UNSTRUCTURED DATA ANALYTICS USING AI-OPTIMIZED SYSTEMS AND METHODS

Abstract

ABSTRACT UNSTRUCTURED DATA ANALYTICS USING AI-OPTIMIZED SYSTEMS AND METHODS The present disclosure introduces unstructured data analytics using AI-optimised systems and methods 100 that efficiently processes multi-format data from diverse sources. The system incorporates a data ingestion module 102 to collect unstructured data and a preprocessing engine 104 to cleanse, tokenize, and normalize it. The AI-optimized analytics engine 106 applies advanced AI models for text, image, video, and audio analysis. Continuous model improvement is facilitated by the machine learning model training and optimization system 108. Insights are presented through the data visualization and reporting module 110, while the model adaptation and learning mechanism 112 ensures real-time adjustments based on evolving data patterns. Security and privacy are maintained by the security and privacy layer 114, ensuring compliance with regulations. The system supports real-time decision-making with the automation engine for decision-making 120 and decentralized learning through the federated learning module 122, making it scalable and adaptable for various industries. Reference Fig 1

Specification

Description:Unstructured Data Analytics Using AI-Optimized Systems and Methods
TECHNICAL FIELD
[0001] The present innovation relates to AI-optimized systems and methods for analyzing unstructured data across various formats and industries.

BACKGROUND

[0002] In the current digital landscape, unstructured data-comprising text, images, videos, and sensor data-represents a significant portion of the information generated daily. However, traditional data processing tools are designed primarily for structured data, leaving organizations with limited capacity to derive meaningful insights from unstructured sources. Existing solutions, such as basic data mining tools or manual categorization methods, often fall short due to their inability to handle the complexity, variability, and sheer volume of unstructured data. While AI and machine learning techniques have been increasingly adopted, many of these systems are resource-intensive, domain-specific, and require extensive manual intervention for effective use, leading to inefficiencies in scalability and adaptability across different industries.

[0003] The primary drawbacks of these available options include the high computational demands, limited flexibility, and inadequate real-time processing capabilities, especially when dealing with multi-format data sources. Furthermore, existing AI solutions often lack optimization for handling diverse unstructured data types such as text, images, and audio simultaneously, forcing users to rely on multiple platforms or systems for comprehensive analysis.

[0004] This invention differentiates itself by offering an AI-optimized system capable of efficiently analyzing and processing unstructured data from a wide variety of formats, all within a single platform. By automating data categorization, pattern recognition, and predictive analytics, the system overcomes the limitations of traditional tools. It is novel in its ability to handle multi-modal data seamlessly, continuously adapt to new data patterns, and scale effortlessly to meet industry needs. The key features include real-time data ingestion, dynamic model adaptation, multi-format data integration, and enhanced visualization capabilities, allowing organizations to gain actionable insights more effectively and make informed decisions in real-time.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to provide an AI-optimized system for efficiently analyzing unstructured data from diverse formats such as text, images, videos, and sensor data.

[0006] Another object of the invention is to enhance decision-making by automating the extraction of actionable insights from unstructured data across various industries.

[0007] Another object of the invention is to offer a scalable, real-time solution for processing and analyzing large volumes of unstructured data, supporting both real-time and batch-mode operations.

[0008] Another object of the invention is to reduce the computational demands typically associated with unstructured data analysis through the use of optimized machine learning and AI algorithms.

[0009] Another object of the invention is to provide a unified platform capable of handling multi-modal data, eliminating the need for multiple separate systems to analyze different types of data.

[00010] Another object of the invention is to enable continuous learning and dynamic model adaptation to ensure that the system improves in accuracy and efficiency over time as new data patterns emerge.

[00011] Another object of the invention is to improve the accessibility of AI-driven unstructured data analytics by offering customizable solutions tailored to specific domains, such as healthcare, finance, and telecommunications.

[00012] Another object of the invention is to enhance data visualization capabilities, allowing users to interact with insights derived from unstructured data through intuitive dashboards and reports.

[00013] Another object of the invention is to offer robust security measures for handling sensitive unstructured data, ensuring compliance with global data privacy regulations.

[00014] Another object of the invention is to overcome the limitations of existing unstructured data analysis systems by providing a comprehensive, AI-powered platform that is adaptable, resource-efficient, and capable of delivering real-time insights across industries

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, unstructured data analytics using AI-optimised systems and methods is presented. It analysis unstructured data from diverse sources such as text, images, videos, and sensor data. It automates tasks like data categorization, pattern recognition, and predictive analytics, improving decision-making across industries. The system is scalable, adaptable, and supports real-time processing, making it ideal for large-scale data environments. It also offers enhanced security and privacy features, ensuring compliance with regulations. Overall, the invention delivers a unified, efficient platform for deriving actionable insights from complex, unstructured data.

[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 unstructured data analytics using AI-optimised systems and methods.

[00021] FIG 2 is working methodology of unstructured data analytics using AI-optimised systems and methods.

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 unstructured data analytics using AI-optimised systems and methods 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, unstructured data analytics using AI-optimised systems and methods 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data ingestion module 102, preprocessing engine 104, AI-optimized analytics engine 106, machine learning model training and optimization system 108, data visualization and reporting module 110, model adaptation and learning mechanism 112, security and privacy layer 114, real-time processing system 116, customization and scalability architecture 118, automation engine for decision-making 120 and federated learning module 122.

[00029] Referring to Fig. 1, the present disclosure provides details of unstructured data analytics using AI-optimised systems and methods 100. It enables efficient processing of multi-format unstructured data such as text, images, videos, and sensor data. The system incorporates key components such as data ingestion module 102, preprocessing engine 104, and AI-optimized analytics engine 106 for comprehensive data analysis. The machine learning model training and optimization system 108 and model adaptation and learning mechanism 112 allow continuous learning and model refinement. Additional components such as data visualization and reporting module 110, security and privacy layer 114, and federated learning module 122 ensure scalability, secure data handling, and decentralized analysis. The system also features real-time processing system 116 and automation engine for decision-making 120 to support real-time insights and action.

[00030] Referring to Fig. 1, the unstructured data analytics system 100 is provided with data ingestion module 102, which is responsible for collecting unstructured data from various sources, such as databases, cloud platforms, IoT devices, and social media. This module ensures seamless data collection at scale, supporting real-time or batch processing. The data ingestion module 102 works closely with the preprocessing engine 104, transferring raw data for further cleaning and normalization. This smooth integration ensures that data flows efficiently through the system, ready for advanced analysis.

[00031] Referring to Fig. 1, the unstructured data analytics system 100 is provided with preprocessing engine 104, which performs critical tasks like data cleansing, tokenization, and normalization. This engine transforms raw unstructured data into a format suitable for AI model analysis. The preprocessing engine 104 works directly with the AI-optimized analytics engine 106, ensuring that the data fed into the AI models is of high quality, improving the accuracy and efficiency of the analysis. It also interacts with the data ingestion module 102 to handle large data volumes seamlessly.

[00032] Referring to Fig. 1, the unstructured data analytics system 100 is provided with AI-optimized analytics engine 106, which serves as the core component responsible for processing and analyzing unstructured data from various sources, including text, images, videos, and sensor data. This engine integrates several specialized subcomponents, each designed to handle specific data formats and tasks.

[00033] The Natural Language Processing (NLP) module within AI-optimized analytics engine 106 processes unstructured text data. It performs tasks such as sentiment analysis, entity recognition, text classification, summarization, and topic modeling. The NLP module enables the system to extract meaningful insights from large volumes of text, such as customer reviews, emails, or research documents.

[00034] The Computer Vision module is designed to handle image and video data, using deep learning techniques such as convolutional neural networks (CNNs) to analyze visual content. It can perform tasks like object detection, image classification, and video segmentation, allowing the system to recognize objects, detect anomalies, and analyze visual patterns.

[00035] The Audio and Speech Recognition module processes unstructured audio data, converting speech to text and performing voice-based sentiment analysis. This subcomponent is useful for analyzing call center recordings, podcasts, or any audio data. It can also identify speakers, detect emotions from voice patterns, and transcribe speech in real-time.

[00036] The Time-Series and Sensor Data Analysis module focuses on handling time-sensitive data from IoT devices and sensors. It applies pattern recognition, anomaly detection, and predictive analytics to identify trends, detect faults, or forecast outcomes based on sensor-generated data, making it critical for applications like predictive maintenance or real-time monitoring.

[00037] Each of these subcomponents works together under the AI-optimized analytics engine 106 to ensure seamless, multi-modal data processing. The engine interacts with the machine learning model training and optimization system 108 to refine models based on processed data and the model adaptation and learning mechanism 112 to continuously improve performance, enabling comprehensive and scalable analysis across industries.

[00038] Referring to Fig. 1, the unstructured data analytics system 100 is provided with machine learning model training and optimization system 108, which manages the training and optimization of AI models. It ensures that the models continuously improve in accuracy and efficiency by adapting to new datasets and evolving trends. The machine learning model training and optimization system 108 works in tandem with the AI-optimized analytics engine 106 to refine models, and it also interacts with the model adaptation and learning mechanism 112 to ensure real-time updates.

[00039] Referring to Fig. 1, the unstructured data analytics system 100 is provided with data visualization and reporting module 110, which transforms processed data into actionable insights displayed through interactive dashboards, charts, and reports. The data visualization and reporting module 110 enables users to explore results from the AI-optimized analytics engine 106. It also connects with the real-time processing system 116 to provide up-to-the-minute insights, allowing users to make informed decisions based on the most current data available.

[00040] Referring to Fig. 1, the unstructured data analytics system 100 is provided with model adaptation and learning mechanism 112, which enables the continuous improvement of AI models by dynamically adjusting to new data patterns and evolving trends. This mechanism ensures that the system remains accurate and efficient over time. The model adaptation and learning mechanism 112 works closely with the machine learning model training and optimization system 108 to fine-tune models and also interacts with the AI-optimized analytics engine 106 to ensure that all analyses reflect the most up-to-date data and patterns.

[00041] Referring to Fig. 1, the unstructured data analytics system 100 is provided with security and privacy layer 114, which safeguards the data being processed by implementing encryption, access controls, and data masking. This layer ensures compliance with global data privacy standards such as GDPR and HIPAA. The security and privacy layer 114 is crucial in protecting sensitive unstructured data that flows through the data ingestion module 102 and the AI-optimized analytics engine 106, ensuring that all operations are secure and privacy-compliant.

[00042] Referring to Fig. 1, the unstructured data analytics system 100 is provided with real-time processing system 116, which allows for the immediate analysis of data as it is ingested. This system supports both real-time and batch-mode processing, offering flexibility for different use cases. The real-time processing system 116 works in conjunction with the data ingestion module 102 to ensure fast data flow and interacts with the data visualization and reporting module 110 to present real-time insights for instant decision-making.

[00043] Referring to Fig. 1, the unstructured data analytics system 100 is provided with customization and scalability architecture 118, which allows the system to be tailored for specific industries and applications. This architecture ensures that the system can scale to meet the needs of small businesses and large enterprises alike. The customization and scalability architecture 118 integrates seamlessly with other components, such as the AI-optimized analytics engine 106 and the model adaptation and learning mechanism 112, enabling users to optimize the system based on their unique data and processing requirements.

[00044] Referring to Fig. 1, the unstructured data analytics system 100 is provided with automation engine for decision-making 120, which automates responses and actions based on the insights generated by the AI models. This engine is especially valuable in scenarios like cybersecurity or risk management, where real-time action is required. The automation engine for decision-making 120 interacts closely with the real-time processing system 116 and the AI-optimized analytics engine 106 to trigger predefined actions, alerts, or decisions based on the analyzed data.

[00045] Referring to Fig. 1, the unstructured data analytics system 100 is provided with federated learning module 122, which enables decentralized AI model training across multiple locations without centralizing sensitive data. This module allows the system to process data locally while aggregating insights from distributed sources, ensuring both privacy and performance. The federated learning module 122 works in conjunction with the machine learning model training and optimization system 108 to ensure continuous model updates while keeping data secure and compliant with privacy regulations.

[00046] Referring to Fig 2, there is illustrated method 200 for unstructured data analytics using AI-optimised systems and methods 100. The method comprises:

At step 202, method 200 includes the system collecting unstructured data from various sources such as databases, IoT devices, and social media using the data ingestion module 102;

At step 204, method 200 includes the system transferring the collected data to the preprocessing engine 104 for data cleansing, tokenization, normalization, and feature extraction;

At step 206, method 200 includes the system passing the preprocessed data to the AI-optimized analytics engine 106 for further analysis based on the data type (text, image, video, or audio);

At step 208, method 200 includes the natural language processing module within the AI-optimized analytics engine 106 analyzing text data for tasks such as sentiment analysis, entity recognition, or topic modeling;

At step 210, method 200 includes the computer vision module within the ai-optimized analytics engine 106 analyzing image or video data for object detection, image classification, or anomaly detection;

At step 212, method 200 includes the audio and speech recognition module within the ai-optimized analytics engine 106 processing audio data for speech-to-text conversion, speaker identification, or emotion detection;

At step 214, method 200 includes the time-series and sensor data analysis module analyzing sensor or IoT data for trend identification, anomaly detection, or predictive maintenance;

At step 216, method 200 includes the system refining the AI models based on real-time or batch data using the machine learning model training and optimization system 108 to improve performance and accuracy;

At step 218, method 200 includes the system dynamically adapting AI models to new data patterns using the model adaptation and learning mechanism 112 to ensure continuous learning and improved results;

At step 220, method 200 includes the system generating visual representations of the analyzed data (such as charts, graphs, and reports) through the data visualization and reporting module 110 for user interpretation;

At step 222, method 200 includes the system automating actions based on the generated insights, such as triggering alerts or executing predefined responses, using the automation engine for decision-making 120;

At step 224, method 200 includes the system performing federated learning across multiple decentralized data sources using the federated learning module 122 to ensure privacy while still benefiting from collective data insights.

[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. An unstructured data analytics using AI-optimised systems and methods 100 comprising of
data ingestion module 102 to collect unstructured data from various sources;
preprocessing engine 104 to cleanse, tokenize, and normalize data for analysis;
AI-optimized analytics engine 106 to analyze multi-format unstructured data using AI models;
machine learning model training and optimization system 108 to refine and improve AI models continuously;
data visualization and reporting module 110 to generate visual representations and reports of analyzed data;
model adaptation and learning mechanism 112 to dynamically adjust models based on evolving data patterns;
security and privacy layer 114 to ensure data protection and compliance with privacy regulations;
real-time processing system 116 to process and analyze data in real-time;
customization and scalability architecture 118 to tailor the system for specific industries and scale as needed;
automation engine for decision-making 120 to automate actions based on AI-generated insights; and
federated learning module 122 to enable decentralized AI model training while maintaining data privacy.

2. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein the data ingestion module 102 is configured to collect unstructured data from multiple sources, including databases, IoT devices, social media, and cloud platforms, ensuring seamless integration of diverse data formats.
3. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein the preprocessing engine 104 is configured to cleanse, tokenize, normalize, and extract features from the collected data, preparing it for analysis by the AI models and ensuring data consistency.

4. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein the AI-optimized analytics engine 106 is configured to analyze unstructured data using machine learning and deep learning models, including natural language processing, computer vision, and audio analysis, to generate actionable insights from text, image, video, and audio data.

5. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein the machine learning model training and optimization system 108 is configured to continuously train and optimize AI models based on new data patterns, improving accuracy and adaptability over time.

6. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein the data visualization and reporting module 110 is configured to generate visual representations, including charts, graphs, and reports, enabling users to interpret and explore insights derived from the analyzed data.

7. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein the model adaptation and learning mechanism 112 is configured to dynamically adjust AI models in real-time based on evolving data patterns, ensuring continuous learning and enhanced model performance.

8. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein the security and privacy layer 114 is configured to ensure data protection through advanced encryption and access control mechanisms, ensuring compliance with global data privacy regulations.

9. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein the federated learning module 122 is configured to enable decentralized AI model training across multiple locations, ensuring data privacy by allowing models to learn from distributed data without centralizing sensitive information.

10. The unstructured data analytics using AI-optimised systems and methods 100 as claimed in claim 1, wherein method comprises of

system collecting unstructured data from various sources such as databases, IOT devices, and social media using the data ingestion module 102;

system transferring the collected data to the pre-processing engine 104 for data cleansing, tokenization, normalization, and feature extraction;

system passing the preprocessed data to the ai-optimized analytics engine 106 for further analysis based on the data type (text, image, video, or audio);

natural language processing module within the ai-optimized analytics engine 106 analyzing text data for tasks such as sentiment analysis, entity recognition, or topic modeling;
computer vision module within the ai-optimized analytics engine 106 analyzing image or video data for object detection, image classification, or anomaly detection;

audio and speech recognition module within the AI-optimized analytics engine 106 processing audio data for speech-to-text conversion, speaker identification, or emotion detection;

time-series and sensor data analysis module analysing sensor or IOT data for trend identification, anomaly detection, or predictive maintenance;

system refining the AI models based on real-time or batch data using the machine learning model training and optimization system 108 to improve performance and accuracy;

system dynamically adapting AI models to new data patterns using the model adaptation and learning mechanism 112 to ensure continuous learning and improved results;

system generating visual representations of the analyzed data (such as charts, graphs, and reports) through the data visualization and reporting module 110 for user interpretation;

system automating actions based on the generated insights, such as triggering alerts or executing predefined responses, using the automation engine for decision-making 120; and

system performing federated learning across multiple decentralized data sources using the federated learning module 122 to ensure privacy while still benefiting from collective data insights.

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