How Computers Find Hidden Connections Between Different Fields of Knowledge
A method for finding related ideas in completely different subjects by using math to map how words appear together, even when the subjects use different vocabulary.
Original patent title: “Method for retrieving semantically distant analogies”
A method for finding related ideas in completely different subjects by using math to map how words appear together, even when the subjects use different vocabulary. Granted to Huntsman International LLC in 2003 with 46 claims and 456 forward citations.
Key facts
Coverage
What does this patent actually cover?
This patent describes a way to find 'analogies' between two unrelated fields, such as finding a biological solution to a mechanical engineering problem. It works by first analyzing a source domain (like a library of biology papers) to create a high-dimensional map of how words appear together. It then uses this map—represented as vectors in a multi-dimensional space—to search a second, completely different domain (like a library of engineering patents) where the same words might not even appear. By comparing the 'meaning' of the words based on their context in the first domain, the system can pull up relevant documents from the second domain that share a functional relationship despite having no overlapping keywords.
The gap
What does this patent NOT cover?
- Does not cover simple keyword-based search engines that rely on matching exact words or synonyms.
- Does not cover systems that require the target domain to contain the same vocabulary as the source domain.
- Does not cover manual categorization or human-led tagging of documents.
- Does not cover basic vector search that does not specifically map relationships across semantically distant domains.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The system ignores the actual words in the target domain and instead maps them into the 'semantic space' of the source domain, allowing it to find functional equivalents without needing a direct translation or shared dictionary.
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
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AI-driven scientific discovery platforms
Why it matters
The bigger picture
This technology is a precursor to modern cross-domain knowledge discovery and semantic search. It addresses the 'vocabulary mismatch' problem, which is a major hurdle in AI and data science where different industries use different jargon to describe the same underlying physical or logical processes.
Filed
October 2, 2000
Granted
February 18, 2003
Market context
Who's building on this
Companies in this space
Large-scale search companies like Google and specialized AI research firms are building on these principles of high-dimensional semantic mapping. The core concept of embedding data into vector spaces is now a foundational technique in modern Large Language Models and retrieval-augmented generation systems.
Market impact
This patent helped formalize the transition from simple string-matching search to semantic search. It enabled organizations to break down information silos by allowing automated systems to identify functional parallels across disparate industries, which is now a standard capability in enterprise data analytics.
Claim 1 — Plain English
What this patent covers
This patent describes a way to find 'analogies' between two unrelated fields, such as finding a biological solution to a mechanical engineering problem. It works by first analyzing a source domain (like a library of biology papers) to create a high-dimensional map of how words appear together. It then uses this map—represented as vectors in a multi-dimensional space—to search a second, completely different domain (like a library of engineering patents) where the same words might not even appear. By comparing the 'meaning' of the words based on their context in the first domain, the system can pull up relevant documents from the second domain that share a functional relationship despite having no overlapping keywords.
The clever bit
The system ignores the actual words in the target domain and instead maps them into the 'semantic space' of the source domain, allowing it to find functional equivalents without needing a direct translation or shared dictionary.
What it does not cover
- Does not cover simple keyword-based search engines that rely on matching exact words or synonyms.
- Does not cover systems that require the target domain to contain the same vocabulary as the source domain.
- Does not cover manual categorization or human-led tagging of documents.
- Does not cover basic vector search that does not specifically map relationships across semantically distant domains.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Strong
Citation count
40/40
Highly cited
Claim breadth
20/20
Very broad protection
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
$96K – $307K
Midpoint $192K · expired or expiring · 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
46 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Gillis, H. R. (2003). How Computers Find Hidden Connections Between Different Fields of Knowledge (U.S. Patent No. 6,523,026). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/6523026/google-search-query-processing
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 Computers Find Hidden Connections Between Different Fields of Knowledge cover?
A method for finding related ideas in completely different subjects by using math to map how words appear together, even when the subjects use different vocabulary.
Who owns patent US 6523026?
Huntsman International LLC owns this patent, granted in 2003.
When does this patent expire?
This patent has expired and is now in the public domain — anyone can use the invention freely.
What is patent US 6523026 cited by?
This patent has been cited by 456 later patents that build on its ideas.
What problem does this patent solve?
This technology is a precursor to modern cross-domain knowledge discovery and semantic search. It addresses the 'vocabulary mismatch' problem, which is a major hurdle in AI and data science where different industries use different jargon to describe the same underlying physical or logical processes.
What does this patent NOT cover?
Does not cover simple keyword-based search engines that rely on matching exact words or synonyms.
Patent monitoring