AR Application Generation Using a DSL for Semantic Data Enrichment
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Solution Overview
Problem
Existing augmented reality (AR) applications are complex and time-consuming to develop, and existing tools do not efficiently enable the enrichment of real-world data with supplementary information from remote sources, particularly in a context-specific manner.
Innovation Solution
A framework and domain-specific language (DSL) are provided to allow developers to easily create AR applications that enrich real-world data by performing semantic fuzzy joins between real-world and remote data using embedding vectors and a declarative grammar, reducing the complexity of development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional AR application development tools are used, then developers can create AR applications with data enrichment capabilities, but the development process becomes complex and time-consuming
Solution Approach 1:
The patent introduces an intermediary processing layer that automatically generates data enrichment code and manages data source integrations. This intermediary system translates high-level developer specifications into complex data joining operations, shielding developers from the underlying complexity while maintaining full functionality.
Solution Approach 2:
The development system is segmented into distinct modular components: a specification layer for defining data enrichment requirements, a code generation layer for automatic program synthesis, and an execution layer for data processing. This segmentation allows each component to be developed and maintained independently, reducing overall system complexity.
2Loss of information
If traditional data joining methods are used to enrich real-world data with remote data, then supplementary information can be retrieved, but the process requires manual connections and context switching between real-world and remote data
Solution Approach 1:
The patent merges real-world data and remote data into a unified data structure with a common namespace. This unified structure allows seamless access to both local and remote data through consistent syntax, eliminating the need for manual context switching and making data integration transparent to the developer.
Solution Approach 2:
The system provides a universal data access interface that works consistently regardless of whether data originates from local sensors, remote databases, or hybrid sources. This multi-functional approach allows the same code patterns to retrieve and join data from any source, greatly simplifying the integration process.
3Loss of information
If comprehensive data enrichment is implemented in AR applications, then users gain access to supplementary information, but the data processing and joining operations become computationally intensive
Solution Approach 1:
The system performs preliminary data processing and indexing of remote data sources before actual AR application execution. By pre-processing and organizing remote data in advance, the system reduces the computational burden during runtime, allowing comprehensive data enrichment without excessive energy consumption during device operation.
Data Source
AI summary
Systems and methods are provided that allow developers to quickly and easily develop augmented reality (AR) applications that enrich the real-world with data from the cloud. Given that the development of an AR application is a complex and time-consuming process, the systems and methods described herein allow software developers to concisely describe their needs in a succinct program, written in the QWL domain-specific language. The systems and methods take this program and automatically generate an AR application.