Website Text Generation Using Hierarchical Content Structures
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Solution Overview
Problem
Existing website building systems lack support for generating and guiding the creation of high-quality text content, often providing only empty components or generic placeholder text, leading to user frustration and incomplete websites.
Innovation Solution
A system integrating a hierarchical data structure (HDS) with AI/ML and NLP engines to analyze text samples, provide recommendations, and enable interactive text generation and editing within the website building process, incorporating user and crowd-sourced information to generate tailored text content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If website building systems provide only empty components or generic placeholder text, then the system complexity remains low, but the quality of text content deteriorates
Solution Approach 1:
The system pre-gathers text samples from multiple sources (internal database, external sources, crowd-sourcing) and pre-processes them into structured formats with metadata before they are needed during website creation. This preliminary preparation enables high-quality text generation without adding complexity during the actual website building process.
Solution Approach 2:
The patent introduces an intermediary text content generation system that sits between the user and the text content. This intermediary automatically selects, generates, and customizes text based on website context, eliminating the need for users to manually write content while maintaining system simplicity from the user perspective.
2Manufacturing precision
If the system integrates AI/ML and NLP engines to analyze text samples and provide recommendations, then the text content quality improves, but the device complexity increases
Solution Approach 1:
The text content generation system is divided into separate functional modules: text sample gathering from multiple sources, AI/ML-based analysis engine, NLP processing component, and recommendation generation module. Each module handles a specific task independently, making the overall complex system manageable and maintainable while delivering high-quality text content.
3Adaptability or versatility
If the system combines user input with crowd-sourced information to generate tailored text content, then the adaptability improves, but the loss of time in processing and integrating multiple data sources increases
Solution Approach 1:
The system pre-gathers and pre-processes text samples from crowd-sourcing and other sources, organizing them into structured databases with metadata during off-peak times. When generating text content during website creation, the system quickly retrieves and combines relevant pre-processed samples with user input, significantly reducing the time required for text generation while maintaining high adaptability.
Data Source
AI summary
A text content generation (TCG) system to generate text for a text field for a website building system (WBS). The TCG system includes a data gatherer to gather text samples from sources internal and external to the WBS; an analysis engine to analyze the text samples and to identify common text structures, substructures and website contexts; an HDS creator to receive the output of the analysis engine and to create a hierarchical data structure (HDS) definition for each text sample accordingly, the HDS creator to also create associated rules for handling the application and behavior for each HDS, where each HDS describes a text element alternative for a given field role and a content management system (CMS) to store the HDS definitions and the text samples.