Sharing AI Knowledge Between Private Datasets Using Synthetic Data
This patent describes a method for two separate AI systems to share learned information by generating artificial data based on common features, without directly exchanging their private training data.
Original patent title: “Systems and methods for heterogeneous federated transfer learning”
This patent describes a method for two separate AI systems to share learned information by generating artificial data based on common features, without directly exchanging their private training data. Granted to Sharecare AI in 2024 with 23 claims and 1 forward citation, and it is expected to expire in 2041.
Coverage
What does this patent actually cover?
This technology allows different AI systems, called "federated endpoints," to learn from each other even when their training data must remain private. It works by first identifying "shared sample features" that are common between the first and second training datasets (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). For example, two hospitals might have patient data, but only the patient's age and gender are shared features. A "generator" AI model is trained on the first endpoint using these shared features and other specific data, creating a "one-hot encoding" of class labels (Claim 1). This trained generator then produces "synthetic samples" (artificial data) that mimic the original data's characteristics. Finally, this generator is used by the second federated endpoint to help it perform its own tasks, like making predictions or classifications (Claim 1). This means the second endpoint gets the benefit of the first endpoint's learning without ever seeing its sensitive raw data.
The gap
What does this patent NOT cover?
- Does not cover federated learning methods that only exchange model weights or gradients without generating synthetic data.
- Does not cover systems where the separate training datasets have no common or 'shared sample features' at all.
- Does not cover transferring raw, un-processed training data directly between the federated endpoints.
- Does not cover methods that use encoding schemes other than 'one-hot encoding' for the class labels of the shared features.
- Does not cover scenarios where a 'generator' is not explicitly trained to produce 'synthetic samples' for inference.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The clever part is enabling knowledge transfer between distinct, private datasets by training a generator on one dataset using shared features, and then using that *trained generator* to create synthetic data for another dataset's tasks. This avoids directly sharing sensitive raw data while still allowing the AI models to benefit from each other's learning.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Collaborative medical research where hospitals train AI models on patient data without sharing individual records.
Financial fraud detection systems that learn from different banks' transaction patterns while keeping customer data private.
Autonomous vehicle systems sharing driving experiences from different car manufacturers without exchanging raw sensor data.
Personalized health recommendations where AI models learn across user groups while maintaining individual privacy.
Why it matters
The bigger picture
This patent addresses a critical challenge in AI: how to leverage large, distributed datasets for training without compromising data privacy or security. It enables collaboration in sensitive fields like healthcare, where data cannot be centrally pooled due to regulations or competitive concerns. By allowing AI models to learn from each other's insights through synthetic data, it can lead to more robust and accurate AI systems across various organizations.
Filed
October 23, 2021
Granted
July 16, 2024
Market context
Who's building on this
Companies in this space
Sharecare AI Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing solutions in this space, likely focusing on healthcare applications given their core business. Beyond Sharecare, major technology companies like Google, NVIDIA, and IBM are investing heavily in federated learning and privacy-preserving AI, as are numerous startups focused on secure data collaboration across industries.
Market impact
This type of federated transfer learning technology helps overcome data silos, enabling AI development in highly regulated or competitive industries. It allows organizations to pool insights without pooling raw data, potentially accelerating AI innovation in areas like drug discovery, personalized medicine, and financial risk assessment. It also contributes to the broader shift towards privacy-preserving machine learning, which is becoming increasingly important with stricter data protection regulations.
Claim 1 — Plain English
What this patent covers
This technology allows different AI systems, called "federated endpoints," to learn from each other even when their training data must remain private. It works by first identifying "shared sample features" that are common between the first and second training datasets (Claim 1). For example, two hospitals might have patient data, but only the patient's age and gender are shared features. A "generator" AI model is trained on the first endpoint using these shared features and other specific data, creating a "one-hot encoding" of class labels (Claim 1). This trained generator then produces "synthetic samples" (artificial data) that mimic the original data's characteristics. Finally, this generator is used by the second federated endpoint to help it perform its own tasks, like making predictions or classifications (Claim 1). This means the second endpoint gets the benefit of the first endpoint's learning without ever seeing its sensitive raw data.
The clever bit
The clever part is enabling knowledge transfer between distinct, private datasets by training a generator on one dataset using shared features, and then using that *trained generator* to create synthetic data for another dataset's tasks. This avoids directly sharing sensitive raw data while still allowing the AI models to benefit from each other's learning.
What it does not cover
- Does not cover federated learning methods that only exchange model weights or gradients without generating synthetic data.
- Does not cover systems where the separate training datasets have no common or 'shared sample features' at all.
- Does not cover transferring raw, un-processed training data directly between the federated endpoints.
- Does not cover methods that use encoding schemes other than 'one-hot encoding' for the class labels of the shared features.
- Does not cover scenarios where a 'generator' is not explicitly trained to produce 'synthetic samples' for inference.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
6/40
Early citations
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$94K – $300K
Midpoint $187K · 15.2 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
ZACCAK, G. G., SHARMA, S. A., VIVONA, S. G., & TITOVA, M. (2024). Sharing AI Knowledge Between Private Datasets Using Synthetic Data (U.S. Patent No. 12,039,012). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12039012/systems-and-methods-for-heterogeneous-federated-transfer-learning
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does Sharing AI Knowledge Between Private Datasets Using Synthetic Data cover?
This patent describes a method for two separate AI systems to share learned information by generating artificial data based on common features, without directly exchanging their private training data.
Who owns patent US 12039012?
Sharecare AI owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on October 23, 2041, when the invention enters the public domain.
What is patent US 12039012 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
This patent addresses a critical challenge in AI: how to leverage large, distributed datasets for training without compromising data privacy or security. It enables collaboration in sensitive fields like healthcare, where data cannot be centrally pooled due to regulations or competitive concerns. By allowing AI models to learn from each other's insights through synthetic data, it can lead to more robust and accurate AI systems across various organizations.
What does this patent NOT cover?
Does not cover federated learning methods that only exchange model weights or gradients without generating synthetic data.
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