Media Query Disambiguation Using Playback Snapshot Context
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Solution Overview
Problem
Digital assistants often struggle with ambiguous user queries related to media assets, leading to inefficient and resource-consuming dialogues to clarify user intent, as they lack effective means to disambiguate queries based on the context of the media content.
Innovation Solution
A system captures a snapshot of the media asset during playback, generating a disambiguating query based on identified objects within the snapshot to clarify user intent, allowing for immediate user feedback without extensive dialogue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the digital assistant engages in extended dialogue to clarify ambiguous queries, then query disambiguation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by capturing a snapshot of the media asset and generating multiple candidate queries before the user responds. This allows the system to prepare disambiguation options in advance, reducing the back-and-forth dialogue needed to clarify user intent while maintaining high accuracy.
Solution Approach 2:
The system creates a visual copy (snapshot) of the media asset being displayed, which is then used to generate candidate queries. This copy serves as a reference to quickly understand the context and generate relevant disambiguation queries without requiring extensive dialogue, thus reducing processing time while maintaining accuracy.
2Measurement precision
If the digital assistant engages in extended dialogue to clarify ambiguous queries, then query disambiguation accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by capturing a snapshot of the media asset and generating multiple candidate queries before the user responds. This allows the system to prepare disambiguation options in advance, reducing the back-and-forth dialogue needed to clarify user intent while maintaining high accuracy.
Solution Approach 2:
The system creates a visual copy (snapshot) of the media asset being displayed, which is then used to generate candidate queries. This copy serves as a reference to quickly understand the context and generate relevant disambiguation queries without requiring extensive dialogue, thus reducing processing time while maintaining accuracy.
3Loss of time
If the system provides only a guess without user clarification, then processing time is reduced, but query response accuracy deteriorates
Solution Approach 1:
The system creates a visual copy (snapshot) of the media asset being displayed, which is then used to generate candidate queries. This copy serves as a reference to quickly understand the context and generate relevant disambiguation queries without requiring extensive dialogue, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The system implements feedback by presenting multiple candidate queries to the user and receiving their selection. This feedback mechanism allows the system to quickly clarify user intent with minimal interaction, achieving both fast processing and high accuracy by leveraging user confirmation rather than extensive dialogue.
4Measurement precision
If the system uses multiple follow-up queries to clarify intent, then query disambiguation accuracy is improved, but user experience deteriorates
Solution Approach 1:
The system creates a visual copy (snapshot) of the media asset being displayed, which is then used to generate candidate queries. This copy serves as a reference to quickly understand the context and generate relevant disambiguation queries without requiring extensive dialogue, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The system implements feedback by presenting multiple candidate queries to the user and receiving their selection. This feedback mechanism allows the system to quickly clarify user intent with minimal interaction, achieving both fast processing and high accuracy by leveraging user confirmation rather than extensive dialogue.
Data Source
AI summary
Systems and methods are described for providing a reply to a query related to a media asset. A query may be received from a user while the media asset is being played on a first device, and in response to determining that the query is related to the media asset, a snapshot of the media asset may be captured, where the snapshot comprises a depiction of a first object and a second object, and the snapshot may be generated for display at a second device. In response to determining there is ambiguity whether the query is related to the first or second object, a disambiguating query based on the first and second objects of the snapshot may be generated for simultaneous output with the snapshot. In response to receiving a reply to the disambiguating query, a response to the query may be generated for output based on the reply.


