Automated Logo Image Selection From Domain and Context Signals
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Solution Overview
Problem
Conventional logo design requires high skills and is time-consuming, necessitating a more efficient and automated process.
Innovation Solution
A computer-based system that automates logo generation by parsing domain names, extracting attributes, using machine learning models to infer profiles and generate keywords, retrieving relevant images, filtering and ranking them, and embedding the selected logo in a website.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional logo design is performed by human designers, then design quality and creativity are improved, but time consumption and cost increase
Solution Approach 1:
The system uses pre-trained machine learning models (profile model, keyword model, image ranking model) that have been trained on extensive design data to generate logos. These models copy successful design patterns and principles from training data, enabling automated generation of high-quality logos without requiring human designers for each new logo, thus reducing time consumption while maintaining design quality.
Solution Approach 2:
The patent replaces the mechanical system of human designers manually creating logos with an automated computer-based system using machine learning models. The system substitutes human creative work with algorithmic processes including domain name parsing, attribute extraction, profile inference, keyword generation, and image ranking, thereby eliminating time consumption associated with human design processes while preserving design quality through trained models.
2Loss of time
If automated logo generation is implemented, then time consumption is reduced, but design complexity and automation level increase
Solution Approach 1:
The automated logo generation system is divided into distinct functional modules: domain name parsing module, attribute extraction module, profile inference module (using profile ML model), keyword generation module (using keyword ML model), image acquisition module, image ranking module (using image ranking model), and logo generation module. This segmentation allows each component to be independently developed, trained, and optimized, managing overall system complexity while achieving rapid automated logo generation.
Solution Approach 2:
The machine learning models used in the system are designed to be universal and adaptable. The profile model can infer different types of attributes from domain names, the keyword model can generate diverse keywords based on inferred profiles, and the image ranking model can rank images across different categories. This multi-functionality reduces the need for separate specialized systems, managing complexity while providing comprehensive automated logo generation capabilities.
3Productivity
If machine learning models are used for profile inference and keyword generation, then automation and speed are improved, but computational resources and processing time increase
Solution Approach 1:
The machine learning models (profile model, keyword model, image ranking model) are pre-trained on extensive datasets before deployment. This preliminary training action allows the models to have already learned optimal patterns and relationships, so during actual logo generation they can quickly infer profiles and generate keywords without requiring intensive real-time computation. The heavy computational work is done beforehand, enabling fast automated logo generation with reduced real-time computational resource requirements.
Data Source
AI summary
In at least one embodiment, a method may include receiving at least one domain name and context information associated with an entity, generating at least one attribute from parsing the at least one domain name, generating an inferred profile based on the at least one attribute and the context information, generating, by utilizing a keyword machine learning model, a plurality of keywords based on the inferred profile, acquiring a first plurality of images from a data source based on the plurality of keywords, and automatically embedding at least one image from the first plurality of images in at least one website, wherein the at least one image is selected based on a predetermined criterion.