DEVELOPMENT METHODOLOGIES FOR AI DEVICES AND PROGRAMS

Abstract

ABSTRACT Development Methodologies for AI Devices and Programs The present disclosure introduces development methodologies for AI devices and programs 100, a comprehensive framework designed to streamline AI development from design to deployment. The invention comprise systematic design framework 102 for structured planning, collaborative development process 104 to integrate multidisciplinary inputs, and data management strategies 106 to ensure ethically sourced, high-quality datasets. Model training module 108 optimizes algorithms and training parameters, while performance evaluation metrics 110 assess model accuracy. Other components are iterative feedback loop 112, ethical guidelines and compliance module 114, deployment strategies 116, continuous monitoring and maintenance system 118, sustainability alignment mechanisms 120, decision support tools 122, dynamic adaptation engine 124, stakeholder engagement framework 126, cross-platform compatibility guidelines 128, ethical audit trail mechanism 130, risk assessment and mitigation protocols 132, knowledge repository and resource library 134, feedback-driven evolution framework 136, impact assessment tools 138 and compliance reporting dashboard 140. 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 development methodologies for AI devices and programs 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, development methodologies for AI devices and programs 100 is disclosed in accordance with one embodiment of the present invention. It comprises of systematic design framework 102, collaborative development process 104, data management strategies 106, model training module 108, performance evaluation metrics 110, iterative feedback loop 112, ethical guidelines and compliance module 114, deployment strategies 116, continuous monitoring and maintenance system 118, sustainability alignment mechanisms 120, decision support tools 122, dynamic adaptation engine 124, stakeholder engagement framework 126, cross-platform compatibility guidelines 128, ethical audit trail mechanism 130, risk assessment and mitigation protocols 132, knowledge repository and resource library 134, feedback-driven evolution framework 136, impact assessment tools 138 and compliance reporting dashboard 140.

[00029] Referring to Fig. 1, the present disclosure provides details of development methodologies for AI devices and programs 100. It is a comprehensive framework designed to streamline AI development, from design to deployment, ensuring ethical compliance and scalability across diverse applications. The framework integrates collaborative development process 104, data management strategies 106, and model training module 108, enabling efficient data handling and model optimization. Key components include performance evaluation metrics 110 and iterative feedback loop 112 for continuous model refinement. The system incorporates ethical guidelines and compliance module 114 and cross-platform compatibility guidelines 128 to enhance responsible deployment and adaptability. Additional features like impact assessment tools 138 and compliance reporting dashboard 140 provide transparency and alignment with sustainability goals.

[00030] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with systematic design framework 102, which structures the entire AI development lifecycle from initial problem definition to data preparation and system architecture. It ensures that all aspects of the AI solution are accounted for early on, reducing the likelihood of oversights. This framework interworks closely with data management strategies 106 to ensure high-quality data is available for model training. By establishing a comprehensive roadmap, the systematic design framework 102 also enhances alignment with ethical guidelines and compliance module 114.

[00031] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with collaborative development process 104, which fosters a multidisciplinary approach involving data scientists, engineers, ethicists, and end-users. This component enables diverse expertise to contribute to the development process, leading to more robust and innovative AI solutions. It facilitates communication between stakeholders and works in conjunction with the systematic design framework 102 to ensure that input from all relevant fields is considered. The collaborative development process 104 further supports the iterative feedback loop 112, promoting continuous improvement based on team insights.

[00032] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with data management strategies 106, which establish protocols for data collection, preprocessing, labeling, and augmentation. This component is essential for maintaining high-quality, diverse, and ethically sourced datasets that enhance AI model accuracy and reduce bias. Working closely with model training module 108, data management strategies 106 ensure that the data used for training is both comprehensive and representative. Additionally, it supports ethical guidelines and compliance module 114 by enforcing data privacy standards and ethical sourcing.

[00033] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with model training module 108, which guides the selection of algorithms, hyperparameter tuning, and cross-validation techniques for optimal model performance. This component directly utilizes the datasets managed by data management strategies 106 to train robust AI models. The model training module 108 also connects with performance evaluation metrics 110 to assess model accuracy, precision, and recall, enabling informed decisions on model refinement and deployment.

[00034] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with performance evaluation metrics 110, a set of standardized criteria for assessing AI model effectiveness. This component includes metrics such as accuracy, precision, recall, and F1 score, tailored to the application context. By working in conjunction with iterative feedback loop 112, performance evaluation metrics 110 enable continuous model refinement based on real-time data and feedback. This standardized approach also enhances accountability, supporting compliance with ethical guidelines and compliance module 114.

[00035] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with iterative feedback loop 112, which enables continuous refinement and adaptation of AI models based on performance evaluations and user feedback. This component plays a crucial role in enhancing the longevity and relevance of AI solutions by facilitating ongoing improvements. The iterative feedback loop 112 works closely with model training module 108 to implement adjustments based on observed outcomes, and it interacts with decision support tools 122 to guide stakeholders on areas for enhancement.

[00036] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with ethical guidelines and compliance module 114, which integrates ethical considerations and regulatory standards throughout the AI development process. This module ensures that data privacy, transparency, and accountability are prioritized, aligning AI solutions with legal and societal expectations. Working in tandem with data management strategies 106, ethical guidelines and compliance module 114 promotes responsible data handling, while supporting deployment strategies 116 to ensure compliant and ethically sound AI implementations.

[00037] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with deployment strategies 116, which outline best practices for implementing AI solutions in real-world environments. This component covers scalability, integration with existing systems, and end-user training to ensure smooth adoption. Deployment strategies 116 interwork with continuous monitoring and maintenance system 118 to provide ongoing support post-deployment, and they also align with cross-platform compatibility guidelines 128 to facilitate broad usability across various operating environments.

[00038] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with continuous monitoring and maintenance system 118, which tracks system performance, detects issues, and implements necessary updates. This component is vital for ensuring that deployed AI solutions remain effective and relevant over time. It works in conjunction with performance evaluation metrics 110 to identify areas needing improvement and supports the iterative feedback loop 112 to keep models optimized and adaptable to changing conditions.

[00039] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with sustainability alignment mechanisms 120, which incorporate the United Nations Sustainable Development Goals (SDGs) into the AI development process. This component ensures that the methodologies not only focus on technical efficiency but also consider social and environmental impacts. It collaborates with impact assessment tools 138 to evaluate the societal implications of AI solutions and aligns with ethical guidelines and compliance module 114 to promote responsible and sustainable innovation.

[00040] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with decision support tools 122, which assist stakeholders in making informed choices throughout the AI development lifecycle. These tools leverage data analytics and visualization to enhance understanding and facilitate effective collaboration. Decision support tools 122 work closely with knowledge repository and resource library 134 to provide stakeholders with access to relevant information and insights, supporting collaborative development process 104 in decision-making.

[00041] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with dynamic adaptation engine 124, which employs machine learning techniques to adjust development processes based on real-time performance data and feedback. This component is essential for optimizing AI solutions as they evolve, responding dynamically to practical experiences. The dynamic adaptation engine 124 operates alongside iterative feedback loop 112 to apply refinements efficiently, enhancing model longevity and robustness.

[00042] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with stakeholder engagement framework 126, which ensures regular input and feedback from all relevant parties throughout the development lifecycle. This component enhances transparency and promotes collaboration, aligning the development process with user needs and expectations. The stakeholder engagement framework 126 supports collaborative development process 104 by facilitating structured input from end-users, data scientists, and ethicists.

[00043] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with cross-platform compatibility guidelines 128, which ensure seamless integration and functionality of AI solutions across different systems and environments. This component promotes scalability and adaptability, allowing AI applications to function effectively in diverse operational contexts. It works closely with deployment strategies 116 to ensure compatibility during implementation and supports continuous monitoring and maintenance system 118 to address any cross-platform challenges post-deployment.

[00044] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with ethical audit trail mechanism 130, which documents the decision-making process regarding ethical considerations throughout the development lifecycle. This component provides transparency and accountability, enabling stakeholders to trace ethical and compliance measures taken at each stage. The ethical audit trail mechanism 130 collaborates with ethical guidelines and compliance module 114 to record all compliance activities and decisions made in line with regulatory standards.
[00045] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with risk assessment and mitigation protocols 132, which identify potential risks and ethical concerns early in the AI development process. This component proactively addresses challenges to ensure reliable and responsible AI solutions. Risk assessment and mitigation protocols 132 work closely with dynamic adaptation engine 124 to implement timely adjustments and align with deployment strategies 116 for safer real-world implementation.

[00046] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with knowledge repository and resource library 134, which acts as a centralized storage for best practices, case studies, and learning resources. This component is a valuable reference for stakeholders, facilitating continuous learning and enhancing collaboration. The knowledge repository and resource library 134 supports decision support tools 122 by providing access to critical information, contributing to informed and effective development decisions.

[00047] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with feedback-driven evolution framework 136, which collects and analyzes feedback post-deployment to inform future iterations of AI systems. This component ensures that AI solutions adapt to changing user needs and evolving environments. Feedback-driven evolution framework 136 interworks with continuous monitoring and maintenance system 118 for ongoing performance assessment and collaborates with impact assessment tools 138 to evaluate user satisfaction and societal impact.

[00048] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with impact assessment tools 138, which evaluate the societal and environmental implications of deployed AI solutions. This component ensures that AI developments are in alignment with broader sustainability and ethical standards. Impact assessment tools 138 work closely with sustainability alignment mechanisms 120 to support SDG objectives and provide valuable insights for compliance reporting dashboard 140 to monitor impact metrics.

[00049] Referring to Fig. 1, development methodologies for AI devices and programs 100 is provided with compliance reporting dashboard 140, which simplifies the process of tracking and reporting adherence to ethical and regulatory standards. This component fosters accountability and transparency, allowing organizations to demonstrate compliance effectively. The compliance reporting dashboard 140 works in tandem with ethical audit trail mechanism 130 to maintain detailed records of compliance activities, ensuring responsible AI development and deployment.

[00050] Referring to Fig 2, there is illustrated method 200 for development methodologies for AI devices and programs 100. The method comprises:
At step 202, method 200 includes initiating the systematic design framework 102 to outline problem definition, gather requirements, and create a system architecture for the AI solution;
At step 204, method 200 includes involving stakeholders in the collaborative development process 104 to bring together multidisciplinary insights, aligning the design with requirements gathered in step 202;
At step 206, method 200 includes applying data management strategies 106 to collect, preprocess, and label datasets, ensuring the data is ethically sourced and meets quality standards for AI training;
At step 208, method 200 includes feeding the preprocessed data from step 206 into the model training module 108, which selects suitable algorithms, tunes hyperparameters, and performs cross-validation for optimal model performance;
At step 210, method 200 includes assessing the trained model using performance evaluation metrics 110 to gauge accuracy, precision, recall, and other relevant indicators, identifying areas for improvement;
At step 212, method 200 includes implementing the iterative feedback loop 112, which refines the model by addressing insights gained from performance metrics in step 210 and feedback from stakeholders in step 204;
At step 214, method 200 includes ensuring adherence to ethical guidelines and compliance module 114 to address data privacy, transparency, and regulatory standards across all stages;
At step 216, method 200 includes deploying the AI solution using deployment strategies 116, focusing on scalability and user training for effective real-world implementation;
At step 218, method 200 includes continuously monitoring the AI system through the continuous monitoring and maintenance system 118 to detect any issues and perform updates as necessary, maintaining system effectiveness over time;
At step 220, method 200 includes conducting impact assessments using sustainability alignment mechanisms 120 and impact assessment tools 138 to evaluate the societal and environmental contributions of the deployed AI solution;
At step 222, method 200 includes providing decision support tools 122 to stakeholders, enabling informed choices and facilitating ongoing improvements based on real-time performance data from step 218;
At step 224, method 200 includes utilizing the dynamic adaptation engine 124 to adjust development processes in response to feedback and performance data, enhancing the adaptability of the solution;
At step 226, method 200 includes engaging stakeholders through the stakeholder engagement framework 126, collecting input on the deployed solution's performance and user satisfaction;
At step 228, method 200 includes ensuring cross-platform functionality of the AI solution with cross-platform compatibility guidelines 128, enabling seamless integration in diverse environments;
At step 230, method 200 includes documenting all ethical considerations and compliance activities in the ethical audit trail mechanism 130, providing transparency and accountability for the development process;
At step 232, method 200 includes proactively identifying risks and mitigating them using risk assessment and mitigation protocols 132 to maintain a robust and reliable AI solution;
At step 234, method 200 includes storing best practices, case studies, and resources in the knowledge repository and resource library 134 to support ongoing development and learning for stakeholders;
At step 236, method 200 includes collecting post-deployment feedback through the feedback-driven evolution framework 136, guiding future improvements to the AI solution based on user experiences;
At step 238, method 200 includes tracking compliance and ethical adherence through the compliance reporting dashboard 140, providing a comprehensive view of regulatory and ethical conformity across the solution lifecycle.
[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 development methodologies for AI devices and programs 100 comprising of
systematic design framework 102 to outline problem definition, gather requirements, and design system architecture;
collaborative development process 104 to engage multidisciplinary teams for input and alignment;
data management strategies 106 to collect, preprocess, label, and augment data ethically and effectively;
model training module 108 to select algorithms, tune hyperparameters, and perform cross-validation for optimal performance;
performance evaluation metrics 110 to assess accuracy, precision, and recall of the AI models;
iterative feedback loop 112 to enable continuous refinement based on performance data and user feedback;
ethical guidelines and compliance module 114 to ensure data privacy, transparency, and adherence to regulations;
deployment strategies 116 to provide best practices for scalable and effective AI implementation;
continuous monitoring and maintenance system 118 to track performance, detect issues, and apply updates;
sustainability alignment mechanisms 120 to incorporate SDG principles and assess social impact;
decision support tools 122 to aid stakeholders in making informed decisions during development;
dynamic adaptation engine 124 to adjust development processes based on real-time feedback and data;
stakeholder engagement framework 126 to collect input on performance and user satisfaction;
cross-platform compatibility guidelines 128 to ensure seamless integration across diverse systems;
ethical audit trail mechanism 130 to document compliance activities and ethical considerations;
risk assessment and mitigation protocols 132 to identify and address potential challenges proactively;
knowledge repository and resource library 134 to store best practices, case studies, and resources;
feedback-driven evolution framework 136 to gather post-deployment feedback for ongoing improvement;
impact assessment tools 138 to evaluate the societal and environmental contributions of AI solutions; and
compliance reporting dashboard 140 to track regulatory adherence and ethical compliance across the lifecycle.
2. The development methodologies for AI devices and programs 100 as claimed in claim 1, wherein the systematic design framework 102 is configured to establish a structured development lifecycle, ensuring comprehensive problem definition, requirement gathering, and system architecture planning for optimized AI solution creation.
3. The development methodologies for AI devices and programs 100 as claimed in claim 1, wherein collaborative development process 104 is configured to facilitate multidisciplinary collaboration, integrating inputs from data scientists, engineers, ethicists, and end-users to enhance innovation and robustness in AI systems.

4. The development methodologies for AI devices and programs 100 as claimed in claim 1, wherein data management strategies 106 are configured to enforce ethical data collection, preprocessing, labeling, and augmentation, providing high-quality, bias-mitigated datasets that enhance model accuracy and fairness.

5. The development methodologies for AI devices and programs 100 as claimed in claim 1, wherein model training module 108 is configured to implement optimized algorithm selection, hyperparameter tuning, and cross-validation, ensuring AI models are trained to achieve maximum precision, reliability, and efficiency.

6. The development methodologies for AI devices and programs 100 as claimed in claim 1, wherein iterative feedback loop 112 is configured to provide continuous model refinement, leveraging performance metrics and user feedback to dynamically adapt and improve model functionality.

7. The development methodologies for AI devices and programs 100 as claimed in claim 1, wherein ethical guidelines and compliance module 114 is configured to integrate regulatory standards, data privacy protocols, and transparency requirements, ensuring AI solutions comply with ethical and legal standards throughout development and deployment.

8. The development methodologies for AI devices and 100 as claimed in claim 1, wherein deployment strategies 116 are configured to implement scalable and user-centered deployment practices, supporting integration with existing systems and facilitating seamless adoption of AI solutions.

9. The development methodologies for AI devices and programs 100 as claimed in claim 1, wherein continuous monitoring and maintenance system 118 is configured to enable post-deployment performance tracking, issue detection, and updates, ensuring the AI system's long-term effectiveness and adaptability in real-world applications.

10. The development methodologies for AI devices and programs 100 as claimed in claim 1, wherein method comprises of
systematic design framework 102 outlines problem definition, gathers requirements, and creates a system architecture for the AI solution;
collaborative development process 104 involves stakeholders to bring together multidisciplinary insights, aligning the design with requirements gathered in systematic design framework 102;
data management strategies 106 apply protocols to collect, preprocess, and label datasets, ensuring the data is ethically sourced and meets quality standards for AI training;
model training module 108 receives the pre-processed data from data management strategies 106, selects suitable algorithms, tunes hyperparameters, and performs cross-validation for optimal model performance;
performance evaluation metrics 110 assess the trained model, gauging accuracy, precision, recall, and other relevant indicators, identifying areas for improvement;
iterative feedback loop 112 refines the model by addressing insights from performance evaluation metrics 110 and feedback from collaborative development process 104;
ethical guidelines and compliance module 114 ensures adherence to data privacy, transparency, and regulatory standards across all stages;
deployment strategies 116 deploy the AI solution, focusing on scalability and user training for effective real-world implementation;
continuous monitoring and maintenance system 118 monitors the AI system to detect issues and perform updates as necessary, maintaining system effectiveness over time;
sustainability alignment mechanisms 120 and impact assessment tools 138 conduct impact assessments to evaluate the societal and environmental contributions of the deployed AI solution;
decision support tools 122 provide stakeholders with informed choices and facilitate ongoing improvements based on real-time performance data from continuous monitoring and maintenance system 118;
dynamic adaptation engine 124 adjusts development processes in response to feedback and performance data, enhancing the adaptability of the solution;
stakeholder engagement framework 126 engages stakeholders, collecting input on the deployed solution's performance and user satisfaction;
cross-platform compatibility guidelines 128 ensure the AI solution's functionality across diverse environments, enabling seamless integration;
ethical audit trail mechanism 130 documents ethical considerations and compliance activities, providing transparency and accountability for the development process;
risk assessment and mitigation protocols 132 proactively identify risks and mitigate them to maintain a robust and reliable AI solution;
knowledge repository and resource library 134 stores best practices, case studies, and resources to support ongoing development and learning for stakeholders;
feedback-driven evolution framework 136 collects post-deployment feedback, guiding future improvements to the AI solution based on user experiences;
compliance reporting dashboard 140 tracks regulatory and ethical adherence, providing a comprehensive view of compliance across the solution lifecycle.

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