NFT Engine for Meaningful Memory Classification and Recreation
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Solution Overview
Problem
Conventional techniques fail to accurately identify, memorialize, and recreate meaningful memories and experiences for retirees, leading to feelings of loneliness and melancholy, as they lack the ability to classify and enrich such memories effectively.
Innovation Solution
A system utilizing a Non-Fungible Token (NFT) engine that analyzes user data to classify memories based on thresholds, generates an NFT, and links it to a blockchain, allowing for sensory feedback to recreate the experience, incorporating data from various sources and enhancing the memory with additional context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to preserve memories, then storage is achieved, but the ability to identify and classify meaningful experiences is lost
Solution Approach 1:
The system employs machine learning algorithms that continuously analyze sensor data, user interactions, and contextual information to identify and classify meaningful memories. The system provides feedback loops where classified memories are refined based on user engagement and sensory feedback responses, improving classification accuracy over time while preserving the semantic meaning of experiences.
Solution Approach 2:
The patent replaces conventional manual or simple digital storage mechanisms with an intelligent system that uses machine learning, neural networks, and automated classification algorithms. This substitution enables the system to automatically identify, classify, and prioritize meaningful memories based on multiple data dimensions without requiring manual curation.
2Ease of operation
If conventional memory storage techniques are used, then memories are stored, but the ability to recreate experiences is lost
Solution Approach 1:
The system nests multiple layers of data within memory representations, including sensor data (audio, visual, haptic), contextual information, emotional states, and metadata. This nested structure allows the system to store comprehensive experience data and selectively recreate aspects of the original experience by accessing different data layers, enabling rich memory recreation without storing every possible detail at full resolution.
Solution Approach 2:
The patent utilizes parameter changes in data representation to enable efficient storage and recreation of experiences. By encoding sensory data in various formats and resolutions, and by dynamically adjusting which parameters are accessed during recreation based on user needs and available resources, the system can recreate experiences with high fidelity when needed while maintaining storage efficiency.
3Loss of information
If comprehensive data collection is implemented, then memory enrichment is improved, but system complexity increases
Solution Approach 1:
The system segments data collection and processing into distinct modular components: sensor data acquisition modules, contextual information gathering modules, machine learning classification modules, and memory storage modules. Each segment handles specific tasks independently, reducing overall system complexity while enabling comprehensive data collection and processing through coordinated operation of specialized subsystems.
4Measurement precision
If machine learning algorithms are used for memory classification, then classification accuracy improves, but processing time increases
Solution Approach 1:
The system applies partial processing by using machine learning algorithms selectively for classification tasks rather than processing all data uniformly. It employs automated classification for routine memory identification while using user feedback and contextual cues for refinement, achieving high accuracy without requiring exhaustive processing of every data point. The system performs excessive action in targeted areas (key classification decisions) while being efficient elsewhere.
Data Source
AI summary
Techniques are described herein for system for preserving memories and experiences through a Non-Fungible Token (NFT) engine. An example system includes one or more memories storing a set of computer-readable instructions including the NFT engine and one or more processors interfacing with the one or more memories. The one or more processors are configured to execute the set of computer-readable instructions to cause the system to: receive data of a user from a time period, and execute the NFT engine to: analyze the data to classify a memory experienced by the user during the time period based on a memory threshold, generate, for display to the user, an NFT based on the memory, and mint the NFT corresponding to the memory to a blockchain, wherein a portion of the data associated with the memory is linked to the NFT.