Conversational Catchphrase Search for Personalized Media Recommendations
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Solution Overview
Problem
In a fragmented media environment with numerous media sources and virtually unlimited content choices, existing systems lack the ability to provide accurate search results and recommendations that keep users engaged with a particular service and identify media assets of interest.
Innovation Solution
Analyzing conversational data to identify catchphrases, associating them with user profiles and media assets, and using this information to generate personalized media asset recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional viewing history and profile metrics are used for recommendations, then the system can provide basic search results, but the recommendations fail to reveal complex user preferences and keep users engaged
Solution Approach 1:
The patent introduces catchphrases as an intermediary element that mediates between user behavior and recommendation systems. Catchphrases serve as observable indicators that reveal underlying user preferences, interests, and affinities without requiring direct access to complex internal user states. By analyzing catchphrase usage patterns, the system can infer user preferences more accurately than traditional viewing history alone.
2Measurement precision
If comprehensive user data is collected to improve recommendations, then user preference accuracy improves, but processing and storage requirements increase
Solution Approach 1:
The patent extracts and focuses on specific, high-value signals (catchphrases) from the broader user data ecosystem. Instead of processing all user interactions equally, the system selectively extracts catchphrase usage as a key indicator of user preference. This extraction approach reduces the volume of data requiring comprehensive processing while maintaining or improving recommendation accuracy.
3Adaptability or versatility
If the media environment remains fragmented with multiple services, then users have more content choices, but users cannot engage with any single service deeply
Solution Approach 1:
The patent implements a feedback mechanism where catchphrase usage data continuously informs and refines recommendation systems across media services. By monitoring how users employ catchphrases in conversation and using this feedback to adjust recommendations, services can adapt to user preferences in real-time, thereby deepening engagement even within a fragmented multi-service environment.
Data Source
AI summary
Methods and systems are provided for detecting user input on a first device, such as a smart television, to identify keywords or phrases indicating uncertainty or a desire for media asset recommendations. This input is transmitted to a second device, which causes the second device to perform a recommendation search. The analysis of user input utilizes a catchphrase data structure and additional information like user profile, viewing history, and preferences. Processing can occur locally on the first device or on remote servers. The catchphrase data structure tracks usage contextually, including time, date, and location. Results from the recommendation search are caused to be generated for output upon later access to the second device.