Service Notification Platform Address Occupancy Data
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Solution Overview
Problem
Service providers face inefficiencies in notifying users about available services due to conflicting and inaccurate address-occupant information, leading to wasted network and computing resources by sending notifications to entities without access to the infrastructure.
Innovation Solution
A service notification platform aggregates and standardizes address-occupant pairs, using granular location identifiers and machine learning to accurately identify occupants with access to infrastructure, discarding duplicates and errors, and aggregating this data with infrastructure information to optimize notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If general notification strategies are used to notify all potential users, then service providers can reach a broader audience, but network and computing resources are wasted by sending notifications to entities without access to infrastructure
Solution Approach 1:
The system performs preliminary actions by aggregating and standardizing address-occupant data before sending notifications. It pre-identifies occupants with infrastructure access using machine learning models, so that notification resources are not wasted on entities without access. This preliminary filtering ensures that subsequent notification efforts are targeted efficiently.
Solution Approach 2:
The system enables self-service by allowing the notification platform to automatically aggregate data from multiple sources, standardize address formats, identify occupants with infrastructure access, and generate targeted notification lists without manual intervention. This automated self-service approach optimizes resource allocation while maintaining broad reach.
2Loss of information
If address information from multiple sources is used, then more complete occupancy data can be obtained, but conflicting and inaccurate information reduces notification accuracy
Solution Approach 1:
The system extracts and separates conflicting information from multiple sources, then applies standardization rules to resolve conflicts. It extracts address-occupant pairs from various sources, standardizes address formats, and uses machine learning to identify the most accurate pairings, thereby maintaining data completeness while improving precision.
Solution Approach 2:
The system changes parameters by standardizing address formats and transforming raw address-occupant data into structured, standardized records. It applies consistent formatting rules and uses machine learning models to transform conflicting data into accurate, standardized occupancy information that can be reliably used for notifications.
3Measurement precision
If machine learning models are used to identify occupants with infrastructure access, then notification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of occupant identification into distinct processing stages: data aggregation from multiple sources, address standardization, duplicate removal, and machine learning-based occupancy prediction. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The system introduces an intermediary layer of address standardization and data preprocessing between raw data inputs and machine learning models. This intermediary processing prepares data in a standardized format, reducing the complexity burden on the machine learning models while maintaining high identification accuracy.
Data Source
AI summary
A system described herein may determine location information (e.g., two-dimensional location information) associated with an asset, generate or receive a first code based on the received location information, determine height information associated with the asset; generate a second code based on the first code and the height information; and store association information associating the first asset with the second code. The system may further receive a first request for location information associated with the asset; and output, in response to the first request, the second code, thus providing three-dimensional location information for the asset in response to the request.