CN Patent Disclosure Generator
CN patent disclosure generation Agent Skill
Description
Why we love this skill
Automatically generates high-quality patent technical disclosures, from patent point mining to novelty search and self-check, ensuring rigor and innovation. An essential tool for patent applications.
From project documents to deliverable technical disclosure: full process of patent point mining, online novelty search (prioritizing CNIPA + Google Patents/Scholar dual channel), desensitization drafting, and self-check closure. Adapted to YouMind platform toolchain, automatically scanning kanban materials, outputting disclosure document + independent novelty search report, supporting generateDiagram diagram generation, iterative revision, and version management. Built-in P1-P7 core protocols (factual integrity/auto desensitization/version protection/confirmation gate/self-check isolation/novelty search positioning/social science context).
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Patent Assistant
label: Patent Assistant description: Provides full-process patent support for R&D staff, covering 5 major scenarios: (1) Writing invention disclosure documents, (2) Writing Chinese invention patent application documents, (3) Patent search and novelty check (generate Chinese and English search keywords + IPC suggestions), (4) Responding to Office Actions (OA), (5) Chinese-English patent translation. Trigger keywords: patent, disclosure, claims, specification, patent search, novelty check, office action, OA, PCT, patent translation, food patent, process patent, packaging patent, formula patent, device patent.
Grant Proposal Review PRO V2.0
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
AI Patent Innovation Miner V5
Many R&D personnel are not lacking in innovation, but rather do not know where their innovation lies. This AI patent innovation miner can help you: 🔍 Find innovation points 💡 Uncover technical highlights 📄 Write technical disclosure documents 🚀 Improve patent output efficiency Whether it's product development, process improvement, experimental research, or paper results, you can quickly turn them into patent ideas. Especially suitable for: R&D personnel in food, biology, materials, medicine, AI, electronics, mechanics, and other technical fields. From technical solutions to patent disclosure documents, done in one step.
CN Patent Disclosure Generator
CN patent disclosure generation Agent Skill
Description
Why we love this skill
Automatically generates high-quality patent technical disclosures, from patent point mining to novelty search and self-check, ensuring rigor and innovation. An essential tool for patent applications.
From project documents to deliverable technical disclosure: full process of patent point mining, online novelty search (prioritizing CNIPA + Google Patents/Scholar dual channel), desensitization drafting, and self-check closure. Adapted to YouMind platform toolchain, automatically scanning kanban materials, outputting disclosure document + independent novelty search report, supporting generateDiagram diagram generation, iterative revision, and version management. Built-in P1-P7 core protocols (factual integrity/auto desensitization/version protection/confirmation gate/self-check isolation/novelty search positioning/social science context).
Related Skills
View allPatent Assistant
label: Patent Assistant description: Provides full-process patent support for R&D staff, covering 5 major scenarios: (1) Writing invention disclosure documents, (2) Writing Chinese invention patent application documents, (3) Patent search and novelty check (generate Chinese and English search keywords + IPC suggestions), (4) Responding to Office Actions (OA), (5) Chinese-English patent translation. Trigger keywords: patent, disclosure, claims, specification, patent search, novelty check, office action, OA, PCT, patent translation, food patent, process patent, packaging patent, formula patent, device patent.
Grant Proposal Review PRO V2.0
🎯 Core Functionality Overview This is an intelligent review and optimization system specially designed for national social science, education ministry, and provincial grant applications. It simulates the thinking mode of a senior review expert with 15 years of experience, ensuring academic rigor and competitiveness through three core mechanisms. 🔧 Three Core Mechanisms 1️⃣ 12-Step Structured Methodology Covers the full lifecycle of grant proposal review: Phase 1-3: Basic Diagnosis - In-depth analysis of announcement (funding priorities, review criteria, application requirements) - Cross-disciplinary type judgment (precise identification of 8 types) - Research GAP five-dimension identification (theory/methodology/empirical/policy/technology) Phase 4-7: Core Element Review - Research question TMAQ model analysis (theory/methodology/approach/question four dimensions) - Research objective SMART principle test - Research content framework completeness assessment - Research approach type matching (6 types) Phase 8-10: Deep Quality Enhancement - Precise extraction of key difficulties (distinguish criteria + breakthrough paths) - Innovation point seven-dimension mining - Feasibility seven-dimension argumentation Phase 11-12: Overall Optimization - Nine-dimension quality check (academic rigor, innovativeness, feasibility, etc.) - Comprehensive optimization suggestions and final report 2️⃣ Dual-Core Adversarial Mechanism (Builder vs Supervisor) Working Principle: - Builder (academic writer): Generates optimization plans based on user materials - Supervisor (top journal reviewer): Challenges Builder's plans with the strictest standards - Adversarial iteration: 3 rounds of confrontation to ensure plans are robust Application Scenarios: - Innovation point mining: Builder proposes innovation points → Supervisor questions novelty → iterative optimization - Feasibility argumentation: Builder designs plan → Supervisor challenges feasibility → supplementary argumentation - Literature citation: Builder cites literature → Supervisor verifies authenticity → ensure academic standards 3️⃣ Literature Authenticity Verification Mechanism Two working modes: Mode A: Placeholder Mode (Default) - Use markers like [Literature Placeholder-001] in place of specific references - Output a Literature Requirement List specifying search requirements for each placeholder - User searches and fills in real references Mode B: Real-Time Verification Mode - Call Google Scholar to verify literature authenticity in real time - Generate Literature Verification Report (authenticity/relevance/authority scores) - Ensure every citation is traceable Preventing AI Hallucination: - Prohibits fabricating authors, journals, DOIs - All references must be verified or marked as placeholders - Guarantees academic integrity bottom line 💡 Core Value and Applicable Scenarios ✅ Key Pain Points Addressed 1. Academic sloppiness: AI-generated content often includes fake references, logical gaps 2. Insufficient innovation: Difficulty uncovering true academic innovation points 3. Weak feasibility: Research plans lack systematic argumentation 4. Cross-disciplinary difficulty: Interdisciplinary topics often fall between two stools 🎓 Target Users - University faculty (social sciences, education, humanities) - Researchers (applying for national and provincial grants) - Academic teams (needing systematic review processes) 📋 Typical Workflow 1. Input: Upload announcement + proposal draft 2. Review: System executes 12-step structured analysis 3. Adversarial: Dual-core mechanism iteratively optimizes key sections 4. Verification: Literature authenticity check 5. Output: Complete review report + optimization suggestions + literature list 🔍 Differences from Traditional Review | Dimension | Traditional Human Review | Expert Review System | |-----------|------------------------|----------------------| | Review depth | Depends on personal experience | 12-step structured + 9D QC | | Academic rigor | Hard to fully audit | Literature verification + dual-core adversarial | | Innovation mining | Subjective judgment | 7-dimension systematic analysis | | Feasibility argumentation | Experience-driven | 7-dimension item-by-item argumentation | | Consistency | Varies by individual | Standardized process | | Efficiency | Days to weeks | 1-2 hours for initial review | The core advantage of this system is: it makes the tacit knowledge of a 15-year senior review expert explicit, structured, and replicable, enabling every user to receive top-level expert review services.
AI Patent Innovation Miner V5
Many R&D personnel are not lacking in innovation, but rather do not know where their innovation lies. This AI patent innovation miner can help you: 🔍 Find innovation points 💡 Uncover technical highlights 📄 Write technical disclosure documents 🚀 Improve patent output efficiency Whether it's product development, process improvement, experimental research, or paper results, you can quickly turn them into patent ideas. Especially suitable for: R&D personnel in food, biology, materials, medicine, AI, electronics, mechanics, and other technical fields. From technical solutions to patent disclosure documents, done in one step.
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