Content Discovery Interface Using Multi-Way Search Algorithm
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Solution Overview
Problem
Home entertainment systems face challenges in navigating a large number of content sources efficiently, as existing search paradigms require users to know what they are looking for and rely on time-consuming tracking of viewing/purchasing habits for recommendations.
Innovation Solution
A method and system for content discovery that uses a multi-way search algorithm with a touch screen interface, allowing users to interact through gestures to minimize the number of questions needed to find desired items by presenting categories and an 'other' option, efficiently narrowing down options using a Double Greedy algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users navigate through multiple display pages or hierarchies of content, then they can access a large number of content sources, but the navigation becomes tedious and time-consuming
Solution Approach 1:
The patent segments the large content catalog into multiple categories and subcategories, allowing users to navigate through organized groups rather than individual items. The system divides content into hierarchical levels (categories, subcategories, items) and uses algorithms to determine optimal navigation paths, reducing the number of steps needed to reach desired content.
Solution Approach 2:
The patent introduces a new navigation dimension by presenting users with multiple choice options (e.g., 5 categories plus an 'other' option) simultaneously on a single screen. This transforms traditional linear navigation into a multi-dimensional decision space, where each user response eliminates a portion of the search space and brings them closer to the desired content faster.
2Ease of operation
If existing search paradigms are used, then users can search for specific content, but users must already know what they are looking for
Solution Approach 1:
The patent performs preliminary actions by proactively analyzing user profiles, viewing history, and preferences before the user initiates a search. The system pre-processes this information to generate personalized category recommendations and content suggestions, so when users do search, they are guided by already-analyzed data rather than having to manually specify all criteria.
Solution Approach 2:
The patent implements continuous feedback loops where user responses to category questions, viewing behavior, and selections are fed back into the system to refine future recommendations. This adaptive feedback mechanism allows the system to learn from user interactions and improve its ability to infer user intent over time, reducing the need for users to explicitly know what they want.
3Adaptability or versatility
If user viewing/purchasing habits are tracked over a long time period, then relevant recommendations can be generated, but the process becomes time-consuming
Solution Approach 1:
The patent performs preliminary analysis of user habits and preferences in advance, building comprehensive user profiles that capture viewing patterns, preferences, and behavior. This pre-processing allows the system to generate recommendations quickly during actual content discovery sessions, as the heavy analytical work has already been completed beforehand.
Solution Approach 2:
The patent uses partial action by analyzing only the most relevant features of user behavior (e.g., viewing frequency, genre preferences, time of day patterns) rather than processing every single interaction in detail. This selective analysis maintains recommendation quality while significantly reducing processing time and computational resources required.
Data Source
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AI summary
A system, method and graphic user interface for facilitating content discovery. The system and method including the selection (1402) of a number of display regions based on a display type, and the selection (1404) of item categories to be displayed in the display regions in accordance with a content discover process. The content discovery process is a greedy process that includes a multi-way search process (700) that tracks a user's item category selections and removes non-selected item categories displayed in the displayed regions from future display in the display regions during the content discovery process, and an item category selection process (900) that selects at least one item category that has not been displayed in the display regions and was not previously non-selected during the multi-way search process (700).