Content Suggestion via Document Classification
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Solution Overview
Problem
Users face difficulty in finding relevant content within large content libraries in graphical user interfaces, particularly when browsing through numerous categories for specific shapes or objects in presentation applications.
Innovation Solution
Implementing machine learning classifiers to analyze document content and provide suggested relevant content by determining document classifications, which can prioritize existing categories and weight certain objects more heavily based on their relevance, thereby streamlining the content selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the content library includes a large number of content elements organized by broad categories, then the content library provides comprehensive content coverage, but users have difficulty sifting through and identifying relevant content elements
Solution Approach 1:
The system performs preliminary classification of content elements by analyzing document content (text, images, audio, video) using machine learning models to determine document classification before the user searches. This pre-processing organizes content based on actual document characteristics rather than requiring users to manually browse categories.
Solution Approach 2:
The patent introduces an intermediary classification layer between the user and the content library. The document classification system acts as a mediator that translates user document content into relevant content suggestions, bridging the gap between user needs and the large content library without requiring direct user browsing.
2Device complexity
If content is categorized by broad categories, then content organization is simple, but users must browse through numerous categories to find specific content
Solution Approach 1:
The system performs preliminary classification of user documents and pre-identifies relevant content suggestions before the user initiates a search. This eliminates the need for users to browse through broad categories by providing direct content recommendations based on document analysis.
Solution Approach 2:
The patent replaces the manual mechanical browsing process with an automated machine learning-based classification and suggestion system. Instead of users manually navigating categories, the system automatically analyzes document content and generates content suggestions, substituting human effort with computational processing.
3Measurement precision
If machine learning classifiers analyze document content to determine classification, then relevant content suggestions are improved, but system complexity increases
Solution Approach 1:
The patent segments the classification task into multiple independent components: text classification, image classification, audio classification, and video classification. Each modality is handled by specialized machine learning models, allowing the system to process different content types separately and combine results, thereby managing complexity through modular architecture.
Solution Approach 2:
The system employs a universal document classification framework that handles multiple content types (text, images, audio, video) through a common architecture. The machine learning models are designed to process various modalities uniformly, enabling the system to maintain high precision across different content types while avoiding the need for separate complex systems for each modality.
Data Source
AI summary
Systems, methods, and devices are provided for determining shape objects to suggest for display on a graphical user interface (GUI). The method may include detecting an input to change one or more objects in an application, in which the object includes an image content, a text content, or both. The method may also include, providing the object to an image classifier, a text classifier, or both in response to detecting the input. Moreover, the method may include receiving a classification of the changed object in response to providing the object. The method may also include identifying suggested shapes for insertion into the application based on the classification. Further, the method may include receiving a request to insert shapes and presenting the suggested shape for insertion in the application.