Personalized Distance Calculation Using Spatial Temporal Topical Social Data
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Solution Overview
Problem
Existing systems fail to effectively capture and utilize user-specific information, such as location, social networks, and activities, to provide personalized experiences and services, leading to missed opportunities for advertisers and retailers.
Innovation Solution
A method and system that calculate and display a 'personalized distance' between two real-world locations using spatial, temporal, topical, and social data, incorporating user preferences and interests to determine the desirability of a route, which is then displayed on a medium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional distance calculation methods are used, then the calculation is simple and fast, but the result does not reflect user-specific preferences and contextual factors
Solution Approach 1:
The patent segments the distance calculation into multiple components: spatial distance, temporal distance, topical distance, and social distance. Each component is calculated separately using different data types and algorithms, then combined to form the personalized distance metric. This segmentation allows the system to handle complexity in a structured manner while improving measurement precision.
Solution Approach 2:
The patent extends the traditional one-dimensional spatial distance measurement by adding multiple dimensions: temporal dimension (time-based factors), topical dimension (interest-based factors), and social dimension (social network factors). This multi-dimensional approach significantly improves personalization accuracy while managing complexity through modular calculation of each dimension.
2Measurement precision
If multiple data types (spatial, temporal, topical, social) are collected and processed, then the personalization accuracy improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent divides data collection and processing into separate modules for each data type (spatial, temporal, topical, social). Each module independently processes its specific data type using appropriate algorithms, then the results are integrated. This segmentation reduces the overall processing complexity by avoiding the need to handle all data types simultaneously in a single complex process.
Solution Approach 2:
The patent introduces intermediary processing layers that transform raw data from each category into standardized distance metrics before final integration. These intermediaries simplify the complexity by providing a common framework for combining diverse data types, making the overall measurement process more manageable.
3Adaptability or versatility
If user-specific information is captured and utilized, then the service relevance improves, but the information capture and storage requirements increase
Solution Approach 1:
The patent extracts only the essential features and patterns from user data that are relevant to distance calculation, rather than storing and processing all raw user information. By extracting key characteristics (spatial patterns, temporal preferences, topical interests, social connections), the system achieves high adaptability while minimizing storage requirements.
Solution Approach 2:
The patent applies different levels of data collection and processing to different aspects of user information based on their relevance to the specific calculation task. Not all user data is captured or processed with equal detail - only the locally relevant quality of data needed for each dimension of personalization is maintained, optimizing the balance between personalization and storage.
Data Source
AI summary
A system and method for determination and display of personalized distance. A request is received for the determination of a personalized distance over a network, wherein the request comprises an identification of a requesting user, and a plurality of real world entities comprising at least a starting location and an ending location. At least one route is determined between the first location and the second location. Spatial, temporal, topical, and social data available to the network relating to the requesting user and each real world entity and the route is retrieved using a global index of data available to the network. A personalized distance is calculated via the network between the first location and the second location using spatial, temporal, topical, and social data relating to the requesting user and each real world entity and the route. A representation of the personalized distance calculated for the route is displayed on a display medium.