Estimating Missing POI Data Using Nearby Proxies
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Solution Overview
Problem
Service providers face challenges in accurately estimating missing dynamic information for points of interest (POIs) when real-time content information is not reported, relying on historical data and information from nearby POIs, which affects decision-making processes.
Innovation Solution
A method and system that determine missing dynamic content for a target POI by processing current values of dynamic content parameters from nearby POIs, calculating distribution means and standard deviations, and using historical values to estimate current values, incorporating a Kalman filter model or Z-value estimation for accurate estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service providers rely on historical information and information from proximate POIs to estimate missing dynamic information, then the estimation can be performed without real-time data from the target POI, but the accuracy and reliability of the estimation deteriorates
Solution Approach 1:
The patent uses proximate POIs as intermediary sources to infer missing information from the target POI. By treating nearby POIs as mediators that can provide proxy data, the system bridges the information gap when direct real-time data from the target POI is unavailable, thereby maintaining reliability through indirect information channels
Solution Approach 2:
The patent creates copies of information patterns from proximate POIs and applies them to estimate the target POI's missing data. By copying the behavioral patterns, information structures, and data characteristics from nearby POIs, the system generates reliable estimates without requiring direct access to the target POI's real-time systems
2Productivity
If service providers use historical information and proximate POI data to estimate missing dynamic content, then the system can operate without direct real-time reporting from all POIs, but the precision of the estimation deteriorates
Solution Approach 1:
The patent merges multiple data sources including historical information from the target POI and real-time data from multiple proximate POIs into a unified estimation model. By combining these diverse information streams, the system achieves both operational efficiency (without requiring all POIs to report) and improved estimation accuracy (through multiple corroborating sources)
Solution Approach 2:
The patent creates a universal estimation framework that can handle missing data from any POI by leveraging information from any available proximate POI. This multi-functional approach allows the same estimation mechanism to work across different POIs and different types of missing information, maintaining both efficiency and precision through a standardized solution
Data Source
AI summary
An approach is provided for determining at least one distribution of a plurality of current values for at least one dynamic content parameter associated with a plurality of points of interest within a predetermined proximity to at least one target point of interest. The approach involves determining at least one distribution mean and at least one distribution standard deviation for the at least one distribution of the plurality of current values. The approach also involves determining at least one set of historical values for the at least one dynamic content parameter for the at least one target point of interest. The approach further involves determining at least one estimated current value for the at least one dynamic content parameter associated with the at least one target point of interest based, at least in part, on the at least one set of historical values, the at least one distribution mean, and the at least one distribution standard deviation.