Working Memory Event Data Chunk Presentation System
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Solution Overview
Problem
Users frequently forget information that they intend to remember, as it is not effectively retained in working memory and is not easily accessible when needed.
Innovation Solution
A system comprising a sensor, processor, and memory that detects a trigger policy based on user attention to categorize and store data chunks, and subsequently presents them when a query policy is satisfied, using a neural network to analyze raw data from sensors like cameras and microphones to identify and retrieve relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users rely on natural working memory to retain information, then the system remains simple and requires no additional technology, but information is quickly forgotten and not accessible when needed
Solution Approach 1:
The patent introduces an external digital system that acts as an intermediary between the user's working memory and long-term storage. Sensors capture raw data from the environment, the processor analyzes this data to identify relevant information chunks, and stores them externally. This intermediary system bridges the gap between temporary working memory and permanent storage, resolving the contradiction between reliable retention and system simplicity.
Solution Approach 2:
The patent replaces the biological mechanical system of human working memory with an electronic/digital system. Instead of relying on the brain's limited working memory capacity, the system uses sensors, processors, and digital storage to capture, analyze, and retain information externally, thereby achieving reliable information retention without overburdening human cognitive resources.
2Loss of information
If the system continuously monitors and stores all raw data to ensure complete information availability, then information accessibility is maximized, but energy consumption and data processing load increase significantly
Solution Approach 1:
The system performs preliminary analysis of raw sensor data to identify and extract only the relevant information chunks before storage. Rather than storing all raw data continuously, the processor pre-processes the data stream, detects meaningful patterns or events, and stores only those identified chunks. This preliminary action reduces the volume of stored data and associated energy consumption while preventing information loss for relevant content.
Solution Approach 2:
The patent extracts only the essential and relevant information from the continuous stream of raw sensor data. The processor analyzes incoming data and pulls out specific information chunks that meet predefined criteria for importance or relevance, storing only these extracted elements. This extraction approach minimizes energy consumption by avoiding the storage and processing of redundant or irrelevant data while maintaining complete information availability for what matters.
3Ease of operation
If the system stores detailed categorized data chunks for future retrieval, then information accessibility upon query is improved, but storage requirements and data processing complexity increase
Solution Approach 1:
The patent segments continuous raw data into discrete, manageable information chunks based on detected events or meaningful boundaries. Each chunk represents a coherent unit of information that can be independently stored and retrieved. This segmentation simplifies the retrieval process by allowing the system to search and return specific chunks rather than processing entire data streams, thereby improving ease of operation while managing storage requirements through organized modular units.
Solution Approach 2:
The system applies different processing and storage strategies to different types of data chunks based on their local characteristics or categories. Rather than uniformly processing all data, the processor identifies specific properties of each chunk (such as type, importance, or context) and applies appropriate storage and retrieval methods tailored to each category. This local quality approach optimizes retrieval ease for each data type while reducing overall processing complexity by avoiding one-size-fits-all methods.
Data Source
AI summary
For presenting data chunks for a working memory event, a processor detects a trigger policy for a working memory event being satisfied by raw data from a sensor. The trigger policy is based on user attention. The processor further categorizes a data chunk for the working memory event from the raw data. The processor detects a query policy being satisfied by a query from subsequent raw data. In response to the query policy being satisfied, the processor identifies the data chunk based on the query. The processor further presents the data chunk.