Case study · Semantic discovery
AI patent search: from keyword matching to semantic search.
A patent search platform built on legacy keyword technology was limited to exact text matching, blind to the concepts and context within complex patent documents. Morphos AI migrated the system from keyword matching to conceptual understanding using Green Vectors.
The challenge
Keyword matching created real risk.
The legacy keyword system created real risk. Researchers could miss critical prior art when search terms did not exactly match a patent's language, even when the concepts were identical. The siloed architecture forced repetitive narrow queries, and concept-based discovery of adjacent technologies was not possible.
The approach
From flat text to semantic vectors.
Morphos AI processed and vectorized the client's entire patent database, transforming flat text into semantic vector representations. Green Vectors managed the resulting large-scale vector database, reducing index size and optimizing vector operations at ingestion so the system was efficient in both storage and retrieval from day one.
The results
Concepts, not just keywords.
Storage cost reduction and conceptual search speed
- Storage cost reduction
- 67%
- Search speed
- 10x faster (90% latency reduction)
- Search behavior
- A search for "self-driving car" now retrieves "autonomous vehicle navigation system"
Figures are drawn from one production patent-search migration. Results on other corpora require separate evaluation.
Why this matters
Discovery beyond specific terminology.
The shift from literal string matching to conceptual understanding lets researchers and legal teams discover previously invisible connections, reduce the risk of missing relevant prior art, and grasp a technology landscape without being limited by specific terminology.
Frequently asked
Questions about this migration.
- Q1
How much did Green Vectors reduce patent search storage costs?
67%, while also delivering 10x faster conceptual search.
- Q2
What changed beyond performance?
The system moved from keyword matching to semantic search, so it retrieves patents by concept rather than exact terminology, surfacing prior art that keyword search would miss.
- Q3
How was latency improved?
Green Vectors reduced index size and optimized vector operations at ingestion, contributing to a 90% reduction in search latency.
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