Spatial Computing Anchoring Security for Malicious Hyperlink Detection

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Solution Overview

Problem

Spatial computing systems are vulnerable to hacking, which can allow malicious actors to alter or corrupt virtual object hyperlinks, potentially redirecting users to hazardous websites, and compromise personal or confidential information.

Innovation Solution

A spatial computing system with a wearable device and dual computer processors, including a sensing apparatus and augmented reality screen, uses deep learning and machine learning algorithms to monitor anchoring parameters, detect anomalies, and generate alerts to prevent unauthorized changes to virtual object placements and hyperlinks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If spatial computing systems enable anchoring of virtual objects to physical environments, then user interaction with online services is enhanced, but the system becomes vulnerable to malicious anchoring and hyperlink corruption

Engineering Contradiction:

Improvevirtual object anchoring capabilityVSAvoidsystem security

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary validation of anchoring parameters and hyperlink integrity before allowing virtual object placement. The processor checks whether the physical environment contains sensitive information and verifies hyperlink authenticity in advance, preventing malicious anchoring before it occurs.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors anchoring parameters and hyperlink status, providing feedback loops that detect unauthorized changes or malicious modifications. This real-time monitoring enables the system to identify and respond to security threats while maintaining legitimate anchoring functionality.

Inventive Principle:

Principle #23Feedback

2Reliability

If the system monitors and validates anchoring parameters to prevent malicious activity, then system security is improved, but processing complexity and computational overhead increase

Engineering Contradiction:

Improvesystem securityVSAvoidmonitoring system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The monitoring system applies different validation levels to different anchoring scenarios. Rather than uniformly complex validation for all cases, the system tailors the depth of parameter checking and hyperlink verification to the specific context, reducing unnecessary computational overhead while maintaining security where needed.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts monitoring parameters and validation thresholds based on environmental context and risk assessment. By changing monitoring intensity and validation depth according to situational factors, the system optimizes the balance between security and computational efficiency.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If deep learning algorithms are used to detect anomalies in anchoring instructions, then detection precision is improved, but computational energy consumption increases

Engineering Contradiction:

Improveanomaly detection accuracyVSAvoidprocessing energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies deep learning-based anomaly detection selectively rather than continuously. By using partial action - activating sophisticated detection only when suspicious patterns are初步 detected or in high-risk scenarios - the system achieves high detection precision when needed while reducing overall energy consumption during normal operation.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12399978B2Apparatus and method to monitor and prevent malicious anchoring in a spatial computing environment

Publication Date: 2025.08.26 BANK OF AMERICA CORP

AI summary

Provided herein is a spatial computing system. The system may include a spatial computing apparatus configured to receive an anchoring instruction and consequently anchor a virtual object to a physical object; an onboarding module configured to interface with an existing spatial computing software platform; a spatial telemetry extraction module configured to extract positioning metadata associated with the physical object; a spatial analyzer module configured to detect an anomaly in the anchoring instruction, wherein the anomaly may indicate a suspected attempt to glean personal information from an image of the physical object; an anchor validation module configured to monitor changes in anchoring instructions; a session management module configured to terminate a spatial computing session, upon receiving an indication of an anomaly; a spatial security rule orchestration module configured to block implementation of the anchoring instruction; and a deep learning module.