Episodic Object Memory for Salient Landmark Extraction
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Solution Overview
Problem
Current systems face challenges in efficiently storing and retrieving relevant memories from vast experiences, as they struggle to selectively encode and access important information due to the bounded nature of cognitive resources, leading to difficulties in recalling specific details from everyday life events.
Innovation Solution
The method involves using episodic object memory systems that integrate content data into an organized metric space through embedding models, ranking, weighting, and scoring embeddings to identify landmark memories, which are then stored and retrieved based on temporal, spatial, and social relationships, enabling efficient access and retrieval of relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If systems store large quantities of experiences and memories, then the quantity of stored information increases, but the ability to selectively recall and reason about relevant content decreases due to bounded cognitive resources
Solution Approach 1:
The patent extracts salient features from experiences and stores only the most important information as landmark memories rather than storing all raw experience data. This extraction process filters and selects only the most relevant information for future recall, resolving the contradiction between storing large quantities of information and maintaining selective recall capability.
Solution Approach 2:
The patent segments memories into landmark memories that capture essential features and non-landmark details that provide context. This segmentation allows the system to store comprehensive information while maintaining efficient retrieval by accessing landmark memories as entry points to related information, thus resolving the contradiction between quantity stored and selectivity of recall.
2Reliability
If systems store all experience data for comprehensive recall, then information completeness improves, but computational resources and memory storage requirements increase
Solution Approach 1:
The system extracts and stores only the salient features and landmark memories from experiences rather than storing all raw data. This extraction maintains recall completeness for important information while significantly reducing memory storage requirements, as only the essential features are preserved rather than all experience data.
Solution Approach 2:
The patent creates compressed representations (embeddings) of experiences that capture essential information in a condensed format. These copying process creates efficient representations that maintain recall completeness while reducing the amount of data that needs to be stored, thus resolving the contradiction between reliability and storage requirements.
3Adaptability or versatility
If systems use unstructured data storage for experiences, then storage flexibility improves, but retrieval efficiency and user recall effectiveness deteriorates
Solution Approach 1:
The patent applies different structural characteristics to different parts of the memory system: landmark memories are stored with structured metadata including temporal, spatial, and contextual information for efficient retrieval, while non-landmark details are stored as supporting context. This local quality differentiation maintains storage flexibility for diverse experience types while improving retrieval efficiency through structured organization of key information.
Data Source
AI summary
A method for identifying and storing a landmark memory in an episodic object memory is provided that includes receiving one or more content items. The content items each have one or more content data. The one or more content data associated with the one or more content items may be provided and integrated into one or more embedding models that represent a growing set of episodic memories. Episodic memory relates to the ability to recall content from one's personal past, such as in the form of landmark memories, which may be filtered from a plurality of memories based on a degree of salience. The one or more landmark memories and references to related content items are inserted into an episodic object memory for later recall and use in the course of context and task at hand.