Use Case: Background AI Image Processing

Overview

This use case describes how the Plings system can leverage AI to analyze uploaded images during object creation to provide intelligent suggestions for object properties, names, classifications, and detect potential duplicates - all while maintaining the non-blocking user experience.

User Story

As a user, I want the system to analyze my uploaded images in the background and suggest object properties, names, and classifications so that I can create objects more efficiently with better metadata while I continue filling out the form.

Business Value

  • Reduced User Effort: Auto-suggest names, descriptions, and properties
  • Improved Data Quality: More accurate and consistent object metadata
  • Duplicate Prevention: Detect potentially identical objects before creation
  • Enhanced Classification: Automatic object categorization and property suggestions
  • Similar Object Discovery: Find related objects for functional relationships

Current Architecture Integration

Existing Foundation

The current image upload system provides perfect integration points:

  • ✅ Non-blocking uploads: Images uploaded to Supabase Storage in background
  • ✅ Progress tracking: User can see upload completion
  • ✅ State management: ImageUploadState tracks upload status
  • ✅ Direct storage access: Images immediately available via public URL

AI Processing Integration Points

  1. Trigger: After successful Supabase upload (when uploadStatus: 'completed')
  2. Processing Window: While user fills name, description, organization fields
  3. Results Display: Suggestions appear as user completes form sections
  4. User Control: All suggestions are optional with clear override options

Workflow Steps

Step 1: User Uploads Image

  • Current Flow: User captures/selects image → immediate thumbnail → background upload
  • AI Enhancement: No change to user experience

Step 2: Background AI Processing Begins

Trigger: When uploadStatus changes to 'completed' for any image Process:

  1. Extract Image URL from upload result
  2. Queue AI Analysis (non-blocking)
  3. Update UI State to show “Analyzing…” indicator
  4. Begin Parallel Processing:
    • Object classification
    • Brand/model detection
    • Similar object search
    • Property extraction
    • Name suggestions

Step 3: Progressive Results Display

As AI analysis completes, suggestions appear in form:

Object Classification:

🤖 AI Suggestion: "Lamp" 
   [Accept] [Different Type] [Ignore]

Name Suggestions:

🤖 Suggested Names:
   • "IKEA Table Lamp CuUY" [Use This]
   • "Black Desk Lamp CuUY" [Use This]  
   • "Modern Lamp CuUY" [Use This]
   [Custom Name...]

Property Suggestions:

🤖 Detected Properties:
   Color: Black ✓     Material: Metal ✓
   Brand: IKEA ✓      Height: ~30cm ✓
   [Edit Properties...]

Similar Objects Alert:

⚠️ Similar Object Found
   "IKEA Lamp in Kitchen" (85% match)
   [View Object] [Create Anyway] [This is Different]

Step 4: User Interaction with Suggestions

  • Accept Suggestions: One-click to apply AI recommendations
  • Modify Suggestions: Edit AI suggestions before applying
  • Ignore Suggestions: Continue with manual entry
  • Override Completely: Disable AI suggestions for this object

Step 5: Object Creation with AI Metadata

  • Enhanced Metadata: Object created with AI-suggested properties
  • Confidence Scores: Track AI suggestion confidence for future improvement
  • User Decisions: Record which suggestions were accepted/rejected
  • Fallback: If AI fails, proceed with normal creation flow

AI Processing Capabilities

1. Object Classification

Input: Image URL from Supabase Storage Processing:

  • Visual object recognition
  • Category classification (furniture, tools, electronics, etc.)
  • Specific type identification (lamp, screwdriver, laptop, etc.) Output: 
    {
category: "Furniture",
type: "Lamp", 
subtype: "Desk Lamp",
confidence: 0.89
}

2. Brand and Model Detection

Input: Image with OCR capabilities Processing:

  • Text extraction from visible labels/logos
  • Brand logo recognition
  • Model number detection Output: 
    {
brand: "IKEA",
model: "FORSÅ", 
detectedText: ["IKEA", "FORSÅ", "Made in China"],
confidence: 0.76
}

3. Visual Property Extraction

Input: Image analysis Processing:

  • Color detection (dominant colors)
  • Material recognition (metal, plastic, wood, etc.)
  • Size estimation (relative to common objects)
  • Condition assessment Output: 
    {
colors: ["Black", "Silver"],
materials: ["Metal", "Plastic"],
estimatedSize: "Small",
condition: "Good",
confidence: 0.82
}

4. Similar Object Detection

Input: Image features + existing object database Processing:

  • Visual similarity matching
  • Feature comparison with existing objects
  • Duplicate detection algorithm Output: 
    {
similarObjects: [
  {
    objectId: "obj_123",
    name: "IKEA Lamp in Kitchen", 
    similarity: 0.85,
    matchReason: "Visual similarity + brand match"
  }
],
isDuplicateRisk: true,
confidence: 0.85
}

5. Smart Name Generation

Input: Classification + brand + properties + user patterns Processing:

  • Combine AI analysis with user naming patterns
  • Apply organization-specific naming conventions
  • Include tag information if available Output: 
    {
suggestions: [
  "IKEA Table Lamp CuUY",
  "Black Desk Lamp CuUY", 
  "Paul's IKEA Lamp CuUY"
],
reasoning: "Based on brand detection + user's naming pattern",
confidence: 0.78
}

Technical Implementation Strategy

Frontend Enhancement

Extend existing ImageUploadState:

interface AIEnhancedImageUploadState {
  localImages: {
    id: string;
    file: File | Blob;
    thumbnail: string;
    uploadStatus: 'pending' | 'uploading' | 'completed' | 'failed';
    uploadProgress: number;
    cloudUrl?: string;
    
    // AI Processing Extensions
    aiAnalysisStatus: 'pending' | 'analyzing' | 'completed' | 'failed';
    aiResults?: {
      classification: ObjectClassification;
      brand: BrandDetection;
      properties: VisualProperties;
      similarObjects: SimilarObject[];
      nameSuggestions: NameSuggestion[];
    };
    aiConfidence: number;
  }[];
}

Backend AI Service Integration

New AI Processing Pipeline:

  1. Image Analysis Service (OpenAI Vision, Google Vision, AWS Rekognition)
  2. Similar Object Search (Vector similarity in database)
  3. Brand Detection (OCR + logo recognition)
  4. Property Extraction (Visual analysis + ML models)
  5. Name Generation (LLM with context)

API Extensions

New GraphQL Operations:

# Trigger AI analysis for uploaded image
mutation analyzeObjectImage($imageUrl: String!, $userId: ID!) {
  analyzeObjectImage(imageUrl: $imageUrl, userId: $userId) {
    analysisId: ID!
    status: String!
    estimatedCompletionTime: Int
  }
}

# Get AI analysis results
query getAIAnalysis($analysisId: ID!) {
  aiAnalysis(id: $analysisId) {
    status: String!
    classification: ObjectClassification
    properties: VisualProperties
    similarObjects: [SimilarObject!]!
    nameSuggestions: [String!]!
    confidence: Float!
  }
}

# Subscription for real-time AI results
subscription aiAnalysisUpdates($analysisId: ID!) {
  aiAnalysisProgress(analysisId: $analysisId) {
    status: String!
    progress: Float!
    partialResults: AIAnalysisResult
  }
}

User Experience Design

AI Suggestions UI Components

1. Analysis Progress Indicator

🔍 Analyzing image... [▓▓▓░░░░] 43%
   Detecting object type...

2. Classification Suggestion

┌─────────────────────────────────────┐
│ 🤖 AI Classification               │
│                                     │
│ We think this is a Desk Lamp        │
│ Confidence: 89%                     │
│                                     │
│ [✓ Accept] [✏️ Edit] [✗ Ignore]    │
└─────────────────────────────────────┘

3. Name Suggestions Panel

┌─────────────────────────────────────┐
│ 💡 Suggested Names                  │
│                                     │
│ ○ "IKEA Table Lamp CuUY"           │
│ ○ "Black Desk Lamp CuUY"           │
│ ○ "Modern Lamp CuUY"               │
│                                     │
│ [Custom Name...] [Use Suggestion]   │
└─────────────────────────────────────┘

4. Property Suggestions

┌─────────────────────────────────────┐
│ 🔧 Detected Properties             │
│                                     │
│ Brand:    IKEA        [✓]          │
│ Color:    Black       [✓]          │
│ Material: Metal       [✓]          │
│ Height:   ~30cm       [✓]          │
│                                     │
│ [Add Property] [Edit All]          │
└─────────────────────────────────────┘

5. Similar Objects Warning

┌─────────────────────────────────────┐
│ ⚠️ Possible Duplicate Detected      │
│                                     │
│ Found similar object:               │
│ "IKEA Lamp in Kitchen" (85% match) │
│                                     │
│ [📷 View Object] [Create Anyway]   │
│ [This is Different]                │
└─────────────────────────────────────┘

Progressive Enhancement

  • Basic Mode: AI disabled, normal object creation
  • Suggestion Mode: AI provides suggestions, user chooses
  • Auto Mode: AI suggestions auto-applied with user confirmation
  • Learning Mode: System learns from user preferences

Privacy and Security Considerations

Image Processing Privacy

  • Local Processing Option: On-device AI for sensitive images
  • Secure Transmission: Images sent to AI services over HTTPS
  • Data Retention: AI services configured for zero data retention
  • User Control: Opt-out of AI processing at user/organization level

AI Service Security

  • API Key Management: Secure storage of AI service credentials
  • Rate Limiting: Prevent abuse of AI services
  • Error Handling: Graceful fallback when AI services unavailable
  • Cost Management: Usage monitoring and limits

Performance Optimization

AI Processing Efficiency

  • Batch Processing: Analyze multiple images together
  • Caching: Cache AI results for identical images
  • Progressive Loading: Start with fast analysis, enhance with detailed
  • Fallback Strategy: Continue without AI if processing takes too long

Network Optimization

  • Image Optimization: Resize images for AI analysis
  • Compression: Compress images before sending to AI services
  • CDN Usage: Leverage Supabase CDN for AI service access
  • Parallel Processing: Simultaneous AI analysis of multiple images

Success Metrics

User Experience Metrics

  • Suggestion Acceptance Rate: % of AI suggestions accepted by users
  • Time to Creation: Reduction in object creation time
  • Metadata Quality: Completeness of object descriptions and properties
  • User Satisfaction: Feedback on AI suggestion usefulness

Technical Performance Metrics

  • Processing Time: AI analysis completion time
  • Accuracy Rate: Correctness of AI classifications and suggestions
  • Service Uptime: Availability of AI processing services
  • Cost Per Analysis: Economic efficiency of AI processing

Business Impact Metrics

  • Object Quality: Improved searchability and organization
  • Duplicate Reduction: Fewer duplicate objects created
  • User Engagement: Increased object creation and system usage
  • Data Richness: More complete object metadata

Future Enhancements

Advanced AI Capabilities

  • Multi-Image Analysis: Analyze object from multiple angles
  • Context Awareness: Consider spatial location for better suggestions
  • User Learning: Personalized suggestions based on user history
  • Collaborative Filtering: Suggest properties based on similar users

Integration Expansions

  • Voice Descriptions: Audio descriptions for accessibility
  • AR Overlay: Augmented reality property identification
  • Barcode Integration: Combine QR/barcode data with visual analysis
  • IoT Integration: Smart device data for enhanced properties

Current System References

Integration Points

  • Phase 3 AI Roadmap: Already planned in object creation requirements
  • Background Upload System: Perfect integration point for AI processing
  • Object Classification: Extends existing ObjectClass system
  • Similar Object Search: Leverages existing object database

Use Case Created: Mån 7 Jul 2025 10:55:32 CEST - Background AI Image Processing for intelligent object creation suggestions