SELECTION FRAMEWORK FOR ARTIFICIAL INTELLIGENCE PROCESSING APPARATUS

Abstract

ABSTRACT Selection Framework for Artificial Intelligence Processing Apparatus The present disclosure introduces a selection framework for artificial intelligence processing apparatus 100, designed to optimize algorithm selection, configuration, and resource allocation in AI systems. Key components are dynamic algorithm selection framework 102, which analyzes dataset characteristics to identify optimal algorithms, and meta-learning module 104, which leverages historical data for predictive selection. The automated hyperparameter optimization system 106 fine-tunes algorithm configurations automatically, while resource profiling and allocation system 108 dynamically assigns computational resources. The adaptive load balancing mechanism 110 distributes workloads efficiently across units, and ensemble learning module 112 combines outputs from multiple algorithms to enhance accuracy. Reinforcement learning agent 116 continuously refines selection strategies based on performance feedback, improving system adaptability. Additionally, explainable AI module 130 provides transparency into the decision-making process by offering insights into algorithm choices. This comprehensive framework enables robust, efficient, and transparent AI processing, suitable for diverse applications and real-time adaptability. 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 selection framework for artificial intelligence processing apparatus 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, selection framework for artificial intelligence processing apparatus 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of dynamic algorithm selection framework 102, meta-learning module 104, automated hyperparameter optimization system 106, resource profiling and allocation system 108, adaptive load balancing mechanism 110, ensemble learning module 112, user interface 114, reinforcement learning agent 116, algorithm benchmarking database 118, context-aware adaptation module 120, cross-validation and ensemble evaluation system 122, predictive maintenance and health monitoring 124, simulation environment 126, time-series data optimization module 128, explainable AI (XAI) module 130 and collaborative filtering module 132.

[00029] Referring to Fig. 1, the present disclosure provides details of a selection framework for artificial intelligence processing apparatus 100, designed to optimize algorithm choice, resource allocation, and configuration. It dynamically adapts to data conditions, improving efficiency and accuracy in AI processing tasks. In one of the embodiments, the selection framework for artificial intelligence processing apparatus 100 may be provided with key components such as dynamic algorithm selection framework 102, meta-learning module 104, and automated hyperparameter optimization system 106, enabling intelligent decision-making. The system incorporates resource profiling and allocation system 108 and adaptive load balancing mechanism 110 for efficient resource usage. It also features explainable AI module 130 for transparent decision-making and collaborative filtering module 132 to recommend optimal algorithms. Additional components like predictive maintenance and health monitoring 124 and simulation environment 126 further enhance system reliability and risk mitigation.

[00030] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with dynamic algorithm selection framework 102, which automatically analyzes data characteristics to select suitable algorithms. This framework evaluates dataset features, such as size and complexity, and determines the appropriate AI model class (e.g., supervised or unsupervised). It works closely with meta-learning module 104 to incorporate historical performance data, improving the selection's accuracy and adaptability to evolving data conditions. This integration ensures that the selected algorithms are optimized for the specific workload without manual intervention.

[00031] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with meta-learning module 104, which leverages data from previous tasks to inform new algorithm selections. By accessing past performance metrics, this module enhances the system's ability to predict the effectiveness of algorithms on new datasets. It dynamically communicates with dynamic algorithm selection framework 102 to reduce the trial-and-error process, thus saving computational resources. The meta-learning module 104 is essential for adapting to changing data conditions, supporting the framework's efficiency and adaptability.

[00032] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with automated hyperparameter optimization system 106, which identifies optimal algorithm configurations. This system utilizes intelligent search methods, such as Bayesian optimization, to fine-tune hyperparameters for better performance. Working in conjunction with dynamic algorithm selection framework 102, it automates the configuration process, enhancing prediction accuracy and system responsiveness. The automated hyperparameter optimization system 106 ensures that AI models operate at peak efficiency, minimizing manual tuning efforts.

[00033] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with resource profiling and allocation system 108, which profiles computational requirements and allocates resources accordingly. It evaluates the demands of selected algorithms to distribute processing tasks efficiently across available hardware, such as CPUs and GPUs. This system collaborates with adaptive load balancing mechanism 110 to optimize resource usage, ensuring minimal latency and preventing bottlenecks. The resource profiling and allocation system 108 contributes to the overall efficiency and scalability of the processing apparatus.

[00034] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with adaptive load balancing mechanism 110, which dynamically distributes workloads across processing units to avoid bottlenecks. It adjusts workload allocation based on real-time resource utilization, enabling smooth system operation. The mechanism operates in conjunction with resource profiling and allocation system 108, which supplies updated information on processing demands, enhancing system reliability. This component is essential for handling high-performance tasks, ensuring stable, efficient processing under variable loads.

[00035] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with ensemble learning module 112, which aggregates outputs from multiple algorithms to improve accuracy and robustness. This module combines algorithm predictions to mitigate the weaknesses of individual models, thereby enhancing overall prediction quality. It works closely with dynamic algorithm selection framework 102 to select and integrate the most complementary algorithms, optimizing system performance. The ensemble learning module 112 is valuable for applications requiring high reliability and accuracy in predictive analytics.

[00036] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with user interface 114, which allows users to interact with and customize the system. This interface provides visualizations of performance metrics, letting users adjust selection criteria for algorithms based on specific needs. It is integrated with all main components, such as dynamic algorithm selection framework 102 and resource profiling and allocation system 108, enabling real-time feedback and customization. The user interface 114 enhances usability, making the system accessible to users with various requirements.

[00037] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with reinforcement learning agent 116, which adapts algorithm and resource selections based on real-time feedback. This agent continually learns from system performance to refine future selections, enhancing processing efficiency and effectiveness. It interacts with both automated hyperparameter optimization system 106 and dynamic algorithm selection framework 102 to make adjustments that improve adaptability. The reinforcement learning agent 116 enables the system to evolve over time, ensuring sustained optimization.

[00038] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with algorithm benchmarking database 118, which catalogs the performance of various algorithms across multiple datasets. This database aids in quick comparisons during the algorithm selection process, supporting informed decisions based on historical performance. It works in conjunction with meta-learning module 104 and dynamic algorithm selection framework 102 to enhance algorithm selection precision. The algorithm benchmarking database 118 is crucial for maintaining efficiency and reducing selection times.

[00039] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with context-aware adaptation module 120, which modifies processing methods based on external factors like network conditions and system load. This component enhances responsiveness, especially in real-time applications, by adjusting algorithm and resource selections accordingly. It operates with resource profiling and allocation system 108 to monitor environmental factors, ensuring optimal performance. The context-aware adaptation module 120 is essential for applications requiring high adaptability to external changes.

[00040] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with cross-validation and ensemble evaluation system 122, which performs data validation and model evaluation. This system ensures that algorithm selections and ensembles are robust and reliable, using multiple data splits to test performance. It works closely with ensemble learning module 112 to validate combined outputs, ensuring comprehensive model testing. The cross-validation and ensemble evaluation system 122 is critical for maintaining accuracy in applications requiring rigorous testing.

[00041] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with predictive maintenance and health monitoring 124, which monitors the apparatus's health and suggests maintenance actions. This component analyzes performance data to detect potential issues before they impact functionality, ensuring continuous operation. It interacts with resource profiling and allocation system 108 to monitor hardware usage, preventing downtime. Predictive maintenance and health monitoring 124 enhances system reliability by proactively managing maintenance.

[00042] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with simulation environment 126, which allows testing of selected algorithms and configurations in a virtual setting. This environment helps validate system performance under simulated conditions, reducing risks before real-world deployment. It supports dynamic algorithm selection framework 102 by providing a safe space to test configurations, ensuring robust results. The simulation environment 126 is essential for applications needing pre-deployment validation to mitigate potential risks.

[00043] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with time-series data optimization module 128, which specializes in processing and optimizing time-series data. This module enhances forecasting and real-time decision-making capabilities by efficiently handling sequential data. It collaborates with context-aware adaptation module 120 to adjust processing based on data flow, making it suitable for time-sensitive applications. The time-series data optimization module 128 is valuable for applications in predictive analytics and IoT.

[00044] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with explainable AI module 130, which offers insights into the rationale behind algorithm selection and parameter configurations. This transparency aids users in understanding how configurations affect performance, improving trust in the system. The explainable AI module 130 works alongside user interface 114 to communicate decisions, enhancing user engagement and understanding. This module is key for applications requiring clarity in decision-making processes.

[00045] Referring to Fig. 1, selection framework for artificial intelligence processing apparatus 100 is provided with collaborative filtering module 132, which suggests algorithms based on user preferences and similar cases. By leveraging community-driven insights, this component enhances the selection process with personalized recommendations. It interacts with dynamic algorithm selection framework 102 to offer optimal options, considering historical data and user-defined criteria. The collaborative filtering module 132 enriches user experience by providing informed and relevant algorithm recommendations.

[00046] Referring to Fig 2, there is illustrated method 200 for selection framework for artificial intelligence processing apparatus 100. The method comprises:
At step 202, method 200 includes the system analyzing the dataset characteristics within dynamic algorithm selection framework 102 to determine data type, complexity, and size for selecting suitable algorithms;
At step 204, method 200 includes the system accessing historical performance data through meta-learning module 104 to predict the effectiveness of candidate algorithms for the current dataset;
At step 206, method 200 includes the system selecting the most appropriate algorithm based on dataset analysis and insights from meta-learning module 104, using dynamic algorithm selection framework 102;
At step 208, method 200 includes the system applying automated hyperparameter optimization system 106 to fine-tune the configurations of the selected algorithm, enhancing prediction accuracy and performance;
At step 210, method 200 includes the system profiling resource requirements with resource profiling and allocation system 108 to evaluate the computational needs of the configured algorithm;
At step 212, method 200 includes the system dynamically allocating resources to meet processing demands based on analysis results from resource profiling and allocation system 108;
At step 214, method 200 includes the system adjusting workloads across processing units with adaptive load balancing mechanism 110 to optimize resource utilization and prevent bottlenecks;
At step 216, method 200 includes the system combining outputs of selected algorithms through ensemble learning module 112 to improve prediction accuracy and robustness;
At step 218, method 200 includes the system providing real-time performance metrics to the user through user interface 114, enabling users to monitor the effectiveness of the selected algorithms and configurations;
At step 220, method 200 includes reinforcement learning agent 116 in the system continuously learning from performance feedback to improve future selections and configurations dynamically;
At step 222, method 200 includes the system referencing algorithm benchmarking database 118 to validate and compare selected algorithms against historical benchmarks, further optimizing performance;
At step 224, method 200 includes the system using context-aware adaptation module 120 to adjust configurations in real-time, optimizing performance based on system load and external factors;
At step 226, method 200 includes the system validating algorithm accuracy and robustness with cross-validation and ensemble evaluation system 122, ensuring reliability of the selected models;
At step 228, method 200 includes the system monitoring apparatus health through predictive maintenance and health monitoring 124, proactively detecting and addressing potential issues;
At step 230, method 200 includes the system testing algorithms and configurations in simulation environment 126 for pre-deployment validation, reducing risks associated with real-world implementation;
At step 232, method 200 includes the system optimizing time-series data handling with time-series data optimization module 128 to enhance forecasting accuracy and real-time decision-making capabilities;
At step 234, method 200 includes the system providing users with insights on algorithm selection through explainable AI module 130, enhancing transparency in the decision-making process;
At step 236, method 200 includes the system suggesting optimal algorithms based on user preferences using collaborative filtering module 132, personalizing the selection process to meet specific requirement.
[00047] 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.

[00048] 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.

[00049] 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 selection framework for artificial intelligence processing apparatus 100 comprising of
dynamic algorithm selection framework 102 to determine suitable algorithms based on dataset characteristics;
meta-learning module 104 to utilize historical performance data for predictive algorithm selection;
automated hyperparameter optimization system 106 to fine-tune algorithm configurations for optimal performance;
resource profiling and allocation system 108 to assess and allocate computational resources dynamically;
adaptive load balancing mechanism 110 to evenly distribute workloads across processing units;
ensemble learning module 112 to combine multiple algorithm outputs for enhanced accuracy;
user interface 114 to enable users to monitor performance metrics and customize settings;
reinforcement learning agent 116 to continuously improve algorithm selection based on performance feedback;
algorithm benchmarking database 118 to validate and compare algorithm performance with historical data;
context-aware adaptation module 120 to adjust processing methods based on external factors;
cross-validation and ensemble evaluation system 122 to ensure algorithm robustness and reliability;
predictive maintenance and health monitoring 124 to proactively manage system health and prevent issues;
simulation environment 126 to validate algorithms and configurations in a virtual setting;
time-series data optimization module 128 to enhance real-time forecasting and decision-making capabilities;
explainable AI module 130 to provide transparency in algorithm selection and configuration impact; and
collaborative filtering module 132 to recommend optimal algorithms based on user preferences and similar cases.
2. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein dynamic algorithm selection framework 102 is configured to analyze dataset characteristics in real time, selecting optimal algorithms based on data type, complexity, and task requirements, thereby enabling enhanced adaptability and accuracy in AI processing.

3. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein meta-learning module 104 is configured to leverage historical performance data for predictive algorithm selection, reducing computational trial-and-error and accelerating decision-making by referencing previous task data.

4. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein automated hyperparameter optimization system 106 is configured to employ intelligent search algorithms, including Bayesian optimization, to automatically fine-tune algorithm configurations, achieving optimal performance with minimal manual intervention.

5. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein resource profiling and allocation system 108 is configured to dynamically assess and allocate computational resources such as CPUs and GPUs based on real-time algorithm demands, thereby preventing bottlenecks and optimizing resource efficiency.

6. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein adaptive load balancing mechanism 110 is configured to dynamically distribute processing workloads across available units, ensuring efficient resource utilization and minimizing processing latency under varying system loads.

7. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein ensemble learning module 112 is configured to combine outputs from multiple selected algorithms, enhancing prediction accuracy and robustness by mitigating individual model weaknesses.

8. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein reinforcement learning agent 116 is configured to continuously adapt algorithm and resource selection strategies based on real-time performance feedback, providing iterative system improvements with reduced need for manual adjustments.

9. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein explainable AI module 130 is configured to deliver transparency by providing insights into algorithm choices and configuration impacts, enabling users to understand and trust the decision-making process within the system
10. The selection framework for artificial intelligence processing apparatus 100 as claimed in claim 1, wherein method comprises of
system analyzing the dataset characteristics within dynamic algorithm selection framework 102 to determine data type, complexity, and size for selecting suitable algorithms;
system accessing historical performance data through meta-learning module 104 to predict the effectiveness of candidate algorithms for the current dataset;
system selecting the most appropriate algorithm based on dataset analysis and insights from meta-learning module 104, using dynamic algorithm selection framework 102;
system applying automated hyperparameter optimization system 106 to fine-tune the configurations of the selected algorithm, enhancing prediction accuracy and performance;
system profiling resource requirements with resource profiling and allocation system 108 to evaluate the computational needs of the configured algorithm;
system dynamically allocating resources to meet processing demands based on analysis results from resource profiling and allocation system 108;
system adjusting workloads across processing units with adaptive load balancing mechanism 110 to optimize resource utilization and prevent bottlenecks;
system combining outputs of selected algorithms through ensemble learning module 112 to improve prediction accuracy and robustness;
system providing real-time performance metrics to the user through user interface 114, enabling users to monitor the effectiveness of the selected algorithms and configurations;
reinforcement learning agent 116 in the system continuously learning from performance feedback to improve future selections and configurations dynamically;
system referencing algorithm benchmarking database 118 to validate and compare selected algorithms against historical benchmarks, further optimizing performance;
system using context-aware adaptation module 120 to adjust configurations in real-time, optimizing performance based on system load and external factors;
system validating algorithm accuracy and robustness with cross-validation and ensemble evaluation system 122, ensuring reliability of the selected models;
system monitoring apparatus health through predictive maintenance and health monitoring 124, proactively detecting and addressing potential issues;
system testing algorithms and configurations in simulation environment 126 for pre-deployment validation, reducing risks associated with real-world implementation;
system optimizing time-series data handling with time-series data optimization module 128 to enhance forecasting accuracy and real-time decision-making capabilities;
system providing users with insights on algorithm selection through explainable AI module 130, enhancing transparency in the decision-making process; and
system suggesting optimal algorithms based on user preferences using collaborative filtering module 132, personalizing the selection process to meet specific requirements.

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