Predictive Performance Assessment Using Ranked Return Advisors
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Solution Overview
Problem
Capturing and benefiting from an individual's insight over an extended period for making informed decisions about future performance of items or entities is difficult due to the lack of effective predictive tools and methods.
Innovation Solution
An system that analyzes messages from multiple decision-makers across networks to identify and rank 'return advisors' based on their risk/return-related decisions, using a server that retrieves and evaluates messages to provide enhanced assessments and recommendations for future performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive tools are used for assessing future performance, then the decision-making process is simple, but the accuracy and reliability of predictions are insufficient
Solution Approach 1:
The patent combines multiple data sources including social media messages, transaction data, and user profiles into a unified predictive system. By merging these diverse information streams, the system achieves higher prediction accuracy while managing complexity through integrated processing architecture.
Solution Approach 2:
The system performs multiple functions including message analysis, user behavior tracking, predictive modeling, and recommendation generation within a single platform. This multi-functionality allows comprehensive assessment of future performance across different domains while maintaining a unified system structure.
2Reliability
If individual insight is captured over extended periods, then the quality of predictive information improves, but the difficulty of capturing and utilizing this insight increases
Solution Approach 1:
The system implements continuous feedback loops where user messages and behaviors are analyzed, predictions are generated, and results are fed back to refine future predictions. This feedback mechanism improves insight quality over time by learning from accumulated data while automating the capture process to manage complexity.
Solution Approach 2:
The system automatically captures and processes user-generated content without requiring manual intervention. By enabling self-service data collection through automated message monitoring and analysis, the system improves insight quality while reducing the operational complexity of data capture.
3Measurement precision
If multiple decision-makers are analyzed to improve predictive accuracy, then the assessment quality improves, but the complexity of analyzing and integrating multiple sources increases
Solution Approach 1:
The system segments the analysis process into distinct modules: message processing, user profiling, behavior analysis, and predictive modeling. Each module handles specific aspects of multi-source analysis independently, improving overall assessment quality while managing complexity through modular architecture.
Solution Approach 2:
The system introduces intermediary processing layers that aggregate and harmonize data from multiple decision-makers before final analysis. These intermediaries standardize diverse input formats and extract key features, improving assessment quality while reducing the complexity of direct multi-source integration.
Data Source
AI summary
In embodiments, a message retriever accesses a plurality of messages and a message filter identifies, within the accessed messages, a set predictive messages. The risk/return-related messages are analyzed to identify associated return advisors. The return advisors are evaluated and ranked according to advisor scores, and risk/return-related decisions referenced in the messages are also identified, evaluated, and ranked. The ranked return advisors and decisions are used to facilitate assessment of future performance of an item or entity.