citation-competitor-intelligence

Discover hidden competitors through academic citation network analysis. Forward: given a paper/professor, trace citation threads (backward to prior art, forward to derivative work) to find researchers who may have commercialized similar technology. Reverse: given a company, map its academic footprint and trace the citation graph to uncover unlisted competitors. 通过学术论文引用网络发现隐性竞品。 正向：给定论文/教授，追踪引用线索（反向至已有成果、正向至衍生研究）找到可能已产业化的同类研究者。 反向：给定公司，绘制其学术足迹并从引用网络挖掘未被媒体覆盖的竞品。Use when researching deep-tech startups, university spin-offs, professor-founded companies, or technology commercialization in hardware/pharma/materials/optics. Triggers: 教授创业、 成果转化、学术竞品、论文引用竞品分析、citation competitor、university spin-off competitor、academic competitor discovery、论文→竞品、引用网络分析

Overview

Media and patent databases cover most commercial competitors. But a significant class of competitors — especially in deep-tech fields where university research drives commercialization — never appears in industry news. They exist in academic citation networks: another professor whose group published similar results six months later, or a postdoc who built on your advisor's work and just spun out a company.

Core insight: Academic citation networks encode technology evolution timelines. By tracing citation threads — backward (who did this first?) and forward (who built on this?) — you can discover competitors that industry databases miss, often years before they appear in commercial registries.

This skill implements a 7-step workflow for both directions:

• Forward (paper/professor → competitors): Given a target paper or research group, trace the citation graph to find other researchers working on the same technology, then verify whether they have commercialized (patents, companies, products).
• Reverse (company → unknown academic competitors): Given a company, map its academic footprint (key papers, inventor network), then trace the citation graph to find unlisted competitors who are still in academic/lab stage but approaching commercialization.

When to Use

Trigger when any of the following applies:

• Researching a university spin-off or professor-founded company — need to find who else in academia is working on the same technology
• Evaluating a deep-tech startup — want to check for stealth competitors still in labs
• Analyzing a technology domain where the key players are academic groups, not companies
• Given a patent or paper and asked "who else is doing this?"
• Keyword triggers (bilingual): 教授创业, 成果转化, 学术竞品, 论文引用竞品, citation competitor, university spin-off competitor, academic startup competitor, 论文→竞品, 引用网络分析, 学术成果产业化

Step 0: Applicability Pre-check

Domain	Applicability	Rationale
Optics / photonics / semiconductors / materials / pharma / biotech	Strong	Heavy academic→commercial pipeline; papers predate products by 3-8 years
AI / ML / software	Medium	Fast-moving; preprints and code repos often better signals than formal papers
Consumer internet / business model innovation	Weak	Academic footprint is minimal or irrelevant
Traditional manufacturing / commodities	Not applicable	Technology differentiation comes from scale/cost, not research

Direction Selection

Direction	Starting point	Goal	Example trigger
Forward	Paper P or Professor X	Find other researchers commercializing similar technology	"陈开鑫的模斑转换器还有谁在做？"
Reverse	Company C	Find academic groups competing with C's core tech	"薄方团队的公司竞品有哪些还在实验室阶段？"

Workflow Overview (7 steps)

1. Anchor identification: Identify the core paper(s) and researcher(s). Map authorship network.
2. Backward citation mining: Trace references and literature review to find prior art and earlier researchers in the same lineage.
3. Forward citation mining: Trace who cited the anchor paper — who built on this work?
4. Similarity filtering: Filter forward/backward results for genuine technology overlap (not tangential citations).
5. Performance/timeline benchmarking: Compare key metrics across discovered groups. Who was first? Who performs best? Who is improving fastest?
6. Author-to-company mapping: For each identified researcher/group, check patents, company registrations, funding rounds, product announcements.
7. Competitor matrix output: Position each competitor on technology maturity × commercialization progress axes.

Detailed operations in references/workflow.md.

Input/Output Contract

Input:

• Forward: Target paper (DOI/title) or researcher name + institution + technology domain
• Reverse: Company name + core technology domain
• Optional: known competitors to exclude, time range, citation depth limit

Output: Competitor intelligence report, including:

• Anchor summary (paper/researcher/company profile)
• Citation graph summary (N backward + M forward citations, K after similarity filter)
• Per-competitor profile: researcher → institution → papers → patents → company status → technology positioning
• Competitor positioning matrix (technology maturity vs. commercialization progress)
• Timeline visualization: who published first, who improved, who commercialized
• Risk assessment: which competitor is closest to commercialization? which has strongest IP?

Tool Integration

Phase	Tools	Purpose
Citation search	WebSearch (Google Scholar, Semantic Scholar, Web of Science)	Backward/forward citation mining
Paper analysis	WebSearch, arXiv API, CNKI	Read abstracts, compare methods/metrics
Patent search	PatSnap patsnap-search connector, tushare patent API	Check if researchers hold related patents
Company search	Tianyancha tyc-mcp, Qichacha qcc-company, WebSearch	Check company registrations, funding, legal entities
Deep-tech verification	WebSearch, industry reports, conference proceedings	Cross-validate commercialization claims

Key Principles

• Citations encode competition timelines: A paper citing your anchor paper means the author is aware of and building on this work — the most direct form of competition
• Not all citations are competitors: Many citations are tangential (background, methodology). Similarity filtering is mandatory.
• The gap between paper and patent is the commercialization window: A group with papers but no patents may still be pre-commercial. A group with both papers and patents is closer to market.
• Reverse direction reveals stealth competitors: Companies without media coverage but WITH academic papers and patent families are the highest-risk discoveries
• Interoperability: Optional bridge to patent-gap-supply-chain skill for deeper supply-chain analysis of discovered companies

Resources

• references/workflow.md — Detailed 7-step operations, directional variations
• references/checklists.md — Similarity filtering checklist, commercialization indicators, confidence scoring
• examples/real-case-spot-size-converter.md — Real case: 陈开鑫 vs 薄方, stepped vs wedge SSC