International business machines corporation (20240094995). GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING simplified abstract
Contents
- 1 GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING
Organization Name
international business machines corporation
Inventor(s)
Swagatam Haldar of Kolkata (IN)
Devika Sondhi of New Delhi (IN)
Diptikalyan Saha of Bangalore (IN)
GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240094995 titled 'GENERATING SURROGATE PROGRAMS USING ACTIVE LEARNING
Simplified Explanation
The abstract describes a method for providing a surrogate program for a program endpoint by generating transformations based on input/output pairs and using a model to classify inputs to these transformations.
- Obtaining a set of input/output pairs generated using the program endpoint
- Generating transformations based on the input/output pairs
- Creating a model that classifies inputs to transformations based on parameters of the inputs
- Receiving a new input
- Selecting a transformation based on the new input using the model
- Generating a new output by applying the selected transformation to the new input
Potential Applications
This technology could be applied in various fields such as software development, artificial intelligence, and automation.
Problems Solved
This technology helps in automating the process of generating surrogate programs for program endpoints, saving time and effort for developers.
Benefits
The benefits of this technology include increased efficiency in program development, improved accuracy in generating surrogate programs, and enhanced automation capabilities.
Potential Commercial Applications
- "Automated Surrogate Program Generation Technology in Software Development"
Possible Prior Art
One possible prior art could be the use of machine learning algorithms to classify inputs and generate outputs in various applications.
Unanswered Questions
How does this technology handle complex input/output relationships?
The method described in the abstract seems to focus on simple input/output pairs. It is unclear how it would handle more complex relationships between inputs and outputs.
What is the computational overhead of using this technology?
The abstract does not mention anything about the computational resources required to implement this method. It would be important to understand the computational overhead before considering widespread adoption of this technology.
Original Abstract Submitted
a method of providing a surrogate program for a program endpoint includes: obtaining, by a processor set, a set of plural input/output pairs generated using the program endpoint; generating, by the processor set, transformations based on the input/output pairs; generating, by the processor set, a model that classifies inputs of the input/output pairs to ones of the transformations based on parameters of one or more strings of the inputs; receiving, by the processor set, a new input; selecting, by the processor set and using the model, one of the transformations based on parameters of one or more strings of the new input; and generating, by the processor set, a new output by applying the selected one of the transformations to the new input.
