Geo-Targeted User Guidance via Machine Learning Location Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current techniques for determining user guidance based on location data are often inaccurate, leading to less relevant results due to reliance on IP addresses or other flawed methods.
Innovation Solution
A machine-learning-based system that receives electronic device location data and account data to identify location patterns, determining a user's current and base locations, and transmitting targeted guidance based on these determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IP address or traditional location methods are used to determine user location, then the system complexity is low, but the location accuracy and relevance of guidance is poor
Solution Approach 1:
The patent introduces machine learning models as an intermediary between raw location data and guidance delivery. The model processes multiple data sources (GPS coordinates, IP addresses, device information) and transforms them into accurate location predictions, resolving the contradiction by adding computational intelligence rather than direct complexity
Solution Approach 2:
The system performs preliminary location pattern analysis by training machine learning models on historical location data before actual guidance delivery. This pre-processing creates accurate location predictions that can be quickly applied during runtime, improving accuracy without adding operational complexity
2Reliability
If machine-learning models are used to analyze location patterns and determine accurate user locations, then the relevance and accuracy of user guidance is improved, but the computational resources and processing time increase
Solution Approach 1:
The machine learning model is trained offline on historical location data and account data before deployment. This preliminary training phase separates the computationally intensive model development from runtime operations, allowing accurate predictions to be made with minimal real-time computational resources
Solution Approach 2:
The system uses partial location data and account data subsets for model training and prediction, rather than processing complete datasets every time. This selective data processing reduces computational overhead while maintaining prediction accuracy through the model's learned patterns
Data Source
AI summary
A method of transmitting user guidance may include receiving captured electronic device location data. The method may include providing the electronic device location data and a plurality of account data of the unique user to a machine-learning model. The machine-learning model may be trained to identify location patterns within the electronic device location data and the plurality of account data of the unique user and output a user current location and a user base location. The method may further include determining a user target location using the user current location and the user base location. The method may further include comparing the user target location to entity target criteria to determine if the user target location satisfies the entity target criteria. The method may further include transmitting the user guidance to the electronic device associated with the unique user based on the user target location satisfying the entity target criteria.