Collaborative Document Audio Transcription and Search
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Solution Overview
Problem
Current collaborative document systems lack the ability to effectively capture and utilize audio data within meetings, making it difficult for users to efficiently access and manage key information, summaries, and actions.
Innovation Solution
A collaborative content management system that transcribes audio data into text, allows for search and playback of specific audio segments, generates meeting summaries, and modifies documents based on identified actions or keywords, using custom lexicons tailored to user vocabularies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If audio data is captured during meetings, then meeting information is preserved, but accessibility and searchability of key information deteriorates
Solution Approach 1:
The patent introduces text transcription as an intermediary between audio data and user interaction. The system transcribes audio recordings into text format, creating a mediating layer that enables efficient searching and access while preserving the original audio content. Users can search and navigate through text transcripts rather than manually reviewing audio files, solving the contradiction between information preservation and ease of access.
2Loss of information
If manual note-taking is used, then meeting summaries can be created, but time consumption and efficiency deteriorates
Solution Approach 1:
The system performs automatic transcription and summary generation without requiring manual intervention. The audio data is automatically transcribed into text, and key information is extracted to create meeting summaries autonomously. This self-service approach eliminates the time-consuming manual note-taking process while preserving all meeting information.
Solution Approach 2:
The system performs transcription and summary generation in advance, before users need to access the meeting information. By automatically processing audio data into searchable text and identifying key points beforehand, the system prepares meeting records ready for efficient retrieval and review, eliminating the need for users to spend time creating summaries manually.
3Loss of information
If audio playback is used, then complete information is available, but time efficiency deteriorates
Solution Approach 1:
The system extracts key information from complete audio recordings and presents it in condensed text formats. Transcripts highlight important segments, and generated summaries extract essential points from the full audio content. This allows users to access extracted key information quickly through text, while the complete audio remains available for full information retrieval when needed.
4Measurement precision
If custom lexicons are created, then transcription accuracy improves, but system complexity increases
Solution Approach 1:
Custom lexicons are created in advance based on user-specific vocabulary, domain terminology, and frequently used terms. By pre-processing and storing these custom lexicons before transcription operations, the system improves transcription accuracy for domain-specific content without adding complexity to the actual transcription process. The lexicon preparation is performed once, and the benefits persist across multiple transcription tasks.
Data Source
AI summary
A collaborative content management system allows multiple users to access and modify collaborative documents. When audio data is recorded by or uploaded to the system, the audio data may be transcribed or summarized to improve accessibility and user efficiency. Text transcriptions are associated with portions of the audio data representative of the text, and users can search the text transcription and access the portions of the audio data corresponding to search queries for playback. An outline can be automatically generated based on a text transcription of audio data and embedded as a modifiable object within a collaborative document. The system associates hot words with actions to modify the collaborative document upon identifying the hot words in the audio data. Collaborative content management systems can also generate custom lexicons for users based on documents associated with the user for use in transcribing audio data, ensuring that text transcription is more accurate.