Asset Ranking Reasoning Using Feature Groups and Optimization
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Solution Overview
Problem
Existing ranking systems do not provide clear explanations for how rankings are derived, making it difficult for observers to understand and compare different ranking lists.
Innovation Solution
A system that retrieves domain data from external data stores, generates feature groups, converts these into values using an optimization engine, and transforms them into sentences to explain the reasoning behind rankings via a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If ranking systems provide detailed explanations for how rankings are derived, then observer understanding improves, but system complexity increases
Solution Approach 1:
The patent segments the complex ranking explanation into distinct feature groups (e.g., performance metrics, statistical data, contextual factors). Each feature group is processed separately through the optimization engine, allowing the system to manage complexity by dividing the explanation task into manageable components while still providing comprehensive reasoning.
Solution Approach 2:
The optimization engine serves as an intermediary between the raw ranking data and the human observer. It transforms complex multi-dimensional ranking criteria into simplified feature groups and ultimately into natural language sentences, mediating between the complexity of the ranking system and the need for human understanding.
2Measurement precision
If multiple data sources are integrated to improve ranking accuracy, then measurement precision improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent merges multiple external data sources and variables into unified feature groups through the optimization engine. By combining disparate data sources (e.g., performance data, statistical information, contextual variables) into integrated feature groups, the system achieves comprehensive ranking accuracy while managing data integration complexity through systematic consolidation.
Solution Approach 2:
The optimization engine performs multiple functions simultaneously: it retrieves data from various sources, processes multiple variables, generates feature groups, and produces explanations. This multi-functionality allows the system to handle diverse data sources uniformly, improving ranking accuracy without proportionally increasing operational complexity.
Data Source
AI summary
An example operation may include one or more of executing queries on one or more external data stores to retrieve domain data of one or more ranked lists of assets and variables corresponding to the domain data, generating a plurality of feature groups based on the retrieved domain data and variables, wherein the plurality of feature groups correspond to a plurality of features used to generate the one or more ranked lists, converting the plurality of features groups into a plurality of values via execution of an optimization engine, transforming the plurality of values into a plurality of sentences describing the plurality of feature groups, and displaying the plurality of sentences via a user interface.