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/paper_outline.md

@@ -11,7 +11,7 @@
 
 ## Abstract (Draft)
 
-Large Language Models (LLMs) are increasingly used for creative ideation, yet they exhibit a phenomenon we term "semantic gravity" - the tendency to generate outputs clustered around high-probability regions of their training distribution. This limits the novelty and diversity of generated ideas. We propose a multi-expert transformation framework that systematically activates diverse semantic regions by conditioning LLM generation on simulated expert perspectives. Our system decomposes concepts into structured attributes, generates ideas through multiple domain-expert viewpoints, and employs semantic deduplication to ensure genuine diversity. Through experiments comparing multi-expert generation against direct LLM generation and single-expert baselines, we demonstrate that our approach produces ideas with [X]% higher semantic diversity and [Y]% lower patent overlap. We contribute a theoretical framework explaining LLM creativity limitations and an open-source system for innovation ideation.
+Large Language Models (LLMs) are increasingly used for creative ideation, yet they exhibit a phenomenon we term "semantic gravity" - the tendency to generate outputs clustered around high-probability regions of their training distribution. This limits the novelty and diversity of generated ideas. We investigate two complementary strategies to overcome this limitation: (1) **attribute decomposition**, which structures the problem space before creative exploration, and (2) **expert perspective transformation**, which conditions LLM generation on simulated domain-expert viewpoints. Through a 2×2 factorial experiment comparing Direct generation, Expert-Only, Attribute-Only, and Full Pipeline (both factors combined), we demonstrate that each factor independently improves semantic diversity, with the combination producing super-additive effects. Our Full Pipeline achieves [X]% higher semantic diversity and [Y]% lower patent overlap compared to direct generation. We contribute a theoretical framework explaining LLM creativity limitations and an open-source system for innovation ideation.
 
 ---
 
@@ -61,8 +61,17 @@ Large Language Models (LLMs) are increasingly used for creative ideation, yet th
 - Evaluation methods (CAT, semantic distance)
 
 ### 2.5 Positioning Our Work
-- Gap: No end-to-end system combining structured decomposition + multi-expert transformation + deduplication
-- Distinction from PersonaFlow: product innovation focus, attribute structure
+
+**Key distinction from PersonaFlow (closest related work)**:
+```
+PersonaFlow:   Query → Experts → Ideas (no problem structure)
+Our approach:  Query → Attributes → (Attributes × Experts) → Ideas
+```
+
+- PersonaFlow applies experts to whole query; we apply experts to decomposed attributes
+- PersonaFlow cannot isolate what helps; our 2×2 factorial design tests each factor
+- We hypothesize attribute decomposition **amplifies** expert effectiveness (interaction effect)
+- PersonaFlow showed experts help; we test whether **structuring the problem first** makes experts more effective
 
 ---
 
@@ -102,30 +111,41 @@ Large Language Models (LLMs) are increasingly used for creative ideation, yet th
 ## 4. Experiments
 
 ### 4.1 Research Questions
-- RQ1: Does multi-expert generation increase semantic diversity?
-- RQ2: Does multi-expert generation reduce patent overlap?
-- RQ3: What is the optimal number of experts?
-- RQ4: How do expert sources affect output quality?
+- RQ1: Does attribute decomposition improve semantic diversity?
+- RQ2: Does expert perspective transformation improve semantic diversity?
+- RQ3: Is there an interaction effect between the two factors?
+- RQ4: Which combination produces the highest patent novelty?
+- RQ5: How do expert sources (LLM vs Curated vs External) affect quality?
+- RQ6: What is the hallucination/nonsense rate of context-free keyword generation?
+
+### 4.1.1 Design Note: Context-Free Keyword Generation
+Our system intentionally excludes the original query during keyword generation:
+- Stage 1: Expert sees attribute only (e.g., "wood" + "accountant"), NOT the query ("chair")
+- Stage 2: Expert applies keyword to original query with context
+- Rationale: Maximize semantic distance for novelty
+- Risk: Some ideas may be too distant (nonsense/hallucination)
+- RQ6 investigates this tradeoff
 
 ### 4.2 Experimental Setup
 
 #### 4.2.1 Dataset
-- N concepts/queries for ideation
-- Selection criteria (diverse domains, complexity levels)
+- 30 queries for ideation (see experimental_protocol.md)
+- Selection criteria: diverse domains, complexity levels
+- Categories: everyday objects, technology/tools, services/systems
 
-#### 4.2.2 Conditions
-| Condition | Description |
-|-----------|-------------|
-| Baseline | Direct LLM: "Generate 20 creative ideas for X" |
-| Single-Expert | 1 expert × 20 ideas |
-| Multi-Expert-4 | 4 experts × 5 ideas each |
-| Multi-Expert-8 | 8 experts × 2-3 ideas each |
-| Random-Perspective | 4 random words as "perspectives" |
+#### 4.2.2 Conditions (2×2 Factorial Design)
+| Condition | Attributes | Experts | Description |
+|-----------|------------|---------|-------------|
+| **C1: Direct** | ❌ | ❌ | Baseline: "Generate 20 creative ideas for [query]" |
+| **C2: Expert-Only** | ❌ | ✅ | Expert personas generate for whole query |
+| **C3: Attribute-Only** | ✅ | ❌ | Decompose query, direct generate per attribute |
+| **C4: Full Pipeline** | ✅ | ✅ | Decompose query, experts generate per attribute |
+| **C5: Random-Perspective** | ❌ | (random) | Control: 4 random words as "perspectives" |
 
 #### 4.2.3 Controls
 - Same LLM model (specify version)
 - Same temperature settings
-- Same total idea count per condition
+- Same total idea count per condition (20 ideas)
 
 ### 4.3 Metrics
 
@@ -142,8 +162,18 @@ Large Language Models (LLMs) are increasingly used for creative ideation, yet th
 - Novelty rating (1-7 Likert)
 - Usefulness rating (1-7 Likert)
 - Creativity rating (1-7 Likert)
+- **Relevance rating (1-7 Likert) - for RQ6**
 - Interrater reliability (Cronbach's alpha)
 
+#### 4.3.4 Nonsense/Hallucination Analysis (RQ6) - Three Methods
+| Method | Metric | Purpose |
+|--------|--------|---------|
+| Automatic | Semantic distance threshold (>0.85) | Fast screening |
+| LLM-as-Judge | GPT-4 relevance score (1-3) | Scalable evaluation |
+| Human | Relevance rating (1-7 Likert) | Gold standard validation |
+
+Triangulate all three to validate findings
+
 ### 4.4 Procedure
 - Idea generation process
 - Evaluation process
@@ -153,27 +183,44 @@ Large Language Models (LLMs) are increasingly used for creative ideation, yet th
 
 ## 5. Results
 
-### 5.1 Semantic Diversity (RQ1)
+### 5.1 Main Effect of Attribute Decomposition (RQ1)
+- Compare: (Attribute-Only + Full Pipeline) vs (Direct + Expert-Only)
 - Quantitative results
-- Visualization (t-SNE/UMAP of idea embeddings)
-- Statistical significance tests
+- Statistical significance (ANOVA main effect)
 
-### 5.2 Patent Novelty (RQ2)
+### 5.2 Main Effect of Expert Perspectives (RQ2)
+- Compare: (Expert-Only + Full Pipeline) vs (Direct + Attribute-Only)
+- Quantitative results
+- Statistical significance (ANOVA main effect)
+
+### 5.3 Interaction Effect (RQ3)
+- 2×2 interaction analysis
+- Visualization: interaction plot
+- Evidence for super-additive vs additive effects
+
+### 5.4 Patent Novelty (RQ4)
 - Overlap rates by condition
+- Full Pipeline vs other conditions
 - Examples of high-novelty ideas
 
-### 5.3 Expert Count Analysis (RQ3)
-- Diversity vs. expert count curve
-- Diminishing returns analysis
-- Optimal expert count recommendation
-
-### 5.4 Expert Source Comparison (RQ4)
-- LLM-generated vs. curated vs. random
+### 5.5 Expert Source Comparison (RQ5)
+- LLM-generated vs curated vs external
 - Unconventionality metrics
+- Within Expert=With conditions only
 
-### 5.5 Human Evaluation Results
-- Rating distributions
-- Condition comparisons
+### 5.6 Control Condition Analysis
+- Expert-Only vs Random-Perspective
+- Validates expert knowledge matters
+
+### 5.7 Hallucination/Nonsense Analysis (RQ6)
+- Nonsense rate by condition (LLM-as-judge)
+- Semantic distance threshold analysis
+- Novelty-usefulness tradeoff visualization
+- Is the context-free design worth the hallucination cost?
+
+### 5.8 Human Evaluation Results
+- Rating distributions by condition
+- 2×2 pattern in human judgments
 - Correlation with automatic metrics
 
 ---
@@ -181,14 +228,14 @@ Large Language Models (LLMs) are increasingly used for creative ideation, yet th
 ## 6. Discussion
 
 ### 6.1 Interpreting the Results
-- Why multi-expert works
-- The role of structured decomposition
-- Deduplication importance
+- Why each factor contributes independently
+- The interaction: why attributes amplify expert effectiveness
+- Theoretical explanation via conceptual blending
 
 ### 6.2 Theoretical Implications
 - Semantic gravity as framework for LLM creativity
-- Expert perspectives as productive constraints
-- Inner crowd wisdom
+- Two complementary escape mechanisms
+- Structured decomposition as "scaffolding" for creative exploration
 
 ### 6.3 Practical Implications
 - When to use multi-expert approach