Brand Engine Automates Data Extraction and Incorporation
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Solution Overview
Problem
The laborious and time-consuming process of manually extracting and incorporating brand data, such as colors and font styles, into various types of content and messaging is inefficient and prone to errors.
Innovation Solution
A method utilizing a brand engine that extracts and presents brand data through user interfaces by processing structure blocks with smart blocks to generate scores, select appropriate smart blocks, and automatically incorporate brand data into content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual extraction and incorporation of brand data is performed using common tools, then brand data can be extracted and incorporated into content, but the process is laborious and time-consuming
Solution Approach 1:
The system automatically extracts brand data from content and incorporates it into templates without requiring manual user intervention. The brand engine performs self-service by autonomously identifying brand elements, extracting their properties, and applying them to appropriate content templates, thereby eliminating the laborious manual process described in the contradiction.
Solution Approach 2:
The patent replaces the manual mechanical process of copying and pasting brand data with an automated computational system. The brand engine uses algorithms to extract brand properties from content and automatically incorporate them into templates, substituting the manual mechanical operations with an automated digital system that significantly reduces time and effort.
2Reliability
If manual extraction and incorporation of brand data is performed, then brand data can be extracted and incorporated into content, but the process is prone to errors
Solution Approach 1:
The automated brand engine performs self-service extraction and incorporation of brand data, eliminating human error-prone manual operations. The system autonomously identifies brand elements, extracts their properties accurately, and incorporates them into content templates without the errors that occur during manual copying and pasting operations.
Solution Approach 2:
The system incorporates feedback mechanisms where the brand engine continuously refines its extraction and incorporation processes based on the content being processed. The engine learns from the structure and properties of brand data across different content types, improving its accuracy over time while maintaining automated operation, thus achieving both high reliability and efficiency.
3Productivity
If automated brand data incorporation is implemented, then time and effort are significantly reduced, but system complexity increases
Solution Approach 1:
The brand engine is segmented into distinct functional modules: a brand data extraction module that identifies and extracts brand properties from content, a processing module that matches extracted data with content templates, and an incorporation module that applies the brand data to templates. This segmentation allows each module to perform its specific function efficiently while keeping the overall system manageable and maintainable.
Solution Approach 2:
The brand engine is designed as a universal system that can extract and incorporate brand data from multiple types of content (text, images, videos) and apply them to various content templates. This multi-functionality is achieved through a standardized interface and processing pipeline that handles different data types uniformly, reducing system complexity compared to having separate systems for each content type.
Data Source
AI summary
A method implements brand engine for extracting and presenting brand data with user interfaces. The method includes receiving a blueprint with a set of structure blocks extracted from a selected content. A structure block of the set of structure blocks includes a set of style parameter requests for a section of the selected content. The method further includes processing the set of structure blocks with a first set of smart blocks to generate a set of scores. A smart block of the first set of smart blocks includes brand data with style parameter selections. The method further includes selecting a second set of smart blocks, for the set of structure blocks, from the first set of smart blocks, using the set of scores. The method further includes presenting the second set of smart blocks with the brand data.