Distance-Based Option Data Object Filtering for Mobile Displays
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mobile device systems fail to effectively filter and present relevant option data objects, such as deal offers, based on user location and deal density, leading to overcrowding of displays with irrelevant offers from distant locations, which degrades user experience due to the inability of predictive models to account for user travel patterns and directionality.
Innovation Solution
A system that determines a user's triangulation location, calculates distances to option data objects, and adjusts weighted values based on deal density and proximity, using a radius-based approach to filter out far-away deals and prioritize locally relevant options, thereby enhancing the presentation of option data objects on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive models are used to generate weighted values for option data objects, then the relevance of deals can be improved, but the ability to account for user travel patterns and directionality is lost
Solution Approach 1:
The system segments the deal recommendation process into two independent components: (1) predictive models that generate base weighted values for deal relevance, and (2) geometric filtering layer that applies distance and directional constraints. This segmentation allows each component to specialize - predictive models focus on deal attractiveness while geometric filters handle spatial constraints and user travel patterns.
Solution Approach 2:
The patent introduces geometric filters as an intermediary layer between predictive models and the final deal presentation. These filters act as a mediator that receives weighted values from predictive models, applies spatial reasoning based on user location and direction, and produces the final filtered deal set. This intermediary resolves the contradiction by adding travel pattern adaptation without compromising predictive model accuracy.
2Quantity of substance
If all option data objects are presented to users, then completeness of information is improved, but display clutter increases and user experience degrades
Solution Approach 1:
The system extracts and removes irrelevant option data objects from the complete set before presentation to users. Geometric filters evaluate each deal's spatial relationship to user location and direction, extracting only those deals that fall within acceptable distance and directional parameters. This extraction process eliminates display clutter while preserving all potentially relevant deals.
Solution Approach 2:
The patent applies local quality by making the deal presentation adaptive to the user's specific geographic context. Instead of presenting all deals uniformly, the system adjusts which deals are presented based on local factors - user current location, heading direction, and distance thresholds. This creates a locally optimized view that enhances user experience by showing only geographically relevant deals.
3Measurement precision
If distance-based filtering is applied to option data objects, then relevance of local deals is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary action by pre-calculating geometric filters and distance thresholds before the actual deal filtering process. User location, direction, and distance parameters are determined in advance, creating a pre-configured filtering framework. When deals are processed, this pre-established geometric framework enables rapid evaluation without complex real-time calculations, reducing computational complexity while maintaining precise location-based relevance.
Data Source
AI summary
An apparatus, method, and computer program product are provided to filter and modify option data objects and weighted values associated with option data objects through the application of specific rule sets based on the relative density of option data objects within a particularized area. In some example implementations, option data objects and related parameters are parsed to identify locations associated with the option data object and a weighted value, such as a weighted value generated by a predictive model. Based at least in part on the location associated with the option data object, a determined location of a user of a mobile device, and location-specific distance criteria, the weighted value associated with the option data object may be modified to reflect distance-related option election probabilities.