Store location algorithm for shopping list fulfillment

Abstract

According to the present invention disclosed herein, there is provided a system for optimizing shopping for a shopper, comprising the steps of: (a) receiving a shopping list from the shopper; (b) mapping the items on the shopping list and their associated attributes and (c) selecting a store and / or combination of stores that allows the shopper to purchase all items on the shopping list optimized for a shopper-defined attribute using an iterative computer computational algorithm implemented on a processor to solve a linear integer programming problem with an integer numbers of stores and items.

Description

TITLESTORE LOCATION ALGORITHM FOR SHOPPING LIST FULFILLMENTCross-Reference to Related Application

[0001] This application claims priority to and the benefit of U.S. Provisional Application No. 63 / 547,363 filed on November 5, 2023.Background

[0002] The present invention relates to the field of shopping optimization and recommendation systems. Specifically, it pertains to algorithms that enable shoppers to input their shopping lists and receive recommendations for optimal stores based on shopper-defined attributes such as price, delivery speed, store ratings, or other criteria. The shopping system optimizes the shopping experience by minimizing the number of stores involved while maximizing the overall value of the selected attributes. This technology encompasses software engineering, data analytics, and shopper behavior modeling, utilizing computational systems to enhance the shopping process, with potential applications in both online and physical retail environments.

[0003] As shopping trends (e.g., online or physical shopping) continue to grow, shoppers encounter challenges , such as finding all items on a shopping list, optimizing the shopping experience based on shopper-defined attributes, and minimizing the number of stores to buy from.

[0004] Shoppers often struggle to locate all the items they need across various e-commerce platforms. Traditional online shopping systems require manual searches for each item, which can become a time-consuming process, especially when items are out of stock or distributed across multiple stores. This inefficiency can result in incomplete shopping experiences, forcing shoppers to either forego certain items or purchase from additional stores, leading to extra delivery fees or increased effort. The struggle also manifests in offline shopping and the effort is amplified as shoppers would need to physically visit multiple stores.

[0005] While many shoppers aim to optimize their shopping experience based on factors such as price, delivery speed, store ratings, or other preferences,existing shopping tools primarily focus on item-by-item price comparisons. These tools require shoppers to manually compare prices for each individual item on their shopping list across different stores, without providing a holistic view of the entire shopping list. This approach is not only labor-intensive but also limits shoppers' ability to consider other important attributes, such as delivery time or store ratings, when making decisions. Moreover, by concentrating on single-item price comparisons, these shopping tools often overlook opportunities to optimize for the full range of shopper preferences or to maximize savings on bulk purchases, delivery consolidation, or other costsaving measures. Additionally, current systems do not efficiently balance multiple shopper-defined attributes across an entire shopping list, nor do they minimize the number of stores involved, often resulting in higher costs, slower deliveries, or suboptimal store choices for online shopping and longer shopping trips with multiple store visits for offline shopping.

[0006] Completing a shopping list by purchasing from multiple stores can complicate the shopping experience, increasing both shipping fees and delivery times for online shopping and extending shopping time and forcing multiple store visits for offline shopping. Shoppers typically prefer a streamlined shopping experience that allows them to source all necessary items from the fewest possible stores. However, existing tools often result in shoppers being directed to multiple stores, which complicates the process and leads to inefficiencies in time, cost, and convenience.

[0007] Current online shopping applications and tools typically provide basic functionalities such as price comparison and store locator features. Several patented systems have attempted to address key aspects of shopping optimization, yet they fall short of providing a comprehensive solution to the challenges shoppers face.

[0008] Moreover, the complexity of generating comprehensive shopping list combinations across numerous stores can quickly escalate, making it impractical, inefficient and computationally prohibitive to use brute force methods to evaluate all potential options in a timely manner. Brute-force search or exhaustive search, also known as generate and test, is a very general problem-solving technique and algorithmic paradigm that consists ofsystematically checking all possible candidates for whether or not each candidate satisfies the problem's statement. For example, a brute-force algorithm that finds the divisors of a natural number n would enumerate all integers from 1 to n, and check whether each of them divides n without remainder. A brute-force approach for the eight queens puzzle would examine all possible arrangements of 8 pieces on the 64-square chessboard and for each arrangement, check whether each (queen) piece can attack any other.

[0009] Overall, existing tools primarily focus on enhancing specific aspects of the shopping experience, such as dietary needs, waste reduction, item recommendations or item-by-item price comparison. However, they largely fail to address the simultaneous optimization of shopper-defined attributes, completeness, and store minimization in a cohesive manner. In addition, brute force solution recommendation is computationally prohibitive given the current state of technologies including but not limited to computing processing power, memory and storage, energy efficiency, etc. Shoppers still encounter barriers in achieving a comprehensive and efficient shopping experience, often resulting in higher costs and increased complexity in the purchasing process.

[0010] The current disclosure provides a solution for this unmet need. In particular, the claimed shopping system optimizes the shopping experience by minimizing the number of stores involved while maximizing the overall value of the shopper-defined and selected attributes.Summary of Invention

[0011] The present invention introduces a highly efficient system for optimizing online shopping by automating the process of selecting stores that fulfill a shopper's shopping list while optimizing key shopper-defined attributes including but not limited to total spend on shopping list, proximity and delivery time, freshness requirements, item substitutions, delivery or pickup time constraints, store loyalty, brand preference, environmental impact, dietary restrictions, quality preferences. The system minimizes the number of stores from which a shopper’s shopping list can be fulfilled, providing a seamless shopping experience. This system leverages an advanced algorithm that processes the shopper's shopping list, selectively retrieves real-time data — such as pricing, availability, and delivery speed — from various stores, anddelivers optimized recommendations based on the shopper's-defined attributes.

[0012] Upon receiving the shopping list, the algorithm first identifies the items and matches them to stores that carry the items. It then evaluates a combination of factors to determine which items require real-time updates for attributes like pricing, or delivery times. These factors may include the variance in item attributes across stores and the number of stores offering the item. By focusing on attributes that have the greatest impact on the shopper's preferences — whether that be price, speed, or store ratings — the algorithm ensures that real-time updates are selectively fetched to conserve computational resources and improve the efficiency of the shopping process.

[0013] Through API integrations with various stores, the system dynamically retrieves updated information on selected items and combines it with static data for other items. The algorithm simultaneously evaluates critical factors such as individual item attributes while also minimizing the number of different stores the shopper must order from. This multifaceted approach allows the system to optimize for multiple shopper-defined attributes, making it highly versatile in addressing the shopper's specific shopping needs, whether they prioritize cost savings, faster delivery, or other attributes.

[0014] The result is a comprehensive recommendation that provides the shopper with an optimal store or store combination recommendation, detailing the selected attributes for each. The system’s efficiency, combined with its selective use of real-time data, enables it to recommend the best possible shopping plan based on the shopper’s-defined attributes, reducing complexity, time, and total effort required by the shopper.

[0015] This invention delivers a robust, automated solution that significantly enhances the shopping experience by selectively using real-time data, optimizing key attributes based on shopper-defined preferences, and minimizing the number of stores the shopper interacts with. This results in a streamlined, efficient, and personalized shopping process tailored to modern shoppers’ needs.

[0016] This invention also delivers a robust, automated solution that significantly enhances the online shopping experience by selectively using real-time data, optimizing key attributes based on shopper-defined preferences, and minimizing the number of stores the shopper interacts with. This results in a streamlined, efficient, and personalized shopping process tailored to modern shoppers’ needs. A similar process could be applied for offline shopping experiences, incorporating the same linear integer programming approach described herein.

[0017] The present invention provides a system for optimizing shopping for a shopper, comprising the steps of: (a) receiving a shopping list from the shopper; (b) mapping the items on the shopping list and their associated attributes and (c) selecting a store or a combination of stores that allows the shopper to purchase all items on the shopping list optimized for a shopper- defined attribute.

[0018] The present invention provides a system for optimizing shopping for a shopper, comprising the steps of: (a) receiving a shopping list from the shopper; (b) refining a list of stores based on shopper preferences; (c) mapping the items on the shopping list and their associated attributes to the refined list of stores; (d) determining stores and multiple combinations of stores that collectively provide all items on the shopping list; (e) calculating the total score of each store and combination of stores based on a shopper- defined attribute; and (f) selecting the store or combination of stores that optimize the shopper-defined attribute.

[0019] The present invention provides a system for optimizing shopping for a shopper, comprising: (a) receiving a shopping list from the shopper; (b) refining a list of stores based on shopper preferences; (c) mapping the items on the shopping list and their associated attributes to the refined list of stores; (d) determining stores and multiple combinations of stores that collectively provide all items on the shopping list (e) calculating the total score for each store and combination based on multiple shopper-defined attributes, each weighted according to shopper preferences; and (f) selecting the store or combination of stores that provides an optimal balance based on the shopper’s defined preferences.

[0020] The present invention provides a system for optimizing shopping for a shopper that can be integrated into existing marketplace applications to enhance shopping capabilities of the shopper.

[0021] The present invention provides a system for optimizing shopping that can function as an independent application, providing a shopper with a comprehensive tool for shopping optimization.

[0022] The present invention provides a system used to fulfill a shopping list of a shopper, wherein the shopping list comprises items to be purchased, and wherein the shopping system reduces overall costs, number of stores required for purchase or pickup, or a combination thereof.Brief Description of the Drawings

[0023] FIG. 1 illustrates the steps of the system for optimizing shopping for a shopper used in embodiments of the present invention.Detailed Description

[0024] The present invention addresses the limitations of existing online shopping tools by introducing an innovative algorithm that simplifies the shopping experience based on any shopper-defined attributes. Once the shopper inputs their shopping list, the algorithm performs all necessary tasks to identify the optimal store(s) for the required items. It not only determines which store(s) carry the items but also compares key attributes across this / these store(s). Moreover, the algorithm minimizes the number of store pickups required, streamlining the shopping process.

[0025] After conducting its analysis, the algorithm recommends the best store or store combinations based on the shopper's-defined attributes for that specific shopping list. For instance, a shopper might prioritize savings (i.e. price attribute) on one shopping list and speed of delivery on another. The shopper can then review the recommendations and complete their order with minimal effort. By automating the process of searching for items, evaluating attributes, and selecting the best stores that meet those shopper-defined attributes, the algorithm offers shoppers a hassle-free shopping experience that saves both time and effort, while optimizing the experience according to their personal needs.

[0026] Additionally, the algorithm leverages advanced computational techniques to enhance the efficiency of the store selection process, integrating shoppers' shopping lists with up-to-date inventory and relevant attribute data. This significantly improves the online shopping experience by empowering shoppers to make informed purchasing decisions without the manual effort of comparing various factors. The algorithm ensures that real-time updates are only fetched when they are likely to impact overall shopping list cost and its recommendation based on shopper-defined attributes, thus conserving computational resources and speeding up the process.

[0027] Furthermore, with potential future applications in physical shopping, the algorithm could eventually assist shoppers in their offline shopping by helping them efficiently locate the store or minimal number of stores that carry all items on their shopping lists while optimizing based on their preferred attributes. This comprehensive solution addresses the evolving needs of modern shoppers both online and offline, ensuring a convenient, personalized, and efficient shopping experience.

[0028] Through API integrations with various stores, the system dynamically retrieves updated information on selected items and combines it with static data for other items. The algorithm simultaneously evaluates attributes such as individual item pricing, bulk discounts, shipping costs, and item availability, all while striving to minimize the number of different stores the shopper would need to make a purchase from. This multi-faceted approach allows the system to optimize both for user-defined attributes and logistical simplicity, a key advantage over existing tools that often rely on manual price comparisons and fail to consider store consolidation.

[0029] The output is a final, comprehensive recommendation that provides the shopper with the optimal store or combination of stores to purchase from, including pricing, item availability, and the number of store pickups or deliveries required. The system’s efficiency and selective use of real-time data allow it to recommend the best possible shopping plan, minimizing shopper effort, complexity, and time spent on shopping.

[0030] In summary, the invention delivers a powerful automated solution that significantly enhances the online and offline shopping experience byselectively using real-time data, optimizing shopper-defined attributes, and minimizing the number of stores a shopper must interact with. This results in a streamlined, personalized, and time-saving shopping process for shoppers.Definitions

[0031] Throughout the definition section, the singular and the plural can be interchangeable. The term “shopper-defined attributes” as used herein refers to requirements, preferences or constraints that a shopper prioritizes for a given shopping list. They can include but are not limited to:Price: the price of an item individually or collectively, which can be used as a parameter when a shopper wants to minimize total spend on a shopping list.Proximity / Delivery time: Distance or travel time from a shopper’s location, which can be used as a parameter for prioritizing closer stores to minimize travel and delivery time.Freshness Requirements: Ensuring perishable items are fresh, often influencing store selection based on perceived product turnover.Item Substitutions: Willingness to substitute items (e.g., organic apples instead of non-organic if unavailable).Timing Constraints: Time of day or specific time limits for shopping trips. In some embodiments, timing constraints is, based on the shopper's availability.Store Loyalty: Preference for certain stores based on loyalty programs, rewards, or familiarity.Brand Preferences: Preference for certain store brands over others, which may be influenced by quality, familiarity, or ethical considerations.Environmental Impact: Preference for eco-friendly or sustainable stores and packaging, or minimizing travel distance to reduce carbon footprint.Dietary Restrictions: Shopper medical needs, religious following, or tastes, for example, gluten free, vegan, Halal or Kosher stores.Quality Preferences: Selecting stores with certain quality levels, such as organic, non-GMO, or fair-trade options.Delivery / Pickup Options: Preference for delivery or curbside pickup versus instore shopping.The term “shopper” as used herein refers to any person whether human or non-human purchasing products / items whether online or offlineThe term “marketplace applications" as used herein refers to existing mobile applications on downloadable application stores including but not limited to Amazon, Instacart, etc.The term “attribute” refers more generally to the requirements, preferences or constraints discussed above in relation to shopper-defined attributes.The term “specific attribute thresholds” refers to absolute requirements and maximum and minimum parameters regarding such attributes as e.g., price and travel distance.The term “improves cost-effectiveness” refers to minimizing price in the context of other related considerations such as travel time and distance which also have a cost.

[0032] The present invention relates to a system for optimizing shopping, which utilizes a computer processor and associated computer memory device to execute at least one of the following steps: (a) receiving a shopping list from the shopper; (b) mapping the items on the shopping list and their associated attributes and (c) selecting a store or combination of stores that allows the shopper to purchase all items on the shopping list optimized for a shopper- defined attribute.

[0033] The present invention also provides a system for optimizing shopping for a shopper, comprising the steps of: (a) receiving a shopping list from the shopper; (b) refining a list of stores based on shopper preferences; (c) mapping the items on the shopping list and their associated attributes to the refined list of stores; (d) determining stores or combinations of stores that collectively provide all items on the shopping list; (e) calculating the total score of each determined result based on a shopper-defined attribute; and (f) selecting the store or combination of stores that optimize the shopper-defined attribute.

[0034] The present invention also provides a system for optimizing shopping for a shopper, comprising: (a) receiving a shopping list from the shopper; (b) refining a list of stores based on shopper preferences; (c) mapping the items on the shopping list and their associated attributes to the refined list of stores; (d) determining store or combinations of stores that collectively provide all items on the shopping list; (e) calculating the total score for the store or each combination based on multiple shopper-defined attributes, each weighted according to shopper preferences; and (f) selecting the store or combination of stores that provide an optimal balance based on the shopper’s defined preferences.

[0035] The present invention provides a system for optimizing shopping for a shopper that can be integrated into existing marketplace applications to enhance shopping capabilities of the shopper.

[0036] The present invention provides a system for optimizing shopping that can function as an independent application, providing a shopper with a comprehensive tool for shopping optimization.

[0037] The present invention provides a system used to fulfill a shopping list of a shopper, wherein the shopping list comprises items to be purchased, and wherein the shopping system reduces overall costs, number of stores required for purchase or pickup, or a combination thereof. In some embodiments, the shopping system uses an algorithm that processes the shopping list wherein the algorithm:(a) selectively retrieves real-time price from stores,(b) selectively retrieves availability data from stores, and(c) provides an optimized recommendation based on price, availability, store minimization, or a combination thereof.

[0038] In some embodiments, the algorithm identifies the items and matches the items to stores that carry the items. In some embodiments, the algorithm uses a combination of factors to decide which items in a shopping list require real-time price and availability updates. In some embodiments, the factors are selected from price variance of the item across stores, number of stores carrying the item, likelihood of item being offered in sale or temporary discount,or a combination thereof. In some embodiments, the current invention optimizes shopping list (e.g., grocery list) resulting in overall cost efficiency. In some embodiments, the current application provides automated cost comparisons. In some embodiments, the current application minimizes store visits. In some embodiments, the current application minimizes store pickups, in some embodiments, the current application does not requires shopper intervention for effective price comparisons. In some embodiments, the current application provides a system for generating optimized shopping lists that account for price fluctuations and / or shopper preferences. In some embodiments, the current application improves cost-effectiveness. In some embodiments, the current application does not rely on shopper input or manual price tracking. In some embodiments, the current application provides automated comparisons across stores. In some embodiments, the current application provides a dynamic optimizing shopping experience.

[0039] Thus, the present invention relates to a system for optimizing shopping by providing a streamlined, attribute-based process that enables shoppers to generate and refine stores or store combinations for purchasing all items on a shopping list. The system leverages shopper-defined preferences and realtime or estimated data to ensure an optimal shopping experience, whether based on price, delivery time, store ratings, or other criteria. In some embodiments, the system for optimizing shopping for a shopper comprises the steps depicted in FIG. 1 .

[0040] Receiving Shopping List: The system begins by receiving a shopping list from the shopper. The list may include any number of items that the shopper intends to purchase, including but not limited to grocery items or household products. The shopping list may be entered manually, imported from other digital sources, or generated via voice input.

[0041] Refining Store List Based on Shopper Preferences: Once the shopping list is received, the system refines a list of potential stores based on shopperspecific preferences. These preferences may include, but are not limited to, proximity of the stores, store loyalty memberships, shopper past experiences, delivery service availability, or store-specific attributes such as delivery speedor shopper ratings. The system may filter stores that do not meet the shopper- defined attributes, such as those with low ratings or longer delivery times.

[0042] Mapping Items to Refined Store List: The system then maps the items on the shopping list to the refined list of stores. Each store is associated with its specific attributes, Including but not limited to pricing of the specific item, delivery time, store reviews. This mapping process may be performed using real-time data from the stores or from estimated information based on prior data or default values if real-time data is not needed.

[0043] Finding Combinations with the Least Number of Stores: After mapping the items, the system identifies multiple combinations of stores that can collectively fulfill the entire shopping list. In this step, the system can identify single stores instead of combinations if single stores can fulfill the order while meeting shopper-defined attributes. Specifically, the system determines a minimum number of combinations that involve the least number of stores, ensuring that each combination has all the items on the list. This can be solved as a linear programming problem, for example using the PuLP library on Python. The problem is actually a linear integer programming problem, since there are integer numbers of stores and items. It needs more constraints — integrality constraints, and it is possible to add that constraint easily on PuLP. This step reduces the complexity of the shopping process by minimizing the number of separate stores the shopper must interact with, while ensuring that all items can still be purchased.

[0044] Calculating the Total Score for Each Combination: The system then calculates a total score for each of the store combinations, factoring in shopper-defined attributes. These attributes may include, but are not limited to, price, delivery time, store ratings. The shopper may also assign custom weightings to these attributes based on personal priorities. For instance, a shopper may place more emphasis on selecting stores with the fastest delivery times or those offering the lowest prices.

[0045] Selecting the Optimal Store Combination: Based on the total score calculated in the previous step, the system selects the optimal store or store combination using for example least squares optimization that best satisfies the shopper’s defined preferences. The optimal store or store combinationmay represent the one that minimizes costs, maximizes delivery speed, or balances several factors such as delivery time and store ratings. The shopper is then presented with the recommended store or store combination for review and confirmation.

[0046] Optional Replacement Preferences: In an optional step, the system may repeat steps 3-6 based on shopper-provided replacement preferences. After presenting the shopper with the initial store or store combination, the shopper may request adjustments based on additional preferences, such as replacing an unavailable item or excluding a store from the combination. The system will then remap the items and recalculate the optimal store or store combinations based on the updated preferences, providing the shopper with an alternative solution.

[0047] This process allows for dynamic and flexible shopping optimization, tailoring the experience to the shopper's needs and reducing the number of stores involved in fulfilling the order, while considering a variety of attributes such as cost, delivery time, and store ratings. The system is particularly suited for integration into a standalone application or as part of a third-party marketplace platform, offering shoppers an efficient and shopper-friendly shopping experience.

[0048] A process according to a recommendation in an embodiment of the present invention may occur as follows:1 . Receive an n-product shopping list as an input2. Receive a list of vendors that are selling at least 1 and at most all n products3. Select a subset of the vendors prioritizing vendors with higher availability of the n products4. Construct all possible carts using brute force5. Remove carts that do not meet certain criteria including but not limited to:- Defined number of deliveries- Vendor basket-size requirements- Total cart price- Vendor reliability- Shopper’s preferences- Vendor product selection - in case a shopper decides to replace certain items6. Score the remaining carts based on criteria from step 57. Recommend the cart with the highest score.

[0049] The system can be programmed to retrieve updates when: a. price: 1 ) price variance is high across stores, 2) product has a history of going on sale 3) product has low availability across stores (ie not popular). b. availability: 1 ) product has low availability across stores (ie not popular) 2) product has a history of going out of stock 3) product availability is in low rated stores (ie likely to be out of stock). c. shopper-defined attributes: 1 ) shopper requests certain delivery / pickup time 2) shopper sets certain budget per product 3) shopper defined certain stores to include and / or exclude for pickup / delivery for all or some categories of items they are shopping for. d. An item that meets any of the criteria mentioned in a to c and has been in shopping list for longer than a set period of time will trigger real-time update when shopper goes to shopping list again to complete their shopping.

[0050] A non-exhaustive list of the features of the present invention includes the following. In some embodiments, the shopping list is optimized for a shopper- defined attribute. In some embodiments, the shopping list is inputted manually, imported from other digital sources, or generated via voice input. In some embodiments, the attributes considered for optimization comprise price, delivery time, store ratings, availability of items, shopper-specific preferences, or a combination thereof. In some embodiments, the system allows the shopper to define custom weighting for multiple attributes when selecting the optimal store or store combination, wherein the attributes comprise price, delivery speed, store reputation, or a combination thereof. In some embodiments, the system determines stores or store combinations based on a combination of real-time data and estimated data. In some embodiments, the system determines store or store combinations based on a combination ofreal-time data and estimated data, including delivery times and store ratings. In some embodiments, the combination of real-time data and estimated data, includes delivery times, store ratings, or a combination thereof. In some embodiments, the shopper can adjust the weighting of a single attribute or multiple attributes. In some embodiments, the shopper can adjust the weighting of a single attribute or multiple attributes by placing higher importance on price or delivery time. In some embodiments, the system suggests an optimal store or store combination based on a predictive model using the shopper's past shopping behavior and attribute preferences. In some embodiments, the system further comprises providing the shopper with stores or multiple store combinations ranked based on different attributes and allowing the shopper to manually select the preferred combination. In some embodiments, the system provides notifications when the attribute or attributes change before the order is finalized. In some embodiments, the system allows the shopper to specify a maximum or minimum value for any attribute. In some embodiments, the system allows the shopper to specify a maximum or minimum value for price or store rating for the selected store or store combination. In some embodiments, the system factors in additional service fees, promotions, or loyalty programs offered by stores when calculating the total score for each store or store combination. In some embodiments, the system provides the shopper with real-time updates on attribute changes. In some embodiments, the system provides the shopper with real-time updates on item availability, price fluctuations, or store delivery times. In some embodiments, the system integrates reviews from third-party platforms to calculate store ratings as part of the optimization. In some embodiments, the system optimizes the store selection based on minimizing number of store pickups while balancing other shopper-defined attributes. In some embodiments, the system optimizes the store selection based on minimizing the number of store pickups while balancing price or delivery time. In some embodiments, the system optimizes the store selection based on minimizing the number of store pickups while balancing price and delivery time. In some embodiments, the system provides an interface for shoppers to manually prioritize or exclude certain stores based on attribute preferences. In some embodiments, the system provides an interface for shoppers tomanually prioritize or exclude certain stores based on attribute preferences, wherein attribute preferences comprise delivery time and ratings. In some embodiments, the system provides suggestions for replacement items from stores when the desired item is out of stock or has unfavorable attribute values In some embodiments, the system provides suggestions for replacement items from stores when the desired item is out of stock or has unfavorable attribute values, wherein the unfavorable attribute values are higher cost, longer delivery time, or a combination thereof. In some embodiments, the shopper can save preferred optimization settings for future shopping sessions, in some embodiments, the shopper can save preferred optimization settings for future shopping sessions, wherein the settings include weighting delivery speed over cost. In some embodiments, the system provides visual representations, showing how each store or store combination scores across different attributes. In some embodiments, the visual representations are graphs or charts. In some embodiments, the system allows the shopper to exclude certain stores based on past experiences or poor ratings, and the system updates the optimization accordingly. In some embodiments, the system integrates with external platforms to track delivery status and estimated arrival times for selected stores or store combinations. In some embodiments, the system recommends stores based on a combination of shopper-defined attributes and location-specific constraints. In some embodiments, the shopper-defined attributes and location-specific constraints include proximity and / or delivery service coverage. In some embodiments, the system automatically adjusts the stores or store combinations in response to significant changes in attributes before the order is finalized, wherein changes in attributes comprise price increases and / or delivery delays. In some embodiments, the shopper can filter stores or store combinations based on specific attribute thresholds. In some embodiments, the specific attribute thresholds comprise excluding stores with ratings below a certain value. In some embodiments, the system calculates total costs, including estimated taxes and additional fees, as part of the store optimization process. In some embodiments, the system provides a confidence rating for stores or store combinations based on the availability of accurate data for each attribute. In some embodiments, the system provides a confidence rating for stores orstore combinations based on the availability of accurate data for real-time prices or verified delivery times. In some embodiments, the shopper can manually adjust individual store selection based on preferred attributes, overriding the automatic optimization. In some embodiments, the system allows shoppers to define preferences for loyalty programs, factoring in any available points, rewards, or discounts into the store or store combination optimization. In some embodiments, the system notifies shoppers of any promotions, limited-time offers, or store discounts that influence the optimization for selected attributes. In some embodiments, the system further comprises providing suggestions for alternative stores when the selected store’s attributes no longer meet shopper-defined preferences. In some embodiments, the shopper-defined preferences comprise price, delivery speed, or a combination thereof.

[0051] The illustrations of embodiments described herein are intended to provide a general understanding of the structure of various embodiments, and they are not intended to serve as a complete description of all the elements and features of apparatus and systems that might make use of the structures described herein. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. Other embodiments may be utilized and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Figures are also merely representational and may not be drawn to scale. Certain proportions thereof may be exaggerated, while others may be minimized. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description. Therefore, it is intended that the disclosure not be limited to the particular embodiments ) disclosed.

Claims

CLAIMSWhat is claimed is:

1. A system for optimizing shopping for a shopper, comprising a processor with an associated computer memory device configured for preforming the steps of: (a) receiving a shopping list from the shopper; (b) mapping the items on the shopping list and their associated attributes and (c) selecting a store and / or combination of stores that allows the shopper to purchase all items on the shopping list optimized for a shopper-defined attribute using an iterative computer computational algorithm implemented on a processor to solve a linear integer programming problem with an integer numbers of stores and items.

2. The system of claim 1 , wherein the shopping list is optimized for a shopper- defined attribute.

3. The system of claim 1 , wherein the system for optimizing shopping for a shopper can be integrated into existing marketplace applications to enhance shopping capabilities of the shopper.

4. The system of claim 1 , wherein the system for optimizing shopping can function as an independent application, providing a shopper with a comprehensive tool for shopping optimization.

5. The system of claim 1 , wherein the shopping list is inputted manually, imported from other digital sources, or generated via voice input.

6. The system of claim 1 , wherein the attributes considered for optimization comprise price, delivery time, store ratings, availability of items, shopper-specific preferences, or a combination thereof.

7. The system of claim 1 , wherein the system allows the shopper to define custom weighting for multiple attributes when selecting the optimal store or store combination, wherein the attributes comprise price, delivery speed, store reputation, or a combination thereof.

8. The system of claim 1 , wherein the system determines store combinations based on a combination of real-time data and estimated data.

9. The system of claim 1 , wherein the system determines store combinations based on a combination of real-time data and estimated data, including delivery times and store ratings.

10. The system of claim 9, wherein the combination of real-time data and estimated data, includes delivery times, store ratings, or a combination thereof.

11. The system of claim 1 , wherein the shopper can adjust the weighting of a single attribute or multiple attributes.

12. The system of claim 1 , wherein the shopper can adjust the weighting of a single attribute or multiple attributes by placing higher importance on price or delivery time.

13. The system of claim 1 , wherein the system suggests an optimal store or store combination based on a predictive model using the shopper's past shopping behavior and attribute preferences.

14. The system of claim 1 , the system further comprising providing the shopper with multiple store combinations ranked based on different attributes and allowing the shopper to manually select the preferred combination.

15. The system of claim 1 , wherein the system provides notifications when the attribute or attributes change before the order is finalized.

16. The system of claim 1 , wherein the system allows the shopper to specify a maximum or minimum value for any attribute.

17. The system of claim 1 , wherein the system allows the shopper to specify a maximum or minimum value for price or store rating for the selected combination.

18. The system of claim 1 , wherein the system factors in additional service fees, promotions, or loyalty programs offered by stores when calculating the total score for each combination.

19. The system of claim 1 , wherein the system provides the shopper with realtime updates on attribute changes.

20. The system of claim 1 , wherein the system provides the shopper with realtime updates on item availability, price fluctuations, or store delivery times.21 . The system of claim 1 , wherein the system integrates reviews from third-party platforms to calculate store ratings as part of the optimization.

22. The system of claim 1 , wherein the system optimizes the store selection based on minimizing number of store pickups while balancing other shopper-defined attributes.

23. The system of claim 1 , wherein the system optimizes the store selection based on minimizing the number of store pickups while balancing price or delivery time.

24. The system of claim 1 , wherein the system optimizes the store selection based on minimizing the number of store pickups while balancing price and delivery time.

25. The system of claim 1 , wherein the system provides an interface for shoppers to manually prioritize or exclude certain stores based on attribute preferences.

26. The system of claim 1 , wherein the system provides an interface for shoppers to manually prioritize or exclude certain stores based on attribute preferences, wherein attribute preferences comprise delivery time and ratings.

27. The system of claim 1 , wherein the system provides suggestions for replacement items from stores when the desired item is out of stock or has unfavorable attribute values28. The system of claim 1 , wherein the system provides suggestions for replacement items from stores when the desired item is out of stock or has unfavorable attribute values, wherein the unfavorable attribute values are higher cost, longer delivery time, or a combination thereof.

29. The system of claim 1 , wherein the shopper can save preferred optimization settings for future shopping sessions.

30. The system of claim 1 , wherein the shopper can save preferred optimization settings for future shopping sessions, wherein the settings include weighting delivery speed over cost.

31. The system of claim 1 , wherein the system provides visual representations, showing how each store or store combination scores across different attributes.

32. The system of claim 31 , wherein the visual representations are graphs or charts.

33. The system of claim 1 , wherein the system allows the shopper to exclude certain stores based on past experiences or poor ratings, and the system updates the optimization accordingly.

34. The system of claim 1 , wherein the system integrates with external platforms to track delivery status and estimated arrival times for selected store or store combination.

35. The system of claim 1 , wherein the system recommends stores based on a combination of shopper-defined attributes and location-specific constraints.

36. The system of claim 35, wherein the shopper-defined attributes and locationspecific constraints include proximity and / or delivery service coverage.

37. The system of claim 1 , wherein the system automatically adjusts the store combinations in response to significant changes in attributes before the order is finalized, wherein changes in attributes comprise price increases and / or delivery delays.

38. The system of claim 1 , wherein the shopper can filter store combinations based on specific attribute thresholds.

39. The system of claim 38, wherein the specific attribute thresholds comprise excluding stores with ratings below a certain value.

40. The system of claim 1 , wherein the system calculates total costs, including estimated taxes and additional fees, as part of the store optimization process.

41. The system of claim 1 , wherein the system provides a confidence rating for store combinations based on the availability of accurate data for each attribute.

42. The system of claim 1 , wherein the system provides a confidence rating for store combinations based on the availability of accurate data for real-time prices or verified delivery times.

43. The system of claim 1 , wherein the shopper can manually adjust individual store selection based on preferred attributes, overriding the automatic optimization.

44. The system of claim 1 , wherein the system allows shoppers to define preferences for loyalty programs, factoring in any available points, rewards, or discounts into the store combination optimization.

45. The system of claim 1 , wherein the system notifies shoppers of any promotions, limited-time offers, or store discounts that influence the optimization for selected attributes.

46. The system of claim 1 , further comprising providing suggestions for alternative stores when the selected store’s attributes no longer meet shopper-defined preferences.

47. The system of claim 1 , the shopper-defined preferences comprise price, delivery speed, or a combination thereof.

48. A system used to fulfill a shopping list of a shopper, wherein the shopping list comprises items to be purchased, and wherein the shopping system reduces overall costs, number of stores required for purchase or pickup, or a combination thereof.

49. The system of claim 48, wherein the shopping system uses an algorithm that processes the shopping list wherein the algorithm:(a) selectively retrieves real-time price from stores,(b) selectively retrieves availability data from stores, and(c) provides an optimized recommendation based on price, availability, store minimization, or a combination thereof.

50. The system of claim 49, wherein the algorithm identifies the items and matches the items to stores that carry the items.

51. The system of claim 50, wherein the algorithm uses a combination of factors to decide which items in a shopping list require real-time price and availability updates.

54. The system of claim 50, wherein the factors are selected from price variance of the item across stores, number of stores carrying the item, likelihood of item being offered in sale or temporary discount, or a combination thereof.

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