Contextual Search Region Projection for Mobile Devices
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Solution Overview
Problem
Conventional mobile search techniques fail to provide relevant results by relying solely on a distance radius from the user's location, ignoring important contextual information such as direction of travel and speed, which can lead to irrelevant options being included in search results.
Innovation Solution
The use of contextual data including current location, direction of travel, and speed to constrain the geographical search region, generating or modifying search queries to provide more relevant results by projecting the search area based on the device's movement and expected location at a specified time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a radius-based search region is used from the user's location, then the search covers a wide area and provides multiple options, but the search results include irrelevant options that the user will not reach during their journey
Solution Approach 1:
The system performs preliminary actions by calculating the user's expected location at a future time point before executing the search. It uses current location, direction of travel, and speed to project where the user will be, then constrains the search region to areas around that expected location. This preliminary calculation ensures that only relevant options the user will actually reach are included in the search results.
Solution Approach 2:
The search region dynamically adapts based on the user's movement characteristics. Instead of a static radius around current location, the system continuously updates the search region based on real-time changes in location, direction, and speed. This dynamic adjustment ensures the search region accurately reflects the user's journey path and maintains relevance as the user moves.
2Reliability
If the search region is constrained to a small radius around current location, then the search results are more relevant to immediate surroundings, but the user may miss relevant options along their travel path
Solution Approach 1:
The system transitions from a two-dimensional static radius search to a three-dimensional temporal-spatial search by incorporating the time dimension. It searches not just around the current location but projects the search region forward in time to where the user will be, creating a dynamic search volume that accounts for both spatial movement and temporal progression. This dimensional expansion allows comprehensive coverage of the travel route while maintaining relevance.
3Measurement precision
If contextual data such as direction of travel and speed are collected and processed, then the search results become more accurate and relevant, but the system complexity increases
Solution Approach 1:
The system leverages existing sensor data (location, direction, speed) that mobile devices already collect for other purposes like navigation and maps. Instead of requiring additional specialized sensors or complex data collection infrastructure, the system self-services by utilizing this readily available contextual data to improve search accuracy. This approach minimizes additional system complexity while achieving high measurement precision.
Data Source
AI summary
Techniques are described by which information needs are satisfied by taking into account a variety of contextual cues that constrain the information searched. Contextual data such as the location, speed, and direction of travel of a mobile device are used to generate or modify a search query in a way that constrains the geographic region being searched to improve the relevance of search results or recommendations provided.