How to Combine Information from Multiple Knowledge Graphs
This patent describes a method for merging information from two or more existing knowledge graphs into a single, unified knowledge graph, using special rules called "graph operators."
Original patent title: “Knowledge graph data fusion”
This patent describes a method for merging information from two or more existing knowledge graphs into a single, unified knowledge graph, using special rules called "graph operators.". Owned by Alipay Hangzhou Information Technology Co with 23 claims and 4 forward citations, and it is expected to expire in 2043.
Coverage
What does this patent actually cover?
The patent outlines a method for combining information from different knowledge graphs. First, it obtains a "target entity field" (like "person" or "product") and a "target relationship description" (like "works for" or "buys") from the "ontology definition data" of two or more existing knowledge graphs (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). Then, it determines "graph operators" which are specific rules for combining these fields and relationships (Claim 1). It gathers "data instances" (actual data, like "John Doe" or "iPhone") corresponding to these fields and relationships from the original graphs (Claim 1). Finally, it uses the graph operators to process these data instances and create a "fused knowledge graph" (Claim 1). For example, if one graph has "customer" and another has "user," a graph operator could fuse them into a single "customer/user" entity, standardizing how their attributes are expressed (Claim 5).
The gap
What does this patent NOT cover?
- Does not cover fusing data that is not structured as a knowledge graph with defined entities and relationships.
- Does not cover data fusion methods that do not use specific "graph operators" to process data instances.
- Does not cover combining knowledge graphs without first selecting target entity fields and relationship descriptions from their ontology definitions.
- Does not cover simple data aggregation without creating a new, fused knowledge graph structure.
- Does not cover fusion where the operators are not determined either by user input or automatic generation (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 3).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in systematically using "graph operators" to not just combine raw data, but to specifically fuse the *ontology definition data* (the blueprints) and then process the *data instances* (the actual facts) from multiple knowledge graphs, ensuring a coherent and structured merged result.
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
Combining customer data from an e-commerce platform and a payment system.
Merging product catalogs from different suppliers in a supply chain.
Integrating medical research data from various hospital systems.
Creating a unified view of financial transactions from multiple banking services.
Why it matters
The bigger picture
In today's world, information is often scattered across many different databases and systems. This patent provides a structured way to bring together knowledge from various sources, like different departments in a company or various online platforms. This allows for a more complete understanding of complex topics, making it easier to find connections and draw conclusions that wouldn't be possible with isolated data.
Filed
December 20, 2023
Market context
Who's building on this
Companies in this space
Major tech companies like Google, Amazon, Microsoft, and Meta heavily use and develop knowledge graph technologies for search, recommendations, and internal data management. Companies specializing in enterprise data integration and AI platforms, such as Neo4j, Ontotext, and various data fabric vendors, are also actively building solutions in this space. Alipay, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a significant player in financial technology, likely using such methods for fraud detection and personalized services.
Market impact
This type of technology enables organizations to break down data silos, creating a unified view of their information assets. This can lead to more accurate AI models, better business intelligence, and improved decision-making. It is crucial for industries that rely on integrating vast and varied datasets, such as finance, healthcare, and e-commerce, by providing a structured approach to complex data integration challenges.
Claim 1 — Plain English
What this patent covers
The patent outlines a method for combining information from different knowledge graphs. First, it obtains a "target entity field" (like "person" or "product") and a "target relationship description" (like "works for" or "buys") from the "ontology definition data" of two or more existing knowledge graphs (Claim 1). Then, it determines "graph operators" which are specific rules for combining these fields and relationships (Claim 1). It gathers "data instances" (actual data, like "John Doe" or "iPhone") corresponding to these fields and relationships from the original graphs (Claim 1). Finally, it uses the graph operators to process these data instances and create a "fused knowledge graph" (Claim 1). For example, if one graph has "customer" and another has "user," a graph operator could fuse them into a single "customer/user" entity, standardizing how their attributes are expressed (Claim 5).
The clever bit
The novelty lies in systematically using "graph operators" to not just combine raw data, but to specifically fuse the *ontology definition data* (the blueprints) and then process the *data instances* (the actual facts) from multiple knowledge graphs, ensuring a coherent and structured merged result.
What it does not cover
- Does not cover fusing data that is not structured as a knowledge graph with defined entities and relationships.
- Does not cover data fusion methods that do not use specific "graph operators" to process data instances.
- Does not cover combining knowledge graphs without first selecting target entity fields and relationship descriptions from their ontology definitions.
- Does not cover simple data aggregation without creating a new, fused knowledge graph structure.
- Does not cover fusion where the operators are not determined either by user input or automatic generation (Claim 3).
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
14/40
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
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
$62K – $200K
Midpoint $125K · 17.3 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
Liang, L. How to Combine Information from Multiple Knowledge Graphs (U.S. Patent No. 20,240,144,032). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20240144032/knowledge-graph-data-fusion
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 Combine Information from Multiple Knowledge Graphs cover?
This patent describes a method for merging information from two or more existing knowledge graphs into a single, unified knowledge graph, using special rules called "graph operators."
Who owns patent US 20240144032?
This patent is owned by Alipay Hangzhou Information Technology Co.
When does this patent expire?
This patent is expected to expire on December 20, 2043, when the invention enters the public domain.
What is patent US 20240144032 cited by?
This patent has been cited by 4 later patents that build on its ideas.
What problem does this patent solve?
In today's world, information is often scattered across many different databases and systems. This patent provides a structured way to bring together knowledge from various sources, like different departments in a company or various online platforms. This allows for a more complete understanding of complex topics, making it easier to find connections and draw conclusions that wouldn't be possible with isolated data.
What does this patent NOT cover?
Does not cover fusing data that is not structured as a knowledge graph with defined entities and relationships.
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