How Computers Group Files Based on Meaning and Context
A system that organizes digital files and data into related groups by analyzing their meaning, context, and how they relate to one another.
Original patent title: “Context based co-operative learning system and method for representing thematic relationships”
A system that organizes digital files and data into related groups by analyzing their meaning, context, and how they relate to one another. Owned by Individual with 22 claims and 17 forward citations.
Key facts
Coverage
What does this patent actually cover?
The system acts as an intelligent librarian for digital data. It uses an identifier to index files and a context determinator to figure out what those files are actually about by analyzing their semantic and syntactic features. It then builds clusters of related objects and maps user search queries to these clusters to return highly relevant results. A key component is the co-operative learner, which allows different instances of the system to share what they have learned about themes and contexts to improve future search accuracy.
The gap
What does this patent NOT cover?
- Does not cover simple keyword-based search engines that lack thematic clustering.
- Does not cover hardware storage devices or physical data retrieval methods.
- Does not cover systems that rely solely on user-provided tags without automated semantic analysis.
- Does not cover real-time streaming data processing that does not involve indexing or clustering.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The system uses a co-operative learner that allows multiple independent systems to share their learned thematic associations, effectively creating a distributed intelligence network for data organization.
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
Enterprise document management systems
Smart database indexing tools
Advanced content recommendation engines
Why it matters
The bigger picture
As data volumes grow, finding specific information becomes harder. This patent addresses the challenge of moving beyond basic keyword matching toward understanding the 'theme' of content, which is essential for modern enterprise search and knowledge management systems.
Filed
April 1, 2015
Market context
Who's building on this
Companies in this space
The technology aligns with research being performed by major cloud providers and AI-focused software firms that specialize in natural language processing and automated knowledge graph construction. These entities are actively refining how machines interpret the relationship between unstructured data objects.
Market impact
This approach contributes to the shift toward semantic search, where systems prioritize intent and context over exact character matching. It reflects a broader industry trend of moving away from static database queries toward dynamic, learning-based information retrieval.
Claim 1 — Plain English
What this patent covers
The system acts as an intelligent librarian for digital data. It uses an identifier to index files and a context determinator to figure out what those files are actually about by analyzing their semantic and syntactic features. It then builds clusters of related objects and maps user search queries to these clusters to return highly relevant results. A key component is the co-operative learner, which allows different instances of the system to share what they have learned about themes and contexts to improve future search accuracy.
The clever bit
The system uses a co-operative learner that allows multiple independent systems to share their learned thematic associations, effectively creating a distributed intelligence network for data organization.
What it does not cover
- Does not cover simple keyword-based search engines that lack thematic clustering.
- Does not cover hardware storage devices or physical data retrieval methods.
- Does not cover systems that rely solely on user-provided tags without automated semantic analysis.
- Does not cover real-time streaming data processing that does not involve indexing or clustering.
Patent timeline
Application submitted to the patent office
PatentBrief Score
Impact Score
Moderate
Citation count
25/40
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
$156K – $499K
Midpoint $312K · 8.8 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
Dwivedi, S., Kulkarni, P. A., & Haribhakta, Y. V. How Computers Group Files Based on Meaning and Context (U.S. Patent No. 20,150,206,070). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20150206070/generative-adversarial-networks-gans
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 Group Files Based on Meaning and Context cover?
A system that organizes digital files and data into related groups by analyzing their meaning, context, and how they relate to one another.
Who owns patent US 20150206070?
This patent is owned by Individual.
What is patent US 20150206070 cited by?
This patent has been cited by 17 later patents that build on its ideas.
What problem does this patent solve?
As data volumes grow, finding specific information becomes harder. This patent addresses the challenge of moving beyond basic keyword matching toward understanding the 'theme' of content, which is essential for modern enterprise search and knowledge management systems.
What does this patent NOT cover?
Does not cover simple keyword-based search engines that lack thematic clustering.
Same assignee
More from Individual
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