# aiva_patent_integration.py import logging import time import hashlib import json from datetime import datetime # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') class PatentIntegrationModule: """ AIVA's deep integration module for the patent ecosystem. This module handles patent understanding, application, learning, revenue tracking, and self-monitoring. """ def __init__(self, patent_database_api, consensus_api, crypto_lib, billing_api): """ Initializes the PatentIntegrationModule. Args: patent_database_api: An API to interact with a patent database (e.g., USPTO, Google Patents). consensus_api: An API for requesting consensus validation from human experts. crypto_lib: A library for cryptographic operations (e.g., signing, hashing). billing_api: An API for billing and revenue tracking. """ self.patent_database_api = patent_database_api self.consensus_api = consensus_api self.crypto_lib = crypto_lib self.billing_api = billing_api self.usage_metrics = {} # Tracks patent usage by category self.validation_history = [] # Stores validation outcomes for learning self.confidence_scores = {} # Tracks confidence scores for different tasks self.audit_trail = [] # Records all patent-related interactions self.hallucination_rates = {} # Tracks hallucination rates for various functionalities def query_patents(self, keywords, limit=10): """ Queries the patent database for patents matching the given keywords. Args: keywords: A list of keywords to search for. limit: The maximum number of patents to return. Returns: A list of patent records (dictionaries). Returns an empty list if an error occurs. """ try: query = " ".join(keywords) patents = self.patent_database_api.search_patents(query, limit=limit) self._log_audit_event("query_patents", {"keywords": keywords, "limit": limit, "result_count": len(patents)}) return patents except Exception as e: logging.error(f"Error querying patents: {e}") return [] def explain_patent(self, patent_id): """ Explains the functionality of a given patent to a user. Args: patent_id: The ID of the patent to explain. Returns: A string containing a human-readable explanation of the patent's functionality. """ try: patent_data = self.patent_database_api.get_patent(patent_id) if not patent_data: return "Patent not found." # Simplified explanation generation (replace with more sophisticated logic) explanation = f"Patent {patent_id}: {patent_data.get('title', 'Untitled')} - {patent_data.get('abstract', 'No abstract available.')}" self._log_audit_event("explain_patent", {"patent_id": patent_id, "explanation_length": len(explanation)}) return explanation except Exception as e: logging.error(f"Error explaining patent {patent_id}: {e}") return "Error explaining patent." def map_task_to_patents(self, user_task, limit=5): """ Maps a user task to relevant patents. Args: user_task: A description of the user's task. limit: The maximum number of patents to return. Returns: A list of patent IDs that are relevant to the user's task. """ try: keywords = self._extract_keywords(user_task) # Replace with proper keyword extraction patents = self.query_patents(keywords, limit=limit) patent_ids = [patent['patent_id'] for patent in patents] self._log_audit_event("map_task_to_patents", {"user_task": user_task, "keywords": keywords, "patent_ids": patent_ids}) return patent_ids except Exception as e: logging.error(f"Error mapping task to patents: {e}") return [] def validate_output(self, output, relevant_patents): """ Automatically validates AIVA's output against relevant patents. Args: output: The output to validate. relevant_patents: A list of patent IDs that are relevant to the output. Returns: A boolean indicating whether the output is valid (doesn't infringe) according to the patents. """ try: # Simplified validation logic (replace with sophisticated analysis) is_valid = True # Assume valid for now for patent_id in relevant_patents: patent_data = self.patent_database_api.get_patent(patent_id) if patent_data: # Check if output potentially infringes on the patent (replace with actual infringement analysis) if "infringement_risk" in patent_data and patent_data["infringement_risk"] > 0.7: is_valid = False break self._log_audit_event("validate_output", {"output_length": len(output), "relevant_patents": relevant_patents, "is_valid": is_valid}) return is_valid except Exception as e: logging.error(f"Error validating output: {e}") return False def request_consensus_validation(self, output, relevant_patents, description="AIVA's decision requires expert validation."): """ Requests consensus validation from human experts for important decisions. Args: output: The output to validate. relevant_patents: A list of patent IDs that are relevant to the output. description: A description of the decision requiring validation. Returns: A boolean indicating whether the output is considered valid by the consensus. """ try: validation_request = { "output": output, "relevant_patents": relevant_patents, "description": description } consensus_result = self.consensus_api.request_validation(validation_request) if consensus_result and consensus_result.get("is_valid", False): self._log_audit_event("request_consensus_validation", {"output_length": len(output), "relevant_patents": relevant_patents, "consensus_valid": True}) return True else: self._log_audit_event("request_consensus_validation", {"output_length": len(output), "relevant_patents": relevant_patents, "consensus_valid": False, "reason": consensus_result.get("reason", "Unknown") if consensus_result else "No response"}) return False except Exception as e: logging.error(f"Error requesting consensus validation: {e}") return False def sign_response(self, response_data): """ Applies a cryptographic signature to a trusted response. Args: response_data: The data to sign. Returns: A dictionary containing the original data and the signature. """ try: data_string = json.dumps(response_data, sort_keys=True) hashed_data = hashlib.sha256(data_string.encode('utf-8')).hexdigest() signature = self.crypto_lib.sign_data(hashed_data) signed_response = { "data": response_data, "signature": signature, "hash": hashed_data } self._log_audit_event("sign_response", {"data_length": len(data_string), "signature_length": len(signature)}) return signed_response except Exception as e: logging.error(f"Error signing response: {e}") return None def track_confidence_score(self, task_type, confidence_level): """ Tracks confidence scores over time for different task types. Args: task_type: The type of task (e.g., "patent_search", "output_validation"). confidence_level: A numerical value representing the confidence level (0.0 to 1.0). """ if task_type not in self.confidence_scores: self.confidence_scores[task_type] = [] self.confidence_scores[task_type].append((time.time(), confidence_level)) self._log_audit_event("track_confidence_score", {"task_type": task_type, "confidence_level": confidence_level}) def record_validation_outcome(self, task_type, is_valid, relevant_patents=None, output=None): """ Records validation outcomes for learning and performance tracking. Args: task_type: The type of task that was validated. is_valid: A boolean indicating whether the task was validated successfully. relevant_patents: A list of relevant patent IDs (optional). output: The output that was validated (optional). """ outcome = { "timestamp": datetime.now().isoformat(), "task_type": task_type, "is_valid": is_valid, "relevant_patents": relevant_patents, "output": output } self.validation_history.append(outcome) self._log_audit_event("record_validation_outcome", outcome) def adjust_behavior(self): """ Adjusts AIVA's behavior based on failure patterns in the validation history. This is a placeholder for more sophisticated machine learning. """ try: failure_count = sum(1 for outcome in self.validation_history if not outcome["is_valid"]) total_count = len(self.validation_history) if total_count > 100 and failure_count / total_count > 0.2: logging.warning("High failure rate detected. Reviewing patent validation logic.") # Placeholder: Implement logic to adjust parameters or re-train models # For example, increase the weight of certain patent features during validation. self._log_audit_event("adjust_behavior", {"failure_rate": failure_count / total_count}) else: self._log_audit_event("adjust_behavior", {"failure_rate": failure_count / total_count, "message": "Failure rate acceptable."}) except Exception as e: logging.error(f"Error adjusting behavior: {e}") def improve_failed_response(self, output, relevant_patents): """ Attempts to improve a response that failed validation. This is a placeholder for more sophisticated response generation. """ try: # Placeholder: Implement logic to rewrite the output, taking the relevant patents into account. # For example, modify the output to avoid infringing on specific patent claims. improved_output = f"Improved version of: {output} (considering patents {relevant_patents})" self._log_audit_event("improve_failed_response", {"original_length": len(output), "improved_length": len(improved_output)}) return improved_output except Exception as e: logging.error(f"Error improving failed response: {e}") return None def generate_insights(self): """ Generates insights from the audit trail and validation history. Returns: A dictionary containing insights. """ try: insights = {} # Example insight: most frequent task type task_types = [outcome["task_type"] for outcome in self.validation_history] if task_types: most_frequent_task = max(set(task_types), key=task_types.count) insights["most_frequent_task"] = most_frequent_task # Example insight: average confidence score if self.confidence_scores: average_confidences = {task_type: sum(score for _, score in scores) / len(scores) for task_type, scores in self.confidence_scores.items()} insights["average_confidence_scores"] = average_confidences self._log_audit_event("generate_insights", {"insights": insights}) return insights except Exception as e: logging.error(f"Error generating insights: {e}") return {} def count_patent_usage(self, category): """ Counts patent usages per category. Args: category: The category of patent usage (e.g., "validation", "explanation"). """ if category not in self.usage_metrics: self.usage_metrics[category] = 0 self.usage_metrics[category] += 1 self._log_audit_event("count_patent_usage", {"category": category, "count": self.usage_metrics[category]}) def generate_licensing_report(self): """ Generates a licensing report based on patent usage. Returns: A dictionary containing the licensing report. """ try: report = { "patent_usage": self.usage_metrics, "report_date": datetime.now().isoformat() } self._log_audit_event("generate_licensing_report", {"report_length": len(json.dumps(report))}) return report except Exception as e: logging.error(f"Error generating licensing report: {e}") return {} def calculate_api_billing(self, api_usage_data): """ Calculates API billing based on usage data. Args: api_usage_data: A dictionary containing API usage data. Returns: The total billing amount. """ try: billing_amount = self.billing_api.calculate_billing(api_usage_data) self._log_audit_event("calculate_api_billing", {"billing_amount": billing_amount}) return billing_amount except Exception as e: logging.error(f"Error calculating API billing: {e}") return 0.0 def calculate_roi(self, investment, revenue): """ Calculates the return on investment (ROI). Args: investment: The amount invested. revenue: The revenue generated. Returns: The ROI as a percentage. """ try: roi = ((revenue - investment) / investment) * 100 self._log_audit_event("calculate_roi", {"investment": investment, "revenue": revenue, "roi": roi}) return roi except Exception as e: logging.error(f"Error calculating ROI: {e}") return 0.0 def track_hallucination_rate(self, function_name, is_hallucination): """ Tracks the hallucination rate for a given function. Args: function_name: The name of the function. is_hallucination: True if the output was a hallucination, False otherwise. """ if function_name not in self.hallucination_rates: self.hallucination_rates[function_name] = {"total": 0, "hallucinations": 0} self.hallucination_rates[function_name]["total"] += 1 if is_hallucination: self.hallucination_rates[function_name]["hallucinations"] += 1 rate = self.hallucination_rates[function_name]["hallucinations"] / self.hallucination_rates[function_name]["total"] if self.hallucination_rates[function_name]["total"] > 0 else 0.0 self._log_audit_event("track_hallucination_rate", {"function_name": function_name, "is_hallucination": is_hallucination, "hallucination_rate": rate}) def monitor_confidence_calibration(self): """ Monitors the calibration of confidence scores. This is a placeholder for more sophisticated calibration monitoring. """ try: # Placeholder: Implement logic to compare confidence scores with actual validation results. # For example, check if low confidence scores consistently lead to failed validations. for task_type, scores in self.confidence_scores.items(): validations = [outcome for outcome in self.validation_history if outcome["task_type"] == task_type] if validations and scores: # Simplified calibration check: see if low confidence scores are associated with invalid results. low_confidence_invalid_count = sum(1 for (timestamp, score), validation in zip(scores, validations) if score < 0.5 and not validation["is_valid"]) total_validations = len(validations) if total_validations > 0: calibration_rate = low_confidence_invalid_count / total_validations logging.info(f"Calibration rate for {task_type}: {calibration_rate}") self._log_audit_event("monitor_confidence_calibration", {"task_type": task_type, "calibration_rate": calibration_rate}) except Exception as e: logging.error(f"Error monitoring confidence calibration: {e}") def monitor_validation_pass_rates(self): """ Monitors validation pass rates over time. """ try: total_validations = len(self.validation_history) if total_validations > 0: pass_rate = sum(1 for outcome in self.validation_history if outcome["is_valid"]) / total_validations logging.info(f"Overall validation pass rate: {pass_rate}") self._log_audit_event("monitor_validation_pass_rates", {"pass_rate": pass_rate}) except Exception as e: logging.error(f"Error monitoring validation pass rates: {e}") def monitor_performance_metrics(self): """ Monitors performance metrics of the module. """ #Placeholder. In a real implementation, this would track things like # API response times, memory usage, CPU utilization, etc. self._log_audit_event("monitor_performance_metrics", {"message": "Performance monitoring triggered."}) logging.info("Performance monitoring triggered.") def _extract_keywords(self, text): """ Placeholder for more advanced keyword extraction. Basic implementation: splits the text into words. """ return text.lower().split() def _log_audit_event(self, event_type, event_data): """ Logs an audit event. Args: event_type: The type of event. event_data: A dictionary containing event data. """ event = { "timestamp": datetime.now().isoformat(), "event_type": event_type, "event_data": event_data } self.audit_trail.append(event) logging.info(f"Audit Event: {event}") # Example Usage (replace with actual API implementations) class MockPatentDatabaseAPI: def search_patents(self, query, limit=10): # Mock patent search if "ai" in query.lower() or "artificial intelligence" in query.lower(): return [{"patent_id": "AI123", "title": "AI Patent 1", "abstract": "A patent related to AI."}, {"patent_id": "AI456", "title": "AI Patent 2", "abstract": "Another AI-related patent."}] else: return [] def get_patent(self, patent_id): # Mock patent retrieval if patent_id == "AI123": return {"patent_id": "AI123", "title": "AI Patent 1", "abstract": "A patent related to AI.", "infringement_risk": 0.8} elif patent_id == "AI456": return {"patent_id": "AI456", "title": "AI Patent 2", "abstract": "Another AI-related patent.", "infringement_risk": 0.2} else: return None class MockConsensusAPI: def request_validation(self, validation_request): # Mock consensus validation if "infringement_risk" in str(validation_request) and "0.8" in str(validation_request): # Simulate high risk leading to invalid return {"is_valid": False, "reason": "Potential infringement detected."} else: return {"is_valid": True} class MockCryptoLib: def sign_data(self, data): # Mock cryptographic signing return f"SIGNED_{data}" class MockBillingAPI: def calculate_billing(self, usage_data): # Mock billing calculation return len(usage_data) * 0.1 if __name__ == '__main__': patent_db_api = MockPatentDatabaseAPI() consensus_api = MockConsensusAPI() crypto_lib = MockCryptoLib() billing_api = MockBillingAPI() patent_module = PatentIntegrationModule(patent_db_api, consensus_api, crypto_lib, billing_api) # Example Usage patents = patent_module.query_patents(["artificial intelligence"]) print(f"Found patents: {patents}") explanation = patent_module.explain_patent("AI123") print(f"Patent explanation: {explanation}") relevant_patents = patent_module.map_task_to_patents("Create an AI model for image recognition") print(f"Relevant patents for image recognition: {relevant_patents}") is_valid = patent_module.validate_output("AI generated image recognition model", relevant_patents) print(f"Output is valid: {is_valid}") signed_response = patent_module.sign_response({"message": "AI model approved."}) print(f"Signed response: {signed_response}") patent_module.count_patent_usage("validation") report = patent_module.generate_licensing_report() print(f"Licensing report: {report}") billing = patent_module.calculate_api_billing({"patent_searches": 10, "validation_requests": 5}) print(f"API billing: {billing}") patent_module.track_hallucination_rate("query_patents", False) patent_module.monitor_confidence_calibration() patent_module.monitor_validation_pass_rates() patent_module.monitor_performance_metrics() insights = patent_module.generate_insights() print(f"Generated Insights: {insights}") #Simulate a failed validation and behavior adjustment patent_module.record_validation_outcome("output_validation", False, relevant_patents, "Some output") patent_module.adjust_behavior() improved_output = patent_module.improve_failed_response("Some output", relevant_patents) print(f"Improved Output: {improved_output}")