How Computers Calculate Probabilities in Large Knowledge Bases
A method for finding answers in a database of uncertain facts by ignoring probabilities to find a solution first, then calculating how likely that solution is based on the underlying evidence.
Original patent title: “Large scale probabilistic ontology reasoning”
A method for finding answers in a database of uncertain facts by ignoring probabilities to find a solution first, then calculating how likely that solution is based on the underlying evidence. Granted to International Business Machines Corp in 2016 with 29 claims and 1 forward citation, and it is expected to expire in 2029.
Coverage
What does this patent actually cover?
This patent describes a way to query a knowledge base where facts (axioms) come with a probability score. Instead of trying to calculate complex probabilities for the entire database at once, the system first ignores the probability scores to find a logical solution using standard reasoning. Once it identifies the 'justification'—the minimal set of facts needed to prove that answer—it then applies the probability scores only to that specific subset. This makes it computationally efficient to determine the 'net probability' of a conclusion in massive datasets that would otherwise be too slow to process.
The gap
What does this patent NOT cover?
- Does not cover calculating probabilities for the entire knowledge base simultaneously without first finding a logical justification.
- Does not cover systems that lack a defined set of axioms or logical rules for deriving answers.
- Does not cover non-probabilistic reasoning systems that treat all facts as 100% certain.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The innovation is the 'divide and conquer' strategy: by decoupling the search for a logical answer from the mathematical calculation of its certainty, the system avoids the exponential complexity of calculating probabilities for every possible combination of facts.
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
Enterprise knowledge graphs used for automated decision support
Semantic search engines that rank results based on probabilistic confidence
Medical diagnostic software evaluating symptoms with varying levels of clinical certainty
Why it matters
The bigger picture
As AI and expert systems deal with increasingly messy, real-world data, they often encounter conflicting or uncertain information. This approach allows developers to build systems that can provide 'likely' answers without getting bogged down in the massive mathematical overhead of traditional probabilistic modeling. It is a key technique for scaling semantic web technologies and enterprise knowledge graphs.
Filed
October 6, 2009
Granted
June 7, 2016
Market context
Who's building on this
Companies in this space
IBM remains a primary developer of these technologies, integrating them into their Watson and enterprise AI platforms. Other major cloud providers and database companies building graph-based AI systems also utilize similar techniques for handling uncertainty in large-scale data.
Market impact
This patent helped enable the transition from rigid, binary databases to more flexible, probabilistic systems. It allowed companies to build AI tools that can reason over 'fuzzy' data, which is essential for modern applications like automated customer service, fraud detection, and complex supply chain management.
Claim 1 — Plain English
What this patent covers
This patent describes a way to query a knowledge base where facts (axioms) come with a probability score. Instead of trying to calculate complex probabilities for the entire database at once, the system first ignores the probability scores to find a logical solution using standard reasoning. Once it identifies the 'justification'—the minimal set of facts needed to prove that answer—it then applies the probability scores only to that specific subset. This makes it computationally efficient to determine the 'net probability' of a conclusion in massive datasets that would otherwise be too slow to process.
The clever bit
The innovation is the 'divide and conquer' strategy: by decoupling the search for a logical answer from the mathematical calculation of its certainty, the system avoids the exponential complexity of calculating probabilities for every possible combination of facts.
What it does not cover
- Does not cover calculating probabilities for the entire knowledge base simultaneously without first finding a logical justification.
- Does not cover systems that lack a defined set of axioms or logical rules for deriving answers.
- Does not cover non-probabilistic reasoning systems that treat all facts as 100% certain.
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
Early stage
Citation count
6/40
Early citations
Claim breadth
19/20
Very broad protection
Recency
5/20
Granted 10–20 years ago
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
$44K – $140K
Midpoint $87K · 3.2 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
29 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Kalyanpur, A., Fokoue-Nkoutche, A. B., Srinivas, K., & Schonberg, E. G. (2016). How Computers Calculate Probabilities in Large Knowledge Bases (U.S. Patent No. 9,361,579). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/9361579/large-scale-probabilistic-ontology-reasoning
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 Calculate Probabilities in Large Knowledge Bases cover?
A method for finding answers in a database of uncertain facts by ignoring probabilities to find a solution first, then calculating how likely that solution is based on the underlying evidence.
Who owns patent US 9361579?
International Business Machines Corp owns this patent, granted in 2016.
When does this patent expire?
This patent is expected to expire on October 6, 2029, when the invention enters the public domain.
What is patent US 9361579 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
As AI and expert systems deal with increasingly messy, real-world data, they often encounter conflicting or uncertain information. This approach allows developers to build systems that can provide 'likely' answers without getting bogged down in the massive mathematical overhead of traditional probabilistic modeling. It is a key technique for scaling semantic web technologies and enterprise knowledge graphs.
What does this patent NOT cover?
Does not cover calculating probabilities for the entire knowledge base simultaneously without first finding a logical justification.
Same assignee
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