Indoor Navigation Routing via Map Data and ML Models
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Solution Overview
Problem
Current routing technologies for indoor environments, such as grocery stores, face challenges in efficiently guiding shoppers to requested items without precise location tracking, leading to inefficiencies and inaccuracies in navigation.
Innovation Solution
A computer-implemented method using machine-learned models that process data signals from mobile devices within a merchant location to generate and update route segments in real-time, based on map data and user interactions, allowing for approximate location determination and dynamic route adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current routing technologies are used for indoor environments, then navigation can be provided, but location tracking precision deteriorates and navigation accuracy worsens
Solution Approach 1:
The patent introduces map data as an intermediary layer between the shopper's mobile device and the routing system. Instead of relying solely on precise location tracking, the system uses map data representing the indoor environment layout to determine routes. This intermediary approach allows navigation accuracy to be maintained through map-based routing rather than dependent on high-precision location tracking.
Solution Approach 2:
The patent replaces the mechanical location tracking system with an information-processing approach using map data. Rather than continuously tracking the shopper's physical position with high precision, the system substitutes this with map-based routing that provides turn-by-turn directions, eliminating the need for constant precise location monitoring while maintaining navigation reliability.
2Reliability
If constant location tracking is implemented, then navigation accuracy improves, but system complexity and resource consumption increase
Solution Approach 1:
The patent extracts the essential navigation function from continuous location tracking. Instead of implementing a complex constant tracking system, the invention takes out only the necessary elements - map data representing the environment and turn-by-turn routing instructions - to provide navigation accuracy without the complexity of continuous tracking infrastructure.
Solution Approach 2:
The system enables self-service navigation where the shopper receives turn-by-turn directions based on map data without requiring complex tracking infrastructure. The navigation system serves itself by using the map representation to generate routes, eliminating the need for complex external location tracking mechanisms.
3Productivity
If real-time route updates are provided based on user interactions, then navigation efficiency improves, but data processing requirements increase
Solution Approach 1:
The patent applies partial action by providing route updates only at necessary points - specifically at turns and intersections based on user interactions with the interface. Rather than continuously processing and updating route data, the system updates routes partially and selectively when needed, improving navigation efficiency while reducing unnecessary data processing power consumption.
Data Source
AI summary
Systems and methods for dynamic mapping and routing using machine learning. The system can access requested items, a map, and process data signals to generate route segments that indicate a path to the requested items. The method includes accessing data associated with a delivery request that includes requested items. The method includes accessing map data associated with the initial location of the items. The method includes processing data signals from computing devices to confirm an initial location of an item, determine a new location of an item, or determine an availability of an item. The method includes iteratively generating a plurality of route segments for a user to follow that indicates a path to the requested items. The method includes outputting a command instruction to generate a user interface that iteratively displays each route segment based on the user selecting or disregarding the respective items.