SCHEDULING PROTOCOLS FOR AI-DRIVEN INFORMATION REQUESTS

Abstract

ABSTRACT SCHEDULING PROTOCOLS FOR AI-DRIVEN INFORMATION REQUESTS The present disclosure introduces scheduling protocols for AI-driven information requests 100 designed to optimize real-time processing through intelligent prioritization and resource allocation. The invention incorporates a request queue management system 102 for organizing requests, and a context-aware request prioritization module 104 that evaluates requests based on urgency and complexity. A machine learning engine 106 refines scheduling, while a dynamic resource allocation system 108 adjusts resources based on demand. Other components are feedback loop mechanism 110, user interface (UI) 112, predictive analytics module 114, load balancing system 116, energy efficiency optimization module 118, security and access control system 120, failure recovery and redundancy mechanism 122, cross-domain compatibility layer 124, customizable alert and notification system 126, priority weighting system 128, external event integration module 130, simulation and modeling tools 132, cloud and edge computing integration layer 134 and adaptive learning for request type classification 136. Reference Fig 1

Specification

Description: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 scheduling protocols for AI-Driven information requests 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, scheduling protocols for AI-Driven information requests 100 is disclosed in accordance with one embodiment of the present invention. It comprises of request queue management system 102, context-aware request prioritization module 104, machine learning engine 106, dynamic resource allocation system 108, feedback loop mechanism 110, user interface (UI) 112, predictive analytics module 114, load balancing system 116, energy efficiency optimization module 118, security and access control system 120, failure recovery and redundancy mechanism 122, cross-domain compatibility layer 124, customizable alert and notification system 126, priority weighting system 128, external event integration module 130, simulation and modeling tools 132, cloud and edge computing integration layer 134 and adaptive learning for request type classification 136.

[00029] Referring to Fig. 1, the present disclosure provides details of scheduling protocols for AI-Driven information requests 100. It is a system designed to optimize the handling of complex AI-generated requests using dynamic prioritization, adaptive resource allocation, and contextual awareness. It enables efficient processing of information requests in real-time, enhancing responsiveness and system efficiency. In one of the embodiments, the scheduling protocols for AI-driven information requests 100 is provided with the following key components such as request queue management system 102, context-aware request prioritization module 104, and machine learning engine 106, facilitating intelligent scheduling and resource allocation. The system incorporates feedback loop mechanism 110 and predictive analytics module 114 to ensure adaptability and anticipate future demand. It also features load balancing system 116 for even distribution of requests and failure recovery and redundancy mechanism 122 to ensure reliability. Additional components such as energy efficiency optimization module 118 and cross-domain compatibility layer 124 enhance sustainability and integration across applications.

[00030] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with request queue management system 102, which organizes incoming information requests by prioritizing them based on urgency, type, and complexity. This component works closely with context-aware request prioritization module 104 to ensure that critical tasks are handled first, optimizing the order of processing to reduce latency. The request queue management system 102 enables streamlined request handling, feeding requests into the dynamic resource allocation system 108 to maintain an efficient processing flow. By ensuring a structured and prioritized queue, this component fulfils the invention's goal of responsive and efficient scheduling.

[00031] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with context-aware request prioritization module 104, which assesses each request using factors such as urgency, data dependencies, and source. This component interacts with the machine learning engine 106 to continuously refine prioritization algorithms based on historical data, adapting to changing requirements in real time. By incorporating contextual data, the context-aware request prioritization module 104 dynamically adjusts the processing order, addressing the invention's objective of optimized request prioritization tailored to the needs of AI-driven applications.

[00032] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with machine learning engine 106, which analyzes historical data to enhance scheduling decisions through adaptive algorithms. The engine integrates with both request queue management system 102 and context-aware request prioritization module 104 to refine processing patterns over time. By learning from system performance metrics collected by feedback loop mechanism 110, the machine learning engine 106 continuously optimizes resource allocation, supporting the invention's feature of adaptive scheduling based on real-time conditions and historical trends.

[00033] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with dynamic resource allocation system 108, which assigns computational resources such as CPU and memory based on real-time demand and availability. This component collaborates with load balancing system 116 to prevent overloading specific resources while maintaining optimal performance across the network. Dynamic resource allocation system 108 adjusts its strategy in coordination with predictive analytics module 114 to anticipate workload fluctuations, fulfilling the invention's objective of efficient resource utilization and load management.
[00034] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with feedback loop mechanism 110, which gathers real-time metrics on processing time, resource usage, and user satisfaction. This component feeds insights back into machine learning engine 106 and context-aware request prioritization module 104 to enable continuous improvement. By providing actionable performance data, feedback loop mechanism 110 ensures that the system adapts and optimizes dynamically, aligning with the invention's goal of enhanced responsiveness and efficiency.

[00035] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with user interface (ui) 112, which allows users to define request parameters, view real-time performance metrics, and adjust settings for scheduling. This component connects to request queue management system 102 and feedback loop mechanism 110 to display up-to-date information on request processing and resource allocation. The user interface (UI) 112 empowers stakeholders with control over scheduling processes, fulfilling the invention's feature of customization and transparency for improved user experience.

[00036] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with predictive analytics module 114, which forecasts future request volumes based on historical data trends. Working closely with dynamic resource allocation system 108, the module pre-emptively adjusts resource distribution to handle anticipated demand spikes. The predictive analytics module 114 enables the system to prepare for workload fluctuations, enhancing the invention's capability to maintain smooth operation and timely processing of AI-driven requests.

[00037] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with load balancing system 116, which distributes requests evenly across available resources, preventing overloads and maintaining system throughput. This component operates alongside dynamic resource allocation system 108 to manage high volumes efficiently, ensuring stability in processing. By effectively balancing the load, load balancing system 116 supports the invention's goal of sustained performance and prevents bottlenecks in information request processing.

[00038] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with energy efficiency optimization module 118, which optimizes resource usage to reduce energy consumption while maintaining high performance. This component collaborates with dynamic resource allocation system 108 to select energy-efficient strategies in resource allocation. By minimizing unnecessary energy use, energy efficiency optimization module 118 aligns with the invention's objective of sustainable and cost-effective AI operations, promoting responsible technology deployment.

[00039] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with security and access control system 120, which manages data access based on user roles and request sensitivity. This component works in conjunction with user interface (ui) 112 to provide secure, role-based access to request data, ensuring confidentiality and privacy. The security and access control system 120 fulfills the invention's goal of protecting sensitive information while enabling authorized access for efficient decision-making.

[00040] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with failure recovery and redundancy mechanism 122, which reroutes requests to alternate resources in case of failure, maintaining uninterrupted operation. This component works closely with load balancing system 116 to ensure that backup systems are utilized effectively, enhancing reliability. By providing redundancy, failure recovery and redundancy mechanism 122 meets the invention's objective of robust and reliable information processing.

[00041] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with cross-domain compatibility layer 124, which facilitates integration with diverse AI systems and data sources, allowing seamless operation across multiple sectors. This component interacts with predictive analytics module 114 and context-aware request prioritization module 104 to adapt to various system architectures and data types. The cross-domain compatibility layer 124 ensures that the invention can support a wide range of applications, meeting the need for flexibility in AI-driven information processing.


[00042] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with customizable alert and notification system 126, which informs users of significant changes in scheduling, delays, or the completion of high-priority requests. This component integrates with user interface (ui) 112 to provide real-time updates, ensuring that users are well-informed. The customizable alert and notification system 126 enhances the invention's objective of transparency and timely communication in information processing.

[00043] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with priority weighting system 128, which allows users to assign weights to requests based on their business importance, enabling customized prioritization. This component collaborates with context-aware request prioritization module 104 to adjust processing order based on organizational needs. By providing tailored prioritization, priority weighting system 128 meets the invention's goal of aligning information processing with business objectives.

[00044] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with external event integration module 130, which adjusts scheduling priorities based on real-world events such as news alerts or system status updates. This component interacts with predictive analytics module 114 to modify priorities in response to external conditions, enhancing responsiveness. The external event integration module 130 fulfils the invention's feature of context-sensitive adaptability in scheduling.
[00045] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with simulation and modelling tools 132, which allow users to simulate various scheduling scenarios to optimize configurations before implementation. This component works with dynamic resource allocation system 108 to test different resource strategies, supporting informed decision-making. Simulation and modelling tools 132 align with the invention's goal of offering proactive, user-driven optimization.

[00046] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with cloud and edge computing integration layer 134, which supports distributed scheduling across cloud and edge environments, enhancing processing flexibility. This component coordinates with load balancing system 116 and failure recovery and redundancy mechanism 122 to optimize performance across different architectures. The cloud and edge computing integration layer 134 fulfils the invention's objective of scalability and reduced latency.

[00047] Referring to Fig. 1, scheduling protocols for AI-driven information requests 100 is provided with adaptive learning for request type classification 136, which categorizes incoming requests to improve scheduling based on their type. This component interacts with machine learning engine 106 to refine classification rules, supporting optimized prioritization. The adaptive learning for request type classification 136 addresses the invention's feature of tailored processing strategies based on request characteristics.

[00048] Referring to Fig 2, there is illustrated method 200 for scheduling protocols for AI-driven information requests 100. The method comprises:
At step 202, method 200 includes receiving an incoming information request at the request queue management system 102;
At step 204, method 200 includes the request queue management system 102 organizing and prioritizing the request based on urgency, type, and other parameters;
At step 206, method 200 includes passing the prioritized request to the context-aware request prioritization module 104, which evaluates the request further using contextual factors such as data dependencies and source;
At step 208, method 200 includes the context-aware request prioritization module 104 coordinating with the machine learning engine 106 to determine the optimal processing sequence based on historical data and real-time analytics;
At step 210, method 200 includes the request entering the dynamic resource allocation system 108, where computational resources are allocated based on current availability and workload demands;
At step 212, method 200 includes the dynamic resource allocation system 108 collaborating with the load balancing system 116 to distribute the request across available resources efficiently, preventing overloads and maximizing throughput;
At step 214, method 200 includes the predictive analytics module 114 forecasting future workload demands to adjust resource allocation proactively for upcoming requests;
At step 216, method 200 includes the feedback loop mechanism 110 collecting real-time performance data such as processing time and resource usage, which is fed back to the machine learning engine 106 to refine scheduling algorithms continuously;
At step 218, method 200 includes users accessing the user interface (ui) 112 to monitor request processing status, adjust parameters, and view system performance metrics;
At step 220, method 200 includes the customizable alert and notification system 126 notifying users of significant scheduling updates, delays, or high-priority request completions;
At step 222, method 200 includes activating the failure recovery and redundancy mechanism 122 to reroute the request to backup resources if any processing failure occurs, ensuring uninterrupted operation;
At step 224, method 200 includes the energy efficiency optimization module 118 adjusting resource usage strategies to minimize energy consumption during request processing;
At step 226, method 200 includes the cross-domain compatibility layer 124 enabling seamless integration with other AI systems and architectures, facilitating compatibility with diverse applications;
At step 228, method 200 includes the priority weighting system 128 applying user-defined weight parameters to further customize request prioritization as needed;
At step 230, method 200 includes the external event integration module 130 adjusting scheduling priorities based on real-world events or system status updates;
At step 232, method 200 includes simulation and modeling tools 132 allowing users to test scheduling scenarios and optimize configurations before full implementation;
At step 234, method 200 includes cloud and edge computing integration layer 134 supporting distributed scheduling across cloud and edge environments, enabling flexible resource utilization and low-latency processing.

[00049] The scheduling protocols for AI-driven information requests 100 offer several key advantages that enhance system performance and adaptability. By improving efficiency, the protocols optimize resource allocation and request prioritization, significantly reducing latency and increasing throughput. They enhance decision-making by providing rapid access to essential information, supporting time-sensitive choices in critical environments. The scalability of these protocols allows them to adjust seamlessly to growing workloads, making them suitable for dynamic applications that demand flexibility. Additionally, the protocols reduce operational costs by maximizing resource utilization, delivering a cost-effective solution for information processing. Overall, this invention 100 provides a novel, adaptable approach to optimizing information systems, addressing challenges in AI-driven applications and improving user experiences and operational outcomes across sectors like healthcare, finance, telecommunications, and smart cities.

[00050] The scheduling protocols for AI-driven information requests 100 can be implemented across various domains in different embodiments to optimize information processing and improve system responsiveness. In Healthcare Applications, these protocols can prioritize requests for patient data retrieval, diagnostics, and treatment recommendations, helping healthcare providers access critical information quickly and improve patient outcomes. In Financial Services, the protocols can streamline market analysis, risk assessments, and trade execution by prioritizing time-sensitive requests, enhancing trading performance and reducing potential financial risks. Within Telecommunications, the protocols efficiently manage data routing, network diagnostics, and customer service inquiries, ultimately boosting service quality and user satisfaction. In Smart Cities, the protocols enable real-time management of data from sensors and IoT devices, optimizing traffic flow, waste management, and energy consumption to enhance urban efficiency. Furthermore, in Manufacturing and Logistics, the protocols can coordinate data from production lines, inventory systems, and supply chain networks, ensuring timely information flow that reduces delays and improves operational productivity.

[00051] 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.

[00052] 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.

[00053] 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. A scheduling protocols for AI-driven information requests 100 comprising of
request queue management system 102 to organize and prioritize incoming information requests;
context-aware request prioritization module 104 to assess requests based on urgency, complexity, and data dependencies;
machine learning engine 106 to analyse historical data and optimize scheduling strategies;
dynamic resource allocation system 108 to allocate computational resources based on real-time demand;
feedback loop mechanism 110 to collect performance data and refine scheduling decisions;
user interface (UI) 112 to allow users to monitor and configure system parameters;
predictive analytics module 114 to forecast request volumes and adjust resource allocation;
load balancing system 116 to evenly distribute requests across resources;
energy efficiency optimization module 118 to minimize energy consumption during processing;
security and access control system 120 to manage data access based on user roles;
failure recovery and redundancy mechanism 122 to ensure uninterrupted operation by rerouting requests during failures;
cross-domain compatibility layer 124 to enable integration with diverse AI systems;
customizable alert and notification system 126 to inform users of scheduling updates and delays;
priority weighting system 128 to apply user-defined priorities to requests;
external event integration module 130 to adjust scheduling based on real-world events;
simulation and modeling tools 132 to test and optimize scheduling configurations;
cloud and edge computing integration layer 134 to support distributed scheduling across cloud and edge environments; and
adaptive learning for request type classification 136 to categorize requests and improve scheduling accuracy.
2. The scheduling protocols for AI-driven information requests 100 as claimed in claim 1, wherein the request queue management system 102 is configured to organize and prioritize incoming information requests based on urgency, complexity, and dependencies, enabling structured processing and reducing system latency.

3. The scheduling protocols for AI-driven information requests 100 as claimed in claim 1, wherein the context-aware request prioritization module 104 dynamically assesses each request's priority using contextual factors, including data source and urgency, to optimize task sequencing and enhance responsiveness.

4. The scheduling protocols for AI-driven information requests 100 as claimed in claim 1, wherein the machine learning engine 106 is configured to analyze historical data and real-time metrics, continuously adapting scheduling strategies through algorithmic refinement to improve system efficiency and resource allocation.

5. The scheduling protocols for AI-driven information requests 100 as claimed in claim 1, wherein the dynamic resource allocation system 108 allocates computational resources in real time based on current workload demand, ensuring balanced resource usage and preventing bottlenecks.

6. The scheduling protocols for AI-driven information requests 100 as claimed in claim 1, wherein the feedback loop mechanism 110 collects real-time performance data, including processing time and resource utilization, and iteratively adjusts scheduling parameters to maintain optimal performance.

7. The scheduling protocols for AI-driven information requests 100 as claimed in claim 1, wherein the predictive analytics module 114 forecasts future request volumes using historical trends, enabling proactive resource distribution to meet anticipated demands and maintain system stability.

8. The scheduling protocols for AI-driven information requests 100 as claimed in claim 1, wherein the load balancing system 116 is configured to evenly distribute incoming requests across available resources, preventing overloads and ensuring consistent throughput.

9. The scheduling protocols for AI-driven information requests 100 as claimed in claim 1, wherein the energy efficiency optimization module 118 actively minimizes energy consumption during request processing by adjusting resource allocation strategies based on usage needs, promoting sustainable operation.

10. The scheduling protocol for AI- Driven information requests 100 as claimed in claim 1, wherein method comprises of
receiving an incoming information request at the request queue management system 102;
the request queue management system 102 organizing and prioritizing the request based on urgency, type, and other parameters;
passing the prioritized request to the context-aware request prioritization module 104, which evaluates the request further using contextual factors such as data dependencies and source;
the context-aware request prioritization module 104 coordinating with the machine learning engine 106 to determine the optimal processing sequence based on historical data and real-time analytics;
the request entering the dynamic resource allocation system 108, where computational resources are allocated based on current availability and workload demands;
the dynamic resource allocation system 108 collaborating with the load balancing system 116 to distribute the request across available resources efficiently, preventing overloads and maximizing throughput;
the predictive analytics module 114 forecasting future workload demands to adjust resource allocation proactively for upcoming requests;
the feedback loop mechanism 110 collecting real-time performance data such as processing time and resource usage, which is fed back to the machine learning engine 106 to refine scheduling algorithms continuously;
users accessing the user interface (UI) 112 to monitor request processing status, adjust parameters, and view system performance metrics;
the customizable alert and notification system 126 notifying users of significant scheduling updates, delays, or high-priority request completions;
activating the failure recovery and redundancy mechanism 122 to reroute the request to backup resources if any processing failure occurs, ensuring uninterrupted operation;
the energy efficiency optimization module 118 adjusting resource usage strategies to minimize energy consumption during request processing;
the cross-domain compatibility layer 124 enabling seamless integration with other AI systems and architectures, facilitating compatibility with diverse applications;
the priority weighting system 128 applying user-defined weight parameters to further customize request prioritization as needed;
the external event integration module 130 adjusting scheduling priorities based on real-world events or system status updates;
simulation and modeling tools 132 allowing users to test scheduling scenarios and optimize configurations before full implementation;
the cloud and edge computing integration layer 134 supporting distributed scheduling across cloud and edge environments, enabling flexible resource utilization and low-latency processing.

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