Personalized Mobile Software Catalog Filtering
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Solution Overview
Problem
Mobile users face inefficiencies in selecting software applications for their devices due to lengthy, non-personalized lists of available options, making it difficult to find applications that align with their interests.
Innovation Solution
A method and system for creating personalized software catalogs for mobile devices by receiving user preferences and filtering or prioritizing available software, allowing users to access a curated selection based on their interests, demographics, and usage history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a complete list of all available applications is presented to all users, then the catalog is comprehensive and contains all possible options, but the catalog becomes excessively long and difficult to navigate
Solution Approach 1:
The patent segments the complete application catalog into multiple categories or groups based on user preferences, demographics, and usage history. Instead of presenting one long undifferentiated list, the system divides applications into relevant segments and presents only those segments to each user, making the catalog manageable while maintaining comprehensiveness.
Solution Approach 2:
The system performs preliminary actions by pre-filtering and organizing applications according to user profiles before the user even views the catalog. User preferences, demographic information, and usage history are analyzed in advance to curate a personalized subset of applications, so the user receives a pre-processed, relevant catalog rather than having to search through everything.
2Productivity
If the catalog is customized to show only relevant applications for each user, then the selection process becomes easier and more efficient, but the system complexity increases due to preference tracking and filtering
Solution Approach 1:
The system implements self-service by automatically collecting user preferences, demographic data, and usage history, then using this information to autonomously filter and organize the application catalog. The system serves itself by making customization decisions without requiring manual intervention from users to configure filters or categories, thereby improving productivity while managing complexity through automation.
Solution Approach 2:
The system uses feedback loops where user interactions with applications, downloads, and usage patterns are continuously monitored and fed back into the preference model. This feedback mechanism allows the system to dynamically adjust the catalog customization, improving selection efficiency over time while the complexity is managed through iterative refinement rather than complex upfront configuration.
3Loss of information
If user-specific criteria are used to filter the application catalog, then the catalog becomes more relevant to individual users, but the processing required to personalize each catalog increases
Solution Approach 1:
The system applies partial action by selectively processing only the most important user-specific criteria rather than analyzing every possible attribute. It focuses on key preference indicators and demographic factors that have the highest impact on application relevance, performing sufficient personalization to maintain information quality without the excessive processing power required for complete analysis of all user data.
Data Source
AI summary
A software download service on a mobile network provides users with personalized catalog presentations identifying software products available for downloading. Customer-specific preferences are established, for example, by input by the customer using a web-interface on a personal computer or on their mobile station, or by processing of user data such as demographics and usage history. The resulting user preferences are stored in a database. When each customer operates a mobile station to access the download service, the listing(s) of available software products are filtered and/or prioritized using the customer's individual preference(s). As a result, the service provides a ‘catalog’ of available software products that has been tailored to the particular user based on that user's preference(s). Consequently, customers can easily find applications that appeal to their interest, which improves both the sales take rate and the customer experience.