How AI Models Learn from Your Health Data Without Sharing It
This patent describes a system where personal health data, like photos or audio, stays on your device while helping train a shared AI model for medical research, improving predictions without sending your raw data.
Original patent title: “System and method with federated learning model for medical research applications”
This patent describes a system where personal health data, like photos or audio, stays on your device while helping train a shared AI model for medical research, improving predictions without sending your raw data. Granted to Sharecare AI in 2023 with 22 claims and 1 forward citation, and it is expected to expire in 2040.
Coverage
What does this patent actually cover?
This system uses a method called federated learning to train artificial intelligence models. It involves multiple 'edge devices' (like your smartphone) that each have a program called a 'federated learner' running a 'deep neural network' (DNN). This DNN predicts characteristics about you, such as your mood or age, from your recorded data like pictures or audio, as mentioned in claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more → 1, 2, 5, and 7. If the prediction is wrong, you can correct it. Your device then uses your private data and corrections to update its part of the DNN model. Instead of sending your raw data, only a 'modified part of the modified tensor' (a small, updated piece of the AI model) is sent to a central 'FL aggregator'. The aggregator combines these updates from many devices to create a better, shared model, which it then sends back to all the devices. For example, claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1 specifies that if the data is a picture, the system only performs this update training if a face is detected in the image.
The gap
What does this patent NOT cover?
- It does not cover systems where raw user data, like pictures or audio recordings, is directly uploaded to a central server for training.
- It does not cover AI training systems that do not perform 'update training' of the deep neural network directly on the end user's device.
- It does not cover federated learning systems that send the entire model parameters from the edge device to the aggregator, rather than just a 'modified part'.
- It does not cover systems that use picture data for training without first detecting a face in the picture, as specified in claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1.
- It does not cover federated learning without an 'FL aggregator' that combines updates from multiple edge devices to produce a new base model.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The clever bit is enabling AI models to learn from highly sensitive personal data, like facial images or voice recordings, directly on a user's device. This on-device training, combined with the selective sharing of only 'modified parts' of the model, allows for continuous improvement of a shared AI without ever exposing individual raw data to a central server. The specific requirement in claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1 to only train on pictures if a face is detected adds a layer of focused privacy and relevance.
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
Google's Gboard keyboard for next-word prediction
Apple's Siri voice recognition improvements
NVIDIA Clara Federated Learning platform for healthcare
Various research initiatives in personalized health and diagnostics
Sharecare AI applications for health management
Why it matters
The bigger picture
This patent addresses a significant challenge in medical research: how to use vast amounts of sensitive personal health data to train powerful AI models without compromising individual privacy. By keeping raw data on user devices and only sharing model updates, it enables the development of more accurate diagnostic and predictive tools. This approach is crucial for advancing personalized medicine and public health initiatives while adhering to strict data privacy regulations.
Filed
March 11, 2020
Granted
December 26, 2023
Market context
Who's building on this
Companies in this space
Sharecare AI, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing systems based on this technology. Major technology companies like Google, Apple, and NVIDIA are also heavily invested in federated learning, applying it to various privacy-sensitive domains including health. Research institutions and startups in the digital health space are exploring similar decentralized AI training methods.
Market impact
This patent contributes to the growing field of privacy-preserving AI, particularly in healthcare. It enables the creation of more robust and accurate AI models by leveraging diverse, real-world data that would otherwise be inaccessible due to privacy concerns. This approach fosters innovation in personalized health applications, diagnostics, and preventative care, potentially leading to new product categories and services that rely on secure, distributed data analysis.
Claim 1 — Plain English
What this patent covers
This system uses a method called federated learning to train artificial intelligence models. It involves multiple 'edge devices' (like your smartphone) that each have a program called a 'federated learner' running a 'deep neural network' (DNN). This DNN predicts characteristics about you, such as your mood or age, from your recorded data like pictures or audio, as mentioned in claims 1, 2, 5, and 7. If the prediction is wrong, you can correct it. Your device then uses your private data and corrections to update its part of the DNN model. Instead of sending your raw data, only a 'modified part of the modified tensor' (a small, updated piece of the AI model) is sent to a central 'FL aggregator'. The aggregator combines these updates from many devices to create a better, shared model, which it then sends back to all the devices. For example, claim 1 specifies that if the data is a picture, the system only performs this update training if a face is detected in the image.
The clever bit
The clever bit is enabling AI models to learn from highly sensitive personal data, like facial images or voice recordings, directly on a user's device. This on-device training, combined with the selective sharing of only 'modified parts' of the model, allows for continuous improvement of a shared AI without ever exposing individual raw data to a central server. The specific requirement in claim 1 to only train on pictures if a face is detected adds a layer of focused privacy and relevance.
What it does not cover
- It does not cover systems where raw user data, like pictures or audio recordings, is directly uploaded to a central server for training.
- It does not cover AI training systems that do not perform 'update training' of the deep neural network directly on the end user's device.
- It does not cover federated learning systems that send the entire model parameters from the edge device to the aggregator, rather than just a 'modified part'.
- It does not cover systems that use picture data for training without first detecting a face in the picture, as specified in claim 1.
- It does not cover federated learning without an 'FL aggregator' that combines updates from multiple edge devices to produce a new base model.
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 · 13.4 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
22 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
BROUWER, W. A. D., THAKORE, K., DOW, P. J., SHARMA, S. A., & KASHYAP, N. R. (2023). How AI Models Learn from Your Health Data Without Sharing It (U.S. Patent No. 11,853,891). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11853891/system-and-method-with-federated-learning-model-for-medical-research-application
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 How AI Models Learn from Your Health Data Without Sharing It cover?
This patent describes a system where personal health data, like photos or audio, stays on your device while helping train a shared AI model for medical research, improving predictions without sending your raw data.
Who owns patent US 11853891?
Sharecare AI owns this patent, granted in 2023.
When does this patent expire?
This patent is expected to expire on March 11, 2040, when the invention enters the public domain.
What is patent US 11853891 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 significant challenge in medical research: how to use vast amounts of sensitive personal health data to train powerful AI models without compromising individual privacy. By keeping raw data on user devices and only sharing model updates, it enables the development of more accurate diagnostic and predictive tools. This approach is crucial for advancing personalized medicine and public health initiatives while adhering to strict data privacy regulations.
What does this patent NOT cover?
It does not cover systems where raw user data, like pictures or audio recordings, is directly uploaded to a central server for training.
Same assignee
More from Sharecare AI
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