Rule-Based Content Generation System for Automated Assembly
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for creating and optimizing display content on electronic devices are labor-intensive and inefficient, as they require human input for each piece of content, leading to a vast number of permutations that are difficult to manage, and fail to effectively utilize human perception principles.
Innovation Solution
A rule-based content generation system that automates content creation by defining permissible relationships among content elements, using a content element repository and a rule management module to generate content configurations that can be assembled into effective pieces of content, aligning with human perception principles such as positional, orientation, and size relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content is created manually by people, then content quality and effectiveness are improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The content creation process is segmented into modular content elements (text, images, video, sounds) that can be independently selected and combined. The system divides the complex task of content creation into manageable components stored in a repository, allowing automated assembly while maintaining quality through structured relationships among elements.
Solution Approach 2:
The content generation system is designed to be universal, handling multiple types of content elements (text, images, video, sounds) through a single automated platform. The rule-based engine can generate various configurations across different display devices and contexts, eliminating the need for separate manual creation processes for each content type.
2Manufacturing precision
If all permutations of content elements are tested, then optimal content configuration is found, but the number of permutations becomes unmanageably large
Solution Approach 1:
The system dynamically adjusts the content generation process by using rules to guide the assembly of content elements. Rather than statically testing all permutations, the rules enable adaptive selection of relevant configurations based on relationships among content elements, reducing the search space while maintaining optimization accuracy.
Solution Approach 2:
The system changes parameters by applying rules that constrain and guide the combination of content elements. These rules modify the effective parameter space by eliminating invalid or suboptimal configurations, allowing the system to find optimal content arrangements without evaluating all possible permutations.
3Productivity
If automated content generation is implemented, then productivity and speed are improved, but the ability to align with human perception principles may be reduced
Solution Approach 1:
The system incorporates feedback mechanisms where rules are derived from and apply human perception principles. The rule-based engine uses feedback loops to ensure that automated content generation adheres to established principles of human perception, maintaining reliability while achieving high productivity through automation.
Solution Approach 2:
Rules act as intermediaries between automated content generation and human perception principles. Rather than directly encoding complex perception theories into the automation engine, the system uses rules as a mediating layer that translates human perception requirements into actionable constraints for the automated assembly process.
4Quantity of substance
If more content elements are used in each piece of content, then content richness and viewer engagement are improved, but the complexity of managing relationships among elements increases
Solution Approach 1:
The system segments content into distinct elemental units with defined relationships, making it easier to manage complexity. Each content element (text, image, video, sound) is treated as a separate manageable unit with explicit relationship rules, allowing the system to handle rich content compositions without overwhelming complexity.
Solution Approach 2:
The system manages relationship complexity by changing parameters through rule-based constraints. Rules define permissible relationships among content elements, effectively parameterizing the complexity management by allowing only certain configurations, thereby enabling rich content assembly without unmanageable complexity.
Data Source
AI summary
At least one aspect of the present disclosure directs to a content generation system including a content generation module and a rule management module. The rule management module is adapted to receive a plurality of rules on content generation. The content generation module is adapted to generate a content configuration, wherein the content configuration comprises a plurality of content elements and one or more relationships among the plurality of content elements, wherein the one or more relationships are in accordance with the plurality of rules on content generation.