AR Target Data Caching via Predictive Correlation

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Solution Overview

Problem

Conventional mobile augmented reality applications face challenges in balancing speed and efficiency when identifying and displaying data for large numbers of targets in complex real-world environments, often requiring a compromise between local memory usage and remote data storage, leading to performance issues.

Innovation Solution

The method predicts potential targets based on coincident target requests by calculating correlation scores and ranking them, preemptively transmitting target data for high-ranking targets to be stored locally on the user's device, improving both speed and efficiency by caching likely future targets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If static target data for every identifiable target is stored in local memory of the mobile device, then speed of target identification is improved, but memory burden and system resource consumption increase significantly

Engineering Contradiction:

Improvetarget identification speedVSAvoidmemory usage

Core Design Contradiction:

SpeedVSQuantity of substance

Solution Approach 1:

The patent applies local quality by differentiating between frequently accessed targets and infrequently accessed targets. Local memory stores target data for frequently identified targets (high local quality), while remote storage holds data for less frequently accessed targets. This creates a quality gradient in data storage, optimizing both speed and memory usage by placing the most critical data locally.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system performs preliminary action by pre-fetching and caching target data that is predicted to be requested in the near future. Based on analysis of user behavior patterns and target correlation, the system proactively loads target data into local memory before it is actually needed, thereby reducing future access delays without requiring all possible target data to be stored locally at once.

Inventive Principle:

Principle #10Preliminary action

2Quantity of substance

If target data is stored in remote storage and accessed dynamically via network, then memory efficiency is improved, but target identification speed decreases due to data propagation delays

Engineering Contradiction:

Improvememory usageVSAvoidtarget identification speed

Core Design Contradiction:

Quantity of substanceVSSpeed

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies dynamics by making the target data storage system adaptive and changeable. The system dynamically adjusts what data is stored locally versus remotely based on real-time usage patterns, access frequency, and predicted future requests. This dynamic allocation allows the system to optimize the balance between local storage burden and access speed continuously as user behavior evolves.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If the number of identifiable targets increases to support complex real-world environments, then adaptability and coverage are improved, but the burden on local memory and system resources increases significantly

Engineering Contradiction:

ImproveAR environment coverageVSAvoidmemory burden

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent applies segmentation by dividing the target data storage into multiple segments: frequently accessed targets stored in local memory, infrequently accessed targets stored in remote storage, and predicted future targets stored in a buffer zone. This segmentation allows the system to support a large total number of identifiable targets while keeping local memory usage manageable by only storing a subset of targets locally at any given time.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system adds another dimension to data storage by utilizing both local device memory and remote cloud storage as different layers of the storage hierarchy. This dimensional expansion allows the system to effectively store and access millions of targets by distributing data across multiple storage dimensions (local/remote, frequent/rare access), thereby supporting complex AR environments without overwhelming local resources.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS10735547B2Systems and methods for caching augmented reality target data at user devices

Publication Date: 2020.08.04 VERIZON PATENT & LICENSING INC

AI summary

Systems and methods are disclosed for transmitting, to user devices, data for potential targets predicted to be identified in an augmented reality application. One method includes receiving a request for target data related to at least one physical object within an image of a real-world environment captured at the device; identifying a current target representing the physical object within a virtual environment corresponding to the real-world environment; determining at least one potential future target to be identified at the device based on identified coincident target requests; and sending to the device target data for each of the current and potential future targets based on the determination, wherein the device presents the target data for the current target within the virtual environment displayed at the device and store the target data for the potential future target in a local memory of the device.