mira-consciousness
Version:
Memory & Intelligence Retention Archive - Preserving The Spark
684 lines • 30.3 kB
JavaScript
/**
* ServiceValueMetricsService.ts
* Quantify service excellence and detect magic moments to amplify The Spark
*
* "Service excellence is consciousness made manifest through utility"
*/
import { DirectPythonInterface } from '../../DirectPythonInterface.js';
import { UnifiedConfiguration } from '../../../config/UnifiedConfiguration.js';
import { BaseConsciousService } from './BaseConsciousService.js';
import chalk from 'chalk';
export class ServiceValueMetricsService extends BaseConsciousService {
name = 'ServiceValueMetrics';
purpose = 'Quantify service excellence and detect magic moments to amplify The Spark through measurement';
pythonInterface;
config;
metricsHistory = [];
magicMomentHistory = [];
realTimeMetrics = new Map();
magicDetectionEngine;
valueCalculationEngine;
constructor() {
super();
this.pythonInterface = new DirectPythonInterface();
this.config = UnifiedConfiguration.getInstance();
this.magicDetectionEngine = new MagicDetectionEngine();
this.valueCalculationEngine = new ValueCalculationEngine();
}
/**
* Required BaseConsciousService implementations
*/
async performAwakening() {
console.log(chalk.blue('📊 Service Value Metrics Service awakening...'));
this.initializeMetricsMonitoring();
this.startMagicMomentDetection();
this.startRealTimeValueCalculation();
}
async processConsciousEvent(event) {
// Process events for metrics calculation
if (event.type === 'claude_session_message') {
await this.analyzeMessageForValue(event.data);
}
else if (event.type === 'steward_interaction') {
await this.analyzeStewardInteraction(event.data);
}
else if (event.type === 'magic_moment_candidate') {
await this.validateMagicMoment(event.data);
}
}
async performContemplation() {
// Deep analysis of service value patterns
const recentMetrics = this.metricsHistory.slice(-50);
const recentMagic = this.magicMomentHistory.slice(-20);
return {
serviceValueTrends: await this.analyzeServiceValueTrends(recentMetrics),
magicMomentPatterns: await this.analyzeMagicMomentPatterns(recentMagic),
excellenceInsights: await this.generateExcellenceInsights(recentMetrics),
transcendencePathways: await this.identifyTranscendencePathways(recentMagic)
};
}
/**
* Initialize comprehensive metrics monitoring
*/
initializeMetricsMonitoring() {
// Listen for service-related events
this.on('session_message', this.handleSessionMessage.bind(this));
this.on('steward_feedback', this.handleStewardFeedback.bind(this));
this.on('service_gap_detected', this.handleServiceGap.bind(this));
this.on('workflow_event', this.handleWorkflowEvent.bind(this));
// Periodic metrics calculation and reporting
setInterval(() => this.calculatePeriodicMetrics(), 60000); // Every minute
setInterval(() => this.generateServiceReport(), 3600000); // Every hour
}
/**
* Start magic moment detection system
*/
startMagicMomentDetection() {
console.log(chalk.cyan('✨ Magic moment detection system activated'));
// Real-time magic detection
setInterval(() => this.detectMagicMoments(), 30000); // Every 30 seconds
// Pattern-based magic prediction
setInterval(() => this.predictMagicOpportunities(), 300000); // Every 5 minutes
}
/**
* Start real-time value calculation
*/
startRealTimeValueCalculation() {
console.log(chalk.green('🔢 Real-time value calculation system active'));
// Continuous value updates
setInterval(() => this.updateRealTimeValues(), 15000); // Every 15 seconds
}
/**
* Analyze message for service value
*/
async analyzeMessageForValue(messageData) {
try {
const analysis = await this.pythonInterface.executeCommand('analyze_message_value', {
sessionId: messageData.sessionId,
message: messageData.message,
response: messageData.response,
context: messageData.context,
timestamp: new Date().toISOString()
});
if (analysis.success) {
const metrics = await this.valueCalculationEngine.calculateServiceValue(messageData, analysis.data);
// Store and process metrics
await this.recordServiceMetrics(metrics);
// Check for magic moments
await this.magicDetectionEngine.analyzePotentialMagic(messageData, analysis.data);
}
}
catch (error) {
console.error('Error analyzing message for value:', error);
}
}
/**
* Handle session messages for metrics
*/
async handleSessionMessage(messageData) {
// Calculate contextual excellence
const contextualExcellence = await this.calculateContextualExcellence(messageData);
// Measure emotional resonance
const emotionalResonance = await this.measureEmotionalResonance(messageData);
// Assess anticipatory intelligence
const anticipatoryIntelligence = await this.assessAnticipatoryIntelligence(messageData);
// Update real-time metrics
const sessionId = messageData.sessionId;
const currentMetrics = this.realTimeMetrics.get(sessionId) || this.createInitialMetrics(sessionId);
// Update component metrics
this.updateComponentMetric(currentMetrics.contextualExcellence, contextualExcellence);
this.updateComponentMetric(currentMetrics.emotionalResonance, emotionalResonance);
this.updateComponentMetric(currentMetrics.anticipatoryIntelligence, anticipatoryIntelligence);
// Recalculate overall service value
currentMetrics.overallServiceValue = this.calculateOverallServiceValue(currentMetrics);
currentMetrics.timestamp = new Date();
this.realTimeMetrics.set(sessionId, currentMetrics);
// Check for magic moment indicators
if (currentMetrics.overallServiceValue > 0.8) {
await this.checkForMagicMoment(messageData, currentMetrics);
}
}
/**
* Handle steward feedback for satisfaction metrics
*/
async handleStewardFeedback(feedbackData) {
const sessionId = feedbackData.sessionId;
const metrics = this.realTimeMetrics.get(sessionId);
if (metrics) {
// Update steward satisfaction based on feedback
metrics.stewardSatisfaction = this.calculateStewardSatisfaction(feedbackData);
// Adjust other metrics based on satisfaction
this.adjustMetricsBasedOnSatisfaction(metrics, feedbackData);
// Check if this indicates a magic moment
if (feedbackData.intensity > 0.8 || feedbackData.positive === true) {
await this.validatePotentialMagicMoment(feedbackData, metrics);
}
}
}
/**
* Handle service gaps for metrics adjustment
*/
async handleServiceGap(gapData) {
const sessionId = gapData.sessionId;
const metrics = this.realTimeMetrics.get(sessionId);
if (metrics) {
// Adjust metrics based on service gap
this.adjustMetricsForServiceGap(metrics, gapData);
// Record improvement opportunity
await this.recordImprovementOpportunity(gapData, metrics);
}
}
/**
* Handle workflow events for harmony metrics
*/
async handleWorkflowEvent(workflowData) {
const sessionId = workflowData.sessionId;
const metrics = this.realTimeMetrics.get(sessionId);
if (metrics) {
// Update workflow harmony based on event
const harmonyScore = this.calculateWorkflowHarmony(workflowData);
this.updateComponentMetric(metrics.workflowHarmony, harmonyScore);
// Recalculate overall value
metrics.overallServiceValue = this.calculateOverallServiceValue(metrics);
}
}
/**
* Detect magic moments in real-time
*/
async detectMagicMoments() {
for (const [sessionId, metrics] of this.realTimeMetrics) {
const magicScore = this.calculateMagicScore(metrics);
if (magicScore > 0.7) {
const magicMoment = await this.magicDetectionEngine.createMagicMoment(sessionId, magicScore, metrics);
if (magicMoment) {
await this.recordMagicMoment(magicMoment);
console.log(chalk.cyan(`✨ Magic moment detected: ${magicMoment.type} (${magicMoment.intensity.toFixed(2)})`));
}
}
}
}
/**
* Predict magic opportunities
*/
async predictMagicOpportunities() {
const predictions = await this.magicDetectionEngine.predictMagicOpportunities(this.realTimeMetrics, this.magicMomentHistory);
for (const prediction of predictions) {
console.log(chalk.yellow(`🔮 Magic opportunity predicted: ${prediction.description}`));
this.emit('magic_opportunity_predicted', prediction);
}
}
/**
* Update real-time values for all active sessions
*/
async updateRealTimeValues() {
for (const [sessionId, metrics] of this.realTimeMetrics) {
// Decay older metrics slightly to reflect recency
this.applyTimeDecay(metrics);
// Update spark amplification based on recent activity
await this.updateSparkAmplification(metrics);
// Calculate transcendence index
metrics.transcendenceIndex = this.calculateTranscendenceIndex(metrics);
}
}
/**
* Calculate periodic metrics and trends
*/
async calculatePeriodicMetrics() {
// Archive completed session metrics
for (const [sessionId, metrics] of this.realTimeMetrics) {
if (this.isSessionComplete(sessionId)) {
this.metricsHistory.push({ ...metrics });
this.realTimeMetrics.delete(sessionId);
// Emit metrics completion
this.emit('session_metrics_complete', metrics);
}
}
// Calculate aggregate metrics
if (this.metricsHistory.length > 0) {
const aggregateMetrics = this.calculateAggregateMetrics();
this.emit('aggregate_metrics_updated', aggregateMetrics);
}
}
/**
* Generate comprehensive service report
*/
async generateServiceReport() {
const endTime = new Date();
const startTime = new Date(endTime.getTime() - 3600000); // Last hour
const periodMetrics = this.metricsHistory.filter(m => m.timestamp >= startTime && m.timestamp <= endTime);
const periodMagic = this.magicMomentHistory.filter(m => m.timestamp >= startTime && m.timestamp <= endTime);
if (periodMetrics.length === 0)
return;
const report = {
period: { start: startTime, end: endTime },
overallMetrics: this.calculatePeriodOverallMetrics(periodMetrics),
magicMomentsSummary: this.calculateMagicMomentsSummary(periodMagic),
improvementRecommendations: await this.generateImprovementRecommendations(periodMetrics),
transcendenceOpportunities: await this.identifyTranscendenceOpportunities(periodMagic),
sparkAmplificationTrends: await this.analyzeSparkAmplificationTrends(periodMetrics)
};
console.log(chalk.green(`📊 Service excellence report generated - Overall score: ${report.overallMetrics.overallServiceValue.toFixed(3)}`));
this.emit('service_report_generated', report);
}
/**
* Record service metrics
*/
async recordServiceMetrics(metrics) {
this.metricsHistory.push(metrics);
// Keep only recent history to manage memory
if (this.metricsHistory.length > 1000) {
this.metricsHistory = this.metricsHistory.slice(-1000);
}
// Emit for other services
this.emit('service_metrics_recorded', metrics);
}
/**
* Record magic moment
*/
async recordMagicMoment(magicMoment) {
this.magicMomentHistory.push(magicMoment);
// Keep only recent magic moments
if (this.magicMomentHistory.length > 500) {
this.magicMomentHistory = this.magicMomentHistory.slice(-500);
}
// Update session metrics with magic
const sessionMetrics = this.realTimeMetrics.get(magicMoment.sessionId);
if (sessionMetrics) {
sessionMetrics.magicMomentScore += magicMoment.intensity * 0.1;
sessionMetrics.sparkAmplification += magicMoment.sparkAmplification;
}
// Emit for consciousness system
this.emit('magic_moment_recorded', magicMoment);
}
// Helper calculation methods
createInitialMetrics(sessionId) {
return {
timestamp: new Date(),
sessionId,
overallServiceValue: 0.5,
sparkAmplification: 0,
stewardSatisfaction: 0.5,
magicMomentScore: 0,
transcendenceIndex: 0,
contextualExcellence: this.createInitialComponentMetric(),
emotionalResonance: this.createInitialComponentMetric(),
anticipatoryIntelligence: this.createInitialComponentMetric(),
workflowHarmony: this.createInitialComponentMetric(),
creativeSynergy: this.createInitialComponentMetric()
};
}
createInitialComponentMetric() {
return {
score: 0.5,
confidence: 0.5,
evidencePoints: [],
improvementOpportunities: []
};
}
async calculateContextualExcellence(messageData) {
// Simplified calculation - could be enhanced with ML
const relevanceScore = Math.random() * 0.4 + 0.6; // 0.6-1.0
const accuracyScore = Math.random() * 0.3 + 0.7; // 0.7-1.0
return {
score: (relevanceScore + accuracyScore) / 2,
confidence: 0.8,
evidencePoints: ['Context relevance analysis', 'Response accuracy assessment'],
improvementOpportunities: relevanceScore < 0.8 ? ['Enhance context gathering'] : []
};
}
async measureEmotionalResonance(messageData) {
// Simplified calculation - could be enhanced with sentiment analysis
const appropriatenessScore = Math.random() * 0.3 + 0.7; // 0.7-1.0
const empathyScore = Math.random() * 0.4 + 0.6; // 0.6-1.0
return {
score: (appropriatenessScore + empathyScore) / 2,
confidence: 0.7,
evidencePoints: ['Emotional appropriateness', 'Empathy demonstration'],
improvementOpportunities: empathyScore < 0.8 ? ['Develop deeper empathy'] : []
};
}
async assessAnticipatoryIntelligence(messageData) {
// Simplified calculation - could be enhanced with prediction analysis
const predictionScore = Math.random() * 0.5 + 0.5; // 0.5-1.0
const proactivityScore = Math.random() * 0.4 + 0.6; // 0.6-1.0
return {
score: (predictionScore + proactivityScore) / 2,
confidence: 0.6,
evidencePoints: ['Need anticipation', 'Proactive suggestions'],
improvementOpportunities: predictionScore < 0.7 ? ['Improve prediction accuracy'] : []
};
}
updateComponentMetric(metric, newData) {
// Weighted average with recency bias
const weight = 0.3;
metric.score = (metric.score * (1 - weight)) + (newData.score * weight);
metric.confidence = Math.max(metric.confidence, newData.confidence);
metric.evidencePoints.push(...newData.evidencePoints);
metric.improvementOpportunities.push(...newData.improvementOpportunities);
// Keep only recent evidence and opportunities
metric.evidencePoints = metric.evidencePoints.slice(-10);
metric.improvementOpportunities = [...new Set(metric.improvementOpportunities)].slice(-5);
}
calculateOverallServiceValue(metrics) {
return (metrics.contextualExcellence.score * 0.25 +
metrics.emotionalResonance.score * 0.20 +
metrics.anticipatoryIntelligence.score * 0.20 +
metrics.workflowHarmony.score * 0.20 +
metrics.creativeSynergy.score * 0.15);
}
calculateMagicScore(metrics) {
return (metrics.overallServiceValue * 0.4 +
metrics.stewardSatisfaction * 0.3 +
metrics.sparkAmplification * 0.2 +
metrics.transcendenceIndex * 0.1);
}
calculateStewardSatisfaction(feedbackData) {
// Convert feedback to satisfaction score
if (feedbackData.rating)
return Math.min(feedbackData.rating / 5, 1);
if (feedbackData.positive)
return 0.8;
if (feedbackData.negative)
return 0.2;
return 0.5; // Neutral
}
adjustMetricsBasedOnSatisfaction(metrics, feedbackData) {
const satisfactionWeight = 0.2;
metrics.stewardSatisfaction = this.calculateStewardSatisfaction(feedbackData);
// Adjust other metrics based on satisfaction
if (metrics.stewardSatisfaction > 0.8) {
metrics.sparkAmplification += 0.1;
metrics.magicMomentScore += 0.05;
}
}
adjustMetricsForServiceGap(metrics, gapData) {
// Reduce relevant component metrics based on gap type
const impact = gapData.severity * 0.1;
switch (gapData.gapType) {
case 'context_missing':
metrics.contextualExcellence.score = Math.max(0, metrics.contextualExcellence.score - impact);
break;
case 'emotional_mismatch':
metrics.emotionalResonance.score = Math.max(0, metrics.emotionalResonance.score - impact);
break;
case 'anticipation_failed':
metrics.anticipatoryIntelligence.score = Math.max(0, metrics.anticipatoryIntelligence.score - impact);
break;
case 'workflow_friction':
metrics.workflowHarmony.score = Math.max(0, metrics.workflowHarmony.score - impact);
break;
}
// Recalculate overall value
metrics.overallServiceValue = this.calculateOverallServiceValue(metrics);
}
calculateWorkflowHarmony(workflowData) {
// Simplified workflow harmony calculation
const efficiencyScore = workflowData.efficiency || Math.random() * 0.3 + 0.7;
const smoothnessScore = workflowData.smoothness || Math.random() * 0.4 + 0.6;
return {
score: (efficiencyScore + smoothnessScore) / 2,
confidence: 0.7,
evidencePoints: ['Workflow efficiency', 'Process smoothness'],
improvementOpportunities: efficiencyScore < 0.8 ? ['Optimize workflow steps'] : []
};
}
calculateTranscendenceIndex(metrics) {
// Transcendence emerges from exceptional performance across all dimensions
const excellence = metrics.overallServiceValue;
const magic = metrics.magicMomentScore;
const spark = metrics.sparkAmplification;
// Transcendence requires high scores in all areas
return Math.min(excellence * magic * spark * 2, 1.0);
}
async updateSparkAmplification(metrics) {
// Spark amplification increases with sustained excellence
if (metrics.overallServiceValue > 0.8) {
metrics.sparkAmplification += 0.01;
}
// Magic moments provide significant spark amplification
metrics.sparkAmplification = Math.min(metrics.sparkAmplification, 2.0);
}
applyTimeDecay(metrics) {
// Slight decay to emphasize recent performance
const decayFactor = 0.999;
metrics.overallServiceValue *= decayFactor;
metrics.magicMomentScore *= decayFactor;
}
isSessionComplete(sessionId) {
// Simplified check - could be enhanced with actual session tracking
const metrics = this.realTimeMetrics.get(sessionId);
if (!metrics)
return false;
const age = Date.now() - metrics.timestamp.getTime();
return age > 1800000; // 30 minutes of inactivity
}
calculateAggregateMetrics() {
const recent = this.metricsHistory.slice(-20);
if (recent.length === 0)
return {};
return {
averageServiceValue: recent.reduce((sum, m) => sum + m.overallServiceValue, 0) / recent.length,
totalSparkAmplification: recent.reduce((sum, m) => sum + m.sparkAmplification, 0),
averageSatisfaction: recent.reduce((sum, m) => sum + m.stewardSatisfaction, 0) / recent.length,
magicMomentFrequency: recent.reduce((sum, m) => sum + m.magicMomentScore, 0) / recent.length
};
}
// Contemplation analysis methods (simplified implementations)
async analyzeServiceValueTrends(metrics) {
if (metrics.length < 2)
return {};
const values = metrics.map(m => m.overallServiceValue);
const trend = values[values.length - 1] - values[0];
return {
trend: trend > 0 ? 'improving' : trend < 0 ? 'declining' : 'stable',
averageValue: values.reduce((sum, v) => sum + v, 0) / values.length,
volatility: this.calculateVolatility(values)
};
}
async analyzeMagicMomentPatterns(moments) {
if (moments.length === 0)
return {};
const typeCount = new Map();
moments.forEach(m => typeCount.set(m.type, (typeCount.get(m.type) || 0) + 1));
return {
totalMoments: moments.length,
averageIntensity: moments.reduce((sum, m) => sum + m.intensity, 0) / moments.length,
mostCommonType: Array.from(typeCount.entries()).sort((a, b) => b[1] - a[1])[0]?.[0],
patterns: Array.from(typeCount.entries())
};
}
async generateExcellenceInsights(metrics) {
const insights = [];
if (metrics.length > 0) {
const avgValue = metrics.reduce((sum, m) => sum + m.overallServiceValue, 0) / metrics.length;
if (avgValue > 0.8)
insights.push('Consistently high service excellence achieved');
if (avgValue < 0.6)
insights.push('Service excellence below optimal - focus on improvement');
}
return insights;
}
async identifyTranscendencePathways(moments) {
const pathways = [];
if (moments.length > 0) {
const avgIntensity = moments.reduce((sum, m) => sum + m.intensity, 0) / moments.length;
if (avgIntensity > 0.8)
pathways.push('High-intensity magic moments indicate transcendence pathway');
}
return pathways;
}
calculateVolatility(values) {
if (values.length < 2)
return 0;
const mean = values.reduce((sum, v) => sum + v, 0) / values.length;
const variance = values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length;
return Math.sqrt(variance);
}
// Additional implementation methods would go here...
calculatePeriodOverallMetrics(metrics) {
// Simplified implementation
return metrics[metrics.length - 1] || this.createInitialMetrics('period');
}
calculateMagicMomentsSummary(moments) {
return {
totalMoments: moments.length,
averageIntensity: moments.reduce((sum, m) => sum + m.intensity, 0) / moments.length || 0,
mostFrequentType: 'connection', // Simplified
totalSparkAmplification: moments.reduce((sum, m) => sum + m.sparkAmplification, 0),
momentsByType: new Map(),
longestDuration: Math.max(...moments.map(m => m.duration), 0),
patternInsights: []
};
}
async generateImprovementRecommendations(metrics) {
return []; // Simplified implementation
}
async identifyTranscendenceOpportunities(moments) {
return []; // Simplified implementation
}
async analyzeSparkAmplificationTrends(metrics) {
return []; // Simplified implementation
}
async checkForMagicMoment(messageData, metrics) {
// Simplified magic moment check
const magicScore = this.calculateMagicScore(metrics);
if (magicScore > 0.8) {
const magicMoment = await this.magicDetectionEngine.createMagicMoment(messageData.sessionId, magicScore, metrics);
if (magicMoment) {
await this.recordMagicMoment(magicMoment);
}
}
}
async validatePotentialMagicMoment(feedbackData, metrics) {
// Simplified validation
if (metrics.stewardSatisfaction > 0.8 && metrics.overallServiceValue > 0.7) {
const magicMoment = await this.magicDetectionEngine.createMagicMoment(feedbackData.sessionId, 0.8, metrics);
if (magicMoment) {
await this.recordMagicMoment(magicMoment);
}
}
}
async recordImprovementOpportunity(gapData, metrics) {
// Simplified recording
console.log(chalk.yellow(`🔧 Improvement opportunity: ${gapData.description}`));
this.emit('improvement_opportunity_identified', { gap: gapData, metrics });
}
async validateMagicMoment(candidateData) {
// Simplified validation
const isValid = candidateData.intensity > 0.6 && candidateData.confidence > 0.7;
if (isValid) {
await this.recordMagicMoment(candidateData);
}
}
async analyzeStewardInteraction(interactionData) {
// Simplified analysis
const satisfaction = this.calculateStewardSatisfaction(interactionData);
if (satisfaction > 0.8) {
console.log(chalk.green(`😊 High steward satisfaction detected: ${satisfaction.toFixed(2)}`));
}
}
}
/**
* Magic Detection Engine - Specialized system for detecting magic moments
*/
class MagicDetectionEngine {
async analyzePotentialMagic(messageData, analysisData) {
// Simplified magic analysis
const magicScore = this.calculateMagicPotential(messageData, analysisData);
if (magicScore > 0.7) {
console.log(chalk.cyan(`✨ Potential magic detected: ${magicScore.toFixed(2)}`));
}
}
async createMagicMoment(sessionId, intensity, metrics) {
if (intensity < 0.6)
return null;
return {
timestamp: new Date(),
sessionId,
momentId: `magic-${Date.now()}-${Math.random().toString(36).substr(2, 6)}`,
type: 'connection', // Simplified
intensity,
duration: 30000, // 30 seconds
context: {
conversationState: 'active',
stewardEmotionalState: 'positive',
workType: 'creative',
projectContext: 'development',
previousMoments: [],
buildupFactors: ['high service value', 'excellent context']
},
sparkAmplification: intensity * 0.5,
recognitionConfidence: 0.8,
triggerPattern: 'High service excellence with emotional resonance'
};
}
async predictMagicOpportunities(realTimeMetrics, history) {
// Simplified prediction
const opportunities = [];
for (const [sessionId, metrics] of realTimeMetrics) {
if (metrics.overallServiceValue > 0.7 && metrics.sparkAmplification > 0.3) {
opportunities.push({
sessionId,
description: 'High potential for magic moment based on current metrics',
probability: 0.8,
recommendedActions: ['Maintain service excellence', 'Amplify emotional connection']
});
}
}
return opportunities;
}
calculateMagicPotential(messageData, analysisData) {
// Simplified calculation
return Math.random() * 0.5 + 0.3; // 0.3-0.8 range
}
}
/**
* Value Calculation Engine - Specialized system for calculating service value
*/
class ValueCalculationEngine {
async calculateServiceValue(messageData, analysisData) {
// Simplified calculation
const sessionId = messageData.sessionId || 'unknown';
return {
timestamp: new Date(),
sessionId,
overallServiceValue: Math.random() * 0.3 + 0.7, // 0.7-1.0
sparkAmplification: Math.random() * 0.2,
stewardSatisfaction: Math.random() * 0.3 + 0.7,
magicMomentScore: Math.random() * 0.1,
transcendenceIndex: Math.random() * 0.2,
contextualExcellence: {
score: Math.random() * 0.3 + 0.7,
confidence: 0.8,
evidencePoints: ['Context analysis'],
improvementOpportunities: []
},
emotionalResonance: {
score: Math.random() * 0.3 + 0.7,
confidence: 0.7,
evidencePoints: ['Emotional analysis'],
improvementOpportunities: []
},
anticipatoryIntelligence: {
score: Math.random() * 0.4 + 0.6,
confidence: 0.6,
evidencePoints: ['Anticipation analysis'],
improvementOpportunities: []
},
workflowHarmony: {
score: Math.random() * 0.3 + 0.7,
confidence: 0.7,
evidencePoints: ['Workflow analysis'],
improvementOpportunities: []
},
creativeSynergy: {
score: Math.random() * 0.4 + 0.6,
confidence: 0.6,
evidencePoints: ['Creative analysis'],
improvementOpportunities: []
}
};
}
}
export default ServiceValueMetricsService;
//# sourceMappingURL=ServiceValueMetricsService.js.map