Contextualized Spell Checker Using Video Conference Metadata
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional spell-checking services inadequately handle contextual terms and proper names with nonstandard spellings, often flagging them as misspelled due to their absence in general lexicons, which can be frustrating for video conference participants, especially in professional or industry-specific settings where jargon and unique names are common.
Innovation Solution
A contextualized spell-checking system that generates a personalized lexicon for each user based on video conference metadata, including participant names, organization-specific terms, and social graph-derived words, allowing for accurate spelling correction without relying on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spell-checking services use general lexicons, then they can check standard words, but they incorrectly flag contextual terms and proper names as misspelled
Solution Approach 1:
The system performs preliminary actions by generating a contextual lexicon from video conference metadata (participant names, organization terms, jargon) before the spell-checking process. This pre-built contextual knowledge base enables the spell-checker to recognize domain-specific terms and proper names as valid, preventing false positives while maintaining accuracy for standard words.
Solution Approach 2:
The invention applies local quality by creating user-specific contextual lexicons tailored to individual users' professional environments. Each user receives a personalized spell-checking experience that adapts to their specific industry, organization, and communication patterns, rather than applying a uniform general lexicon to all users.
2Reliability
If spell-checking services flag all non-standard words, then they maintain strict spelling rules, but they reduce communication efficiency and user satisfaction
Solution Approach 1:
The system dynamically adjusts spelling validation based on contextual information from the user's video conference history and professional environment. Words are not statically flagged as misspelled; instead, the system adapts its criteria in real-time based on the user's specific context, maintaining reliability for standard spelling while improving productivity by accepting valid domain-specific terminology.
3Adaptability or versatility
If users manually add words to the dictionary, then they can improve contextual recognition, but it increases user effort and time consumption
Solution Approach 1:
The system performs self-service by automatically generating contextual lexicons from video conference metadata without requiring user intervention. The system extracts participant names, organization-specific terms, and industry jargon from conference data and builds personalized dictionaries autonomously, eliminating the need for users to manually add words while improving contextual recognition.
Solution Approach 2:
The system uses feedback from video conference communications to continuously improve each user's contextual lexicon. By analyzing actual usage patterns and terminology from conference chats and transcripts, the system refines its understanding of user-specific vocabulary, automatically adapting to new terms and proper names without additional user effort.
Data Source
AI summary
In some implementations, techniques disclosed herein may include receiving, from a chat session by a video conference provider, text from a first client device associated with a first user. In addition, the techniques may include segmenting the text into one or more words. The techniques may include identifying one or more preliminarily misspelled words based on a first lexicon. Moreover, the techniques may include determining, for at least one of the one or more preliminarily misspelled words, whether the respective preliminarily misspelled word is correctly spelled based on a second lexicon. Also, the techniques may include responsive to determining the preliminarily misspelled word is correctly spelled, identifying the preliminarily misspelled word as correctly spelled.