Intent-Based Preset System for Digital Image Editing
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Solution Overview
Problem
Conventional digital image editing systems are inflexible and inefficient, as they fail to adapt to the needs of client devices in providing preset configurations, leading to significant user interaction to select relevant presets.
Innovation Solution
The intent-based preset system intelligently analyzes user queries to extract editing intent and objects, then searches among available presets to recommend flexible editing presets that target specific editing scenarios, reducing user interaction by providing relevant presets based on editing intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional digital image editing systems provide preset configurations, then users can modify digital images without manually selecting editing settings, but the systems fail to flexibly adapt to user needs, leading to significant user interaction to select relevant presets
Solution Approach 1:
The system automatically analyzes user queries and retrieves relevant editing presets without requiring manual selection. The natural language processing system extracts editing intent from user queries and automatically searches the preset database, allowing the system to serve itself by understanding and fulfilling user needs without intervention.
Solution Approach 2:
The system uses feedback from user queries to dynamically retrieve and recommend relevant presets. By analyzing the user's natural language input and understanding their editing intent, the system adapts its preset recommendations based on the specific needs expressed in each query, creating a feedback loop that improves ease of operation.
2Adaptability or versatility
If the system provides a wide range of available presets, then users have more options for editing, but users require significant interaction to locate and select the relevant preset
Solution Approach 1:
The system performs preliminary analysis of user queries before presenting preset options. By extracting editing intent and relevant parameters from the user's natural language input in advance, the system pre-filters the large database of presets to show only those that match the user's specific needs, eliminating the time required to manually search through all available options.
Solution Approach 2:
The natural language processing system acts as an intermediary between the user and the preset database. It translates user queries into structured editing intent parameters and uses these to search and retrieve relevant presets, serving as a mediator that connects user needs with the appropriate presets without requiring direct user interaction with the full preset list.
3Productivity
If the system requires manual selection of editing settings, then users have precise control over edits, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system automatically extracts editing intent from user queries and retrieves relevant presets without requiring manual configuration. Users simply describe what they want to achieve in natural language, and the system handles the entire process of understanding their intent, searching the preset database, and presenting appropriate options, dramatically improving editing efficiency while maintaining ease of operation.
Solution Approach 2:
The system replaces the mechanical process of manual preset selection and parameter adjustment with an intelligent natural language processing system. Instead of users manually navigating through menus and adjusting settings, the system uses NLP to understand user intent and automatically retrieves and applies relevant presets, substituting manual mechanical interaction with intelligent automation.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that recommend editing presets based on editing intent. For instance, in one or more embodiments, the disclosed systems receive, from a client device, a user query corresponding to a digital image to be edited. The disclosed systems extract, from the user query, an editing intent for editing the digital image. Further, the disclosed systems determine an editing preset that corresponds to the editing intent based on an editing state of an edited digital image associated with the editing preset. The disclosed systems generate a recommendation for the editing preset for provision to the client device.