Location Refinement via Affirmative User Inputs
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Solution Overview
Problem
Existing location data association methods often result in inaccuracies due to ambiguous or low-confidence location identifiers, leading to incorrect associations between user location data and entities, such as geographic locations or points of interest.
Innovation Solution
The method involves using affirmative user inputs, like locational queries and user actions, to adjust and refine the association of location data with entities, increasing the confidence level of these associations and resolving ambiguities by updating database entries to reflect the user's actual location or intended destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If location data is automatically associated with entities using existing methods, then the process is efficient and automated, but the accuracy and confidence level of the association deteriorates due to ambiguous location identifiers
Solution Approach 1:
The system implements feedback by monitoring user actions (such as searches, directions requests, and check-ins) and using this feedback to refine and correct location associations. When users interact with location-related features, the system adjusts the confidence levels and associations in the database, creating a continuous improvement loop that enhances accuracy while maintaining automation.
Solution Approach 2:
The system enables self-service by allowing users to implicitly correct location associations through their natural interactions with the service. User actions such as requesting directions to a specific location or searching for an entity serve as self-correcting mechanisms that automatically refine the location database without requiring manual intervention from system administrators.
2Measurement precision
If affirmative user inputs are collected to refine location associations, then the accuracy and confidence level improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The system achieves multi-functionality by using the same user interaction framework for multiple purposes: initial location association, ambiguity resolution, and continuous refinement. The same interface and data collection mechanisms serve both to gather user preferences and to monitor location accuracy, reducing the need for separate complex subsystems.
Solution Approach 2:
The system performs preliminary actions by establishing initial location associations using available data before user interactions occur. This preliminary association provides a baseline that can be quickly refined using user inputs, reducing the complexity of real-time processing by separating the initial association step from the refinement step.
3Measurement precision
If ambiguous location data is resolved using user signals, then the confidence level in entity identification improves, but the time required to resolve ambiguities increases
Solution Approach 1:
The system applies partial action by resolving ambiguities to the degree necessary for providing useful services without requiring complete certainty. When user signals provide sufficient confidence for practical purposes, the system proceeds with the association rather than continuing to gather additional data, thus balancing accuracy with time efficiency.
Solution Approach 2:
The system performs preliminary resolution of ambiguities using available user signals before full processing is complete. By anticipating likely associations based on user behavior patterns and pre-processing signals, the system reduces the time required for final resolution while maintaining high confidence levels.
Data Source
AI summary
Methods and apparatus related to associating location data with one or more entities. Location data from, for example, mobile devices carried by users, may indicate a first entity as being associated with the given location data. However, one or more affirmative user inputs may indicate that a second entity is additionally, and/or alternatively associated with location data. Accordingly, location data may be associated with the second entity. In some implementations the first entity may be dissociated from the first location data. In some implementations second location data may be identified as being associated with the first entity and the second location data may be associated with the first entity.