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>
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@@ -59,6 +59,27 @@ With Marine Biologist Expert:
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Result: Novel ideas like "pressure-adaptive seating" or "coral-inspired structural support"
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```
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#### The Semantic Distance Tradeoff
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However, semantic distance is not always beneficial. There exists a tradeoff:
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```
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Semantic Distance Spectrum:
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Too Close Optimal Zone Too Far
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(Semantic Gravity) (Creative) (Hallucination)
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├────────────────────────────┼────────────────────────────────┼────────────────────────────┤
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"Ergonomic office chair" "Pressure-adaptive seating" "Quantum-entangled
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"Coral-inspired support" chair consciousness"
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High usefulness High novelty + useful High novelty, nonsense
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Low novelty Low usefulness
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```
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**Our Design Choice**: Context-free keyword generation (Stage 1 excludes original query) intentionally pushes toward the "far" end to maximize novelty. Stage 2 re-introduces query context to ground the ideas.
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**Research Question**: What is the hallucination/nonsense rate of this approach, and is the tradeoff worthwhile?
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#### 2. Conceptual Blending Theory (Fauconnier & Turner, 2002)
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> "Creative products emerge from blending elements of two input spaces into a novel integrated space."
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@@ -136,12 +157,22 @@ Our "Inner Crowd":
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Aggregation → Diverse idea pool (simulated crowd)
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```
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### Why Multiple Experts Work
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### Why This Approach Works: Two Complementary Mechanisms
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1. **Coverage**: Different experts activate different semantic regions
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2. **Redundancy Reduction**: Deduplication removes overlapping ideas
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3. **Diversity by Design**: Expert selection can be optimized for maximum diversity
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4. **Diminishing Returns**: Beyond ~4-6 experts, marginal diversity gains decrease
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**Factor 1: Attribute Decomposition**
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- Structures the problem space before creative exploration
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- Prevents premature fixation on holistic solutions
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- Ensures coverage across different aspects of the target concept
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**Factor 2: Expert Perspectives**
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- Different experts activate different semantic regions
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- Forces semantic jumps that LLMs wouldn't naturally make
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- Each expert provides a distinct input space for conceptual blending
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**Combined Effect (Interaction)**
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- Experts are more effective when given structured attributes to transform
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- Attributes without expert perspectives still generate predictable ideas
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- The combination creates systematic exploration of remote conceptual spaces
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---
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@@ -231,32 +262,43 @@ Output:
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---
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## Testable Hypotheses
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## Testable Hypotheses (2×2 Factorial Design)
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### H1: Semantic Diversity
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> Multi-expert generation produces higher semantic diversity than single-expert or direct generation.
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Our experimental design manipulates two independent factors:
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1. **Attribute Decomposition**: With / Without
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2. **Expert Perspectives**: With / Without
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### H1: Main Effect of Attribute Decomposition
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> Conditions with attribute decomposition produce higher semantic diversity than those without.
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**Prediction**: (Attribute-Only + Full Pipeline) > (Direct + Expert-Only)
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**Measurement**: Mean pairwise cosine distance between idea embeddings
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### H2: Novelty
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> Ideas from multi-expert generation have lower patent overlap than direct generation.
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### H2: Main Effect of Expert Perspectives
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> Conditions with expert perspectives produce higher semantic diversity than those without.
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**Measurement**: Percentage of ideas with existing patent matches
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**Prediction**: (Expert-Only + Full Pipeline) > (Direct + Attribute-Only)
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**Measurement**: Mean pairwise cosine distance between idea embeddings
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### H3: Expert Count Effect
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> Semantic diversity increases with expert count, with diminishing returns beyond 4-6 experts.
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### H3: Interaction Effect
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> The combination of attributes and experts produces super-additive benefits.
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**Measurement**: Diversity vs. expert count curve
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**Prediction**: Full Pipeline > (Attribute-Only + Expert-Only - Direct)
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**Rationale**: Experts are more effective when given structured problem decomposition to work with.
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**Measurement**: Interaction term in 2×2 ANOVA
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### H4: Expert Source Effect
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> LLM-generated experts produce more unconventional ideas than curated/database experts.
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### H4: Novelty
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> The Full Pipeline produces ideas with lowest patent overlap.
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**Measurement**: Semantic distance from query centroid
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**Prediction**: Full Pipeline has highest novelty rate across all conditions
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**Measurement**: Percentage of ideas without existing patent matches
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### H5: Fixation Breaking
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> Multi-expert approach produces more ideas outside the top-3 semantic clusters than direct generation.
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### H5: Expert vs Random Control
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> Expert perspectives outperform random word perspectives.
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**Measurement**: Cluster distribution analysis
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**Prediction**: Expert-Only > Random-Perspective
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**Rationale**: Validates that domain knowledge (not just any perspective shift) drives improvement
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**Measurement**: Semantic diversity and human creativity ratings
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---
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@@ -271,10 +313,29 @@ Output:
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## Positioning Against Related Work
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### Key Differentiator: Attribute Decomposition
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```
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PersonaFlow (2024): Query → Experts → Ideas
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Our Approach: Query → Attributes → (Attributes × Experts) → Ideas
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```
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**Why this matters**: Attribute decomposition provides **scaffolding** that makes expert perspectives more effective. An expert seeing "chair materials" generates more focused ideas than an expert seeing just "chair."
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### Comparison Table
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| Approach | Limitation | Our Advantage |
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|----------|------------|---------------|
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| Direct LLM generation | Semantic gravity, fixation | Expert-forced semantic jumps |
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| Human brainstorming | Cognitive fatigue, social dynamics | Tireless LLM generation |
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| PersonaFlow (2024) | Research-focused, no attribute structure | Product innovation, structured decomposition |
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| PopBlends (2023) | Two-concept blending only | Multi-expert, multi-attribute blending |
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| BILLY (2025) | Vector fusion less interpretable | Sequential generation, explicit control |
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| Direct LLM generation | Semantic gravity, fixation | Two-factor enhancement (attributes + experts) |
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| **PersonaFlow (2024)** | **No problem structure, experts see whole query** | **Attribute decomposition amplifies expert effect** |
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| PopBlends (2023) | Two-concept blending only | Systematic attribute × expert exploration |
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| BILLY (2025) | Cannot isolate what helps | 2×2 factorial design isolates contributions |
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| Persona prompting alone | Random coverage | Systematic coverage via attribute × expert matrix |
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### What We Can Answer That PersonaFlow Cannot
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1. **Does problem structure alone help?** (Attribute-Only vs Direct)
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2. **Do experts help beyond structure?** (Full Pipeline vs Attribute-Only)
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3. **Is there an interaction effect?** (Full Pipeline > Attribute-Only + Expert-Only - Direct)
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PersonaFlow showed experts help, but never tested whether **structuring the problem first** makes experts more effective.
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