Hyperintelligence System for Real-Time Decision Error Correction
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Solution Overview
Problem
Existing information decision tools are reactive and fail to address information and decision errors in real-time, leading to system risks and reduced confidence in decision-making.
Innovation Solution
The development of a hyperintelligence system that processes inputted information using artificial intelligence, machine learning, and data science to make fast, accurate decisions and learn from real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional information decision tools are used, then system complexity is reduced, but decision accuracy and real-time error correction capability deteriorate
Solution Approach 1:
The system segments decision-making into multiple specialized intelligence modules (predictive analytics module, prescriptive analytics module, descriptive analytics module, generative AI module) that each handle specific aspects of decision analysis, allowing complex decisions to be broken down into manageable components while maintaining high accuracy
Solution Approach 2:
The patent introduces an intermediary intelligence layer that sits between data input and decision output, using machine learning models and algorithms to process information and generate recommendations, thereby improving decision accuracy without requiring end-users to understand the underlying complexity
2Reliability
If traditional reactive error correction is used, then system simplicity is maintained, but information persistence time and risk exposure increase
Solution Approach 1:
The system performs preliminary actions by proactively detecting and correcting information errors before they persist in the computing system. The predictive analytics module continuously monitors data quality and identifies potential errors in real-time, enabling correction before decisions are made based on erroneous information
Solution Approach 2:
The patent implements continuous feedback loops where the system monitors decision outcomes and information quality, uses this feedback to retrain machine learning models, and continuously improves its error detection and correction capabilities, thereby reducing risk exposure over time
3Productivity
If real-time learning from feedback is implemented, then decision quality improves, but computational resource consumption increases
Solution Approach 1:
The system applies partial learning actions by selectively retraining models based on the significance and recency of feedback. Not all feedback triggers full model retraining - instead, the system prioritizes learning from high-impact errors and uses incremental learning techniques to reduce computational overhead while maintaining decision quality
Solution Approach 2:
The patent dynamically adjusts learning parameters such as learning rate, batch size, and model complexity based on available computational resources and the criticality of decisions. This allows the system to optimize the balance between decision quality and resource consumption by changing operational parameters rather than maintaining fixed high-resource settings
Data Source
AI summary
Methods and systems for interpreting inputted information are described herein. In some embodiments, a method comprises processing inputted information wherein processing inputted information uses one or more intelligence modules using one or more intelligence models to process the inputted information; making, by the one or more intelligence modules, one or more decisions about inputted information based on the one or more intelligence models; learning, by the one or more intelligence modules, to update the one or more intelligence models; and interpreting inputted information based on the one or more decisions.