Automatic Translator Identification via Document Mining

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Solution Overview

Problem

Existing systems lack an efficient method to automatically identify and provide suitable translators for users in need of language translation services within organizations, often relying on manual and outdated databases, which can be inaccurate and costly.

Innovation Solution

A computer-implemented method that analyzes electronic communications and documents to identify users with language capabilities, using machine learning algorithms and data mining techniques to determine proficiency in specific languages, and provides contact information of suitable candidates for translation tasks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If manual databases are used to identify translators, then implementation is simple, but accuracy and timeliness of translation capability identification deteriorates

Engineering Contradiction:

Improveease of implementationVSAvoidaccuracy of translation capability identification

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The system automatically analyzes electronic communications and documents to identify user language capabilities without requiring manual input or feedback from users. The analysis engine autonomously processes communication data, extracts language information, and maintains the translation capability database, eliminating the need for manual database updates while improving accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual database maintenance with an automated analysis engine that uses data mining and machine learning techniques to identify translation capabilities. This substitution of mechanical manual processes with automated computational analysis significantly improves both the accuracy and timeliness of capability identification

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If manual feedback is used to update translation databases, then system complexity is low, but productivity and timeliness of translation service deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidtranslation service efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The analysis engine continuously monitors and analyzes electronic communications and documents in real-time, automatically updating the translation capability database as new information becomes available. This continuous operation eliminates gaps in data freshness and ensures the system always has current translation capability information without requiring periodic manual updates

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system self-updates the translation capability database by automatically analyzing communication data and extracting language capability information. This autonomous operation eliminates the need for manual database maintenance while significantly improving the timeliness and productivity of translation service provision

Inventive Principle:

Principle #25Self-service

3Measurement precision

If comprehensive analysis of electronic communications is performed, then accuracy of translator identification is improved, but processing time and computational resources increase

Engineering Contradiction:

Improveaccuracy of translator identificationVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary analysis by continuously monitoring and pre-processing electronic communications as they are generated, rather than analyzing them on-demand. This advance preparation ensures that when translation capability identification is needed, the analysis is already complete or near-complete, reducing response time while maintaining comprehensive analysis accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The analysis engine employs efficient data sampling and feature selection techniques, analyzing only the most relevant portions of electronic communications that indicate language capabilities. This selective analysis approach maintains high accuracy in translator identification while significantly reducing the overall processing time and computational resources required

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS9444773B2Automatic translator identification

Publication Date: 2016.09.13 MIMECAST SERVICES LTD

AI summary

The technology described in this document can be embodied in a method that includes receiving information via a user interface provided on a display of a user device from a first user associated with a pre-determined user group. The information includes an identification of a) a source language and b) a target language to which translation from the source language is requested. The method also includes determining that one or more second users of the pre-determined user group is associated with the source language and associated with the target language. This can be determined based on accessing a data repository that stores language capabilities of users within the pre-determined user group, wherein the language capabilities are determined automatically based on mining a corpus of electronic documents associated with the pre-determined user group. An identification of the one or more second users can be displayed on the user interface.