feat: add multi-company batch processing

- Add CompanyAnalysisResult and BatchAnalysisResult dataclasses
- Implement analyze_companies() for concurrent batch analysis
- Implement analyze_companies_sequential() for rate-limited scenarios
- Add progress callback support for monitoring batch jobs
- Include 5 new tests for batch processing functionality
- Fix pre-existing test mock issue in test_llm.py

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

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

README.md

@@ -15,7 +15,8 @@ SPARC automatically collects, parses, and analyzes patents from companies to pro
 - **Content Minimization**: Removes verbose descriptions to reduce LLM token usage
 - **AI Analysis**: Uses Claude 3.5 Sonnet via OpenRouter to analyze innovation quality and market potential
 - **Portfolio Analysis**: Evaluates multiple patents holistically for comprehensive insights
-- **Robust Testing**: 26 tests covering all major functionality
+- **Batch Processing**: Analyze multiple companies concurrently with progress tracking
+- **Robust Testing**: 31 tests covering all major functionality
 
 ## Architecture
 
@@ -99,6 +100,33 @@ result = analyzer.analyze_single_patent(
 )
 ```
 
+### Multi-Company Batch Analysis
+
+```python
+from SPARC.analyzer import CompanyAnalyzer
+
+analyzer = CompanyAnalyzer()
+
+# Analyze multiple companies concurrently (default 3 workers)
+batch_result = analyzer.analyze_companies(
+    ["nvidia", "amd", "intel", "qualcomm"],
+    max_workers=3
+)
+
+# Access results
+print(f"Analyzed: {batch_result.total_companies}")
+print(f"Successful: {batch_result.successful}")
+print(f"Failed: {batch_result.failed}")
+
+for result in batch_result.results:
+    if result.success:
+        print(f"{result.company_name}: {result.patent_count} patents")
+        print(result.analysis)
+
+# Or use sequential processing (safer for rate limits)
+batch_result = analyzer.analyze_companies_sequential(["nvidia", "amd"])
+```
+
 ## Running Tests
 
 ```bash
@@ -130,7 +158,7 @@ pytest tests/ --cov=SPARC --cov-report=term-missing
 - [X] Extract and minimize patent content
 - [X] LLM integration for analysis
 - [X] Company performance estimation
-- [ ] Multi-company batch processing
+- [X] Multi-company batch processing
 - [ ] FastAPI web service wrapper
 - [X] Docker containerization
 - [X] Results persistence (database)

SPARC/analyzer.py

@@ -4,10 +4,12 @@ This module ties together patent retrieval, parsing, and LLM analysis
 to provide company performance estimation based on patent portfolios.
 """
 
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Callable
+
 from SPARC.serp_api import SERP
 from SPARC.llm import LLMAnalyzer
-from SPARC.types import Patent
+from SPARC.types import Patent, CompanyAnalysisResult, BatchAnalysisResult
-from typing import List
 
 
 class CompanyAnalyzer:
@@ -110,3 +112,154 @@ class CompanyAnalyzer:
 
         except Exception as e:
             return f"Failed to analyze patent {patent_id}: {e}"
+
+    def _analyze_company_safe(self, company_name: str) -> CompanyAnalysisResult:
+        """Internal wrapper that catches exceptions and returns structured result.
+
+        Args:
+            company_name: Name of the company to analyze
+
+        Returns:
+            CompanyAnalysisResult with success/failure status
+        """
+        try:
+            patents = SERP.query(company_name)
+            patent_count = len(patents.patents) if patents.patents else 0
+
+            analysis = self.analyze_company(company_name)
+
+            # Check if analysis indicates failure
+            if analysis.startswith("No patents found") or analysis.startswith(
+                "Failed to process"
+            ):
+                return CompanyAnalysisResult(
+                    company_name=company_name,
+                    analysis=analysis,
+                    patent_count=patent_count,
+                    success=False,
+                    error=analysis,
+                )
+
+            return CompanyAnalysisResult(
+                company_name=company_name,
+                analysis=analysis,
+                patent_count=patent_count,
+                success=True,
+            )
+
+        except Exception as e:
+            return CompanyAnalysisResult(
+                company_name=company_name,
+                analysis="",
+                patent_count=0,
+                success=False,
+                error=str(e),
+            )
+
+    def analyze_companies(
+        self,
+        companies: list[str],
+        max_workers: int = 3,
+        progress_callback: Callable[[str, int, int], None] | None = None,
+    ) -> BatchAnalysisResult:
+        """Analyze multiple companies' patent portfolios in batch.
+
+        Processes companies concurrently for improved performance while
+        respecting API rate limits.
+
+        Args:
+            companies: List of company names to analyze
+            max_workers: Maximum concurrent analyses (default 3 to avoid rate limits)
+            progress_callback: Optional callback(company_name, completed, total)
+
+        Returns:
+            BatchAnalysisResult containing all individual results and summary stats
+        """
+        results: list[CompanyAnalysisResult] = []
+        total = len(companies)
+
+        print(f"Starting batch analysis of {total} companies...")
+
+        with ThreadPoolExecutor(max_workers=max_workers) as executor:
+            future_to_company = {
+                executor.submit(self._analyze_company_safe, company): company
+                for company in companies
+            }
+
+            completed = 0
+            for future in as_completed(future_to_company):
+                company = future_to_company[future]
+                completed += 1
+
+                try:
+                    result = future.result()
+                    results.append(result)
+
+                    status = "✓" if result.success else "✗"
+                    print(f"[{completed}/{total}] {status} {company}")
+
+                    if progress_callback:
+                        progress_callback(company, completed, total)
+
+                except Exception as e:
+                    results.append(
+                        CompanyAnalysisResult(
+                            company_name=company,
+                            analysis="",
+                            patent_count=0,
+                            success=False,
+                            error=str(e),
+                        )
+                    )
+                    print(f"[{completed}/{total}] ✗ {company}: {e}")
+
+        successful = sum(1 for r in results if r.success)
+        failed = total - successful
+
+        print(f"\nBatch complete: {successful} succeeded, {failed} failed")
+
+        return BatchAnalysisResult(
+            results=results,
+            total_companies=total,
+            successful=successful,
+            failed=failed,
+        )
+
+    def analyze_companies_sequential(
+        self, companies: list[str]
+    ) -> BatchAnalysisResult:
+        """Analyze multiple companies sequentially (safer for rate limits).
+
+        Use this when you want more control over API rate limiting or
+        when debugging issues.
+
+        Args:
+            companies: List of company names to analyze
+
+        Returns:
+            BatchAnalysisResult containing all individual results
+        """
+        results: list[CompanyAnalysisResult] = []
+        total = len(companies)
+
+        print(f"Starting sequential analysis of {total} companies...")
+
+        for idx, company in enumerate(companies, 1):
+            print(f"\n[{idx}/{total}] Analyzing {company}...")
+            result = self._analyze_company_safe(company)
+            results.append(result)
+
+            status = "✓" if result.success else "✗"
+            print(f"[{idx}/{total}] {status} {company}")
+
+        successful = sum(1 for r in results if r.success)
+        failed = total - successful
+
+        print(f"\nBatch complete: {successful} succeeded, {failed} failed")
+
+        return BatchAnalysisResult(
+            results=results,
+            total_companies=total,
+            successful=successful,
+            failed=failed,
+        )

SPARC/types.py

@@ -1,4 +1,5 @@
-from dataclasses import dataclass
+from dataclasses import dataclass, field
+from datetime import datetime
 
 
 @dataclass
@@ -12,3 +13,26 @@ class Patent:
 @dataclass
 class Patents:
     patents: list[Patent]
+
+
+@dataclass
+class CompanyAnalysisResult:
+    """Result of analyzing a single company's patent portfolio."""
+
+    company_name: str
+    analysis: str
+    patent_count: int
+    success: bool
+    error: str | None = None
+    timestamp: datetime = field(default_factory=datetime.now)
+
+
+@dataclass
+class BatchAnalysisResult:
+    """Result of batch analyzing multiple companies."""
+
+    results: list[CompanyAnalysisResult]
+    total_companies: int
+    successful: int
+    failed: int
+    timestamp: datetime = field(default_factory=datetime.now)

tests/test_analyzer.py

@@ -1,9 +1,9 @@
 """Tests for the high-level company analyzer orchestration."""
 
 import pytest
-from unittest.mock import Mock, patch
+from unittest.mock import Mock, patch, call
 from SPARC.analyzer import CompanyAnalyzer
-from SPARC.types import Patent, Patents
+from SPARC.types import Patent, Patents, CompanyAnalysisResult, BatchAnalysisResult
 
 
 class TestCompanyAnalyzer:
@@ -176,3 +176,177 @@ class TestCompanyAnalyzer:
 
         assert "Failed to analyze patent US999" in result
         assert "PDF not found" in result
+
+
+class TestBatchProcessing:
+    """Test multi-company batch processing functionality."""
+
+    def test_analyze_companies_success(self, mocker):
+        """Test batch analysis of multiple companies."""
+        mock_query = mocker.patch("SPARC.analyzer.SERP.query")
+        mock_save = mocker.patch("SPARC.analyzer.SERP.save_patents")
+        mock_parse = mocker.patch("SPARC.analyzer.SERP.parse_patent_pdf")
+        mock_minimize = mocker.patch("SPARC.analyzer.SERP.minimize_patent_for_llm")
+        mock_llm = mocker.patch("SPARC.analyzer.LLMAnalyzer")
+
+        # Setup mock returns
+        def query_side_effect(company):
+            patent = Patent(
+                patent_id=f"US-{company}",
+                pdf_link=f"http://example.com/{company}.pdf",
+            )
+            return Patents(patents=[patent])
+
+        mock_query.side_effect = query_side_effect
+
+        def save_side_effect(patent):
+            patent.pdf_path = f"patents/{patent.patent_id}.pdf"
+            return patent
+
+        mock_save.side_effect = save_side_effect
+        mock_parse.return_value = {"abstract": "Test"}
+        mock_minimize.return_value = "Content"
+
+        mock_llm_instance = Mock()
+        mock_llm_instance.analyze_patent_portfolio.return_value = "Analysis result"
+        mock_llm.return_value = mock_llm_instance
+
+        analyzer = CompanyAnalyzer()
+        result = analyzer.analyze_companies(["CompanyA", "CompanyB"], max_workers=2)
+
+        assert isinstance(result, BatchAnalysisResult)
+        assert result.total_companies == 2
+        assert result.successful == 2
+        assert result.failed == 0
+        assert len(result.results) == 2
+
+    def test_analyze_companies_with_failures(self, mocker):
+        """Test batch analysis handles partial failures."""
+        mock_query = mocker.patch("SPARC.analyzer.SERP.query")
+        mocker.patch("SPARC.analyzer.LLMAnalyzer")
+
+        def query_side_effect(company):
+            if company == "FailCorp":
+                return Patents(patents=[])
+            patent = Patent(
+                patent_id=f"US-{company}",
+                pdf_link=f"http://example.com/{company}.pdf",
+            )
+            return Patents(patents=[patent])
+
+        mock_query.side_effect = query_side_effect
+
+        analyzer = CompanyAnalyzer()
+        result = analyzer.analyze_companies(["GoodCorp", "FailCorp"], max_workers=1)
+
+        assert result.total_companies == 2
+        assert result.failed >= 1  # At least FailCorp should fail
+
+        # Find the failed result
+        fail_result = next(r for r in result.results if r.company_name == "FailCorp")
+        assert fail_result.success is False
+
+    def test_analyze_companies_sequential(self, mocker):
+        """Test sequential batch analysis."""
+        mock_query = mocker.patch("SPARC.analyzer.SERP.query")
+        mock_save = mocker.patch("SPARC.analyzer.SERP.save_patents")
+        mock_parse = mocker.patch("SPARC.analyzer.SERP.parse_patent_pdf")
+        mock_minimize = mocker.patch("SPARC.analyzer.SERP.minimize_patent_for_llm")
+        mock_llm = mocker.patch("SPARC.analyzer.LLMAnalyzer")
+
+        def query_side_effect(company):
+            patent = Patent(
+                patent_id=f"US-{company}",
+                pdf_link=f"http://example.com/{company}.pdf",
+            )
+            return Patents(patents=[patent])
+
+        mock_query.side_effect = query_side_effect
+
+        def save_side_effect(patent):
+            patent.pdf_path = f"patents/{patent.patent_id}.pdf"
+            return patent
+
+        mock_save.side_effect = save_side_effect
+        mock_parse.return_value = {"abstract": "Test"}
+        mock_minimize.return_value = "Content"
+
+        mock_llm_instance = Mock()
+        mock_llm_instance.analyze_patent_portfolio.return_value = "Analysis"
+        mock_llm.return_value = mock_llm_instance
+
+        analyzer = CompanyAnalyzer()
+        result = analyzer.analyze_companies_sequential(["Corp1", "Corp2", "Corp3"])
+
+        assert result.total_companies == 3
+        assert len(result.results) == 3
+
+    def test_analyze_companies_progress_callback(self, mocker):
+        """Test that progress callback is invoked correctly."""
+        mock_query = mocker.patch("SPARC.analyzer.SERP.query")
+        mock_save = mocker.patch("SPARC.analyzer.SERP.save_patents")
+        mock_parse = mocker.patch("SPARC.analyzer.SERP.parse_patent_pdf")
+        mock_minimize = mocker.patch("SPARC.analyzer.SERP.minimize_patent_for_llm")
+        mock_llm = mocker.patch("SPARC.analyzer.LLMAnalyzer")
+
+        def query_side_effect(company):
+            patent = Patent(
+                patent_id=f"US-{company}",
+                pdf_link=f"http://example.com/{company}.pdf",
+            )
+            return Patents(patents=[patent])
+
+        mock_query.side_effect = query_side_effect
+
+        def save_side_effect(patent):
+            patent.pdf_path = f"patents/{patent.patent_id}.pdf"
+            return patent
+
+        mock_save.side_effect = save_side_effect
+        mock_parse.return_value = {"abstract": "Test"}
+        mock_minimize.return_value = "Content"
+
+        mock_llm_instance = Mock()
+        mock_llm_instance.analyze_patent_portfolio.return_value = "Analysis"
+        mock_llm.return_value = mock_llm_instance
+
+        callback = Mock()
+        analyzer = CompanyAnalyzer()
+        analyzer.analyze_companies(["A", "B"], max_workers=1, progress_callback=callback)
+
+        assert callback.call_count == 2
+
+    def test_company_analysis_result_structure(self, mocker):
+        """Test CompanyAnalysisResult has correct structure."""
+        mock_query = mocker.patch("SPARC.analyzer.SERP.query")
+        mock_save = mocker.patch("SPARC.analyzer.SERP.save_patents")
+        mock_parse = mocker.patch("SPARC.analyzer.SERP.parse_patent_pdf")
+        mock_minimize = mocker.patch("SPARC.analyzer.SERP.minimize_patent_for_llm")
+        mock_llm = mocker.patch("SPARC.analyzer.LLMAnalyzer")
+
+        patent = Patent(patent_id="US123", pdf_link="http://example.com/test.pdf")
+        mock_query.return_value = Patents(patents=[patent])
+
+        def save_side_effect(p):
+            p.pdf_path = "patents/US123.pdf"
+            return p
+
+        mock_save.side_effect = save_side_effect
+        mock_parse.return_value = {"abstract": "Test"}
+        mock_minimize.return_value = "Content"
+
+        mock_llm_instance = Mock()
+        mock_llm_instance.analyze_patent_portfolio.return_value = "Strong innovation"
+        mock_llm.return_value = mock_llm_instance
+
+        analyzer = CompanyAnalyzer()
+        result = analyzer.analyze_companies(["TestCorp"], max_workers=1)
+
+        assert len(result.results) == 1
+        company_result = result.results[0]
+        assert company_result.company_name == "TestCorp"
+        assert company_result.analysis == "Strong innovation"
+        assert company_result.patent_count == 1
+        assert company_result.success is True
+        assert company_result.error is None
+        assert company_result.timestamp is not None

tests/test_llm.py

@@ -25,6 +25,8 @@ class TestLLMAnalyzer:
         mock_openai = mocker.patch("SPARC.llm.OpenAI")
         mock_config = mocker.patch("SPARC.llm.config")
         mock_config.openrouter_api_key = "config-key-456"
+        mock_config.use_database = False
+        mock_config.database_url = "postgresql://localhost/test"
 
         analyzer = LLMAnalyzer()