Method and system for generating data-enriching augmented reality applications from a domain-specific language

Patent metadata

Field	Value
Publication number	US-12469236-B2
Application number	US-202418661998-A
Country	US
Kind code	B2
Filing date	May 13, 2024
Priority date	May 22, 2020
Publication date	Nov 11, 2025
Grant date	Nov 11, 2025

Abstract

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.

First claim

What is claimed:

1 . A system for generating data-enriching augmented reality (AR) applications from a domain-specific language, the system comprising:
a data store; and an AR application generating module configured to generate an AR application using a domain-specific language (DSL) and a fuzzy join operation, wherein the AR application is configured to augment real-world data with supplementary data from the data source by: expressing the real-world data as a vector; determining at least one vector of a plurality of vectors that is similar to the vector of the real-world data using a search executed over the plurality of vectors, wherein each vector of the plurality of vectors expresses the supplementary data; and performing the fuzzy join operation of the vector of the real-world data and the determined at least one vector of the plurality of vectors to augment the real-world data with the supplementary data from the data source.

2 . The system of claim 1 , wherein the AR application generating module is configured to receive a program written in the DSL, and generate the AR application based on the received program.

3 . The system of claim 1 , wherein the AR application is defined in a stateful manner and wherein the fuzzy join operation is defined in a declarative manner.

4 . The system of claim 1 , wherein the DSL is based on a grammar, wherein the grammar is defined with data sources and with transformations to join remotely retrieved data with real-world data.

5 . The system of claim 1 , wherein the real-world data is live data from a camera feed.

6 . The system of claim 1 , wherein the DSL is configured to provide scripts that define a directed acyclic graph (DAG) of nodes that represent event-driven data flow elements.

7 . The system of claim 1 , wherein the data store comprises at least one of a remote source, an embedding store, or a real-world context.

8 . The system of claim 1 , wherein the fuzzy join operation is configured to perform a left-outer join between real-world context and supplementary data, which have both been transformed by embedding functions.

9 . A method for generating data-enriching augmented reality (AR) applications from a domain-specific language, the method comprising:
receiving a program written in a domain-specific language (DSL); and
generating an AR application based on the received program using the DSL and a fuzzy join operation, wherein the AR application is configured to augment real-world data with supplementary data from a data source by:
expressing the real-world data as a vector;
determining at least one vector of a plurality of vectors that is similar to the vector of the real-world data using a search executed over the plurality of vectors, wherein each vector of the plurality of vectors expresses the supplementary data; and
performing the fuzzy join operation of the vector of the real-world data and the determined at least one vector of the plurality of vectors to augment the real-world data with the supplementary data from the data source.

10 . The method of claim 9 , further comprising defining the AR application defined in a stateful manner and wherein the fuzzy join operation is defined in a declarative manner.

11 . The method of claim 9 , wherein the DSL is based on a grammar, wherein the grammar is defined with data sources and with transformations to join the supplementary data with the real-world data.

12 . The method of claim 9 , wherein the real-world data is live data from a camera feed.

13 . The method of claim 9 , wherein the DSL is configured to provide scripts that define a directed acyclic graph (DAG) of nodes that represent event-driven data flow elements.

14 . The method of claim 9 , wherein the fuzzy join operation is configured to perform a left-outer join between real-world context and remote data from remote sources, which have both been transformed by embedding functions.

Assignees

• Ohio State Innovation Foundation

Inventors

• Nandi Arnab
• Burley Codi
• Sarkhel Ritesh

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F16/2468

  Fuzzy queries · CPC title

• G06F8/433

  Dependency analysis; Data or control flow analysis · CPC title

• G06F16/24544

  Join order optimisation · CPC title

• G06N7/023

  Learning or tuning the parameters of a fuzzy system · CPC title