Translation Time Estimation with Preparation Coefficients
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Solution Overview
Problem
Current computer-aided translation systems lack an efficient method to accurately estimate translation time, which is crucial for resource allocation in managing complex translation projects, especially when dealing with varying content types and levels of prior translation.
Innovation Solution
A system that dynamically estimates translation time based on factors such as the number of words, pages, band percentage (percentage of previously translated content), and content type, using pre-determined coefficients that are adjusted based on actual translation times to improve future estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer-aided translation systems use statistical methods to improve translation accuracy, then translation quality improves, but the complexity of managing translation projects increases
Solution Approach 1:
The system automatically estimates translation time by analyzing content characteristics (word count, band percentage, content type) without requiring manual intervention. The estimation process self-adjusts using historical data and pre-determined coefficients, enabling the system to serve itself in the time estimation task rather than requiring complex manual project management.
Solution Approach 2:
The patent replaces manual time estimation mechanisms with an automated computational system. Instead of relying on human translators or project managers to manually assess and estimate translation time, the system uses algorithms that process content parameters and calculate estimates automatically, substituting mechanical manual processes with electronic automation.
2Measurement precision
If manual time estimation methods are used for translation tasks, then accuracy can be maintained through human judgment, but productivity decreases due to manual errors and time consumption
Solution Approach 1:
The system incorporates feedback mechanisms where actual translation times are recorded and used to refine future estimates. The pre-determined coefficients are adjusted based on historical performance data, allowing the system to learn from past translations and improve its estimation accuracy over time, creating a self-improving feedback loop.
Solution Approach 2:
The system changes the parameters used for time estimation from subjective human judgment to objective measurable factors such as word count, band percentage (percentage of previously translated content), and content type. These quantifiable parameters enable automated processing while maintaining estimation accuracy through mathematical relationships between parameters and translation time.
3Device complexity
If translation time estimates do not include preparation time, then the estimation process is simpler, but the reliability of resource allocation decreases
Solution Approach 1:
The patent segments the translation process into distinct time components: preparation time (for analyzing content, accessing resources) and translation time (for actual translation work). By separating these segments, the system can accurately estimate each component independently and sum them for a comprehensive total time estimate, improving resource allocation reliability without overwhelming complexity.
Solution Approach 2:
The system performs preliminary actions by analyzing content characteristics (word count, band percentage, content type) before the actual translation begins. This preliminary analysis enables the system to pre-calculate preparation time requirements and provide accurate total time estimates, allowing resources to be allocated in advance based on reliable predictions.
Data Source
AI summary
A method for automatically estimating a translation time comprises receiving translation data, determining one or more translation parameters based on the translation data, retrieving one or more pre-determined translation coefficients associated with the one or more translation parameters, calculating an estimated translation time based on the one more or more translation parameters and the one or more pre-determined translation coefficients, and a base time to prepare the translation in addition to the translation itself. The method may further comprise reporting the estimated translation time to a user, receiving, from the user, a request to perform a translation associated with the translation data, performing the translation, recording an actual translation time, comparing the actual translation time to the estimated translation time, and based on the comparison, revising the one or more pre-determined translation coefficients to improve the estimating of the translation time.