Conversational Media Search with Speaker-Priority Result Refinement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing media guidance systems struggle to effectively prioritize and integrate multiple viewer opinions in conversations to refine search results, leading to irrelevant or uninteresting media recommendations.
Innovation Solution
A media guidance application that utilizes a home assistant device to listen to viewer conversations, analyze speech, and adjust search results based on prioritization of speakers' comments, preferences, and viewing histories, refining results iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all viewer comments and opinions in a conversation are used to update search results, then the system integrates comprehensive feedback, but the results become less relevant and interesting because opinions may differ and conflict
Solution Approach 1:
The system introduces a home assistant device as an intermediary that captures, transcribes, and analyzes conversation data. This mediator processes the raw conversation between multiple viewers, extracts relevant search terms, and updates the search results without directly conflicting with individual viewer opinions. The intermediary layer enables comprehensive feedback integration while maintaining result quality through systematic processing.
Solution Approach 2:
The system implements a feedback loop where search results are updated based on conversation analysis, and the updated results are presented back to viewers for further interaction. This continuous feedback mechanism allows the system to learn from viewer reactions and refine search results iteratively, balancing comprehensive feedback integration with maintaining relevance through ongoing optimization.
2Ease of operation
If the system monitors and analyzes viewer conversations in real-time to update search results, then the responsiveness and personalization improve, but the device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by capturing and transcribing conversations before full analysis. The home assistant device records the conversation and converts speech to text in advance, preparing the data for subsequent search term extraction and result updates. This preliminary processing reduces the complexity of real-time analysis by pre-processing the conversation data.
Solution Approach 2:
The system segments the conversation analysis process into distinct stages: conversation capture by the home assistant device, speech-to-text transcription, search term extraction from the transcribed text, and search result updates. This segmentation divides the complex real-time analysis task into manageable components, reducing overall system complexity while maintaining responsiveness.
3Measurement precision
If the system prioritizes one viewer's comments over another's in a conversation, then the search results can be more precisely tailored, but it becomes difficult to determine which viewer's preferences to prioritize
Solution Approach 1:
The system implements dynamic prioritization where viewer importance weights are not fixed but can be adjusted based on conversation context, user profiles, and interaction patterns. The home assistant device and media guidance application can dynamically determine which viewer's preferences to prioritize in different situations, allowing flexible adaptation to various social contexts and viewer relationships.
Solution Approach 2:
The system changes parameters by adjusting the weight or priority assigned to different viewers' comments based on various factors such as user profiles, conversation context, and historical interaction data. This parameter adjustment enables the system to dynamically prioritize certain viewers over others in different scenarios, making the prioritization process adaptable rather than static.
Data Source
AI summary
Methods and systems are described herein updating search results based on a conversation. For example, the system may capture audio data and determining that the audio data includes first voice inputs corresponding to a first user and second voice inputs corresponding to a second user. The system may generate for display a list of media asset identifiers selected based at least in part on the first voice inputs and the second voice inputs and a search trail corresponding to the first voice inputs and the second voice inputs. For example, the search trail may include one or more selectable options. The system may receive a selection of at least one of the one or more selectable options and generating for display an updated list of media asset identifiers based on the selection of the at least one of the one or more selectable options.