Personalized Moment Extraction in Meeting Transcripts
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Solution Overview
Problem
Existing technologies for transcribing and reviewing meeting recordings are too verbose, making it difficult to quickly identify important moments, and rely on a one-size-fits-all approach to keyword searches, which may not surface moments that are subjectively important to individual users.
Innovation Solution
A moments service that uses machine learning to identify relevant moments in a conversation by accessing a machine learning model specific to each participant, applying the transcription as input, and generating information about the relevant moments for display to the participant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If meeting recordings and transcriptions are provided in their entirety, then users can review all conversations, but the content becomes too verbose to quickly identify important moments
Solution Approach 1:
The system extracts only the most relevant and important moments from the complete meeting transcription, presenting them as highlighted snippets to users. This extraction process removes unnecessary verbose content while preserving the essential information, allowing users to quickly identify and review important moments without sifting through the entire conversation transcript.
Solution Approach 2:
The system creates simplified copies or representations of the important moments from the full transcription, presenting them in a condensed format that maintains the essential information while reducing verbosity. These copied moments are then ranked and presented to users in an easily consumable format.
2Ease of operation
If keyword searches are used to find important moments, then users can search for specific terms, but the approach is one-size-fits-all and may not surface moments that are subjectively important to individual users
Solution Approach 1:
The system dynamically adapts the moment identification process to each user's individual preferences and needs. By analyzing user interactions, feedback, and behavioral patterns, the system continuously adjusts which moments are identified as important for each user, transforming a static one-size-fits-all approach into a dynamic personalized experience that evolves with user preferences.
Solution Approach 2:
The system incorporates user feedback mechanisms where users can indicate which moments are helpful or unhelpful. This feedback is then used to refine and personalize the moment identification algorithm for each user, allowing the system to learn from user interactions and improve the relevance of surfaced moments over time, moving beyond simple keyword matching.
3Adaptability or versatility
If a machine learning model specific to each participant is used, then personalized moments can be identified, but the system complexity increases
Solution Approach 1:
The system segments the machine learning model into separate individual models for each participant rather than using a single monolithic model. This segmentation allows each participant to have their own personalized model that captures their unique preferences and behaviors, while the overall system architecture remains modular and manageable through the use of multiple independent models.
Data Source
AI summary
A system and a method are disclosed for identifying a subjectively interesting moment in a transcript. In an embodiment, a device receives a transcription of a conversation, and identifies a participant of the conversation. The device accesses a machine learning model corresponding to the participant, and applies, as input to the machine learning model, the transcription. The device receives as output from the machine learning model a portion of the transcription having relevance to the participant, and generates for display, to the participant, information pertaining to the portion.