APPARATUS AND METHOD FOR AI-DRIVEN PROGRAM EXECUTION

Abstract

ABSTRACT APPARATUS AND METHOD FOR AI-DRIVEN PROGRAM EXECUTION The present disclosure introduces an apparatus and method for AI-driven program execution 100, designed to optimize software execution using artificial intelligence and machine learning. The system comprises of data collection module 102 to gather real-time execution data, data processing and analysis unit 104 to analyze this data, identifying performance bottlenecks and inefficiencies. A machine learning engine 106 uses the analyzed data to train predictive models. The execution optimization module 108 applies these insights to adjust resource allocation and task prioritization. A user interaction layer 110 provides users with real-time feedback while the real-time feedback module 112 continuously updates the system. The resource management module 114 ensures efficient use of system resources while error detection and recovery module 116 predicts and corrects potential execution errors. Additionally, the cloud-based data storage and analytics 118 stores execution data, and the API integration layer 120 enables seamless integration with existing software applications. Reference Fig 1

Specification

Description:Apparatus and Method for AI-Driven Program Execution
TECHNICAL FIELD
[0001] The present innovation relates to AI-driven systems for optimizing software program execution using machine learning, data analytics, and real-time processing.

BACKGROUND

[0002] In the rapidly evolving field of software development, traditional program execution models often struggle to meet the demands of modern applications. Existing options typically rely on fixed algorithms and predetermined instructions, which offer limited adaptability and optimization in dynamic environments. This static approach can cause inefficiencies, particularly in applications requiring real-time processing, such as cloud computing, data analytics, and the Internet of Things (IoT). Users have the option of employing rule-based execution models or manual optimization techniques, but these are time-consuming, resource-intensive, and often fail to deliver optimal performance under varying conditions.

[0003] A key drawback of traditional models is their inability to leverage vast amounts of execution data for predictive analytics and real-time adjustments. These systems cannot adapt to changing workloads, resource availability, or user preferences, leading to suboptimal performance, higher resource consumption, and potential operational bottlenecks. Moreover, the lack of dynamic error detection and recovery mechanisms further hampers reliability and overall system stability.

[0004] The invention, an "Apparatus and Method for AI-Driven Program Execution," overcomes these limitations by incorporating artificial intelligence (AI) and machine learning (ML) techniques to enhance execution strategies dynamically. Unlike existing systems, this invention continuously learns from historical execution data and real-time metrics, allowing it to predict execution outcomes and optimize resource allocation proactively. It introduces novelty through adaptive execution strategies, real-time error detection, and recovery mechanisms, and a user-centric approach that adjusts based on user behavior and system conditions. The AI-driven optimization not only improves performance but also reduces operational costs and energy consumption. This invention's ability to dynamically adjust execution in real-time, based on intelligent insights, sets it apart from existing solutions and offers a more efficient, scalable, and reliable execution framework across diverse computing environments.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to optimize software program execution by employing artificial intelligence to dynamically adjust execution strategies based on real-time data.

[0006] Another object of the invention is to enhance resource utilization, ensuring efficient allocation of CPU, memory, and other computing resources during program execution.

[0007] Another object of the invention is to improve system adaptability by enabling programs to respond to changing conditions and workloads in real-time, reducing inefficiencies and operational bottlenecks.

[0008] Another object of the invention is to increase execution reliability through predictive error detection and recovery mechanisms, minimizing downtime and improving overall system stability.

[0009] Another object of the invention is to enable seamless integration of AI-driven execution capabilities across various computing environments, including cloud, IoT, and enterprise software systems.
[00010] Another object of the invention is to reduce operational costs by optimizing energy consumption and computational resource usage during program execution.

[00011] Another object of the invention is to provide a user-centric execution system that adapts to individual user preferences and behaviors, enhancing user experience and productivity.

[00012] Another object of the invention is to facilitate continuous learning and improvement by using machine learning models to refine execution strategies based on historical performance data.

[00013] Another object of the invention is to offer a scalable solution that can handle increasing workloads and complexity without compromising execution performance.

[00014] Another object of the invention is to promote responsible resource consumption and align with sustainable practices by optimizing program execution for minimal environmental impact.

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, apparatus and method for ai-driven program execution is presented. It optimizes software execution by leveraging artificial intelligence and machine learning to dynamically adjust execution strategies in real-time. It enhances resource utilization, system adaptability, and performance through continuous learning and predictive analytics. The system improves reliability by detecting and mitigating errors, while also offering user-centric optimization based on behavior and preferences. This innovation ensures scalable, efficient, and sustainable program execution across diverse computing environments.
[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for apparatus and method for AI-driven program execution.

[00021] FIG 2 is working methodology of apparatus and method for AI-driven program execution.




DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of apparatus and method for AI-driven program execution and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, apparatus and method for AI-driven program execution 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data collection module 102, data processing and analysis unit 104, machine learning engine 106, execution optimization module 108, user interaction layer 110, real-time feedback module 112, resource management module 114, error detection and recovery module 116, cloud-based data storage and analytics 118 and API integration layer 120.

[00029] Referring to Fig. 1, the present disclosure provides details of the apparatus and method for AI-driven program execution 100. It is designed to optimize software execution using artificial intelligence and machine learning to dynamically adjust execution strategies in real-time. The system includes key components such as the data collection module 102, data processing and analysis unit 104, and machine learning engine 106, which analyze and learn from historical and real-time data. The execution optimization module 108 adjusts resource allocation, while the user interaction layer 110 enables users to input preferences and receive real-time feedback. Additional components like the real-time feedback module 112 and resource management module 114 ensure continuous improvement and efficient resource utilization, respectively.

[00030] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with data collection module 102, which gathers real-time execution data, including CPU usage, memory consumption, and user interactions. It plays a vital role in capturing the raw data needed for optimization. This data collection module 102 continuously feeds into the data processing and analysis unit 104, ensuring that all necessary metrics are available for in-depth analysis. It works in harmony with the real-time feedback module 112 to ensure that live data is always integrated into the system.

[00031] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with data processing and analysis unit 104, which processes the data collected from data collection module 102. It applies machine learning techniques to identify inefficiencies, trends, and performance bottlenecks. The processed insights are passed on to the machine learning engine 106 to train predictive models. This unit works closely with the execution optimization module 108 to ensure that the system remains responsive to real-time execution conditions.

[00032] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with machine learning engine 106, which uses historical and real-time data from data processing and analysis unit 104 to train models that predict execution performance. These models enable the system to adjust program execution dynamically. The machine learning engine 106 collaborates with the execution optimization module 108 to provide real-time insights that influence resource allocation and task prioritization.
[00033] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with execution optimization module 108, which applies the insights from machine learning engine 106 to optimize program execution in real time. It adjusts resource allocations and task priorities to improve efficiency. This module continuously interacts with the resource management module 114 to ensure that computational resources like CPU and memory are used optimally during execution.

[00034] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with user interaction layer 110, which allows users to input preferences, view real-time execution feedback, and access optimization suggestions. It is the primary interface between the user and the system, facilitating communication between the AI-driven framework and its human operators. The user interaction layer 110 works in conjunction with the real-time feedback module 112 to provide updated performance metrics to users.

[00035] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with real-time feedback module 112, which feeds execution outcomes back into the system for continuous learning and adaptation. It ensures that the data collection module 102 and data processing and analysis unit 104 receive updated information, enabling iterative improvements to execution strategies. The module also updates the machine learning engine 106 with new data to refine its predictive models.

[00036] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with resource management module 114, which dynamically allocates computational resources based on real-time data from the execution optimization module 108. It ensures efficient use of CPU, memory, and I/O resources, reducing costs and improving system performance. The resource management module 114 works closely with the error detection and recovery module 116 to maintain optimal resource distribution, even in the event of potential execution failures.

[00037] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with error detection and recovery module 116, which leverages predictive analytics from machine learning engine 106 to detect potential errors or bottlenecks during execution. It proactively implements recovery measures, minimizing downtime and enhancing system reliability. The module interacts with the resource management module 114 to reallocate resources as necessary during recovery.

[00038] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with cloud-based data storage and analytics 118, which stores execution data and machine learning models for long-term analysis and scalability. It provides a platform for continuous data-driven improvements and integrates seamlessly with the machine learning engine 106 for model retraining. The cloud-based data storage and analytics 118 ensures that the system remains scalable and capable of handling large data volumes.

[00039] Referring to Fig.1, apparatus and method for AI-driven program execution 100 is provided with API integration layer 120, which enables easy integration with various software environments and development platforms. It provides the necessary tools and APIs for developers to incorporate AI-driven execution features into their existing applications. The API integration layer 120 works in conjunction with the user interaction layer 110 to provide customizable user experiences.

[00040] Referring to Fig 2, there is illustrated method 200 for apparatus and method for AI-driven program execution 100. The method comprises:


At step 202, method 200 includes initializing the data collection module 102 to begin gathering real-time execution data from the software program;

At step 204, method 200 includes the data processing and analysis unit 104 receiving the gathered data and analyzing it to identify trends, performance bottlenecks, and inefficiencies;
At step 206, method 200 includes the machine learning engine 106 using the processed data to train predictive models for future program execution performance;

At step 208, method 200 includes the execution optimization module 108 dynamically adjusting resource allocation and task prioritization based on real-time insights from the machine learning engine 106;

At step 210, method 200 includes the user interaction layer 110 displaying real-time feedback to the user regarding the execution performance and optimization suggestions;

At step 212, method 200 includes the real-time feedback module 112 feeding back execution outcomes into the system for continuous learning and iterative improvement;

At step 214, method 200 includes the resource management module 114 allocating CPU, memory, and I/O resources based on the current execution needs to optimize performance and reduce costs;

At step 216, method 200 includes the error detection and recovery module 116 identifying potential execution errors or performance bottlenecks using predictive analytics and applying corrective measures;

At step 218, method 200 includes the cloud-based data storage and analytics 118 storing execution data and updated machine learning models for future analysis and system scalability;

At step 220, method 200 includes the API integration layer 120 facilitating the integration of AI-driven execution capabilities into existing software applications, allowing seamless operation across platforms.

[00041] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00042] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00043] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.

, Claims:WE CLAIM:
1. An apparatus and method for AI-driven program execution 100 comprising of
data collection module 102 to gather real-time execution data;
data processing and analysis unit 104 to analyze gathered data and identify inefficiencies;
machine learning engine 106 to train predictive models for optimized execution;
execution optimization module 108 to dynamically adjust resource allocation and task prioritization;
user interaction layer 110 to display real-time feedback and optimization suggestions;
real-time feedback module 112 to continuously update execution outcomes for iterative improvement;
resource management module 114 to efficiently allocate cpu, memory, and i/o resources;
error detection and recovery module 116 to predict and correct potential execution errors;
cloud-based data storage and analytics 118 to store execution data and machine learning models and
API integration layer 120 to facilitate seamless integration with existing software applications.

2. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein the data collection module 102 is configured to gather real-time execution data, including resource usage, system performance metrics, and user interactions, enabling comprehensive data analysis for optimizing program execution.

3. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein the data processing and analysis unit 104 is configured to analyze the collected data using advanced machine learning algorithms, identifying patterns, performance bottlenecks, and inefficiencies to enhance execution performance.

4. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein the machine learning engine 106 is configured to utilize historical and real-time data to train predictive models, allowing dynamic adjustments to program execution strategies based on current system conditions.

5. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein the execution optimization module 108 is configured to dynamically adjust resource allocation and task prioritization during program execution, based on insights provided by the machine learning engine 106, optimizing performance and resource efficiency.

6. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein the user interaction layer 110 is configured to display real-time execution performance feedback and optimization suggestions, allowing users to interact with and input preferences for the system's execution strategies.

7. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein the real-time feedback module 112 is configured to continuously update the system with execution outcomes, enabling iterative improvements to execution strategies through continuous learning and adaptation.

8. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein the resource management module 114 is configured to allocate CPU, memory, and I/O resources dynamically based on real-time execution needs, optimizing system performance and reducing operational costs.

9. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein the error detection and recovery module 116 is configured to predict potential execution errors and performance bottlenecks using predictive analytics, proactively applying corrective measures to ensure system reliability and stability.

10. The apparatus and method for AI-driven program execution 100 as claimed in claim 1, wherein method comprises of
initializing the data collection module 102 to begin gathering real-time execution data from the software program;

data processing and analysis unit 104 receiving the gathered data and analyzing it to identify trends, performance bottlenecks, and inefficiencies;

machine learning engine 106 using the processed data to train predictive models for future program execution performance;

execution optimization module 108 dynamically adjusting resource allocation and task prioritization based on real-time insights from the machine learning engine 106;

user interaction layer 110 displaying real-time feedback to the user regarding the execution performance and optimization suggestions;
real-time feedback module 112 feeding back execution outcomes into the system for continuous learning and iterative improvement;

resource management module 114 allocating CPU, memory, and I/O resources based on the current execution needs to optimize performance and reduce costs;

error detection and recovery module 116 identifying potential execution errors or performance bottlenecks using predictive analytics and applying corrective measures;

cloud-based data storage and analytics 118 storing execution data and updated machine learning models for future analysis and system scalability;

API integration layer 120 facilitating the integration of AI-driven execution capabilities into existing software applications, allowing seamless operation across platforms.

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