A system for using augmented reality to improve product placement in physical and virtual stores.

Abstract

An AI-powered augmented reality (AR) system for optimizing product placement and customer loyalty. The system includes: • a user interaction data collection unit configured to monitor consumer behavior in real time in any retail scenario (point-of-sale POS, online e-commerce and shopping areas within a public virtual technology space), with the unit analyzing consumer movement patterns, engagement metrics, eye-tracking and purchase intent; • an intelligent spatial mapping module to build a contextual understanding of high-traffic zones, underutilized areas and high-quality product placements using heatmap analysis, other spatial computer algorithms and the live movement of consumers; • A model-based product placement recommender based on machine learning, which uses one or more DL-based predictive models trained by importing current sales data, historical text interaction data, and consumer preference data to suggest optimal product placement so that customers see the advertisement and are likely to click through it to ensure a conversion. • a customer-centric, real-time AR visualization interface configured to allow customers to experience digitally enhanced product presentations directly via mobile AR applications, smart glasses, or in-store digital kiosks, enabling customers to preview product placements, rotate and resize objects, and visualize interactive advertising content before purchasing; • a customized advertising and sales promotion engine configured to generate augmented reality-based marketing content and provide users with tailored promotions and interactive ads based at least in part on an analysis of consumer sentiment by the system, as well as information on location data or consumer interaction history, wherein the customized advertising and sales promotion engine personalizes the configuration instructions for advertising and sales promotion to encourage consumer interaction and achieve higher sales revenues; • A secure, cloud-based data processing and analytics unit that uses an automated engagement model to link online and in-store sales information with established activity models for product placement decisions. The unit leverages AI-powered consumer insights to continuously recommend product placement changes based on a variety of programmatic retail trends; • a privacy-friendly AI module based on federated learning and on-device AI processing, providing consumer behavior analytics while complying with global data protection regulations and preventing the use of open data; • a processing framework characterized by a low-power mode suitable for AR rendering and AI calculations, and implemented via dynamic voltage scaling and energy management techniques for seamless and sustainable operation; The system according to claim 1, wherein the user interaction data capture unit further comprises an eye-tracking and sentiment analysis sub-module, wherein the sub-module detects facial expressions, emotional reactions, and attention levels of consumers in real time to determine the effectiveness of product placements and advertising strategies.

Description

Field of the invention

[0001] The present invention relates generally to marketing and retail technologies, but more particularly to a system for utilizing augmented reality to enhance product placement in physical and virtual stores. Customers can experience products interactively and immersively, improving engagement, decision-making, and conversion rates. Background of the invention

[0002] Digital technologies, artificial intelligence (AI), and augmented reality (AR) have fundamentally transformed retail. For years, traditional product placement techniques in brick-and-mortar stores leveraged static layouts, trends, and consumer psychology to influence purchasing decisions. They rely on manual adjustment, observational patterns, and historical sales trends, lacking real-time customization and the ability to create personalized customer journeys. Here, too, predefined algorithms, sponsored product placements, and static product listings dominated the world of e-commerce and virtual marketplaces. None of this offered the dynamic interactivity or immersive engagement that modern users increasingly expect.

[0003] Errors due to a lack of dynamic placement strategies, immature in-store merchandise, or uncalibrated product positions can occur in both brick-and-mortar and online retail, despite some existing product placement strategies. Traditional product placement methods in brick-and-mortar stores rely on static shelf configurations, seasonal patterns, and uniform merchandising plans, and therefore remain rigid. They cannot respond promptly to changing customer preferences or in-store customer loyalty metrics. Retailers develop general segmentation models to analyze their product placement, but rarely consider individual characteristics, shopping behavior such as browsing history, and situational factors at the time of purchase. Brick-and-mortar stores need support to identify the busiest locations, efficiently allocate shelf space, and flexibly adjust product placements.Retailers face the challenge of maximizing customer engagement and sales without real-time analytics and AR-powered visualization. Unlike online stores, which rely primarily on search algorithms, static banners, and manually curated product grids with minimal interactive features to allow customers to visualize and experience products before purchase, there is no seamless connection between physical retail spaces, commoditized online platforms, and AR-based shopping options, resulting in disparate product discovery experiences, divergent pricing tactics, and ineffective audience engagement.

[0004] With the increasing adoption of AR technology, AI-powered recommendation engines, and real-time behavioral analytics, there's an opportunity to reinvent product placement and, in the process, influence the way consumers interact with products. From AR-enabled shopping experiences that help users visualize the fit of products in their real-world environment via AR displays on mobile devices, AR glasses, or in-store digital kiosks, to merging the physical world and virtual elements into an interactive environment through AR beacons, supported by behavioral insights, we can provide consumers with the perfect recommendations as well as the placement of product locations within the physical and virtual store environment. This enables real-time interactive advertising, where advertising messages, product details, and sales promotions can be changed at any time based on user interaction.

[0005] Due to limited analytics, interactivity, and static merchandising, retailers and brands often struggle to identify optimal touchpoints across brick-and-mortar and digital marketplaces. For example, they fail to adapt in real time to the combined use of past consumer purchase data and historical footfall heatmaps in physical stores to cost-effectively create product placement and product visualization using AR. This allows consumers to seamlessly experience, customize, and engage with the product long before the transaction. Concise, dynamic in-store promotions effortlessly scan for movement patterns based on customer engagement levels, sentiment data, and in-store traffic traces to curate such events.

[0006] This invention relates to a product recognition system, in particular an AI-driven augmented reality system for placing products in physical or virtual stores to support the decision-making process and overcome some of the challenges of bringing products to market.This system combines augmented reality (AR) visualization, which allows consumers to virtually position, compare, and interact with items in their home, office, or shopping space; AI-powered product placement optimization using machine learning algorithms, computer vision, and spatial mapping to determine optimal product placements in stores and online shelves; real-time monitoring of consumer engagement using heat map analysis, behavior tracking, and sentiment analysis for on-the-fly adjustments to product placement; personalized shopping recommendations that provide context-aware suggestions based on consumer preferences, purchase history, and real-time engagement levels; and seamless integration that coordinates physical and virtual shopping experiences, ensuring consistent product placement across AR-enabled store shelves, online stores, and digital displays.

[0007] It offers AI-powered analytics and AR-based visualization, helping retailers and brands present products in a way that maximizes viewable space, achieves higher conversion rates, and improves customer engagement through real-time data processing. These benefits include higher conversion rates, as customers can view and interact with products in-store before purchasing. This minimizes uncertainty and builds shoppers' confidence in their purchasing decisions. Few are aware that AI enables optimized space utilization in retail. AI-powered spatial analytics allow retailers to optimally adjust product placement based on customer traffic and customer interaction data. The system also offers optimized, personalized advertising in real time.This leverages engagement and purchase history, as well as demographic information, to optimize the AR-based advertising content for each customer. Furthermore, it helps minimize return rates, as the improved product visualization helps customers better understand what they want to buy. This reduces returns due to false expectations.

[0008] Finally, it enables interactive shopping experiences, playful and immersive experiences, thus strengthening user engagement and brand loyalty.

[0009] The system consists of several key components: an AR-based visualization module for displaying products in virtual real-world store environments, allowing users to preview items in a range of placements, colors, and configurations; an AI-powered recommendation engine that uses deep learning and predictive analytics to optimize product positioning based on customer preferences and sales data; a real-time consumer behavior tracking module that monitors eye tracking, facial recognition, and interaction time to measure consumer interest and adjust product placements on the fly and effectively; and a context-aware AR advertising system for creating personalized AR ads / promotions based on consumer demographics, past purchases, and in-store activity.and integration with retail and e-commerce platforms: Synchronizes with existing retail systems, digital advertising networks, and e-commerce stores to provide a seamless shopping experience.;

[0010] Leverage augmented reality, artificial intelligence, and real-time consumer analytics to create a store that blurs the lines between physical and digital retail. This is achieved through optimal product placement, personalized promotions, and interactive shopping experiences. This solution guarantees strategic product positioning, AI-powered engagement tracking, and integrated shopping experiences with every purchase by combining AI-driven insights with AR-enabled visualization. This technology is fundamentally transforming retail, enabling brands, retailers, and advertisers alike to transform everyday product placement techniques into intelligent, reactive, and highly engaging customer experiences, as well as highly interactive ones. Summary of the invention

[0011] The present invention describes an AI-powered augmented reality system for improving product placement and customer interaction in virtual or real-world stores. It utilizes machine learning, computer vision, and behavioral analytics to ensure real-time optimization of product placement, tailored suggestions, and integrated shopping experiences. The system combines AR-powered visualization, AI-powered insights, and real-time customer feedback to enable effective retail interactions, increase brand visibility and sales, and ensure a seamless, interactive, and engaging AR shopping experience.

[0012] This includes the development of an AR visualization module that allows consumers to virtually place products in their shopping environment and interact with them in real time on mobile devices, AR glasses, or smart in-store displays. Shoppers can preview product placements, compare different variants, and make informed purchases. These insights are then fed into a proprietary recommendation engine that uses machine learning algorithms to generate real-time recommendations based on real-time behavioral data, historical consumer preferences, and spatial analysis. This displays the most relevant product placements. Brands can position their merchandise to achieve the highest customer engagement, increasing the likelihood of conversion.

[0013] It stands for innovative, contextual advertising and promotions. These use real-time analytics of consumer sentiment, location of delivery, and AI-powered content adaptation to promote dynamic ads and products that match user preferences and the current shopping context. This module also uses AR to create advertising experiences for consumers by providing real-time discounts, interactive ads, and digital product demonstrations, thus improving consumer purchase decisions.

[0014] Additionally, the system leverages real-time engagement tracking and AI-powered analytics to capture customer interactions such as eye tracking, dwell time, purchase behavior, and product engagement metrics. These insights enable the system to adjust product placement plans, determine shelf placement, and improve the overall user experience, regardless of whether the customer is shopping in a brick-and-mortar or digital store. Furthermore, the system optimizes store efficiency through spatial mapping and AI-powered retail heatmaps that identify popular areas, underutilized shelf space, and the best advertising spots.

[0015] To ensure optimal functionality and integration across multiple retail platforms, the system is deployable in both physical and virtual spaces, supporting brick-and-mortar stores, e-commerce websites, digital marketplaces, and metaverse shopping environments. Its cloud-based architecture, combined with AI-powered synchronization, makes it adaptable across platforms and delivers real-time updates for a consistent product experience.

[0016] The system is complemented by a privacy-preserving AI framework that leverages federated learning, edge computing, and anonymized data processing, and complies with GDPR, CCPA, and other global data protection regulations. This system focuses on consumer privacy while providing personalized, product-based recommendations and promotions—unlike traditional ad tracking techniques.

[0017] To ensure the system's scalability without wasting resources, an energy-efficient AI processing module executes various machine learning algorithms. These features enable retailers to improve their product placement strategies using AI without incurring the costs of new infrastructure, making them affordable for large retail chains, medium-sized companies, and online platforms alike.

[0018] This invention integrates AI-powered analytics, AR-enabled interactivity, and real-time personalization, creating a next-generation platform for dynamic product placement, immersive shopping, and data-driven retail optimization. It unites customer engagement and brand interaction, offering retailers the ability to maximize product placement, conversion, and hyper-personalization across all retail channels—physical and digital. Detailed description of the invention

[0019] The current idea presents a process of augmented reality (AR)-based artificial intelligence (AI) that controls product placement and customer engagement in the physical and digital worlds. Machine learning, computer vision, real-time behavioral analysis, and AR visualization leverage this technology to radically transform product positioning and marketing, as well as the consumer experience. Data-driven, interactive, and adaptive sales floor management, a holistic shopping experience for customers, and the integration of dynamic advertising models bridge the gap between physical, digital, and virtual stores.

[0020] Fig.shows a block diagram of an AI-driven augmented reality (AR) system for optimizing product placement. The system 100 leverages AI, ML, real-time behavioral analytics, and AR visualization to transform product positioning, merchandising, and experience in physical and digital retail.

[0021] In addition, the user interaction data collection unit (102) captures real-time data on customer behavior in various retail environments. It implements motion tracking, eye-tracking, and engagement monitoring to determine product interest, in-store movement, and purchase intent. This unit captures dwell time analysis, interaction frequency, and click-through rates, enabling retailers to dynamically optimize product presentation. Using heat map analysis and AI-assisted spatial mapping, the context-aware spatial mapping module (104) analyzes store data to identify high-traffic areas, under-consumption zones, and the most effective product placement points. It designs store layouts and shelf placements to be more attractive to customers and improve their conversion rates.

[0022] The deep learning algorithm for predictive analytics (106) leverages historical sales data, real-time engagement metrics, and customer preferences to optimize product placement strategies by analyzing past performance and engagement trends, thus improving the visibility of targeted products. Using reinforcement learning, the system dynamically adapts product placement to changing customer interaction patterns, stock availability, and promotional approaches, providing a continuously evolving mechanism that ensures that high-interest products remain visible and attractive to customers. Recommended shelf placement, digital, and ad placement are calculated in real time to leverage engagement, conversion rates, and site availability to ensure optimal product positioning.

[0023] With the real-time AR visualization interface (108), consumers can virtually view products in their physical environment, e.g., via mobile AR applications, smart glasses, or in-store digital kiosks. They can flexibly position, rotate, and compare them. They can experience product previews in an interactive experience, try out virtual demonstrations, and use 3D visualization features to make informed purchasing decisions. This feature closes the physical-digital gap and creates a real-time virtual shopping experience. This module uses AR-assisted object recognition and spatial mapping to allow consumers to experience products as they would appear at home or at work before making a purchase decision.

[0024] The personalized advertising and promotion system (110) generates tailored marketing content, particularly AR-based marketing content, based on individual consumer preferences. Using facial expression tracking, sentiment analysis, and biometric data analysis, the system offers tailored offers, instant discounts, and personalized product recommendations based on purchase history, demographic data, and geolocation. This AR-based advertising content is mostly adaptive, providing consumers with highly relevant and interactive advertising at the optimal time during their purchase. Using real-time consumer sentiment analysis, geolocation data, and AI to analyze marketing data, the system individually adapts ad content and delivery.

[0025] Module (112) Provides a highly scalable and agile architecture for data processing and analytics in the cloud. This also enables real-time synchronization of brick-and-mortar and online sales channels, ensuring customers have a similar product experience across different shopping channels. Leveraging AI-powered consumer insights, cloud-based machine learning models, and distributed data processing, this unit provides retailers with continuous opportunities to optimize their product positioning and marketing strategies. Centralized updates to AI machine learning models for product placement and ad targeting ensure continuous improvements and enable cross-platform data integration under one roof, maintaining retail parity.

[0026] Data collection occurs across sensors without compromising consumer privacy. Federated learning, edge computing, and anonymized data tracking are used by the privacy-preserving AI module (114) in conjunction with the data information integration module. This enables even smarter personalization. Unlike traditional centralized data collection methods, this system performs behavioral analytics processing locally on the user device, ensuring compliance with global data privacy regulations, including GDPR and CCPA. By leveraging differential privacy mechanisms to conceal individual identities while simultaneously extracting behavior, this module provides the perfect balance between AI-powered personalization without compromising the security of consumer data.

[0027] The energy-efficient processing framework (116) dynamically adjusts voltage and frequency to meet application requirements. It uses DVFS techniques to maximize the efficiency of AI computations and AR rendering while minimizing power consumption, ensuring the user experience remains unaffected. In high-performance retail environments, the system automatically adjusts its computational load to real-time demand, ensuring smooth, energy-efficient operation. This architecture meets the requirements of real-time AR visualization and AI-driven decision-making while remaining energy-efficient.

[0028] This revolutionary system combines AI-driven, real-time adaptation of AR-based interactive shopping experiences with dynamic marketing content, providing retailers with enhanced product visibility, stronger customer engagement, and optimized sales performance. Cross-platform synchronization between physical retail environments, online marketplaces, and virtual shopping experiences ensures a unified shopping experience, improving the entire customer journey, and providing companies with valuable insights gained through AI as an intermediary. By training the data, consumers consistently receive product recommendations and AR-optimized interactions, whether they're shopping in-store, via an online catalog, or in a metaverse-based virtual shopping experience.

[0029] This system is suitable for various retail formats, including brick-and-mortar stores looking to improve shelf utilization and product interaction, e-commerce websites with AI-powered, personalized product recommendations, AR shopping experiences with interactive in-store trials, and metaverse retail environments with fully digital, interactive shopping areas. It also enables integration between retail platforms, allowing them to align product placement on their websites with that in their brick-and-mortar stores.

[0030] This augmented reality-based shopping interface allows users to interact with 3D product displays and offers detailed product specifications, reviews, and even a guided shopping experience. A virtual showroom allows consumers to experience digital product twins, compare features of different models, and receive tailored, AI-powered recommendations. This further enhances the shopping experience, as retailers can create dynamic digital storefronts where consumers can immerse themselves in the products through AR overlays.

[0031] In addition to improved product placement, the system also increases customer engagement through interactive shopping experiences with gamified experiences, personalized loyalty programs, and AI-based shopping assistants. The system's recommendation engine, trained with consumer data, learns from every interaction, making each shopping experience increasingly personalized. Based on purchasing behavior, browsing history, and customer data, the AI ​​can recommend the most relevant products and help customers find products that match their interests.

[0032] Novel and intelligent product placement based on predictive analytics and augmented reality (AR) visualization. This invention is a state-of-the-art device that significantly improves traditional methods of product placement with AI by automating predictive analytics and AR-specific visualizations in retail to a far greater extent. While taking user experience and data privacy into account, this system enables retailers, brands, and advertisers to offer data-driven and immersive shopping experiences that lead to higher sales, better customer satisfaction, and more efficient marketing.It enables a kind of interactive product discovery, optimized store space utilization, a seamless integration of digital and physical shopping experiences, and a next-generation retail solution that is optimally tailored to consumer needs.

[0033] This AI-induced phenomenon brings many benefits, including higher conversion rates, as consumers can see and interact with the product before purchasing. This eliminates uncertainty and builds confidence in the purchase. Robust space optimization through AI-powered spatial analytics enables retailers to optimize product placement based on customer interaction data and in-store footfall patterns. The system also reduces return rates by allowing customers to visualize the product in augmented reality and make an informed purchase decision, avoiding false expectations. The system's integrated shopping channels provide a highly engaging and consistent customer experience.

[0034] This system leverages advanced technologies such as AI, machine learning, and AR visualization to give companies a competitive advantage, increase product engagement, and improve brand visibility and sales through intelligent product placement and adaptive marketing strategies. AI-powered decision-making allows retailers to modify real-time data to increase the efficiency of all processes and ensure products are optimally presented and sold. With such innovative systems, not only will the future of shopping, consumer behavior, and product placement strategies in retail be personalized and enhanced, but this invention also represents a revolutionary step toward retail technology that helps us with another aspect: data protection.

Claims

[1] An AI-driven augmented reality (AR) system for optimizing product placement and customer engagement. The system includes: • a user interaction data collection unit configured to monitor consumer behavior in real time in any retail scenario (point-of-sale POS, online e-commerce, and shopping areas within a public virtual technology space), analyzing movement patterns, engagement numbers, eye gaze detection, and consumer purchase intent; • an intelligent spatial mapping module to build a contextual understanding of high-traffic zones, under-utilized areas and high-value product placements using heatmap analysis, other spatial computer algorithms and live consumer movement; • a model-based machine learning product placement recommender that uses one or more DL-based predictive models trained by importing current sales data, historical text interaction data, and consumer preference data to suggest optimal product placement so that customers see the advertisement and are likely to click through the advertisement to ensure conversion, • a customer-facing, real-time AR visualization interface configured to enable customers to experience digitally enhanced product representations directly through mobile AR applications, smart glasses, or in-store digital kiosks, through which the customer can preview product placements, rotate and resize objects, and visualize interactive promotional content before purchasing; • a customized advertising and promotion engine configured to generate augmented reality-based marketing content and to provide the user with customized promotions and interactive ads based at least in part on an analysis of consumer sentiment by the system, as well as information about location data or consumer interaction history, wherein the customized advertising and promotion engine personalizes the advertising and promotion configuration instructions to encourage consumer interaction and achieve higher sales; • A secure, cloud-based data processing and analytics unit powered by an automated engagement model that connects online and in-store sales information with established activity models for product placement decisions. The unit leverages AI-powered consumer insights to continuously recommend product placement changes from a variety of programmatic retail trends. • a privacy-preserving AI module based on federated learning and on-device AI processing, providing consumer behavior analytics while adhering to global privacy regulations and preventing the use of open data; • a processing framework characterized by a low-power mode suitable for AR rendering and AI computations, implemented via dynamic voltage scaling and power management techniques for seamless and sustainable operation; The system of claim 1, wherein the user interaction data acquisition unit further comprises a gaze tracking and sentiment analysis sub-module, the sub-module detecting consumers' real-time facial expressions, emotional responses, and attention levels to determine the effectiveness of product placements and advertising strategies. [2] The system of claim 1, wherein the user interaction data collection unit further comprises a gaze tracking and sentiment analysis sub-module, the gaze tracking and sentiment analysis sub-module detecting consumer facial expressions, emotional reactions, and attention levels in real time to evaluate the effectiveness of product placements and advertising strategies. [3] The system of claim 1, wherein the context-aware spatial mapping module further includes AI-driven retail heatmap analysis, the analysis adaptively changing product placement in response to foot traffic intensity, interaction areas, and consumer dwell time. [4] The system of claim 1, wherein the product placement recommendation engine further comprises a reinforcement learning model that continuously learns in real time from consumer interaction and optimizes the product placement strategy based on conversion rates. [5] The system of claim 1, wherein the AR visualization interface is configured to provide a pair of AI-enabled virtual showrooms in which consumers can browse and interact with different variants of digital product twins, as well as visualize the product in AR-enhanced devices in different environments. [6] The system of claim 1, wherein the personalized advertising and promotion system further integrates real-time behavioral targeting, wherein the system changes the advertising content based on individual shopping patterns, past purchases, and engagement levels of the consumer. [7] The system of claim 1, wherein the secure cloud-based data processing and analysis unit further comprises a blockchain-based transaction verification system, the system securing consumer data and transaction records, thus ensuring transparency and fraud prevention in digital commerce environments. [8] The system of claim 1, wherein the privacy-preserving AI module further comprises an edge computing architecture, the architecture facilitating the execution of AI models on the device for personalized recommendations while reducing the dependence on centralized data storage. [9] The system of claim 1, wherein the energy-efficient processing framework further comprises a thermal management system that maintains optimal processing temperatures during high-performance AI computations and corresponding AR rendering, thus ensuring system reliability and long-term usability. [10] The system of claim 1, wherein the AR-based visualization interface further comprises an interactive gamification module where consumers can interact with AR-based loyalty rewards, augmented reality product testing, and in-app promotional events to achieve greater brand engagement and purchase motivation. [11] The system of claim 1, wherein the system is further adapted to enable cross-platform synchronization such that the synchronization synchronizes the virtual and augmented reality shopping experiences to leverage AI for product recommendations on both physical and digital marketplaces, including metaverse-integrated shopping experiences. [12] The system according to claim 1. Furthermore, the system is equipped with an AI-driven inventory management assistant. The assistant predicts changes in demand, maintains optimal inventory levels, and sends automatic reorder alerts based on real-time sales data and consumer purchasing behavior. [13] The system of claim 1, wherein the real-time AR visualization interface further provides support for multi-user collaboration, allowing multiple users to interact simultaneously within shared AR-based product previews and personalized recommendations. [14] The system according to claim 1 provides a dynamic price optimization model for adapting pricing strategies in real time using demand trends, competitive analysis and consumer purchasing power. [15] The system of claim 1, further comprising a machine learning-based fraud detection and prevention module, the module monitoring consumer interactions, detecting changes in shopping behavior, and preventing fraudulent activity in online and in-store transactions. [16] The system of claim 1, wherein the AR visualization interface further includes support for haptic feedback and multi-sensory interactions, whereby consumers can receive touch sensations and auditory feedback while interacting with AR-enhanced product demos on the stage. [17] The system of claim 1, further comprising a voice-activated AI shopping assistant that enables consumers to navigate AR-enabled shopping experiences, request product recommendations, and receive personalized promotions using real-time voice commands. [18] The system according to claim 1 further comprises an AI component for virtual social shopping, in which customers can exchange AR-based product suggestions, work together on virtual shopping scenarios and communicate with influencers or brand ambassadors in augmented reality environments. [19] The system of claim 1, further comprising an AI-powered market forecasting subsystem that uses global trends and customer behavior data to predict emerging retail trends so retailers can adapt their marketing strategies. [20] The system of claim 1, wherein the AR-based visualization interface further comprises direct checkout functionality that allows consumers to instantly purchase products within the AR interface without having to scroll to standalone checkout pages, thereby creating a seamless shopping experience.

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