A real-time anomaly detection framework for embedded BI systems using LLMs

Abstract

A real-time anomaly detection framework for embedded BI systems (100) using LLMs, consisting of: (a) a data collection and pre-processing module configured to collect, cleanse, normalise and encrypt structured and unstructured data from multiple enterprise data sources in real time; b) a contextual embedding and feature extraction module configured to transform processed data into semantically rich, domain-aware embeddings that capture temporal, correlational, and contextual features; c) an LLM-based anomaly detection engine configured to analyze said embeddings using fine-tuned large language models to identify anomalies based on semantic inference, historical trends, and adaptive learning; d) a real-time stream processing and low-latency pipeline configured to perform high-throughput parallel in-memory computations to deliver anomaly detection results with minimal latency; (e) a root cause analysis and interpretability module configured to identify probable causes for detected anomalies and generate human-readable explanations, confidence scores and causal narratives; (f) an alerting and visualisation module configured to deliver real-time notifications via embedded BI dashboards, messaging platforms and graphical representations such as heat maps and trend charts; and g) a continuous learning and feedback optimization module configured to refine the accuracy of anomaly detection by incorporating user feedback, updated historical data, and evolving business rules.

Description

[0001] The present invention relates to the field of artificial intelligence and business intelligence (BI) systems, particularly real-time anomaly detection in embedded BI environments. It utilizes large language models (LLMs) for advanced pattern recognition, contextual data interpretation, and dynamic alarm generation. The invention addresses the need for intelligent, adaptive, and low-latency anomaly detection to improve the accuracy of decision-making within integrated analytics platforms.

[0002] Translated with DeepL.com (free version) Business intelligence (BI) systems have evolved from static reporting tools to highly interactive, embedded analytics solutions that deliver real-time insights within enterprise applications. These systems enable organizations to visualize operational data, monitor key performance indicators, and support strategic decision-making. However, the increasing complexity, volume, and velocity of data streams in modern organizations pose significant challenges in accurately and quickly detecting unusual patterns or anomalies. Traditional statistical or rule-based anomaly detection approaches are often unable to efficiently process dynamic, unstructured, and context-rich data, resulting in delayed or inaccurate detection results.

[0003] With the rise of artificial intelligence (AI) and natural language processing (NLP), large language models (LLMs) have emerged as a powerful tool for understanding complex data sets, interpreting diverse contexts, and generating actionable insights. Unlike traditional algorithms, LLMs can process heterogeneous data sources, adapt to changing data trends, and provide human-like reasoning capabilities for anomaly identification. Despite these advantages, their integration into real-time embedded BI frameworks remains limited due to challenges in latency optimization, data protection, and contextual adaptation to enterprise-specific workflows.

[0004] There is a growing need for an intelligent, adaptable, and scalable anomaly detection framework that can be seamlessly integrated into embedded BI environments, enabling immediate analytics without disrupting existing analytics pipelines. The proposed invention closes this gap by combining LLM-based contextual reasoning with real-time data processing pipelines, enabling proactive anomaly detection, root cause interpretation, and automated reporting. This integration enables organizations to make faster and more accurate decisions, reduce operational risks, and improve overall business performance in rapidly changing environments.

[0005] One objective of the present disclosure is to enable real-time anomaly detection in embedded BI systems to immediately identify unusual patterns and thus minimize operational risks.

[0006] Another objective of the present disclosure is to utilize large language models (LLMs) for contextual analysis, thereby improving recognition accuracy over conventional rule-based and statistical approaches.

[0007] Another goal of this disclosure is to enable seamless integration into existing BI dashboards so that decision makers can access anomaly insights without having to switch platforms.

[0008] Another goal of the present disclosure is to provide human-readable explanations for anomalies, thereby improving transparency, trust, and interpretability in AI-driven analytics.

[0009] Another objective of the present disclosure is to support multi-channel alerting and visualization so that users can receive actionable notifications through dashboards, messaging platforms, and automated workflows.

[0010] Another objective of the present disclosure is to reduce false alarms by combining semantic reasoning, historical pattern analysis, and adaptive learning.

[0011] Another objective of the present disclosure is to ensure scalability and low latency, making the framework suitable for large, mission-critical enterprise data streams.

[0012] Another objective of the present disclosure is to incorporate continuous learning from user feedback and evolving data sets to ensure long-term adaptability to changing business conditions.

[0013] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention relates to a real-time anomaly detection framework for embedded business intelligence (BI) systems that utilizes large language models (LLMs) for context data interpretation, advanced pattern recognition, and adaptive decision support in integrated analytics environments.

[0015] Another embodiment of the present invention is to provide a data collection and preprocessing module that collects, cleanses, normalizes, and encrypts structured and unstructured real-time data from multiple enterprise sources to enable seamless processing.

[0016] Another embodiment of the present invention is the inclusion of a contextual embedding and feature extraction module that transforms incoming data into semantically rich, domain-aware embeddings that capture temporal, correlative, and contextual patterns to improve the accuracy of anomaly detection.

[0017] Another embodiment of the present invention is the implementation of an LLM-based anomaly detection engine that applies fine-tuned large language models to identify critical deviations using semantic reasoning, historical trend analysis, and adaptive learning mechanisms.

[0018] Another embodiment of the present invention is the integration of a root cause analysis and interpretability module that provides human-readable causal explanations, confidence values, and contextual descriptions for detected anomalies to increase transparency and trust.

[0019] Another embodiment of the present invention is the use of an alerting and visualization module that provides anomaly notifications via embedded BI dashboards, multi-channel alarms, and graphical tools such as heatmaps and trend charts.

[0020] Another embodiment of the present invention is the inclusion of a continuous learning and feedback optimization module that updates recognition models with user feedback, evolving business rules, and historical data to ensure sustainable accuracy and adaptability.

[0021] The present invention relates to a real-time anomaly detection framework for embedded BI systems using LLMs, enabling intelligent detection of unusual patterns in low-latency enterprise analytics environments. It comprises seven integrated modules: data ingestion and preprocessing, contextual embedding and feature extraction, LLM-based anomaly detection engine, real-time stream processing pipeline, root cause analysis and interpretability, alerting and visualization, and continuous learning and feedback optimization. Together, these modules provide accurate anomaly detection, contextual explanations, and adaptive enhancements within embedded BI platforms.

[0022] The system is described using various functional components, with the modules of the system being as follows: Data acquisition and preprocessing module

[0023] This module is responsible for ingesting real-time data streams from various enterprise systems, IoT devices, APIs, and transactional databases integrated into the embedded BI platform. It supports both structured and unstructured data formats, including numeric metrics, text logs, and event data. Preprocessing steps such as data cleansing, normalization, noise filtering, and timestamp synchronization ensure that the incoming data is accurate, consistent, and suitable for subsequent anomaly detection. The module also incorporates lightweight data compression and encryption mechanisms to ensure low latency and data security. Module for contextual embedding and feature extraction

[0024] In this phase, the system transforms incoming data into semantically rich representations using advanced embedding techniques. Numerical, categorical, and textual features are extracted and encoded into a unified vector space for efficient LLM processing. The module uses contextual embeddings to capture domain-specific nuances, seasonal patterns, and correlation structures between multiple data variables. This ensures that the LLM receives not only raw data but also the contextual clues necessary for accurate anomaly interpretation. LLM-based anomaly detection engine

[0025] This core module leverages a fine-tuned Large Language Model to analyze contextual embeddings and identify deviations from expected patterns in real time. Unlike traditional statistical thresholds, the LLM detects anomalies based on semantic reasoning, historical behavior modeling, and cross-functional relationship analysis. It is able to distinguish between harmless fluctuations and critical anomalies by incorporating industry-specific rules, natural language instructions, and adaptive learning mechanisms that evolve with changing business dynamics. Real-time stream processing and low-latency pipeline

[0026] To enable immediate insights, this module includes a high-throughput, low-latency data processing pipeline that leverages stream processing frameworks and in-memory computations. It ensures minimal delays between data arrival, LLM inference, and anomaly output generation. The pipeline also supports parallel processing to process large, high-frequency data streams without bottlenecks, making the system suitable for mission-critical BI operations. Module for root cause analysis and interpretability

[0027] Once anomalies are detected, this module performs in-depth analysis to determine probable causes. It leverages the natural language capabilities of the LLM to generate human-readable explanations that link anomalies to specific data trends, events, or external factors. The interpretability layer provides transparency by providing confidence scores, causal factors, and contextual explanations, allowing BI users to understand the "why" behind the detected anomalies. Warning and visualization module

[0028] This component integrates directly with embedded BI dashboards and provides real-time alerts, visual indicators, and drill-down analytics. It supports multi-channel notifications via email, messaging platforms, and API hooks for automated workflows. Visualization tools such as anomaly heatmaps, trend charts, and contextual storyboards help decision makers quickly understand the severity and impact of anomalies without leaving their BI environment. Module for continuous learning and feedback optimization

[0029] The system features a self-improvement mechanism that continuously refines its detection accuracy based on user feedback, updated historical data, and evolving business rules. Detected anomalies and their resolutions are fed back into the training loop, allowing the LLM to adapt to seasonal changes, shifts in data distribution, and emerging anomaly patterns. This ensures that the framework remains robust and relevant over time, aligned with business objectives.

[0030] The invention is explained again below with reference to the figures. Herein: Fig. a real-time anomaly detection framework for embedded BI systems (100) using LLMs.

[0031] Fig.presents a framework for real-time anomaly detection for embedded BI systems (100) using LLMs. The system's operation begins with the data collection and preprocessing module, which continuously collects structured and unstructured real-time data from enterprise applications, IoT sensors, APIs, and transactional databases, and cleans, normalizes, and encrypts it to ensure accuracy and security. The processed data is then passed to the contextual embedding and feature extraction module, where it is transformed into domain-aware vector embeddings that capture semantic, temporal, and correlation patterns between variables.These enriched representations are fed into the LLM-based anomaly detection engine, which uses fine-tuned large-scale language models to identify deviations from expected behavior through semantic reasoning, historical trend analysis, and adaptive learning. Real-time stream processing and the low-latency pipeline minimize delays between data arrival, inference, and output generation by leveraging in-memory computations and parallel processing techniques. Upon anomaly detection, the root cause analysis and interpretability module examines influencing factors and generates natural language explanations, confidence scores, and causal narratives to ensure transparency in decision-making.The alerting and visualization module then delivers instant alerts via BI dashboards, messaging platforms, and visual tools such as anomaly heatmaps and trend charts to enable immediate understanding and action. Finally, the continuous learning and feedback optimization module integrates user feedback, updated historical data, and business rule changes into the training process, allowing the system to continuously adapt in real time to evolving patterns, seasonal fluctuations, and emerging risks.

Claims

[1] A real-time anomaly detection framework for embedded BI systems (100) using LLMs, consisting of: a) a data collection and preprocessing module configured to collect, clean, normalize and encrypt structured and unstructured data from multiple enterprise data sources in real time; b) a context-based embedding and feature extraction module configured to transform processed data into semantically rich, domain-aware embeddings that capture temporal, correlational, and contextual features; c) an LLM-based anomaly detection engine configured to analyze the aforementioned embeddings using finely tuned large language models to identify anomalies based on semantic inference, historical trends, and adaptive learning; d) a real-time stream processing and low-latency pipeline configured to perform parallel, high-throughput in-memory computations to deliver anomaly detection results with minimal latency; e) a root cause analysis and interpretability module configured to identify likely causes for detected anomalies and generate human-readable explanations, confidence scores and causal narratives; f) an alert and visualization module configured to provide real-time notifications via embedded BI dashboards, messaging platforms, and graphical representations such as heatmaps and trend charts; and g) a continuous learning and feedback optimization module configured to refine the accuracy of anomaly detection by incorporating user feedback, updated historical data and evolving business rules. [2] The real-time anomaly detection framework for embedded BI systems (100) using LLMs according to claim 1, wherein the data acquisition module and preprocessing module supports heterogeneous data formats, including numerical metrics, log data, text reports and sensor streams. [3] The real-time anomaly detection framework for embedded BI systems (100) using LLMs according to claim 1, wherein the contextual embedding and feature extraction module uses domain-specific embedding models to capture seasonal fluctuations and correlation patterns between multiple data sources. [4] The real-time anomaly detection framework for embedded BI systems (100) using LLMs according to claim 1, wherein the LLM-based anomaly detection engine is fine-tuned with enterprise-specific datasets to improve detection accuracy and reduce false alarms. [5] The real-time anomaly detection framework for embedded BI systems (100) using LLMs according to claim 1, wherein the real-time stream processing and low-latency pipeline utilizes distributed computing power and memory-optimized storage to process high-frequency, large-scale data streams. [6] The real-time anomaly detection framework for embedded BI systems (100) using LLMs according to claim 1, wherein the root cause analysis and interpretability module generates anomaly explanations in natural language so that end users can understand the underlying cause of anomalies without technical expertise. [7] The real-time anomaly detection framework for embedded BI systems (100) using LLMs according to claim 1, wherein the alert and visualization module supports configurable severity levels for alerts and is integrated into tools for automating business processes. [8] The real-time anomaly detection framework for embedded BI systems (100) using LLMs according to claim 1, wherein the continuous learning and feedback optimization module incorporates incremental learning techniques to adapt to newly emerging anomaly patterns without having to completely retrain the model.

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