Title: Conformal Prediction for Inventors
Description: This book was written because a gap in the available literature was noticed when the author began filing patents. Existing books were found to either focus solely on the legal aspects of patenting or provide guidelines on generating ideas and innovations. What was missing was a practical example that demonstrated how to design an innovative solution using applied conformal prediction for specific problem statements within risk-sensitive domains, particularly in enterprise data storage.

The book expertly blends theoretical concepts with practical applications, guiding industry professionals through the complexity of applied conformal prediction in the enterprise sector. Readers are shown real-world scenarios, exploring the intersection of machine learning principles and risk-sensitive domains, with a focus on uncertainty quantification and conformal prediction for robust real-time decision-making in high-risk areas. The sections covered in the book includes the Internet of Things, persistent storage memory, data storage reliability, and the blockchain technology. Each chapter offers fresh insights drawn from valuable industry experience, serving as a reliable companion for aspiring inventors and providing a roadmap for creative solutions. Beyond a mere compilation of methods, the book acts as a vibrant guide, empowering industry professionals to fully embrace the transformative potential of conformal prediction through practical wisdom and innovative application.

The book chapters six sections with below chapters namely, conformal prediction, hardware security, data storage, persistent memory, telemetry, and DNA based storage.

1. Enterprise Disk Management
2. Risk Aware Decision in Healthcare Using UQ
3. Discerning Scrub Based on Multi-Probabilistic Approach
4. Machine Learning for Hardware Trojan Detection
5. Multimodal Deep Learning with Uncertainty Consideration
6. Stochastic Risk Scoring for Storage Capacity
7. SCM Configuration in a De-duplication Based Storage
8. Hash Structure for NVME with a Dual-Relationship
9. Predictive Logging Analytical Engine
10. Smart Telemetry Driven State Transition Forecasting
11. Securely Archiving Digital Information in Oligonucleotides
Subjects: Computer science, Artificial intelligence, Uncertainty (Information theory), Machine learning, Patents