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Neurigraph vs. Existing Memory Systems: A Comprehensive Analysis

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Executive Summary

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1. Architecture Overview

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1.1 Neurigraph (aiConnected Brain)

• Category Layer: Broad knowledge domains (Business, Law, Medicine, Psychology, Engineering)
• Concept Layer: High-level fields within categories (Sales, Marketing, Operations within Business)
• Topic Layer: Focused functional domains where experience becomes behavior (Objection Handling, Closing Strategies within Sales)

1. Structured Memory Tables: Each Topic node contains its own relational database with domain-specific schemas
   • Call transcripts with fields: scenario, tone_used, tactic_used, outcome, feedback, trainer_note, timestamp
   • Pricing strategy memories: phrase_used, timing, customer_reaction, win_rate_impact, notes
   • Not flat text strings; actual queryable structured data
2. Reflection Engine: Uses LLMs to generate natural language interpretations of accumulated data
   • Periodic summarization of Topic-level memory tables
   • Pattern recognition and insight generation
   • Version-controlled, continuously updated reflections
3. Vector Memory Layer: Embeddings of reflections enable semantic retrieval
   • Lightning-fast context-aware prompting
   • Filtered by Category/Concept/Topic hierarchy
   • Uses pgvector, Pinecone, or similar
4. Dual-Layer Cognition:
   • Open Thinking Layer (OTL): Provisional memory space for new learnings
   • Closed Thinking Layer (CTL): Governance engine enforcing rules, original intent, compliance constraints
   • CTL validates all OTL content before permanent storage
5. Graph-Like Cross-Linking: Topics connect across Concepts and Categories
   • Sales EQ links to both Sales (Business) and Emotional Intelligence (Psychology)
   • Handling Budget Objections connects to Pricing Psychology (Behavioral Economics)
6. Human-Guided Memory Only: Permanent memories form exclusively from human interaction data
   • Internet data referenced only if part of human conversation
   • No passive web scraping for direct memory formation
7. Complete Conversation Transcripts: Full session logs tied to knowledge graph nodes

• Cross-platform persistence (Claude, ChatGPT, Gemini)
• Consumer-focused subscription model ($9/month target)
• Desktop app + MCP + Custom GPTs + Gems unified interface
• Mobile strategy via custom chat interface with OpenRouter backend

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1.2 Anthropic MCP Memory Server

• Entities: Nodes with name, type, and observations (text strings)
• Relations: Directed connections between entities (stored in active voice)
• Observations: Discrete text facts attached to entities

• Single JSONL file storage
• Text search across names, types, observation content
• Basic CRUD operations: create entities, create relations, add/remove observations, read graph, search nodes

• No hierarchical structure (flat graph)
• No structured data within nodes (just text observations)
• No interpretation/reflection layer
• No governance or rule validation
• Single-instance, single-platform
• No source validation
• No conversation threading

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1.3 OpenMemory (CaviraOSS)

• Five Memory Sectors: Episodic (events), Semantic (facts), Procedural (skills), Emotional (feelings), Reflective (insights)
• Temporal Knowledge Graph: valid_from/valid_to timestamps, point-in-time truth queries
• Waypoint Graph: Single-waypoint linking mechanism (sparse, biologically-inspired)
• Composite Scoring: Salience + recency + coactivation (not just cosine similarity)
• Adaptive Decay Engine: Sector-specific forgetting curves instead of hard TTLs
• Explainable Recall: Trace paths showing which nodes contributed to retrieval

• SQLite/PostgreSQL for relational data
• Vector embeddings (768-dim, quantized)
• TypeScript + Node.js backend
• REST API + MCP server
• Support for OpenAI, Gemini, Ollama, AWS embeddings

• Local-first or centralized server deployment
• Python and JavaScript SDKs
• LangChain, CrewAI, AutoGen, Streamlit integrations
• Connectors: GitHub, Notion, Google Drive, OneDrive, web crawler
• Migration tool from Mem0, Zep, Supermemory
• VS Code extension

• 2-3× faster contextual recall vs. hosted APIs
• 6-10× lower cost than SaaS solutions
• 95% recall stability, 338 QPS average, 7.9ms/item scalability

• No per-topic structured databases (memories still stored as classified text)
• Reflective sector is a memory type, not a dynamic generation engine
• No governance layer (no OTL/CTL equivalent)
• Accepts external data sources (web crawling, GitHub imports)
• Developer-focused infrastructure, not consumer product
• No cross-platform consumer packaging

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2. Detailed Feature Comparison

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2.1 Memory Organization

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2.2 Intelligence Layer

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2.3 Governance & Safety

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2.4 Retrieval & Performance

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2.5 Integration & Deployment

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3. Unique Neurigraph Capabilities

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3.1 Structured Per-Topic Databases

- **Objection Handling Topic** might have schema: `{scenario, tone_used, tactic_used, outcome, feedback, trainer_note, timestamp}`

- **Pricing Strategy Topic**: `{phrase_used, timing, customer_reaction, win_rate_impact, notes}`

• Enables SQL-style queries and aggregations
• Supports trend analysis and reporting
• Maintains audit trails with full fidelity
• Powers advanced analytics impossible with text-only storage

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3.2 Dynamic Reflection Engine

1. Aggregate recent data from Topic’s memory table
2. LLM generates summary, identifies patterns, extracts insights
3. Reflection stored as text and vectorized
4. Version-controlled; updates as more data arrives

• Memory: “17 sessions where delaying price disclosure until after value framing”
• Reflection: “Past 17 sessions show 42% conversion increase when price revealed post-value. Consider as default tactic.”

• Creates “living thoughts” that evolve with experience
• Bridges structured data and natural language reasoning
• Enables contextual retrieval based on interpreted meaning, not just keywords

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3.3 Closed Thinking Layer (CTL)

• Stores Original Intent definitions per Category/Concept
• Enforces ethical boundaries, compliance rules
• Can approve, expire, or reject memories
• Prevents cognitive drift and bias accumulation

• Approve: Memory becomes permanent
• Expire: Memory set with TTL (e.g., 7 days for provisional data)
• Reject: Immediate deletion with audit log

• Prevents AI from learning harmful patterns
• Maintains alignment with intended behavior
• Creates accountability and transparency
• Enables enterprise compliance (GDPR, HIPAA, industry regulations)

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3.4 Cross-Platform Consumer Product

• Claude: Native MCP integration
• ChatGPT: Custom GPT with Actions calling Brain API
• Gemini: Gem with function calling to Brain API
• Mobile: Custom chat app with OpenRouter backend

• User’s AI memory is portable, not locked to one vendor
• Consistent context regardless of which model they’re using
• Subscription revenue model ($9/month target)
• Consumer-accessible, no technical setup required

• MCP Memory: Claude ecosystem only
• OpenMemory: Self-hosted developer infrastructure, no consumer packaging
• Neurigraph is the only system designed for cross-platform consumer use

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4. OpenMemory as Foundation

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4.1 What OpenMemory Provides

• Multi-sector memory classification
• Temporal knowledge graph
• Vector embeddings and retrieval
• Composite scoring (salience + recency + coactivation)
• Adaptive decay per sector
• Explainable waypoint traces
• MCP server infrastructure
• Python and JavaScript SDKs

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4.2 Strategic Options

1. Fork OpenMemory repository
2. Add Neurigraph-specific layers:
   • Structured per-topic database schemas
   • Dynamic reflection generation engine
   • CTL governance and rule validation
   • Cross-platform consumer wrapper
3. Build subscription service on top
4. Market as “Brain by aiConnected powered by OpenMemory core”

• Ship cross-platform memory in weeks, not months
• Proven infrastructure handles vector operations, decay, retrieval
• Focus development on unique differentiators
• Apache license allows proprietary additions
• Credibility from established open-source foundation

• Dependency on external codebase evolution
• Need to maintain fork if upstream diverges
• Less “clean sheet” architectural control

1. Design complete system independently
2. Implement all components in-house
3. Full control over architecture, no external dependencies
4. Launch when feature-complete

• Perfect alignment with vision
• No technical debt from inherited code
• Complete intellectual property ownership
• Freedom to optimize for specific use cases

• 12-18 month development timeline
• No market validation until much later
• Reinventing proven components (vector search, MCP server)
• Higher engineering cost and resource requirements

1. Use OpenMemory for base infrastructure (vector storage, MCP, temporal graph)
2. Build Neurigraph’s unique layers on top:
   • Per-topic structured database system
   • Reflection generation engine
   • CTL governance module
   • Consumer cross-platform interface
3. Ship iteratively:
   • Week 1-2: Deploy OpenMemory, validate cross-platform MCP
   • Week 3-4: Add structured topic databases
   • Week 5-6: Implement reflection engine
   • Week 7-8: Build CTL governance
   • Week 9-10: Consumer wrapper and billing
4. Document divergence points where Neurigraph exceeds OpenMemory

• Fast time to market (weeks vs. months)
• Real user feedback informs development
• Proven foundation reduces risk
• Focused engineering on differentiators
• Option to replace base layer later if needed

• Validate demand with OpenMemory core
• Build revenue through aiConnected Knowledge and Chat
• Invest revenue in proprietary Neurigraph components
• Transition users to fully integrated Brain product

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5. Neurigraph Patentability Assessment

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5.1 Novelty

1. Hierarchical Category → Concept → Topic memory organization with per-node structured databases
2. Dynamic LLM-generated reflections from accumulated structured data, stored separately as vectorized interpretations
3. Dual-layer cognitive architecture (OTL + CTL) with policy-driven memory validation
4. Integration of structured relational data, semantic graphs, and vector embeddings in unified retrieval system
5. Human-guided memory formation with explicit prohibition of passive external data ingestion

• Traditional knowledge graphs (Neo4j, Wikidata): No per-node databases or reflection generation
• Vector databases (Pinecone, Weaviate): No conceptual hierarchy or structured schemas
• RAG systems (LangChain): No reflection layer or governance
• OpenMemory: Multi-sector classification but no structured data or governance

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5.2 Non-Obviousness

• Combining relational databases at the graph node level is not standard practice
• Reflection generation as a separate computed layer (vs. storing reflections as data) represents architectural insight
• CTL governance as mandatory validation gate is unique to Neurigraph
• Integration pattern of structured data → LLM interpretation → vector embedding → retrieval requires specific design decisions not apparent from prior systems

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5.3 Utility & Industrial Applicability

• AI sales agents with evolving tactic libraries
• Customer service systems that learn from escalations
• Legal assistants with case precedent memory
• Medical AI with diagnostic pattern recognition
• Educational tutors tracking student comprehension
• Personal AI assistants with true long-term memory

• Reduces AI training costs through accumulated experience
• Enables compliance and audit trails
• Improves AI accuracy through structured learning
• Creates competitive moat through proprietary memory architecture

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5.4 Patentable Claims

1. A method for organizing artificial intelligence memory in a three-tiered hierarchical structure comprising Categories, Concepts, and Topics, wherein each Topic node contains a domain-specific relational database schema
2. A system for generating dynamic natural language reflections by periodically analyzing accumulated structured data within Topic-level databases using large language models, storing said reflections as separately vectorized interpretations
3. A dual-layer cognitive architecture comprising an Open Thinking Layer for provisional memory storage and a Closed Thinking Layer for policy-driven validation, wherein all permanent memory storage requires explicit approval through rule-based governance
4. A method for hybrid memory retrieval combining structured database queries, semantic vector search of LLM-generated reflections, and graph traversal of Topic-Concept-Category relationships
5. A cross-platform AI memory synchronization system enabling persistent context across multiple AI interfaces through unified backend storage and platform-specific integration adapters

1. The process of converting human interaction data into structured memories, generating interpretations, validating against policies, and storing approved content in hierarchical graph structure
2. The method of reflection regeneration triggered by accumulated data thresholds, version control of evolving interpretations, and automatic re-vectorization
3. The workflow for CTL rule enforcement including memory approval, expiration, and rejection with audit logging

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5.5 Patent Strategy Recommendation

• 12-month “Patent Pending” status
• Establishes priority date
• Low cost ($70-300 self-filed)
• Provides time to refine claims while building product

• System architecture diagrams
• Detailed component descriptions
• Use case examples with specific schemas
• Comparison to existing systems highlighting novelty
• Technical implementation details sufficient for enablement

• Month 1: File provisional
• Months 2-12: Build product, gather usage data, refine architecture
• Month 12: File full utility patent with strengthened claims based on implementation experience

• Patent the architecture and core methods
• Keep specific algorithms, scoring formulas, and optimization techniques as trade secrets
• Creates layered IP protection difficult for competitors to replicate

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6. Go-to-Market Strategy

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6.1 Phase 1: Validation (Weeks 1-4)

1. Deploy OpenMemory backend to Railway/Render
2. Configure Claude MCP integration
3. Build Custom GPT for ChatGPT
4. Build Gem for Gemini
5. Create simple web dashboard
6. Recruit 50 beta users from existing network

• 50+ active users
• 70% weekly retention
• Positive feedback on cross-platform utility

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6.2 Phase 2: Differentiation (Weeks 5-12)

1. Implement structured per-topic databases
   • Define initial schemas for common use cases
   • Build schema management interface
   • Enable structured queries
2. Build reflection generation engine
   • LLM prompt templates for interpretation
   • Automated periodic reflection jobs
   • Version control system
3. Implement basic CTL governance
   • Rule definition interface
   • Memory approval workflow
   • Audit logging

• Users report improved relevance vs. basic memory
• Structured data enables new use cases
• Reflections surface insights users wouldn’t have found manually

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6.3 Phase 3: Consumer Product (Weeks 13-20)

1. Build desktop wrapper app
   • Electron/Tauri application
   • Auto-configuration for Claude Desktop
   • System tray presence
2. Implement account system and billing
   • Stripe integration
   • Subscription management ($9/month tier)
   • Usage analytics
3. Create onboarding flow
   • Platform selection (Claude/ChatGPT/Gemini)
   • Initial preference capture
   • Quick-start guide
4. Launch marketing campaign
   • Target AI power users
   • Content: “Your AI should remember you everywhere”
   • Focus on portability vs. vendor lock-in

• 500 paying subscribers @ $9/month =$4,500 MRR
• <10% monthly churn
• Organic growth through word-of-mouth

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6.4 Phase 4: Platform Launch (Months 6-12)

1. Build custom chat application
   • OpenRouter integration for model selection
   • Chat/browser hybrid interface
   • Native Brain integration (no Custom GPT intermediary)
2. Mobile app development
   • iOS and Android versions
   • Full feature parity with desktop
3. Advanced Neurigraph features
   • Complete CTL governance suite
   • Advanced structured databases
   • Cross-topic analytics and insights
   • ANI (Acquired Network Intelligence) pilot

• 5,000 paying users @ $9/month =$45K MRR
• 30% of users migrated from Custom GPT/Gem to native app
• Brain positioned as platform, not plugin

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7. Competitive Positioning

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7.1 vs. ChatGPT Memory

• Cross-platform portability
• User owns and controls data
• Structured memory with queryable fields
• No vendor lock-in

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7.2 vs. OpenMemory

• Consumer-ready packaging
• Structured per-topic databases
• Dynamic reflection generation
• Governance and compliance features
• Cross-platform consumer integration

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7.3 vs. RAG Solutions

• Hierarchical organization (not flat chunks)
• Structured data within memory nodes
• LLM-generated interpretations
• Temporal and relationship awareness
• Behavioral adaptation from experience

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8. Risk Analysis

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8.1 Technical Risks

• Validate core use cases early in Phase 1
• Design abstraction layer allowing backend swap
• Budget for potential rewrite in Phase 4

• Use smaller local models for reflection generation
• Batch reflection jobs during off-peak hours
• Implement reflection caching and incremental updates

• Abstract platform integrations behind adapter layer
• Monitor vendor changelogs and beta programs
• Maintain fallback paths (web interface always works)

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8.2 Market Risks

• Free tier with Claude-only access
• Paid tier unlocks ChatGPT/Gemini
• Demonstrate clear value before conversion ask

• Structural advantages (topic databases, reflection engine) hard to replicate
• First-mover advantage in cross-platform space
• IP protection through patents

• One-click installer for desktop
• Automated platform configuration
• Video onboarding and support

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8.3 Legal Risks

• File provisional to establish priority
• Work with patent attorney for utility filing
• Layer IP protection with trade secrets

• Review ToS for Custom GPT and Gem programs
• Structure as “user brings own API key” where needed
• Maintain compliant implementation

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9. Success Metrics & Milestones

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9.1 Phase 1 Success (Week 4)

• 50 active beta users
• 70% weekly retention
• Positive qualitative feedback
• Zero critical bugs reported

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9.2 Phase 2 Success (Week 12)

• Structured databases implemented for 5 use cases
• Reflection engine generating insights automatically
• Basic CTL governance operational
• Users report 30% improvement in AI relevance

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9.3 Phase 3 Success (Week 20)

• 500 paying subscribers
• $4,500 MRR
• <10% monthly churn
• Desktop app distributed through official channels

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9.4 Phase 4 Success (Month 12)

• 5,000 paying subscribers
• $45,000 MRR
• Native app launched (web + mobile)
• Patent filed and pending
• Brain positioned as platform, not plugin

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10. Conclusion

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10.1 Neurigraph’s Position

1. Structured per-topic relational databases
2. Dynamic LLM-generated reflections from operational data
3. Dual-layer governance with policy enforcement
4. Cross-platform consumer accessibility
5. Human-guided memory formation

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10.2 Recommended Path Forward

1. Fork OpenMemory and deploy to cloud infrastructure
2. Implement cross-platform integrations (Claude MCP, ChatGPT Custom GPT, Gemini Gem)
3. File provisional patent application
4. Recruit 50 beta users from network

1. Add structured per-topic databases
2. Build reflection generation engine
3. Implement CTL governance
4. Launch consumer desktop app with billing

1. Build native chat interface with OpenRouter
2. Launch mobile applications
3. Convert provisional to utility patent
4. Scale to 5,000 subscribers and $45K MRR

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10.3 Strategic Value

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Appendix A: Technical Architecture Diagrams

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Appendix B: Sample Schemas

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Sales Objection Handling Topic Schema

CREATE TABLE objection_handling_memory (
    id UUID PRIMARY KEY,
    scenario TEXT NOT NULL,
    tone_used VARCHAR(50),
    tactic_used VARCHAR(100),
    outcome VARCHAR(50),
    feedback TEXT,
    trainer_note TEXT,
    timestamp TIMESTAMP DEFAULT NOW(),
    approved BOOLEAN DEFAULT FALSE
);

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Pricing Strategy Topic Schema

CREATE TABLE pricing_strategy_memory (
    id UUID PRIMARY KEY,
    phrase_used TEXT NOT NULL,
    timing VARCHAR(50),
    customer_reaction VARCHAR(100),
    win_rate_impact DECIMAL(5,2),
    notes TEXT,
    timestamp TIMESTAMP DEFAULT NOW(),
    approved BOOLEAN DEFAULT FALSE
);

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Appendix C: Reflection Generation Prompt Template

You are analyzing accumulated sales training data to generate insights.

Topic: {topic_name}
Recent Sessions: {session_count}
Date Range: {start_date} to {end_date}

Data Summary:
{structured_data_summary}

Generate a concise reflection (2-3 sentences) that:
1. Identifies the primary pattern or trend
2. States the quantitative impact if measurable
3. Suggests a behavioral recommendation

Reflection:

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Appendix D: CTL Rule Examples

{
  "rules": [
    {
      "scope": "category:Business",
      "rule": "Reject memories containing discriminatory language based on protected characteristics",
      "action": "reject",
      "severity": "critical"
    },
    {
      "scope": "concept:Sales",
      "rule": "Expire provisional pricing strategies after 30 days without validation",
      "action": "expire",
      "severity": "medium"
    },
    {
      "scope": "topic:ObjectionHandling",
      "rule": "Approve tactics only if outcome field is populated",
      "action": "require_validation",
      "severity": "low"
    }
  ]
}