Machine Learning Trajectory Prediction for Intent-Aware Access Control
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Solution Overview
Problem
Existing access control systems face challenges in accurately predicting user trajectories and intentions due to user-specific variations in movement and the lack of seamless credential exchange, leading to inefficiencies and potential unauthorized access when using mobile devices for long-range communication.
Innovation Solution
A system that utilizes machine learning techniques, including conditioned variational autoencoders, to process observed user trajectories and behavior information to predict future positions and intentions, enabling precise access control operations based on predicted trajectories and user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning techniques are used to predict user trajectories and intentions, then access control accuracy and security are improved, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning prediction system as an intermediary component between trajectory tracking and access control decisions. This mediator processes observed trajectories and behavior information to generate predicted trajectories, which then inform access control operations, thereby improving accuracy while managing complexity through modular architecture
Solution Approach 2:
The system performs preliminary trajectory prediction and intent determination before executing access control operations. By predicting future trajectories and assessing user intent in advance, the system prepares access control decisions ahead of time, improving response accuracy and reducing real-time processing complexity
2Ease of operation
If long-range wireless communication is used for credential exchange, then ease of operation is improved, but security risks increase due to potential unauthorized access
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors user trajectories and behavior information, compares predicted trajectories against access control policies, and adjusts access decisions accordingly. This feedback loop allows the system to maintain security while enabling long-range communication by validating each access attempt against real-time trajectory predictions
Solution Approach 2:
The access control system dynamically adjusts its behavior based on real-time trajectory predictions and user behavior patterns. The system adapts access decisions to the current contextual situation, allowing long-range credential exchange when trajectory predictions indicate legitimate intent while blocking access when unauthorized patterns are detected
3Measurement precision
If trajectory prediction is performed for multiple users with variations in movement patterns, then access control precision is improved, but processing time increases
Solution Approach 1:
The patent segments the trajectory prediction process into distinct modules: observed trajectory processing, behavior information extraction, prediction generation, and access control decision-making. This segmentation allows each component to be optimized independently, improving precision for diverse user patterns while managing processing time through modular execution
Solution Approach 2:
The system adapts prediction parameters and model complexity based on the specific characteristics of each user's movement patterns and the contextual situation. By adjusting parameters dynamically rather than using fixed parameters for all users, the system achieves high precision for diverse movement patterns while optimizing processing efficiency for each individual case
Data Source
AI summary
Methods and systems for trajectory and intent prediction are provided. The methods and systems include operations comprising: receiving an observed trajectory of a user and user behavior information; processing the observed trajectory by a machine learning technique to generate a plurality of predicted trajectories, the machine learning technique being trained to establish a relationship between a plurality of training observed trajectories and training predicted trajectories; adjusting the plurality of predicted trajectories based on the user behavior information to determine user intent to operate a target access control device; determining that the target access control device within a threshold range of a given one of the plurality of predicted trajectories; and in response to determining that the target access control device is within the threshold range of the given one of the plurality of predicted trajectories, performing an operation associated with the target access control device.