Explore Patents and Literature Semantically with Sciverse

Use semantic retrieval across patents and academic literature to discover technical connections

Scenario

R&D teams need to understand how a technology appears across patents and academic papers, and discover overlaps or gaps between the two sources.

Estimated calls

~10-20 API calls

Tools

agentic-searchcontent

Pipeline

agentic-search(专利关键词)→ agentic-search(学术关键词)→ content(验证)→ 对比分析

Input example

Research topic:
"solid-state electrolyte interface stabilization for lithium metal batteries"
Need: compare academic literature and patent coverage.

Output example

Cross-source summary:
- Academic papers emphasize mechanisms and experimental characterization.
- Patents emphasize formulations, manufacturing processes, and application claims.
- Shared themes: interface coatings, sulfide electrolytes, polymer composites.

Agent Prompt example

You are a technology intelligence assistant. Use Sciverse semantic search to collect academic and patent-related evidence, then compare topics, claims, and source types.

Implementation steps

Step 1: Set up the environment

Configure token and HTTP client

!pip install httpx anthropic
import os
os.environ["SCIVERSE_API_TOKEN"] = "sv-your-token-here"  # 替换为你的真实值
import os
os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..."  # 替换为你的真实值

Step 2: Retrieve patents and academic papers semantically

Search the topic from multiple phrasings to collect diverse evidence

import os
import asyncio
import httpx

BASE = "https://api.sciverse.space"
TOKEN = os.environ["SCIVERSE_API_TOKEN"]
HEADERS = {"Authorization": f"Bearer {TOKEN}"}

async def search(query: str, top_k: int = 15):
    async with httpx.AsyncClient(timeout=30) as client:
        resp = await client.post(
            f"{BASE}/agentic-search",
            headers=HEADERS,
            json={"query": query, "top_k": top_k}
        )
        resp.raise_for_status()
        return (resp.json().get("hits") or [])

async def main():
    # 检索专利相关内容
    patent_hits = await search("CRISPR base editing patent method composition")
    print(f"Patent-related: {len(patent_hits)} chunks")

    # 检索学术文献
    academic_hits = await search("CRISPR base editing adenine cytosine mechanism")
    print(f"Academic-related: {len(academic_hits)} chunks")

    return patent_hits, academic_hits

patent_hits, academic_hits = await main()

Step 3: Cross-analyze and generate a report

Group results by source type, theme, and claim focus

from anthropic import Anthropic

client = Anthropic()

patent_summary = "\
".join([
    f"- [{h['doc_id']}] (score: {h['score']:.2f}) {h['title']}: {h.get('chunk', '')[:80]}..."
    for h in patent_hits[:8]
])
academic_summary = "\
".join([
    f"- [{h['doc_id']}] (score: {h['score']:.2f}) {h['title']}: {h.get('chunk', '')[:80]}..."
    for h in academic_hits[:8]
])

msg = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    messages=[{
        "role": "user",
        "content": f"""分析以下两组检索结果的技术关联：

## 专利相关片段
{patent_summary}

## 学术文献片段
{academic_summary}

请输出：
1) 两组结果中的技术主题对比
2) 可能的专利-论文关联（基于内容相似性）
3) 技术发展脉络推测

注意：所有结论必须基于上述检索结果，标注 doc_id。"""
    }]
)
print(msg.content[0].text)

Notes

Use multiple query phrasings to improve recall.
Do not treat patent claims and academic findings as equivalent evidence.
Keep source type and metadata in the final report.
Use structured filters when you need a strict year or venue range.

FAQ

适合什么任务?

适合 prior art、技术调研、专利与论文交叉验证。

输出时要注意什么?

应区分论文证据和专利证据，不要混成同一种来源。

为什么要交叉检索?

同一技术可能先出现在论文、专利或产品材料中，交叉检索能提高覆盖率。

如何组织结果?

建议按技术主题、论文证据、专利证据和风险判断分组输出。

Next steps

Need an API key?

Create one in Console > Tokens.The same API key works for enabled Sciverse, DianShi, and Skills capabilities, with starter quota available according to account permissions.

Open console