Training AI Models Together with Unlabeled Data Using a Teacher
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.
Original patent title: “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. Owned by Nokia Technologies Oy with 23 claims and 37 forward citations, and it is expected to expire in 2041.
Key facts
Coverage
What does this patent actually cover?
This patent describes an apparatus, or node, within a federated machine learning system. This node contains a 'federated student machine learning network' that updates its own AI model by considering the updated models from other nodes in the system (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). Crucially, it also has a 'teacher machine learning network' which receives data that has not been manually labeled. The teacher network then creates 'pseudo-labels' for this unlabeled data (Claim 1). The federated student network then uses this unlabeled data along with the teacher's pseudo-labels to perform supervised learning (Claim 1). For example, a network on a phone could learn to identify new types of objects in photos by getting rough labels from a local 'teacher' AI, while also sharing its learning with other phones to improve overall accuracy without sending private photos to a central server.
The gap
What does this patent NOT cover?
- Does not cover federated learning systems that rely solely on manually labeled data for training.
- Does not cover machine learning systems where a 'student' network does not update its model based on other 'nodes' in a federated system.
- Does not cover traditional centralized machine learning where all data is sent to one server for training.
- Does not cover systems that use a teacher network but do not involve a federated student network.
- Does not cover systems that only use unsupervised learning without generating pseudo-labels for supervised learning.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The clever part is combining federated learning, where multiple devices collaboratively train an AI model without sharing raw data, with a 'teacher-student' approach that generates its own labels for previously unlabeled data. This allows the system to learn from much more data without needing expensive human labeling or compromising privacy.
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
AI models on smartphones learning to improve photo recognition without uploading personal photos.
IoT devices collaboratively learning to detect anomalies in sensor data.
Edge computing devices improving predictive maintenance models.
Healthcare systems training AI on patient data while maintaining privacy.
Why it matters
The bigger picture
This technology is important for training powerful AI models while protecting user privacy. By allowing AI models to learn from unlabeled data directly on devices (like smartphones or IoT sensors) and only share model updates, it reduces the need to send sensitive raw data to a central server. This approach can also make AI training more efficient, as it reduces the massive human effort typically required to manually label vast amounts of data.
Filed
July 8, 2021
Market context
Who's building on this
Companies in this space
Nokia Technologies Oy, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, continues to develop technologies in telecommunications and AI, including federated learning for network optimization and edge computing. Other major technology companies like Google and Apple are also heavily invested in federated learning for on-device AI improvements, particularly for privacy-sensitive applications on smartphones and other consumer electronics.
Market impact
This patent contributes to the growing field of federated learning, which addresses critical challenges in data privacy and the cost of data labeling. By enabling more efficient training on decentralized, unlabeled data, it supports the development of more powerful and private AI applications across various industries. This approach helps reduce the need for large, centralized datasets, potentially lowering barriers for smaller entities to develop AI solutions and fostering innovation at the network edge.
Claim 1 — Plain English
What this patent covers
This patent describes an apparatus, or node, within a federated machine learning system. This node contains a 'federated student machine learning network' that updates its own AI model by considering the updated models from other nodes in the system (Claim 1). Crucially, it also has a 'teacher machine learning network' which receives data that has not been manually labeled. The teacher network then creates 'pseudo-labels' for this unlabeled data (Claim 1). The federated student network then uses this unlabeled data along with the teacher's pseudo-labels to perform supervised learning (Claim 1). For example, a network on a phone could learn to identify new types of objects in photos by getting rough labels from a local 'teacher' AI, while also sharing its learning with other phones to improve overall accuracy without sending private photos to a central server.
The clever bit
The clever part is combining federated learning, where multiple devices collaboratively train an AI model without sharing raw data, with a 'teacher-student' approach that generates its own labels for previously unlabeled data. This allows the system to learn from much more data without needing expensive human labeling or compromising privacy.
What it does not cover
- Does not cover federated learning systems that rely solely on manually labeled data for training.
- Does not cover machine learning systems where a 'student' network does not update its model based on other 'nodes' in a federated system.
- Does not cover traditional centralized machine learning where all data is sent to one server for training.
- Does not cover systems that use a teacher network but do not involve a federated student network.
- Does not cover systems that only use unsupervised learning without generating pseudo-labels for supervised learning.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
32/40
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
$225K – $719K
Midpoint $449K · 15.0 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
Cricri, F., Tavakoli, H. R., & Aksu, E. B. Training AI Models Together with Unlabeled Data Using a Teacher (U.S. Patent No. 20,220,012,637). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20220012637/federated-teacher-student-machine-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 Training AI Models Together with Unlabeled Data Using a Teacher cover?
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.
Who owns patent US 20220012637?
This patent is owned by Nokia Technologies Oy.
When does this patent expire?
This patent is expected to expire on July 8, 2041, when the invention enters the public domain.
What is patent US 20220012637 cited by?
This patent has been cited by 37 later patents that build on its ideas.
What problem does this patent solve?
This technology is important for training powerful AI models while protecting user privacy. By allowing AI models to learn from unlabeled data directly on devices (like smartphones or IoT sensors) and only share model updates, it reduces the need to send sensitive raw data to a central server. This approach can also make AI training more efficient, as it reduces the massive human effort typically required to manually label vast amounts of data.
What does this patent NOT cover?
Does not cover federated learning systems that rely solely on manually labeled data for training.
Patent monitoring