View source for 17806794. SCENARIO AWARE DYNAMIC CODE BRANCHING OF SELF-EVOLVING CODE simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

=SCENARIO AWARE DYNAMIC CODE BRANCHING OF SELF-EVOLVING CODE=


==Organization Name==

[[:Category:INTERNATIONAL BUSINESS MACHINES CORPORATION|INTERNATIONAL BUSINESS MACHINES CORPORATION]]


[[Category:INTERNATIONAL BUSINESS MACHINES CORPORATION]]

==Inventor(s)==

[[:Category:Saraswathi Sailaja Perumalla of Visakhapatnam (IN)|Saraswathi Sailaja Perumalla of Visakhapatnam (IN)]][[Category:Saraswathi Sailaja Perumalla of Visakhapatnam (IN)]]

[[:Category:Sarbajit K. Rakshit of Kolkata (IN)|Sarbajit K. Rakshit of Kolkata (IN)]][[Category:Sarbajit K. Rakshit of Kolkata (IN)]]

[[:Category:Sowjanya Rao of Hyderabad (IN)|Sowjanya Rao of Hyderabad (IN)]][[Category:Sowjanya Rao of Hyderabad (IN)]]

==SCENARIO AWARE DYNAMIC CODE BRANCHING OF SELF-EVOLVING CODE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17806794 titled 'SCENARIO AWARE DYNAMIC CODE BRANCHING OF SELF-EVOLVING CODE

==Simplified Explanation==

The patent application describes computer technology that allows an AI-enabled edge device, such as an autonomous vehicle or industrial robotic device, to dynamically adapt its code based on successful execution of a contextual scenario. This technology predicts a second contextual scenario where the device can perform a specific activity and proactively deploys self-adapted code on the device.

* The technology enables dynamic code branching of self-adapted code on an AI-enabled edge device.
* It relies on the successful execution of a contextual scenario by the device.
* The technology predicts a second contextual scenario where the device can perform a predetermined activity.
* It proactively deploys self-adapted code on the device to handle the predicted scenario.

== Potential Applications ==
This technology has potential applications in various fields, including:

* Autonomous vehicles: The AI-enabled edge device can adapt its code based on successful execution of contextual scenarios, improving its ability to handle different driving situations.
* Industrial robotics: The technology allows robotic devices to dynamically adapt their code to perform specific activities based on contextual scenarios, enhancing their efficiency and versatility.
* Smart home systems: AI-enabled edge devices in smart homes can use this technology to adapt their code and perform different tasks based on changing environmental conditions or user preferences.

== Problems Solved ==
The technology addresses several problems:

* Lack of adaptability: Traditional code in AI-enabled edge devices is typically static and cannot adapt to changing scenarios. This technology solves this problem by enabling dynamic code branching and self-adaptation.
* Inefficiency: Without proactive deployment of self-adapted code, AI-enabled edge devices may not be able to efficiently handle predicted scenarios. This technology solves this problem by proactively deploying the necessary code.
* Limited versatility: AI-enabled edge devices may have limited capabilities to handle different contextual scenarios. This technology solves this problem by predicting and preparing for specific scenarios, expanding the device's range of applications.

== Benefits ==
The technology offers several benefits:

* Improved performance: By dynamically adapting its code, the AI-enabled edge device can optimize its performance based on successful execution of contextual scenarios.
* Enhanced efficiency: Proactively deploying self-adapted code allows the device to efficiently handle predicted scenarios, reducing response times and resource consumption.
* Increased versatility: The ability to predict and prepare for specific scenarios enables the device to perform a wider range of activities, making it more versatile and adaptable to different environments.


==Original Abstract Submitted==

Computer technology for performing dynamic code branching of self-adapted code upon successful execution of a contextual scenario by artificial intelligence (AI) enabled edge device (for example, an autonomous vehicle or an industrial robotic device). predicting a second contextual scenario where the AI enabled edge device can perform a predetermined activity, and proactively deploying self-adapted code on the AI enabled edge device.



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