Natural-language search
FindIP is a semantic search engine. The more you frame your query as a sentence describing intent and the problem to solve — rather than a few keywords — the better the results.
This page covers the basics that apply to every workflow. For role-specific deep dives (prior-art, FTO, invalidity, landscape), see the cards near the bottom.
Write a concrete sentence, not a keyword list
Semantic search doesn't look for documents that share words; it finds the documents whose meaning is closest to your question. Even when an exact technical term is missing, the engine still surfaces patents that describe the same problem-solving principle or use a synonym.
Not recommended — keyword soup
"all-solid-state battery lithium dendrite suppression solid electrolyte"
- Lacks context, so it falls back to plain word matching
- Misses patents that say "dendritic crystal" instead of "dendrite"
Recommended — sentence with intent
"Solid-electrolyte technology that suppresses lithium dendrite growth in all-solid-state batteries to improve stability"
- Goal (stability) and challenge (suppress growth) are explicit
- The vector engine cleanly clusters patents that share the same intent
Powerful applicant-aware search
When you prompt an LLM to find a company's patents, you don't need to know the exact English / Korean / Japanese name. FindIP links tens of thousands of applicant-name variants to a single representative Entity ID under the hood.
Example
Prompt
"Find Apple's recent patents on augmented-reality (AR) HMDs."
That prompt alone is enough — the engine auto-expands to Apple Inc., 애플 인코포레이티드, アップル インコーポレイテッド, and so on, sweeping up Apple's patents across US · KR · JP · CN · EP.
Search vs Trends
Switching the way you prompt the LLM (which endpoint gets called) based on what you actually want from the answer makes a huge difference in efficiency.
1. When you need deep dives into individual patents
Use this when you want to find the most similar patents in a technology area and read their content.
Example prompt
"Summarize the abstract and claims of the 5 most similar core patents that improve the interfacial resistance of solid electrolytes."
2. When you want market-wide trends
Use this when you need to skim thousands of documents fast for stats like annual filing volume or top-applicant rankings.
Example prompt
"Give me a table of yearly filing volume for autonomous-driving LiDAR sensors over the past 5 years and the share of the top 5 companies."
Guides by work type
Different goals call for different prompting styles, filters, and tools.