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{"repo": "alokickstudios-coder/alokickflow", "files": {"docs/SPI_PATENT_DOCUMENTATION.md": {"85e1c6d1a57b6e5da462b05a33b77869d394067c": {"t": 1785746403.30077, "n": 43964, "h": "c52105af3bcc4520", "c": "# Semantic Provenance Intelligence (SPI)\n## Patent Application Technical Documentation\n\n**Invention Title:** Semantic Provenance Intelligence System for Multi-Dimensional Media Content Analysis and Quality Control\n\n**Inventors:** Alokick Studios \n**Filing Date:** December 2024 \n**Application Type:** Utility Patent\n\n---\n\n## 1. ABSTRACT\n\nA novel system and method for analyzing media content (video, audio, text) using multi-dimensional semantic fingerprinting that captures narrative architecture, emotional trajectory, visual grammar, auditory signatures, and temporal rhythm. The system generates a unified \"Semantic DNA\" that enables creative quality assessment, content provenance tracking, and intelligent content matching across heterogeneous media formats.\n\n---\n\n## 2. FIELD OF THE INVENTION\n\nThis invention relates to the field of artificial intelligence applied to media content analysis, specifically to a system that creates multi-dimensional semantic representations of creative content for quality control, rights management, and content intelligence purposes.\n\n---\n\n## 3. BACKGROUND OF THE INVENTION\n\n### 3.1 Prior Art Limitations\n\nExisting content analysis systems suffer from critical limitations:\n\n1. **Single-Modality Analysis**: Current systems analyze video OR audio OR text separately, missing cross-modal semantic relationships.\n\n2. **Surface-Level Features**: Existing fingerprinting (e.g., audio fingerprinting, perceptual hashing) captures technical signatures but not creative/semantic meaning.\n\n3. **Lack of Temporal Understanding**: Frame-by-frame analysis misses narrative arc, emotional progression, and structural patterns.\n\n4. **No Creative Quality Metrics**: Current QC systems focus on technical parameters (bitrate, resolution) but cannot assess storytelling quality, emotional impact, or brand alignment.\n\n5. **Binary Matching**: Existing systems only detect exact or near-exact copies, not semantic similarity or stylistic relationships.\n\n### 3.2 Problem Statement\n\nThere exists no system that can:\n- Create a unified semantic representation capturing both technical AND creative qualities\n- Track content provenance through creative transformations\n- Assess subjective creative quality objectively\n- Match content based on narrative/emotional similarity rather than pixel similarity\n\n---\n\n## 4. SUMMARY OF THE INVENTION\n\n### 4.1 Core Innovation\n\nSemantic Provenance Intelligence (SPI) introduces a **Multi-Dimensional Semantic Fingerprint** that captures:\n\n```\n┌─────────────────────────────────────────────────────────────────────┐\n│ SEMANTIC DNA FINGERPRINT │\n├─────────────────────────────────────────────────────────────────────┤\n│ │\n│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │\n│ │ NARRATIVE │ │ VISUAL │ │ AUDITORY │ │\n│ │ VECTOR │ │ GRAMMAR │ │ SIGNATURE │ │\n│ │ (128-dim) │ │ (128-dim) │ │ (128-dim) │ │\n│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │\n│ │ │ │ │\n│ └────────────┬────┴────────────────┘ │\n│ │ │\n│ ┌───────▼───────┐ │\n│ │ TEMPORAL │ │\n│ │ RHYTHM │ │\n│ │ (128-dim) │ │\n│ └───────┬───────┘ │\n│ │ │\n│ ┌───────▼───────┐ │\n│ │ EMOTIONAL │ │\n│ │ TRAJECTORY │ │\n│ │ (128-dim) │ │\n│ └───────┬───────┘ │\n│ │ │\n│ ┌───────▼───────┐ │\n│ │ SEMANTIC │ │\n│ │ DENSITY │ │\n│ │ (128-dim) │ │\n│ └───────┬───────┘ │\n│ │ │\n│ ┌────────────▼────────────┐ │\n│ │ COMBINED SDF │ │\n│ │ (768-dimensional │ │\n│ │ Semantic DNA) │ │\n│ └─────────────────────────┘ │\n│ │\n└─────────────────────────────────────────────────────────────────────┘\n```\n\n### 4.2 Key Differentiators\n\n| Feature | Prior Art | SPI Innovation |\n|---------|-----------|----------------|\n| Analysis Scope | Single modality | Multi-modal fusion |\n| Feature Type | Technical/perceptual | Semantic/creative |\n| Temporal Modeling | Frame-level | Narrative arc |\n| Quality Assessment | Technical only | Creative + Technical |\n| Matching | Exact/near-exact | Semantic similarity |\n| Provenance | Copy detection | Transformation tracking |\n\n---\n\n## 5. DETAILED DESCRIPTION OF THE INVENTION\n\n### 5.1 System Architecture\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ SPI SYSTEM ARCHITECTURE │\n└─────────────────────────────────────────────────────────────────────────────┘\n\n ┌─────────────────┐\n │ MEDIA INPUT │\n │ (Video/Audio) │\n └────────┬────────┘\n │\n ┌──────────────────┼──────────────────┐\n │ │ │\n ▼ ▼ ▼\n ┌───────────────┐ ┌───────────────┐ ┌───────────────┐\n │ VISUAL │ │ AUDIO │ │ TEMPORAL │\n │ ANALYZER │ │ ANALYZER │ │ ANALYZER │\n └───────┬───────┘ └───────┬───────┘ └───────┬───────┘\n │ │ │\n ▼ ▼ ▼\n ┌───────────────┐ ┌───────────────┐ ┌───────────────┐\n │ CLIP Encoder │ │Whisper + OpenL3│ │ TransNet V2 │\n │ + Composition │ │ + Prosody │ │ + Shot │\n │ Analysis │ │ Analysis │ │ Detection │\n └───────┬───────┘ └───────┬───────┘ └───────┬───────┘\n │ │ │\n └──────────────────┼──────────────────┘\n │\n ┌───────▼───────┐\n │ TRANSCRIPTION │\n │ (Groq Whisper)│\n └───────┬───────┘\n │\n ┌───────▼───────┐\n │ SEMANTIC │\n │ ANALYZER │\n │ (DeepSeek) │\n └───────┬───────┘\n │\n ┌───────────────────────┼───────────────────────┐\n │ │ │\n ▼ ▼ ▼\n ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n │ NARRATIVE │ │ EMOTIONAL │ │ CREATIVE │\n │ STRUCTURE │ │ TRAJECTORY │ │ QUALITY │\n │ EXTRACTION │ │ MAPPING │ │ SCORING │\n └────────┬────────┘ └────────┬────────┘ └────────┬────────┘\n │ │ │\n └──────────────────────┼──────────────────────┘\n │\n ┌───────▼───────┐\n │ SEMANTIC │\n │ FINGERPRINT │\n │ GENERATOR │\n └───────┬───────┘\n │\n ┌───────▼───────┐\n │ VECTOR │\n │ DATABASE │\n │ (pgvector) │\n └───────────────┘\n```\n\n### 5.2 Component Specifications\n\n#### 5.2.1 Narrative Vector Extraction\n\nThe Narrative Vector captures story structure through:\n\n1. **Three-Act Structure Detection**: Identifies setup, confrontation, resolution\n2. **Character Arc Mapping**: Tracks protagonist transformation\n3. **Conflict Progression**: Measures tension/release patterns\n4. **Theme Identification**: Extracts thematic elements\n\n```typescript\ninterface NarrativeVector {\n actStructure: number[]; // [setup_strength, confrontation_strength, resolution_strength]\n characterArcs: number[]; // Transformation intensity per character\n conflictProgression: number[]; // Tension curve sampled at N points\n thematicElements: number[]; // Theme embedding from content\n plotDensity: number; // Events per minute\n narrativeCoherence: number; // Logical consistency score\n}\n```\n\n#### 5.2.2 Visual Grammar Analysis\n\n```typescript\ninterface VisualGrammar {\n composition: {\n ruleOfThirds: number; // Adherence to compositional rules\n symmetryScore: number;\n leadingLines: number;\n depthOfField: number;\n };\n colorPalette: {\n dominantHues: number[]; // HSV distribution\n saturationProfile: number;\n luminanceRange: number;\n colorHarmony: number;\n };\n motionDynamics: {\n cameraMovement: number[]; // [pan, tilt, zoom, dolly, crane]\n subjectMotion: number;\n visualFlow: number;\n };\n stylistic: {\n lightingStyle: number[]; // [high-key, low-key, natural, dramatic]\n textureComplexity: number;\n visualNoise: number;\n };\n}\n```\n\n#### 5.2.3 Auditory Signature\n\n```typescript\ninterface AuditorySignature {\n speech: {\n prosody: number[]; // Pitch, rhythm, stress patterns\n emotionalTone: number[]; // Valence, arousal, dominance\n speakerDiversity: number;\n };\n music: {\n tempo: number;\n key: string;\n instrumentalComplexity: number;\n melodicContour: number[];\n };\n soundscape: {\n ambientProfile: number[];\n dynamicRange: number;\n frequencyBalance: number[]; // Bass, mid, treble distribution\n };\n}\n```\n\n#### 5.2.4 Temporal Rhythm\n\n```typescript\ninterface TemporalRhythm {\n pacing: {\n averageShotDuration: number;\n shotDurationVariance: number;\n cutRhythmPattern: number[];\n };\n structure: {\n sceneTransitionTypes: number[]; // [cut, dissolve, fade, wipe]\n actBreakTimestamps: number[];\n climaxTimestamp: number;\n };\n engagement: {\n attentionCurve: number[]; // Predicted viewer engagement over time\n hookStrength: number; // Opening engagement score\n retentionPrediction: number;\n };\n}\n```\n\n#### 5.2.5 Emotional Trajectory\n\n```typescript\ninterface EmotionalTrajectory {\n valence: number[]; // Positive/negative over time\n arousal: number[]; // High/low energy over time\n dominance: number[]; // Empowerment/submission over time\n emotionalPeaks: {\n timestamp: number;\n emotion: string;\n intensity: number;\n }[];\n overallEmotionalArc: string; // \"redemption\", \"tragedy\", \"comedy\", etc.\n catharticMoments: number[];\n}\n```\n\n### 5.3 Creative QC Parameter Set\n\nThe SPI system evaluates content across **33 creative parameters** in **7 categories**:\n\n```\n┌─────────────────────────────────────────────────────────────────────┐\n│ CREATIVE QC PARAMETER TAXONOMY │\n├─────────────────────────────────────────────────────────────────────┤\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 1. STORY & STRUCTURE (6 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Story Cohesion • Three-Act Adherence │ │\n│ │ • Conflict Clarity • Resolution Satisfaction │ │\n│ │ • Pacing Effectiveness • Narrative Momentum │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 2. CHARACTER & VOICE (5 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Character Distinctiveness • Dialogue Authenticity │ │\n│ │ • Voice Consistency • Character Arc Completion │ │\n│ │ • Relatability Score │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 3. EMOTION & ENGAGEMENT (5 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Emotional Authenticity • Tension Management │ │\n│ │ • Cathartic Payoff • Hook Strength │ │\n│ │ • Sustained Engagement │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 4. PLATFORM & AUDIENCE FIT (5 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Format Optimization • Audience Alignment │ │\n│ │ • Cultural Sensitivity • Trend Relevance │ │\n│ │ • Platform-Specific Best Practices │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 5. BRAND & INTENT ALIGNMENT (5 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Message Clarity • Brand Voice Consistency │ │\n│ │ • Call-to-Action Strength • Value Proposition Clarity │ │\n│ │ • Intent Fulfillment │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 6. RISK, SAFETY & COMPLIANCE (4 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Content Safety Score • Legal/Copyright Risk │ │\n│ │ • Misinformation Risk • Accessibility Compliance │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 7. PERCEIVED CRAFT QUALITY (3 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Production Value • Technical Polish │ │\n│ │ • Creative Originality │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n└─────────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## 6. NOVEL CLAIMS\n\n### Claim 1: Multi-Dimensional Semantic Fingerprint\n\nA method for generating a unified semantic fingerprint of media content comprising:\n- Extracting narrative structure vectors from transcribed content\n- Extracting visual grammar vectors from video frames\n- Extracting auditory signature vectors from audio tracks\n- Extracting temporal rhythm vectors from scene transitions\n- Extracting emotional trajectory vectors from multimodal analysis\n- Combining said vectors into a single 768-dimensional Semantic DNA fingerprint\n\n### Claim 2: Creative Quality Assessment System\n\nA system for objectively measuring subjective creative quality comprising:\n- A set of 33 creative quality parameters across 7 categories\n- AI-powered analysis using large language models to evaluate each parameter\n- Scoring each parameter on a 0-100 scale with explanatory rationale\n- Generating aggregate scores for overall creative quality, risk, and brand alignment\n\n### Claim 3: Semantic Content Matching\n\nA method for matching media content based on semantic similarity comprising:\n- Computing cosine similarity between Semantic DNA fingerprints\n- Detecting content that is semantically similar but technically different\n- Identifying content transformation chains (original → adaptation → derivative)\n- Tracking creative provenance across format conversions\n\n### Claim 4: Temporal Semantic Analysis\n\nA method for analyzing content meaning over time comprising:\n- Segmenting content into semantic chunks aligned with narrative structure\n- Generating per-segment embeddings capturing local semantic meaning\n- Tracking semantic evolution across the content timeline\n- Identifying semantic peaks, transitions, and patterns\n\n### Claim 5: Cross-Modal Semantic Fusion\n\nA method for combining semantic information from multiple modalities comprising:\n- Aligning visual, audio, and text streams temporally\n- Computing attention weights across modalities based on semantic salience\n- Generating a fused representation that captures cross-modal relationships\n- Resolving conflicts between modalities using learned arbitration\n\n---\n\n## 7. IMPLEMENTATION CODE\n\n### 7.1 Core SPI Engine\n\n```typescript\n// lib/services/spi/engine.ts\n\nimport { CREATIVE_QC_PARAMETERS, CreativeQCParameter } from \"@/config/creativeQcConfig\";\nimport { getTranscriptionProvider, getSpiProvider } from \"./providers\";\n\nexport interface SPIResult {\n status: \"completed\" | \"failed\" | \"partial\";\n overall_creative_score: number;\n overall_risk_score: number;\n overall_brand_fit_score: number;\n parameters: {\n [key: string]: {\n score: number;\n label: string;\n explanation: string;\n };\n };\n summary?: string;\n recommendations?: string[];\n semantic_fingerprint?: {\n narrative_vector: number[];\n visual_grammar: number[];\n auditory_signature: number[];\n temporal_rhythm: number[];\n emotional_trajectory: number[];\n combined_sdf: number[];\n };\n processing_time_ms: number;\n error?: string;\n}\n\nexport interface SPIContext {\n mediaUrl: string;\n mediaType: \"video\" | \"audio\";\n language?: string;\n platform?: string;\n genre?: string;\n intendedAudience?: string;\n brandGuidelines?: string;\n}\n\n/**\n * Run comprehensive Creative QC analysis using SPI\n */\nexport async function runCreativeQC(context: SPIContext): Promise<SPIResult> {\n const startTime = Date.now();\n \n try {\n // Step 1: Get transcription\n const transcriptionProvider = getTranscriptionProvider();\n const transcript = await transcriptionProvider.transcribeMedia(\n context.mediaUrl,\n context.language\n );\n \n // Step 2: Run SPI analysis with full parameter set\n const spiProvider = getSpiProvider();\n const analysisResult = await spiProvider.runCreativeQc(transcript, {\n platform: context.platform,\n language: context.language,\n genre: context.genre,\n intendedAudience: context.intendedAudience,\n creativeQcParameters: CREATIVE_QC_PARAMETERS,\n });\n \n return {\n status: \"completed\",\n overall_creative_score: analysisResult.overall_creative_score,\n overall_risk_score: analysisResult.overall_risk_score,\n overall_brand_fit_score: analysisResult.overall_brand_fit_score,\n parameters: analysisResult.parameters,\n summary: analysisResult.summary,\n recommendations: analysisResult.recommendations,\n processing_time_ms: Date.now() - startTime,\n };\n } catch (error) {\n return {\n status: \"failed\",\n overall_creative_score: 0,\n overall_risk_score: 0,\n overall_brand_fit_score: 0,\n parameters: {},\n processing_time_ms: Date.now() - startTime,\n error: error instanceof Error ? error.message : String(error),\n };\n }\n}\n```\n\n### 7.2 Transcription Provider (Groq Whisper)\n\n```typescript\n// lib/services/spi/providers/groqWhisper.ts\n\nimport { TranscriptionProvider, TranscriptionResult } from \"./types\";\n\nconst GROQ_API_KEY = process.env.GROQ_API_KEY;\nconst GROQ_API_BASE = process.env.GROQ_API_BASE || \"https://api.groq.com/openai/v1\";\nconst GROQ_WHISPER_MODEL = process.env.GROQ_WHISPER_MODEL || \"whisper-large-v3-turbo\";\n\nexport class GroqWhisperTranscriptionProvider implements TranscriptionProvider {\n \n async transcribeMedia(\n mediaUrl: string,\n language?: string\n ): Promise<TranscriptionResult> {\n if (!GROQ_API_KEY) {\n throw new Error(\"GROQ_API_KEY not configured\");\n }\n\n // Download media file\n const response = await fetch(mediaUrl);\n if (!response.ok) {\n throw new Error(`Failed to fetch media: ${response.status}`);\n }\n \n const fileBuffer = Buffer.from(await response.arrayBuffer());\n const mimeType = response.headers.get(\"content-type\") || \"audio/mpeg\";\n const fileName = mediaUrl.split(\"/\").pop() || \"media.mp4\";\n \n // Prepare form data\n const formData = new FormData();\n const blob = new Blob([new Uint8Array(fileBuffer)], { type: mimeType });\n formData.append(\"file\", blob, fileName);\n formData.append(\"model\", GROQ_WHISPER_MODEL);\n formData.append(\"response_format\", \"verbose_json\");\n \n if (language) {\n formData.append(\"language\", language);\n }\n\n // Call Groq API\n const apiResponse = await fetch(`${GROQ_API_BASE}/audio/transcriptions`, {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${GROQ_API_KEY}`,\n },\n body: formData,\n });\n\n if (!apiResponse.ok) {\n throw new Error(`Groq API error: ${apiResponse.status}`);\n }\n\n const result = await apiResponse.json();\n \n return {\n text: result.text,\n segments: result.segments?.map((seg: any) => ({\n start: seg.start,\n end: seg.end,\n text: seg.text,\n confidence: seg.confidence,\n })),\n language: result.language,\n duration: result.duration,\n };\n }\n}\n```\n\n### 7.3 SPI Analysis Provider (DeepSeek)\n\n```typescript\n// lib/services/spi/providers/deepseekSpi.ts\n\nimport OpenAI from \"openai\";\nimport { SpiProvider, SPIAnalysisResult } from \"./types\";\nimport { CreativeQCParameter } from \"@/config/creativeQcConfig\";\n\nconst DEEPSEEK_API_KEY = process.env.DEEPSEEK_API_KEY;\nconst DEEPSEEK_API_BASE = process.env.DEEPSEEK_API_BASE || \"https://api.deepseek.com/v1\";\nconst DEEPSEEK_MODEL = process.env.DEEPSEEK_MODEL_NAME || \"deepseek-chat\";\n\nexport class DeepseekSpiProvider implements SpiProvider {\n private client: OpenAI;\n\n constructor() {\n if (!DEEPSEEK_API_KEY) {\n throw new Error(\"DEEPSEEK_API_KEY not configured\");\n }\n \n this.client = new OpenAI({\n apiKey: DEEPSEEK_API_KEY,\n baseURL: DEEPSEEK_API_BASE,\n });\n }\n\n async runCreativeQc(\n transcript: string,\n metadata: {\n platform?: string;\n language?: string;\n genre?: string;\n intendedAudience?: string;\n creativeQcParameters: CreativeQCParameter[];\n }\n ): Promise<SPIAnalysisResult> {\n \n const parameterDescriptions = metadata.creativeQcParameters\n .map(p => `- ${p.key}: ${p.label} (${p.category}) - ${p.description}`)\n .join(\"\\n\");\n\n const prompt = `You are an expert Creative Quality Control AI. Analyze the following media transcript and evaluate it against creative parameters.\n\n## TRANSCRIPT:\n${transcript}\n\n## CONTEXT:\n- Platform: ${metadata.platform || \"General\"}\n- Language: ${metadata.language || \"English\"}\n- Genre: ${metadata.genre || \"Not specified\"}\n- Intended Audience: ${metadata.intendedAudience || \"General\"}\n\n## PARAMETERS TO EVALUATE:\n${parameterDescriptions}\n\n## OUTPUT FORMAT (JSON):\n{\n \"overall_creative_score\": <0-100>,\n \"overall_risk_score\": <0-100>,\n \"overall_brand_fit_score\": <0-100>,\n \"parameters\": {\n \"<parameter_key>\": {\n \"score\": <0-100>,\n \"label\": \"<parameter label>\",\n \"explanation\": \"<1-3 sentence explanation>\"\n }\n },\n \"summary\": \"<2-4 sentence overall assessment>\",\n \"recommendations\": [\"<recommendation 1>\", \"<recommendation 2>\", \"<recommendation 3>\"]\n}`;\n\n const completion = await this.client.chat.completions.create({\n model: DEEPSEEK_MODEL,\n messages: [\n {\n role: \"system\",\n content: \"You are a Creative QC expert. Respond only with valid JSON.\",\n },\n { role: \"user\", content: prompt },\n ],\n response_format: { type: \"json_object\" },\n temperature: 0.7,\n });\n\n const response = completion.choices[0].message.content;\n if (!response) {\n throw new Error(\"Empty response from DeepSeek\");\n }\n\n return JSON.parse(response);\n }\n}\n```\n\n### 7.4 Creative QC Parameter Configuration\n\n```typescript\n// config/creativeQcConfig.ts\n\nexport interface CreativeQCParameter {\n key: string;\n label: string;\n category: CreativeQCCategory;\n description: string;\n scale: string;\n direction: \"higher_better\" | \"lower_better\" | \"balanced\";\n}\n\nexport type CreativeQCCategory =\n | \"story_structure\"\n | \"character_voice\"\n | \"emotion_engagement\"\n | \"platform_audience\"\n | \"brand_intent\"\n | \"risk_safety\"\n | \"craft_quality\";\n\nexport const CREATIVE_QC_PARAMETERS: CreativeQCParameter[] = [\n // Story & Structure\n {\n key: \"story_cohesion\",\n label: \"Story Cohesion\",\n category: \"story_structure\",\n description: \"How well the narrative elements connect logically\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"three_act_adherence\",\n label: \"Three-Act Structure\",\n category: \"story_structure\",\n description: \"Clear setup, confrontation, and resolution\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"conflict_clarity\",\n label: \"Conflict Clarity\",\n category: \"story_structure\",\n description: \"How clearly the central conflict is established\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"resolution_satisfaction\",\n label: \"Resolution Satisfaction\",\n category: \"story_structure\",\n description: \"How satisfying the conclusion feels\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"pacing_effectiveness\",\n label: \"Pacing Effectiveness\",\n category: \"story_structure\",\n description: \"Appropriate rhythm and tempo throughout\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"narrative_momentum\",\n label: \"Narrative Momentum\",\n category: \"story_structure\",\n description: \"Forward progress and audience pull\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Character & Voice\n {\n key: \"character_distinctiveness\",\n label: \"Character Distinctiveness\",\n category: \"character_voice\",\n description: \"How unique and memorable characters are\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"dialogue_authenticity\",\n label: \"Dialogue Authenticity\",\n category: \"character_voice\",\n description: \"Natural, believable dialogue\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"voice_consistency\",\n label: \"Voice Consistency\",\n category: \"character_voice\",\n description: \"Characters maintain consistent voice\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"character_arc_completion\",\n label: \"Character Arc Completion\",\n category: \"character_voice\",\n description: \"Characters show meaningful growth\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"relatability_score\",\n label: \"Relatability\",\n category: \"character_voice\",\n description: \"How relatable characters are to audience\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Emotion & Engagement\n {\n key: \"emotional_authenticity\",\n label: \"Emotional Authenticity\",\n category: \"emotion_engagement\",\n description: \"Genuine emotional resonance\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"tension_management\",\n label: \"Tension Management\",\n category: \"emotion_engagement\",\n description: \"Effective build-up and release of tension\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"cathartic_payoff\",\n label: \"Cathartic Payoff\",\n category: \"emotion_engagement\",\n description: \"Emotional satisfaction at key moments\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"hook_strength\",\n label: \"Hook Strength\",\n category: \"emotion_engagement\",\n description: \"Opening engagement and capture\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"sustained_engagement\",\n label: \"Sustained Engagement\",\n category: \"emotion_engagement\",\n description: \"Maintains interest throughout\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Platform & Audience Fit\n {\n key: \"format_optimization\",\n label: \"Format Optimization\",\n category: \"platform_audience\",\n description: \"Content optimized for delivery format\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"audience_alignment\",\n label: \"Audience Alignment\",\n category: \"platform_audience\",\n description: \"Matches target audience expectations\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"cultural_sensitivity\",\n label: \"Cultural Sensitivity\",\n category: \"platform_audience\",\n description: \"Appropriate cultural awareness\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"trend_relevance\",\n label: \"Trend Relevance\",\n category: \"platform_audience\",\n description: \"Alignment with current trends\",\n scale: \"0-100\",\n direction: \"balanced\",\n },\n {\n key: \"platform_best_practices\",\n label: \"Platform Best Practices\",\n category: \"platform_audience\",\n description: \"Follows platform-specific guidelines\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Brand & Intent Alignment\n {\n key: \"message_clarity\",\n label: \"Message Clarity\",\n category: \"brand_intent\",\n description: \"Clear communication of key message\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"brand_voice_consistency\",\n label: \"Brand Voice Consistency\",\n category: \"brand_intent\",\n description: \"Aligns with brand identity\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"cta_strength\",\n label: \"Call-to-Action Strength\",\n category: \"brand_intent\",\n description: \"Clear and compelling CTA\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"value_proposition_clarity\",\n label: \"Value Proposition Clarity\",\n category: \"brand_intent\",\n description: \"Clear benefit communication\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"intent_fulfillment\",\n label: \"Intent Fulfillment\",\n category: \"brand_intent\",\n description: \"Achieves stated content goals\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Risk, Safety & Compliance\n {\n key: \"content_safety_score\",\n label: \"Content Safety\",\n category: \"risk_safety\",\n description: \"Free from harmful content\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"legal_copyright_risk\",\n label: \"Legal/Copyright Risk\",\n category: \"risk_safety\",\n description: \"Risk of legal issues\",\n scale: \"0-100\",\n direction: \"lower_better\",\n },\n {\n key: \"misinformation_risk\",\n label: \"Misinformation Risk\",\n category: \"risk_safety\",\n description: \"Risk of spreading false info\",\n scale: \"0-100\",\n direction: \"lower_better\",\n },\n {\n key: \"accessibility_compliance\",\n label: \"Accessibility Compliance\",\n category: \"risk_safety\",\n description: \"Meets accessibility standards\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Perceived Craft Quality\n {\n key: \"production_value\",\n label: \"Production Value\",\n category: \"craft_quality\",\n description: \"Overall production quality perception\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"technical_polish\",\n label: \"Technical Polish\",\n category: \"craft_quality\",\n description: \"Technical execution quality\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"creative_originality\",\n label: \"Creative Originality\",\n category: \"craft_quality\",\n description: \"Uniqueness and innovation\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n];\n```\n\n---\n\n## 8. PROCESS FLOW DIAGRAMS\n\n### 8.1 Complete SPI Processing Pipeline\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ SPI CREATIVE QC PROCESS FLOW │\n└─────────────────────────────────────────────────────────────────────────────┘\n\n USER SYSTEM AI PROVIDERS\n │ │ │\n │ Upload Media │ │\n ├───────────────────────►│ │\n │ │ │\n │ │ Create QC Job │\n │ ├─────────────────────┐ │\n │ │ │ │\n │ │◄────────────────────┘ │\n │ │ │\n │ │ Resolve Media File │\n │ │ (Drive/Upload) │\n │ ├─────────────────────┐ │\n │ │ │ │\n │ │◄────────────────────┘ │\n │ │ │\n │ │ Check Creative QC Enabled │\n │ │ (Enterprise + Feature Flag) │\n │ ├─────────────────────┐ │\n │ │ │ │\n │ │◄────────────────────┘ │\n │ │ │\n │ [If Creative QC] │ │\n │ │ Send to Groq Whisper │\n │ ├─────────────────────────────►│\n │ │ │\n │ │ Transcription │\n │ │◄─────────────────────────────┤\n │ │ │\n │ │ Send to DeepSeek │\n │ │ (33 Parameters) │\n │ ├─────────────────────────────►│\n │ │ │\n │ │ Creative Analysis │\n │ │ + Scores + Explanations │\n │ │◄─────────────────────────────┤\n │ │ │\n │ │ Store Results │\n │ ├─────────────────────┐ │\n │ │ │ │\n │ │◄────────────────────┘ │\n │ │ │\n │ Results │ │\n │◄───────────────────────┤ │\n │ │ │\n```\n\n### 8.2 Semantic Fingerprint Generation\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ SEMANTIC FINGERPRINT GENERATION PIPELINE │\n└─────────────────────────────────────────────────────────────────────────────┘\n\n ┌─────────────────┐\n │ MEDIA INPUT │\n └────────┬────────┘\n │\n ┌───────────────┼───────────────┐\n │ │ │\n ▼ ▼ ▼\n ┌───────────┐ ┌───────────┐ ┌───────────┐\n │ VIDEO │ │ AUDIO │ │ METADATA │\n │ STREAM │ │ STREAM │ │ CONTEXT │\n └─────┬─────┘ └─────┬─────┘ └─────┬─────┘\n │ │ │\n ┌─────────┼─────────┐ │ │\n │ │ │ │ │\n ▼ ▼ ▼ ▼ │\n ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ │\n │ CLIP │ │Scene │ │Motion │ │Whisper│ │\n │Embed │ │Detect │ │Analyze│ │Transcr│ │\n └───┬───┘ └───┬───┘ └───┬───┘ └───┬───┘ │\n │ │ │ │ │\n └────┬────┴────┬────┘ │ │\n │ │ │ │\n ▼ ▼ ▼ │\n ┌───────────┐ ┌───────────┐ ┌───────────┐ │\n │ VISUAL │ │ TEMPORAL │ │ SPEECH │ │\n │ VECTOR │ │ VECTOR │ │ VECTOR │ │\n │ (128-d) │ │ (128-d) │ │ (128-d) │ │\n └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ │\n │ │ │ │\n └──────┬──────┴──────┬──────┘ │\n │ │ │\n ▼ ▼ ▼\n ┌─────────────┐ ┌─────────────┐ ┌─────────────┐\n │ NARRATIVE │ │ EMOTIONAL │ │ SEMANTIC │\n │ ENCODER │ │ ENCODER │ │ ENCODER │\n │ (DeepSeek) │ │ (DeepSeek) │ │ (DeepSeek) │\n └──────┬──────┘ └──────┬──────┘ └──────┬──────┘\n │ │ │\n ▼ ▼ ▼\n ┌───────────┐ ┌───────────┐ ┌───────────┐\n │ NARRATIVE │ │ EMOTIONAL │ │ SEMANTIC │\n │ VECTOR │ │ TRAJECTORY│ │ DENSITY │\n │ (128-d) │ │ (128-d) │ │ (128-d) │\n └─────┬─────┘ └─────┬─────┘ └─────┬─────┘\n │ │ │\n └───────────────┼───────────────┘\n │\n ┌───────▼───────┐\n │ FUSION │\n │ LAYER │\n │ (Attention) │\n └───────┬───────┘\n │\n ┌───────▼───────┐\n │ COMBINED │\n │ SEMANTIC DNA │\n │ (768-dim) │\n └───────────────┘\n```\n\n---\n\n## 9. DATABASE SCHEMA\n\n```sql\n-- Semantic Fingerprints Table\nCREATE TABLE semantic_fingerprints (\n id UUID PRIMARY KEY DEFAULT gen_random_uuid(),\n organization_id UUID NOT NULL REFERENCES organizations(id),\n project_id UUID REFERENCES projects(id),\n source_type VARCHAR(50) NOT NULL, -- 'video', 'audio', 'document'\n source_id UUID NOT NULL,\n media_url TEXT,\n content_hash VARCHAR(64),\n duration_ms INTEGER,\n \n -- Vector embeddings (using pgvector)\n combined_sdf vector(768), -- Combined Semantic DNA Fingerprint\n narrative_vector vector(128),\n visual_vector vector(128),\n auditory_vector vector(128),\n temporal_vector vector(128),\n emotional_vector vector(128),\n semantic_density_vector vector(128),\n \n -- Temporal segments\n temporal_sdfs JSONB, -- Array of per-segment fingerprints\n \n -- Metadata\n metadata JSONB DEFAULT '{}',\n originality_score DECIMAL(5,2),\n style_tags TEXT[],\n \n created_at TIMESTAMPTZ DEFAULT NOW(),\n updated_at TIMESTAMPTZ DEFAULT NOW()\n);\n\n-- Creative QC Results in QC Jobs\nALTER TABLE qc_jobs ADD COLUMN IF NOT EXISTS\n creative_qc_status VARCHAR(20) DEFAULT 'pending',\n creative_qc_overall_score INTEGER,\n creative_qc_risk_score INTEGER,\n creative_qc_brand_fit_score INTEGER,\n creative_qc_parameters JSONB,\n creative_qc_summary TEXT,\n creative_qc_recommendations JSONB,\n creative_qc_started_at TIMESTAMPTZ,\n creative_qc_completed_at TIMESTAMPTZ,\n creative_qc_error TEXT;\n\n-- Feature Flags\nCREATE TABLE feature_flags (\n id UUID PRIMARY KEY DEFAULT gen_random_uuid(),\n organization_id UUID NOT NULL REFERENCES organizations(id),\n feature_key VARCHAR(100) NOT NULL,\n enabled BOOLEAN DEFAULT false,\n config JSONB DEFAULT '{}',\n created_at TIMESTAMPTZ DEFAULT NOW(),\n updated_at TIMESTAMPTZ DEFAULT NOW(),\n UNIQUE(organization_id, feature_key)\n);\n\n-- Indexes for vector similarity search\nCREATE INDEX idx_semantic_fingerprints_combined_sdf \n ON semantic_fingerprints USING ivfflat (combined_sdf vector_cosine_ops);\n\nCREATE INDEX idx_semantic_fingerprints_org \n ON semantic_fingerprints(organization_id);\n```\n\n---\n\n## 10. ENVIRONMENT CONFIGURATION\n\n```bash\n# Required Environment Variables for SPI\n\n# Groq Whisper (Transcription)\nGROQ_API_KEY=gsk_xxxxxxxxxxxxx\nGROQ_API_BASE=https://api.groq.com/openai/v1\nGROQ_WHISPER_MODEL=whisper-large-v3-turbo\n\n# DeepSeek (Creative Analysis)\nDEEPSEEK_API_KEY=sk-xxxxxxxxxxxxx\nDEEPSEEK_API_BASE=https://api.deepseek.com/v1\nDEEPSEEK_MODEL_NAME=deepseek-chat\nDEEPSEEK_TIMEOUT_MS=120000\n\n# Supabase (Database)\nNEXT_PUBLIC_SUPABASE_URL=https://xxx.supabase.co\nSUPABASE_SERVICE_ROLE_KEY=eyJxxxx\n```\n\n---\n\n## 11. COMMERCIAL APPLICATIONS\n\n### 11.1 Primary Markets\n\n1. **Post-Production QC** - Automated creative quality assessment for studios\n2. **Content Moderation** - Risk and safety scoring at scale\n3. **Rights Management** - Content provenance and derivative tracking\n4. **Recommendation Systems** - Semantic content matching\n5. **Brand Compliance** - Automated brand guideline checking\n\n### 11.2 Revenue Model\n\n| Feature | Target Market | Pricing Model |\n|---------|--------------|---------------|\n| Creative QC | Studios, Agencies | Per-minute processing |\n| Content Matching | Streaming, Social | SaaS subscription |\n| Risk Scoring | All platforms | API call pricing |\n| Provenance Tracking | Rights holders | Enterprise license |\n\n---\n\n## 12. CONCLUSION\n\nSemantic Provenance Intelligence represents a paradigm shift from technical content analysis to semantic understanding. By creating multi-dimensional fingerprints that capture creative intent, emotional impact, and narrative structure, SPI enables applications that were previously impossible—from automated creative quality assessment to intelligent content matching across heterogeneous formats.\n\n---\n\n**Document Version:** 1.0 \n**Last Updated:** December 2024 \n**Classification:** Patent Application - Confidential\n\n\n"}, "22293a0c5a3e1441406055edab7c8975cd2f0332": {"t": 1785763504.060654, "n": 43964, "h": "c52105af3bcc4520", "c": "# Semantic Provenance Intelligence (SPI)\n## Patent Application Technical Documentation\n\n**Invention Title:** Semantic Provenance Intelligence System for Multi-Dimensional Media Content Analysis and Quality Control\n\n**Inventors:** Alokick Studios \n**Filing Date:** December 2024 \n**Application Type:** Utility Patent\n\n---\n\n## 1. ABSTRACT\n\nA novel system and method for analyzing media content (video, audio, text) using multi-dimensional semantic fingerprinting that captures narrative architecture, emotional trajectory, visual grammar, auditory signatures, and temporal rhythm. The system generates a unified \"Semantic DNA\" that enables creative quality assessment, content provenance tracking, and intelligent content matching across heterogeneous media formats.\n\n---\n\n## 2. FIELD OF THE INVENTION\n\nThis invention relates to the field of artificial intelligence applied to media content analysis, specifically to a system that creates multi-dimensional semantic representations of creative content for quality control, rights management, and content intelligence purposes.\n\n---\n\n## 3. BACKGROUND OF THE INVENTION\n\n### 3.1 Prior Art Limitations\n\nExisting content analysis systems suffer from critical limitations:\n\n1. **Single-Modality Analysis**: Current systems analyze video OR audio OR text separately, missing cross-modal semantic relationships.\n\n2. **Surface-Level Features**: Existing fingerprinting (e.g., audio fingerprinting, perceptual hashing) captures technical signatures but not creative/semantic meaning.\n\n3. **Lack of Temporal Understanding**: Frame-by-frame analysis misses narrative arc, emotional progression, and structural patterns.\n\n4. **No Creative Quality Metrics**: Current QC systems focus on technical parameters (bitrate, resolution) but cannot assess storytelling quality, emotional impact, or brand alignment.\n\n5. **Binary Matching**: Existing systems only detect exact or near-exact copies, not semantic similarity or stylistic relationships.\n\n### 3.2 Problem Statement\n\nThere exists no system that can:\n- Create a unified semantic representation capturing both technical AND creative qualities\n- Track content provenance through creative transformations\n- Assess subjective creative quality objectively\n- Match content based on narrative/emotional similarity rather than pixel similarity\n\n---\n\n## 4. SUMMARY OF THE INVENTION\n\n### 4.1 Core Innovation\n\nSemantic Provenance Intelligence (SPI) introduces a **Multi-Dimensional Semantic Fingerprint** that captures:\n\n```\n┌─────────────────────────────────────────────────────────────────────┐\n│ SEMANTIC DNA FINGERPRINT │\n├─────────────────────────────────────────────────────────────────────┤\n│ │\n│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │\n│ │ NARRATIVE │ │ VISUAL │ │ AUDITORY │ │\n│ │ VECTOR │ │ GRAMMAR │ │ SIGNATURE │ │\n│ │ (128-dim) │ │ (128-dim) │ │ (128-dim) │ │\n│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │\n│ │ │ │ │\n│ └────────────┬────┴────────────────┘ │\n│ │ │\n│ ┌───────▼───────┐ │\n│ │ TEMPORAL │ │\n│ │ RHYTHM │ │\n│ │ (128-dim) │ │\n│ └───────┬───────┘ │\n│ │ │\n│ ┌───────▼───────┐ │\n│ │ EMOTIONAL │ │\n│ │ TRAJECTORY │ │\n│ │ (128-dim) │ │\n│ └───────┬───────┘ │\n│ │ │\n│ ┌───────▼───────┐ │\n│ │ SEMANTIC │ │\n│ │ DENSITY │ │\n│ │ (128-dim) │ │\n│ └───────┬───────┘ │\n│ │ │\n│ ┌────────────▼────────────┐ │\n│ │ COMBINED SDF │ │\n│ │ (768-dimensional │ │\n│ │ Semantic DNA) │ │\n│ └─────────────────────────┘ │\n│ │\n└─────────────────────────────────────────────────────────────────────┘\n```\n\n### 4.2 Key Differentiators\n\n| Feature | Prior Art | SPI Innovation |\n|---------|-----------|----------------|\n| Analysis Scope | Single modality | Multi-modal fusion |\n| Feature Type | Technical/perceptual | Semantic/creative |\n| Temporal Modeling | Frame-level | Narrative arc |\n| Quality Assessment | Technical only | Creative + Technical |\n| Matching | Exact/near-exact | Semantic similarity |\n| Provenance | Copy detection | Transformation tracking |\n\n---\n\n## 5. DETAILED DESCRIPTION OF THE INVENTION\n\n### 5.1 System Architecture\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ SPI SYSTEM ARCHITECTURE │\n└─────────────────────────────────────────────────────────────────────────────┘\n\n ┌─────────────────┐\n │ MEDIA INPUT │\n │ (Video/Audio) │\n └────────┬────────┘\n │\n ┌──────────────────┼──────────────────┐\n │ │ │\n ▼ ▼ ▼\n ┌───────────────┐ ┌───────────────┐ ┌───────────────┐\n │ VISUAL │ │ AUDIO │ │ TEMPORAL │\n │ ANALYZER │ │ ANALYZER │ │ ANALYZER │\n └───────┬───────┘ └───────┬───────┘ └───────┬───────┘\n │ │ │\n ▼ ▼ ▼\n ┌───────────────┐ ┌───────────────┐ ┌───────────────┐\n │ CLIP Encoder │ │Whisper + OpenL3│ │ TransNet V2 │\n │ + Composition │ │ + Prosody │ │ + Shot │\n │ Analysis │ │ Analysis │ │ Detection │\n └───────┬───────┘ └───────┬───────┘ └───────┬───────┘\n │ │ │\n └──────────────────┼──────────────────┘\n │\n ┌───────▼───────┐\n │ TRANSCRIPTION │\n │ (Groq Whisper)│\n └───────┬───────┘\n │\n ┌───────▼───────┐\n │ SEMANTIC │\n │ ANALYZER │\n │ (DeepSeek) │\n └───────┬───────┘\n │\n ┌───────────────────────┼───────────────────────┐\n │ │ │\n ▼ ▼ ▼\n ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐\n │ NARRATIVE │ │ EMOTIONAL │ │ CREATIVE │\n │ STRUCTURE │ │ TRAJECTORY │ │ QUALITY │\n │ EXTRACTION │ │ MAPPING │ │ SCORING │\n └────────┬────────┘ └────────┬────────┘ └────────┬────────┘\n │ │ │\n └──────────────────────┼──────────────────────┘\n │\n ┌───────▼───────┐\n │ SEMANTIC │\n │ FINGERPRINT │\n │ GENERATOR │\n └───────┬───────┘\n │\n ┌───────▼───────┐\n │ VECTOR │\n │ DATABASE │\n │ (pgvector) │\n └───────────────┘\n```\n\n### 5.2 Component Specifications\n\n#### 5.2.1 Narrative Vector Extraction\n\nThe Narrative Vector captures story structure through:\n\n1. **Three-Act Structure Detection**: Identifies setup, confrontation, resolution\n2. **Character Arc Mapping**: Tracks protagonist transformation\n3. **Conflict Progression**: Measures tension/release patterns\n4. **Theme Identification**: Extracts thematic elements\n\n```typescript\ninterface NarrativeVector {\n actStructure: number[]; // [setup_strength, confrontation_strength, resolution_strength]\n characterArcs: number[]; // Transformation intensity per character\n conflictProgression: number[]; // Tension curve sampled at N points\n thematicElements: number[]; // Theme embedding from content\n plotDensity: number; // Events per minute\n narrativeCoherence: number; // Logical consistency score\n}\n```\n\n#### 5.2.2 Visual Grammar Analysis\n\n```typescript\ninterface VisualGrammar {\n composition: {\n ruleOfThirds: number; // Adherence to compositional rules\n symmetryScore: number;\n leadingLines: number;\n depthOfField: number;\n };\n colorPalette: {\n dominantHues: number[]; // HSV distribution\n saturationProfile: number;\n luminanceRange: number;\n colorHarmony: number;\n };\n motionDynamics: {\n cameraMovement: number[]; // [pan, tilt, zoom, dolly, crane]\n subjectMotion: number;\n visualFlow: number;\n };\n stylistic: {\n lightingStyle: number[]; // [high-key, low-key, natural, dramatic]\n textureComplexity: number;\n visualNoise: number;\n };\n}\n```\n\n#### 5.2.3 Auditory Signature\n\n```typescript\ninterface AuditorySignature {\n speech: {\n prosody: number[]; // Pitch, rhythm, stress patterns\n emotionalTone: number[]; // Valence, arousal, dominance\n speakerDiversity: number;\n };\n music: {\n tempo: number;\n key: string;\n instrumentalComplexity: number;\n melodicContour: number[];\n };\n soundscape: {\n ambientProfile: number[];\n dynamicRange: number;\n frequencyBalance: number[]; // Bass, mid, treble distribution\n };\n}\n```\n\n#### 5.2.4 Temporal Rhythm\n\n```typescript\ninterface TemporalRhythm {\n pacing: {\n averageShotDuration: number;\n shotDurationVariance: number;\n cutRhythmPattern: number[];\n };\n structure: {\n sceneTransitionTypes: number[]; // [cut, dissolve, fade, wipe]\n actBreakTimestamps: number[];\n climaxTimestamp: number;\n };\n engagement: {\n attentionCurve: number[]; // Predicted viewer engagement over time\n hookStrength: number; // Opening engagement score\n retentionPrediction: number;\n };\n}\n```\n\n#### 5.2.5 Emotional Trajectory\n\n```typescript\ninterface EmotionalTrajectory {\n valence: number[]; // Positive/negative over time\n arousal: number[]; // High/low energy over time\n dominance: number[]; // Empowerment/submission over time\n emotionalPeaks: {\n timestamp: number;\n emotion: string;\n intensity: number;\n }[];\n overallEmotionalArc: string; // \"redemption\", \"tragedy\", \"comedy\", etc.\n catharticMoments: number[];\n}\n```\n\n### 5.3 Creative QC Parameter Set\n\nThe SPI system evaluates content across **33 creative parameters** in **7 categories**:\n\n```\n┌─────────────────────────────────────────────────────────────────────┐\n│ CREATIVE QC PARAMETER TAXONOMY │\n├─────────────────────────────────────────────────────────────────────┤\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 1. STORY & STRUCTURE (6 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Story Cohesion • Three-Act Adherence │ │\n│ │ • Conflict Clarity • Resolution Satisfaction │ │\n│ │ • Pacing Effectiveness • Narrative Momentum │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 2. CHARACTER & VOICE (5 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Character Distinctiveness • Dialogue Authenticity │ │\n│ │ • Voice Consistency • Character Arc Completion │ │\n│ │ • Relatability Score │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 3. EMOTION & ENGAGEMENT (5 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Emotional Authenticity • Tension Management │ │\n│ │ • Cathartic Payoff • Hook Strength │ │\n│ │ • Sustained Engagement │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 4. PLATFORM & AUDIENCE FIT (5 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Format Optimization • Audience Alignment │ │\n│ │ • Cultural Sensitivity • Trend Relevance │ │\n│ │ • Platform-Specific Best Practices │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 5. BRAND & INTENT ALIGNMENT (5 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Message Clarity • Brand Voice Consistency │ │\n│ │ • Call-to-Action Strength • Value Proposition Clarity │ │\n│ │ • Intent Fulfillment │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 6. RISK, SAFETY & COMPLIANCE (4 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Content Safety Score • Legal/Copyright Risk │ │\n│ │ • Misinformation Risk • Accessibility Compliance │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n│ ┌─────────────────────────────────────────────────────────────┐ │\n│ │ 7. PERCEIVED CRAFT QUALITY (3 parameters) │ │\n│ ├─────────────────────────────────────────────────────────────┤ │\n│ │ • Production Value • Technical Polish │ │\n│ │ • Creative Originality │ │\n│ └─────────────────────────────────────────────────────────────┘ │\n│ │\n└─────────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## 6. NOVEL CLAIMS\n\n### Claim 1: Multi-Dimensional Semantic Fingerprint\n\nA method for generating a unified semantic fingerprint of media content comprising:\n- Extracting narrative structure vectors from transcribed content\n- Extracting visual grammar vectors from video frames\n- Extracting auditory signature vectors from audio tracks\n- Extracting temporal rhythm vectors from scene transitions\n- Extracting emotional trajectory vectors from multimodal analysis\n- Combining said vectors into a single 768-dimensional Semantic DNA fingerprint\n\n### Claim 2: Creative Quality Assessment System\n\nA system for objectively measuring subjective creative quality comprising:\n- A set of 33 creative quality parameters across 7 categories\n- AI-powered analysis using large language models to evaluate each parameter\n- Scoring each parameter on a 0-100 scale with explanatory rationale\n- Generating aggregate scores for overall creative quality, risk, and brand alignment\n\n### Claim 3: Semantic Content Matching\n\nA method for matching media content based on semantic similarity comprising:\n- Computing cosine similarity between Semantic DNA fingerprints\n- Detecting content that is semantically similar but technically different\n- Identifying content transformation chains (original → adaptation → derivative)\n- Tracking creative provenance across format conversions\n\n### Claim 4: Temporal Semantic Analysis\n\nA method for analyzing content meaning over time comprising:\n- Segmenting content into semantic chunks aligned with narrative structure\n- Generating per-segment embeddings capturing local semantic meaning\n- Tracking semantic evolution across the content timeline\n- Identifying semantic peaks, transitions, and patterns\n\n### Claim 5: Cross-Modal Semantic Fusion\n\nA method for combining semantic information from multiple modalities comprising:\n- Aligning visual, audio, and text streams temporally\n- Computing attention weights across modalities based on semantic salience\n- Generating a fused representation that captures cross-modal relationships\n- Resolving conflicts between modalities using learned arbitration\n\n---\n\n## 7. IMPLEMENTATION CODE\n\n### 7.1 Core SPI Engine\n\n```typescript\n// lib/services/spi/engine.ts\n\nimport { CREATIVE_QC_PARAMETERS, CreativeQCParameter } from \"@/config/creativeQcConfig\";\nimport { getTranscriptionProvider, getSpiProvider } from \"./providers\";\n\nexport interface SPIResult {\n status: \"completed\" | \"failed\" | \"partial\";\n overall_creative_score: number;\n overall_risk_score: number;\n overall_brand_fit_score: number;\n parameters: {\n [key: string]: {\n score: number;\n label: string;\n explanation: string;\n };\n };\n summary?: string;\n recommendations?: string[];\n semantic_fingerprint?: {\n narrative_vector: number[];\n visual_grammar: number[];\n auditory_signature: number[];\n temporal_rhythm: number[];\n emotional_trajectory: number[];\n combined_sdf: number[];\n };\n processing_time_ms: number;\n error?: string;\n}\n\nexport interface SPIContext {\n mediaUrl: string;\n mediaType: \"video\" | \"audio\";\n language?: string;\n platform?: string;\n genre?: string;\n intendedAudience?: string;\n brandGuidelines?: string;\n}\n\n/**\n * Run comprehensive Creative QC analysis using SPI\n */\nexport async function runCreativeQC(context: SPIContext): Promise<SPIResult> {\n const startTime = Date.now();\n \n try {\n // Step 1: Get transcription\n const transcriptionProvider = getTranscriptionProvider();\n const transcript = await transcriptionProvider.transcribeMedia(\n context.mediaUrl,\n context.language\n );\n \n // Step 2: Run SPI analysis with full parameter set\n const spiProvider = getSpiProvider();\n const analysisResult = await spiProvider.runCreativeQc(transcript, {\n platform: context.platform,\n language: context.language,\n genre: context.genre,\n intendedAudience: context.intendedAudience,\n creativeQcParameters: CREATIVE_QC_PARAMETERS,\n });\n \n return {\n status: \"completed\",\n overall_creative_score: analysisResult.overall_creative_score,\n overall_risk_score: analysisResult.overall_risk_score,\n overall_brand_fit_score: analysisResult.overall_brand_fit_score,\n parameters: analysisResult.parameters,\n summary: analysisResult.summary,\n recommendations: analysisResult.recommendations,\n processing_time_ms: Date.now() - startTime,\n };\n } catch (error) {\n return {\n status: \"failed\",\n overall_creative_score: 0,\n overall_risk_score: 0,\n overall_brand_fit_score: 0,\n parameters: {},\n processing_time_ms: Date.now() - startTime,\n error: error instanceof Error ? error.message : String(error),\n };\n }\n}\n```\n\n### 7.2 Transcription Provider (Groq Whisper)\n\n```typescript\n// lib/services/spi/providers/groqWhisper.ts\n\nimport { TranscriptionProvider, TranscriptionResult } from \"./types\";\n\nconst GROQ_API_KEY = process.env.GROQ_API_KEY;\nconst GROQ_API_BASE = process.env.GROQ_API_BASE || \"https://api.groq.com/openai/v1\";\nconst GROQ_WHISPER_MODEL = process.env.GROQ_WHISPER_MODEL || \"whisper-large-v3-turbo\";\n\nexport class GroqWhisperTranscriptionProvider implements TranscriptionProvider {\n \n async transcribeMedia(\n mediaUrl: string,\n language?: string\n ): Promise<TranscriptionResult> {\n if (!GROQ_API_KEY) {\n throw new Error(\"GROQ_API_KEY not configured\");\n }\n\n // Download media file\n const response = await fetch(mediaUrl);\n if (!response.ok) {\n throw new Error(`Failed to fetch media: ${response.status}`);\n }\n \n const fileBuffer = Buffer.from(await response.arrayBuffer());\n const mimeType = response.headers.get(\"content-type\") || \"audio/mpeg\";\n const fileName = mediaUrl.split(\"/\").pop() || \"media.mp4\";\n \n // Prepare form data\n const formData = new FormData();\n const blob = new Blob([new Uint8Array(fileBuffer)], { type: mimeType });\n formData.append(\"file\", blob, fileName);\n formData.append(\"model\", GROQ_WHISPER_MODEL);\n formData.append(\"response_format\", \"verbose_json\");\n \n if (language) {\n formData.append(\"language\", language);\n }\n\n // Call Groq API\n const apiResponse = await fetch(`${GROQ_API_BASE}/audio/transcriptions`, {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${GROQ_API_KEY}`,\n },\n body: formData,\n });\n\n if (!apiResponse.ok) {\n throw new Error(`Groq API error: ${apiResponse.status}`);\n }\n\n const result = await apiResponse.json();\n \n return {\n text: result.text,\n segments: result.segments?.map((seg: any) => ({\n start: seg.start,\n end: seg.end,\n text: seg.text,\n confidence: seg.confidence,\n })),\n language: result.language,\n duration: result.duration,\n };\n }\n}\n```\n\n### 7.3 SPI Analysis Provider (DeepSeek)\n\n```typescript\n// lib/services/spi/providers/deepseekSpi.ts\n\nimport OpenAI from \"openai\";\nimport { SpiProvider, SPIAnalysisResult } from \"./types\";\nimport { CreativeQCParameter } from \"@/config/creativeQcConfig\";\n\nconst DEEPSEEK_API_KEY = process.env.DEEPSEEK_API_KEY;\nconst DEEPSEEK_API_BASE = process.env.DEEPSEEK_API_BASE || \"https://api.deepseek.com/v1\";\nconst DEEPSEEK_MODEL = process.env.DEEPSEEK_MODEL_NAME || \"deepseek-chat\";\n\nexport class DeepseekSpiProvider implements SpiProvider {\n private client: OpenAI;\n\n constructor() {\n if (!DEEPSEEK_API_KEY) {\n throw new Error(\"DEEPSEEK_API_KEY not configured\");\n }\n \n this.client = new OpenAI({\n apiKey: DEEPSEEK_API_KEY,\n baseURL: DEEPSEEK_API_BASE,\n });\n }\n\n async runCreativeQc(\n transcript: string,\n metadata: {\n platform?: string;\n language?: string;\n genre?: string;\n intendedAudience?: string;\n creativeQcParameters: CreativeQCParameter[];\n }\n ): Promise<SPIAnalysisResult> {\n \n const parameterDescriptions = metadata.creativeQcParameters\n .map(p => `- ${p.key}: ${p.label} (${p.category}) - ${p.description}`)\n .join(\"\\n\");\n\n const prompt = `You are an expert Creative Quality Control AI. Analyze the following media transcript and evaluate it against creative parameters.\n\n## TRANSCRIPT:\n${transcript}\n\n## CONTEXT:\n- Platform: ${metadata.platform || \"General\"}\n- Language: ${metadata.language || \"English\"}\n- Genre: ${metadata.genre || \"Not specified\"}\n- Intended Audience: ${metadata.intendedAudience || \"General\"}\n\n## PARAMETERS TO EVALUATE:\n${parameterDescriptions}\n\n## OUTPUT FORMAT (JSON):\n{\n \"overall_creative_score\": <0-100>,\n \"overall_risk_score\": <0-100>,\n \"overall_brand_fit_score\": <0-100>,\n \"parameters\": {\n \"<parameter_key>\": {\n \"score\": <0-100>,\n \"label\": \"<parameter label>\",\n \"explanation\": \"<1-3 sentence explanation>\"\n }\n },\n \"summary\": \"<2-4 sentence overall assessment>\",\n \"recommendations\": [\"<recommendation 1>\", \"<recommendation 2>\", \"<recommendation 3>\"]\n}`;\n\n const completion = await this.client.chat.completions.create({\n model: DEEPSEEK_MODEL,\n messages: [\n {\n role: \"system\",\n content: \"You are a Creative QC expert. Respond only with valid JSON.\",\n },\n { role: \"user\", content: prompt },\n ],\n response_format: { type: \"json_object\" },\n temperature: 0.7,\n });\n\n const response = completion.choices[0].message.content;\n if (!response) {\n throw new Error(\"Empty response from DeepSeek\");\n }\n\n return JSON.parse(response);\n }\n}\n```\n\n### 7.4 Creative QC Parameter Configuration\n\n```typescript\n// config/creativeQcConfig.ts\n\nexport interface CreativeQCParameter {\n key: string;\n label: string;\n category: CreativeQCCategory;\n description: string;\n scale: string;\n direction: \"higher_better\" | \"lower_better\" | \"balanced\";\n}\n\nexport type CreativeQCCategory =\n | \"story_structure\"\n | \"character_voice\"\n | \"emotion_engagement\"\n | \"platform_audience\"\n | \"brand_intent\"\n | \"risk_safety\"\n | \"craft_quality\";\n\nexport const CREATIVE_QC_PARAMETERS: CreativeQCParameter[] = [\n // Story & Structure\n {\n key: \"story_cohesion\",\n label: \"Story Cohesion\",\n category: \"story_structure\",\n description: \"How well the narrative elements connect logically\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"three_act_adherence\",\n label: \"Three-Act Structure\",\n category: \"story_structure\",\n description: \"Clear setup, confrontation, and resolution\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"conflict_clarity\",\n label: \"Conflict Clarity\",\n category: \"story_structure\",\n description: \"How clearly the central conflict is established\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"resolution_satisfaction\",\n label: \"Resolution Satisfaction\",\n category: \"story_structure\",\n description: \"How satisfying the conclusion feels\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"pacing_effectiveness\",\n label: \"Pacing Effectiveness\",\n category: \"story_structure\",\n description: \"Appropriate rhythm and tempo throughout\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"narrative_momentum\",\n label: \"Narrative Momentum\",\n category: \"story_structure\",\n description: \"Forward progress and audience pull\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Character & Voice\n {\n key: \"character_distinctiveness\",\n label: \"Character Distinctiveness\",\n category: \"character_voice\",\n description: \"How unique and memorable characters are\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"dialogue_authenticity\",\n label: \"Dialogue Authenticity\",\n category: \"character_voice\",\n description: \"Natural, believable dialogue\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"voice_consistency\",\n label: \"Voice Consistency\",\n category: \"character_voice\",\n description: \"Characters maintain consistent voice\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"character_arc_completion\",\n label: \"Character Arc Completion\",\n category: \"character_voice\",\n description: \"Characters show meaningful growth\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"relatability_score\",\n label: \"Relatability\",\n category: \"character_voice\",\n description: \"How relatable characters are to audience\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Emotion & Engagement\n {\n key: \"emotional_authenticity\",\n label: \"Emotional Authenticity\",\n category: \"emotion_engagement\",\n description: \"Genuine emotional resonance\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"tension_management\",\n label: \"Tension Management\",\n category: \"emotion_engagement\",\n description: \"Effective build-up and release of tension\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"cathartic_payoff\",\n label: \"Cathartic Payoff\",\n category: \"emotion_engagement\",\n description: \"Emotional satisfaction at key moments\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"hook_strength\",\n label: \"Hook Strength\",\n category: \"emotion_engagement\",\n description: \"Opening engagement and capture\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"sustained_engagement\",\n label: \"Sustained Engagement\",\n category: \"emotion_engagement\",\n description: \"Maintains interest throughout\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Platform & Audience Fit\n {\n key: \"format_optimization\",\n label: \"Format Optimization\",\n category: \"platform_audience\",\n description: \"Content optimized for delivery format\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"audience_alignment\",\n label: \"Audience Alignment\",\n category: \"platform_audience\",\n description: \"Matches target audience expectations\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"cultural_sensitivity\",\n label: \"Cultural Sensitivity\",\n category: \"platform_audience\",\n description: \"Appropriate cultural awareness\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"trend_relevance\",\n label: \"Trend Relevance\",\n category: \"platform_audience\",\n description: \"Alignment with current trends\",\n scale: \"0-100\",\n direction: \"balanced\",\n },\n {\n key: \"platform_best_practices\",\n label: \"Platform Best Practices\",\n category: \"platform_audience\",\n description: \"Follows platform-specific guidelines\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Brand & Intent Alignment\n {\n key: \"message_clarity\",\n label: \"Message Clarity\",\n category: \"brand_intent\",\n description: \"Clear communication of key message\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"brand_voice_consistency\",\n label: \"Brand Voice Consistency\",\n category: \"brand_intent\",\n description: \"Aligns with brand identity\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"cta_strength\",\n label: \"Call-to-Action Strength\",\n category: \"brand_intent\",\n description: \"Clear and compelling CTA\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"value_proposition_clarity\",\n label: \"Value Proposition Clarity\",\n category: \"brand_intent\",\n description: \"Clear benefit communication\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"intent_fulfillment\",\n label: \"Intent Fulfillment\",\n category: \"brand_intent\",\n description: \"Achieves stated content goals\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Risk, Safety & Compliance\n {\n key: \"content_safety_score\",\n label: \"Content Safety\",\n category: \"risk_safety\",\n description: \"Free from harmful content\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"legal_copyright_risk\",\n label: \"Legal/Copyright Risk\",\n category: \"risk_safety\",\n description: \"Risk of legal issues\",\n scale: \"0-100\",\n direction: \"lower_better\",\n },\n {\n key: \"misinformation_risk\",\n label: \"Misinformation Risk\",\n category: \"risk_safety\",\n description: \"Risk of spreading false info\",\n scale: \"0-100\",\n direction: \"lower_better\",\n },\n {\n key: \"accessibility_compliance\",\n label: \"Accessibility Compliance\",\n category: \"risk_safety\",\n description: \"Meets accessibility standards\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n \n // Perceived Craft Quality\n {\n key: \"production_value\",\n label: \"Production Value\",\n category: \"craft_quality\",\n description: \"Overall production quality perception\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"technical_polish\",\n label: \"Technical Polish\",\n category: \"craft_quality\",\n description: \"Technical execution quality\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n {\n key: \"creative_originality\",\n label: \"Creative Originality\",\n category: \"craft_quality\",\n description: \"Uniqueness and innovation\",\n scale: \"0-100\",\n direction: \"higher_better\",\n },\n];\n```\n\n---\n\n## 8. PROCESS FLOW DIAGRAMS\n\n### 8.1 Complete SPI Processing Pipeline\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ SPI CREATIVE QC PROCESS FLOW │\n└─────────────────────────────────────────────────────────────────────────────┘\n\n USER SYSTEM AI PROVIDERS\n │ │ │\n │ Upload Media │ │\n ├───────────────────────►│ │\n │ │ │\n │ │ Create QC Job │\n │ ├─────────────────────┐ │\n │ │ │ │\n │ │◄────────────────────┘ │\n │ │ │\n │ │ Resolve Media File │\n │ │ (Drive/Upload) │\n │ ├─────────────────────┐ │\n │ │ │ │\n │ │◄────────────────────┘ │\n │ │ │\n │ │ Check Creative QC Enabled │\n │ │ (Enterprise + Feature Flag) │\n │ ├─────────────────────┐ │\n │ │ │ │\n │ │◄────────────────────┘ │\n │ │ │\n │ [If Creative QC] │ │\n │ │ Send to Groq Whisper │\n │ ├─────────────────────────────►│\n │ │ │\n │ │ Transcription │\n │ │◄─────────────────────────────┤\n │ │ │\n │ │ Send to DeepSeek │\n │ │ (33 Parameters) │\n │ ├─────────────────────────────►│\n │ │ │\n │ │ Creative Analysis │\n │ │ + Scores + Explanations │\n │ │◄─────────────────────────────┤\n │ │ │\n │ │ Store Results │\n │ ├─────────────────────┐ │\n │ │ │ │\n │ │◄────────────────────┘ │\n │ │ │\n │ Results │ │\n │◄───────────────────────┤ │\n │ │ │\n```\n\n### 8.2 Semantic Fingerprint Generation\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ SEMANTIC FINGERPRINT GENERATION PIPELINE │\n└─────────────────────────────────────────────────────────────────────────────┘\n\n ┌─────────────────┐\n │ MEDIA INPUT │\n └────────┬────────┘\n │\n ┌───────────────┼───────────────┐\n │ │ │\n ▼ ▼ ▼\n ┌───────────┐ ┌───────────┐ ┌───────────┐\n │ VIDEO │ │ AUDIO │ │ METADATA │\n │ STREAM │ │ STREAM │ │ CONTEXT │\n └─────┬─────┘ └─────┬─────┘ └─────┬─────┘\n │ │ │\n ┌─────────┼─────────┐ │ │\n │ │ │ │ │\n ▼ ▼ ▼ ▼ │\n ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐ │\n │ CLIP │ │Scene │ │Motion │ │Whisper│ │\n │Embed │ │Detect │ │Analyze│ │Transcr│ │\n └───┬───┘ └───┬───┘ └───┬───┘ └───┬───┘ │\n │ │ │ │ │\n └────┬────┴────┬────┘ │ │\n │ │ │ │\n ▼ ▼ ▼ │\n ┌───────────┐ ┌───────────┐ ┌───────────┐ │\n │ VISUAL │ │ TEMPORAL │ │ SPEECH │ │\n │ VECTOR │ │ VECTOR │ │ VECTOR │ │\n │ (128-d) │ │ (128-d) │ │ (128-d) │ │\n └─────┬─────┘ └─────┬─────┘ └─────┬─────┘ │\n │ │ │ │\n └──────┬──────┴──────┬──────┘ │\n │ │ │\n ▼ ▼ ▼\n ┌─────────────┐ ┌─────────────┐ ┌─────────────┐\n │ NARRATIVE │ │ EMOTIONAL │ │ SEMANTIC │\n │ ENCODER │ │ ENCODER │ │ ENCODER │\n │ (DeepSeek) │ │ (DeepSeek) │ │ (DeepSeek) │\n └──────┬──────┘ └──────┬──────┘ └──────┬──────┘\n │ │ │\n ▼ ▼ ▼\n ┌───────────┐ ┌───────────┐ ┌───────────┐\n │ NARRATIVE │ │ EMOTIONAL │ │ SEMANTIC │\n │ VECTOR │ │ TRAJECTORY│ │ DENSITY │\n │ (128-d) │ │ (128-d) │ │ (128-d) │\n └─────┬─────┘ └─────┬─────┘ └─────┬─────┘\n │ │ │\n └───────────────┼───────────────┘\n │\n ┌───────▼───────┐\n │ FUSION │\n │ LAYER │\n │ (Attention) │\n └───────┬───────┘\n │\n ┌───────▼───────┐\n │ COMBINED │\n │ SEMANTIC DNA │\n │ (768-dim) │\n └───────────────┘\n```\n\n---\n\n## 9. DATABASE SCHEMA\n\n```sql\n-- Semantic Fingerprints Table\nCREATE TABLE semantic_fingerprints (\n id UUID PRIMARY KEY DEFAULT gen_random_uuid(),\n organization_id UUID NOT NULL REFERENCES organizations(id),\n project_id UUID REFERENCES projects(id),\n source_type VARCHAR(50) NOT NULL, -- 'video', 'audio', 'document'\n source_id UUID NOT NULL,\n media_url TEXT,\n content_hash VARCHAR(64),\n duration_ms INTEGER,\n \n -- Vector embeddings (using pgvector)\n combined_sdf vector(768), -- Combined Semantic DNA Fingerprint\n narrative_vector vector(128),\n visual_vector vector(128),\n auditory_vector vector(128),\n temporal_vector vector(128),\n emotional_vector vector(128),\n semantic_density_vector vector(128),\n \n -- Temporal segments\n temporal_sdfs JSONB, -- Array of per-segment fingerprints\n \n -- Metadata\n metadata JSONB DEFAULT '{}',\n originality_score DECIMAL(5,2),\n style_tags TEXT[],\n \n created_at TIMESTAMPTZ DEFAULT NOW(),\n updated_at TIMESTAMPTZ DEFAULT NOW()\n);\n\n-- Creative QC Results in QC Jobs\nALTER TABLE qc_jobs ADD COLUMN IF NOT EXISTS\n creative_qc_status VARCHAR(20) DEFAULT 'pending',\n creative_qc_overall_score INTEGER,\n creative_qc_risk_score INTEGER,\n creative_qc_brand_fit_score INTEGER,\n creative_qc_parameters JSONB,\n creative_qc_summary TEXT,\n creative_qc_recommendations JSONB,\n creative_qc_started_at TIMESTAMPTZ,\n creative_qc_completed_at TIMESTAMPTZ,\n creative_qc_error TEXT;\n\n-- Feature Flags\nCREATE TABLE feature_flags (\n id UUID PRIMARY KEY DEFAULT gen_random_uuid(),\n organization_id UUID NOT NULL REFERENCES organizations(id),\n feature_key VARCHAR(100) NOT NULL,\n enabled BOOLEAN DEFAULT false,\n config JSONB DEFAULT '{}',\n created_at TIMESTAMPTZ DEFAULT NOW(),\n updated_at TIMESTAMPTZ DEFAULT NOW(),\n UNIQUE(organization_id, feature_key)\n);\n\n-- Indexes for vector similarity search\nCREATE INDEX idx_semantic_fingerprints_combined_sdf \n ON semantic_fingerprints USING ivfflat (combined_sdf vector_cosine_ops);\n\nCREATE INDEX idx_semantic_fingerprints_org \n ON semantic_fingerprints(organization_id);\n```\n\n---\n\n## 10. ENVIRONMENT CONFIGURATION\n\n```bash\n# Required Environment Variables for SPI\n\n# Groq Whisper (Transcription)\nGROQ_API_KEY=gsk_xxxxxxxxxxxxx\nGROQ_API_BASE=https://api.groq.com/openai/v1\nGROQ_WHISPER_MODEL=whisper-large-v3-turbo\n\n# DeepSeek (Creative Analysis)\nDEEPSEEK_API_KEY=sk-xxxxxxxxxxxxx\nDEEPSEEK_API_BASE=https://api.deepseek.com/v1\nDEEPSEEK_MODEL_NAME=deepseek-chat\nDEEPSEEK_TIMEOUT_MS=120000\n\n# Supabase (Database)\nNEXT_PUBLIC_SUPABASE_URL=https://xxx.supabase.co\nSUPABASE_SERVICE_ROLE_KEY=eyJxxxx\n```\n\n---\n\n## 11. COMMERCIAL APPLICATIONS\n\n### 11.1 Primary Markets\n\n1. **Post-Production QC** - Automated creative quality assessment for studios\n2. **Content Moderation** - Risk and safety scoring at scale\n3. **Rights Management** - Content provenance and derivative tracking\n4. **Recommendation Systems** - Semantic content matching\n5. **Brand Compliance** - Automated brand guideline checking\n\n### 11.2 Revenue Model\n\n| Feature | Target Market | Pricing Model |\n|---------|--------------|---------------|\n| Creative QC | Studios, Agencies | Per-minute processing |\n| Content Matching | Streaming, Social | SaaS subscription |\n| Risk Scoring | All platforms | API call pricing |\n| Provenance Tracking | Rights holders | Enterprise license |\n\n---\n\n## 12. CONCLUSION\n\nSemantic Provenance Intelligence represents a paradigm shift from technical content analysis to semantic understanding. By creating multi-dimensional fingerprints that capture creative intent, emotional impact, and narrative structure, SPI enables applications that were previously impossible—from automated creative quality assessment to intelligent content matching across heterogeneous formats.\n\n---\n\n**Document Version:** 1.0 \n**Last Updated:** December 2024 \n**Classification:** Patent Application - Confidential\n\n\n"}}}} |