User Vector Prediction for Next Location Tracking
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Solution Overview
Problem
Conventional techniques can detect locations visited by a user based on position information but cannot predict the next location the user will visit.
Innovation Solution
An information processing apparatus and method that acquires position data and user features, generates user and sequence vectors, and uses machine learning to predict the next location visited by the user, incorporating region-of-interest, location-of-interest, and transportation vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional position information detection techniques are used, then location tracking capability is achieved, but prediction capability for next location is lost
Solution Approach 1:
The system performs preliminary actions by collecting and storing position information, user features, and movement patterns before prediction is needed. This includes acquiring historical position data, extracting user features (demographics, behavior patterns), and pre-processing this data into structured formats that can be quickly utilized for prediction when needed.
Solution Approach 2:
The patent introduces intermediary components including a machine learning model that acts as a mediator between raw position data and prediction results, and a feature extraction module that serves as an intermediary to transform raw data into meaningful user features. These intermediaries enable the system to bridge the gap between simple location tracking and intelligent prediction.
2Measurement precision
If machine learning models are trained with comprehensive user features, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the complex prediction task into distinct components: position information acquisition, user feature extraction, movement pattern analysis, and prediction generation. Each component is handled by a separate module, allowing independent optimization and reducing overall system complexity while maintaining high prediction accuracy through comprehensive feature processing.
Solution Approach 2:
The system dynamically changes parameters based on user context and behavior patterns. It adjusts the weight and importance of different user features (demographics, preferences, behavior patterns) and modifies processing depth according to the specific prediction scenario, thereby achieving high accuracy without consistently requiring maximum processing complexity for all cases.
Data Source
AI summary
An information processing apparatus acquires data on positions of a user accompanying movement of the user, acquires a user feature representing a feature of the user, generates a user vector representing a feature of the movement of the user based on the position data and the user feature, generates a sequence vector representing a sequence of locations visited by the user based on the position data; and predicts a next location that the user will visit based on the user victor and the sequence vector through machine learning.