# 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. - **Patent:** US 12039012 - **Original title:** Systems and methods for heterogeneous federated transfer learning - **Owner:** Sharecare AI - **Granted:** 2024 - **Status:** Active - **Times cited:** 1 - **Field:** ai_ml, software, telecommunications, healthcare, finance ## What it does 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. ## 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. ## 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. ## Real-world examples 1. Collaborative medical research where hospitals train AI models on patient data without sharing individual records. 2. Financial fraud detection systems that learn from different banks' transaction patterns while keeping customer data private. 3. Autonomous vehicle systems sharing driving experiences from different car manufacturers without exchanging raw sensor data. 4. Personalized health recommendations where AI models learn across user groups while maintaining individual privacy. ## Why it matters 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. ## 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. **Full plain-English explainer:** https://patentbrief.org/patent/us/12039012/systems-and-methods-for-heterogeneous-federated-transfer-learning **Original patent:** https://patents.google.com/patent/US12039012 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [How Cloud Systems Automatically Create and Train AI Data Models](https://patentbrief.org/patent/us/11615208/dall-e-text-to-image-generation) — A cloud-based system that generates fake, privacy-safe data to train AI models, ensuring they remain accurate while protecting sensitive personal information. - [How Training Systems Share AI Knowledge Without Sharing Private Student Data](https://patentbrief.org/patent/us/11915111/federated-machine-learning-in-adaptive-training-systems) — This patent describes a system where different student training centers can improve their AI models by sharing general learning patterns, not private student data, to make training better for everyone. - [Training AI Models Together with Unlabeled Data Using a Teacher](https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-learning) — This patent describes a way for multiple AI systems to learn together from data that hasn't been manually labeled, using a 'teacher' AI to create temporary labels for a 'student' AI. - [How to Train AI Models with Fake Data Using Generative Networks](https://patentbrief.org/patent/us/20230297446/data-model-generation-using-generative-adversarial-networks) — This patent describes a method for training artificial intelligence models using specially generated fake, or 'synthetic,' data created by a generative adversarial network, ensuring the synthetic data is high-quality and safe for training.