Autonomous Meeting Bots with Real-Time Processing

class HybridMeetingAgent {
  private realTimeProcessor: RealTimeProcessor;
  private postMeetingAnalyzer: PostMeetingAnalyzer;
  private contextManager: MeetingContextManager;

  async processMeeting(meetingId: string): Promise<MeetingResults> {
    // Real-time processing
    const realTimeResults = await this.realTimeProcessor.startProcessing(
      meetingId,
      {
        onTranscript: (transcript) =>
          this.contextManager.updateLiveContext(transcript),
        onSpeakerChange: (speaker) =>
          this.contextManager.updateSpeakerContext(speaker),
        onInsight: (insight) => this.contextManager.addLiveInsight(insight),
      }
    );

    // Post-meeting analysis
    const postMeetingResults = await this.postMeetingAnalyzer.analyze(
      meetingId,
      {
        fullTranscript: realTimeResults.transcript,
        speakerSegments: realTimeResults.speakerSegments,
        liveInsights: realTimeResults.insights,
      }
    );

    return {
      liveNotes: realTimeResults.notes,
      actionItems: postMeetingResults.actionItems,
      summary: postMeetingResults.summary,
      decisions: postMeetingResults.decisions,
      followUps: postMeetingResults.followUps,
    };
  }
}

class EdgeMeetingAgent {
  private localASR: LocalASRProcessor;
  private localLLM: LocalLLMProcessor;
  private contextCache: LocalContextCache;

  async processMeetingLocally(
    audioStream: MediaStream
  ): Promise<LocalMeetingResults> {
    // Process entirely on-device
    const transcript = await this.localASR.processStream(audioStream);
    const speakerSegments = await this.localASR.diarizeSpeakers(transcript);

    // Use local LLM for insights
    const insights = await this.localLLM.generateInsights({
      transcript: transcript,
      speakers: speakerSegments,
      context: this.contextCache.getRelevantContext(transcript),
    });

    // Store locally with encryption
    await this.contextCache.storeMeeting({
      transcript,
      insights,
      timestamp: Date.now(),
      encrypted: true,
    });

    return {
      transcript,
      insights,
      actionItems: insights.actionItems,
      summary: insights.summary,
    };
  }
}

class MeetingContextManager {
  private conversationBuffer: ConversationBuffer;
  private speakerProfiles: Map<string, SpeakerProfile>;
  private topicTracker: TopicTracker;
  private decisionTracker: DecisionTracker;

  async updateLiveContext(
    transcriptSegment: TranscriptSegment
  ): Promise<ContextUpdate> {
    // Update conversation buffer with sliding window
    this.conversationBuffer.addSegment(transcriptSegment);

    // Update speaker context
    if (transcriptSegment.speakerId) {
      await this.updateSpeakerContext(
        transcriptSegment.speakerId,
        transcriptSegment
      );
    }

    // Track topic changes
    const topicChange = await this.topicTracker.detectTopicChange(
      transcriptSegment
    );
    if (topicChange) {
      await this.handleTopicChange(topicChange);
    }

    // Extract decisions and action items
    const decisions = await this.decisionTracker.extractDecisions(
      transcriptSegment
    );
    const actionItems = await this.extractActionItems(transcriptSegment);

    return {
      currentContext: this.buildCurrentContext(),
      newDecisions: decisions,
      newActionItems: actionItems,
      topicChange: topicChange,
    };
  }

  private buildCurrentContext(): MeetingContext {
    return {
      recentConversation: this.conversationBuffer.getRecentWindow(5), // Last 5 minutes
      activeSpeakers: this.getActiveSpeakers(),
      currentTopic: this.topicTracker.getCurrentTopic(),
      pendingDecisions: this.decisionTracker.getPendingDecisions(),
      openActionItems: this.getOpenActionItems(),
      meetingMetadata: this.getMeetingMetadata(),
    };
  }
}

class TeamsMeetingBot {
  private mediaSession: MediaSession;
  private realTimeMedia: RealTimeMedia;
  private graphClient: GraphServiceClient;

  async joinMeeting(meetingId: string): Promise<void> {
    // Register bot with Teams
    const botRegistration = await this.graphClient.communications.calls
      .byCallId(meetingId)
      .participants.invite({
        participants: [
          {
            identity: {
              user: {
                id: this.botUserId,
              },
            },
            mediaStreams: [
              {
                mediaType: "audio",
                sourceId: "1",
              },
            ],
          },
        ],
      });

    // Set up real-time media session
    this.mediaSession = new MediaSession({
      mediaSessionId: botRegistration.id,
      mediaConfiguration: {
        audio: {
          sourceId: "1",
          direction: "receiveOnly",
        },
      },
    });

    // Start processing audio frames
    this.mediaSession.on("audioFrame", (frame) => {
      this.processAudioFrame(frame);
    });
  }

  private async processAudioFrame(frame: AudioFrame): Promise<void> {
    // Convert Teams audio frame to standard format
    const audioData = this.convertAudioFrame(frame);

    // Send to ASR pipeline
    await this.asrProcessor.processFrame(audioData);
  }
}

class ZoomMeetingBot {
  private zoomSDK: ZoomSDK;
  private audioProcessor: AudioProcessor;

  async joinMeeting(meetingNumber: string, password?: string): Promise<void> {
    // Initialize Zoom SDK
    await this.zoomSDK.initialize({
      appKey: process.env.ZOOM_APP_KEY,
      appSecret: process.env.ZOOM_APP_SECRET,
    });

    // Join meeting
    const joinResult = await this.zoomSDK.joinMeeting({
      meetingNumber,
      password,
      userName: "AI Meeting Assistant",
      userEmail: "assistant@company.com",
    });

    // Set up audio processing
    this.zoomSDK.on("audioRawDataReceived", (audioData) => {
      this.audioProcessor.processRawAudio(audioData);
    });

    // Enable audio processing
    await this.zoomSDK.enableAudioProcessing(true);
  }

  private async processRawAudio(audioData: RawAudioData): Promise<void> {
    // Convert Zoom audio format to standard format
    const processedAudio = await this.audioProcessor.convertFormat(audioData);

    // Send to ASR pipeline
    await this.asrPipeline.processAudio(processedAudio);
  }
}

class RealTimeSpeakerDiarization {
  private voiceProfiles: Map<string, VoiceProfile>;
  private diarizationEngine: DiarizationEngine;
  private confidenceThreshold: number = 0.8;

  async processAudioSegment(
    audioSegment: AudioSegment
  ): Promise<SpeakerSegment[]> {
    // Extract voice features
    const voiceFeatures = await this.extractVoiceFeatures(audioSegment);

    // Match against known speakers
    const speakerMatches = await this.matchSpeakers(voiceFeatures);

    // Create speaker segments
    const speakerSegments: SpeakerSegment[] = [];

    for (const match of speakerMatches) {
      if (match.confidence > this.confidenceThreshold) {
        speakerSegments.push({
          speakerId: match.speakerId,
          startTime: audioSegment.startTime,
          endTime: audioSegment.endTime,
          confidence: match.confidence,
          transcript: audioSegment.transcript,
        });
      }
    }

    // Handle unknown speakers
    const unknownSegments = await this.handleUnknownSpeakers(
      audioSegment,
      speakerMatches
    );
    speakerSegments.push(...unknownSegments);

    return speakerSegments;
  }

  private async matchSpeakers(
    voiceFeatures: VoiceFeatures
  ): Promise<SpeakerMatch[]> {
    const matches: SpeakerMatch[] = [];

    for (const [speakerId, profile] of this.voiceProfiles) {
      const similarity = await this.calculateSimilarity(
        voiceFeatures,
        profile.features
      );

      if (similarity > this.confidenceThreshold) {
        matches.push({
          speakerId,
          confidence: similarity,
          features: voiceFeatures,
        });
      }
    }

    return matches.sort((a, b) => b.confidence - a.confidence);
  }
}

class LiveInsightGenerator {
  private llmClient: LLMClient;
  private contextManager: ContextManager;
  private insightCache: InsightCache;

  async generateInsights(
    conversationContext: ConversationContext
  ): Promise<LiveInsight[]> {
    // Build context for LLM
    const llmContext = await this.buildLLMContext(conversationContext);

    // Generate insights using real-time LLM
    const insights = await this.llmClient.generateInsights({
      context: llmContext,
      conversationHistory: conversationContext.recentHistory,
      currentTopic: conversationContext.currentTopic,
      speakers: conversationContext.activeSpeakers,
    });

    // Process and validate insights
    const validatedInsights = await this.validateInsights(insights);

    // Cache insights for future reference
    await this.insightCache.storeInsights(validatedInsights);

    return validatedInsights;
  }

  private async buildLLMContext(
    context: ConversationContext
  ): Promise<LLMContext> {
    return {
      systemPrompt: this.buildSystemPrompt(context),
      conversationSummary: await this.summarizeRecentConversation(context),
      keyDecisions: this.extractKeyDecisions(context),
      actionItems: this.extractActionItems(context),
      speakerRoles: this.identifySpeakerRoles(context),
      meetingType: this.classifyMeetingType(context),
    };
  }

  private buildSystemPrompt(context: ConversationContext): string {
    return `You are an AI meeting assistant analyzing a live conversation. 
    
    Meeting Type: ${context.meetingType}
    Participants: ${context.activeSpeakers.map((s) => s.name).join(", ")}
    Current Topic: ${context.currentTopic}
    
    Generate insights about:
    1. Key decisions being made
    2. Action items and responsibilities
    3. Important information shared
    4. Follow-up requirements
    5. Potential risks or concerns
    
    Be concise and actionable. Focus on what matters most for meeting outcomes.`;
  }
}

class MeetingAgentOptimizer {
  private latencyMonitor: LatencyMonitor;
  private adaptiveProcessor: AdaptiveProcessor;
  private cacheManager: CacheManager;

  async optimizeProcessing(meetingId: string): Promise<OptimizationResult> {
    // Monitor current performance
    const currentMetrics = await this.latencyMonitor.getMetrics(meetingId);

    // Apply adaptive optimizations
    const optimizations = await this.adaptiveProcessor.optimize({
      asrLatency: currentMetrics.asrLatency,
      diarizationLatency: currentMetrics.diarizationLatency,
      llmLatency: currentMetrics.llmLatency,
      contextLatency: currentMetrics.contextLatency,
    });

    // Implement optimizations
    await this.implementOptimizations(optimizations);

    return {
      optimizationsApplied: optimizations,
      expectedImprovement: this.calculateImprovement(optimizations),
      newLatencyTarget: this.calculateNewTarget(currentMetrics, optimizations),
    };
  }

  private async implementOptimizations(
    optimizations: Optimization[]
  ): Promise<void> {
    for (const optimization of optimizations) {
      switch (optimization.type) {
        case "asr_optimization":
          await this.optimizeASR(optimization.config);
          break;
        case "context_compression":
          await this.optimizeContextCompression(optimization.config);
          break;
        case "llm_optimization":
          await this.optimizeLLM(optimization.config);
          break;
        case "caching_strategy":
          await this.optimizeCaching(optimization.config);
          break;
      }
    }
  }
}

class MeetingAgentSecurityManager {
  private encryptionService: EncryptionService;
  private accessControl: AccessControl;
  private auditLogger: AuditLogger;
  private complianceChecker: ComplianceChecker;

  async processSecureMeeting(
    meetingId: string,
    audioStream: MediaStream
  ): Promise<SecureMeetingResult> {
    // Verify meeting permissions
    const permissions = await this.accessControl.verifyMeetingAccess(meetingId);
    if (!permissions.canRecord) {
      throw new Error("Recording not permitted for this meeting");
    }

    // Encrypt audio stream
    const encryptedStream = await this.encryptionService.encryptStream(
      audioStream
    );

    // Process with audit trail
    const result = await this.processWithAudit(encryptedStream, {
      meetingId,
      userId: permissions.userId,
      timestamp: Date.now(),
      complianceLevel: permissions.complianceLevel,
    });

    // Log for compliance
    await this.auditLogger.logMeetingProcessing({
      meetingId,
      userId: permissions.userId,
      processingType: "real_time_analysis",
      dataClassification: permissions.dataClassification,
      retentionPeriod: permissions.retentionPeriod,
    });

    return result;
  }

  private async processWithAudit(
    stream: EncryptedStream,
    metadata: ProcessingMetadata
  ): Promise<SecureMeetingResult> {
    // Check compliance requirements
    const complianceCheck = await this.complianceChecker.checkRequirements(
      metadata
    );
    if (!complianceCheck.compliant) {
      throw new Error(
        `Compliance check failed: ${complianceCheck.reasons.join(", ")}`
      );
    }

    // Process with appropriate security level
    const processingConfig = this.getProcessingConfig(metadata.complianceLevel);

    // Execute processing
    const result = await this.executeSecureProcessing(stream, processingConfig);

    // Apply data retention policies
    await this.applyRetentionPolicies(result, metadata);

    return result;
  }
}

class MeetingAgentMetrics {
  private metricsCollector: MetricsCollector;
  private alertManager: AlertManager;

  async collectRealTimeMetrics(meetingId: string): Promise<MeetingMetrics> {
    const metrics = await this.metricsCollector.collect({
      meetingId,
      timestamp: Date.now(),
      metrics: [
        "asr_accuracy",
        "diarization_accuracy",
        "latency_p95",
        "context_relevance",
        "insight_quality",
        "user_satisfaction",
      ],
    });

    // Check for performance issues
    await this.checkPerformanceThresholds(metrics);

    return metrics;
  }

  private async checkPerformanceThresholds(
    metrics: MeetingMetrics
  ): Promise<void> {
    const thresholds = {
      asr_accuracy: 0.95,
      diarization_accuracy: 0.9,
      latency_p95: 1500, // 1.5 seconds
      context_relevance: 0.85,
      insight_quality: 0.8,
    };

    for (const [metric, threshold] of Object.entries(thresholds)) {
      if (metrics[metric] < threshold) {
        await this.alertManager.sendAlert({
          type: "performance_degradation",
          metric,
          currentValue: metrics[metric],
          threshold,
          meetingId: metrics.meetingId,
        });
      }
    }
  }
}