# patent_unified_validator.py # The master orchestrator for all 9 patent systems import hashlib import hmac import json import time import uuid from typing import Dict, List, Tuple, Union # --- Mock Implementations of the 9 Patent Systems --- # Replace these with actual implementations! class CryptographicValidation: """Cryptographic Validation (HMAC-SHA256)""" def validate(self, data: str, key: str) -> Tuple[bool, Dict]: """Validates data using HMAC-SHA256.""" try: hmac_obj = hmac.new(key.encode('utf-8'), data.encode('utf-8'), hashlib.sha256) signature = hmac_obj.hexdigest() return True, {"cryptographic_signature": signature} # Assume valid for now except Exception as e: return False, {"error": str(e)} class CurrencyValidation: """Currency Validation (financial accuracy) - Mock""" def validate(self, transaction_data: Dict) -> Tuple[bool, Dict]: """Validates financial accuracy.""" # Replace with actual financial validation logic amount = transaction_data.get("amount", 0) if amount <= 0: return False, {"error": "Invalid transaction amount."} return True, {"currency_validation_status": "Passed (Mock)"} class MultiDimensionalRiskAssessment: """Multi-Dimensional Risk Assessment - Mock""" def assess_risk(self, data: Dict) -> Tuple[float, Dict]: """Assesses risk based on multiple dimensions.""" # Replace with actual risk assessment logic # Mock: Assign a random risk score risk_score = hash(json.dumps(data)) % 100 / 100.0 # Normalize to 0-1 return risk_score, {"risk_score": risk_score, "risk_factors": ["factor_a", "factor_b"]} class ImmutableAuditTrail: """Immutable Audit Trail - Mock""" def __init__(self): self.audit_log = [] def log_event(self, event_data: Dict) -> str: """Logs an event to the audit trail.""" timestamp = time.time() event_id = str(uuid.uuid4()) event = {"event_id": event_id, "timestamp": timestamp, "data": event_data} self.audit_log.append(event) return event_id def get_trail(self) -> List[Dict]: """Returns the entire audit trail.""" return self.audit_log class MultiModelConsensus: """Multi-Model Consensus - Mock""" def achieve_consensus(self, model_outputs: List[Dict]) -> Tuple[bool, Dict]: """Achieves consensus across multiple models.""" # Replace with actual consensus mechanism # Mock: Check if any model flagged an error for output in model_outputs: if "error" in output: return False, {"consensus_status": "Failed - Error in one or more models"} return True, {"consensus_status": "Achieved (Mock)"} class DynamicConfidenceScoring: """Dynamic Confidence Scoring - Mock""" def calculate_confidence(self, validation_results: List[Dict]) -> Tuple[float, Dict]: """Calculates a confidence score based on validation results.""" # Replace with actual confidence calculation logic # Mock: Count the number of successful validations successful_validations = sum(1 for result in validation_results if result.get("status", False)) confidence_score = successful_validations / len(validation_results) if validation_results else 0.5 return confidence_score, {"confidence_score": confidence_score} class HallucinationDetection: """Hallucination Detection - Mock""" def detect_hallucinations(self, text: str) -> Tuple[bool, Dict]: """Detects hallucinations in generated text.""" # Replace with actual hallucination detection logic # Mock: Check for certain keywords hallucination_keywords = ["unicorns", "flying pigs", "impossible"] if any(keyword in text.lower() for keyword in hallucination_keywords): return True, {"hallucination_detected": True, "hallucination_reason": "Keyword match"} return False, {"hallucination_detected": False} class PrivacyPreservingValidation: """Privacy-Preserving Validation - Mock""" def validate_privacy(self, data: Dict) -> Tuple[bool, Dict]: """Validates that privacy is preserved.""" # Replace with actual privacy validation logic (e.g., differential privacy) # Mock: Check for personally identifiable information (PII) pii_keywords = ["name", "address", "phone", "email"] if any(keyword in str(data).lower() for keyword in pii_keywords): return False, {"privacy_violation": True, "privacy_reason": "Potential PII detected"} return True, {"privacy_violation": False} class SelfImprovingThresholds: """Self-Improving Thresholds - Mock""" def __init__(self): self.thresholds = {"risk_threshold": 0.8, "confidence_threshold": 0.6} def adjust_thresholds(self, feedback: Dict) -> None: """Adjusts thresholds based on feedback.""" # Replace with actual threshold adjustment logic (e.g., reinforcement learning) # Mock: Adjust the risk threshold based on error rate error_rate = feedback.get("error_rate", 0.0) if error_rate > 0.1: self.thresholds["risk_threshold"] += 0.01 elif error_rate < 0.05: self.thresholds["risk_threshold"] -= 0.01 def get_thresholds(self) -> Dict: """Returns the current thresholds.""" return self.thresholds # --- Master Orchestrator --- class UnifiedPatentValidator: """ The master orchestrator that chains all validation systems together, implements the Triple Gate pattern, provides a unified API, and generates comprehensive validation reports. """ def __init__(self): self.cryptographic_validator = CryptographicValidation() self.currency_validator = CurrencyValidation() self.risk_assessor = MultiDimensionalRiskAssessment() self.audit_trail = ImmutableAuditTrail() self.consensus_engine = MultiModelConsensus() self.confidence_scorer = DynamicConfidenceScoring() self.hallucination_detector = HallucinationDetection() self.privacy_validator = PrivacyPreservingValidation() self.threshold_manager = SelfImprovingThresholds() def validate(self, data: Union[str, Dict], validation_key: str = None, model_outputs: List[Dict] = None) -> Dict: """ Validates AI output using the chained validation systems and the Triple Gate pattern. Args: data: The AI output to validate. Can be a string or a dictionary. validation_key: An optional key for cryptographic validation. model_outputs: A list of outputs from different AI models for consensus validation. Returns: A comprehensive validation report. """ # --- Alpha Gate --- (Basic Validation & Preprocessing) alpha_results = self._alpha_gate(data, validation_key) if not alpha_results["status"]: self.audit_trail.log_event({"stage": "alpha", "input": data, "results": alpha_results}) return self._generate_report(data, alpha_results, "Alpha Gate Failed") # --- Beta Gate --- (Risk Assessment & Privacy) beta_results = self._beta_gate(data) if not beta_results["status"]: self.audit_trail.log_event({"stage": "beta", "input": data, "results": beta_results}) return self._generate_report(data, beta_results, "Beta Gate Failed") # --- Gamma Gate --- (Advanced Validation & Consensus) gamma_results = self._gamma_gate(data, model_outputs) if not gamma_results["status"]: self.audit_trail.log_event({"stage": "gamma", "input": data, "results": gamma_results}) return self._generate_report(data, gamma_results, "Gamma Gate Failed") # --- Final Confidence Scoring --- all_validation_results = [ alpha_results, beta_results, gamma_results, ] confidence_score, confidence_details = self.confidence_scorer.calculate_confidence( [result["results"] for result in all_validation_results if "results" in result] ) # --- Update Thresholds (Example) --- # This is a simplified example. More sophisticated feedback mechanisms # should be implemented in a real-world scenario. error_count = sum(1 for result in all_validation_results if not result["status"]) total_validations = len(all_validation_results) error_rate = error_count / total_validations if total_validations > 0 else 0.0 self.threshold_manager.adjust_thresholds({"error_rate": error_rate}) # --- Generate Final Report --- final_report = self._generate_report(data, { "alpha": alpha_results, "beta": beta_results, "gamma": gamma_results, "confidence": {"score": confidence_score, "details": confidence_details} }, "Validation Passed", confidence_score=confidence_score) self.audit_trail.log_event({"stage": "final", "input": data, "results": final_report}) return final_report def _alpha_gate(self, data: Union[str, Dict], validation_key: str) -> Dict: """Basic validation and preprocessing (Cryptographic & Currency).""" results = {} try: if isinstance(data, str) and validation_key: status, crypto_details = self.cryptographic_validator.validate(data, validation_key) results["cryptographic_validation"] = crypto_details elif isinstance(data, dict): status, currency_details = self.currency_validator.validate(data) results["currency_validation"] = currency_details else: status = False results["error"] = "Invalid data type or missing key" except Exception as e: status = False results["error"] = str(e) return {"status": status, "results": results} def _beta_gate(self, data: Union[str, Dict]) -> Dict: """Risk assessment and privacy validation.""" results = {} try: if isinstance(data, str): data_dict = {"text": data} # Convert string to dict for risk assessment else: data_dict = data risk_score, risk_details = self.risk_assessor.assess_risk(data_dict) results["risk_assessment"] = risk_details status, privacy_details = self.privacy_validator.validate_privacy(data_dict) results["privacy_validation"] = privacy_details #Check if risk exceeds threshold if risk_score > self.threshold_manager.get_thresholds()["risk_threshold"]: status = False results["error"] = f"Risk score {risk_score} exceeds threshold." else: status = status #Preserve the result of privacy validation except Exception as e: status = False results["error"] = str(e) return {"status": status, "results": results} def _gamma_gate(self, data: Union[str, Dict], model_outputs: List[Dict]) -> Dict: """Advanced validation and consensus (Hallucination Detection & Multi-Model Consensus).""" results = {} try: if isinstance(data, str): hallucination_status, hallucination_details = self.hallucination_detector.detect_hallucinations(data) results["hallucination_detection"] = hallucination_details status = not hallucination_status # If hallucination is detected, status is False else: hallucination_status = False status = True results["hallucination_detection"] = {"hallucination_detected": False} #No hallucination to detect if model_outputs: consensus_status, consensus_details = self.consensus_engine.achieve_consensus(model_outputs) results["multi_model_consensus"] = consensus_details status = status and consensus_status # Status is false if either hallucination or consensus fails else: results["multi_model_consensus"] = {"consensus_status": "No models provided for consensus."} except Exception as e: status = False results["error"] = str(e) return {"status": status, "results": results} def _generate_report(self, input_data: Union[str, Dict], validation_results: Dict, status_message: str, confidence_score: float = None) -> Dict: """Generates a comprehensive validation report.""" report = { "input_data": input_data, "validation_results": validation_results, "status": status_message, "audit_trail_id": self.audit_trail.log_event({"report_generated": True, "status": status_message}), "thresholds": self.threshold_manager.get_thresholds() } if confidence_score is not None: report["confidence_score"] = confidence_score return report def get_audit_trail(self) -> List[Dict]: """Retrieves the entire audit trail.""" return self.audit_trail.get_trail() def get_thresholds(self) -> Dict: """Retrieves the current thresholds.""" return self.threshold_manager.get_thresholds() # --- Example Usage --- if __name__ == "__main__": validator = UnifiedPatentValidator() # Example 1: Validating a simple string with cryptographic validation data_string = "This is a test message." validation_key = "secret_key" report1 = validator.validate(data_string, validation_key=validation_key) print("Report 1 (String Validation):\n", json.dumps(report1, indent=2)) # Example 2: Validating a currency transaction transaction_data = {"amount": 100.0, "currency": "USD", "recipient": "Alice"} report2 = validator.validate(transaction_data) print("\nReport 2 (Currency Validation):\n", json.dumps(report2, indent=2)) # Example 3: Validating with multiple model outputs model_outputs = [ {"model_name": "Model A", "result": "Positive", "confidence": 0.9}, {"model_name": "Model B", "result": "Positive", "confidence": 0.8}, {"model_name": "Model C", "result": "Negative", "error": "Something went wrong"} ] data_string_2 = "This is another test message." report3 = validator.validate(data_string_2, validation_key="another_key", model_outputs=model_outputs) print("\nReport 3 (Multi-Model Validation):\n", json.dumps(report3, indent=2)) # Example 4: Accessing the audit trail audit_trail = validator.get_audit_trail() print("\nAudit Trail:\n", json.dumps(audit_trail, indent=2)) # Example 5: Accessing current thresholds thresholds = validator.get_thresholds() print("\nCurrent Thresholds:\n", json.dumps(thresholds, indent=2))