Hybrid Memory Retention System Using Blockchain Storage
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Solution Overview
Problem
Human memory retention and recall functions are limited, leading to challenges in retaining and retrieving short-term and long-term memories effectively, necessitating a system that augments biological memory capabilities.
Innovation Solution
A computer-implemented method and system that constructs electronic memory data structures from speech, text, and image inputs, using context parameters like entities, emotions, and locations, and stores them in a blockchain-based database for easy retrieval via a natural language interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human biological memory is used for retaining and retrieving information, then the system is simple and requires no external components, but the retention and recall functions are limited and unreliable
Solution Approach 1:
The patent merges biological memory with an electronic memory retention system. The system captures memories through multiple input modalities (speech, text, images), processes them through AI models, and stores them in a blockchain-based database, creating a hybrid memory architecture that supplements human biological memory with enhanced reliability and capacity.
Solution Approach 2:
The patent introduces an intermediary system between the user and memory storage. This intermediary includes AI processing models that convert various input formats into standardized memory data structures, and a blockchain-based database that provides secure, permanent storage, thereby bridging the gap between human memory limitations and ideal memory performance.
2Adaptability or versatility
If multiple types of memory data structures are constructed from different input modalities, then the system can capture comprehensive memory information, but the system complexity increases
Solution Approach 1:
The patent implements a universal memory data structure that can accommodate multiple input modalities (speech, text, images). The system uses AI processing models to convert each modality into a standardized representation that fits within the same data structure framework, allowing the system to handle diverse memory inputs without requiring separate storage mechanisms for each type.
Solution Approach 2:
The patent changes the representation parameters of memory data based on input modality. Speech inputs are transcribed and converted to text representations, images are processed into descriptive data, and text inputs are directly formatted into memory structures. This parameter transformation allows diverse inputs to be unified into a consistent data structure format.
3Reliability
If a blockchain-based database is used for storing memory data, then data security and permanence are improved, but system complexity and storage requirements increase
Solution Approach 1:
The patent creates a digital copy of memory data and stores it on a blockchain-based database. Instead of requiring direct access to or manipulation of the original memory storage mechanisms, the system copies memory information into a secure, permanent digital format that can be reliably stored and retrieved, thereby enhancing security while simplifying the interaction layer.
4Productivity
If automatic construction of memory data structures is implemented, then memory retrieval efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of memory inputs immediately upon capture. AI processing models automatically convert speech to text, extract features from images, and format all inputs into standardized memory data structures in advance. This preliminary action prepares the data for rapid retrieval later, reducing the time required during actual memory access operations.
Solution Approach 2:
The patent replaces manual memory construction processes with automated AI-based processing. Instead of requiring users to manually organize and format memory data, the system uses AI models to automatically interpret, process, and structure raw inputs into retrievable memory data structures, thereby improving retrieval efficiency while the computational processing time is amortized over the duration of memory capture.
Data Source
AI summary
The present disclosure generally relates to a computer-implemented system for intelligently retaining and recalling memory data. An exemplary method comprises receiving, via a microphone of an electronic device, a speech input of the user; receiving a text input of the user; constructing a first instance of a memory data structure based on the speech input; constructing a second instance of the memory data structure based on the text input; adding the first instance and the second instance of the memory data structure to a memory stack of the user; displaying a user interface for retrieving memory data of the user; receiving, via the user interface, a beginning of a statement from the user; retrieving a particular instance of the memory data structure from the memory stack based on the beginning of the statement; and automatically displaying a completion of the statement.