US 2024/0403003 A1Published application

Building software applications using natural language and machine-learning models

AssigneeBrain Technologies, Inc.

InventorsSheng Yue, Clive Gomes

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Abstract

A system may receive nodes and edges representing a dynamic-layout software program. The program is represented by nodes and edges, at least one node represents an initial state for an end user to provide an input and at least one edge represents an executable routine whose functionality is specified in a natural language by a software developer. The system executes the initial state of the program at the runtime, receives the input from the end user, and extracts executable elements of the program. The executable elements include the input and the executable routine specified in one of edges. The system applies a machine-learning model to analyze the executable elements to generate a machine-learnable scripting language that connects the executable elements. The machine-learnable scripting language includes runtime layout parameters of the program. When executing the machine-learnable scripting language, the program displays one or more runtime-determined layout elements.

Claims20

Dependent claims

A system comprising:

a natural-language software builder configured to display a build interface that allows a software developer to design a dynamic-layout software program that is represented by nodes and one or more edges, at least one node representing an initial state for an end user to provide an input and at least one edge representing an executable routine whose functionality is specified in a natural language by the software developer;
and
a computing server in communication with the natural language software builder, the computing server comprising one or more processors and memory configured to store code comprising instructions, wherein the instructions, when executed by the one or more processors, cause the one or more processors to:
receive the nodes and edges representing the dynamic-layout software program;
execute, at runtime of the dynamic-layout software program, the initial state of the dynamic-layout software program;
receive the input from the end user directed at the dynamic-layout software program;
extract executable elements of the dynamic-layout software program, the executable elements comprising the input and the executable routine specified in one of edges;
apply a machine-learning model to analyze the executable elements to generate, during the runtime of the dynamic-layout software program, a machine-learnable scripting language that connects the executable elements, the machine-learnable scripting language comprising runtime layout parameters of the dynamic-layout software program;
execute the machine-learnable scripting language to cause the dynamic-layout software program to display one or more runtime-determined layout elements.

6 more elements

A computer-implemented method, comprising:

receiving nodes and edges representing a dynamic-layout software program, wherein the dynamic-layout software program is represented by the nodes and edges, at least one node representing an initial state for an end user to provide an input and at least one edge representing an executable routine whose functionality is specified in a natural language by a software developer;
executing, at runtime of the dynamic-layout software program, the initial state of the dynamic-layout software program;
receiving the input from the end user directed at the dynamic-layout software program;
extracting executable elements of the dynamic-layout software program, the executable elements comprising the input and the executable routine specified in one of edges;
applying a machine-learning model to analyze the executable elements to generate, during the runtime of the dynamic-layout software program, a machine-learnable scripting language that connects the executable elements, the machine-learnable scripting language comprising runtime layout parameters of the dynamic-layout software program;
and
execute the machine-learnable scripting language to cause the dynamic-layout software program to display one or more runtime-determined layout elements.

4 more elements

A non-transitory computer readable storage medium comprising stored program code, the program code comprising instructions, the instructions when executed causes a processor system to:

receive nodes and edges representing a dynamic-layout software program, wherein the dynamic-layout software program is represented by the nodes and edges, at least one node representing an initial state for an end user to provide an input and at least one edge representing an executable routine whose functionality is specified in a natural language by a software developer;
execute, at runtime of the dynamic-layout software program, the initial state of the dynamic-layout software program;
receive the input from the end user directed at the dynamic-layout software program;
extract executable elements of the dynamic-layout software program, the executable elements comprising the input and the executable routine specified in one of edges;
apply a machine-learning model to analyze the executable elements to generate, during the runtime of the dynamic-layout software program, a machine-learnable scripting language that connects the executable elements, the machine-learnable scripting language comprising runtime layout parameters of the dynamic-layout software program;
execute the machine-learnable scripting language to cause the dynamic-layout software program to display one or more runtime-determined layout elements.

3 more elements

Dependencies are inferred from the claim text.