Truth Maintenance System for Accurate Predictions
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Solution Overview
Problem
Generating predictions from data retrieved from various sources is an inefficient process with little flexibility, often resulting in inaccurate predictions due to lack of consideration for additional data.
Innovation Solution
A method involving a computer processor that receives event data from RFID tags, associates it with RFID tags, derives assumption data using an inference engine and non-monotonic logic, retrieves previous assumption data, executes non-monotonic logic to generate updated assumption data, and stores it in a truth maintenance system database, enabling flexible and accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictions are generated without considering additional data, then the prediction process is simple and fast, but the prediction accuracy deteriorates
Solution Approach 1:
The patent segments the prediction process into multiple independent components: data collection module, data storage module, assumption generation module, and prediction module. Each module handles specific tasks independently, allowing the system to consider comprehensive data without creating a monolithic complex process. The segmentation enables modular processing of additional data while maintaining overall system manageability.
Solution Approach 2:
The patent introduces an intermediary assumption generation module that processes raw sensor data and transforms it into structured assumption data before feeding it to the prediction module. This intermediary layer organizes and filters additional data, making it easier for the prediction algorithm to process comprehensive information without being overwhelmed by raw data complexity.
2Measurement precision
If comprehensive data is considered in prediction, then prediction accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by collecting and storing sensor data in advance in a database before prediction is needed. The assumption generation module also pre-processes data into structured formats ahead of time. When prediction is required, the system retrieves pre-processed data rather than processing raw sensor data from scratch, significantly reducing real-time processing time while still considering comprehensive data.
3Adaptability or versatility
If traditional prediction methods are used, then the system is simple to implement, but flexibility in handling diverse data sources deteriorates
Solution Approach 1:
The patent implements a universal data collection module that can interface with multiple types of sensors and data sources through standardized protocols. The assumption generation module uses universal data structures that can accommodate diverse input formats. This multi-functionality allows the system to handle various data sources (temperature sensors, motion detectors, RFID tags) without requiring separate processing pipelines for each type, achieving flexibility through standardized universal interfaces.
Data Source
AI summary
A truth maintenance method and system. The method includes receiving by a computer processor from RFID tags embedded in sensors, event data associated with events detected by said sensors. The computer processor associates portions of the event data with associated RFID tags and derives assumption data associated with each portion of the portions. The computer processor retrieves previous assumption data derived from and associated with previous portions of previous event data retrieved from the RFID tags and executes non monotonic logic with respect to the assumption data and the previous assumption data. In response, the computer processor generates and stores updated assumption data associated with the assumption data and the previous assumption data.