How AI Models Learn from Both Local and Central AI Systems
This patent describes a system for training specialized AI models at local sites, like factories, by combining insights from small local AI systems and large central AI systems, especially when data patterns change.
Original patent title: “Knowledge acquisition for distributed learning models”
This patent describes a system for training specialized AI models at local sites, like factories, by combining insights from small local AI systems and large central AI systems, especially when data patterns change. Owned by Dell Products LP with 23 claims, and it is expected to expire in 2044.
Coverage
What does this patent actually cover?
The patent describes a "distributed learning knowledge acquirer" which is a system designed to improve how AI models learn. This acquirer sits at a "distributed edge site," like a factory (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 2), and gathers data from three places: a "local small foundation system" (a smaller AI model at the edge), a "distributed endpoint management system" (which manages devices at the edge), and a "centralized large foundation system" (a powerful AI model in a central location). The acquirer checks if this incoming data shows a "conceptual drift" (a change in data patterns). If it does, this new data is used as "local training data" to update a "local discriminative model." This local model then learns from both the local small AI's classifications and the central large AI's classifications (Claim 1), making it better at quickly classifying new input data at the edge site (Claim 3).
The gap
What does this patent NOT cover?
- Does not cover training discriminative AI models without using data from both a local small foundation system and a centralized large foundation system.
- Does not cover training AI models that do not perform a "drift evaluation" to identify data causing "conceptual drift" before using it for training.
- Does not cover systems where the local small foundation system has more than 10 billion parameters (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 5).
- Does not cover training discriminative AI models solely on human-labeled data, rather than on output data from other AI models.
- Does not cover systems that lack a "distributed learning knowledge acquirer" to coordinate data reception and model training across edge and central systems.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in intelligently combining the strengths of small, local generative AI models with large, centralized generative AI models to train discriminative AI models. It specifically uses the concept of "conceptual drift" to identify when local data changes enough to warrant updating the local model, ensuring the edge AI remains relevant and accurate.
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
Quality control AI in smart factories
Predictive maintenance systems on industrial equipment
Edge computing devices for real-time data analysis
Automated inspection systems in manufacturing
Smart city infrastructure monitoring
Why it matters
The bigger picture
In today's world, many devices, like those in smart factories, need to make quick decisions without sending all their data to a central cloud. This patent helps train "discriminative AI models" (AI that classifies things) more effectively and cheaply right where they are used. By combining local, specialized knowledge with broad, central intelligence, these models can adapt quickly to new situations, like changes on a factory floor, making the entire system more efficient and responsive.
Filed
November 29, 2024
Market context
Who's building on this
Companies in this space
Dell Products LP, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a major player in enterprise IT and edge computing, making this relevant to their hardware and software solutions. Other companies like NVIDIA, Intel, and various cloud providers (AWS, Azure, Google Cloud) are also heavily invested in developing and deploying distributed AI and edge inference systems. Startups focused on industrial AI and IoT platforms would also find this technology relevant.
Market impact
This patent addresses a critical challenge in deploying AI at scale: keeping models accurate and up-to-date in dynamic, distributed environments. By enabling more efficient and localized training, it could reduce the cost and latency associated with AI inference at the edge. This approach could accelerate the adoption of AI in industries like manufacturing and logistics, where real-time decision-making is crucial, potentially leading to more customized and robust AI applications.
Claim 1 — Plain English
What this patent covers
The patent describes a "distributed learning knowledge acquirer" which is a system designed to improve how AI models learn. This acquirer sits at a "distributed edge site," like a factory (Claim 2), and gathers data from three places: a "local small foundation system" (a smaller AI model at the edge), a "distributed endpoint management system" (which manages devices at the edge), and a "centralized large foundation system" (a powerful AI model in a central location). The acquirer checks if this incoming data shows a "conceptual drift" (a change in data patterns). If it does, this new data is used as "local training data" to update a "local discriminative model." This local model then learns from both the local small AI's classifications and the central large AI's classifications (Claim 1), making it better at quickly classifying new input data at the edge site (Claim 3).
The clever bit
The novelty lies in intelligently combining the strengths of small, local generative AI models with large, centralized generative AI models to train discriminative AI models. It specifically uses the concept of "conceptual drift" to identify when local data changes enough to warrant updating the local model, ensuring the edge AI remains relevant and accurate.
What it does not cover
- Does not cover training discriminative AI models without using data from both a local small foundation system and a centralized large foundation system.
- Does not cover training AI models that do not perform a "drift evaluation" to identify data causing "conceptual drift" before using it for training.
- Does not cover systems where the local small foundation system has more than 10 billion parameters (Claim 5).
- Does not cover training discriminative AI models solely on human-labeled data, rather than on output data from other AI models.
- Does not cover systems that lack a "distributed learning knowledge acquirer" to coordinate data reception and model training across edge and central systems.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Limited data
Citation count
0/40
No citations yet
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
$31K – $100K
Midpoint $62K · 18.1 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
23 claims as filed with the patent office.
Concepts involved
Cite this patent
White, W. J. How AI Models Learn from Both Local and Central AI Systems (U.S. Patent No. 20,260,065,072). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20260065072/knowledge-acquisition-for-distributed-learning-models
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 Both Local and Central AI Systems cover?
This patent describes a system for training specialized AI models at local sites, like factories, by combining insights from small local AI systems and large central AI systems, especially when data patterns change.
Who owns patent US 20260065072?
This patent is owned by Dell Products LP.
When does this patent expire?
This patent is expected to expire on November 29, 2044, when the invention enters the public domain.
What problem does this patent solve?
In today's world, many devices, like those in smart factories, need to make quick decisions without sending all their data to a central cloud. This patent helps train "discriminative AI models" (AI that classifies things) more effectively and cheaply right where they are used. By combining local, specialized knowledge with broad, central intelligence, these models can adapt quickly to new situations, like changes on a factory floor, making the entire system more efficient and responsive.
What does this patent NOT cover?
Does not cover training discriminative AI models without using data from both a local small foundation system and a centralized large foundation system.
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