Location Intelligence System Using Autoencoder Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current geocoding methods do not provide insights into specific characteristics of vehicles and driver actions at particular locations, limiting the ability to analyze trends and patterns.
Innovation Solution
A location intelligence system that combines vehicle datasets to form location tensors, extracts embeddings using autoencoder neural networks, and stores these embeddings in a database to characterize geographic locations, enabling the identification of vehicle types and driver actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional geocoding methods are used to convert addresses to geographic coordinates, then location data can be obtained, but specific characteristics of vehicles and driver actions at locations cannot be determined
Solution Approach 1:
The patent segments location intelligence into multiple dimensions: geographic coordinates, vehicle characteristics, driver actions, and temporal patterns. Each dimension is processed separately through dedicated neural network layers before being integrated into a comprehensive location embedding, thereby preserving all location characteristics without information loss
Solution Approach 2:
The patent introduces location tensors as an intermediary data structure that bridges raw geocoding data and final location embeddings. These tensors organize multi-dimensional location characteristics in a structured format that can be processed by neural networks, enabling comprehensive information retention while maintaining system manageability
2Measurement precision
If comprehensive vehicle datasets are collected and processed to extract detailed location intelligence, then accurate location characterizations are achieved, but data processing complexity and computational resources increase
Solution Approach 1:
The patent transforms raw vehicle and location data into standardized tensors with specific dimensional parameters. This parameter standardization enables consistent processing across diverse data sources while maintaining high measurement precision through structured data representation suitable for neural network processing
Solution Approach 2:
The patent creates composite location embeddings that integrate multiple data types (geographic coordinates, vehicle characteristics, driver actions, temporal information) into a unified representation. This composite approach achieves comprehensive location characterization accuracy while the integrated structure simplifies downstream processing compared to handling separate data streams
3Productivity
If location intelligence data is processed in real-time to provide immediate insights, then timely decision-making is enabled, but computational processing time and resources increase
Solution Approach 1:
The patent performs preliminary processing by organizing raw location and vehicle data into standardized tensors before neural network processing. This pre-organization reduces the computational burden during real-time inference, enabling faster processing while maintaining comprehensive data analysis
Solution Approach 2:
The patent creates compressed embedding representations that serve as simplified copies of comprehensive location data. These embeddings retain essential location characteristics in a compact form that can be rapidly processed and queried, enabling real-time decision-making without processing the full complexity of original datasets
Data Source
AI summary
System, methods, and other embodiments described herein relate to location intelligence. In one embodiment, a method of obtaining location intelligence includes receiving a plurality of datasets from a plurality of vehicles, the datasets being associated with a same geographical location and respectively including at least vehicle descriptive information that describes one or more aspects of the respective vehicles and feature data that indicates a status of at least one respective feature of the respective vehicles, combining the plurality of datasets to form a location tensor associated with the geographical location, extracting, from the location tensor, an embedding that indicates information contained in the location tensor, and storing the embedding in a database in association with the geographical location.