Automated AI for Adapting to New Data Without Retraining
This patent describes an automated system that builds artificial intelligence models capable of adapting to new, different data without needing full retraining, by learning to ignore irrelevant changes.
Original patent title: “System and Method for Automated Transfer Learning with Domain Disentanglement”
This patent describes an automated system that builds artificial intelligence models capable of adapting to new, different data without needing full retraining, by learning to ignore irrelevant changes. Owned by Mitsubishi Electric Research Laboratories with 22 claims and 46 forward citations, and it is expected to expire in 2042.
Key facts
Coverage
What does this patent actually cover?
This system automatically builds artificial neural network architectures. It uses interfaces to receive training, validation, and testing data, which includes 'task labels Y' (what the AI needs to identify) and 'nuisance variations S' (irrelevant changes, like lighting or background). Memory banks store 'reconfigurable deep neural network (DNN) blocks' and settings called 'hyperparameters'. A processor then explores different hyperparameters and methods, including 'auxiliary regularization modules', to adjust how the DNN blocks work. The goal is to make the AI's predictions 'insensitive to the nuisance variations S', meaning it can identify the task labels accurately even when the irrelevant parts of the data change. For example, an AI trained to recognize a specific object in bright light could still recognize it in dim light without needing to be fully retrained.
The gap
What does this patent NOT cover?
- Does not cover manually designed neural network architectures for transfer learning, as it specifies 'automated construction'.
- Does not cover transfer learning methods that do not explicitly disentangle latent variables from nuisance variations using auxiliary regularization modules.
- Does not cover systems that do not explore hyperparameters of regularization, pre-processing, or post-processing methods to achieve nuisance robustness.
- Does not cover non-deep neural network (DNN) architectures, as it specifically references 'reconfigurable deep neural network (DNN) blocks'.
- Does not cover methods that do not aim for 'nuisance-robust Bayesian inference' to handle domain shifts.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The innovation lies in automatically exploring and configuring neural network components and methods to actively 'disentangle' important features from irrelevant 'nuisance variations'. This allows the AI to learn what truly matters for a task, making it highly adaptable and robust when encountering new, slightly different data environments.
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
Autonomous vehicle perception systems adapting to different weather or lighting conditions
Medical image analysis systems working across various scanner types or patient demographics
Industrial inspection systems identifying defects on different production lines
Speech recognition systems handling varying background noise or speaker accents
Why it matters
The bigger picture
AI models often struggle when deployed in real-world situations where data differs slightly from their training data, a problem known as 'domain shift'. This patent offers a way to automatically build AI models that are robust to these changes, making them more reliable and reducing the need for expensive, time-consuming retraining. This is crucial for deploying AI in diverse environments, from self-driving cars encountering varied weather to medical imaging systems used in different hospitals.
Filed
February 1, 2022
Market context
Who's building on this
Companies in this space
Mitsubishi Electric Research Laboratories Inc. is the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more → and continues to be active in AI research, particularly in areas like machine learning, computer vision, and robotics. Major technology companies like Google, Microsoft, Amazon, and IBM, along with numerous AI startups, are also heavily invested in developing more robust and adaptable AI systems, including advanced transfer learning and automated machine learning (AutoML) techniques.
Market impact
This technology addresses a fundamental challenge in AI deployment: the brittleness of models to real-world data variability. By enabling the automated creation of more robust and transferable AI, it can significantly lower the cost and increase the reliability of AI solutions across industries. This could accelerate the adoption of AI in critical applications where models must perform consistently despite changing conditions, potentially leading to more reliable AI products and services.
Claim 1 — Plain English
What this patent covers
This system automatically builds artificial neural network architectures. It uses interfaces to receive training, validation, and testing data, which includes 'task labels Y' (what the AI needs to identify) and 'nuisance variations S' (irrelevant changes, like lighting or background). Memory banks store 'reconfigurable deep neural network (DNN) blocks' and settings called 'hyperparameters'. A processor then explores different hyperparameters and methods, including 'auxiliary regularization modules', to adjust how the DNN blocks work. The goal is to make the AI's predictions 'insensitive to the nuisance variations S', meaning it can identify the task labels accurately even when the irrelevant parts of the data change. For example, an AI trained to recognize a specific object in bright light could still recognize it in dim light without needing to be fully retrained.
The clever bit
The innovation lies in automatically exploring and configuring neural network components and methods to actively 'disentangle' important features from irrelevant 'nuisance variations'. This allows the AI to learn what truly matters for a task, making it highly adaptable and robust when encountering new, slightly different data environments.
What it does not cover
- Does not cover manually designed neural network architectures for transfer learning, as it specifies 'automated construction'.
- Does not cover transfer learning methods that do not explicitly disentangle latent variables from nuisance variations using auxiliary regularization modules.
- Does not cover systems that do not explore hyperparameters of regularization, pre-processing, or post-processing methods to achieve nuisance robustness.
- Does not cover non-deep neural network (DNN) architectures, as it specifically references 'reconfigurable deep neural network (DNN) blocks'.
- Does not cover methods that do not aim for 'nuisance-robust Bayesian inference' to handle domain shifts.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
33/40
Moderately cited
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
0/20
Older than 20 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
$187K – $599K
Midpoint $374K · 15.6 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
22 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Akino, T. K., Smedemark-Margulies, N., & Wang, Y. Automated AI for Adapting to New Data Without Retraining (U.S. Patent No. 20,230,162,023). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20230162023/system-and-method-for-automated-transfer-learning-with-domain-disentanglement
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 Automated AI for Adapting to New Data Without Retraining cover?
This patent describes an automated system that builds artificial intelligence models capable of adapting to new, different data without needing full retraining, by learning to ignore irrelevant changes.
Who owns patent US 20230162023?
This patent is owned by Mitsubishi Electric Research Laboratories.
When does this patent expire?
This patent is expected to expire on February 1, 2042, when the invention enters the public domain.
What is patent US 20230162023 cited by?
This patent has been cited by 46 later patents that build on its ideas.
What problem does this patent solve?
AI models often struggle when deployed in real-world situations where data differs slightly from their training data, a problem known as 'domain shift'. This patent offers a way to automatically build AI models that are robust to these changes, making them more reliable and reducing the need for expensive, time-consuming retraining. This is crucial for deploying AI in diverse environments, from self-driving cars encountering varied weather to medical imaging systems used in different hospitals.
What does this patent NOT cover?
Does not cover manually designed neural network architectures for transfer learning, as it specifies 'automated construction'.
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