SYSTEM AND METHOD FOR AUTONOMOUS UPDATE OF AI MODEL IN FACTORY ENVIRONMENT

Abstract

ABSTRACT SYSTEM AND METHOD FOR AUTONOMOUS UPDATE OF AI MODEL IN FACTORY ENVIRONMENT The present disclosure introduces system and method of autonomous update of AI model in factory environment 100, enabling continuous adaptation to real-time conditions. The system comprises of data acquisition unit 102 for continuous data collection and preprocessing, and model monitoring unit 104 to assess AI model performance with adaptive thresholds. The auto-update module 106 autonomously initiates incremental model retraining, supported by dynamic data pipeline unit 108 for seamless data flow integration. Contextual awareness module 110 enriches data with environmental metadata, enhancing model accuracy. Feedback loop unit 112 integrates user feedback and operational insights, while anomaly detection and self-correction unit 122 autonomously identifies irregularities, adjusting model parameters to maintain stability. Energy efficiency optimization module 124 dynamically reduces energy consumption based on model insights. Additionally, edge computing and distributed processing unit 120 ensures low-latency, real-time responsiveness, with security and data protection unit 126 safeguarding data integrity and privacy across the system. Reference Fig 1

Specification

Description:SYSTEM AND METHOD FOR AUTONOMOUS UPDATE OF AI MODEL IN FACTORY ENVIRONMENT
TECHNICAL FIELD
[0001] The present innovation relates to autonomous systems for real-time updating of AI models in manufacturing environments to enhance operational efficiency and adaptability.

BACKGROUND

[0002] In the era of Industry 4.0, manufacturing processes increasingly rely on artificial intelligence (AI) for automation, predictive maintenance, quality control, and process optimization. However, one significant challenge in such AI-driven manufacturing environments is maintaining the accuracy and relevance of AI models in real-time. As production lines evolve and data accumulates from sensors, machines, and IoT devices, traditional methods for updating AI models require manual intervention from data scientists and engineers. These conventional approaches are time-consuming and inefficient, resulting in delays that can reduce AI models' effectiveness and lead to outdated or inaccurate predictions. Users typically have two options: manually retraining models on a set schedule or conducting performance assessments periodically to determine if retraining is necessary. Both methods, however, are resource-intensive, prone to error, and slow to respond to the frequent shifts in factory environments, making them inadequate for modern manufacturing needs.

[0003] The invention overcomes these drawbacks by introducing a fully autonomous system for updating AI models in real-time, continuously adapting to new data and operational changes without manual intervention. Unlike existing options, this invention incorporates advanced machine learning techniques like incremental learning, transfer learning, and dynamic threshold monitoring. It utilizes a data acquisition module to continuously collect and preprocess data, a model monitoring system to evaluate performance against adaptive thresholds, and an auto-update mechanism that initiates retraining when necessary. Additionally, a feedback loop integrates user and environmental data, enhancing the model's contextual awareness. This system's modular and scalable architecture allows seamless integration with legacy manufacturing setups, ensuring compatibility and broad applicability. Novel features, such as real-time data integration, cross-model learning capabilities, and edge computing support, make this invention a transformative solution, optimizing production processes by improving model accuracy, reducing waste, and enhancing sustainability in manufacturing.

OBJECTS OF THE INVENTION

[0004] The primary object of the invention is to enable real-time updates to AI models in manufacturing environments, ensuring models remain accurate and relevant.

[0005] Another object of the invention is to improve operational efficiency by automating the model updating process, reducing the need for manual intervention and resource allocation.

[0006] Another object of the invention is to enhance production flexibility by enabling AI models to adapt autonomously to changes in production schedules, equipment usage, and environmental factors.

[0007] Another object of the invention is to minimize waste and energy consumption by optimizing manufacturing processes through continuous model learning and adaptation.

[0008] Another object of the invention is to increase the accuracy of predictive maintenance, quality control, and process optimization by maintaining up-to-date AI models that reflect real-time data.

[0009] Another object of the invention is to reduce downtime and production delays by ensuring AI models quickly respond to performance dips and operational changes.

[00010] Another object of the invention is to provide a scalable and modular architecture that integrates easily with existing factory systems, promoting wider adoption across various industrial settings.

[00011] Another object of the invention is to enable continuous learning through feedback integration, allowing AI models to improve based on user input and operational context.

[00012] Another object of the invention is to support sustainability goals by reducing resource wastage and optimizing energy usage in manufacturing operations.

[00013] Another object of the invention is to offer enhanced security and data protection features to safeguard AI model integrity and factory data from potential cyber threats.

SUMMARY OF THE INVENTION

[00014] In accordance with the different aspects of the present invention, system and method for autonomous update of AI model in factory environment is presented. The invention provides an autonomous system for updating AI models in factory environments, ensuring continuous adaptation to real-time data and changing production conditions. It integrates components like data acquisition, model monitoring, and an auto-update mechanism to maintain model accuracy and operational efficiency without manual intervention. The system's modular architecture allows easy integration with existing infrastructure, promoting scalability and flexibility. Additionally, it includes feedback loops and security protocols, optimizing production processes while supporting sustainability goals. This invention significantly improves manufacturing responsiveness, reduces resource wastage, and enhances predictive capabilities.

[00015] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00016] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00017] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00018] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00019] FIG. 1 is component wise drawing for system and method for autonomous update of AI model in factory environment.

[00020] FIG 2 is working methodology of system and method for autonomous update of AI model in factory environment.

DETAILED DESCRIPTION

[00021] 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.

[00022] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of system and method for autonomous update of AI model in factory environment 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.

[00023] 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.

[00024] 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.

[00025] 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.

[00026] 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.

[00027] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data acquisition unit 102, model monitoring unit 104, auto-update module 106, dynamic data pipeline unit 108, contextual awareness module 110, feedback loop unit 112, modular architecture framework 114, user interface and visualization tools 116, simulation and scenario analysis module 118, edge computing and distributed processing unit 120, anomaly detection and self-correction unit 122, energy efficiency optimization module 124, security and data protection unit 126.

[00028] Referring to Fig. 1, the present disclosure provides details of system and method for autonomous update of AI model in factory environment 100. It is a system designed to enable real-time adaptation of AI models using continuous learning, contextual awareness, and modular scalability. The system incorporates data acquisition unit 102, model monitoring unit 104, and auto-update module 106, ensuring timely and efficient updates based on operational changes. Key components are dynamic data pipeline unit 108 and contextual awareness module 110 to process and contextualize real-time data. Additional features such as feedback loop unit 112 and modular architecture framework 114 facilitate user input integration and seamless integration with existing factory systems. Components like edge computing and distributed processing unit 120 enhance responsiveness, while security and data protection unit 126 safeguard sensitive data, making this system an effective and secure solution for AI-driven manufacturing optimization.

[00029] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with data acquisition unit 102, which continuously collects and preprocesses data from sensors, IoT devices, and other data sources in the manufacturing environment. This unit ensures the AI models have access to accurate, real-time data, essential for maintaining model relevancy. It works in tandem with model monitoring unit 104 to supply fresh data streams that aid in evaluating model performance. The data acquisition unit 102 also feeds directly into the dynamic data pipeline unit 108, which facilitates smooth data integration across components.

[00030] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with model monitoring unit 104, which constantly evaluates the AI model's performance against predefined and adaptive thresholds. By analyzing metrics such as accuracy, processing speed, and error rates, this unit identifies when an update is necessary. The model monitoring unit 104 interacts closely with auto-update module 106 to trigger retraining based on performance dips, ensuring timely adjustments. It also shares insights with feedback loop unit 112 to further refine the system based on user input.

[00031] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with auto-update module 106, which autonomously initiates model retraining when notified by model monitoring unit 104 of performance declines. Using incremental and transfer learning, it efficiently updates the model without complete retraining, conserving computational resources. This module interacts directly with data acquisition unit 102 to access the most recent data and collaborates with contextual awareness module 110 to incorporate environmental and operational factors during the update process.

[00032] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with dynamic data pipeline unit 108, which enables the seamless flow of structured and unstructured data from various sources to the AI model. This unit ensures compatibility across different data formats and structures, crucial for integrating real-time data into model training. The dynamic data pipeline unit 108 works in close coordination with data acquisition unit 102 for data intake and contextual awareness module 110 for aligning data with environmental conditions, making it a critical component for robust data handling.

[00033] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with contextual awareness module 110, which adds critical environmental context to the data processed by the system. By integrating metadata such as machine status, production schedules, and environmental conditions, this module ensures the AI model's decisions are contextually accurate. It communicates with dynamic data pipeline unit 108 to provide enriched data streams and auto-update module 106 to ensure contextual factors are considered in model retraining, supporting more precise and adaptable AI decision-making.

[00034] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with feedback loop unit 112, which allows user feedback and operational data to be incorporated into model updates. This unit captures insights from operators and end-users, feeding this information back into model monitoring unit 104 to refine thresholds and performance expectations. The feedback loop unit 112 also enhances the functionality of auto-update module 106 by introducing practical, real-world inputs into the retraining process, improving model adaptability and accuracy.

[00035] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with modular architecture framework 114, which provides a scalable and flexible structure that can integrate with existing manufacturing systems. This framework allows for easy adaptation and expansion of the system across various industrial setups without significant infrastructural changes. The modular architecture framework 114 works with all other components, enabling seamless interaction and scalability, making the system versatile across different production environments.

[00036] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with user interface and visualization tools 116, offering an intuitive interface that displays model performance metrics, real-time data, and feedback analytics. This component aids operators in monitoring AI system activity and understanding model behavior. The user interface and visualization tools 116 are closely linked with model monitoring unit 104 and feedback loop unit 112 to present relevant insights in a user-friendly format, facilitating communication and stakeholder engagement.

[00037] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with simulation and scenario analysis module 118, allowing manufacturers to perform predictive analyses based on historical and real-time data. This module supports operational decision-making by running various scenarios to forecast potential outcomes of adjustments. The simulation and scenario analysis module 118 collaborates with contextual awareness module 110 and feedback loop unit 112 to consider environmental and user-based factors in simulations, enhancing predictive accuracy.

[00038] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with edge computing and distributed processing unit 120, which performs data processing and model updates closer to the data source, reducing latency. By enabling distributed data handling, this unit enhances real-time responsiveness and operational efficiency. The edge computing and distributed processing unit 120 works closely with data acquisition unit 102 and auto-update module 106, ensuring that model updates are timely and aligned with on-site production demands.

[00039] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with anomaly detection and self-correction unit 122, which identifies irregular patterns or deviations in production data. This unit can autonomously adjust model parameters or alert operators to maintain consistent performance. Anomaly detection and self-correction unit 122 operates alongside model monitoring unit 104 to proactively address inconsistencies, helping prevent issues before they affect production quality.

[00040] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with energy efficiency optimization module 124, dynamically adjusting operational parameters to minimize energy usage while maintaining productivity. By analyzing model insights, this module optimizes energy consumption across processes. The energy efficiency optimization module 124 coordinates with contextual awareness module 110 to factor in environmental conditions, supporting sustainable manufacturing practices.

[00041] Referring to Fig. 1, system and method for autonomous update of AI model in factory environment 100 is provided with security and data protection unit 126, which incorporates encryption, access control, and regular assessments to safeguard data and AI models from cyber threats. This unit is critical for protecting sensitive operational data and model integrity. Security and data protection unit 126 works with data acquisition unit 102 and feedback loop unit 112 to ensure all data streams and feedback inputs are secure, providing a safe environment for autonomous AI operations.

[00042] Referring to Fig 2, there is illustrated method 200 for system and method for autonomous update of AI model in factory environment 100. The method comprises:

At step 202, method 200 includes data acquisition unit 102 continuously collecting and preprocessing data from sensors, IoT devices, and machinery in the factory environment;

At step 204, method 200 includes model monitoring unit 104 evaluating AI model performance by analyzing metrics like accuracy, speed, and error rates to detect any performance dips;

At step 206, method 200 includes model monitoring unit 104 identifying when model performance falls below a set threshold, triggering auto-update module 106 to initiate model retraining;

At step 208, method 200 includes auto-update module 106 retrieving fresh data from data acquisition unit 102 and utilizing incremental learning techniques to efficiently retrain the AI model;

At step 210, method 200 includes contextual awareness module 110 adding relevant metadata, such as machine status and environmental conditions, to ensure model updates align with real-time factory conditions;

At step 212, method 200 includes dynamic data pipeline unit 108 managing seamless data flow from various sources, integrating structured and unstructured data for consistent model training;

At step 214, method 200 includes feedback loop unit 112 incorporating user feedback and operational data to further refine the updated AI model, enhancing accuracy and relevance;

At step 216, method 200 includes simulation and scenario analysis module 118 testing the updated model under various conditions using historical and real-time data to validate performance;

At step 218, method 200 includes edge computing and distributed processing unit 120 processing the updated model closer to data sources, reducing latency and ensuring real-time responsiveness;

At step 220, method 200 includes anomaly detection and self-correction unit 122 monitoring the newly updated model for any irregularities, autonomously adjusting parameters as needed to maintain consistent output;

At step 222, method 200 includes energy efficiency optimization module 124 analyzing the model's insights to adjust operational parameters, optimizing energy usage and reducing resource consumption;

At step 224, method 200 includes security and data protection unit 126 securing all data interactions and protecting the updated model from cyber threats, ensuring safe and reliable AI operations.

[00043] 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.

[00044] 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.

[00045] 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 system and method for autonomous update of AI model in factory environment 100 comprising of
data acquisition unit 102 to continuously collect and preprocess data from factory sensors and IoT devices;
model monitoring unit 104 to evaluate AI model performance and detect any drops in accuracy;
auto-update module 106 to initiate model retraining when performance falls below thresholds;
dynamic data pipeline unit 108 to ensure seamless data flow and integration from various sources;
contextual awareness module 110 to add metadata like machine status and production schedules;
feedback loop unit 112 to incorporate user feedback into model adjustments;
modular architecture framework 114 to allow integration with existing manufacturing systems;
user interface and visualization tools 116 to display real-time metrics and model performance insights;
simulation and scenario analysis module 118 to validate model updates under different conditions;
edge computing and distributed processing unit 120 to process model updates closer to data sources for low latency; anomaly detection and self-correction unit 122 to autonomously adjust model parameters when irregularities occur;
energy efficiency optimization module 124 to analyze and adjust operational parameters for reduced energy consumption; and
security and data protection unit 126 to safeguard data and AI models from cyber threats.

2. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein data acquisition unit 102 is configured to continuously collect and preprocess real-time data from sensors and IoT devices, ensuring immediate access to accurate and relevant data for AI model updates.

3. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein model monitoring unit 104 is configured to evaluate AI model performance using adaptive thresholds for metrics including accuracy and processing speed, enabling automatic detection of performance dips that trigger model retraining.

4. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein auto-update module 106 is configured to autonomously initiate incremental retraining of the AI model when performance falls below predefined standards, utilizing fresh data and reducing computational resource requirements.

5. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein dynamic data pipeline unit 108 is configured to facilitate seamless flow and integration of structured and unstructured data from diverse sources, ensuring consistent data handling for model training.

6. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein contextual awareness module 110 is configured to enrich incoming data with metadata such as machine status and environmental conditions, enhancing model accuracy and relevance in real-time factory settings.

7. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein feedback loop unit 112 is configured to incorporate user feedback and operational data into model adjustments, allowing continuous improvement and increased model adaptability.

8. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein anomaly detection and self-correction unit 122 is configured to autonomously identify irregularities in production data, adjusting model parameters or alerting operators as needed to maintain stable performance.

9. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein energy efficiency optimization module 124 is configured to dynamically adjust operational parameters based on AI model insights, reducing energy consumption and supporting sustainable manufacturing practices.

10. The system and method for autonomous update of AI model in factory environment 100 as claimed in claim 1, wherein method comprises of
data acquisition unit 102 continuously collecting and preprocessing data from sensors, IoT devices, and machinery in the factory environment;

model monitoring unit 104 evaluating AI model performance by analyzing metrics like accuracy, speed, and error rates to detect any performance dips;

model monitoring unit 104 identifying when model performance falls below a set threshold, triggering auto-update module 106 to initiate model retraining;

auto-update module 106 retrieving fresh data from data acquisition unit 102 and utilizing incremental learning techniques to efficiently retrain the AI model;

contextual awareness module 110 adding relevant metadata, such as machine status and environmental conditions, to ensure model updates align with real-time factory conditions;

dynamic data pipeline unit 108 managing seamless data flow from various sources, integrating structured and unstructured data for consistent model training;

feedback loop unit 112 incorporating user feedback and operational data to further refine the updated AI model, enhancing accuracy and relevance;

simulation and scenario analysis module 118 testing the updated model under various conditions using historical and real-time data to validate performance;

edge computing and distributed processing unit 120 processing the updated model closer to data sources, reducing latency and ensuring real-time responsiveness;

anomaly detection and self-correction unit 122 monitoring the newly updated model for any irregularities, autonomously adjusting parameters as needed to maintain consistent output;

energy efficiency optimization module 124 analyzing the model's insights to adjust operational parameters, optimizing energy usage and reducing resource consumption;

security and data protection unit 126 securing all data interactions and protecting the updated model from cyber threats, ensuring safe and reliable AI operations.

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