ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING BASED USER BEHAVIOUR ANALYSIS SYSTEM AND METHOD THEREOF

Abstract

The present invention discloses an Artificial Intelligence (AI) and Machine Learning (ML) Based User Behavior Analysis System and Method that enables real-time, scalable, and efficient analysis and prediction of user behaviors in digital environments. The system incorporates novel hardware components such as User Behavior Data Collection Sensors, Edge Processing Units (EPUs), and AI-Optimized Processors, alongside advanced software modules for data preprocessing, feature extraction, and machine learning model training. The system processes large volumes of user interaction data locally at the edge, reducing latency and improving system efficiency. It leverages machine learning models to predict user actions and provide personalized recommendations, which can be utilized for applications such as content recommendation, fraud detection, and personalized marketing. The system’s continuous optimization and retraining capabilities ensure that it adapts to evolving user behaviors, providing highly accurate, real-time insights.

Specification

Description:[016] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[017] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[018] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[019] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[020] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[021] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[022] In an embodiment of the invention and referring to Figures 1, the present invention relates to an Artificial Intelligence (AI) and Machine Learning (ML) Based User Behavior Analysis System and Method Thereof, which enables enhanced, real-time analysis and prediction of user behaviors in various digital environments. It employs a unique combination of novel hardware and software components that interact seamlessly to gather, process, and analyze user interaction data, thereby facilitating adaptive and personalized decision-making. This system is designed to be scalable, efficient, and highly responsive, ensuring that organizations can derive actionable insights from user behavior at an unprecedented speed and accuracy.
[023] The system comprises multiple interconnected hardware and software components that work in concert to process large volumes of user data in real-time. The hardware components, including sensors, edge processing units, data storage systems, and specialized AI-optimized processors, are integrated to ensure that the system is both scalable and responsive to the dynamic nature of user behavior. On the software side, advanced modules for data collection, preprocessing, feature extraction, machine learning, and prediction are incorporated to ensure that the analysis is accurate and adapts quickly to changes in user behavior.
[024] The hardware components are essential in enabling the seamless flow of data throughout the system. First and foremost, User Behavior Data Collection Sensors are embedded within digital platforms (e.g., mobile applications, websites, and smart devices). These sensors collect real-time data such as clickstreams, time spent on pages, scroll activities, purchase history, search terms, and other behavioral metrics. The sensors also capture contextual data, such as location, device type, and user demographics, which are critical for delivering accurate predictions and recommendations.
[025] Next, the system utilizes Edge Processing Units (EPUs). These EPUs are deployed at the network edge, closer to the data sources, to process the data locally and reduce latency. Edge processing minimizes the amount of raw data that needs to be sent to centralized servers, ensuring faster response times for real-time analysis. The EPUs perform initial data filtering, anomaly detection, and basic analytics before sending relevant, pre-processed data to the cloud or central server for deeper analysis. This process significantly reduces the network load and enhances the overall system efficiency.
[026] The system's data is stored in a Distributed Data Storage System, which comprises both on-premise and cloud storage solutions. The distributed nature of the storage ensures scalability, as it can dynamically expand to accommodate increasing amounts of data. It also ensures high availability and fault tolerance, ensuring that even in the event of a system failure, data integrity is preserved. The storage system is designed to handle structured and unstructured data, storing raw user interaction logs, behavioral features, and model results.
[027] To power the AI models, the system integrates AI-Optimized Processors such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs). These specialized processors are designed to accelerate machine learning tasks, such as training deep learning models and performing inference on large datasets. GPUs and TPUs are optimized to handle the parallel processing demands of AI algorithms, enabling the system to run complex models at scale and deliver real-time predictions with minimal delay.
[028] On the software side, the system includes several advanced modules that contribute to the efficiency and accuracy of the behavior analysis. The Data Preprocessing Module is the first software component that handles raw data. It performs tasks such as filtering out noisy data, handling missing values, and normalizing the data to ensure consistency across datasets. This module also generates derived features by processing raw interaction data to capture meaningful patterns, such as session duration, frequency of interactions, and changes in user behavior over time.
[029] Following preprocessing, the Behavioral Feature Extraction Engine extracts deeper insights from the processed data. This engine leverages techniques such as Natural Language Processing (NLP) to analyze textual data (such as reviews or social media posts) and extract sentiment information. It also utilizes statistical methods to calculate patterns in time-series data, such as session patterns, frequency of purchases, and interaction sequences. These extracted features are critical in training machine learning models for user behavior prediction.
[030] The heart of the system lies in the Machine Learning Model Training and Inference Module. This module trains multiple machine learning models on historical data to predict user behavior. These models include supervised learning algorithms like Random Forest, Support Vector Machines (SVM), and Gradient Boosting Machines (GBM) for classification and regression tasks. Additionally, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used to analyze sequential and unstructured data, enabling the system to understand complex, time-dependent user behavior.
[031] The Behavior Prediction and Recommendation Engine takes the output of the machine learning models to generate actionable insights. The system predicts what actions a user is likely to take next, such as making a purchase, engaging with a specific piece of content, or abandoning a cart. The engine uses these predictions to offer personalized recommendations, tailor marketing strategies, or alert system administrators to potential fraud or abnormal behavior patterns.
[032] The Model Optimization and Retraining Framework is a crucial component that ensures the system remains accurate over time. This module monitors the performance of the deployed models and assesses whether they need to be updated. By incorporating online learning and transfer learning techniques, the system can update the models in real-time based on new data, ensuring that the predictions remain relevant even as user behavior evolves.
[033] The User Interaction Interface is a front-end software component that allows users to interact with the system, visualize predictions, and provide feedback. This interface is critical for presenting the results of user behavior analysis in a manner that is easily understandable. It provides a dashboard where stakeholders can visualize behavioral trends, monitor performance metrics, and receive real-time alerts. The interface may also include APIs for integration with third-party platforms, enabling businesses to leverage the insights in other applications.
[034] The hardware and software components of the system are tightly integrated to ensure smooth data flow and efficient processing. Data from the User Behavior Data Collection Sensors is initially processed by the Edge Processing Units (EPUs). These units perform basic data filtering and anomaly detection before sending the relevant pre-processed data to the Distributed Data Storage System. Once stored, the data is accessed by the Data Preprocessing Module, which prepares the data for further analysis.
[035] The Behavioral Feature Extraction Engine then analyzes the pre-processed data, extracting actionable features that are required for training machine learning models. These features are passed to the Machine Learning Model Training and Inference Module, where various algorithms are applied to develop predictive models. The trained models are used by the Behavior Prediction and Recommendation Engine to provide real-time insights and recommendations.
[036] The Model Optimization and Retraining Framework ensures that the system continuously improves its predictions. As new data is collected, the framework assesses the performance of the models and retrains them as necessary. This ongoing optimization ensures that the system remains adaptive and relevant even in rapidly changing environments.
[037] In this architecture, the AI-Optimized Processors play a crucial role in handling the computational demands of training deep learning models and running high-throughput inference tasks. Their ability to parallel-process large datasets significantly enhances the system's capacity to deliver predictions at scale.
[038] The system's efficiency is greatly enhanced by the combination of edge processing and centralized AI optimization. By utilizing Edge Processing Units (EPUs), the system reduces the amount of data that must be transmitted to centralized servers, cutting down on network congestion and minimizing latency. This local data processing ensures that the system can deliver real-time predictions, which is essential for applications like dynamic content recommendation or fraud detection.
[039] The Distributed Data Storage System provides the scalability needed to accommodate vast amounts of user interaction data. The system is designed to scale horizontally, meaning that as the number of users grows, the system can add more processing units and storage nodes to meet the increased demand without compromising performance.
Table 1: Performance Validation:

Table 2: System Efficiency: Latency and Throughput Performance

[040] Table 1 demonstrates the superior prediction accuracy of the present invention when compared to traditional methods, confirming that the AI and ML-based system provides more accurate insights into user behavior. Table 2 shows the system's performance in terms of latency and throughput, highlighting its efficiency and scalability in handling large data volumes.
[041] The present invention provides a robust, scalable, and efficient AI and ML-based system for user behavior analysis. By integrating novel hardware components such as Edge Processing Units (EPUs), AI-Optimized Processors, and a Distributed Data Storage System, alongside advanced software modules for data preprocessing, feature extraction, and machine learning, the system is capable of delivering highly accurate, real-time insights into user behavior. The continuous optimization and adaptability of the system ensure that it remains effective even as user behavior evolves, making it an invaluable tool for industries that rely on understanding and predicting user actions. , Claims:1. An Artificial Intelligence (AI) and Machine Learning (ML) Based User Behavior Analysis System, comprising:
a) a plurality of User Behavior Data Collection Sensors embedded within digital platforms configured to collect real-time user interaction data, including but not limited to clickstreams, time spent on pages, scroll activities, purchase history, search terms, location, device type, and user demographics;
b) one or more Edge Processing Units (EPUs) deployed at the network edge to locally process the collected data, including performing data filtering, anomaly detection, and basic analytics;
c) a Distributed Data Storage System configured to store the collected and pre-processed user data, wherein the storage system includes on-premise and cloud storage solutions for scalability and fault tolerance;
d) one or more AI-Optimized Processors designed to accelerate machine learning tasks, including GPUs and TPUs for model training and inference;
e) a Machine Learning Model Training and Inference Module configured to train machine learning models on the collected data and perform real-time predictions based on trained models;
f) a Behavior Prediction and Recommendation Engine configured to generate actionable insights and personalized recommendations based on the predicted user behavior;
g) a Model Optimization and Retraining Framework configured to continuously monitor model performance and retrain the models based on new data using online learning and transfer learning techniques.
2. The system as claimed in claim 1, wherein the User Behavior Data Collection Sensors capture both structured and unstructured data including but not limited to textual data from user reviews, social media posts, and other interactions, which are then processed by the system for behavioral analysis.
3. The system as claimed in claim 1, wherein the Edge Processing Units (EPUs) perform initial data filtering and anomaly detection, and forward relevant pre-processed data to the Distributed Data Storage System for deeper analysis.
4. The system as claimed in claim 1, wherein the Behavioral Feature Extraction Engine extracts actionable features from the processed data by utilizing techniques such as Natural Language Processing (NLP) to analyze textual data and statistical methods to calculate patterns in time-series data.
5. The system as claimed in claim 1, wherein the Machine Learning Model Training and Inference Module applies both supervised learning algorithms, including but not limited to Random Forest, Support Vector Machines (SVM), and Gradient Boosting Machines (GBM), and deep learning models, including but not limited to Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
6. The system as claimed in claim 1, wherein the Behavior Prediction and Recommendation Engine generates real-time personalized recommendations based on predictions, including but not limited to predicting a user's likelihood to make a purchase, engage with specific content, or abandon a cart.
7. The system as claimed in claim 1, wherein the Distributed Data Storage System is designed to scale horizontally by dynamically adding additional storage nodes as the volume of user data increases, ensuring high availability and data integrity.
8. The system as claimed in claim 1, wherein the AI-Optimized Processors including GPUs and TPUs are configured to perform parallel processing of large datasets to accelerate the training of deep learning models and high-throughput inference tasks.
9. The system as claimed in claim 1, wherein the Model Optimization and Retraining Framework incorporates continuous model assessment and real-time updates based on new incoming data to ensure the accuracy and relevance of the predictions over time.
10. A method for analyzing user behavior using the system of claim 1, comprising the steps of:
i. collecting real-time user interaction data via User Behavior Data Collection Sensors;
ii. preprocessing and filtering the data through the Edge Processing Units (EPUs);
iii. storing the processed data in the Distributed Data Storage System;
iv. extracting behavioral features from the data using the Behavioral Feature Extraction Engine;
v. training and applying machine learning models to the data through the Machine Learning Model Training and Inference Module to predict user behavior;
vi. generating personalized recommendations and actionable insights using the Behavior Prediction and Recommendation Engine;
vii. continuously optimizing and retraining the models using the Model Optimization and Retraining Framework to maintain prediction accuracy over time.

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