Dynamic Copyfitting Parameter Estimation Using Trained SVMs
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Solution Overview
Problem
Existing systems for copyfitting in electronic documents face challenges such as delayed design processes, lack of real-time feedback, and inefficiencies due to complex calculations and remote server architectures, particularly in in-browser design applications.
Innovation Solution
A machine learning-based approach using trained support vector machines to dynamically estimate copyfitting parameters by applying weights to features of input text and content areas, enabling real-time prediction of remaining characters, words, or lines available for additional content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex calculations and remote server architectures are used for copyfitting, then measurement precision of copyfitting parameters is improved, but loss of time and device complexity increase
Solution Approach 1:
The patent replaces complex mechanical calculation systems with machine learning models. Instead of using traditional computational geometry and iterative layout algorithms that require significant processing time, the system uses trained neural networks to directly predict copyfitting parameters from text and layout features, achieving both accuracy and speed.
Solution Approach 2:
The patent performs preliminary action by pre-training machine learning models offline using extensive training data and computational resources. Once trained, these models can make rapid predictions in real-time without requiring complex calculations during the actual design process, thus eliminating the time loss associated with real-time complex computations.
2Measurement precision
If complex calculations and remote server architectures are used for copyfitting, then measurement precision of copyfitting parameters is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical calculation systems with machine learning models. Instead of using traditional computational geometry and iterative layout algorithms that require significant processing time, the system uses trained neural networks to directly predict copyfitting parameters from text and layout features, achieving both accuracy and speed.
Solution Approach 2:
The patent extracts the complex calculation logic from the runtime system and encapsulates it in pre-trained machine learning models. This separation allows the runtime system to remain simple while still achieving accurate copyfitting parameter estimation through the extracted knowledge contained in the trained models.
3Productivity
If real-time estimation is implemented, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models offline using extensive training data and computational resources. Once trained, these models can make rapid predictions in real-time without requiring complex calculations during the actual design process, thus eliminating the time loss associated with real-time complex computations.
Solution Approach 2:
The patent changes the fundamental parameters of the estimation system by transitioning from deterministic calculation-based approaches to probabilistic machine learning-based approaches. This parameter change allows the system to achieve both real-time performance and high accuracy by leveraging patterns learned from extensive training data rather than relying on complex real-time computations.
Data Source
AI summary
Embodiments are disclosed for real-time copyfitting using a shape of a content area and input text. A content area and an input text for performing copyfitting using a trained classifier is received. A number of remaining characters in the content area is computed in real-time using the input, the computing performed in response to receiving additional input text, wherein computing, in real-time, the number of remaining characters in the content area using the input text includes generating, by the trained classifier, a set of weights including a first set of one or more weights for the input text and a second set of one or more weights for the content area. The first set of one or more weights, the second set of one or more weights, the input text, and the additional input text, and a copyfitting parameter indicating a number of additional characters to be fitted into the content area are determined based on the content area. The copyfitting parameter and the number of remaining characters are presented in real-time.