
• People with higher scores are prioritized for treatment or vaccination under the logic that they’re more likely to spread the disease if infected.
• The entire system can be configured to work locally on devices or with minimal server involvement, while preserving user anonymity.

The idea is to use anonymous interaction patterns, not personal health data, to guide limited medical interventions during an epidemic.

To test how novel or widespread this idea is, we distilled the patent into a plain-English description and fed it into PQAI:

“Privacy-first vaccine prioritization using smartphones to calculate individual transmission risk from anonymous proximity interactions”

Source – PQAI

We limited the scope of the search to patents only, as we found the broader dataset included an overwhelming number of academic papers. Since our goal was to understand how this concept has evolved from a legal and commercial standpoint, we focused exclusively on granted and published patents.

Here are some of the most intriguing patents PQAI surfaced, not just for their similarity, but for how they expand, adapt, or challenge the same core idea.

#1. Trajectory Coincidence Analysis Using Privacy Intersection

At the height of the pandemic, researchers at the Inspur Academy of Sciences in China filed a bold idea: what if we could identify people at risk in a different manner? That too not through Bluetooth beacons or shared location data, but through encrypted comparisons of their actual movement paths?

The CN115828001A patent outlines a system that does exactly that. When someone is diagnosed, their phone sends out a privacy-protected request to nearby users. But instead of revealing where anyone has been, the system quietly checks whether their GPS trails intersected, even briefly. If there’s a match, the exposed person is alerted. If not, no one knows anything.

It’s a leap forward from Bluetooth-based contact tracing. It uses location data for richer insights, but wraps it in privacy-preserving computation to keep sensitive paths completely hidden.

#2. Privacy-Preserving Contact Tracing Using Zero-Knowledge Proofs 

Filed in late 2021 by researchers at Raytheon BBN Technologies, the US2021365585A1 patent presents a cryptographic approach to a familiar pandemic challenge. It notifies people of potential exposure without revealing who was infected or where they were.

Instead of sharing raw contact data or relying on centralized health databases, the system uses a sophisticated technique called pp-zk-SNARKs.

Here’s how it works in plain terms: every time two smartphones come into proximity, they exchange anonymous tokens. If one user later tests positive, their device can generate a cryptographic proof that confirms exposure without disclosing identity, health status, or precise interactions.

The result is a public exposure alert system that’s mathematically verifiable yet deeply private. This system enables users to participate in public health safety without compromising their autonomy and sovereignty. It’s a reminder that contact tracing doesn’t have to be surveillance. 

#3. Multi-Source Epidemic Traceability with Anonymized Alerts 

In the thick of the COVID-19 pandemic, researchers at East China Normal University filed this patent proposing a bold yet privacy-conscious vision for outbreak response. 

Instead of relying solely on GPS or Bluetooth, the system taps into Wi-Fi probe signals to detect nearby devices by reading their MAC addresses, without needing user input or app installations.

The innovation lies in how the system stitches together multi-source data from Wi-Fi strength and timestamp differences, as well as ride history and e-payment logs, to assess infection risks. 

Source – Google Patents

Crucially, all alerts and data sharing happen anonymously. If a user is flagged as high-risk, the system pushes a silent notification to others who may have crossed paths, while also escalating high-risk contacts to local CDC servers for action.

CN111885502A is an example of how diverse data streams can be fused into early warning systems that are both powerful and privacy-aware.

#4. Anonymous Proximity Detection Using Stepwise Data Sharing

Long before the world had heard of social distancing, engineers at Lenovo Singapore envisioned a system that could calculate how “close” two people were. That is, either physically or based on shared interests, without either party ever revealing their exact location or personal data.

Filed in 2010, the CN100583923C patent introduced an early form of what we now call privacy-preserving computation. Instead of exchanging raw data, users send fragments, such as partial coordinates, preference vectors, or encoded traits, which are processed separately and evaluated locally to compute a final proximity score.

What’s compelling is its dual flexibility: the system can detect proximity in geographic terms or in interest-based dimensions. This seems like a secure way to find people near you who also like baseball or ballroom dancing, without anyone knowing where you are or what you like.

At its core, it’s the same principle found in modern epidemic-response tech: keep what’s private as private,  but still act on shared risk or context.

#5. Vaccination-Based Anonymization in Public Surveillance Systems

In 2023, researchers at Hitachi Kokusai Electric Inc. filed JP2023093912A for a system that blends video surveillance with vaccination status checks. 

The idea? When a person enters a monitored area,  their vaccination status is checked against pre-registered data. If the system can’t confirm that they’re vaccinated, it applies anonymization filters to their image before displaying it on public-facing screens.

Unlike proximity-based or interaction-driven models, this system works by combining live imaging with health status verification. It’s positioned as a privacy-protecting method, but only for those whose medical records don’t meet certain thresholds.

While technically intriguing, it introduces a murkier ethical terrain: Should privacy protection be conditional on vaccination status?  And what happens when health data becomes a filter for visibility in public spaces?

This patent doesn’t follow the same logic as others in our list. But it’s part of the same post-pandemic push: figuring out where the boundaries lie between public safety, medical data, and individual privacy.

As we sifted through these diverse inventions, one thing became clear: privacy-preserving epidemic tech isn’t a fringe concept. It’s a global design movement. Each patent reflects a different philosophy on how to balance urgency, data, and dignity in times of crisis.

Some lean on math. Others on behavioral heuristics. Some even on conditional anonymity. But they all grapple with the same core challenge: how do we respond collectively without surrendering personal control?

And that’s exactly where PQAI comes in.

By mapping patterns across this fragmented landscape, PQAI helps innovators, researchers, and policymakers surface not just similar patents, but adjacent ideas, blind spots, and emerging standards. 

In the next section, we’ll walk you through how the tool works and why it matters more than ever in building the next generation of responsible tech.

Turning Patterns into Possibilities: Why PQAI Matters

Innovation in epidemic tech doesn’t just come from breakthroughs. It comes from recognizing patterns, avoiding collisions, and asking smarter questions early.

That’s what PQAI is built for.

Instead of relying on complex Boolean searches or spending thousands on searches, you can simply describe your idea in natural language.

PQAI will scan millions of patents and research papers to find filings with overlapping logic. Not just titles, but actual concept-level matches, backed by text snippets that show you why each result was returned.

Whether you’re validating white space, preparing a provisional patent, or fine-tuning your invention, PQAI makes the search process transparent and fast. You can try it for free here and explore more ideas with confidence, without worrying about privacy.