POI Filtering and Creation Using Crowd Data Profiles
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Solution Overview
Problem
Current systems for displaying and filtering Points-of-Interest (POIs) do not effectively account for the user's experience of the surroundings, such as the ambiance or affinity of a location, and may not be aware of newly established POIs.
Innovation Solution
A system and method that filters POIs based on crowd data, including identifying crowds relevant to a bounding region and applying crowd-based filtering criteria to provide a curated list of POIs, and creates POIs through crowd-sourced requests, identifying relevant crowds to define new POIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If POIs are filtered using predefined categories and static data, then filtering speed is improved, but the ability to capture user experience and surroundings affinity deteriorates
Solution Approach 1:
The system pre-computes and stores crowd attribute profiles and surrounding characteristics for POIs before user queries. This preliminary action enables fast filtering by comparing user preferences against pre-analyzed data, achieving both speed and accuracy without real-time computation overhead
Solution Approach 2:
The system creates simplified copies of complex surrounding environments and crowd characteristics as structured data profiles. These copies capture essential attributes (ambiance, affinity, crowd demographics) that can be quickly queried and compared, preserving information richness while enabling efficient processing
2Device complexity
If POIs are selected from existing databases, then system complexity is reduced, but the ability to identify newly established POIs deteriorates
Solution Approach 1:
The system implements feedback loops where user interactions, crowd data, and surrounding information continuously update POI profiles and validate new POI candidates. This feedback mechanism ensures newly established POIs are automatically discovered and integrated without manual database updates, maintaining reliability while managing complexity through automated processes
Solution Approach 2:
The system enables new POIs to self-register through crowd-sourced requests and automated detection of surrounding characteristics. POIs automatically generate their own profiles based on observed crowd behavior and environmental data, eliminating the need for manual database maintenance while ensuring current and accurate information
3Measurement precision
If crowd data is collected and analyzed for each POI, then POI relevance to user experience is improved, but data processing complexity increases
Solution Approach 1:
The system segments crowd data and surrounding characteristics into distinct attribute categories (demographics, interests, ambiance, affinity) for individual POIs. This segmentation allows precise characterization of each POI while simplifying data processing through modular, category-based analysis rather than holistic complex processing
Solution Approach 2:
The system transforms raw crowd data and surrounding information into standardized parameter profiles with defined attributes and weightings. By changing parameters from raw unstructured data to structured measurable quantities, the system achieves precise POI characterization while reducing processing complexity through consistent parameter formats and comparison metrics
Data Source
AI summary
Systems and methods are provided for filtering and/or creating Points-of-Interest (POIs). In one embodiment, a list of POIs is obtained and then filtered based on crowd data related to the list of POIs to provide a filtered list of POIs. In another embodiment, one or more crowd-sourced POIs are created based on one or more crowds relevant to a corresponding bounding region for POI creation.