AI-powered system for rating and classifying disputes

Abstract

An AI-powered dispute triage and classification system (100) that includes: (a) a dispute input module configured to receive dispute inputs from multiple communication channels, including text, voice, email and chatbot interfaces; b) a Natural Language Understanding (NLU) module that uses machine learning models to extract intent, sentiment and contextual metadata from the dispute content; (c) a classification and prioritization module configured to categorize disputes into predefined taxonomies and assign priority levels based on predefined rules and AI-based predictions; (d) an intelligent routing module that routes disputes to specific human agents, automated systems or escalation paths based on classification results and organizational policies; (e) a solution assistance module configured to suggest or autonomously execute solution actions using AI-generated answers or predefined templates; and (f) a continuous learning module configured to retrain AI models using feedback data from dispute outcomes, user interactions and resolution performance, enabling automatic triage and classification of disputes in real time to optimise resolution time, accuracy and resource utilisation.

Description

[0001] The present invention relates to the use of artificial intelligence to automate the triage, classification and escalation of disputes in customer service, legal or financial areas.

[0002] In traditional dispute resolution systems—especially in areas like customer service, finance, and legal—handling and categorizing disputes is a manual, time-consuming process prone to errors and delays. Disputes are often submitted through multiple channels, such as emails, chat logs, or complaint forms, requiring human agents to read, interpret, and classify each issue before escalating it to the appropriate department. This not only slows the resolution process but also leads to inconsistent handling of similar disputes, resulting in customer dissatisfaction, non-compliance, and operational inefficiencies.

[0003] The volume and complexity of disputes have increased significantly with digital transformation and the proliferation of online services. The larger a company grows, the greater the challenge of managing the various types of disputes—from billing errors and service outages to legal claims and fraud alerts. Many companies lack a structured, intelligent mechanism for prioritizing urgent or high-risk cases. As a result, critical disputes can be delayed or misdirected, while routine cases consume valuable expert resources, leading to poor allocation and increased backlogs.

[0004] To address these challenges, a system is needed that automatically ingests disputes from diverse sources, intelligently interprets their context and severity, and classifies them into meaningful categories for efficient triage. By incorporating AI techniques such as natural language processing (NLP), machine learning, and rule-based decision engines, the proposed invention enables faster, more accurate, and scalable dispute resolution. This not only reduces the burden on human agents but also improves resolution time, transparency, and overall stakeholder satisfaction.

[0005] One objective of this disclosure is to automate the classification and review of disputes and reduce manual workload.

[0006] Another objective of this disclosure is to accelerate resolution time through intelligent prioritization and routing.

[0007] Another objective of this disclosure is to improve the accuracy and consistency in dispute resolution.

[0008] Another objective of this disclosure is to increase customer satisfaction through faster and more relevant responses.

[0009] Another objective of the present disclosure is to enable scalable processing of high volumes of disputes across different channels.

[0010] Another objective of the present disclosure is to support agents with AI-driven recommendations and response templates.

[0011] Another objective of this disclosure is to minimize SLA violations through proactive escalation management.

[0012] Another objective of this disclosure is to continuously learn and adapt to evolving conflict patterns and workflows.

[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 system that accepts disputes from various input sources such as emails, web forms, chatbots, and voice transcripts. It unifies these inputs into a unified structure for further processing. This enables centralized handling of disputes from different communication platforms.

[0015] In another embodiment of the present invention, the system leverages natural language processing and deep learning to accurately interpret the context, tone, and intent of each dispute. It identifies core issues, sub-issues, and sentiment. This improves the understanding of customer complaints and enables intelligent categorization.

[0016] Another embodiment of the present invention involves classifying disputes into specific categories using trained machine learning models and company-specific taxonomies. Furthermore, priority levels are assigned based on context and risk. This helps companies manage disputes with consistent and scalable logic.

[0017] Another embodiment of the present invention is that each dispute is assessed for severity, urgency, and customer impact using rule-based and predictive analytics. High-priority disputes are flagged for immediate processing. This allows time-critical cases to be quickly routed through the resolution pipeline.

[0018] Another embodiment of the present invention is that the system dynamically routes disputes to the appropriate team, department, or automation module based on classification, priority, and resource availability. Escalation rules are embedded to ensure SLAs are met, reducing manual intervention and ensuring accountability.

[0019] Another embodiment of the present invention is that the system proposes or executes automatic solutions based on templates or decision trees for recurring and less complex disputes. In complex cases, it supports human employees with recommended actions and responses. This increases the speed of resolution and employee productivity.

[0020] Another embodiment of the present invention is that an integrated dashboard provides an overview of dispute volume, categories, resolution times, and performance trends. Managers can track KPIs and bottlenecks in real time. This promotes data-driven decision-making and operational transparency.

[0021] Another embodiment of the present invention is that the system retrains its models through a feedback loop of dispute outcomes and user responses to improve them over time. It adapts to new dispute types, customer behavior, and regulatory requirements. This ensures long-term relevance, accuracy, and ROI.

[0022] The present invention relates to the AI-powered dispute triage and classification system (100) designed to automate the ingestion, understanding, categorization, and escalation of disputes across various industries. It includes modules for dispute ingestion, natural language understanding, classification and prioritization, intelligent escalation, automatic resolution, and continuous learning. These modules work together to process disputes from various channels, interpret their context and urgency, and assign them to the appropriate resolution paths. The system reduces manual intervention, improves response times, and ensures consistent case handling. Thanks to its self-learning capabilities, it can adapt over time, thus increasing its accuracy and operational efficiency. Module for recording and pre-processing disputes:

[0023] This module serves as an entry point for capturing dispute data from various channels such as emails, web forms, chatbots, customer service portals, voice-to-text transcripts, and third-party APIs. It standardizes input by converting unstructured and semi-structured text into a normalized format suitable for analysis. Key processes include speech recognition, text cleaning (removing stop words, special characters, etc.), entity extraction (e.g., customer ID, invoice number), and metadata tagging (timestamp, source, channel). This ensures that all disputes are consistently structured and ready for downstream AI analysis. Natural Language Understanding (NLU) and Intent Recognition Module:

[0024] Using advanced NLP techniques and transformer-based models (e.g., BERT, RoBERTa), this module interprets the semantic meaning of each dispute. It identifies the primary issue (intention), secondary concerns (sub-intentions), tone or sentiment (e.g., angry, urgent, neutral), and contextual cues. The intent detection model is trained on labeled datasets to detect various types of disputes, such as billing errors, service quality issues, policy complaints, or fraud reports. This module transforms raw text descriptions into machine-readable labels and confidence scores, which are critical for classification and triage. Module for classifying and prioritizing disputes:

[0025] Once the intent and context are identified, the classification engine categorizes the dispute into predefined classes or taxonomies based on business rules and AI predictions. For example, a complaint about an "excessive internet bill" might be classified under "Billing > Excessive Charges > Internet Services." At the same time, the prioritization engine assesses urgency based on sentiment, complaint type, customer value (e.g., premium customer), and SLA parameters. It assigns a priority value (e.g., P1-P4) that indicates how quickly and by whom the dispute should be handled. Automated forwarding and escalation module:

[0026] Based on the classification and prioritization results, this module dynamically routes the dispute to the most appropriate resolution channel—be it a human agent, legal, a specialized bot, or an escalation team. It integrates with CRM systems, help desk software, and workflow management tools to ensure seamless routing. Routing logic can be customized based on corporate policies, agent knowledge, load balancing, and jurisdiction rules. Escalation triggers are also integrated to ensure that unresolved or high-risk disputes are automatically escalated within a specified timeframe. Self-solution and reaction recommendation module:

[0027] For disputes that fall into common, repeatable categories, this module suggests or executes automatic resolution actions. It uses a pre-trained response generation model or a template library to suggest empathetic, concise responses to the customer. It can also trigger automated workflows, such as issuing a refund, resending a document, or resetting account credentials. The module continuously learns from past resolutions and feedback to improve the accuracy of its suggestions. In semi-automatic mode, it supports human agents with recommended actions and draft responses. Feedback loop and continuous learning module:

[0028] To ensure the system's adaptability and accuracy over time, this module captures results, agent interventions, and customer feedback. It uses this data to retrain classification, intent recognition, and routing models, improving their precision and relevance. This module also supports A / B testing, performance tracking (e.g., resolution time, accuracy, customer satisfaction), and rule refinement.

[0029] The invention is explained again below with reference to the figure. It shows: Fig. : an AI-based dispute resolution and classification system (100).

[0030] Fig.illustrates an AI-powered dispute resolution and classification system (100). The AI-powered dispute resolution and classification system operates as a fully integrated, intelligent end-to-end pipeline that first ingests disputes from various communication channels such as emails, chat systems, web forms, and voice interactions. Once the data is ingested, it undergoes preprocessing to normalize and clean the text, followed by Natural Language Understanding (NLU) to interpret the intent, context, and emotional tone of the message. The system then classifies the dispute into hierarchical categories and assigns a priority score based on factors such as sentiment, SLA impact, and customer value.This information is fed into the automated routing engine, which forwards the dispute to the appropriate department, agent, or automated resolution path while monitoring for escalation triggers.

[0031] For standard and recurring cases, the system can suggest resolution actions or perform them autonomously using a library of templates and AI-generated responses. While processing disputes, the system continuously collects performance data, user feedback, and outcome metrics and feeds this information into its learning models to improve classification accuracy, routing efficiency, and resolution quality over time. The entire process is orchestrated in real time and integrates seamlessly with existing enterprise systems such as CRM, case management tools, and knowledge bases, resulting in faster resolution, improved consistency, and higher customer satisfaction.

Claims

[1] An AI-based dispute triage and classification system (100) comprising: (a) a dispute input module configured to receive dispute inputs from multiple communication channels, including text, voice, email and chatbot interfaces; b) a Natural Language Understanding (NLU) module that uses machine learning models to extract intent, sentiment and contextual metadata from the dispute content; (c) a classification and prioritization module configured to categorize disputes into predefined taxonomies and assign priority levels based on predefined rules and AI-based predictions; (d) an intelligent routing module that routes disputes to specific human agents, automated systems or escalation paths based on classification results and organizational policies; (e) a solution assistance module configured to suggest or autonomously execute solution actions using AI-generated answers or predefined templates; and (f) a continuous learning module configured to retrain AI models using feedback data from dispute outcomes, user interactions and resolution performance, enabling automatic triage and classification of disputes in real time to optimise resolution time, accuracy and resource utilisation. [2] The system (100) of claim 1, wherein the dispute input module includes natural language preprocessing components for text normalization, entity recognition, and source metadata tagging. [3] The system (100) of claim 1, wherein the NLU module uses transformer-based language models to identify primary and secondary intents from the dispute text. [4] The system (100) of claim 1, wherein the prioritization engine calculates urgency ratings based on sentiment polarity, customer level, dispute topic, and SLA compliance thresholds. [5] The system (100) of claim 1, wherein the routing module is integrated with the company's customer relationship management (CRM) and ticketing systems to enable seamless dispute routing. [6] The system (100) of claim 1, wherein the solution aid module can operate in an autonomous or semi-autonomous mode based on the confidence level of the classification result. [7] The system (100) of claim 1, wherein the continuous learning module incorporates agent corrections and customer feedback to regularly update classification and routing models. [8] The system (100) of claim 1, wherein real-time dashboards are provided to display dispute analyses, system performance metrics, and model decision rationale to system administrators.

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