feat: Enhance patent search and update research documentation

- Improve patent search service with expanded functionality
- Update PatentSearchPanel UI component
- Add new research_report.md
- Update experimental protocol, literature review, paper outline, and theoretical framework

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

research/literature_review.md

@@ -14,7 +14,26 @@ Groups of people tend to generate more diverse ideas than individuals because ea
 
 PersonaFlow provides multiple perspectives by using LLMs to simulate domain-specific experts. User studies showed it increased the perceived relevance and creativity of ideated research directions and promoted users' critical thinking activities without increasing perceived cognitive load.
 
-**Gap for our work**: PersonaFlow focuses on research ideation. Our system applies to product/innovation ideation with structured attribute decomposition.
+**Critical Gap - Our Key Differentiation**:
+
+```
+PersonaFlow approach:
+  Query → Experts → Ideas
+  (Experts see the WHOLE query, no problem structure)
+
+Our approach:
+  Query → Attribute Decomposition → (Attributes × Experts) → Ideas
+  (Experts see SPECIFIC attributes, systematic coverage)
+```
+
+| Limitation of PersonaFlow | Our Solution |
+|---------------------------|--------------|
+| No problem structure | Attribute decomposition structures the problem space |
+| Experts applied to whole query | Experts applied to specific attributes |
+| Cannot test what helps (experts vs structure) | 2×2 factorial isolates each contribution |
+| Implicit/random coverage of idea space | Systematic coverage via attribute × expert matrix |
+
+**Our unique contribution**: We hypothesize that attribute decomposition **amplifies** expert effectiveness (interaction effect). PersonaFlow cannot test this because they never decomposed the problem.
 
 ### 1.3 PopBlends: Conceptual Blending with LLMs
 **PopBlends: Strategies for Conceptual Blending with Large Language Models** (CHI 2023)