How Computers Build Knowledge Maps from Text Documents
Adobe's patent describes a system that automatically builds a structured map of information, called a knowledge graph, by identifying key concepts (noun-phrases) and their connections within written text, then using this map to modify online platforms.
Original patent title: “Creating a knowledge graph based on text-based knowledge corpora”
Adobe's patent describes a system that automatically builds a structured map of information, called a knowledge graph, by identifying key concepts (noun-phrases) and their connections within written text, then using this map to modify online platforms. Granted to Adobe in 2024 with 23 claims and 1 forward citation, and it is expected to expire in 2042.
Coverage
What does this patent actually cover?
This patent describes a method for creating a knowledge graph from a collection of text documents, referred to as a 'text-based knowledge corpus'. First, the system extracts individual sentences from the text. For each sentence, it identifies 'noun-phrases' and determines the relationships between them using both the sentence's grammatical structure ('dependency paths') and a 'relation classifier' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). This classifier can predict connections between any pair of noun-phrases, not just pre-defined 'named entities' (AbstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →). Based on these noun-phrases and their relations, a small 'sub-graph' is created for each sentence. Finally, all these sub-graphs are merged together to form a complete knowledge graph for the entire text collection (Claim 1). For example, if a sentence says 'Adobe developed Photoshop', the system would identify 'Adobe' and 'Photoshop' as noun-phrases, determine the 'developed' relation between them, and add this to the graph. The resulting knowledge graph can then be used to identify specific entities or relations for modifying an online platform (Claim 1).
The gap
What does this patent NOT cover?
- Does not cover methods that use other types of phrases (like verb phrases or adjective phrases) to build the graph, as it specifically claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more → using 'noun-phrases in the sentence without using other types of phrases in the sentence' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover determining relations solely based on statistical co-occurrence without considering 'dependency paths of the sentence' or relations 'predicted by a relation classifier' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover knowledge graphs where entities are restricted to a predefined set of named entities, as the relation classifier is 'configured to predict a relation between a pair of entities without restricting the entities to a set of named entities' (AbstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →).
- Does not cover systems that don't merge sentence-level sub-graphs to form the final knowledge graph (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
- Does not cover systems that don't transmit identified entities or relations to an 'online platform' for 'modifying the online platform' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The cleverness lies in combining grammatical analysis ('dependency paths') with a machine learning model ('relation classifier') to find connections between *any* noun-phrases, not just pre-identified 'named entities'. This allows for a more flexible and comprehensive understanding of text. Building the larger graph by merging smaller, sentence-level 'sub-graphs' is also a key innovation, enabling a scalable approach to knowledge extraction.
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
Google's Knowledge Graph
Wikipedia's structured data (Wikidata)
Content management systems
Semantic search engines
Recommendation systems for online platforms
AI-powered assistants that answer factual questions
Why it matters
The bigger picture
Knowledge graphs are crucial for helping computers understand and organize vast amounts of information, much like how humans connect ideas. This patent offers a way to automatically build these structured maps from unstructured text, which is a fundamental challenge in artificial intelligence. By extracting precise relationships between concepts, it can power more intelligent search, content recommendations, and automated content generation across various online services.
Filed
November 17, 2022
Granted
January 9, 2024
Market context
Who's building on this
Companies in this space
Adobe Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing and integrating AI and machine learning into its creative and enterprise software, where knowledge graphs are essential for content understanding and intelligent features. Major tech companies like Google, Microsoft, and Amazon also heavily invest in knowledge graph technologies for their search, cloud, and AI products. Startups focused on natural language processing and data intelligence are also building solutions in this space.
Market impact
This type of technology has significantly impacted the market by enabling more sophisticated content understanding and retrieval. It allows online platforms to move beyond simple keyword matching to grasp the meaning and relationships within text, leading to more accurate search results, personalized user experiences, and automated content enrichment. It underpins many of the 'smart' features we see in modern applications, driving innovation in AI-powered services and data management.
Claim 1 — Plain English
What this patent covers
This patent describes a method for creating a knowledge graph from a collection of text documents, referred to as a 'text-based knowledge corpus'. First, the system extracts individual sentences from the text. For each sentence, it identifies 'noun-phrases' and determines the relationships between them using both the sentence's grammatical structure ('dependency paths') and a 'relation classifier' (Claim 1). This classifier can predict connections between any pair of noun-phrases, not just pre-defined 'named entities' (Abstract). Based on these noun-phrases and their relations, a small 'sub-graph' is created for each sentence. Finally, all these sub-graphs are merged together to form a complete knowledge graph for the entire text collection (Claim 1). For example, if a sentence says 'Adobe developed Photoshop', the system would identify 'Adobe' and 'Photoshop' as noun-phrases, determine the 'developed' relation between them, and add this to the graph. The resulting knowledge graph can then be used to identify specific entities or relations for modifying an online platform (Claim 1).
The clever bit
The cleverness lies in combining grammatical analysis ('dependency paths') with a machine learning model ('relation classifier') to find connections between *any* noun-phrases, not just pre-identified 'named entities'. This allows for a more flexible and comprehensive understanding of text. Building the larger graph by merging smaller, sentence-level 'sub-graphs' is also a key innovation, enabling a scalable approach to knowledge extraction.
What it does not cover
- Does not cover methods that use other types of phrases (like verb phrases or adjective phrases) to build the graph, as it specifically claims using 'noun-phrases in the sentence without using other types of phrases in the sentence' (Claim 1).
- Does not cover determining relations solely based on statistical co-occurrence without considering 'dependency paths of the sentence' or relations 'predicted by a relation classifier' (Claim 1).
- Does not cover knowledge graphs where entities are restricted to a predefined set of named entities, as the relation classifier is 'configured to predict a relation between a pair of entities without restricting the entities to a set of named entities' (Abstract).
- Does not cover systems that don't merge sentence-level sub-graphs to form the final knowledge graph (Claim 1).
- Does not cover systems that don't transmit identified entities or relations to an 'online platform' for 'modifying the online platform' (Claim 1).
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
Moderate
Citation count
6/40
Early citations
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 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
$62K – $200K
Midpoint $125K · 16.1 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
Citations
Patent lineage
Cite this patent
Aditya, S., & Sinha, A. (2024). How Computers Build Knowledge Maps from Text Documents (U.S. Patent No. 11,868,733). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11868733/creating-a-knowledge-graph-based-on-text-based-knowledge-corpora
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
Embed
Add this patent to your site
Drop this plain-English patent card into any blog post or article — free, no signup. It always links back to the full breakdown here.
<div data-patentlens-widget data-patent-number="US11868733"></div> <script src="https://patentbrief.org/embed.js" async></script>
Stay in the loop
Get a weekly digest of new patents.
One email per week. No spam. Unsubscribe anytime.
Keep exploring
Related patents you should know
US 4683195 · 1987
How to Make Billions of Copies of a DNA Segment
This patent describes the Polymerase Chain Reaction (PCR), a method to rapidly create many copies of a specific piece of DNA or RNA, enabling its detection and analysis.
Cetus Corp
US 8697359 · 2014
How to Edit Genes in Human Cells Using an Engineered CRISPR System
This patent describes an engineered CRISPR-Cas9 system for precisely cutting DNA in eukaryotic cells to change how genes work, opening the door for gene editing in complex organisms.
Massachusetts Institute of Technology
US 7657849 · 2010
How the iPhone's Slide-to-Unlock Gesture Works
Apple's 2010 patent describes unlocking a device by dragging a specific graphical image across the touchscreen along a predefined path, a gesture that became iconic with the original iPhone.
Apple Inc
US 4733665 · 1988
How Doctors Implant a Permanent Stent Using a Balloon
This patent describes the method for placing a permanent, expandable wire mesh tube inside a blood vessel or other body tube using a balloon-tipped catheter to widen it and keep it open.
Expandable Grafts Partnership
US 4965188 · 1990
How to Make Many Copies of a DNA Piece with Heat
This patent describes the Polymerase Chain Reaction (PCR) method, a technique to make millions of copies of a specific DNA segment using a heat-resistant enzyme and repeated temperature changes.
Cetus Corp
US 4235871 · 1980
How to Encapsulate Active Materials in Lipid Bubbles Efficiently
This patent describes a method for trapping biologically active substances inside tiny, multi-layered fat bubbles called liposomes, using a specific water-in-oil emulsion and gel-forming process to improve how much material gets captured.
Individual
Semantically similar
You might also find these interesting
US 12340316 · 2025 · Oracle International
How to Build a Smart Knowledge Graph for Specific Topics
US 11798529 · 2023 · Microsoft Technology Licensing
Improving AI Language Understanding with Aligned Knowledge Graphs
US 12531820 · 2026 · Amazon Technologies
How Chatbots Use AI and Fact Databases to Learn and Improve
US 11507851 · 2022 · Samsung Electronics Co
How AI Connects Different Databases Using Knowledge Graphs
More to explore
More in Software & Internet
US 4405829 · 1983 · Massachusetts Institute of Technology
How RSA Public-Key Encryption Keeps Digital Messages Secret
US 6285999 · 2001 · Leland Stanford Junior University
How Websites Get Ranked by Importance
US 5960411 · 1999 · Amazon com Inc
How Amazon's One-Click Ordering Works for Online Purchases
US 7669123 · 2010 · Facebook Inc
Displaying Friends' Activities in a Social Network Feed
New to patents?
Common Questions
Frequently Asked Questions
What does How Computers Build Knowledge Maps from Text Documents cover?
Adobe's patent describes a system that automatically builds a structured map of information, called a knowledge graph, by identifying key concepts (noun-phrases) and their connections within written text, then using this map to modify online platforms.
Who owns patent US 11868733?
Adobe owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on November 17, 2042, when the invention enters the public domain.
What is patent US 11868733 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
Knowledge graphs are crucial for helping computers understand and organize vast amounts of information, much like how humans connect ideas. This patent offers a way to automatically build these structured maps from unstructured text, which is a fundamental challenge in artificial intelligence. By extracting precise relationships between concepts, it can power more intelligent search, content recommendations, and automated content generation across various online services.
What does this patent NOT cover?
Does not cover methods that use other types of phrases (like verb phrases or adjective phrases) to build the graph, as it specifically claims using 'noun-phrases in the sentence without using other types of phrases in the sentence' (Claim 1).
Same assignee
More from Adobe
Patent monitoring