Hyperlocal Data Smoothing via Inference Engine
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Solution Overview
Problem
Hyperlocal services face data scarcity and challenges in defining search parameters due to varying geographic and demographic factors, leading to sparse, outdated, or unreliable data.
Innovation Solution
The implementation of an inference data server and inference engine that combines sparse data with inferred or extrapolated data from related locales, using contextual analysis to provide relevant information, even in data-scarce areas, by calculating or augmenting attribute data and generating alerts based on updated values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If hyperlocal services focus on narrowly targeted geographies to provide relevant information, then information relevance is improved, but data scarcity worsens
Solution Approach 1:
The patent combines sparse explicit data from a specific hyperlocal area with inferred or extrapolated data from related locales to create a comprehensive view. The smoothing application merges data from multiple sources including explicit attribute data, inferred attribute data, and extrapolated data to overcome data scarcity while maintaining hyperlocal relevance.
Solution Approach 2:
The patent introduces an inference engine as an intermediary between data collection and service delivery. This intermediary component generates inferred attribute data and extrapolated data to bridge the gap between available sparse data and the comprehensive information needed for hyperlocal services.
2Measurement precision
If hyperlocal services use sparse data from specific locales, then geographic precision is improved, but data reliability worsens
Solution Approach 1:
The patent applies different data quality strategies to different geographic areas. For areas with sufficient explicit data, the system uses that data directly. For areas with sparse data, the system applies inference and extrapolation techniques. This local quality approach ensures high reliability in data-rich areas while maintaining service capability in data-scarce areas through alternative data generation methods.
3Adaptability or versatility
If hyperlocal services define search parameters for varying geographic areas, then service adaptability is improved, but system complexity worsens
Solution Approach 1:
The patent implements dynamic search parameter definition that adapts to the characteristics of each hyperlocal area. The system automatically adjusts search parameters based on the geographic area being queried, the types of entities present, and the availability of explicit data. This dynamic approach allows the system to handle varying geographic areas with different characteristics without requiring manual configuration for each area.
4Quantity of substance
If hyperlocal services infer attribute data from related locales, then data completeness is improved, but computational complexity worsens
Solution Approach 1:
The patent applies partial inference by selecting only the most relevant related locales for data extrapolation rather than analyzing all possible sources. The smoothing application determines what attribute data is needed and infers only that specific data from related locales, rather than performing comprehensive analysis of all available data. This partial action approach achieves sufficient data completeness while limiting computational complexity to what is necessary.
Data Source
AI summary
Concepts and technologies are described herein for hyperlocal smoothing. The hyperlocal smoothing solutions described herein provide a smooth view of data and events across hyperlocal geographic areas by combining sparse data available with inferred or extrapolated data. Additionally, the hyperlocal smoothing solutions described herein make use of contextual analysis to interpret service requests in a manner appropriate for a targeted hyperlocal area. Thus, the smooth view of data can be queried in a contextually sensitive manner to return relevant information for a hyperlocal geographic area, even in circumstances wherein data relevant to the hyperlocal geographic area is sparse or even non-existent.