Location-Based Alter-Ego Search Query Processing
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Solution Overview
Problem
Current search engines fail to effectively integrate location-based and alter-ego queries, which require personalized results based on a user's geographical location and the interests of their social network connections, leading to inefficient search results and user experience.
Innovation Solution
The system processes location-based and alter-ego queries by utilizing geo-fences, user interests, and social network attributes to generate refined search queries, prioritizing results based on the user's location and the interests of their social connections, allowing users to see results that would be relevant to their friends or groups as if they were present at the user's location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines provide general search results based on keywords, then search coverage is comprehensive, but result relevance to user location and social context is poor
Solution Approach 1:
The patent segments the search query processing into multiple components: location-based filtering, alter-ego identification, and interest-based ranking. Each component processes specific aspects of the query independently and combines results to provide personalized search outcomes while maintaining overall query flexibility.
Solution Approach 2:
The patent applies local quality by customizing search results based on the user's specific location and the alter-ego's particular interests. The search engine adjusts result relevance locally for each user-alter-ego-location combination rather than applying uniform filtering rules globally.
2Productivity
If search results are personalized based on location and social network attributes, then user engagement increases, but computational resources and network traffic increase
Solution Approach 1:
The patent performs preliminary actions by pre-identifying alter-egos and their interests, and pre-filtering results based on location criteria before final result generation. This reduces computational overhead during the actual search execution by preparing filtering criteria in advance.
Solution Approach 2:
The patent extracts only the necessary location and social attributes required for personalization, rather than processing all available user data. This selective extraction reduces the volume of data processed and transmitted, lowering computational and network costs while maintaining personalization effectiveness.
3Measurement precision
If users manually specify location and interest criteria for searches, then search accuracy improves, but user effort and time increase
Solution Approach 1:
The patent implements self-service by automatically identifying the user's location and determining relevant alter-egos based on social network attributes. The system performs these filtering and personalization tasks autonomously without requiring manual user input, thereby maintaining high search accuracy while minimizing user effort.
Solution Approach 2:
The patent uses feedback mechanisms to learn from user interactions with search results, continuously refining alter-ego identification and location-based filtering. This automated feedback loop improves search accuracy over time without increasing user effort, as the system adapts based on implicit user preferences.
Data Source
AI summary
A user at a geographical location may submit a search query and receive results responsive to the search query. The search results provided to the user may be based on the user's geographical location. The search results may also be based on one or more attributes of the user or an alter ego. The alter ego may be an individual user or another type of entity, such as a group or a business. A user at a geographical location submitting an alter-ego search query may see the results that would be presented to the alter ego if the alter ego were at the geographical location. Each user's interests may be selected through an interest-selection interface, automatically generated as the user interacts with search results, dynamically generated as a user follows or likes search results, or otherwise determined.