AI DECISION-MAKING SYSTEM FOR DATA OBJECT ANALYSIS AND MANAGEMENT

Abstract

ABSTRACT AI Decision-Making System for Data Object Analysis and Management The present disclosure introduces an AI decision-making system for data object analysis and management 100, which automates data processing using data ingestion and pre-processing module 102. The system is powered by an AI-based analysis engine 104 for identifying patterns and anomalies, and priority assignment and decision-making logic 106 to dynamically assign priorities based on urgency and relevance. Efficient resource use is managed by the resource optimization and load balancing module 108, while the user interface and interaction module 110 displays real-time outputs and allows user intervention. Transparency is ensured through the explainability and transparency module 112, which provides clear explanations for AI decisions. Security is maintained by security and data privacy mechanisms 114, while the system continually improves through the self-learning feedback loop 116. Threat detection is handled by the automated threat detection and mitigation engine 118, and seamless integration is achieved through cross-platform integration and API support 120. Reference Fig 1

Specification

Description:AI Decision-Making System for Data Object Analysis and Management
TECHNICAL FIELD
[0001] The present innovation relates to artificial intelligence (AI) systems for automated decision-making in data object analysis and management across various industries.

BACKGROUND

[0002] The exponential growth of data across industries has created significant challenges in managing, analyzing, and making decisions based on this vast amount of information. Traditional data management systems rely heavily on manual or semi-automated processes, which are not scalable and often lead to inefficiencies, errors, and delays in decision-making. These systems generally follow predefined rules, limiting their ability to adapt to the dynamic nature of modern data environments. As industries like healthcare, finance, cybersecurity, and logistics become increasingly data-driven, the need for a more responsive, intelligent system to handle complex data management tasks has become evident.

[0003] Current options available to users include rule-based systems, which offer limited flexibility, and AI models that are often specialized for specific tasks, such as analyzing structured data or detecting anomalies. While these systems provide some level of automation, they are either too rigid to adapt to new data inputs or too resource-intensive to operate efficiently. Additionally, many of these solutions lack transparency and explainability, which are critical in regulated sectors like finance and healthcare.

[0004] The AI Decision-Making System for Data Object Analysis and Management addresses these shortcomings by offering an adaptive, multi-modal system capable of analyzing, categorizing, and prioritizing diverse data objects in real-time. The invention integrates advanced AI algorithms with machine learning models that continuously learn from data environments, making it highly responsive to evolving business needs. Unlike existing solutions, the system offers transparency through explainable AI (XAI), providing users with understandable insights into how decisions are made. Its resource optimization features ensure sustainable operations by dynamically allocating processing power based on task priority, reducing energy consumption. The system's novelty lies in its ability to handle complex, multi-source data while delivering real-time, context-aware decision-making, making it a versatile tool across industries.

OBJECTS OF THE INVENTION
[0005] The primary object of the invention is to automate the analysis and management of data objects using advanced AI and machine learning techniques.

[0006] Another object of the invention is to improve decision-making efficiency by identifying, categorizing, and prioritizing data objects in real-time.

[0007] Another object of the invention is to offer a versatile solution that can be applied across industries such as cybersecurity, healthcare, finance, and logistics.

[0008] Another object of the invention is to reduce human intervention in data management processes, minimizing the risk of errors and delays.

[0009] Another object of the invention is to enhance resource optimization by dynamically allocating computational resources based on task priority and complexity.

[00010] Another object of the invention is to provide explainable AI (XAI) features, offering transparency and understandable insights into the decision-making process.

[00011] Another object of the invention is to detect and mitigate potential threats or anomalies in data, particularly in cybersecurity applications.

[00012] Another object of the invention is to support sustainability by reducing unnecessary energy consumption in data processing tasks.

[00013] Another object of the invention is to allow for seamless integration with existing enterprise systems, ensuring smooth workflows and operational continuity.

[00014] Another object of the invention is to continuously improve decision accuracy through a self-learning feedback loop that adapts to evolving data environments.

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, AI decision-making system for data object analysis and management is presented. It is designed to autonomously analyze, categorize, and prioritize data objects across multiple industries. By leveraging advanced AI algorithms and machine learning, the system optimizes data handling, enhances decision-making efficiency, and reduces human intervention. It provides real-time insights, resource optimization, and explainable AI features, ensuring transparency and adaptability. The system is applicable in domains such as cybersecurity, healthcare, finance, and logistics, improving both operational efficiency and sustainability. Its novelty lies in its dynamic prioritization, multi-source data processing, and continuous learning capabilities.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

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

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

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

[00020] FIG. 1 is component wise drawing for AI decision-making system for data object analysis and management.

[00021] FIG 2 is working methodology of AI decision-making system for data object analysis and management.

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 AI decision-making system for data object analysis and management 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, AI decision-making system for data object analysis and management 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data ingestion and pre-processing module 102, ai-based analysis engine 104, priority assignment and decision-making logic 106, resource optimization and load balancing module 108, user interface and interaction module 110, explainability and transparency module 112, security and data privacy mechanisms 114, self-learning feedback loop 116, automated threat detection and mitigation engine 118, cross-platform integration and API support 120.

[00029] Referring to Fig. 1, the present disclosure provides details of AI decision-making system for data object analysis and management 100. It automates the process of analysing, categorizing, and prioritizing data objects across multiple domains using advanced AI and machine learning techniques. In one of the embodiments, the system is provided with the following key components such as data ingestion and pre-processing module 102, AI-based analysis engine 104, and priority assignment and decision-making logic 106, enabling efficient real-time data processing and decision-making. It also includes resource optimization and load balancing module 108 and user interface and interaction module 110 to ensure dynamic resource allocation and user control. Additional components like explainability and transparency module 112 and security and data privacy mechanisms 114 ensure transparency, accountability, and data protection.

[00030] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with data ingestion and preprocessing module 102, which is responsible for collecting and preparing data for analysis. It ingests data from various sources, including databases, real-time sensors, and external streams, and performs data cleaning, normalization, and transformation to standardize formats. This module works closely with AI-based analysis engine 104 by ensuring the data is in the right format for further processing, enabling accurate pattern recognition and categorization. It plays a critical role in ensuring that all incoming data is prepared for efficient analysis by the system.

[00031] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with AI-based analysis engine 104, which performs the core task of analyzing data objects using machine learning algorithms. It identifies patterns, classifies data, and detects anomalies across various data types, including structured and unstructured formats. The engine continuously interacts with priority assignment and decision-making logic 106 to determine which data objects require immediate attention based on real-time insights. Its continuous learning capabilities allow it to adapt to new data inputs, improving the overall decision-making process.

[00032] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with priority assignment and decision-making logic 106, which dynamically assigns priorities to data objects based on context, relevance, and urgency. It integrates with AI-based analysis engine 104 to ensure that critical data is flagged for immediate action, while less important data is managed in the background. This component ensures that time-sensitive tasks, such as cybersecurity threats, are handled with the highest priority, enhancing the system's efficiency in complex environments.

[00033] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with resource optimization and load balancing module 108, which ensures optimal allocation of computational resources. This module dynamically assigns resources based on task complexity and priority, working closely with priority assignment and decision-making logic 106 to ensure high-priority tasks receive the necessary resources for fast processing. It reduces energy consumption by deferring non-critical tasks during off-peak times, optimizing the system's overall performance.

[00034] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with user interface and interaction module 110, which allows users to monitor and control the system in real-time. This interface provides visual dashboards, manual overrides, and audit trails, offering transparency and control over the AI's decision-making process. It works with explainability and transparency module 112 to ensure users understand the AI's decisions, providing insights into how the system processes and prioritizes data, enhancing user trust and engagement.

[00035] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with explainability and transparency module 112, which offers clear and understandable explanations for decisions made by the AI. This module generates visual decision paths and provides confidence levels for each action, ensuring that users can comprehend why certain data objects were prioritized or categorized in specific ways. It interacts with user interface and interaction module 110 to display these explanations, ensuring that decision-making is transparent, especially in regulated sectors like finance or healthcare.

[00036] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with security and data privacy mechanisms 114, which ensure that all data handled by the system is secured through encryption and access controls. These mechanisms protect sensitive information and comply with privacy regulations, such as GDPR. They work alongside data ingestion and preprocessing module 102 to anonymize data when necessary, ensuring that the system maintains strict data privacy standards while handling large volumes of information.

[00037] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with self-learning feedback loop 116, which continuously refines the AI models based on new data and feedback. This component enables the system to improve its decision-making accuracy over time without manual retraining. It interacts dynamically with AI-based analysis engine 104, feeding updated insights back into the analysis process, allowing the system to adapt to evolving data environments and improve over time.

[00038] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with automated threat detection and mitigation engine 118, which monitors data for potential cybersecurity threats or anomalies. It works in real-time to detect irregularities in data and can trigger predefined security actions to mitigate risks. This engine collaborates closely with priority assignment and decision-making logic 106 to ensure that any detected threats are prioritized and dealt with immediately, enhancing the system's security capabilities.

[00039] Referring to Fig. 1, the AI decision-making system for data object analysis and management 100 is provided with cross-platform integration and API support 120, which allows the system to integrate seamlessly with other enterprise systems, such as ERP and CRM platforms. This component facilitates the exchange of data between systems, enabling the AI decision-making process to incorporate external data sources and trigger actions in other platforms. It works with resource optimization and load balancing module 108 to ensure that external interactions do not overload the system, maintaining smooth and efficient operations across all platforms.



[00040] Referring to Fig 2, there is illustrated method 200 for AI decision-making system for data object analysis and management 100. The method comprises:

At step 202, method 200 includes the system collecting data from various sources, including databases, sensors, and external streams, using the data ingestion and preprocessing module 102;

At step 204, method 200 includes the system cleaning, normalizing, and transforming the ingested data to standardize its format for further analysis;

At step 206, method 200 includes the AI-based analysis engine 104 analyzing the preprocessed data to identify patterns, categorize data objects, and detect anomalies;

At step 208, method 200 includes the system assigning priorities to the analyzed data objects based on context, urgency, and relevance using the priority assignment and decision-making logic 106;

At step 210, method 200 includes the system allocating computational resources dynamically using the resource optimization and load balancing module 108 to ensure high-priority tasks receive sufficient processing power;

At step 212, method 200 includes the system displaying real-time decision outputs and explanations to the user through the user interface and interaction module 110, allowing for manual intervention if necessary;

At step 214, method 200 includes the system providing transparent explanations for AI-driven decisions, including decision paths and confidence levels, through the explainability and transparency module 112;

At step 216, method 200 includes the system ensuring data security and privacy by encrypting data and controlling access using the security and data privacy mechanisms 114;

At step 218, method 200 includes the system continuously learning from new data inputs and user feedback using the self-learning feedback loop 116, enhancing decision-making over time;

At step 220, method 200 includes the system monitoring data for potential threats using the automated threat detection and mitigation engine 118, and triggering automated actions when a threat is detected;

At step 222, method 200 includes the system integrating external data and interacting with other enterprise systems through the cross-platform integration and API support 120, ensuring smooth system operation.

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

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

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

, Claims:WE CLAIM:
1. An AI decision-making system for data object analysis and management 100 comprising of
data ingestion and preprocessing module 102 to collect and prepare data from various sources for analysis;
AI-based analysis engine 104 to analyze data objects for patterns, categorization, and anomaly detection;
priority assignment and decision-making logic 106 to dynamically assign priorities based on context and urgency;
resource optimization and load balancing module 108 to allocate computational resources efficiently for high-priority tasks;
user interface and interaction module 110 to display real-time decision outputs and allow user interaction;
explainability and transparency module 112 to provide clear explanations of AI decisions and decision paths;
security and data privacy mechanisms 114 to ensure data encryption and control access to sensitive information;
self-learning feedback loop 116 to continuously learn from new data and improve decision-making;
automated threat detection and mitigation engine 118 to monitor for threats and trigger automated defensive actions;
cross-platform integration and API support 120 to facilitate seamless interaction with external enterprise systems.

2. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein data ingestion and preprocessing module 102 is configured to collect, clean, normalize, and transform data from various sources to prepare it for real-time analysis.

3. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein AI-based analysis engine 104 is configured to analyze data objects using machine learning algorithms, identifying patterns, categorizing data, and detecting anomalies.

4. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein priority assignment and decision-making logic 106 is configured to dynamically assign priorities to data objects based on their context, urgency, and relevance to operational processes.

5. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein resource optimization and load balancing module 108 is configured to dynamically allocate computational resources based on task complexity and priority, optimizing system performance and energy efficiency.

6. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein user interface and interaction module 110 is configured to display real-time decision outputs, explanations, and allow users to manually intervene or adjust system settings when necessary.

7. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein explainability and transparency module 112 is configured to provide transparent explanations of AI decisions, including visual decision paths and confidence levels, enhancing user trust and accountability.

8. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein automated threat detection and mitigation engine 118 is configured to continuously monitor data for potential threats and trigger automated security actions in response to detected risks.

9. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein cross-platform integration and API support 120 is configured to enable seamless interaction with external enterprise systems, facilitating data exchange and system integration across multiple platforms

10. The AI decision-making system for data object analysis and management 100 as claimed in claim 1, wherein method comprises of
data ingestion and preprocessing module 102 collecting data from various sources, including databases, sensors, and external streams;

system cleaning, normalizing, and transforming the ingested data to standardize its format for further analysis;

AI-based analysis engine 104 analyzing the preprocessed data to identify patterns, categorize data objects, and detect anomalies;

priority assignment and decision-making logic 106 assigning priorities to the analyzed data objects based on context, urgency, and relevance;

resource optimization and load balancing module 108 dynamically allocating computational resources to ensure high-priority tasks receive sufficient processing power;

user interface and interaction module 110 displaying real-time decision outputs and explanations to the user, allowing for manual intervention if necessary;
explainability and transparency module 112 providing transparent explanations for AI-driven decisions, including decision paths and confidence levels;

security and data privacy mechanisms 114 ensuring data security and privacy by encrypting data and controlling access;

self-learning feedback loop 116 continuously learning from new data inputs and user feedback, enhancing decision-making over time;

automated threat detection and mitigation engine 118 monitoring data for potential threats and triggering automated actions when a threat is detected;

cross-platform integration and API support 120 integrating external data and interacting with other enterprise systems, ensuring smooth system operation.

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