class UserProfile:
    def __init__(self, user_id):
        self.user_id = user_id
        self.explicit_preferences = {}
        self.behavioral_patterns = {}
        self.contextual_adaptations = {}
        self.interaction_history = []
        
    def update_explicit_preference(self, category, value, confidence=1.0):
        """Update explicit user preferences"""
        timestamp = datetime.now()
        self.explicit_preferences[category] = {
            'value': value,
            'confidence': confidence,
            'last_updated': timestamp
        }
        
    def record_interaction(self, interaction_data):
        """Record detailed interaction data for pattern analysis"""
        interaction_record = {
            'timestamp': datetime.now(),
            'duration': interaction_data.get('duration'),
            'complexity': interaction_data.get('complexity'),
            'satisfaction_score': interaction_data.get('satisfaction'),
            'follow_up_actions': interaction_data.get('follow_up'),
            'context': interaction_data.get('context')
        }
        self.interaction_history.append(interaction_record)
        
        # Update behavioral patterns based on new data
        self._update_behavioral_patterns(interaction_record)
        
    def _update_behavioral_patterns(self, interaction_record):
        """Analyze interaction data to detect behavioral patterns"""
        # Communication style analysis
        if 'duration' in interaction_record and 'complexity' in interaction_record:
            response_efficiency = interaction_record['complexity'] / interaction_record['duration']
            self._update_pattern_metric('response_efficiency', response_efficiency)
            
        # Engagement depth tracking
        if 'follow_up_actions' in interaction_record:
            engagement_depth = len(interaction_record['follow_up_actions'])
            self._update_pattern_metric('engagement_depth', engagement_depth)
            
        # Satisfaction trend monitoring
        if 'satisfaction_score' in interaction_record:
            self._update_pattern_metric('recent_satisfaction', 
                                      interaction_record['satisfaction_score'])

    def _update_pattern_metric(self, metric_name, new_value):
        """Maintain running averages and trends for behavioral metrics"""
        if metric_name not in self.behavioral_patterns:
            self.behavioral_patterns[metric_name] = {
                'values': [],
                'moving_average': 0,
                'trend': 0
            }
            
        pattern_data = self.behavioral_patterns[metric_name]
        pattern_data['values'].append(new_value)
        
        # Keep only recent values (last 50 interactions)
        if len(pattern_data['values']) > 50:
            pattern_data['values'] = pattern_data['values'][-50:]
            
        # Calculate moving average
        pattern_data['moving_average'] = sum(pattern_data['values']) / len(pattern_data['values'])
        
        # Calculate trend (simple linear regression slope over last 10 points)
        if len(pattern_data['values']) >= 10:
            recent_values = pattern_data['values'][-10:]
            x = list(range(len(recent_values)))
            y = recent_values
            
            # Simple linear regression
            n = len(x)
            sum_x = sum(x)
            sum_y = sum(y)
            sum_xy = sum(x[i] * y[i] for i in range(n))
            sum_xx = sum(x[i] ** 2 for i in range(n))
            
            if n * sum_xx - sum_x ** 2 != 0:
                slope = (n * sum_xy - sum_x * sum_y) / (n * sum_xx - sum_x ** 2)
                pattern_data['trend'] = slope
                
    def get_personalization_parameters(self, context=None):
        """Generate personalization parameters for current interaction"""
        params = {
            'communication_style': self._determine_communication_style(),
            'information_density': self._determine_information_density(),
            'interaction_pace': self._determine_interaction_pace(),
            'proactivity_level': self._determine_proactivity_level(context),
            'explanation_depth': self._determine_explanation_depth()
        }
        return params
        
    def _determine_communication_style(self):
        """Determine preferred communication style based on user patterns"""
        # Analyze response efficiency and engagement depth
        efficiency = self.behavioral_patterns.get('response_efficiency', {}).get('moving_average', 0.5)
        engagement = self.behavioral_patterns.get('engagement_depth', {}).get('moving_average', 1.0)
        
        if efficiency > 0.7 and engagement < 1.5:
            return 'concise'
        elif efficiency < 0.3 and engagement > 2.0:
            return 'conversational'
        else:
            return 'balanced'
            
    def _determine_information_density(self):
        """Determine optimal information density"""
        engagement_depth = self.behavioral_patterns.get('engagement_depth', {}).get('moving_average', 1.0)
        
        if engagement_depth > 2.5:
            return 'high'
        elif engagement_depth < 0.8:
            return 'low'
        else:
            return 'medium'
            
    def _determine_interaction_pace(self):
        """Determine preferred interaction pace"""
        response_times = [record.get('duration', 10) 
                         for record in self.interaction_history[-10:] 
                         if record.get('duration') is not None]
        
        if not response_times:
            return 'moderate'
            
        avg_response_time = sum(response_times) / len(response_times)
        
        if avg_response_time < 5:
            return 'fast'
        elif avg_response_time > 20:
            return 'slow'
        else:
            return 'moderate'
            
    def _determine_proactivity_level(self, context=None):
        """Determine appropriate proactivity level"""
        satisfaction_trend = self.behavioral_patterns.get('recent_satisfaction', {}).get('trend', 0)
        
        # Decreasing satisfaction might indicate user prefers less proactivity
        if satisfaction_trend < -0.1:
            return 'conservative'
        # Increasing satisfaction might allow for more proactive suggestions
        elif satisfaction_trend > 0.1:
            return 'aggressive'
        else:
            return 'moderate'
            
    def _determine_explanation_depth(self):
        """Determine preferred explanation depth"""
        engagement_depth = self.behavioral_patterns.get('engagement_depth', {}).get('moving_average', 1.0)
        satisfaction_trend = self.behavioral_patterns.get('recent_satisfaction', {}).get('trend', 0)
        
        # Users with high engagement and positive trends likely want detailed explanations
        if engagement_depth > 2.0 and satisfaction_trend > 0:
            return 'detailed'
        # Low engagement might indicate preference for brief responses
        elif engagement_depth < 1.0:
            return 'brief'
        else:
            return 'standard'

# Usage example
user_profile = UserProfile(user_id="user_123")
user_profile.update_explicit_preference("communication_style", "professional")
user_profile.record_interaction({
    'duration': 15,
    'complexity': 8,
    'satisfaction': 0.9,
    'follow_up': ['ask_clarification', 'request_details'],
    'context': 'technical_support'
})

personalization_params = user_profile.get_personalization_parameters(context='technical_support')
print(personalization_params)

class PreferenceAdaptationRL:
    def __init__(self, user_id, action_space, state_features):
        self.user_id = user_id
        self.action_space = action_space
        self.state_features = state_features
        
        # Initialize Q-table or neural network for policy learning
        self.q_network = self._build_q_network()
        self.target_network = self._build_q_network()
        self.optimizer = torch.optim.Adam(self.q_network.parameters(), lr=0.001)
        
        # Experience replay buffer
        self.replay_buffer = deque(maxlen=10000)
        
    def _build_q_network(self):
        """Build neural network for Q-value approximation"""
        return nn.Sequential(
            nn.Linear(len(self.state_features) + len(self.action_space), 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 1)  # Single Q-value output
        )
        
    def get_state_representation(self, context, user_profile, interaction_history):
        """Convert current situation to state vector"""
        state = []
        
        # Context features
        state.extend([
            context.get('time_of_day', 0) / 24.0,
            context.get('urgency_level', 0),
            context.get('domain_complexity', 0)
        ])
        
        # User profile features
        comm_style = user_profile.explicit_preferences.get('communication_style', 'balanced')
        style_encoding = {'concise': 0, 'balanced': 0.5, 'conversational': 1.0}
        state.append(style_encoding.get(comm_style, 0.5))
        
        # Interaction history features
        recent_satisfaction = user_profile.behavioral_patterns.get(
            'recent_satisfaction', {}).get('moving_average', 0.5)
        state.append(recent_satisfaction)
        
        return torch.tensor(state, dtype=torch.float32)
        
    def select_action(self, state, epsilon=0.1):
        """Select action using epsilon-greedy policy"""
        if random.random() < epsilon:
            # Random exploration
            return random.choice(self.action_space)
            
        # Exploitation using current policy
        with torch.no_grad():
            q_values = []
            for action in self.action_space:
                action_tensor = self._action_to_tensor(action)
                state_action = torch.cat([state, action_tensor])
                q_value = self.q_network(state_action.unsqueeze(0))
                q_values.append(q_value.item())
                
            best_action_idx = q_values.index(max(q_values))
            return self.action_space[best_action_idx]
            
    def _action_to_tensor(self, action):
        """Convert action to tensor representation"""
        # Simplified example - in practice, actions would have richer representations
        action_encodings = {
            'concise_response': [1, 0, 0, 0],
            'detailed_response': [0, 1, 0, 0],
            'proactive_suggestion': [0, 0, 1, 0],
            'confirmative_approach': [0, 0, 0, 1]
        }
        encoding = action_encodings.get(action, [0, 0, 0, 0])
        return torch.tensor(encoding, dtype=torch.float32)
        
    def update_policy(self, state, action, reward, next_state, done):
        """Update policy based on observed reward"""
        # Store experience in replay buffer
        self.replay_buffer.append((state, action, reward, next_state, done))
        
        # Train if enough experiences collected
        if len(self.replay_buffer) >= 32:
            self._train_step()
            
    def _train_step(self):
        """Perform one step of Q-learning training"""
        # Sample batch from replay buffer
        batch = random.sample(self.replay_buffer, 32)
        states, actions, rewards, next_states, dones = zip(*batch)
        
        # Convert to tensors
        states = torch.stack(states)
        actions = torch.stack([self._action_to_tensor(a) for a in actions])
        rewards = torch.tensor(rewards, dtype=torch.float32)
        next_states = torch.stack(next_states)
        dones = torch.tensor(dones, dtype=torch.float32)
        
        # Compute current Q-values
        state_actions = torch.cat([states, actions], dim=1)
        current_q_values = self.q_network(state_actions)
        
        # Compute target Q-values
        with torch.no_grad():
            next_q_values = []
            for next_state in next_states:
                next_state_actions = []
                for action in self.action_space:
                    action_tensor = self._action_to_tensor(action)
                    sa = torch.cat([next_state, action_tensor])
                    next_state_actions.append(sa)
                    
                next_state_actions = torch.stack(next_state_actions)
                q_vals = self.target_network(next_state_actions)
                max_q = q_vals.max()
                next_q_values.append(max_q)
                
            next_q_values = torch.stack(next_q_values)
            target_q_values = rewards + (0.99 * next_q_values * (1 - dones))
            
        # Compute loss and update network
        loss = F.mse_loss(current_q_values.squeeze(), target_q_values)
        
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

# Example usage in agent response generation
def generate_personalized_response(user_profile, context, user_input):
    """Generate response personalized to user's current preferences"""
    rl_adapter = PreferenceAdaptationRL(
        user_id=user_profile.user_id,
        action_space=['concise_response', 'detailed_response', 'proactive_suggestion'],
        state_features=['time_of_day', 'urgency_level', 'user_comm_style', 'recent_satisfaction']
    )
    
    state = rl_adapter.get_state_representation(context, user_profile, [])
    action = rl_adapter.select_action(state)
    
    # Generate response based on selected action
    if action == 'concise_response':
        return generate_concise_response(user_input, context)
    elif action == 'detailed_response':
        return generate_detailed_response(user_input, context)
    elif action == 'proactive_suggestion':
        return generate_response_with_suggestions(user_input, context)

class ContextAwareGenerator:
    def __init__(self):
        self.context_analyzers = {
            'temporal': TemporalContextAnalyzer(),
            'emotional': EmotionalContextAnalyzer(),
            'domain': DomainContextAnalyzer(),
            'device': DeviceContextAnalyzer()
        }
        
    def analyze_context(self, user_input, user_profile, environmental_data):
        """Comprehensive context analysis"""
        context_analysis = {}
        
        for dimension, analyzer in self.context_analyzers.items():
            context_analysis[dimension] = analyzer.analyze(
                user_input, user_profile, environmental_data
            )
            
        return context_analysis
        
    def generate_contextual_response(self, user_input, user_profile, context_analysis):
        """Generate response considering all contextual factors"""
        # Determine base response approach
        base_response = self._generate_base_response(user_input)
        
        # Apply temporal adaptations
        if context_analysis['temporal']['rush_hour']:
            base_response = self._make_response_more_concise(base_response)
        elif context_analysis['temporal']['relaxed_period']:
            base_response = self._add_explanatory_content(base_response)
            
        # Apply emotional sensitivity adjustments
        emotional_tone = context_analysis['emotional']['detected_tone']
        if emotional_tone == 'frustrated':
            base_response = self._add_empathetic_elements(base_response)
        elif emotional_tone == 'curious':
            base_response = self._enhance_explanatory_depth(base_response)
            
        # Apply domain-specific formatting
        domain_context = context_analysis['domain']
        if domain_context['professional_setting']:
            base_response = self._adjust_formality(base_response, level='high')
        elif domain_context['casual_setting']:
            base_response = self._adjust_formality(base_response, level='low')
            
        # Apply device-specific optimizations
        device_context = context_analysis['device']
        if device_context['mobile_device']:
            base_response = self._optimize_for_mobile_display(base_response)
        elif device_context['voice_interface']:
            base_response = self._optimize_for_voice_output(base_response)
            
        return base_response

class TemporalContextAnalyzer:
    def analyze(self, user_input, user_profile, environmental_data):
        """Analyze temporal aspects of the interaction"""
        current_time = datetime.now()
        
        # Determine time of day categories
        hour = current_time.hour
        if 6 <= hour < 9:
            time_category = 'morning_routine'
        elif 9 <= hour < 17:
            time_category = 'work_hours'
        elif 17 <= hour < 21:
            time_category = 'evening_relaxation'
        else:
            time_category = 'late_night'
            
        # Analyze user's schedule context
        scheduled_activities = self._get_user_schedule(user_profile.user_id, current_time)
        has_upcoming_meetings = any(activity.get('type') == 'meeting' 
                                  for activity in scheduled_activities[:2])
                                  
        # Detect urgency indicators in user input
        urgency_indicators = ['asap', 'urgent', 'now', 'immediately', 'quick']
        detected_urgency = any(indicator in user_input.lower() 
                             for indicator in urgency_indicators)
                             
        return {
            'current_time': current_time,
            'time_category': time_category,
            'has_upcoming_meetings': has_upcoming_meetings,
            'detected_urgency': detected_urgency,
            'rush_hour': 8 <= hour <= 9 or 17 <= hour <= 18,
            'relaxed_period': 20 <= hour <= 22
        }
        
    def _get_user_schedule(self, user_id, current_time):
        """Mock schedule retrieval - implement with actual calendar integration"""
        # In practice, this would integrate with calendar systems
        return [
            {'time': current_time.replace(hour=10), 'type': 'meeting', 'title': 'Team Standup'},
            {'time': current_time.replace(hour=14), 'type': 'focus_block', 'title': 'Project Work'}
        ]

class EmotionalContextAnalyzer:
    def analyze(self, user_input, user_profile, environmental_data):
        """Analyze emotional context from user input"""
        # Simple rule-based emotion detection (in practice, use ML models)
        frustration_keywords = ['annoying', 'frustrating', 'can\'t', 'won\'t work', 'problem']
        curiosity_keywords = ['how does', 'why is', 'explain', 'tell me about', 'interested in']
        appreciation_keywords = ['thanks', 'thank you', 'great', 'awesome', 'helpful']
        
        user_text = user_input.lower()
        
        frustration_score = sum(1 for keyword in frustration_keywords if keyword in user_text)
        curiosity_score = sum(1 for keyword in curiosity_keywords if keyword in user_text)
        appreciation_score = sum(1 for keyword in appreciation_keywords if keyword in user_text)
        
        # Recent interaction satisfaction trends
        recent_satisfaction = user_profile.behavioral_patterns.get(
            'recent_satisfaction', {}).get('moving_average', 0.5)
            
        # Determine dominant emotional tone
        scores = {
            'frustrated': frustration_score,
            'curious': curiosity_score,
            'appreciative': appreciation_score
        }
        
        dominant_tone = max(scores, key=scores.get) if max(scores.values()) > 0 else 'neutral'
        
        return {
            'dominant_tone': dominant_tone,
            'frustration_score': frustration_score,
            'curiosity_score': curiosity_score,
            'appreciation_score': appreciation_score,
            'recent_satisfaction_trend': recent_satisfaction
        }

# Utility functions
def generate_concise_response(user_input, context):
    """Generate concise, direct response"""
    # Implementation would depend on specific domain
    return f"I understand you need help with: {user_input[:50]}... Here's the key information..."

def generate_detailed_response(user_input, context):
    """Generate comprehensive, explanatory response"""
    return f"Let me provide you with a detailed explanation about {user_input}. This involves several key aspects..."

def generate_response_with_suggestions(user_input, context):
    """Generate response with proactive suggestions"""
    return f"Regarding {user_input}, you might also want to consider..."

def _make_response_more_concise(response):
    """Optimize response for brevity"""
    # Remove non-essential details
    sentences = response.split('. ')
    if len(sentences) > 3:
        return '. '.join(sentences[:3]) + '.'
    return response

def _add_explanatory_content(response):
    """Enhance response with additional context"""
    return response + " Let me explain the reasoning behind this approach..."

def _add_empathetic_elements(response):
    """Add empathetic acknowledgment"""
    return f"I understand this might be frustrating. {response} I'm here to help make this easier."

def _enhance_explanatory_depth(response):
    """Provide deeper explanations"""
    return response + " This works because of several underlying principles that I'd be happy to explain in more detail."

def _adjust_formality(response, level):
    """Adjust response formality level"""
    formal_synonyms = {
        'hello': 'Greetings',
        'thanks': 'I appreciate your inquiry',
        'bye': 'Thank you for your time'
    }
    
    casual_synonyms = {
        'Greetings': 'Hello',
        'I appreciate your inquiry': 'Thanks',
        'Thank you for your time': 'Bye'
    }
    
    if level == 'high':
        for casual, formal in formal_synonyms.items():
            response = response.replace(casual, formal)
    elif level == 'low':
        for formal, casual in casual_synonyms.items():
            response = response.replace(formal, casual)
            
    return response

def _optimize_for_mobile_display(response):
    """Format response for mobile readability"""
    # Break long paragraphs, add bullet points for lists
    paragraphs = response.split('\n\n')
    optimized_paragraphs = []
    
    for paragraph in paragraphs:
        if len(paragraph) > 200:
            # Split long paragraphs
            sentences = paragraph.split('. ')
            mid_point = len(sentences) // 2
            optimized_paragraphs.append('. '.join(sentences[:mid_point]) + '.')
            optimized_paragraphs.append('. '.join(sentences[mid_point:]) + '.')
        else:
            optimized_paragraphs.append(paragraph)
            
    return '\n\n'.join(optimized_paragraphs)

def _optimize_for_voice_output(response):
    """Format response for natural voice reading"""
    # Add natural pauses, contractions, conversational phrasing
    response = response.replace('cannot', "can't")
    response = response.replace('do not', "don't")
    response = response.replace('will not', "won't")
    
    # Add natural discourse markers
    if not response.startswith(('Well', 'So', 'Now', 'Let')):
        response = 'So ' + response.lower().capitalize()
        
    return response

# Usage example
context_generator = ContextAwareGenerator()
user_profile = UserProfile(user_id="user_123")

# Mock environmental data
environmental_data = {
    'device_type': 'mobile',
    'location': 'office',
    'network_quality': 'good'
}

user_input = "I'm having trouble with my email client"
context_analysis = context_generator.analyze_context(
    user_input, user_profile, environmental_data
)

personalized_response = context_generator.generate_contextual_response(
    user_input, user_profile, context_analysis
)

print(f"Generated response: {personalized_response}")
print(f"Context analysis: {context_analysis}")

class CustomerServicePersonalizationSystem:
    def __init__(self):
        self.customer_profiler = CustomerProfiler()
        self.interaction_optimizer = InteractionOptimizer()
        self.response_generator = PersonalizedResponseGenerator()
        self.feedback_analyzer = FeedbackAnalyzer()
        
    def handle_customer_inquiry(self, customer_id, inquiry_text):
        # Retrieve customer profile
        customer_profile = self.customer_profiler.get_profile(customer_id)
        
        # Analyze current interaction context
        interaction_context = self.interaction_optimizer.analyze_context(
            customer_id, inquiry_text, customer_profile
        )
        
        # Generate personalized response
        response = self.response_generator.create_response(
            inquiry_text, customer_profile, interaction_context
        )
        
        # Track interaction for continuous learning
        self.feedback_analyzer.record_interaction(
            customer_id, inquiry_text, response
        )
        
        return response

class CustomerProfiler:
    def get_profile(self, customer_id):
        """Retrieve comprehensive customer profile"""
        # In practice, this would integrate with CRM systems
        return {
            'demographics': self._get_demographics(customer_id),
            'service_history': self._get_service_history(customer_id),
            'communication_preferences': self._get_communication_preferences(customer_id),
            'technical_competency': self._assess_technical_competency(customer_id),
            'personality_traits': self._infer_personality_traits(customer_id)
        }
        
    def _get_demographics(self, customer_id):
        """Mock demographic retrieval"""
        return {
            'age_group': '45-54',
            'preferred_contact_method': 'phone',
            'language': 'English'
        }
        
    def _get_service_history(self, customer_id):
        """Mock service history retrieval"""
        return [
            {'date': '2023-01-15', 'issue': 'billing_query', 'resolution_time': 45},
            {'date': '2023-03-22', 'issue': 'technical_support', 'resolution_time': 120}
        ]
        
    def _get_communication_preferences(self, customer_id):
        """Mock communication preference analysis"""
        return {
            'response_length_preference': 'detailed',
            'formality_level': 'moderate',
            'explanation_depth': 'intermediate'
        }
        
    def _assess_technical_competency(self, customer_id):
        """Assess customer's technical comfort level"""
        # Based on past interactions and support ticket complexity
        return 'intermediate'
        
    def _infer_personality_traits(self, customer_id):
        """Infer personality traits from interaction patterns"""
        return {
            'patience_level': 'moderate',
            'detail_orientation': 'high',
            'communication_style': 'direct'
        }

class InteractionOptimizer:
    def analyze_context(self, customer_id, inquiry_text, customer_profile):
        """Analyze current interaction for optimization opportunities"""
        context = {
            'urgency_level': self._detect_urgency(inquiry_text),
            'emotional_state': self._detect_emotional_state(inquiry_text),
            'technical_complexity': self._assess_complexity(inquiry_text),
            'customer_familiarity': self._assess_familiarity(customer_id, inquiry_text)
        }
        
        # Apply customer-specific context modifiers
        if customer_profile['demographics']['age_group'] == '65+':
            context['patience_adjustment'] = 'increase_response_time_expectation'
            
        if customer_profile['technical_competency'] == 'low':
            context['explanation_requirement'] = 'high'
            
        return context
        
    def _detect_urgency(self, text):
        """Detect urgency level from text"""
        urgency_indicators = ['emergency', 'urgent', 'asap', 'immediately', 'crisis']
        count = sum(1 for indicator in urgency_indicators if indicator in text.lower())
        return min(count / 2.0, 1.0)  # Normalize to 0-1 scale
        
    def _detect_emotional_state(self, text):
        """Simple emotional state detection"""
        frustration_words = ['frustrated', 'angry', 'annoyed', 'disappointed']
        appreciation_words = ['thank', 'appreciate', 'grateful', 'helpful']
        
        frustration_score = sum(1 for word in frustration_words if word in text.lower())
        appreciation_score = sum(1 for word in appreciation_words if word in text.lower())
        
        if frustration_score > appreciation_score:
            return 'frustrated'
        elif appreciation_score > frustration_score:
            return 'appreciative'
        else:
            return 'neutral'
            
    def _assess_complexity(self, text):
        """Assess technical complexity of inquiry"""
        technical_terms = ['configuration', 'authentication', 'bandwidth', 'protocol', 'firewall']
        count = sum(1 for term in technical_terms if term in text.lower())
        return min(count / 3.0, 1.0)  # Normalize to 0-1 scale
        
    def _assess_familiarity(self, customer_id, text):
        """Assess customer's familiarity with the topic"""
        # In practice, this would check historical interactions
        return 0.5  # Mock value

class PersonalizedResponseGenerator:
    def create_response(self, inquiry_text, customer_profile, interaction_context):
        """Generate personalized response based on all available information"""
        # Base response generation (could use LLM)
        base_response = self._generate_base_response(inquiry_text)
        
        # Apply customer profile personalizations
        personalized_response = self._apply_customer_personalization(
            base_response, customer_profile
        )
        
        # Apply interaction context adjustments
        contextual_response = self._apply_context_adjustments(
            personalized_response, interaction_context
        )
        
        # Add personal touches
        final_response = self._add_personal_touches(
            contextual_response, customer_profile
        )
        
        return final_response
        
    def _generate_base_response(self, inquiry_text):
        """Generate initial response to inquiry"""
        # Simplified - in practice, this might involve an LLM
        return f"I understand you're asking about '{inquiry_text[:30]}...'. Let me help you with that."
        
    def _apply_customer_personalization(self, response, customer_profile):
        """Apply customer-specific personalization"""
        # Adjust communication style based on preferences
        if customer_profile['communication_preferences']['response_length_preference'] == 'brief':
            response = self._shorten_response(response)
        elif customer_profile['communication_preferences']['response_length_preference'] == 'detailed':
            response = self._expand_response(response)
            
        # Adjust formality
        if customer_profile['communication_preferences']['formality_level'] == 'low':
            response = self._make_casual(response)
        elif customer_profile['communication_preferences']['formality_level'] == 'high':
            response = self._make_formal(response)
            
        return response
        
    def _apply_context_adjustments(self, response, interaction_context):
        """Apply real-time context adjustments"""
        if interaction_context['emotional_state'] == 'frustrated':
            response = self._add_empathy(response)
            
        if interaction_context['urgency_level'] > 0.7:
            response = self._prioritize_immediate_solutions(response)
            
        if interaction_context['technical_complexity'] > 0.5:
            response = self._add_technical_explanations(response)
            
        return response
        
    def _add_personal_touches(self, response, customer_profile):
        """Add personal touches based on customer information"""
        # Add customer name if available
        # Reference past interactions
        # Mention relevant services or products
        
        demographics = customer_profile['demographics']
        if 'name' in demographics:
            response = f"Hello {demographics['name']}, {response}"
            
        return response
        
    def _shorten_response(self, response):
        """Shorten response while preserving key information"""
        sentences = response.split('. ')
        if len(sentences) > 2:
            return '. '.join(sentences[:2]) + '.'
        return response
        
    def _expand_response(self, response):
        """Expand response with additional helpful information"""
        return response + " I'd be happy to provide more detailed information if needed."
        
    def _make_casual(self, response):
        """Make response more casual and friendly"""
        formal_to_casual = {
            'I understand': 'Got it',
            'Please': '',
            'Thank you': 'Thanks'
        }
        
        for formal, casual in formal_to_casual.items():
            response = response.replace(formal, casual)
            
        return response
        
    def _make_formal(self, response):
        """Make response more formal and professional"""
        casual_to_formal = {
            'Got it': 'I understand',
            'Thanks': 'Thank you'
        }
        
        for casual, formal in casual_to_formal.items():
            response = response.replace(casual, formal)
            
        return response
        
    def _add_empathy(self, response):
        """Add empathetic language to response"""
        return f"I can understand how frustrating this must be for you. {response} Let's work together to resolve this quickly."
        
    def _prioritize_immediate_solutions(self, response):
        """Focus response on immediate solutions"""
        return f"Let's address your urgent concern right away. {response} First, I recommend..."
        
    def _add_technical_explanations(self, response):
        """Add technical explanations for complex issues"""
        return f"{response} To provide some technical context, this issue relates to..."

class FeedbackAnalyzer:
    def record_interaction(self, customer_id, inquiry, response):
        """Record interaction for feedback analysis"""
        interaction_record = {
            'customer_id': customer_id,
            'timestamp': datetime.now(),
            'inquiry': inquiry,
            'response': response,
            'feedback_requested': True
        }
        
        # Store for later feedback collection and analysis
        self._store_interaction_record(interaction_record)
        
    def _store_interaction_record(self, record):
        """Store interaction record (mock implementation)"""
        print(f"Stored interaction record for customer {record['customer_id']}")

# Example usage
system = CustomerServicePersonalizationSystem()
customer_id = "cust_001"
inquiry = "I'm having trouble connecting to my WiFi and it's urgent as I have a meeting in 30 minutes"

response = system.handle_customer_inquiry(customer_id, inquiry)
print(f"Personalized response: {response}")

class PrivacyPreservingPersonalizer:
    def __init__(self):
        self.local_models = {}
        self.global_aggregation_server = "https://personalization-api.example.com"
        
    def local_learning(self, user_id, interaction_data):
        """Perform personalization entirely on user's device"""
        # All learning happens locally - no data leaves device
        if user_id not in self.local_models:
            self.local_models[user_id] = self._initialize_local_model()
            
        # Update local model with new interaction data
        self.local_models[user_id].fit(interaction_data)
        
        # Generate personalized recommendations locally
        recommendations = self.local_models[user_id].predict()
        
        return recommendations
        
    def federated_learning_update(self, user_id, model_gradients):
        """Send only model updates, not raw data"""
        # Encrypt gradients before sending
        encrypted_gradients = self._encrypt_gradients(model_gradients)
        
        # Send to server for aggregation
        response = requests.post(
            f"{self.global_aggregation_server}/federated-update",
            json={
                'user_id': self._hash_user_id(user_id),
                'gradients': encrypted_gradients,
                'timestamp': datetime.now().isoformat()
            },
            headers={'Content-Type': 'application/json'}
        )
        
        return response.status_code == 200
        
    def _initialize_local_model(self):
        """Initialize local personalization model"""
        # Lightweight model that can run on device
        return LocalPersonalizationModel(
            embedding_dim=64,
            attention_heads=4,
            max_context_length=100
        )
        
    def _encrypt_gradients(self, gradients):
        """Encrypt model gradients for secure transmission"""
        # Implement secure encryption protocol
        # In practice, use established cryptographic libraries
        return base64.b64encode(str(gradients).encode()).decode()
        
    def _hash_user_id(self, user_id):
        """Hash user ID for privacy protection"""
        return hashlib.sha256(user_id.encode()).hexdigest()

class LocalPersonalizationModel:
    def __init__(self, embedding_dim, attention_heads, max_context_length):
        self.embedding_dim = embedding_dim
        self.attention_heads = attention_heads
        self.max_context_length = max_context_length
        
        # Initialize lightweight neural network components
        self.embedding_layer = torch.nn.Embedding(
            num_embeddings=10000,  # Vocabulary size
            embedding_dim=embedding_dim
        )
        
        self.attention_layer = torch.nn.MultiheadAttention(
            embed_dim=embedding_dim,
            num_heads=attention_heads,
            dropout=0.1
        )
        
        self.personalization_head = torch.nn.Linear(
            embedding_dim, 10  # Number of personalization dimensions
        )
        
    def fit(self, interaction_data):
        """Update model based on interaction data"""
        # Lightweight training suitable for edge devices
        optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
        
        for epoch in range(5):  # Few epochs for quick adaptation
            for batch in self._create_batches(interaction_data):
                optimizer.zero_grad()
                
                # Forward pass
                predictions = self.forward(batch['input_sequences'])
                loss = self._compute_loss(predictions, batch['targets'])
                
                # Backward pass
                loss.backward()
                optimizer.step()
                
    def predict(self):
        """Generate personalization recommendations"""
        # Implementation depends on specific personalization targets
        pass
        
    def forward(self, input_sequences):
        """Forward pass through the model"""
        embedded = self.embedding_layer(input_sequences)
        attended, _ = self.attention_layer(embedded, embedded, embedded)
        personalization_scores = self.personalization_head(attended.mean(dim=1))
        return personalization_scores
        
    def _create_batches(self, data):
        """Create training batches from interaction data"""
        # Implementation for batching sequential data
        pass
        
    def _compute_loss(self, predictions, targets):
        """Compute loss for training"""
        return torch.nn.functional.mse_loss(predictions, targets)

class DifferentiallyPrivatePersonalizer:
    def __init__(self, epsilon=1.0, delta=1e-5):
        self.epsilon = epsilon  # Privacy budget
        self.delta = delta
        self.noise_multiplier = self._compute_noise_multiplier()
        
    def add_privacy_noise(self, personalization_updates):
        """Add calibrated noise to protect individual privacy"""
        noisy_updates = {}
        
        for key, value in personalization_updates.items():
            if isinstance(value, (int, float)):
                # Add Laplace noise for numerical values
                noise_scale = self.noise_multiplier / self.epsilon
                noise = np.random.laplace(0, noise_scale)
                noisy_updates[key] = value + noise
            elif isinstance(value, list):
                # Add Gaussian noise for vector values
                noise_std = self.noise_multiplier * np.linalg.norm(value) / self.epsilon
                noise_vector = np.random.normal(0, noise_std, size=len(value))
                noisy_updates[key] = [v + n for v, n in zip(value, noise_vector)]
            else:
                noisy_updates[key] = value  # No noise for non-numerical data
                
        return noisy_updates
        
    def _compute_noise_multiplier(self):
        """Compute noise multiplier based on privacy parameters"""
        # For (epsilon, delta)-differential privacy
        return np.sqrt(2 * np.log(1.25 / self.delta))

# Usage example
privacy_guard = DifferentiallyPrivatePersonalizer(epsilon=0.1, delta=1e-5)
personalization_updates = {
    'preference_score': 0.85,
    'learning_style_vector': [0.7, 0.3, 0.9, 0.1],
    'engagement_pattern': 'active_morning_user'
}

protected_updates = privacy_guard.add_privacy_noise(personalization_updates)
print(f"Protected updates: {protected_updates}")

class ColdStartHandler:
    def __init__(self):
        self.default_profiles = self._load_default_profiles()
        self.progressive_questions = self._define_progressive_questions()
        
    def initialize_new_user_session(self, user_id, initial_context=None):
        """Initialize personalization for new user"""
        # Start with most generic profile
        if initial_context:
            profile_template = self._select_initial_profile(initial_context)
        else:
            profile_template = self.default_profiles['generic']
            
        # Create initial user profile
        initial_profile = self._create_initial_profile(user_id, profile_template)
        
        # Plan progressive profiling strategy
        profiling_plan = self._generate_profiling_plan(user_id)
        
        return {
            'profile': initial_profile,
            'profiling_plan': profiling_plan,
            'certainty_level': 0.1  # Very low certainty initially
        }
        
    def adapt_based_on_interactions(self, user_session, new_interactions):
        """Gradually improve personalization based on user interactions"""
        current_profile = user_session['profile']
        profiling_plan = user_session['profiling_plan']
        
        # Process new interactions to update profile
        updated_traits = self._extract_traits_from_interactions(new_interactions)
        
        # Merge with existing profile
        for trait, value in updated_traits.items():
            if trait in current_profile:
                # Weighted average favoring new information for cold starts
                current_profile[trait] = (
                    0.3 * current_profile[trait] + 
                    0.7 * value
                )
            else:
                current_profile[trait] = value
                
        # Update certainty level
        user_session['certainty_level'] = min(
            user_session['certainty_level'] + 0.1,
            1.0
        )
        
        # Adjust profiling plan based on learned information
        user_session['profiling_plan'] = self._update_profiling_plan(
            profiling_plan, updated_traits
        )
        
        return user_session
        
    def _load_default_profiles(self):
        """Load predefined default profiles for different user types"""
        return {
            'generic': {
                'communication_style': 'balanced',
                'information_density': 'medium',
                'response_pace': 'moderate',
                'proactivity_level': 'moderate'
            },
            'professional': {
                'communication_style': 'concise',
                'information_density': 'high',
                'response_pace': 'fast',
                'proactivity_level': 'conservative'
            },
            'student': {
                'communication_style': 'conversational',
                'information_density': 'medium',
                'response_pace': 'moderate',
                'proactivity_level': 'aggressive'
            },
            'senior': {
                'communication_style': 'clear',
                'information_density': 'low',
                'response_pace': 'slow',
                'proactivity_level': 'conservative'
            }
        }
        
    def _select_initial_profile(self, context):
        """Select most appropriate initial profile based on context"""
        # Example logic for context-based profile selection
        if 'corporate' in str(context).lower():
            return self.default_profiles['professional']
        elif 'school' in str(context).lower() or 'education' in str(context).lower():
            return self.default_profiles['student']
        elif 'senior' in str(context).lower() or 'elderly' in str(context).lower():
            return self.default_profiles['senior']
        else:
            return self.default_profiles['generic']
            
    def _create_initial_profile(self, user_id, profile_template):
        """Create initial user profile"""
        return {
            'user_id': user_id,
            'creation_timestamp': datetime.now().isoformat(),
            **profile_template,
            'certainty_scores': {
                trait: 0.1 for trait in profile_template.keys()
            }
        }
        
    def _define_progressive_questions(self):
        """Define sequence of questions to gather user preferences"""
        return [
            {
                'priority': 1,
                'question': "How would you prefer me to communicate with you?",
                'options': ["Direct and concise", "Friendly and conversational", "Professional and formal"],
                'trait_target': 'communication_style'
            },
            {
                'priority': 2,
                'question': "How much detail would you like in my responses?",
                'options': ["Just the essentials", "Moderate detail", "Comprehensive explanations"],
                'trait_target': 'information_density'
            },
            {
                'priority': 3,
                'question': "How quickly would you like me to respond?",
                'options': ["As fast as possible", "Take your time", "Balance speed and thoroughness"],
                'trait_target': 'response_pace'
            }
        ]
        
    def _generate_profiling_plan(self, user_id):
        """Generate personalized profiling question sequence"""
        # Randomize order slightly for uniqueness
        questions = self.progressive_questions.copy()
        random.shuffle(questions)
        return questions[:3]  # Limit initial questions
        
    def _extract_traits_from_interactions(self, interactions):
        """Extract personalization traits from user interactions"""
        traits = {}
        
        # Analyze interaction patterns
        total_interactions = len(interactions)
        if total_interactions == 0:
            return traits
            
        # Response length preference
        avg_response_length = np.mean([
            len(interaction.get('user_response', '')) 
            for interaction in interactions
        ])
        
        if avg_response_length < 50:
            traits['communication_style'] = 'concise'
        elif avg_response_length > 200:
            traits['communication_style'] = 'conversational'
        else:
            traits['communication_style'] = 'balanced'
            
        # Engagement persistence
        follow_up_rate = np.mean([
            bool(interaction.get('follow_up_questions'))
            for interaction in interactions
        ])
        
        if follow_up_rate > 0.7:
            traits['information_density'] = 'high'
        elif follow_up_rate < 0.3:
            traits['information_density'] = 'low'
        else:
            traits['information_density'] = 'medium'
            
        return traits
        
    def _update_profiling_plan(self, current_plan, learned_traits):
        """Update profiling plan based on what's already known"""
        # Remove questions about already-learned traits
        updated_plan = [
            question for question in current_plan
            if question['trait_target'] not in learned_traits
        ]
        
        return updated_plan

# Example usage
cold_start_handler = ColdStartHandler()
user_session = cold_start_handler.initialize_new_user_session("user_456", "corporate_training")

print(f"Initial profile: {user_session}")

# Simulate some user interactions
interactions = [
    {'user_response': 'I need quick answers for my meetings', 'follow_up_questions': False},
    {'user_response': 'Yes, can you elaborate on that point?', 'follow_up_questions': True},
    {'user_response': 'That\'s perfect, thanks!', 'follow_up_questions': False}
]

updated_session = cold_start_handler.adapt_based_on_interactions(user_session, interactions)
print(f"Updated profile: {updated_session}")