How Cloud Systems Automatically Create and Train AI Data Models
A cloud-based system that generates fake, privacy-safe data to train AI models, ensuring they remain accurate while protecting sensitive personal information.
Original patent title: “Systems and methods for synthetic data generation”
A cloud-based system that generates fake, privacy-safe data to train AI models, ensuring they remain accurate while protecting sensitive personal information. Granted to Capital One Services LLC in 2023 with 23 claims and 4 forward citations.
Key facts
Coverage
What does this patent actually cover?
This patent describes a cloud system that automates the creation of AI models by using synthetic data—fake data that mimics the statistical properties of real, sensitive information. The system takes a reference dataset, turns categories into numbers, and uses a 'dataset generator' to create synthetic versions. During training, the system constantly compares the model's output to the original data, using a 'similarity metric' and a 'prediction metric' to ensure the model is both accurate and statistically similar to real-world patterns. If the model drifts too far from the desired accuracy or similarity, the system applies a penalty to a loss function, forcing the model to adjust itself until it meets specific quality criteria.
The gap
What does this patent NOT cover?
- Does not cover the use of real, unmasked personal data for training purposes.
- Does not cover manual, non-automated methods of data labeling or model training.
- Does not cover hardware-specific AI acceleration (e.g., specific GPU architectures).
- Does not cover methods that do not involve a penalty-based loss function for synthetic data generation.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The system uses a feedback loop that treats the 'similarity' of the synthetic data as a constraint in the loss function, effectively forcing the AI to learn the structure of the data without ever seeing the actual sensitive values.
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
Fraud detection systems in banking
Credit risk assessment models
Automated customer service chatbots
Privacy-compliant data analysis platforms
Why it matters
The bigger picture
In industries like banking, companies cannot use real customer data (like account numbers or social security numbers) to train AI models due to strict privacy laws. This patent provides a technical framework for 'privacy-preserving' AI development, allowing companies to build powerful machine learning tools without risking data breaches or violating regulations like GDPR or CCPA.
Filed
October 4, 2018
Granted
March 28, 2023
Market context
Who's building on this
Companies in this space
Capital One is the primary assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more → and continues to integrate these methods into their internal machine learning pipelines. Other major financial institutions and cloud providers like AWS and Google Cloud are actively developing similar synthetic data generation tools to solve the 'data privacy vs. model utility' trade-off.
Market impact
This patent reinforces the shift toward 'synthetic data' as a standard industry practice for regulated sectors. It helps move the industry away from risky data-sharing practices and toward automated, secure model training pipelines that satisfy both data scientists and legal compliance teams.
Claim 1 — Plain English
What this patent covers
This patent describes a cloud system that automates the creation of AI models by using synthetic data—fake data that mimics the statistical properties of real, sensitive information. The system takes a reference dataset, turns categories into numbers, and uses a 'dataset generator' to create synthetic versions. During training, the system constantly compares the model's output to the original data, using a 'similarity metric' and a 'prediction metric' to ensure the model is both accurate and statistically similar to real-world patterns. If the model drifts too far from the desired accuracy or similarity, the system applies a penalty to a loss function, forcing the model to adjust itself until it meets specific quality criteria.
The clever bit
The system uses a feedback loop that treats the 'similarity' of the synthetic data as a constraint in the loss function, effectively forcing the AI to learn the structure of the data without ever seeing the actual sensitive values.
What it does not cover
- Does not cover the use of real, unmasked personal data for training purposes.
- Does not cover manual, non-automated methods of data labeling or model training.
- Does not cover hardware-specific AI acceleration (e.g., specific GPU architectures).
- Does not cover methods that do not involve a penalty-based loss function for synthetic data generation.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Moderate
Citation count
14/40
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 · 12.3 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.
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Walters, A., Key, K., Watson, M., Goodsitt, J., Walters, M., Pham, V., TATSUMI, N., Truong, A., Taylor, K., Farivar, R., & Abad, F. A. T. (2023). How Cloud Systems Automatically Create and Train AI Data Models (U.S. Patent No. 11,615,208). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11615208/dall-e-text-to-image-generation
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 Cloud Systems Automatically Create and Train AI Data Models cover?
A cloud-based system that generates fake, privacy-safe data to train AI models, ensuring they remain accurate while protecting sensitive personal information.
Who owns patent US 11615208?
Capital One Services LLC owns this patent, granted in 2023.
When does this patent expire?
This patent is expected to expire on March 28, 2043, when the invention enters the public domain.
What is patent US 11615208 cited by?
This patent has been cited by 4 later patents that build on its ideas.
What problem does this patent solve?
In industries like banking, companies cannot use real customer data (like account numbers or social security numbers) to train AI models due to strict privacy laws. This patent provides a technical framework for 'privacy-preserving' AI development, allowing companies to build powerful machine learning tools without risking data breaches or violating regulations like GDPR or CCPA.
What does this patent NOT cover?
Does not cover the use of real, unmasked personal data for training purposes.
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