How a Camera-Based System Monitors Artificial Neural Network Creativity
A system that uses a camera to watch a screen displaying neural network activity, identifying new patterns and using a critic to decide if those patterns are worth keeping.
Original patent title: “Electro-optical device and method for identifying and inducing topological states formed among interconnecting neural modules”
A system that uses a camera to watch a screen displaying neural network activity, identifying new patterns and using a critic to decide if those patterns are worth keeping. Granted to Individual in 2019 with 56 claims and 1 forward citation, and it is expected to expire in 2035.
Coverage
What does this patent actually cover?
The system monitors an artificial neural network by displaying its internal states—such as reconstruction errors or neuron activations—as color-coded values on an optical display. A camera captures this display, and a processor called a 'thalamobot' analyzes the video feed to identify new, novel topologies (patterns) in the neural chains. If the thalamobot finds a new pattern, it sends it to a 'critic' component that evaluates the merit of that pattern. Based on this evaluation, the system injects noise into an 'imagitron' to either strengthen or weaken the neural connections, effectively guiding the network's learning or idea-generation process.
The gap
What does this patent NOT cover?
- Does not cover systems that analyze neural network data directly from memory or digital buses without an optical display and camera interface.
- Does not cover general-purpose neural network training methods that lack the specific thalamobot-critic-imagitron feedback loop architecture.
- Does not cover systems that do not use color-coded visual representations of neural reconstruction errors or activation histories.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The system treats the neural network as an external environment to be observed via a camera, using a visual feedback loop to bypass traditional software bottlenecks and introduce a 'critic' to curate machine-generated ideas.
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
Experimental neural network monitoring interfaces
Conceptual AI systems simulating biological thalamic filtering
Why it matters
The bigger picture
This patent represents a conceptual approach to artificial intelligence that mimics biological processes, specifically the role of the thalamus in filtering information. By using an external optical loop to monitor internal neural states, it attempts to solve processing bottlenecks in large-scale machine learning models. It highlights a unique, albeit unconventional, methodology for managing machine creativity and learning through externalized feedback.
Filed
January 2, 2015
Granted
September 24, 2019
Market context
Who's building on this
Companies in this space
The technology is primarily associated with the work of Stephen L. Thaler and his company, Imagination Engines, Inc. The approach remains largely academic and experimental, focusing on autonomous machine intelligence rather than mainstream commercial deep learning frameworks.
Market impact
The patent has had minimal impact on mainstream AI development, as the industry has largely converged on gradient-based optimization and digital backpropagation rather than optical monitoring loops. It serves as a niche example of alternative architectures for artificial intelligence.
Claim 1 — Plain English
What this patent covers
The system monitors an artificial neural network by displaying its internal states—such as reconstruction errors or neuron activations—as color-coded values on an optical display. A camera captures this display, and a processor called a 'thalamobot' analyzes the video feed to identify new, novel topologies (patterns) in the neural chains. If the thalamobot finds a new pattern, it sends it to a 'critic' component that evaluates the merit of that pattern. Based on this evaluation, the system injects noise into an 'imagitron' to either strengthen or weaken the neural connections, effectively guiding the network's learning or idea-generation process.
The clever bit
The system treats the neural network as an external environment to be observed via a camera, using a visual feedback loop to bypass traditional software bottlenecks and introduce a 'critic' to curate machine-generated ideas.
What it does not cover
- Does not cover systems that analyze neural network data directly from memory or digital buses without an optical display and camera interface.
- Does not cover general-purpose neural network training methods that lack the specific thalamobot-critic-imagitron feedback loop architecture.
- Does not cover systems that do not use color-coded visual representations of neural reconstruction errors or activation histories.
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
Early stage
Citation count
6/40
Early citations
Claim breadth
20/20
Very broad protection
Recency
10/20
Granted 5–10 years ago
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
$77K – $246K
Midpoint $154K · 8.5 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
56 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Thaler, S. L. (2019). How a Camera-Based System Monitors Artificial Neural Network Creativity (U.S. Patent No. 10,423,875). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/10423875/electro-optical-device-and-method-for-identifying-and-inducing-topological-states-formed-among-interconnecting-neural-modules
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 a Camera-Based System Monitors Artificial Neural Network Creativity cover?
A system that uses a camera to watch a screen displaying neural network activity, identifying new patterns and using a critic to decide if those patterns are worth keeping.
Who owns patent US 10423875?
Individual owns this patent, granted in 2019.
When does this patent expire?
This patent is expected to expire on January 2, 2035, when the invention enters the public domain.
What is patent US 10423875 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
This patent represents a conceptual approach to artificial intelligence that mimics biological processes, specifically the role of the thalamus in filtering information. By using an external optical loop to monitor internal neural states, it attempts to solve processing bottlenecks in large-scale machine learning models. It highlights a unique, albeit unconventional, methodology for managing machine creativity and learning through externalized feedback.
What does this patent NOT cover?
Does not cover systems that analyze neural network data directly from memory or digital buses without an optical display and camera interface.
Same assignee
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