Intelligent IoT and artificial intelligence-based systems for interactive processes and experiences in stores

Abstract

An intelligent system for interactive processes and experiences in the store, consisting of: a network of IoT sensors, including RFID readers, computer vision cameras, LiDAR sensors and environmental sensors, configured to capture real-time data on customer movements, product interactions and store conditions; an AI-powered analytics engine configured to process real-time and historical sensor data using machine learning models, customer behavior analysis, and forecasting techniques to generate dynamic insights into customer preferences, purchasing patterns, and inventory optimization; a real-time advertising module that is operationally connected to the AI-supported analysis engine, whereby the module dynamically generates and delivers personalized advertising content via digital signage, mobile applications, interactive kiosks or AI-supported smart carts based on individual customer profiles, customer traffic analyses and product demand trends; a subsystem for inventory management that includes RFID-based tracking, computerized shelf monitoring and predictive analytics, and is configured to automate inventory level detection, trigger replenishment alerts and optimize product placement in the store; an AI-driven workforce management module configured to dynamically allocate store staff based on real-time customer density, service demand, and predicted workload distribution, with staff assignments adaptively updated using AI-driven planning techniques; an interactive checkout subsystem that includes RFID-based automatic product identification, mobile payment integration, and AI-powered fraud detection; a cloud-integrated data processing server that communicates with the IoT sensor network and the AI-powered analytics engine, with the server performing distributed computing tasks to refine machine learning models, store historical data, and synchronize operational parameters across multiple retail locations; and a user interface system consisting of mobile applications, AR-enabled shopping assistants and voice-interactive kiosks, configured to provide customers with guided navigation, product recommendations and virtual try-on features based on real-time store interactions and AI-driven engagement models.

Description

Field of the InventionThe present invention relates to an interactive system designed to optimize business and improve customers' shopping experience through the use of Internet of Things (IoT), Artificial Intelligence (AI) sensors and real-time data processing. More specifically, the invention provides a smart retail infrastructure that integrates AI-controlled analyses into the environment of physical stores to offer personalized advertising actions, optimize inventory management, and improve overall operating efficiency. More particularly, the present invention relates to a smart IoT and artificial intelligence based system for providing interactive business and experience.BACKGROUND OF THE INVENTIONConventional stationary retail stores are challenging to personalize the customer experience while maintaining operating efficiency. Traditional marketing approaches are based on static advertisements and generic advertising actions that do not take into account any customer preferences, shopping patterns, or real-time activities in the store. Moreover, manual inventory management and inefficient personnel assignment often result in mis inventory, inventory, and suboptimal resource usage.Existing solutions such as RFID (Radio Frequency Identification) systems and POS-based tracking provide only limited real-time insights into customer behavior and store operation. While e-commerce platforms use KI and data analysis to personalize the shopping experience, similar technologies for interactive engagement and efficiency optimization are not yet fully integrated in stationary retail.To address these constraints, the present invention proposes an advanced interactive shop system that uses IoT sensors to monitor conditions in the shop, track customer movements, and analyze behavioral data using AI techniques. By integrating dynamic digital marketing, mobile app interactions, and predictive analyses, the system provides personalized advertising actions in real time and optimizes shop operation based on data-driven insights.Retail has experienced significant changes over the years that have been advanced through advances in digital technology, automation, and artificial intelligence areas. Traditional stationary stores, the primary shopping destinations, are experiencing increasing contention by e-commerce platforms that offer personalized recommendations, seamless navigation, and data-driven shopping experiences. To counteract this competition, retailers have incorporated various technological solutions into their store procedures to increase customer binding, improve inventory management, and optimize overall efficiency of stores. Despite these advances, existing solutions still have numerous limitations that prevent them from fully optimizing the charging experience and remedying operational inefficiencies.One of the earliest and most widely used retail technologies is the use of radio frequency identification (RFID) for inventory tracking. RFID tags allow retailers to automate inventory monitoring, thereby reducing dependency on manual inventory checks and improving inventory accuracy. Unlike barcode systems that require visual scanning, RFID technology enables simultaneous tracking of multiple products within a particular area, thereby making inventory checks faster and more efficient. Although RFID systems provide inventory management advantages, they have limitations when dealing with real-time customer binding and shop analysis. RFID focuses primarily on product tracking, but does not provide insight into customer behavior, preferences, or patterns of movement within the business. Moreover, the cost of implementing RFID tags for all products may be cost prohibitive for many retailers, particularly those handling low-margin goods.Another widely used solution in retail commerce is beacon technology. Bluetooth Low Energy (BLE) devices are used to send targeted advertisements and notifications to the customers' smartphones as they enter certain areas of a store. Large retailers use beacons to improve personalized marketing policies and increase customer binding. When a customer installing the retailer's mobile app comes near a beacon, he receives customized advertising actions, product recommendations, or location-based assistance. Although beacon technology provides some level of personalization, its effectiveness is limited by the customer's involvement. Many buyers may not have installed or activate Bluetooth or location services the retailer's app, which significantly limits the range and effectiveness of beacon-based marketing strategies. Moreover, beacon technology does not provide detailed analysis of customer interactions with physical products, nor optimizes store operation beyond marketing actions.Computer vision-based systems have also been incorporated into retail environments to analyze customer traffic, determine demographic data of the customers, and improve security. These systems use AI-controlled cameras to monitor customer movements, identify frequently visited areas of the store, and track interactions with products. Retailers use this data to optimize shop layouts, improve product placements, and adapt advertising strategies. Despite the advantages of computer vision, technology related to data protection concerns and implementation costs presents challenges. Customers may feel uncomfortable when monitored by AI-controlled cameras, leading to potential regulatory and ethical concerns. Moreover, processing large amounts of video data in real time requires considerable computing power, making them a costly investment for many retailers.The present invention is intended to enclose this gap by incorporating an advanced interactive shop system that incorporates IoT sensors and AI-controlled decision making to improve both customer experience and operating efficiency. By overcoming the limitations of existing technologies and providing a scalable, low cost solution, this invention is intended to revolutionize the traditional retail landscape and provide a seamless, smart, and highly efficient shopping environment.SUMMARY OF THE INVENTIONThe present invention provides a retail interactive system that includes a network of IoT sensors, AI-controlled analysis modules, and customer interactive interfaces to improve the shopping experience and increase operating efficiency. The system consists of strategicly placed smart sensors, including RFID, computer vision cameras, and LiDAR, to capture real-time data on customer traffic, product interactions, and business environmental conditions.An AI-based recommendation engine processes the collected data to generate personalized advertising actions that are displayed on digital advertising panels at the store or sent directly to a buyer's mobile device via a particular application. The system dynamically adjusts advertising actions to contextual factors such as product availability, shopping trends, and individual customer preferences.To increase operating efficiency, the invention utilizes AI-based predictive analyses to optimize store layout, manage inventory, and rationalize personnel assignment. A real-time dashboard offers family leaders implementable insights into customer binding, sales performance and inventory.Moreover, an interactive slide or smart shopping cart with AI control is incorporated into the system so that customers can receive customized product recommendations, access digital coupons, and navigate through the store using augmented reality (AR).BRIEF DESCRIPTION OF THE FIGUREThese and other features, aspects, and advantages of the present invention will become more fully understood when the following detailed description is read with reference to the accompanying drawings, in which like numerals represent like parts throughout. The following applies here: Figure 1 shows a block diagram of an in-store interactive operations and experience system that utilizes IoT sensors and AI for personalized advertising actions and operational efficiency.Moreover, those skilled in the art will appreciate that elements are shown in the drawing for simplicity and need not necessarily be drawn to scale. For example, the flowcharts illustrate the method using the major steps to improve understanding of aspects of the present disclosure. Moreover, with respect to the construction of the apparatus, one or more components of the apparatus may have been represented in the drawing by conventional symbols, and the drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art having the benefit of the description herein.DETAILED DESCRIPTION OF THE INVENTIONIn order to aid in the understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation of the scope of the invention is intended, since such changes and further modifications to the illustrated system and such further applications of the principles of the invention as illustrated therein are contemplated, as would normally occur to one skilled in the art to which the invention pertains.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean 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 phrase "in one embodiment," "in another embodiment," and similar language in this specification may or may not all refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps not only comprises those steps, but may also comprise other steps not expressly listed or inherent in such a process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" do not exclude, without further limitations, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The system, methods, and examples provided herein are illustrative only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.The present invention relates to a intelligent IoT and artificial intelligence based system for providing interactive business and experience.Referring now to FIG. 1, a block diagram of an intelligent interactive in store operations and experience system that utilizes IoT sensors and AI for personalized advertising actions and operational efficiency is shown. The system 100 includes: a network of IoT sensors (102) including RFID readers, computer vision cameras (102a), LiDAR sensors, and environmental sensors configured to acquire real-time data regarding customer movements, product interactions, and load conditions; an AI-based analysis engine (104) configured to process real-time and historical sensor data using machine learning models, customer behavior analyses, and prediction techniques to generate dynamic insights regarding customer preferences, shopping patterns, and inventory optimization; a real-time advertising module (106) operatively connected to the AI-based analysis engine, the module generating and providing dynamically personalized advertising content via digital marketing, mobile applications, interactive gravel, or AI-based smart cars based on individual customer profiles, pedestrian traffic analyses, and product demand trends; an inventory management subsystem (108) comprising RFID-based tracking, computerized shelf monitoring, and predictive analyses and configured to automate inventory detection, trigger inventory alerts, and optimize product placement in the store; A AI-controlled personnel management module (110) configured to dynamically assign store personnel based on real-time customer density, service demand, and predicted workload distribution, wherein personnel assignments are adaptively updated using AI-controlled scheduling techniques; an interactive point-of-sale subsystem (112) comprising RFID-based automatic product identification, mobile payment integration, and AI-controlled fraud detection, and configured to allow smooth point-of-sale processes and reduce transaction times by avoiding manual barcode scanning; a cloud-integrated computing server (114) communicatively coupled to the IoT sensor network and the AI-based analysis engine, the server executing distributed computer tasks to refine machine learning models, store historical data, and synchronize operating parameters across multiple retail locations; a user interface system (116) including mobile applications, AR-enabled shopping assistants, and voice interactive kissing and configured to provide customers with guided navigation, product recommendations, and virtual try-on functions based on real-time load interactions and AI-controlled engagement models.In one embodiment, the AI-based analysis engine (104) also includes an edge computing unit that locally processes sensor data, thereby reducing latency in decision making and enabling dynamic real-time adjustments to the load operation without requiring continuous cloud connectivity.In one embodiment, the IoT sensor network (102) also includes thermal and pressure sensors embedded in store racks configured to detect product interactions, measure inventory depletion rates, and transmit data to the AI-controlled inventory management subsystem to provide predictive inventory replenishment recommendations.In one embodiment, the real-time advertising module (106) also includes an adaptive pricing technique configured to dynamically adapt product prices to demand variations, competition pricing trends, and spoilage inventory condition, thus ensuring optimal revenue generation and inventory turnover.In one embodiment, the computer vision cameras (102a) utilize AI-based face recognition and demographic analysis models configured to classify customers by age, sex, and estimated mood, thereby enabling hyper-personalized advertising content and optimization of store layout.In one embodiment, the AI-controlled personnel management module (110) also includes a predictive task assignment engine configured to evaluate movement patterns of customers in the store, transaction loads in real time, and historical data on personnel efficiency to dynamically assign employees to the cash registers, fill tasks, or service areas.In one embodiment, the interactive point-of-sale subsystem (112) also includes a biometric authentication interface configured to enable secure transactions through face recognition or fingerprint scanning, thus reducing fraud risks associated with mobile payments and self-checkpointing processes.In one embodiment, the thermal and pressure sensors also include AI-controlled anomaly detection techniques configured to distinguish between genuine product purchases and rack manipulations, wherein anomalous patterns trigger safety warnings or automated loss prevention measures.In one embodiment, the AI-based analysis engine (104) also includes an adaptive reinforcement learning recommendation system configured to dynamically refine product recommendations based on real-time customer feedback, dwell time analyses, and tracking shopping behavior across multiple sessions.In one embodiment, the cloud-integrated computing server (114) also includes a federated learning architecture in which AI models are trained from remote store records at multiple retail locations without directly exchanging sensitive customer data. This ensures compliance with the data protection regulations.The present invention relates to an advanced interactive in store operation and experience system that integrates IoT sensors, AI-controlled analyses, personalized real-time advertising actions, inventory automation, and personal optimization to improve retail efficiency and customer binding. This system closes the gap between traditional physical retail and AI-based e-commerce experiences and allows decisions in real-time and automated in-store procedures. The system is designed to dynamically adapt to customer behaviour, optimize resource allocation and rationalize the checkpointing process. Deep learning techniques, reinforcement learning techniques and federated AI model training are used to improve continuously.The AI-based analysis engine processes sensory data in real time using deep learning models. These models utilize convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for object detection, customer behavior analysis, and prediction of future interactions. The analysis engine also integrates reinforcement learning based recommendation techniques that adaptively refine product suggestions based on historical shopping patterns, dwell times, and user preferences.In addition, an edge computing framework is employed to locally process high frequency data to reduce latency in generating real-time findings and advertising triggers. The federated learning mechanism allows training of AI models across multiple branch locations while ensuring data protection and compliance with consumer protection regulations.A key component of the system is the real-time advertising module, which uses AI-controlled prediction models to dynamically provide personalized advertising actions. The module uses a multi-artned bandit technique to optimize the selection of advertising content by continuously testing and adjusting various recommendations based on customer responses in real time. Personalized advertising actions are provided via digital markings, interactive gravel, mobile applications and intelligent shopping cars and thus ensure a context-dependent interaction. The gaze tracking and mood analysis interface evaluates customer responses to displayed advertisements, thus allowing automatic optimization of the advertisements sequences based on real-time effectiveness metrics.The inventory management subsystem employs a combination of RFID tracking, computer assisted inventory monitoring, and AI-controlled demand prediction. A You Only Look Once (YOLO) based object recognition model analyzes video feeds from shelf-mounted cameras and thus recognizes instances of inventory depletion or improperly placed products. The predictive analysis model, based on long short-term memory (LSTM) networks, predicts future needs from historical sales data, seasonal trends, and real-time purchase patterns of customers. The system automatically triggers refill alerts, optimizes shelf placement, and recommends dynamic pricing adjustments in response to inventory variations. Moreover, an autonomous robotic refill unit equipped with Simultaneous Localization and Mapping (SLAM) technology navigates through the store threads to perform automated inventory checks and refill actions.For personnel management, an AI-controlled task assignment engine dynamically assigns employees based on real-time customer traffic patterns, queue density at the cash register, and predicted service requirements. The system uses deep reinforcement learning techniques such as Proximal Policy Optimization (PPO) to continuously optimize personnel mission planning and task prioritization. A hierarchical reinforcement learning framework allows for adaptive workload distribution and ensures that employees are optimally positioned for business, cashing, or inventory replenishment assistance if needed. Moreover, a staff performance optimization technique that includes natural language processing (NLP) models evaluates service interactions and provides personalized training recommendations to improve ownership.The interactive check-out subsystem is designed for smooth payment handling and uses RFID-based automatic product identification, mobile payment integration and biometric authentication. A deep learning based fraud detection technique analyzes transaction patterns to identify unusual purchase behavior, potential theft attempts, and discrepancies in the checkpointing. The system supports contactless payments by blockchain-based transaction checking and thus ensures secure and tamper-proof financial transactions. Computer vision-based object detection additionally improves the accuracy of the checkpoint by automatically identifying products placed in the shopping cart, thus eliminating manual scanning.The user interface system includes a multi-modal conversation AI assistant that understands voice commands, gesture-based inputs, and text interactions, thus improving customer binding and business navigation. The assistant operates with transformer-based NLP models such as BERT (Bidirectional Encoder Representations from Transformers) and enables context-sensitive responses and interactive product consulting. The system also integrates augmented reality (AR) shopping assistants based on LiDAR-based spatial mapping to provide AR overlays of product locations, personalized shopping routes, and detailed product details in real-time as the customer views them through the smartphone or smart glasses.The invention also includes sustainability enhancing functions, including AI-controlled dynamic energy management in which IoT-enabled environmental sensors adjust lighting, heating and cooling settings based on customer traffic and load occupancy in real-time. The dynamic shelf lighting system intelligently adjusts light intensity to customer proximity and product demand trends, thus increasing energy efficiency while improving product visibility.The AI-based decision making framework continues to refine itself using a synthetic data generation module that simulates customer interactions in scenarios where real world data is only limitedly available. By generating synthetic training data for under-represented customer segments or new retail locations, the AI system provides robust decision making and adaptation to novel shopping environments.In addition, the geofencing-based personalized addressing mechanism allows retailers to interact with customers off-load as well by recommending them prior to visit, giving indications of business navigation, and giving them subsequent post-purchase offers via the retailer's mobile application. This location dependent engagement system increases brand fidelity and extends the impact of retail AI-controlled interactions beyond the store.Upon entering the store, customers are detected by IoT sensors that communicate with the AI-controlled system. Once the customer has installed a mobile app of the store, his preferences and previous purchase history are retrieved to personalize the shopping experience. As the customer navigates through the store, smart displays and gravel dynamically present relevant advertising actions and product recommendations based on real-time interactions and movement patterns.At the same time, the system continuously monitors the store operation and adjusts product placement and inventory to demand variations. When a heavily requested item runs out, the AI-based inventory system initiates an automatic refill alert. The work force management module provides optimal personnel allocation by predicting peak times and dynamically allocating tasks.At the cash register, the smart shopping cart or cookie enables a smooth transaction using RFID-based scanning and mobile payment integration. The system then updates the customer's purchase history and refines its profile for future visits.The present invention relates to the field of interactive retailing, intelligent financial management and AI-controlled customer binding systems. More particularly, the invention relates to a networked charging system that utilizes Internet of Things (IoT) sensors, artificial intelligence (AI), machine learning-controlled analytics, and real-time automation to optimize retail operations, improve customer experience, and increase operating efficiency. The invention includes autonomous inventory tracking, dynamic personnel assignment, personalized advertising actions, AI-controlled checkpointing processes, and data-controlled store optimization strategies. It integrates several advanced technologies such as computer vision, deep learning, edge computing, blockchain-based transaction security, and federated learning architectures to provide a real-time, adaptive retail ecosystem. This invention is particularly applicable to stationary retail stores, supermarkets, department stores, and multi-brand shopping environments where real-time operating intelligence and personalized customer experiences are critical to efficiency and portability.The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow chart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimension, and material usage, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages, and solutions to problems have been described above with respect to certain embodiments. However, the advantages, benefits, solutions to problems, and any components that may result in an advantage, solution occurring or becoming clearer are not to be construed as a critical, required, or essential feature or component of any or all claims.REFERENCES100 A smart, interactive in store operations & experience system, The lot sensors And KI Take advantage of personalized advertising actions and operational efficiency. 102 Network of lot sensors 102 a Computer vision cameras 104 An AI-based analysis engine 106 Real-time advertising module 108 Inventory management subsystem 110 AI-controlled work force management module 112 Interactive check-out subsystem 114 Cloud-integrated data processing server 116 User interface system

Claims

An intelligent business interactive sequences and experiences system comprising: a network of IoT sensors including RFID readers, computer vision cameras, LiDAR sensors, and environmental sensors configured to acquire real-time data regarding customer movements, product interactions, and store conditions; an AI-based analysis engine configured to process real-time and historical sensor data using machine learning models, customer behavior analyses, and prediction techniques to generate dynamic findings regarding customer preferences, shopping patterns, and inventory optimization; a real-time advertising module operatively connected to the AI-based analysis engine, the module generating and providing dynamically personalized advertising content via digital signage, mobile applications, interactive kissy, or AI-based smart cars based on individual customer profiles, customer traffic analyses, and product demand trends; a inventory management subsystem comprising RFID-based tracking, computerized shelf monitoring, and predictive analytics and configured to automate inventory level detection, trigger inventory alerts, and optimize product placement at the store; an AI-controlled work force management module configured to dynamically assign shop personnel based on real-time customer density, service demand, and predicted workload distribution, wherein the personnel assignments are adaptively updated using AI-controlled scheduling techniques; an interactive check-out subsystem including RFID-based automatic product identification, mobile payment integration, and AI-controlled fraud detection; a computing server integrated into the cloud communicatively coupled to the IoT sensor network and the AI-based analysis engine, the server executing distributed computing tasks to refine machine learning models, store historical data, and synchronize operating parameters across multiple retail locations; and a user interface system comprised of mobile applications, AR-enabled shopping assistants, and voice interactive kissies and configured to provide customers with guided navigation, product recommendations, and virtual try-on functions based on real-time load interactions and AI-controlled engagement models.The system of claim 1, wherein the AI-based analysis engine further comprises an edge computing unit that locally processes sensor data, thereby reducing latency in decision making and enabling dynamic real-time adjustments to the load operation without requiring continuous cloud connectivity.The system of claim 1, wherein the IoT sensor network further comprises thermal and pressure sensors embedded in store racks configured to detect product interactions, measure inventory depletion rates, and transmit data to the AI-controlled inventory management subsystem to provide predictive inventory replenishment recommendations.The system of claim 1, wherein the computer vision cameras use AI-based face recognition and demographic analysis models configured to classify customers by age, sex, and estimated mood, thereby enabling hyper-personalized advertising content and optimization of store layout.The system of claim 1, wherein the AI-controlled personnel management module further comprises a predictive task assignment engine configured to evaluate movement patterns of the in-store customers, transaction loads in real time, and historical data for personnel efficiency to dynamically assign employees to the cash registers, fill tasks, or service areas.The system of claim 1, wherein the interactive point-of-sale subsystem further comprises a biometric authentication interface configured to enable secure transactions using face recognition or fingerprint scanning, thereby reducing fraud risks associated with mobile payments and self-checkpointing processes.The system of claim 3, wherein the thermal and pressure sensors further include AI-controlled anomaly detection techniques configured to distinguish between genuine product purchases and manipulations on shelves, wherein anomalous patterns trigger safety warnings or automated loss prevention measures.The system of claim 1, wherein the AI-based analysis engine further comprises an adaptive reinforcement learning-based recommendation system configured to dynamically refine product recommendations based on real-time customer feedback, dwell analyses, and tracking shopping behavior across multiple sessions.The system of claim 1, wherein the cloud-integrated computing server further comprises a federated learning architecture in which AI models are trained from decentralized data sets in stores at multiple retail locations without directly exchanging confidential customer data.

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