System for autonomous reconciliation of product hierarchies in distributed retail systems

Abstract

System (100) for the autonomous reconciliation of product hierarchies in distributed retail systems, comprising: (a) a data entry module configured to collect product hierarchy data from a variety of heterogeneous retail data sources; b) a hierarchy extraction and structuring module configured to extract, normalise and standardise hierarchical relationships and category attributes from the input data; c) an AI-based hierarchy matching module configured to detect similarities, inconsistencies, and conflicts between the hierarchical structures using machine learning and semantic analysis; d) an autonomous reconciliation engine configured to resolve the identified conflicts and map disparate hierarchies into a unified structure using adaptive rules and learned mappings; (e) a feedback and governance module configured to log decisions, enable audit trails, and provide exception handling with optional manual overrides; (f) a unified hierarchy repository configured to store the agreed product hierarchy and make it accessible through APIs and user interfaces; and (g) a monitoring and adaptation module configured to continuously track changes across source systems and improve reconciliation performance over time; h) the system operates without manual intervention to achieve scalable and consistent product hierarchy reconciliation across distributed retail platforms in real time.

Description

[0001] The present invention relates to the field of data management systems in retail, particularly to automated systems for harmonizing product hierarchies. It addresses the challenge of inconsistent or fragmented product categorizations in distributed retail networks. The invention utilizes intelligent algorithms to enable seamless and autonomous reconciliation of hierarchical product data across disparate systems.

[0002] In large retail environments, product data is often stored and managed across different systems, departments, and partner organizations, each maintaining its own version of the product hierarchy. This fragmentation leads to inconsistencies in categorization, naming conventions, and classification logic, resulting in data silos that hinder unified product analytics, inventory optimization, and seamless customer experiences. Manual efforts to reconcile these disparate hierarchies are time-consuming, error-prone, and unscalable.

[0003] The problem becomes even more complex in distributed retail ecosystems where companies operate across multiple locations, platforms (e.g., brick-and-mortar stores, e-commerce, marketplaces), and business partners. In such scenarios, maintaining product hierarchy consistency is critical for synchronized operations, real-time reporting, pricing strategies, and regulatory compliance. Traditional master data management systems are often static and struggle to adapt to dynamic changes or unstructured inputs from disparate sources, leading to operational inefficiencies.

[0004] To address these challenges, an autonomous, intelligent system is needed that can automatically identify, reconcile, and standardize product hierarchies in distributed systems without human intervention. Such a system should be able to learn from historical mappings, detect hierarchy conflicts, and continuously adapt to new data sources and product structures. The present invention provides a scalable, AI-driven solution that automates the reconciliation process and ensures the consistency, accuracy, and alignment of product data across the retail enterprise.

[0005] One goal of the present disclosure is to eliminate manual reconciliation effort by automating the reconciliation of the product hierarchy.

[0006] Another objective of the present disclosure is to ensure consistent and accurate product categorization across distributed systems.

[0007] Another objective of the present disclosure is to improve operational efficiency in managing and analyzing retail data.

[0008] Another objective of this disclosure is to adapt to new data sources and structural changes through continuous learning.

[0009] Another goal of this disclosure is to reduce data silos and improve cross-platform integration and visibility.

[0010] Another objective of this disclosure is to provide real-time hierarchy updates for responsive decision making.

[0011] Another objective of this disclosure is to support auditability and governance with detailed logs and override controls.

[0012] Another objective of this disclosure is to enable effortless scaling across multiple retail domains and geographic areas.

[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 an AI-driven system for autonomously reconciling product hierarchies across multiple distributed retail platforms, ensuring consistency and operational efficiency. It eliminates manual data reconciliation by intelligently analyzing different hierarchical structures and mapping them into a unified format.

[0015] Another embodiment of the present invention is that the system first reads data from various retail sources such as ERP, e-commerce, and POS systems via secure connectors and APIs. It processes both structured and unstructured inputs to ensure comprehensive cross-platform data coverage.

[0016] Another embodiment of the present invention is an extraction and structuring module that standardizes the incoming data, flattens inconsistent schemas, and interprets complex category relationships. This ensures a consistent representation before the matching process begins.

[0017] Another embodiment of the present invention involves using advanced AI models and semantic algorithms to detect inconsistencies, naming conflicts, redundant nodes, and structural inconsistencies. These models learn from historical data to improve the accuracy of detecting hierarchy similarities and conflicts.

[0018] Another embodiment of the present invention is the autonomous voting engine, which uses adaptive rules and machine-learned logic to transform disparate hierarchies into a standardized format. It continuously refines its results through real-time feedback and performance metrics.

[0019] Another embodiment of the present invention is a feedback and governance layer that provides transparency through logs and audit trails, supports compliance, and enables manual intervention when necessary. It enables category managers to validate decisions or override them in exceptional cases.

[0020] Another embodiment of the present invention is that the reconciled hierarchy is stored in a central repository and made accessible via APIs and dashboards for downstream use in analytics, inventory management, pricing, and merchandising.

[0021] Another embodiment of the present invention is a monitoring and adaptation module that tracks data changes and system performance and continuously improves the reconciliation process. This dynamic learning capability ensures long-term adaptability to evolving retail ecosystems.

[0022] The present invention relates to the "System for Autonomous Product Hierarchy Reconciliation Across Distributed Retail Systems," which is designed as a multi-module, AI-driven framework that automatically detects, reconciles, and harmonizes product hierarchies from multiple retail systems. It ensures data consistency across platforms, improves operational efficiency, and reduces manual reconciliation effort. The invention comprises the following key modules: Data entry and source integration module:

[0023] This module connects to a variety of distributed retail systems, including ERP platforms, e-commerce engines, POS databases, and partner catalogs. It supports APIs, ETL connectors, and batch uploads for retrieving hierarchical product data in structured or semi-structured formats. Furthermore, the solution enables real-time synchronization and version tracking of source hierarchies to ensure that updates and changes from different systems are continuously captured. Module for extracting and structuring product hierarchies:

[0024] This component extracts hierarchical metadata from the incoming product records, including category levels, parent-child relationships, attribute groupings, and classification labels. It standardizes and flattens inconsistent schemas into a normalized, intermediate representation. The module uses natural language processing and pattern recognition to interpret textual category definitions and unstructured labels, facilitating further comparison. AI-based module for comparing hierarchies and detecting conflicts:

[0025] This core module uses machine learning models and heuristic algorithms to detect equivalences, overlaps, and divergences between product hierarchies from different sources. It detects name mismatches, redundant levels, orphaned nodes, and structurally similar hierarchies using similarity scoring, clustering, and contextual learning. The module flags conflicts and automatically suggests reconciliation strategies. Autonomous comparison and mapping engine:

[0026] As soon as conflicts or inconsistencies are detected, this module autonomously reorganizes product hierarchies into a unified, canonical structure. It applies learned mapping rules, semantic matching techniques, and reinforcement learning models to resolve discrepancies without human intervention. The engine dynamically updates the matching logic based on feedback loops and evolving data patterns, ensuring long-term adaptability. Feedback, audit and governance module:

[0027] This module maintains a detailed log of all mapping decisions, reconciliation actions, and version changes for traceability and auditability. It supports human-in-the-loop validation for exceptions and provides override options for administrators. It also ensures compliance with corporate policies, data governance standards, and category management protocols through rule-based access and approval workflows. Unified hierarchy repository and access interface:

[0028] After reconciliation, the unified product hierarchy is stored in a central repository accessible via APIs, dashboards, and export interfaces. This module provides customizable views for various stakeholders (e.g., category managers, merchandisers, data analysts) and supports integration with downstream systems for analytics, reporting, pricing, and supply chain operations. Monitoring, analysis and adaptation module:

[0029] This module continuously monitors hierarchy changes in the source systems and tracks the performance of the reconciliation models. It provides KPIs, visual insights, and anomaly detection for hierarchy shifts. Based on the insights gained, the AI ​​models are fine-tuned and the reconciliation logic is adjusted to account for business growth, seasonal changes, or catalog expansions.

[0030] The invention is explained again below with reference to the figure. It shows: Fig. : a system (100) for autonomously reconciling product hierarchies across distributed retail systems.

[0031] Fig.illustrates a system (100) for autonomously matching product hierarchies across distributed retail systems. The system operates by first establishing secure integrations with various distributed retail platforms to ingest product hierarchy data from sources such as ERP systems, e-commerce catalogs, point-of-sale databases, and partner inventories. After ingestion, hierarchical structures are extracted and standardized using schema normalization and NLP-based interpretation to process unstructured labels. The AI-powered matching engine then analyzes these hierarchies to identify structural similarities, semantic overlaps, and mismatches through contextual learning and similarity scoring algorithms.Detected conflicts are independently resolved by the Reconciliation Engine, which reconciles diverging hierarchies into a unified structure using adaptive rules and machine learning models. All mapping actions and decisions are logged in the audit and governance layer, where exceptions can be manually reviewed and approved as needed. The harmonized product hierarchy is stored in a central repository and made accessible via APIs, dashboards, and data exports to downstream systems for reporting, inventory management, and merchandising operations. At the same time, the system continuously monitors changes at the source, evaluates reconciliation results, and adjusts its logic using real-time analytics and feedback to ensure consistency and accuracy across the entire system.

Claims

[1] System (100) for the autonomous reconciliation of product hierarchies in distributed retail systems, comprising: (a) a data entry module configured to collect product hierarchy data from a variety of heterogeneous retail data sources; b) a hierarchy extraction and structuring module configured to extract, normalise and standardise hierarchical relationships and category attributes from the input data; c) an AI-based hierarchy matching module configured to detect similarities, inconsistencies, and conflicts between the hierarchical structures using machine learning and semantic analysis; d) an autonomous reconciliation engine configured to resolve the identified conflicts and map disparate hierarchies into a unified structure using adaptive rules and learned mappings; (e) a feedback and governance module configured to log decisions, enable audit trails, and provide exception handling with optional manual overrides; (f) a unified hierarchy repository configured to store the agreed product hierarchy and make it accessible through APIs and user interfaces; and (g) a monitoring and adaptation module configured to continuously track changes across source systems and improve reconciliation performance over time; h) the system operates without manual intervention to achieve scalable and consistent product hierarchy reconciliation across distributed retail platforms in real time. [2] The system (100) of claim 1, wherein the data entry module supports integration through RESTful APIs, file uploads, ETL pipelines, and direct database connections. [3] The system (100) of claim 1, wherein the hierarchy extraction and structuring module uses natural language processing (NLP) techniques to interpret and normalize category labels and attribute metadata from unstructured product data. [4] The system (100) of claim 1, wherein the hierarchy matching module uses a combination of fuzzy string matching, vector-based similarity models, and hierarchical clustering to identify equivalent or related categories. [5] The system (100) of claim 1, wherein the autonomous voting module applies reinforcement learning algorithms to optimize allocation decisions based on historical voting results. [6] The system (100) of claim 1, wherein the feedback and control module includes a human review user interface that enables category managers to validate, override, or approve certain assignments. [7] The system (100) of claim 1, wherein the unified hierarchy repository supports multiple version storage, access control, and export to external enterprise systems in various formats including JSON, XML, and CSV. [8] The system (100) of claim 1, wherein the monitoring and adjustment module generates performance metrics and visual dashboards indicating matching accuracy, hierarchy variance, and system confidence levels. [9] The system (100) of claim 1, further comprising a scheduling mechanism to perform periodic reconciliation at predefined intervals or in response to detected changes in source hierarchies. [10] The system (100) of claim 1, wherein the system allows domain-specific configuration to accommodate industry-specific taxonomies such as food, clothing, electronics, or pharmaceuticals.

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