Automated Business Hours Update via Mobile Location Data
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Solution Overview
Problem
Current methods for updating business hours in databases are labor-intensive and prone to inaccuracies, as they often rely on human curation or crowd-sourcing, which can be time-consuming and lacks incentives for businesses to provide correct information.
Innovation Solution
A computer-implemented method using machine learning algorithms that analyze time-stamped mobile device location data to determine the likelihood of changes in business hours, allowing for automated updates based on patterns of mobile device activity, with human verification when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If human curation or crowd-sourcing methods are used to update business hours, then the process can be performed with existing infrastructure, but the labor intensity and time consumption increase significantly
Solution Approach 1:
The patent replaces manual human curation and crowd-sourcing mechanisms with an automated machine learning system that processes mobile device location data. The ML model automatically detects changes in business hours by analyzing patterns in location data, eliminating the need for human labor while significantly increasing update speed and efficiency.
Solution Approach 2:
The system enables businesses to be automatically monitored and updated through passive analysis of mobile device location data in the area. The ML model continuously self-updates business hours information by detecting patterns in location data without requiring active participation from businesses or manual intervention from curators.
2Reliability
If manual verification processes are implemented to ensure accuracy, then the reliability of hours data improves, but the time and resources required increase
Solution Approach 1:
The patent replaces manual verification processes with automated machine learning analysis of mobile device location data. The ML model provides continuous automated verification by analyzing location patterns, maintaining high data accuracy while eliminating the time loss associated with manual verification steps.
3Quantity of substance
If comprehensive databases of business hours are maintained through traditional methods, then complete coverage can be achieved, but the cost and labor requirements become prohibitive
Solution Approach 1:
The patent replaces expensive manual maintenance operations with automated machine learning processing of mobile device location data. This substitution enables comprehensive database coverage at a fraction of the cost, as the ML system can process and analyze location data from thousands of devices simultaneously without proportional increases in labor costs.
Solution Approach 2:
The machine learning system serves multiple functions simultaneously: it monitors business hours, detects changes, verifies accuracy, and updates databases across all locations. This multi-functional approach enables comprehensive database maintenance through a single automated system, dramatically improving cost-effectiveness compared to traditional location-specific manual processes.
Data Source
AI summary
Computer-implemented methods and systems for sourcing hours of operation for a business or other location entity can include receiving a request to update hours of operation. A number of mobile devices present at the location entity during one or more periods of time can be determined based at least in part on a collection of time-stamped mobile device location data that identifies mobile devices associated with discrete users present at the location entity. The number of mobile devices and initial operating hours for the location entity can be provided as input to a statistical model (e.g., neural network, support-vector machine (SVM) or logistic regression model). An output of the model can indicate a likelihood that operating hours for the location entity have changed relative to the initial operating hours. The operating hours for the location entity can then be updated based at least in part on the model output.