Adaptive Data Aggregation via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data aggregation services rely on APIs or pre-programmed scripts to access data, which are unreliable and costly to maintain, especially when websites change their layouts or formats, and lack coverage in industries like utility, healthcare, and insurance due to high development and maintenance costs.
Innovation Solution
The use of natural language processing (NLP) and machine learning (ML) techniques to automate data aggregation from websites without pre-programmed scripts, allowing for adaptive operation across multiple websites with different schemas and layouts, reducing maintenance costs and improving reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data aggregation services use APIs or pre-programmed scripts to access data, then they can obtain structured data access, but they become unreliable and costly to maintain when websites change their layouts or formats
Solution Approach 1:
The system enables self-service by allowing the data aggregation system to automatically adapt to website changes without requiring manual intervention. The machine learning models continuously learn from website structures and automatically adjust to layout changes, eliminating the need for developers to maintain and update scripts when websites change.
Solution Approach 2:
The system changes from using fixed, pre-programmed parameters (hardcoded scripts) to dynamic, learned parameters (machine learning models that adapt). The models learn website structures and navigate based on learned patterns rather than fixed parameters, allowing automatic adaptation when website layouts change.
2Adaptability or versatility
If data aggregation services develop custom scripts for each website, then they can access multiple data sources, but the development and maintenance costs increase significantly
Solution Approach 1:
The system implements universality by creating a single, general-purpose data aggregation platform that can access multiple different websites without requiring custom development for each site. The machine learning models learn the structure of each website automatically and can navigate any website that follows similar patterns, eliminating the need for separate scripts for each data source.
Solution Approach 2:
The system uses copying by training machine learning models on example website structures and then applying these learned patterns to navigate and extract data from similar websites. Instead of creating unique scripts for each website, the system copies and adapts navigation patterns from learned examples to new websites.
3Quantity of substance
If conventional systems use pre-programmed scripts for data aggregation, then they can access data from known sources, but they lack coverage in industries like utility, healthcare, and insurance
Solution Approach 1:
The system transitions from static, pre-programmed scripts to dynamic, adaptive machine learning models. The models continuously learn from website structures and can automatically adapt to new website formats in industries like utility, healthcare, and insurance without requiring upfront script development for each industry.
Data Source
AI summary
Systems, apparatuses, and methods for automated data aggregation, automated webpage navigation, or automatically performing a task by entering data into multiple webpages. In some embodiments, this is achieved by use of techniques such as natural language processing (NLP) and machine learning to enable the automation of data aggregation and other tasks involving websites without the use of pre-programmed scripts.