TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining
TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining explores TRIZ-RAGNER enhances patent analysis by combining retrieval-augmented LLM with TRIZ knowledge for effective contradiction mining.. Commercial viability score: 7/10 in Patent Analysis.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisCiMRAG: Cim-Aware Domain-Adaptive and Noise-Resilient Retrieval-Augmented Generation for Edge-Based LLMsViability: 6.0
- Builds On ThisEvaluating Social Bias in RAG Systems: When External Context Helps and Reasoning HurtsViability: 5.0
- Builds On ThisT-Retriever: Tree-based Hierarchical Retrieval Augmented Generation for Textual GraphsViability: 5.0
- Prior WorkNot All Entities are Created Equal: A Dynamic Anonymization Framework for Privacy-Preserving Retrieval-Augmented GenerationViability: 7.0
- Prior Work