Identity Mapping Tool for Cross-Technology Website Management
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Solution Overview
Problem
Existing website-management tools are specialized for specific underlying technologies, making it difficult to manage and update websites independently of their original data sources, leading to complexity and lack of flexibility, which restricts their use to specific ecosystems and hinders efficient management and maintenance.
Innovation Solution
The Identity Mapping Tool enables the creation of a broad management system that interfaces with various silos, allowing for the analysis and mapping of website content, layouts, and templates, independent of the original data sources, to provide a user-friendly interface for website lifecycle management, enabling direct editing and management without requiring access to the underlying technology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If website-management tools are specialized for specific underlying technologies, then they can effectively manage websites built with that technology, but they lack flexibility and cannot be used across different technology ecosystems
Solution Approach 1:
The patent introduces an intermediary layer (the website management tool) that sits between the user and multiple underlying website technologies. This intermediary uses machine learning models to understand and translate between different technology ecosystems, enabling a single tool to manage websites across various platforms without requiring technology-specific implementations.
Solution Approach 2:
The website management tool is designed with universal functionality that can operate across multiple technology ecosystems. By implementing a unified interface and using machine learning to adapt to different underlying technologies, the tool provides multi-functional capabilities that work regardless of the specific technology stack being used.
2Adaptability or versatility
If website-management tools provide comprehensive functionality for all underlying technologies, then they can manage diverse websites, but the user interface becomes excessively complex and difficult to use
Solution Approach 1:
The patent segments the complex functionality of website management into distinct, manageable components. The machine learning model breaks down the management tasks into standardized operations that can be handled independently, allowing the user interface to present simplified controls while maintaining comprehensive underlying capabilities across different technologies.
Solution Approach 2:
The machine learning model acts as an intermediary that handles the complexity of technology-specific operations behind the scenes. Users interact with a simplified interface, and the ML model translates these high-level commands into appropriate technology-specific actions, shielding users from the underlying complexity.
3Productivity
If website-management tools require direct access to underlying technology and data sources, then they can perform precise management operations, but they lose flexibility and cannot operate independently
Solution Approach 1:
The website management tool uses machine learning to enable self-service capabilities. The ML model automatically understands the structure and content of websites by analyzing publicly available information, eliminating the need for direct access to underlying data sources. The system serves itself by autonomously learning and adapting to different website structures.
Solution Approach 2:
The patent replaces the mechanical system of direct database connections and API access with an intelligent system based on machine learning. Instead of requiring programmed access paths to underlying technologies, the ML model uses pattern recognition and inference to understand and manage website content, providing independence from specific data source implementations.
4Reliability
If website-management tools are designed for specific ecosystems, then they can provide optimized functionality, but they are not interoperable with the entire ecosystem
Solution Approach 1:
The machine learning model dynamically adjusts its parameters and behavior based on the detected ecosystem and technology stack. By changing its operational parameters according to the specific environment, the tool maintains reliable functionality across different ecosystems while adapting to their unique characteristics and requirements.
Data Source
AI summary
Disclosed herein are systems and methods for an identity mapping tool that bridges the gap between editing and displaying the contents of a website, regardless of the underlying technology, so as to enable development of an intuitive, visual website-editing experience. The disclosure obtains a website's content, determines relationships between the content and website, and maps the website. The tool obtains access to the content, layouts/templates, and black-box generation process of a website and deduces the correlation between the content's fields and the elements of any of the website's resulting webpages. It taints the content-source data, iteratively feeds it into a site generation process, and inspects the resulting artifacts to determine correlations between fields in the content source and the resulting website. The artifacts may be further employed to generate a user-interface display with controls for facilitating management of the website.