SMART RETAIL SHOPPING CART SYSTEM WITH REAL-TIME ANALYTICS, DYNAMIC PRICING, AND PERSONALIZED RECOMMENDATIONS

Abstract

Smart Retail Shopping Cart System with Real-Time Analytics, Dynamic Pricing, and Personalized Recommendations This invention describes an Intelligent Retail Shopping Cart System integrates cutting-edge hardware and software to enhance consumer shopping experiences and deliver real-time business analytics. Equipped with an interactive touch screen, barcode scanner, and weighing system, the cart facilitates seamless product scanning, weight-based pricing for loose items, and dynamic real-time price adjustments. Powered by the Cross-Transaction High Utility Itemset Mining (CTC-HUIM) framework, the system provides personalized product recommendations and health-conscious or sustainability suggestions based on customer preferences and product data. The backend analytics system enables retailers to optimize stock levels, product placement, and promotional strategies by mining transaction data for actionable insights into high-profit itemsets and customer behavior trends. The secure digital transaction mechanism allows for seamless checkout via digital wallets and contactless payments, eliminating the need for traditional cashier lines. This holistic system improves both customer convenience and business efficiency, promoting data-driven decisions and enhancing profitability.

Specification

Description:[0001] This invention relates to the field of computer sciences more particularly an intelligent retail shopping systems that integrate hardware and software technologies to enhance consumer shopping experiences while providing real-time business analytics. Specifically, the invention relates to smart shopping carts equipped with barcode scanners, weighing systems, and interactive touch screens that dynamically interact with a backend system. This backend utilizes advanced data mining algorithms, such as the Cross-Transaction High Utility Itemset Mining (CTC-HUIM) framework, to provide personalized recommendations, dynamic pricing adjustments, and seamless digital transactions. Additionally, the system offers business owners comprehensive insights into customer behavior, inventory management, and promotional effectiveness, thereby optimizing store performance and profitability.

PRIOR ART AND PROBLEM TO BE SOLVED

[0002] Retail environments are in the midst of a transformation, driven by evolving consumer behavior, the rapid expansion of e-commerce, and growing expectations for enhanced, personalized shopping experiences. However, despite these shifts, physical retail spaces have been slow to adopt intelligent systems that can bridge the gap between customer experience and business optimization. Current retail systems primarily rely on basic technologies such as barcode scanning and digital payment processing. While these technologies streamline operational processes, they are limited in scope and fail to provide the advanced features needed to meet modern consumer demands.

For instance, barcode scanners are adept at product identification and pricing, but they offer no insights into customer preferences, in-store behaviors, or inventory trends beyond basic stock updates. Digital payment systems, though efficient in facilitating transactions, lack the intelligence to offer real-time analytics or support personalized customer engagement. As a result, many physical retailers are left behind in the digital race, unable to compete with online platforms that use sophisticated processs to tailor customer experiences and optimize business outcomes in real time. This gap in technological adoption has widened the divide between the personalized, data-driven experiences consumers encounter online and the more static, impersonal experiences they find in physical stores.
Beacon technology, which involves the use of small devices to send notifications to smartphones based on proximity, has also been explored as a way to enhance the in-store experience. Retailers use beacons to send personalized promotions or product information when a customer is near a particular item. However, this approach has seen limited adoption due to concerns about privacy and the overall effectiveness of the technology. Consumers are often reluctant to download retailer-specific apps or share their location data, reducing the potential reach of beacon technology. Traditional shopping carts, whether in physical stores or online platforms, are designed to serve a single, basic purpose: helping customers transport or organize items they wish to purchase. In brick-and-mortar retail environments, shopping carts are simple, physical structures that assist customers in carrying items throughout the store, while in e-commerce, they are digital containers for selected products before checkout. However, despite being an essential part of the shopping experience, these carts remain largely generic and passive tools. They do not offer any personalized guidance or interaction with the customer beyond the fundamental function of holding goods.

One of the major drawbacks of regular shopping carts, both physical and digital, is their lack of ability to guide customers through the shopping process. In physical stores, a standard cart is simply a passive tool. It does not help customers find items more efficiently, alert them to deals or promotions, or suggest complementary products based on their preferences or purchase history. For instance, a customer pushing a cart through a large supermarket might wander aimlessly, unsure of where to find a particular product or unaware of related items they might want to purchase. This lack of guidance can lead to a less satisfying shopping experience and missed opportunities for retailers to increase sales through cross-selling or upselling.

Furthermore, regular shopping carts do not track or enhance the overall shopping experience. In a time where data-driven personalization is becoming increasingly important, traditional carts provide no insights into customer behavior. They do not record which products customers have interacted with, how long they spend in certain aisles, or which items they add to the cart and later abandon. This lack of data limits retailers' ability to understand and optimize the customer journey. In contrast, modern digital tools in other areas of retail, such as online browsing histories and personalized suggestions, help businesses tailor experiences and improve customer satisfaction. The static nature of traditional carts means that retailers are missing out on valuable opportunities to collect and use data in meaningful ways.

[0003] To resolve the above mentioned problem here a smart cart system is designed for retail shopping by incorporating smart hardware and advanced software analytics. Designed to optimize both customer convenience and business insights, it features a multifunctional shopping cart equipped with an interactive touch screen, a barcode scanner, and a built-in scale for loose items. Customers benefit from personalized recommendations, dynamic pricing, health alerts, and sustainability suggestions, enhancing their shopping journey. The backend analytics system, based on the CTC-HUIM framework, mines transaction data to help businesses uncover high-profitable itemsets and trends in consumer behavior. This enables retailers to optimize inventory, product placement, and marketing strategies. Additional features include personalized navigation, product subscriptions, and seamless checkout through secure billing integration with digital wallets. Smart shopping Cart empowers customers with real-time information while providing businesses with valuable data for operational efficiency and customer loyalty.

THE OBJECTIVES OF THE INVENTION:

[0004] It has already been proposed that currently retail systems such as POS, CRM, and RFID typically operate in isolation from one another, each providing data relevant to their specific function but failing to create a unified view of the customer or store operations. For instance, while POS systems may offer sales insights and CRM tools track customer interactions, there is little communication between these systems, leaving retailers unable to piece together a comprehensive picture of customer behavior. This siloed approach limits the ability of retailers to generate actionable insights that could enhance both the customer experience and operational efficiency. Another major issue with existing solutions is their reliance on historical rather than real-time data. Most systems are designed to analyze transactions or interactions after they occur, meaning that retailers are always a step behind. This reactive model does not allow retailers to dynamically adjust their strategies based on what is happening in-store at any given moment. Without real-time analytics, retailers cannot respond to sudden shifts in consumer preferences or optimize their operations in a way that maximizes customer satisfaction. Several alternative methods have been explored in an effort to close the gap between customer experience and business optimization in retail environments, though challenges remain.

[0005] The principal objective of the invention is an Advanced Intelligent Retail Shopping Cart System that integrates a Cross-Transaction High Utility Itemset Mining (CTC-HUIM) framework to optimize the shopping experience for consumers while providing actionable business insights in real-time. The system will incorporate features such as dynamic pricing models, personalized product recommendations, health and sustainability suggestions, and a secure digital billing mechanism. This unified system enhances consumer convenience while enabling businesses to make data-driven decisions on inventory management, product placement, and promotional strategies.

[0006] Another objective of the invention is To provide real-time personalized recommendations through an interactive UI based on the customer's shopping history, preferences, and current cart contents. This includes cross-sell and upsell suggestions, such as complementary products or related promotional items. The system also uses behavioral nudging techniques to encourage customers to try new products or explore alternative options.

[0007] The further objective of the invention is a dynamic pricing features that adjust in real-time based on multiple factors, such as stock levels, foot traffic, and product shelf life. The system will offer flash discounts or promotional pricing when necessary to optimize stock turnover and improve the customer's shopping experience. This ensures that customers receive competitive pricing while businesses benefit from improved inventory management.

[0008] The further objective of the invention is to mine high-utility itemsets across transactions, offering critical insights into consumer behavior patterns, popular product combinations, and purchasing trends. This data will enable businesses to optimize product placement, adjust marketing strategies, and make informed decisions about stock replenishment to maximize profitability and customer satisfaction.

[0009] The further objective of the invention is to integrate a secure digital billing mechanism that connects the shopping cart system with customer accounts or digital wallets for a seamless checkout process. The system ensures accurate item entry, pricing, and secure transactions, reducing the time spent at checkout while enhancing customer convenience. The two-step verification for scanning and weighing items ensures that billing data is processed without error.

[0010] The further objective of the invention is to offer health-conscious and sustainable product suggestions. This includes scanning items for allergens, high sugar, or sodium content and providing healthier alternatives when applicable. For sustainability, the system highlights eco-friendly or ethically produced products, promoting environmentally conscious shopping choices for customers while giving businesses insight into consumer sustainability trends.

[0011] The further objective of the invention is to provide customers with optimized navigation through the store based on their shopping list and preferences. This feature reduces the time spent shopping by guiding customers directly to products and highlighting cross-sell opportunities they might otherwise miss. The system also integrates a subscription or reordering option for frequently purchased items, automating the process for enhanced convenience.
Objective 7: Real-Time Product Validation and Error Prevention

[0012] The further objective of the invention is to ensure product accuracy and error prevention by incorporating real-time validation mechanisms that cross-reference scanned products with the store's inventory system. If a product is faulty, mislabelled, or out of stock, the system will immediately notify the customer and suggest alternative options, ensuring a smooth and reliable shopping experience.

[0013] The further objective of the invention is toenable customers to submit feedback or reviews directly through the shopping cart interface after purchasing products. This customer-driven feedback system will help improve the quality of product recommendations and business decision-making over time, creating a loop of continuous improvement in both the customer experience and store operations.

SUMMARY OF THE INVENTION

[0014] In physical retail environments, customers often navigate through large stores with little direction, using carts solely to gather items they can find. There is no system in place that assists customers in discovering new products, receiving personalized recommendations, or optimizing their shopping journey based on their preferences or shopping history. The cart itself provides no interaction, feedback, or intelligence, leaving customers to rely on their own instincts or store staff for help. Similarly, digital shopping carts, while more integrated into the e-commerce experience, still serve a static function of organizing selected products without offering real-time support or customization based on the customer's behaviors or interests. As retail trends shift towards greater personalization and customer engagement, the limitations of traditional shopping carts become more apparent. Modern consumers are accustomed to tailored experiences, particularly in online shopping, where recommendations and smart suggestions are commonplace. However, in both online and offline retail, the shopping cart remains a missed opportunity to provide intelligent, interactive guidance that could enhance the customer experience and improve business outcomes.
[0015] So here in this invention a smart shopping cart is engineered for for both customer convenience and business optimization. Its hardware includes an interactive touch screen, a barcode scanner, and a weighing scale, all integrated into the shopping cart. On the software side, the backend employs the CTC-HUIM framework, mining high-utility itemsets across multiple transactions to offer actionable insights to retailers. These insights enable inventory optimization, product placement adjustments, and dynamic pricing based on real-time factors. For customers, the system provides personalized recommendations, sustainability suggestions, and health-related alerts, fostering a more conscious shopping experience. The intuitive UI also helps with navigation, reordering options, and subscription services, enhancing convenience. With built-in billing integration and real-time data processing, Smart shopping Cart streamlines the checkout process while offering dynamic pricing models that respond to current store conditions. Overall, Smart shopping Cart provides a comprehensive, smart shopping experience that benefits both shoppers and businesses.

DETAILED DESCRIPTION OF THE INVENTION

[0016] While the present invention is described herein by example, using various embodiments and illustrative drawings, those skilled in the art will recognise recognize invention is neither intended to be limited that to the embodiment of drawing or drawings described nor designed to represent the scale of the various components. Further, some features that may form a part of the invention may not be illustrated with specific figures for ease of illustration. Such omissions do not limit the embodiment outlined in any way. The drawings and detailed description are not intended to restrict the invention to the form disclosed. Still, on the contrary, the invention covers all modification/s, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the description's size or the claims. As used throughout this specification, the worn "may" be used in a permissive sense (That is, meaning having the potential) rather than the mandatory sense (That is, meaning, must).

[0017] Further, the words "an" or "a" mean "at least one" and the word "plurality" means one or more unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents and any additional subject matter not recited, and is not supposed to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents acts, materials, devices, articles and the like are included in the specification solely to provide a context for the present invention.
[0018] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is also understood that it contemplates the same component or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group comprising", "including", or "is" preceding the recitation of the element or group of elements and vice versa. Before explaining at least one embodiment of the invention in detail, it is to be understood that the present invention is not limited in its application to the details outlined in the following description or exemplified by the examples. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for description and should not be regarded as limiting.
[0019] 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 the invention belongs. Besides, the descriptions, materials, methods, and examples are illustrative only and not intended to be limiting. Methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention.

[0020] The present invention is an Advanced Intelligent Retail Shopping Cart System Utilizing CTC-HUIM for Enhanced Consumer Experience and Real-Time Business Analytics with Integrated Dynamic Pricing, Personalized Recommendations, and Secure Digital Transaction Mechanisms is designed to revolutionize the retail shopping experience by merging advanced data analytics with real-time customer interaction. Its primary purpose is to create a seamless, efficient, and personalized shopping environment for consumers while providing valuable, data-driven insights to businesses. By harnessing the power of the Cross-Transaction High Utility Itemset Mining (CTC-HUIM) framework, the system empowers businesses to gain a deeper understanding of customer behavior and product performance, facilitating more informed decision-making across various aspects of retail operations.
[0021] This system enhances the shopping journey by offering real-time, personalized product recommendations based on customer preferences and shopping patterns. It intelligently suggests complementary or alternative products, guiding customers toward items they may find relevant or appealing. These recommendations are not only designed to elevate customer satisfaction but also to increase overall sales and product discovery, creating a more engaging shopping experience. As customers move through the store, they receive real-time prompts tailored to their specific preferences, transforming what could be a routine shopping trip into an interactive and personalized event.
[0022] In addition to personalized recommendations, the system introduces dynamic pricing, which adjusts prices in real-time based on factors such as inventory levels, foot traffic, and product freshness. This dynamic model ensures that customers receive the most competitive prices at any given time while helping businesses optimize stock levels and reduce waste. The system's ability to respond to real-time conditions allows retailers to offer flash discounts, promote high-turnover products, or incentivize purchases during peak shopping periods, creating a more adaptive and responsive retail environment.
[0023] Furthermore, the system incorporates features designed to promote health-conscious and sustainable shopping. It alerts customers to potential allergens, high sugar or sodium content, and other health considerations, while also offering suggestions for healthier alternatives. Additionally, the system encourages environmentally conscious choices by recommending sustainable or ethically produced products, supporting both the health and values of the consumer and contributing to a broader culture of responsible shopping.
[0024] The integration of secure digital transaction mechanisms further enhances the shopping experience by streamlining the checkout process. Customers can complete their purchases through a secure digital wallet or account system, minimizing delays at checkout and ensuring that transactions are both swift and secure. This digital integration not only simplifies the payment process but also supports an overall seamless and enjoyable shopping experience, free from traditional checkout bottlenecks. By employing advanced analytics through the CTC-HUIM framework, the system goes beyond enhancing customer experiences and provides retailers with actionable insights into consumer purchasing trends and patterns. This allows businesses to optimize their inventory management, adjust product placement strategies, and develop targeted marketing campaigns based on real-time data. The system empowers retailers to respond quickly to changes in consumer behavior and market demands, ensuring operational efficiency and maximizing profitability.
[0025] Here the cart is mounted at the front with an interactive touch screen display that forms the primary interface for the customer. The screen's position is intuitive, at eye level for most users, ensuring easy access without requiring the shopper to bend or strain. The display itself is bright and responsive, using high-resolution graphics and a large enough interface to ensure that text and images are easily readable even in varying lighting conditions within the store. Despite its advanced functionality, the interface is minimalist, maintaining a clean and non-intrusive presence while offering all necessary information at the customer's fingertips. The handle of the cart integrates seamlessly with the technology, housing touch-sensitive controls and a scanner embedded discreetly into its structure. Unlike traditional carts, this design emphasizes comfort with its soft, ergonomic grips, ensuring that even prolonged use remains comfortable for the shopper. The scanner is hidden within the handle, with only a subtle indication that it exists, preventing visual clutter while remaining easily accessible for scanning items. This ensures the customer's interaction with the technology is fluid and intuitive, as every aspect of the design has been considered to minimize distractions and complications. The central body of the cart itself features a sleek and polished basket area, with no visible wires or technology components interrupting the clean lines of the design. Hidden within the cart's body are sophisticated sensors and mechanisms that contribute to the cart's functionality, but these remain completely invisible to the user, maintaining the appearance of simplicity. The basket is spacious, able to accommodate a large number of products while the overall dimensions of the cart ensure it remains easy to navigate even in crowded aisles. A subtle but essential addition to the cart's aesthetics is its smart lighting system. The cart may feature soft, ambient lighting around the base or screen that subtly changes color based on interactions or system alerts, giving the shopper real-time visual cues without overwhelming their attention. This visual feedback helps guide the customer through their shopping experience, whether through gentle reminders of promotions or highlighting health or sustainability alerts.
[0026] The cart is built upon a complex integration of hardware and software components that work together seamlessly to create an intelligent, data-driven shopping experience. Each component of the system is meticulously designed to fulfill specific functions, and their interactions form the foundation for a smooth, user-friendly shopping journey that is simultaneously powerful in its business analytics capabilities.
[0027] At the heart of the system is the interactive touch screen interface, which serves as the central hub for customer interaction. This screen is where users scan items, view product details, receive personalized recommendations, and interact with various system features. The touch screen is designed to be highly responsive, ensuring that users can easily navigate through menus and options. Its connection to the system's backend enables it to display real-time data, such as dynamic pricing and promotional offers, as well as customer-specific information, like personalized recommendations based on shopping history. This interface also serves as the point of interaction for submitting feedback or navigating through the store via optimized routes.
[0028] One of the core functions of the interactive touch screen interface is its scanning feature, which allows users to scan products by either manually entering a barcode or using the integrated barcode scanner within the cart's handle. Once a product is scanned, the item's details, such as price, weight, nutritional information, and any ongoing promotions, are instantly displayed on the screen. This interaction occurs through real-time communication with the backend system, which retrieves the data from the store's database. The item is also added to the customer's digital cart, which is continuously updated and displayed on the screen, giving the customer a clear overview of their total items and running cost.
[0029] The personalized recommendations feature of the touch screen interface is another vital component that interacts deeply with the backend analytics system. As customers add items to their cart, the system generates suggestions for complementary products based on their current selections, previous shopping habits, or related promotions. For example, if a customer scans a box of pasta, the interface may suggest a discounted bottle of sauce or a promotional offer on cheese. These suggestions are driven by the CTC-HUIM framework, which mines high-utility itemsets from transaction data, identifying common product pairings and high-profit items that align with the customer's preferences. This ensures that the recommendations are not only relevant but also tailored to the individual's shopping behavior, enhancing the overall shopping experience.
[0030] Another key interaction facilitated by the touch screen is the dynamic pricing model. As customers scan items, the system dynamically adjusts prices based on various real-time factors such as inventory levels, store traffic, and product freshness. The backend system continuously monitors these factors and pushes updated pricing data to the touch screen interface, ensuring that customers always see the most current prices. For example, if a product is approaching its sell-by date, the interface may display a flash discount, encouraging the customer to purchase it at a reduced price. This dynamic interaction between the backend and the touch screen ensures that customers are always informed of the best available deals in real time.
[0031] The navigation feature within the touch screen interface adds another layer of convenience to the shopping experience. Customers can input their shopping lists into the system, and the interface will generate an optimized route through the store, guiding them efficiently from one product to the next. This feature not only saves time but also encourages cross-selling opportunities by directing customers through sections of the store they might otherwise bypass. The navigation system interacts with the store's layout data, ensuring that the customer follows the shortest possible path while being presented with additional product suggestions along the way.
[0032] The feedback and review submission feature is another crucial function of the touch screen interface, allowing customers to provide immediate input on products they have purchased. After scanning or selecting an item, customers can rate it, leave a review, or provide feedback on their shopping experience. This information is fed back into the system's backend, where it is aggregated and analyzed to improve product recommendations, promotional strategies, and overall store operations. The interaction between the customer's input on the touch screen and the backend ensures a continuous feedback loop that enhances both customer satisfaction and business decision-making.
[0033] Additionally, the touch screen provides real-time access to health and sustainability suggestions. As customers scan items, the system cross-references product data with allergen databases and nutritional guidelines. If an item contains allergens or is high in sugar or sodium, the touch screen will display a warning and suggest healthier alternatives. Similarly, the system can recommend eco-friendly or sustainable product options, providing customers with the information they need to make more conscious choices. This feature is particularly useful for health-conscious customers or those looking to shop ethically, and its interaction with the backend system ensures that all data is up to date and accurate.
[0034] Finally, the checkout process on the touch screen is streamlined for maximum convenience. As the customer finishes their shopping, they can initiate the checkout process directly through the touch screen. The system totals the items in their cart and offers multiple payment options, including digital wallets, store loyalty accounts, or even contactless payments. The secure digital transaction mechanism ensures that all payment information is encrypted and securely transmitted to the store's payment processor, minimizing the risk of fraud or data breaches. This seamless integration between the payment system and the touch screen allows for a smooth and hassle-free checkout experience, eliminating the need for traditional cashier lines.
[0035] The barcode scanner is another critical component embedded within the cart's handle, enabling users to scan products effortlessly as they shop. The scanner is directly linked to both the touch screen and the backend system. When a product is scanned, the information is instantly relayed to the backend, where the product details, price, and any applicable promotions are retrieved and displayed on the screen. This seamless interaction between the scanner, the touch screen, and the backend system allows the user to get real-time updates about their cart contents, ensuring accuracy in the item list and avoiding any confusion about pricing or availability. This two-step verification system, which also cross-references store inventory data, ensures that faulty or mislabeled items are immediately flagged, adding an extra layer of security and efficiency.
[0036] The barcode scanner, which is ergonomically embedded within the cart's handle or a designated scanning station on the cart, serves as the primary input mechanism for identifying packaged products and retrieving associated data from the backend system. Upon the scanning of a product's barcode, the scanner, through an optical recognition system, decodes the barcode symbology and transmits the encoded data to the system's backend. This instantaneous transmission is facilitated by a high-speed, wireless communication interface between the scanner and the backend database. Once the backend receives the decoded product identification, it immediately cross-references the item against the centralized product database, retrieving pertinent details such as the product name, price, applicable discounts, promotional offers, and any other associated metadata, including health or sustainability information.
[0037] This information is then transmitted back to the touch screen interface in real time, where it is presented to the customer in a user-friendly format. The product details are displayed in a clear, intuitive layout, which may include the product's image, price, weight, and any relevant discounts or dynamic pricing adjustments derived from the CTC-HUIM algorithm. In the event that the product is subject to a promotion or flash discount based on real-time store conditions, this is immediately reflected on the screen, providing the customer with full transparency regarding the pricing of the item at the moment of purchase. The tight integration between the barcode scanner, backend system, and touch screen interface ensures that no manual intervention is required, thereby streamlining the shopping process, reducing potential human error, and providing an optimized user experience.
[0038] The weighing system integrated into the cart's body is another sophisticated component that plays a crucial role in handling loose or unbarcoded items, such as fresh produce. When a customer places loose items on the scale, the system automatically weighs them and calculates the price based on the current per-unit cost. This data is then instantly updated on the touch screen, ensuring that the customer knows the exact cost of the items before proceeding. This component is tightly integrated with the rest of the system, as it ensures accurate item entry without requiring manual inputs from customers or store staff, making the shopping process faster and more intuitive.
[0039] The weighing system employs advanced digital load cells that are highly sensitive to weight fluctuations, ensuring precise measurement of the item's mass. Upon the placement of an item on the weighing platform, the system instantly calculates the item's weight and cross-references this data with the per-unit pricing information stored in the backend database. The weighing system is calibrated to handle a broad range of weights, accommodating both lightweight items and heavier produce with equal accuracy, ensuring that the weight is measured to a high degree of precision As soon as the weight of the item is recorded, the backend system dynamically calculates the total price by multiplying the item's weight by the current per-unit price. This pricing data is transmitted back to the touch screen interface, where the customer is presented with a detailed breakdown of the item's weight, per-unit cost, and total price, along with any applicable promotions or discounts. The real-time interaction between the weighing system and the backend ensures that customers are informed of the exact price of loose items before finalizing their purchase, thus enhancing transparency and customer satisfaction.
def __init__(self, per_unit_prices, weight_fluctuation_threshold):
self.per_unit_prices = per_unit_prices # Price per unit weight for each product
self.weight_fluctuation_threshold = weight_fluctuation_threshold # Allowed weight fluctuation threshold

# Measure the weight of an item using a digital load cell
def measure_weight(self, item):
# Simulated function to get the current weight of the item
weight = item['current_weight']
return weight

# Calculate the total price based on weight and per-unit price
def calculate_price(self, item):
weight = self.measure_weight(item)
per_unit_price = self.per_unit_prices.get(item['name'], 0)
total_price = weight * per_unit_price
return total_price

# Apply the weight fluctuation threshold
def validate_weight(self, item):
measured_weight = self.measure_weight(item)
expected_weight = item['expected_weight']

# Check if the measured weight is within the allowable threshold
if abs(measured_weight - expected_weight) <= self.weight_fluctuation_threshold:
return True
return False

# Process the item and return pricing details
def process_item(self, item):
if self.validate_weight(item):
price = self.calculate_price(item)
return {
'name': item['name'],
'weight': item['current_weight'],
'total_price': price
}
else:
return {
'error': "Weight fluctuation exceeds allowable threshold. Please re-weigh the item."
}

# Example data for loose items and per-unit prices
per_unit_prices = {
"apples": 3.0, # $3 per kilogram
"oranges": 2.5,
"potatoes": 1.8,
}

# Weight fluctuation threshold (e.g., 0.05 kg)
weight_fluctuation_threshold = 0.05

# Weighing system instance
weighing_system = WeighingSystem(per_unit_prices, weight_fluctuation_threshold)

# Simulated loose item
item = {
"name": "apples",
"current_weight": 1.52, # 1.52 kg measured weight
"expected_weight": 1.5, # Expected weight based on the system's last recorded data
}

# Process the item and calculate price
result = weighing_system.process_item(item)
print(result)

[0040] This above process simulates the behavior of the weighing system integrated into the shopping cart. The process begins by measuring the weight of an item using a digital load cell. The system measures the current weight of the item placed on the weighing platform and compares it against an expected weight, ensuring that any minor fluctuations in weight do not affect the calculation. Once the weight is measured, the algorithm cross-references the item's per-unit price from a backend database that holds the price information for various loose items (such as fruits or vegetables). The total price is then calculated by multiplying the measured weight by the per-unit price of the item. This calculated price is then relayed back to the system and displayed on the touch screen interface, ensuring that the customer is fully informed of the item's weight, per-unit price, and total cost. A weight fluctuation threshold is incorporated into the algorithm to account for small, unavoidable variations in the measured weight due to sensor precision or environmental factors. For example, if the threshold is set to 0.05 kg (50 grams), the system will allow small fluctuations up to this limit. If the difference between the expected weight (either from a previous measurement or estimated weight) and the current weight exceeds the threshold, the system will flag the issue and prompt the customer to re-weigh the item. This prevents potential errors from being introduced into the pricing process due to inconsistent weight measurements.
[0041] The weight fluctuation threshold is critical in ensuring both the accuracy and stability of the weighing system. Weight fluctuations can occur due to various factors, including external vibrations, minor inaccuracies in the digital load cell, or even subtle changes in the item's positioning on the weighing platform. By defining a threshold value (for example, 0.05 kg), the system can differentiate between minor, acceptable fluctuations and more significant discrepancies that may indicate an error in the weighing process. This threshold is particularly useful because it prevents the system from generating unnecessary warnings or recalculations for very small changes in weight that do not materially affect the total price. If the weight difference falls within the threshold, the system assumes the measurement is accurate and processes the item accordingly. However, if the weight difference exceeds the threshold, the system flags the issue, prompting the customer to re-weigh the item to ensure that the total price is calculated correctly.
[0042] This use of a threshold helps to maintain the system's precision and reliability, ensuring that the customer is provided with accurate, real-time pricing information for loose or unbarcoded items. It also enhances the overall transparency and trustworthiness of the system by ensuring that minor fluctuations do not introduce errors into the shopping experience. This dynamic and adaptive approach helps the system balance accuracy and responsiveness, ensuring that customers are presented with reliable, real-time data while shopping.
[0043] Both the barcode scanner and weighing system operate under a unified backend infrastructure that manages all data processing, ensuring that the product information and pricing are accurately reflected in real time. The high degree of integration between these components allows for automated updates to the cart's total price, item list, and applicable discounts, thereby eliminating the need for manual entry or verification by store staff. This interaction not only increases operational efficiency but also reduces the likelihood of pricing discrepancies, ensuring that customers are consistently provided with accurate and up-to-date information.
[0044] The barcode scanner and weighing system, therefore, constitute essential components within the advanced shopping cart system, operating in concert with the backend analytics engine to ensure that the customer is provided with precise, real-time pricing and product information. The instant relay of data from the scanner and scale to the backend, and subsequently to the touch screen, enhances both the speed and accuracy of the shopping process, thereby contributing to the system's overall objective of creating an optimized, data-driven, and user-friendly retail experience. The integration of these components within the broader system infrastructure also supports the seamless application of dynamic pricing models and personalized recommendations, further augmenting the value proposition for both customers and retailers.
[0045] A key feature of the system is the CTC-HUIM-powered backend, which serves as the system's analytical brain. This backend processes vast amounts of transaction data, using the CTC-HUIM (Cross-Transaction High Utility Itemset Mining) framework to identify high-utility itemsets across multiple customer transactions. This data analysis enables the system to mine customer behavior patterns, uncovering which products are frequently bought together, which items are most profitable, and which trends are emerging in consumer preferences. The results of these analytics are used to inform the recommendations and dynamic pricing that appear on the touch screen, creating a deeply personalized shopping experience while simultaneously offering valuable insights to retailers.
# Example data
transaction_data = [
{"items": ["pasta", "sauce", "cheese"]},
{"items": ["pasta", "sauce"]},
{"items": ["bread", "butter"]},
{"items": ["pasta", "cheese"]},
]

utility_table = {
"pasta": 10,
"sauce": 8,
"cheese": 12,
"bread": 6,
"butter": 5,
}
min_utility_threshold = 15
# Personalized recommendations based on the current cart items
recommendation_system = HUIM_RecommendationSystem(transaction_data, utility_table, min_utility_threshold)
current_cart_items = ["pasta"]
recommendations = recommendation_system.generate_recommendations(current_cart_items)
print(f"Recommendations: {recommendations}")

[0046] The CTC-HUIM framework used in this personalized recommendation system aims to identify itemsets with high utility values across multiple transactions. Utility in this context refers to the significance or profit value of an item in a transaction. For example, "pasta" may have a high utility because it is frequently purchased and has a high margin. When a customer adds an item like "pasta" to their cart, the process searches for item pairs or larger itemsets that have historically been bought together and exceed a certain utility threshold. These itemsets are then used to generate personalized recommendations. The process begins by analyzing the transaction data, which consists of previous customer purchases. Each item in the transaction is assigned a utility value (based on factors like sales margin, frequency of purchase, or customer preferences). The process then looks for combinations of items (in this case, 2-itemsets or pairs) that exceed a minimum utility threshold. The threshold is critical here-it ensures that only itemsets with enough significance (both in terms of profit and customer relevance) are considered.
[0047] Once the high-utility itemsets are identified, the system compares them with the items currently in the customer's cart. If any item from these high-utility itemsets matches an item in the cart, the remaining items in the set are suggested as recommendations. For example, if a customer adds "pasta" to their cart, and the system detects that "pasta" and "sauce" frequently appear together in profitable transactions, it may suggest "sauce" to the customer as a complementary product.
[0048] The minimum utility threshold is crucial in this process as it acts as a filter to discard itemsets that are not significant enough to be recommended. If the threshold is set too low, the system may suggest irrelevant or low-profit items, overwhelming the customer with unnecessary information. On the other hand, if the threshold is too high, the system may miss out on potentially valuable recommendations. Therefore, the threshold is carefully selected to balance the quantity and quality of recommendations. For example, in the code provided, the threshold is set to 15. This means that only itemsets whose combined utility exceeds this value will be considered for recommendations. The idea is to ensure that the system focuses on recommending items that have a strong historical relevance to the customer's current purchase, providing them with meaningful suggestions that enhance their shopping experience while increasing the likelihood of cross-sell and upsell opportunities for the business.
[0049] Using this threshold-driven utility filtering mechanism ensures that the recommendations made are both contextually relevant and profitable. It helps the system focus on high-utility pairs or sets of items that are likely to be of interest to the customer based on their cart contents. This not only enhances the customer's shopping journey by offering them useful, personalized suggestions, but it also helps businesses by promoting items that have a higher chance of being purchased, thus increasing overall sales and customer satisfaction.
[0050] The dynamic pricing process, which is also tied to the backend system, plays a crucial role in providing competitive, real-time price adjustments. This feature uses data on current store conditions-such as foot traffic, inventory levels, and product shelf life-to determine the most effective pricing strategies. For example, if a particular product is nearing its expiration date, the system might offer a flash discount to encourage a quick sale, while items in high demand may see a price increase. These adjustments are communicated immediately through the interactive touch screen, ensuring customers are always aware of the best available deals. The seamless integration between the backend and the touch screen ensures that these pricing updates occur in real time without any noticeable delay.

class DynamicPricingSystem:
def __init__(self, product_data, inventory_data, min_discount_threshold):
self.product_data = product_data # Product details like price, freshness, etc.
self.inventory_data = inventory_data # Inventory levels for each product
self.min_discount_threshold = min_discount_threshold # Minimum threshold for triggering a discount

# Calculate discount based on freshness and inventory
def calculate_discount(self, product):
freshness_discount = 0
inventory_discount = 0

# Freshness-based discount (e.g., if approaching expiration date)
if product['expiry_date'] - datetime.now() < timedelta(days=3): # Example threshold: 3 days to expiry
freshness_discount = 0.30 # 30% discount for near-expiry products

# Inventory-based discount (e.g., if stock is high)
if self.inventory_data[product['name']] > 50: # Example threshold: overstocked if more than 50 units
inventory_discount = 0.20 # 20% discount for high inventory

return max(freshness_discount, inventory_discount) # Apply the higher of the two discounts

# Adjust price based on dynamic factors
def adjust_price(self, product):
base_price = product['price']
discount = self.calculate_discount(product)

# Only apply discount if it meets the minimum discount threshold
if discount >= self.min_discount_threshold:
adjusted_price = base_price * (1 - discount)
else:
adjusted_price = base_price

return adjusted_price

# Update product prices dynamically
def update_dynamic_prices(self):
updated_prices = {}
for product in self.product_data:
new_price = self.adjust_price(self.product_data[product])
updated_prices[product] = new_price
return updated_prices

# Example product and inventory data
product_data = {
"pasta": {"name": "pasta", "price": 10.0, "expiry_date": datetime(2024, 10, 12)},
"sauce": {"name": "sauce", "price": 8.0, "expiry_date": datetime(2024, 10, 18)},
"cheese": {"name": "cheese", "price": 12.0, "expiry_date": datetime(2024, 10, 10)},
}

inventory_data = {
"pasta": 60, # High inventory for pasta
"sauce": 30,
"cheese": 10,
}

min_discount_threshold = 0.10 # Minimum 10% discount for dynamic pricing to apply

# Dynamic pricing system
dynamic_pricing = DynamicPricingSystem(product_data, inventory_data, min_discount_threshold)

# Update and display dynamic prices
updated_prices = dynamic_pricing.update_dynamic_prices()

print(f"Updated Prices: {updated_prices}")

[0051] The dynamic pricing process in this system uses real-time factors such as product freshness and inventory levels to determine the final price displayed to customers. When a customer scans a product, the system evaluates these two key factors to decide whether to apply a discount and what percentage of the discount should be applied. The system first looks at the freshness of the product by comparing its current date to the product's expiry date. If the product is nearing its expiration (e.g., within three days of the sell-by date), the system assigns a freshness-based discount, such as 30%. This feature encourages the customer to purchase the product before it becomes unsellable, thereby reducing wastage and improving inventory turnover. The system also examines inventory levels. For overstocked items-where the available quantity exceeds a predefined threshold, such as 50 units-the system assigns an inventory-based discount. For instance, if a product has more than 50 units in stock, it might trigger a 20% discount to encourage faster sales and help clear excess inventory. The process compares the freshness and inventory-based discounts and selects the higher of the two to apply to the product's price.
[0052] Once the discount is calculated, the system ensures that it meets the minimum discount threshold, which is a safeguard to avoid insignificant price changes. If the calculated discount is below the minimum threshold (for example, less than 10%), the system keeps the base price unchanged. However, if the discount meets or exceeds this threshold, the system reduces the product's price accordingly. After recalculating the price for each product based on these real-time factors, the system updates the product's price and pushes this new price to the touch screen interface, ensuring customers are always informed of the most up-to-date prices, including any applicable discounts.
[0053] The minimum discount threshold in this process ensures that only meaningful price changes are applied. For instance, if the calculated discount is less than 10%, the system will not change the price, as minor discounts may not be impactful or beneficial for either the retailer or the customer. This threshold prevents unnecessary adjustments, which could confuse customers or create operational challenges. The threshold is crucial for maintaining balance in dynamic pricing. If the threshold were too low, it might result in frequent, negligible price fluctuations, leading to customer dissatisfaction or pricing confusion. On the other hand, setting the threshold too high could result in missed opportunities to move products nearing their expiry or to clear excess inventory effectively. By carefully setting the threshold, the system can apply meaningful discounts that optimize sales while keeping the shopping experience smooth and transparent.
[0054] This dynamic pricing mechanism, supported by real-time data monitoring, allows the system to optimize product sales while ensuring customers are presented with the best possible deals, thereby improving customer satisfaction and operational efficiency in the retail environment.
[0055] The secure digital transaction system is another key component of the shopping cart, allowing for a frictionless checkout experience. The cart is integrated with digital wallets and customer accounts, enabling users to make payments directly from the cart without the need for a traditional checkout line. As items are scanned and added to the cart, the system builds a running total of the customer's purchases, which is displayed on the touch screen. When the customer is ready to check out, they simply select their preferred payment method on the screen, and the system processes the payment securely. The transaction data is encrypted and sent to the central server, ensuring that all financial information remains protected throughout the process. This integration of secure digital payments not only enhances customer convenience but also reduces the need for physical cashiers, improving store efficiency.
[0056] The sustainability and health recommendation features are additional layers that add value to the customer's shopping experience. As the system scans products, it cross-references them with databases containing information on allergens, high sugar or sodium content, and environmental sustainability. If a product contains an allergen or exceeds a certain threshold for unhealthy ingredients, the system will notify the customer through the touch screen and suggest healthier alternatives. Similarly, when a product is deemed unsustainable or non-eco-friendly, the system may offer recommendations for more environmentally conscious choices. These features are made possible by the system's tight integration with external databases and the backend analytics engine, which ensures that customers receive real-time, personalized advice to make better health and environmental decisions.
def __init__(self, product_data, allergen_database, nutritional_guidelines, sustainability_data, health_threshold, eco_threshold):
self.product_data = product_data # Product details (e.g., nutritional info, allergens)
self.allergen_database = allergen_database # Allergen information for products
self.nutritional_guidelines = nutritional_guidelines # Nutritional standards (e.g., sugar/sodium limits)
self.sustainability_data = sustainability_data # Eco-friendliness of products
self.health_threshold = health_threshold # Maximum allowed levels for health warnings (e.g., sugar/sodium)
self.eco_threshold = eco_threshold # Minimum sustainability score for eco-friendly recommendations

# Check for allergens
def check_allergens(self, product):
if product['name'] in self.allergen_database:
return f"Warning: {product['name']} contains allergens: {self.allergen_database[product['name']]}"
return None

# Check for nutritional issues (e.g., high sugar or sodium content)
def check_nutritional_values(self, product):
for nutrient, value in product['nutritional_values'].items():
if value > self.nutritional_guidelines.get(nutrient, float('inf')):
return f"Warning: {product['name']} is high in {nutrient}."
return None

# Check for sustainability suggestions
def check_sustainability(self, product):
sustainability_score = self.sustainability_data.get(product['name'], 0)
if sustainability_score < self.eco_threshold:
return f"Suggestion: Choose eco-friendly alternatives for {product['name']}."
return None

# Generate health and sustainability suggestions
def generate_suggestions(self, scanned_item):
product = self.product_data[scanned_item]
recommendations = []

# Check allergens
allergen_warning = self.check_allergens(product)
if allergen_warning:
recommendations.append(allergen_warning)

# Check nutritional values (e.g., high sugar or sodium)
nutritional_warning = self.check_nutritional_values(product)
if nutritional_warning:
recommendations.append(nutritional_warning)

# Check for sustainability suggestions
sustainability_suggestion = self.check_sustainability(product)
if sustainability_suggestion:
recommendations.append(sustainability_suggestion)

return recommendations

# Example product and database info
product_data = {
"cereal": {"name": "cereal", "nutritional_values": {"sugar": 20, "sodium": 200}},
"chips": {"name": "chips", "nutritional_values": {"sugar": 5, "sodium": 500}},
"milk": {"name": "milk", "nutritional_values": {"sugar": 10, "sodium": 100}},
}

allergen_database = {
"cereal": "gluten, nuts",
"chips": "gluten",
}

nutritional_guidelines = {
"sugar": 15, # Max grams of sugar allowed per serving
"sodium": 300, # Max mg of sodium allowed per serving
}

sustainability_data = {
"cereal": 85, # Sustainability score out of 100
"chips": 40, # Low sustainability score
"milk": 90, # High sustainability score
}

# Health and sustainability thresholds
health_threshold = 15 # Threshold for sugar and sodium
eco_threshold = 50 # Minimum sustainability score for eco-friendly products

# Initialize system and get suggestions for scanned items
health_sustainability_system = HealthSustainabilitySystem(product_data, allergen_database, nutritional_guidelines, sustainability_data, health_threshold, eco_threshold)

# Simulate scanning an item
scanned_item = "chips"
suggestions = health_sustainability_system.generate_suggestions(scanned_item)

print(f"Suggestions for {scanned_item}: {suggestions}")

[0057] The Health and Sustainability Recommendation Process is designed to provide real-time warnings and suggestions to customers as they scan products using the touch screen interface. This process is essential for customers who are health-conscious or want to make environmentally responsible choices. The system works by cross-referencing scanned product data with an allergen database, nutritional guidelines, and sustainability data to ensure customers are informed of potential health risks or environmental concerns related to the products they are purchasing.
[0058] When a customer scans an item, the system first checks whether the product contains any allergens by referencing the allergen database. If allergens are found, the system immediately displays a warning on the touch screen. For instance, if a product like "cereal" contains gluten and nuts, the system will notify the customer with a message such as "Warning: This cereal contains allergens: gluten, nuts." This is especially useful for customers with dietary restrictions, ensuring they are aware of any risks before proceeding with their purchase.
[0059] Next, the system evaluates the nutritional content of the product by comparing its sugar and sodium levels (or other key nutrients) to a predefined set of nutritional guidelines. These guidelines specify the maximum acceptable levels for sugar, sodium, or other unhealthy ingredients. If the product exceeds these thresholds, the system displays a warning. For example, if "chips" contain 500 mg of sodium, which exceeds the recommended threshold of 300 mg, the system will warn the customer: "Warning: Chips are high in sodium." This feature allows customers to make more informed, health-conscious decisions, helping them avoid products that may not align with their dietary preferences or health goals.
[0060] Finally, the system checks the product's sustainability score by cross-referencing the product with a sustainability database. Each product is assigned a score based on factors such as its environmental impact, carbon footprint, or ethical production practices. If the product's sustainability score falls below a certain threshold (e.g., 50 out of 100), the system suggests alternative, eco-friendly products. For example, if the customer scans "chips" with a low sustainability score of 40, the system may suggest: "Consider eco-friendly alternatives to chips." This feature is particularly valuable for customers who prioritize sustainability and want to make environmentally responsible purchasing decisions.
[0061] The health threshold and eco threshold values are critical in filtering and triggering appropriate warnings or suggestions. The health threshold (for example, 15 grams of sugar or 300 mg of sodium) ensures that the system only flags products that exceed a certain level of unhealthy ingredients. This prevents the system from overwhelming customers with minor alerts that are not significant to their health. If the product's sugar or sodium content exceeds this threshold, the system warns the customer, helping them avoid excessive intake of unhealthy substances. The eco threshold (for example, a sustainability score of 50 out of 100) ensures that the system only suggests eco-friendly alternatives for products that are genuinely harmless to the environment. If a product falls below this score, the system encourages the customer to consider more sustainable options. By setting this threshold, the system avoids pushing unnecessary suggestions for products that are already relatively sustainable, ensuring that customers only receive meaningful and actionable eco-friendly recommendations.
[0062] These threshold values are essential for delivering precise and useful feedback to the customer, enhancing their shopping experience without overwhelming them with excessive or irrelevant information. By ensuring that only significant warnings and suggestions are triggered, the process helps customers make more conscious health and sustainability choices while shopping, supporting their lifestyle preferences.
[0063] Finally, the backend analytics system also integrates with the store's central database, providing business owners with comprehensive insights into consumer behavior and store performance. As the CTC-HUIM framework mines transaction data, it generates actionable reports that can be used to optimize stock levels, improve product placement, and refine marketing strategies. Retailers can use this information to understand which products are driving the most profit, how customer preferences are shifting over time, and what types of promotions are most effective. This integration helps businesses create a more data-driven approach to inventory management and marketing, ultimately leading to higher profitability and customer satisfaction.
class BackendAnalyticsSystem:
def __init__(self, transaction_data, utility_threshold):
self.transaction_data = transaction_data # Transaction history (list of itemsets)
self.utility_threshold = utility_threshold # Minimum utility threshold for itemsets

# Calculate utility for a given itemset based on profit and frequency
def calculate_utility(self, itemset, product_data):
utility = 0
for item in itemset:
# Add profit from sales and frequency as utility components
utility += product_data.get(item, {}).get('profit', 0) * product_data.get(item, {}).get('frequency', 0)
return utility

# Mine high-utility itemsets from transaction data
def mine_high_utility_itemsets(self, product_data):
high_utility_itemsets = {}
for transaction in self.transaction_data:
items = transaction["items"]
for i in range(len(items)):
for j in range(i + 1, len(items)):
itemset = frozenset([items[i], items[j]]) # Generate 2-itemsets
utility = self.calculate_utility(itemset, product_data)
if utility >= self.utility_threshold: # Only include itemsets above the threshold
if itemset not in high_utility_itemsets:
high_utility_itemsets[itemset] = utility
else:
high_utility_itemsets[itemset] += utility
return high_utility_itemsets

# Generate actionable reports based on mined data
def generate_reports(self, product_data):
high_utility_itemsets = self.mine_high_utility_itemsets(product_data)
report = []
for itemset, utility in high_utility_itemsets.items():
report.append({
'itemset': list(itemset),
'total_utility': utility,
'recommendation': "Optimize stock for these products and cross-sell opportunities"
})
return report

# Example transaction data
transaction_data = [
{"items": ["pasta", "sauce", "cheese"]},
{"items": ["pasta", "sauce"]},
{"items": ["bread", "butter"]},
{"items": ["pasta", "cheese"]},
]

# Example product data (profit per unit and sales frequency)
product_data = {
"pasta": {"profit": 2.5, "frequency": 100}, # profit in dollars and frequency of sales
"sauce": {"profit": 3.0, "frequency": 80},
"cheese": {"profit": 4.0, "frequency": 60},
"bread": {"profit": 1.5, "frequency": 50},
"butter": {"profit": 2.0, "frequency": 45},
}

# Utility threshold (minimum utility for an itemset to be included in reports)
utility_threshold = 250 # Set based on business's utility expectations

# Initialize the backend analytics system
backend_system = BackendAnalyticsSystem(transaction_data, utility_threshold)

# Generate and print actionable reports for business owners
reports = backend_system.generate_reports(product_data)
for report in reports:
print(report)

[0064] This backend analytics system operates using the CTC-HUIM (Cross-Transaction High Utility Itemset Mining) framework to analyze transaction data and extract high-utility itemsets, providing business owners with meaningful insights into their store's performance. The algorithm is designed to help businesses optimize their inventory management, product placement, and marketing strategies by identifying which product combinations generate the most utility (profit and frequency of sale). The algorithm starts by processing transaction data, which consists of itemsets representing the products customers have purchased together. For each transaction, the system generates itemsets (pairs of items) and calculates their utility, which is a measure of the profit and frequency of the item. For example, if "pasta" and "sauce" are frequently bought together, and both have high profit margins, the utility of this itemset will be high. The utility of an itemset is calculated by summing the product of the profit and frequency of each item in the set. This utility value is then compared against a threshold to determine whether the itemset is significant enough to include in the analysis. Only those itemsets whose utility exceeds the threshold are considered high-utility itemsets and are included in the final report.
[0065] Once the high-utility itemsets are mined, the system generates actionable reports for the business owner. These reports include the identified itemsets, their total utility, and recommendations such as "Optimize stock for these products and cross-sell opportunities." The reports give the business owner insights into which products are driving sales and profits and how they can optimize inventory levels, adjust product placement to encourage cross-selling, or promote specific item combinations to enhance profitability.
[0066] The utility threshold is a critical component of the algorithm, acting as a filter to ensure that only itemsets with significant utility are included in the analysis. This threshold is essential for focusing the business owner's attention on the most profitable and frequently purchased products, avoiding the clutter of low-utility itemsets that may not offer substantial value for inventory or marketing decisions. For instance, setting a utility threshold of 250 ensures that only itemsets generating substantial profit and frequency are flagged for optimization. If the threshold were set too low, the system would include many itemsets with minimal impact on the business, potentially overwhelming the retailer with unnecessary data. Conversely, if the threshold were set too high, the system might miss valuable itemsets that could provide insights into cross-selling opportunities or emerging customer preferences
[0067] By carefully setting the threshold, the system ensures that the high-utility itemsets presented in the report are both actionable and relevant to the retailer's strategy. This enables the business to take a more data-driven approach to managing stock levels, adjusting product placement, and refining promotional strategies, ultimately leading to better-informed decisions, improved customer satisfaction, and higher profitability.
[0068] So, the components of the Advanced Intelligent Retail Shopping Cart System are intricately interconnected to create a seamless, high-tech shopping experience. Each component-whether it is the touch screen interface, barcode scanner, weighing system, or the backend analytics powered by CTC-HUIM-works in concert with the others to fulfill distinct roles that enhance both customer convenience and business efficiency. Through their integration, these components ensure that the system is not only responsive and user-friendly but also capable of delivering valuable real-time insights to retailers, making it a comprehensive solution for modern retail environments.

[0069] The working process begins when a customer engages with the interactive touch screen interface embedded in the shopping cart. The customer can enter a shopping list or simply start scanning items as they go. The barcode scanner, embedded in the cart's handle, automatically reads each product's barcode and transmits this information to the backend system. In real time, the backend retrieves the product details from a central database, including pricing, promotions, and any relevant nutritional or sustainability data. The scanned item's details are instantly displayed on the touch screen, providing the customer with a clear overview of the product's name, price, and any applicable discounts.
[0070] For loose or unbarcoded items, the system incorporates an advanced weighing system. When a customer places an item, such as fruits or vegetables, on the cart's built-in weighing scale, the system precisely measures the weight using digital load cells. This weight data is sent to the backend, which calculates the total price by multiplying the weight by the per-unit cost stored in the database. The calculated price is displayed on the touch screen alongside other item details. Throughout the process, the system ensures accuracy by applying a weight fluctuation threshold, which prevents small, insignificant weight variations from affecting the final pricing. The dynamic pricing model adjusts product prices in real time based on factors such as inventory levels, store traffic, and product freshness. For instance, if a product is nearing its expiration date or the store has excess inventory, the system may apply a discount, which is immediately reflected on the touch screen. These price adjustments help retailers optimize inventory and reduce waste while offering customers better deals.
[0071] The system also provides personalized recommendations based on the customer's current purchases, shopping history, and preferences. The CTC-HUIM framework continuously mines transaction data to identify high-utility itemsets, which represent product combinations frequently bought together and yielding high profit margins. For example, if a customer scans pasta, the system might suggest sauce or cheese based on historical data. These recommendations are shown on the touch screen, promoting relevant upsell or cross-sell opportunities. As the customer shops, the system offers health-conscious and sustainability suggestions by cross-referencing the product data with allergen databases and sustainability guidelines. If a scanned item contains allergens or exceeds thresholds for sugar or sodium, the system warns the customer and suggests healthier alternatives. Similarly, if an item is deemed environmentally unfriendly, the system may recommend eco-friendly products instead.
[0072] When the customer is ready to check out, the secure digital transaction mechanism allows for seamless payment directly from the cart. The system totals the items in the cart and enables payment through digital wallets or linked customer accounts. The backend securely processes the transaction, ensuring a smooth and hassle-free checkout experience without the need for traditional cashier lines. All financial data is encrypted and protected, guaranteeing the customer's privacy and security. For retailers, the backend system continuously mines transaction data using the CTC-HUIM framework to generate insights into consumer behavior. The system identifies high-profit products, tracks shifting customer preferences, and suggests optimizations for stock levels and product placement. These reports allow retailers to fine-tune marketing strategies, adjust inventory, and create targeted promotions that enhance profitability.

[0073] Case Study: Suppose a young boy, around 10 years old, arrives at the grocery store with a shopping list provided by his parents. The list includes items like apples, pasta, tomato sauce, and cheese. He is using the Advanced Intelligent Retail Shopping Cart System to complete his shopping. Engaging the Cart System Upon entering the store, the boy approaches an available smart shopping cart. He places his parents' list on the cart's touch screen by manually entering or scanning the pre-prepared list. The system processes the list and generates a route through the store, guiding the boy to the specific aisles where the items are located. Scanning Products The boy starts with the fresh produce section. He places a bag of apples on the cart's built-in scale. The weighing system instantly measures the apples' weight (e.g., 1.2 kg) and calculates the price based on the store's per-unit rate for apples. The touch screen displays this information, showing that the total cost for the apples is $3.60. The boy moves to the pasta section, where he scans a package of pasta using the cart's barcode scanner. The system retrieves the price and promotions, displaying them on the screen.
[0074] Dynamic Pricing and Recommendations As the boy scans the pasta, the system checks inventory levels and notices an overstock of pasta sauce. Using the dynamic pricing model, the system offers a flash discount on the pasta sauce, displaying the discounted price on the screen. The system also suggests a cheese promotion, prompting the boy to add it to the cart. The boy follows the recommendations, scanning the discounted sauce and the suggested cheese. Health and Sustainability Suggestions The boy heads to the snack aisle to grab a treat but scans a bag of chips. The system cross-references the chips with an allergen database, detecting that the chips contain high levels of sodium. A warning appears on the screen, alerting him of the high sodium content, and the system suggests a healthier alternative-baked vegetable chips. The boy, concerned about his parents' dietary preferences, follows the recommendation and swaps the chips for the healthier option.
[0075] Finalizing the Purchase With all the items from the list in the cart, the boy proceeds to checkout. The secure digital transaction mechanism calculates the total cost of the items in his cart, including any discounts applied through dynamic pricing. The system allows him to use a digital wallet linked to his parents' account for payment. After confirming the transaction, the cart's screen displays a "Thank You" message, and the boy is ready to leave the store without ever needing to queue at a traditional checkout line. Retailer Insights Behind the scenes, the system uses the transaction data from this shopping trip to update the retailer's backend analytics. The CTC-HUIM framework identifies the cross-sell success of pairing pasta with sauce and cheese, reinforcing the effectiveness of the promotional strategy. The retailer receives a report detailing which product bundles are yielding high profits and how the dynamic pricing of overstocked items helped reduce excess inventory. Based on these insights, the retailer can further optimize product placement and promotional strategies to boost future sales.

[0076] While there has been illustrated and described embodiments of the present invention, those of ordinary skill in the art, to be understood that various changes may be made to these embodiments without departing from the principles and spirit of the present invention, modifications, substitutions and modifications, the scope of the invention being indicated by the appended claims and their equivalents.

FIGURE DESCRIPTION

[0077] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate an exemplary embodiment and explain the disclosed embodiment together with the description. The left and rightmost digit(s) of a reference number identifies the figure in which the reference number first appears in the figures. The same numbers are used throughout the figures to reference like features and components. Some embodiments of the System and methods of an embodiment of the present subject matter are now described, by way of example only, and concerning the accompanying figures, in which:
[0078] Figure - 1 illustrates the ne diagram of the Advanced Intelligent Retail Shopping Cart System, The interactive touch screen is mounted at the top front of the cart, easily accessible to the customer. This interface serves as the primary point of interaction between the customer and the system. It displays product information, personalized recommendations, and real-time dynamic pricing based on the items scanned or weighed. Additionally, it provides navigation assistance and enables secure digital transactions, allowing customers to complete their purchases directly from the cart. The barcode scanner is integrated into the cart handle, offering a convenient position for users to scan packaged products. When a product is scanned, the information is instantly relayed to the backend system, which retrieves relevant product details such as pricing, promotions, and nutritional information. The scanner ensures fast and accurate item identification, contributing to an efficient shopping experience. Located at the base of the cart is the weighing system, designed to handle loose or unbarcoded items like produce. When an item is placed on the scale, it calculates the weight with precision and transmits the data to the backend system, which multiplies the weight by the per-unit price. The total price is then displayed on the touch screen, ensuring that the customer has full visibility of the cost of their loose items. the secure digital transaction mechanism is placed near the touch screen, allowing customers to pay directly from the cart using digital wallets or other contactless payment methods. It processes the transaction securely through encrypted connections, ensuring a safe and seamless checkout experience without the need for traditional cashier lines. , Claims:1. An Advanced Intelligent Retail Shopping Cart System for enhancing the consumer shopping experience and providing real-time business analytics, comprising:
an interactive touch screen interface operatively connected to a backend system, wherein said interface facilitates customer interaction by displaying product details, personalized recommendations, and real-time pricing data;
a barcode scanner integrated into the shopping cart, operatively linked to said backend system, configured to scan product barcodes and retrieve corresponding product details from a central database;
a weighing system embedded in the shopping cart, configured to measure the weight of loose or unbarcoded items, and operatively connected to said backend system for calculating the total price based on the weight and per-unit price of the measured item;
a Cross-Transaction High Utility Itemset Mining (CTC-HUIM) framework embedded within the backend system, operatively configured to mine transaction data, identify high-utility itemsets, and generate personalized product recommendations based on customer preferences, shopping history, and high-profit itemsets;
a dynamic pricing mechanism integrated into the backend system, operatively configured to adjust product prices in real time based on store inventory levels, product freshness, and customer traffic, and display said adjusted prices on the interactive touch screen;
a secure digital transaction mechanism integrated into the system, enabling customers to finalize purchases via a digital payment interface on the touch screen without requiring traditional checkout lines, and securely processing payment data through encryption protocols.
2. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the interactive touch screen interface is further configured to display real-time health-conscious recommendations by cross-referencing product data with a nutritional and allergen database, providing alerts if a scanned product contains allergens or exceeds pre-set thresholds for sugar, sodium, or other dietary concerns.
3. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the interactive touch screen interface is further configured to display sustainability suggestions, said system cross-referencing product data with an eco-friendly product database, providing recommendations for environmentally friendly or ethically produced alternatives based on sustainability scores associated with the scanned product.
4. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the barcode scanner is configured with an error-checking mechanism that cross-references scanned product data with store inventory data, identifying and flagging mislabeled or faulty products, and notifying the customer via the interactive touch screen interface.
5. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the weighing system employs advanced digital load cells calibrated to account for minor fluctuations in weight, with a pre-set weight fluctuation threshold to ensure precision in weight measurement and price calculation for loose items, thereby ensuring accurate pricing information is displayed in real time.
6. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the backend system integrates with the store's central database, said system configured to generate business intelligence reports through the CTC-HUIM framework, wherein the transaction data is analyzed to provide actionable insights into consumer purchasing behavior, high-profit product combinations, inventory optimization, and promotional strategies.
7. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the dynamic pricing mechanism is configured to apply discounts and promotional pricing in real time, based on product freshness, store traffic patterns, and inventory levels, and is capable of initiating flash sales or time-sensitive discounts displayed on the touch screen interface.
8. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the secure digital transaction mechanism is configured to process payments via multiple digital methods, including but not limited to digital wallets, store loyalty accounts, and contactless payment systems, said mechanism employing encryption and secure transmission protocols to ensure privacy and prevent unauthorized access to payment data.
9. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the interactive touch screen interface is further configured to provide real-time navigation assistance, guiding customers to the locations of items in the store based on their entered shopping list and optimizing the path to reduce shopping time and enhance cross-sell opportunities.
10. The Advanced Intelligent Retail Shopping Cart System of Claim 1, wherein the system is configured to offer personalized promotional suggestions based on mined transaction data, customer shopping history, and product preferences, said suggestions being displayed on the interactive touch screen interface in response to scanned items, with real-time adjustments made through the CTC-HUIM framework.

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