Episodic Memory Store for Adaptive Semantic Querying
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data management techniques face challenges in extracting actionable information from vast datasets, often requiring computationally intensive processes and static training stages, which limits their ability to adapt and provide personalized insights in real-time.
Innovation Solution
The system employs an episodic memory store that contextualizes data with labels, allowing for semantic queries to extract information efficiently, reducing the need for extensive training and enabling adaptive learning and personalized feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data management techniques are used to extract actionable information from vast datasets, then comprehensive data analysis can be performed, but the process becomes computationally intensive and requires extensive training
Solution Approach 1:
The patent segments data into discrete episodes with contextual labels, organizing information into manageable units that can be stored and queried efficiently. This segmentation allows the system to process only relevant episodes during queries rather than analyzing entire datasets, reducing computational intensity while maintaining extraction accuracy.
Solution Approach 2:
The system performs preliminary organization of data into episodic memory structures with contextual labels during the data ingestion phase. This preliminary action creates an indexed, labeled framework that enables rapid retrieval and analysis during queries, eliminating the need for computationally intensive training processes while preserving information extraction capabilities.
2Stability of the object's composition
If static training stages are used in traditional systems, then model structure can be fixed, but the system loses adaptability and cannot provide personalized insights in real-time
Solution Approach 1:
The patent implements a dynamic system where episodic memory structures can be continuously updated and modified without requiring retraining. The contextual labels and episode associations are maintained in a flexible structure that adapts to new information in real-time, enabling personalized insights while preserving the stable core architecture of the memory system.
Solution Approach 2:
The system performs self-adaptation through automated episode creation and contextual labeling without requiring external training interventions. The episodic memory structure automatically organizes new data and updates associations based on incoming information, providing real-time adaptability while maintaining structural stability through consistent organizational principles.
3Adaptability or versatility
If exhaustive training is performed to enable adaptive learning and personalized feedback, then system intelligence can be enhanced, but computational and memory requirements increase significantly
Solution Approach 1:
The patent extracts only the essential contextual information and relationships from data, storing them as labeled episodes in memory rather than retaining complete datasets or complex trained models. This extraction approach enables adaptive learning capabilities by maintaining key contextual associations while minimizing memory consumption by storing only relevant episodic information.
Solution Approach 2:
The system uses lightweight episodic representations that can be created, stored, and discarded efficiently without requiring persistent large-scale model structures. Each episode is a self-contained, low-memory unit that provides adaptive learning functionality without the heavy computational burden of traditional trained models, allowing flexible memory management.
Data Source
AI summary
An example computing system may operate on episodes stored in an episodic memory using a semantic query language. The semantic query language may associate contextual labels with the data ingested from the data sources. Systems described herein may determine probabilities of an event based on the episodes including previous observations, counts, similarities, anomalies, and causality among many other techniques and methodologies. In some examples, the systems described herein may provide result explanations by providing references to source data pertinent to a given result. The user may provide feedback to the query results and update the semantic query.