How to Build a Smart Knowledge Graph for Specific Topics
Oracle's patent describes a method for automatically expanding a small, topic-specific knowledge graph by intelligently pulling relevant information from a much larger knowledge graph, helping applications understand user requests better.
Original patent title: “Techniques for building a knowledge graph in limited knowledge domains”
Oracle's patent describes a method for automatically expanding a small, topic-specific knowledge graph by intelligently pulling relevant information from a much larger knowledge graph, helping applications understand user requests better. Granted to Oracle International in 2025 with 23 claims and 1 forward citation, and it is expected to expire in 2043.
Coverage
What does this patent actually cover?
This patent describes a computer-implemented method for creating a specialized knowledge graph for a particular application. It starts by taking information from a user's own data, like conversations or documents, to build a basic "seed graph" of important entities and how they connect (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). The system then looks for groups of related entities in this seed graph, called "weakly connected components" (Claim 1). It maps these entities to a much larger, existing "reference knowledge graph." Using a "finite state machine," it explores the reference graph from these mapped points to find new, potentially relevant entities and connections (Claim 1). A special "priority function" then scores these potential additions, considering factors like how many weakly connected components are present and the overall "graph density" (Claim 1). The highest-scoring new entities and links are added to the seed graph, making it a more complete "customized knowledge graph." Finally, this expanded graph is used to understand what a user means when they ask a question or give a command, and then provide a suitable answer (Claim 1). For example, if a user dataset is about a specific company's products, the system could expand this with general product knowledge from a large reference graph to better answer customer service questions.
The gap
What does this patent NOT cover?
- Does not cover building a knowledge graph without first creating a "seed graph" from a specific "user dataset" (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 expanding a knowledge graph without using a separate, pre-existing "reference knowledge graph" for additional information (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 traversing the reference knowledge graph without using a "finite state machine" to guide the search for new entities (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 selecting new entities and links without a "priority function" that specifically considers the number of "weakly connected components" and the "graph density" (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 graph generation if it is not ultimately used for "determining a user intent" and "providing a response" to an utterance (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 clever part is how the system intelligently expands a small, focused knowledge graph by using a priority function. This function doesn't just add nearby information; it specifically evaluates new entities based on how they improve the overall structure and interconnectedness (measured by weakly connected components and graph density) of the growing graph, ensuring relevance and coherence.
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
Customer service chatbots that answer specific product questions
Virtual assistants that understand industry-specific jargon
Enterprise search engines that provide highly relevant internal documents
Healthcare systems that interpret patient queries based on medical knowledge bases
Why it matters
The bigger picture
This technology is important for making artificial intelligence systems smarter and more useful in specific areas. By customizing knowledge graphs, applications can better understand nuanced user requests and provide more accurate, context-aware responses. This is critical for improving the performance of chatbots, virtual assistants, and specialized search tools in fields like customer support, healthcare, or financial services.
Filed
March 3, 2023
Granted
June 24, 2025
Market context
Who's building on this
Companies in this space
Oracle, as the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing and applying knowledge graph technologies in its enterprise software and cloud services. Major tech companies like Google, Microsoft, and Amazon also invest heavily in knowledge graph research and development to power their search engines, virtual assistants, and cloud AI platforms. Numerous startups are also emerging to create specialized knowledge graph solutions for various industries.
Market impact
This type of technology enhances the ability of AI systems to operate effectively in specialized domains, moving beyond general knowledge to provide highly accurate and contextual responses. It enables the creation of more sophisticated conversational AI, intelligent search, and data analysis tools, directly impacting industries reliant on understanding complex, domain-specific information. This capability allows companies to build more effective internal tools and customer-facing applications, potentially reducing operational costs and improving user experience.
Claim 1 — Plain English
What this patent covers
This patent describes a computer-implemented method for creating a specialized knowledge graph for a particular application. It starts by taking information from a user's own data, like conversations or documents, to build a basic "seed graph" of important entities and how they connect (Claim 1). The system then looks for groups of related entities in this seed graph, called "weakly connected components" (Claim 1). It maps these entities to a much larger, existing "reference knowledge graph." Using a "finite state machine," it explores the reference graph from these mapped points to find new, potentially relevant entities and connections (Claim 1). A special "priority function" then scores these potential additions, considering factors like how many weakly connected components are present and the overall "graph density" (Claim 1). The highest-scoring new entities and links are added to the seed graph, making it a more complete "customized knowledge graph." Finally, this expanded graph is used to understand what a user means when they ask a question or give a command, and then provide a suitable answer (Claim 1). For example, if a user dataset is about a specific company's products, the system could expand this with general product knowledge from a large reference graph to better answer customer service questions.
The clever bit
The clever part is how the system intelligently expands a small, focused knowledge graph by using a priority function. This function doesn't just add nearby information; it specifically evaluates new entities based on how they improve the overall structure and interconnectedness (measured by weakly connected components and graph density) of the growing graph, ensuring relevance and coherence.
What it does not cover
- Does not cover building a knowledge graph without first creating a "seed graph" from a specific "user dataset" (Claim 1).
- Does not cover expanding a knowledge graph without using a separate, pre-existing "reference knowledge graph" for additional information (Claim 1).
- Does not cover traversing the reference knowledge graph without using a "finite state machine" to guide the search for new entities (Claim 1).
- Does not cover selecting new entities and links without a "priority function" that specifically considers the number of "weakly connected components" and the "graph density" (Claim 1).
- Does not cover knowledge graph generation if it is not ultimately used for "determining a user intent" and "providing a response" to an utterance (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
Strong
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
20/20
Major company or institution
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
$75K – $240K
Midpoint $150K · 16.5 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
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Singaraju, G., & Ammanabrolu, P. V. (2025). How to Build a Smart Knowledge Graph for Specific Topics (U.S. Patent No. 12,340,316). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12340316/techniques-for-building-a-knowledge-graph-in-limited-knowledge-domains
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 to Build a Smart Knowledge Graph for Specific Topics cover?
Oracle's patent describes a method for automatically expanding a small, topic-specific knowledge graph by intelligently pulling relevant information from a much larger knowledge graph, helping applications understand user requests better.
Who owns patent US 12340316?
Oracle International owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on March 3, 2043, when the invention enters the public domain.
What is patent US 12340316 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
This technology is important for making artificial intelligence systems smarter and more useful in specific areas. By customizing knowledge graphs, applications can better understand nuanced user requests and provide more accurate, context-aware responses. This is critical for improving the performance of chatbots, virtual assistants, and specialized search tools in fields like customer support, healthcare, or financial services.
What does this patent NOT cover?
Does not cover building a knowledge graph without first creating a "seed graph" from a specific "user dataset" (Claim 1).
Patent monitoring