feat: add patent content minimization for LLM consumption

Implemented minimize_patent_for_llm() function that reduces patent
content by keeping only essential sections (abstract, claims, summary)
and explicitly excludes the verbose detailed description section.

This reduces token usage while preserving core innovation details
needed for company performance estimation.

Added comprehensive test coverage (5 new tests) for:
- Essential section inclusion
- Description section exclusion
- Missing section handling
- Empty section handling
- Section separator formatting

All 13 tests passing.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

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

SPARC/serp_api.py

@@ -45,18 +45,28 @@ class SERP:
     return patent
 
   def parse_patent_pdf(pdf_path: str) -> Dict:
-    """Extract structured sections from patent PDF"""
-    
+    """Extract structured sections from patent PDF.
+
+    Extracts all major sections from a patent PDF including abstract,
+    claims, summary, and detailed description.
+
+    Args:
+      pdf_path: Path to the patent PDF file
+
+    Returns:
+      Dictionary containing all extracted sections
+    """
+
     with pdfplumber.open(pdf_path) as pdf:
       # Extract all text
       full_text = ""
       for page in pdf.pages:
         full_text += page.extract_text() + "\n"
-    
+
     # Define section patterns (common in patents)
     sections = {
       'abstract': SERP.extract_section(
-        full_text, 
+        full_text,
         start_patterns=[r'ABSTRACT', r'Abstract of the Disclosure'],
         end_patterns=[r'BACKGROUND', r'FIELD OF', r'BRIEF DESCRIPTION']
       ),
@@ -76,9 +86,41 @@ class SERP:
         end_patterns=[r'What is claimed', r'CLAIMS?:', r'I claim:']
       )
     }
-    
+
     return sections
 
+  def minimize_patent_for_llm(sections: Dict) -> str:
+    """Minimize patent content for LLM consumption.
+
+    Removes bloated sections (detailed description) and keeps only
+    essential information: abstract, claims, and summary. This reduces
+    token usage while preserving the core innovation details.
+
+    Args:
+      sections: Dictionary of parsed patent sections from parse_patent_pdf()
+
+    Returns:
+      Concatenated string of essential patent sections ready for LLM analysis
+    """
+    essential_parts = []
+
+    # Abstract: Concise overview of the invention
+    if sections.get('abstract'):
+      essential_parts.append("ABSTRACT:\n" + sections['abstract'])
+
+    # Claims: The actual legal claims defining the invention (most important)
+    if sections.get('claims'):
+      essential_parts.append("CLAIMS:\n" + sections['claims'])
+
+    # Summary: High-level description of the invention
+    if sections.get('summary'):
+      essential_parts.append("SUMMARY:\n" + sections['summary'])
+
+    # Explicitly exclude 'description' - it's too verbose and contains
+    # implementation details not needed for high-level analysis
+
+    return "\n\n".join(essential_parts)
+
   def extract_section(text: str, start_patterns: list, end_patterns: list) -> str:
     """Extract text between start and end patterns"""