Projector Inferencing for Accurate Model Selection and Data Classification
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Solution Overview
Problem
Existing systems fail to accurately identify the degree of inaccuracy in iterative data analysis and lack adequate user-provided data intake and processing capabilities, leading to potential misrepresentation of complex phenomena.
Innovation Solution
An apparatus and method using projector inferencing, which includes a processor and memory, to receive and project data through first and second projectors, score operational values, and generate an interface data structure for user input to select between models, incorporating fuzzy inferencing and metamodels to improve data classification and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems perform iterative data analysis, then computational efficiency is improved, but accuracy in identifying inaccuracy degree deteriorates
Solution Approach 1:
The patent introduces projectors as intermediary components that map input data to operational values in a structured space. These projectors act as mediators between raw data and decision-making processes, enabling accurate uncertainty quantification without sacrificing computational efficiency. The projectors transform complex data relationships into manageable projections that can be scored and evaluated systematically.
Solution Approach 2:
The patent segments the model selection process into distinct components: data projection, operational value scoring, uncertainty assessment, and model selection. This segmentation allows each component to be optimized independently, with projectors handling the transformation and scoring mechanisms handling the evaluation, thereby resolving the contradiction between efficiency and accuracy.
2Productivity
If existing systems process user-provided data, then data processing capability is improved, but adequacy of data intake deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the scoring of operational values provides information about data quality and completeness. This feedback loop allows the system to assess whether sufficient user-provided data has been received and to identify areas where additional data intake is needed, thereby improving reliability without compromising processing capability.
3Adaptability or versatility
If existing systems represent complex phenomena, then coverage of phenomena is improved, but accuracy of representation deteriorates
Solution Approach 1:
The patent transitions from direct representation of complex phenomena to a projected dimensional space where relationships can be more accurately captured. By mapping data to operational values through projectors, the system preserves essential relationships while reducing complexity, achieving accurate representation across diverse phenomena.
Data Source
AI summary
An apparatus for model selection between a first model and a second model using projector inferencing is provided. The apparatus includes a processor and a memory connected to the processor. The memory contains instructions configuring the processor to receive an entity datum from an entity device and a second datum from a client device connected to the processor. The second datum describes matching the entity datum based on a preferred allocation with target values using the models. The processor may run two projectors capable of outputting operational values by projecting the entity datum over a defined duration. The processor may score operational values to target values using a fuzzy inferencing system. Scoring the operational values may include classifying an operational value and the second datum to categories organized sequentially in multiple discrete increments.