import logging from typing import List, Dict, Any from datetime import datetime # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') class PatentIntelligenceModule: """ AIVA's Patent Intelligence Module - the brain that gives AIVA full awareness of her patent ecosystem. """ def __init__(self, knowledge_graph_connection): """ Initializes the PatentIntelligenceModule. Args: knowledge_graph_connection: An object representing the connection to the knowledge graph. This allows the module to fetch and update patent data. """ self.knowledge_graph = knowledge_graph_connection self.patent_data: Dict[str, Dict[str, Any]] = {} # Patent ID -> Patent Details self.patent_usage_log: List[Dict[str, Any]] = [] # List of patent usage records self.load_patent_knowledge() # Immediately load patents upon initialization logging.info("Patent Intelligence Module initialized.") def load_patent_knowledge(self): """ Loads all patent knowledge from the knowledge graph into the module's memory. """ try: self.patent_data = self.knowledge_graph.get_all_patents() logging.info(f"Loaded {len(self.patent_data)} patents from the knowledge graph.") except Exception as e: logging.error(f"Error loading patent knowledge: {e}") raise def answer_patent_question(self, question: str) -> str: """ Answers questions about patents based on the loaded knowledge. Args: question: The question to answer. Returns: An answer to the question. """ question = question.lower() if "hallucination detection" in question: # Example response based on a keyword. More sophisticated NLP needed for real-world use. for patent_id, patent_details in self.patent_data.items(): if "hallucination" in patent_details.get("title", "").lower() or "hallucination" in patent_details.get("abstract", "").lower(): return f"Patent {patent_id} covers hallucination detection." return "No patent found directly related to hallucination detection in the current knowledge base." elif "patent portfolio worth" in question: valuation = self.perform_portfolio_valuation() return f"Your patent portfolio is estimated to be worth ${valuation:.2f}." else: return "I am sorry, I cannot answer that question based on my current knowledge." def validate_output_with_patent(self, output: str, patent_id: str) -> Dict[str, Any]: """ Applies patent validations to a given output. Records the usage of the patent. Args: output: The output to validate. patent_id: The ID of the patent to use for validation. Returns: A dictionary containing the validation results and patent usage information. """ if patent_id not in self.patent_data: return {"valid": False, "message": f"Patent {patent_id} not found."} patent = self.patent_data[patent_id] validation_result = self.apply_patent_validation(output, patent) # Delegate validation logic if validation_result["valid"]: self.log_patent_usage(patent_id, "validation") logging.info(f"Patent {patent_id} used for validation of output.") else: logging.warning(f"Patent {patent_id} validation failed for output.") return validation_result def apply_patent_validation(self, output: str, patent: Dict[str, Any]) -> Dict[str, Any]: """ Applies the actual patent validation logic. This is a placeholder and needs to be implemented. Args: output: The output to validate. patent: The patent details. Returns: A dictionary containing the validation results. """ # Placeholder implementation - Replace with actual validation logic # This example checks if the output contains a keyword from the patent's abstract. abstract_keywords = patent.get("abstract", "").split() for keyword in abstract_keywords: if keyword.lower() in output.lower(): return {"valid": True, "message": f"Output validated using patent {patent['patent_id']} based on keyword: {keyword}"} return {"valid": False, "message": f"Output does not align with patent {patent['patent_id']}."} def log_patent_usage(self, patent_id: str, usage_type: str): """ Logs the usage of a patent. Args: patent_id: The ID of the patent used. usage_type: The type of usage (e.g., "validation", "generation"). """ usage_record = { "patent_id": patent_id, "usage_type": usage_type, "timestamp": datetime.now().isoformat(), # Add more relevant information here, like task ID, user ID, etc. } self.patent_usage_log.append(usage_record) logging.info(f"Patent {patent_id} usage logged: {usage_type}") def track_patent_usage_for_revenue_attribution(self) -> Dict[str, float]: """ Tracks patent usage and attributes revenue based on the usage. Returns: A dictionary mapping patent IDs to their attributed revenue. """ revenue_attribution: Dict[str, float] = {} # Placeholder implementation - Replace with actual revenue attribution logic # This is a simplified example that attributes equal revenue to each used patent. total_revenue = 1000000.0 # Example total revenue unique_patents_used = set(usage["patent_id"] for usage in self.patent_usage_log) if unique_patents_used: revenue_per_patent = total_revenue / len(unique_patents_used) for patent_id in unique_patents_used: revenue_attribution[patent_id] = revenue_per_patent else: logging.warning("No patents used, revenue attribution is zero.") logging.info(f"Revenue attribution calculated: {revenue_attribution}") return revenue_attribution def generate_patent_licensing_recommendations(self) -> List[Dict[str, Any]]: """ Generates patent licensing recommendations based on patent usage and market trends. Returns: A list of dictionaries, each containing a patent ID and a licensing recommendation. """ licensing_recommendations: List[Dict[str, Any]] = [] # Placeholder implementation - Replace with actual licensing recommendation logic # This is a simplified example that recommends licensing patents with high usage. usage_counts: Dict[str, int] = {} for usage in self.patent_usage_log: patent_id = usage["patent_id"] usage_counts[patent_id] = usage_counts.get(patent_id, 0) + 1 for patent_id, usage_count in usage_counts.items(): if usage_count > 5: # Example threshold for high usage licensing_recommendations.append({ "patent_id": patent_id, "recommendation": "Consider licensing this patent due to high internal usage." }) logging.info(f"Generated {len(licensing_recommendations)} licensing recommendations.") return licensing_recommendations def perform_portfolio_valuation(self) -> float: """ Performs a valuation analysis of the patent portfolio. Returns: The estimated value of the portfolio. """ # Placeholder implementation - Replace with a more sophisticated valuation model # This example simply sums up the estimated value of each patent. total_value = 0.0 for patent_id, patent in self.patent_data.items(): # Replace with a more realistic valuation based on patent features total_value += patent.get("estimated_value", 10000.0) # Default value if not specified logging.info(f"Patent portfolio valued at: ${total_value}") return total_value # Example Knowledge Graph Interface (Replace with your actual implementation) class MockKnowledgeGraph: """ A mock class for simulating a connection to a knowledge graph. In a real application, this would be replaced with a connection to a database or other knowledge storage system. """ def __init__(self): self.patents = { "P1": {"patent_id": "P1", "title": "Cryptographic Proof System", "abstract": "A method for generating cryptographic proofs...", "estimated_value": 50000}, "P2": {"patent_id": "P2", "title": "Data Encryption Algorithm", "abstract": "An improved data encryption algorithm...", "estimated_value": 75000}, "P3": {"patent_id": "P3", "title": "Secure Communication Protocol", "abstract": "A protocol for secure communication...", "estimated_value": 60000}, "P4": {"patent_id": "P4", "title": "Machine Learning Model", "abstract": "A novel machine learning model...", "estimated_value": 40000}, "P5": {"patent_id": "P5", "title": "Image Recognition System", "abstract": "A system for recognizing images...", "estimated_value": 30000}, "P6": {"patent_id": "P6", "title": "Natural Language Processing Technique", "abstract": "A technique for natural language processing...", "estimated_value": 35000}, "P7": {"patent_id": "P7", "title": "Hallucination Detection in NLP", "abstract": "A method for detecting hallucinations in natural language processing models...", "estimated_value": 80000}, } def get_all_patents(self): return self.patents if __name__ == '__main__': # Example usage knowledge_graph = MockKnowledgeGraph() patent_brain = PatentIntelligenceModule(knowledge_graph) # Example 1: Answering a question question = "What patent covers hallucination detection?" answer = patent_brain.answer_patent_question(question) print(f"Question: {question}\nAnswer: {answer}\n") # Example 2: Validating an output output = "This output uses a cryptographic proof system." validation_result = patent_brain.validate_output_with_patent(output, "P1") print(f"Output: {output}\nValidation Result: {validation_result}\n") # Example 3: Portfolio Valuation portfolio_value = patent_brain.perform_portfolio_valuation() print(f"Patent Portfolio Value: ${portfolio_value}\n") # Example 4: Tracking and Attributing Revenue patent_brain.log_patent_usage("P2", "generation") revenue_attribution = patent_brain.track_patent_usage_for_revenue_attribution() print(f"Revenue Attribution: {revenue_attribution}\n") # Example 5: Licensing Recommendations licensing_recommendations = patent_brain.generate_patent_licensing_recommendations() print(f"Licensing Recommendations: {licensing_recommendations}\n")