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`FIG. 15 is a flow chart. for the method of makinga list and identifying what and
`{0185]
`where to buy item(s) spending the minimum amountwithin defined Shopperpreferences. At step
`800, the shopperlogs on to the website or starts the shopper app on the mobile device. At step
`
`804, the shopper enters preferences data. At step 806, the shopper creates a shoppinglist. At
`
`. Step 808 and 810, the system issues one or more not-on-list (NOL) couponoffers for
`
`consideration. At step 812, the shopperselects ‘““Done”endinglist creation andinitiating cost
`
`minimization algorithm. At step 814, the Minimization Algorithm Calculates DNP for each on
`
`the list, shopper preference identified item substitutesat all alternate retail locations. At step 816,
`the minimization function Identifies “Initial Lowest Cost” (ILC)list/basket andretail location
`
`10
`
`providing. At step 818, a determination is made whetherthere are anyretailer incentives (RIs).
`
`If so, step 820 is performed. If not step 822 is performed. At step 820 the Minimization
`Algorithm incorporates RI’s and recalculates basket cost, now the “Subsequent Lowest Cost”list
`andretail location providing. At step 822, if there are no RIs, the ILC becomes the SLC. Atstep
`
`824, SLC is displayed to the shopper along with MIs. At step 826, a determination is made
`
`15
`
`whether there are any marketing incentives (MIs). If so, step 830 is performed. If not step 828 is
`
`performed. At step 830, the Minimization Algorithm recalculates basket or item cost including
`
`all MIs; the final SLC becomesthe “Final Lowest Cost (FLC)item(s) orlist. At step 828 the
`SLC becomes the FLC. At step 832, the Lowest cost for item andretail store (FLC) are
`
`presented to shopper for the shoppingtrip.
`
`-20
`
`FIG. 16 is a flow chart of an example ofa Retail store, Shopper uses FLClist to
`[0186]
`shop for itemsin streamlined fashion. At step 902, the shopper takes the shoppinglist on smart
`phone to a retail store. At step 904, As the shopperenters the store, the shopper is presented with
`
`a splash page of “In-StoreIncentives” (ISIs) or Ad. At step 906, a determination is made
`
`whetherthe ISI is accepted. If so, step 908 is performed, and the selected Items(if any) is added
`
`25
`
`to list and basket total. At step 910, the shopper picks up itemsonthe list. At step 912, the
`
`shopper checks out, scanning smart phoneor presents a frequent shoppercard, frequent shopper
`
`numberorother identifier to record and validate purchases, redeem e-couponsandcreate valid
`
`redemption record for coupon fulfillment.
`
`[0187]:
`
`The system described herein captures key shopper demographic and preference
`
`30
`
`data including: decision heuristics, substitutable brands, preferred/substitutable stores or retail
`
`shopping locations, preferred sizes, flavors, packs, etc. This information comes both from
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`internal sources upon registration and also from shared information from loyalty programsofthe
`
`retailers participating in the program, as well as other external sources and co-developed sources,
`
`such as manually entered data, screen scraped data, or the like, . Additional information about
`
`the user is derived from data mining and analysis of the above data.
`[0188]
`The system can use these data in calculating the dead-net price for an item and the
`Final Lowest Cost (FLC) representing the lowest overall item and basket cost to the shopper,
`
`simultaneously identifying the store to purchasethe list of items for the FLC costor price. In
`
`addition to streamlining the preparation, selection and shopping process for shoppers the system
`allowsretailers and Brand Marketers or Manufacturers the opportunity to influence shopper
`choice just prior to actual purchase by providing targeted incentives based on the immediately
`planned shopping trip list, preferences, demographics, geographic or competitive inputs, prior
`trip behavior, forecasted future behavior, competitive factors, weather, shopping location or
`
`other means.
`
`
`
`[0189] =~—Application. The system includesat least one programmedprocessorthat
`
`automatically: 1, Matches items on the shopper's list (General or specific) to specific substitute
`
`items at the targeted retail shoppingstore or stores, calculates the item and overall list basket
`
`price and dead-netprices for the items and basket and recommendsa specificretail store for the
`
`shopper to achieve the lowest overall basket price or cost inclusive of available retail pricing,
`
`promotions and couponsfor the items on the list and suitable, shopper defined substitutes.
`
`20
`
`[0190]
`
`The greater the user's willingness to specify items more generally and purchase
`
`equivalent products of comparable quality, the more the system is able to reducethetotal cost of
`
`the basket at one or more stores. The system automatically advises the user whenselection of an
`
`item at a broaderclassification level may lead to improved savings.
`
`[0191]
`
`FIGS. 17A — 17D show anexample ofthis list process.
`
`In FIG. 17A,the user
`
`25
`
`enters a plurality of items. In FIG. 17B, when the user has selected a sub-classification
`
`sufficiently low-level for the products remaining to be substitutes, , but at a sufficiently high
`
`level that the system can select from several substitutable products to save the shopper more
`
`money.
`
`the mobile device displays that sub-classification with the ADD button. Upon
`
`selecting the "SAVEON"button, the system executes the cost minimization and identifies a
`
`30
`
`basket of products and a store. The results of the computation are shown in FIG. 17C. Thetotal
`basket cost and percentage savings (relative to the same items at the most expensive store
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`analyze) are shown. Details about each SKU # are displayed, with the numberofunits nexttoit.
`
`An "Extra Savings" button next to one of the SKU #'s indicates an RI or MIthat is available, and
`
`which the user can view and accept by selecting the extra savings button. For example, the
`
`system may suggest that the cost perjar is lowerif the user buy's a third jar of spaghetti sauce.
`
`After the user purchases the recommended basket of items at the check outofthe store, in FIG.
`
`17D, the system optionally emails e-couponsto the user, instructs the user to print them and to
`check in upon entering the store (for additional offers).
`[0192]
`FIGS. 18A — 18D are wireframes showing an example ofthe store check in
`procedure. Uponentering the store the user clicks the check in button of FIG. 17C.
`In FIG. 18A,
`
`10
`
`if the system has recommendeda first store, but the user checks into a secondstore, the system
`
`recommendsthat the user goto thefirst store to save more money. Ifthe user selects the button
`
`indicating that the userstill wants to shop atthe secondstore, the system re-computesthe user's
`
`basket at the second store. In some embodiments,if the user has entered a store which is not on
`the user's list of stores, the system prompts the user to perform a search using the system store
`search function, and to add thestore to the user's list.
`
`15
`
`[0193]
`
`FIG. 18B showsthe case in which the user checksin at the recommendedstore
`
`for the greatest savings. In FIG. 18B, a check in screen remindsthe user to turn on location
`
`services, so that a GPS equipped mobile device precisely locates the user and automatically
`
`checksin the user uponarrival at the recommendedstore. FIG. 18B also shows control buttons
`
`20
`
`for home, specials & coupons, mystores and myprofile. The specials and couponscan assist the
`
`user in manually finding any current specials, The "My Stores" button allows the user to add or
`
`subtract up to a predetermined number(e.g., 4) of stores from which the user wants the app to
`
`recommenda store to find the lowest basket price on any given day. Selection of the "My-
`
`25
`
`Profile" button takes the user to a screen for updating the user's registration information. FIG.
`24 shows an example of a display for selecting stores. In some embodiments,the stores are
`added by the user typing in the nameofthe store. Each store so entered appears on the display
`
`of the mobile device with a checkbox to allow the userto select the store as a candidate for the
`
`current shoppingtrip (from which the system will recommenda store).
`
`In other embodiments,
`
`the user enters a zip code and the mobile device locates stores within that region as well as
`
`30
`
`displaying them on a map(for example, by checking a local white pages). Ifno store is found an
`
`appropriate message is conveyedto the user.
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`[0194]
`
`In FIG. 18C, uponarrival, the mobile device app identifies when the user has
`
`reachedthe store, and displays an e-circular or additional in-store promotions. For example, in
`
`FIG. 18C, the user is offered a special price on a baguette, which wasnotpart of the user'slist
`
`prior to entering the store. The mobile device displays an ADD button which, if selected, adds
`
`the offered product to the user's list. In the example of FIG. 18C, there are plural offers available.
`
`The user can select one of the offers by selecting a numberor the arrow on the number menu
`
`below the add button.
`
`In FIG. 18D, the mobile app checks whetherthe user's profile includes a loyalty
`[0195]
`card numberforthe store at which the user has checkedin. If not, the mobile device prompts the
`user to enter the loyalty card numberfor greater savings.
`
`[0196]
`
`FIG. 20 shows a manual check in screen displayed by a mobile device without a
`
`GPS. Buttons are provided to enable the userto select: check in, not shopping yet, shopping at a
`
`different store, or skip check in. If the user skips check in, some offers may be unavailable to the
`
`user.
`
`In some embodiments, the system is able to matchlist descriptors to specific
`[0197]
`items and then provide the shopper with a recommendation of where to shop and what to buy
`(item level) reflecting actualretail pricing (shelf prices, promoted prices and combinations
`
`thereof). The processor(s) running the shopping application program in essence automates the —
`way a shopperselects a store and makes purchasedecisions, in a way that was previously
`unachievable, thereby streamlining the grocery item purchase process. In addition, because the
`
`system is used as part of the shopper’s shopping process, the system enables bothretail store
`
`marketers, herein “retailers” and brand marketers, herein “marketers” to deliver marketing
`
`incentives of various kinds including additional price-related incentives, directly to the shopper
`
`during the preparation and execution of their shopping list and shopping trip. This capability is
`unique for the grocery industry.
`‘These incentives are included into the basket cost and can be
`targeted by retailers and Marketers based on a broad numberof shopper characteristics,
`
`preferences, behaviors, purchase history and the like. The system further provides post and pre-
`shoppingtrip analytics to help Marketers and Retails evaluate their promotional efforts, better |
`understand the shopperandprofitably grow their respective businesses.
`
`10
`
`15
`
`20
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`25
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`[0198]
`This system can influence shoppersafter they have indicated what they intend to
`purchase on their shoppingtrip — but before the actual purchase has been made — in a streamlined
`highly functional application.
`|
`[0199]
`Some embodiments comprise a physical system architecture, software, internet
`webportals, a mobile device(e.g., smart phone) programmedwith application(s) or “Apps”,
`databases, business systems and methods of capturing, analyzing information quickly to deliver
`in near-real time powerful analytic capabilities. In addition some embodiments includes a
`
`system for delivering electronic coupons, marketing incentives, and advertising to consumers
`
`ready to make a purchaseataretail store. Some embodiments comprise a system of hardware,
`
`software, databases and servers, processors and communication networks both wired and
`
`wireless that provides a robust, extremely fast and highly reliable experience for target customers
`
`including consumersor shoppers, retailer marketing or staff personnel and brand marketers/staff
`
`personnel and researchers andother professionals. An embodiment comprises fourinter-linked
`
`technology platforms targeting shoppers, retailers and brand marketers.
`
`[0200]
`
`The first component platform, herein the “Shopper App,” is a web portal and
`
`smartphone, tablet or other mobile access device application for consumersor herein “shoppers”
`
`that:
`
`[0201]
`
`(1) Allows shoppersto enter a shoppinglist in the shopper App (for example,
`
`but not limited to 1-250 items items) using a website interface or other electronic means
`
`20
`
`(smartphone,tablet, terminal, computer) app by either voice command(voice recognition
`
`software), typing in a keyboard interface or smartphone touch pad/keyboardorvia item
`scanning, photo or handwriting recognition with the smart phone/device, accessing an item look-
`up databaseas the item is typedorfilled-in.
`
`[0202]
`
`(2) Includes an item level database stored in a non-transitory computer
`
`25
`
`readable storage medium,with all (or a large number) of the unique itemscarried in grocery
`
`retail outlets in the targeted geography(e.g., United States, Canada, France, Germany, England,
`
`Japan, China, South Korea, Brazil, Argentina, Switzerland and Denmark- or other region
`
`sharing a brand andretail base). In some embodiments,the item database can have as many as
`
`~500,000 unique SKUsor such amount(s) as to cover more than 50% of the items most
`
`30
`
`‘purchased and of concern to shoppersata retail location. In other embodiments, the database
`
`size is smaller or larger The database is highly structured with each item being dynamically
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`categorized and tagged so as to enable rapid matching of category level descriptions to specific
`products.
`.
`[0203]
`(3) Allows shoppersto access, store and/or modify shopping preferences
`including favorite supermarket stores(e.g., 1-25), brand, sizes, flavors and price decision rules
`
`relating to when a productis to be considered over another or when twodifferent brands or
`
`products are to be considered “substitutes” by the shopper. This expedites learning by the
`
`system.
`
`In some embodiments, one of the preferences specifies the way in whichpriceis
`~
`[0204]
`calculated and compared (on a SKUorunit/area/count/weightbasis). Thatis, the user can
`
`specify whether the system finds the equivalent product having the lowest cost per package
`
`(usually a smaller package) or lowest amongsta single like size to minimize this trip's cost, or
`
`the lowest cost per unit of product, to minimize total long term costs.
`
`[0205]
`
`(4) Learns from shopper usage about the shopper’s product, brand,flavor,
`
`size, pack, retail shopping location and other factor preferences. The system updates shopper
`specific preference database “on the fly” and incorporates such decisions in recommendationsto
`shopperand in targeting of Marketer or retailer promotions and advertising. In some
`
`embodiments, the preference database is not updated on-the-fly throughout the day, but is
`
`periodically updated (e.g., daily).
`
`[0206]
`
`In some embodiments, the system initializes each user to have a default set of
`
`20
`
`preferences, which are subsequently updated. For example, the system mayinitially choosea set
`
`of default preferred products(e.g., the 3 or 4 top sellers) for each sub-classification. The system
`
`may recommend oneofthese products; if the shopper deselects that product, that productis
`
`dropped from the user's preferencesor is reduced in rank.
`
`[0207]
`
`In some embodiments, the learning includes beginning with a large set of
`
`25
`
`prospective recommendedproducts, and reducing the candidate set for a given user based on that
`
`user's behavior. For example, the initial set may begin with the top market share products
`
`accounting for 80% of total sales in category. This is intended to eliminate the low quality
`products from the candidate set. Unless the user enters a specific product outside of the top 80%,
`the modification of the user's preferences results in subtracting products from the user's
`
`30
`
`preferences mostofthe time.
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`[0208]
`
`In other embodiments, the initial preferences and/or updates to the user's
`
`preferences may be varied based on the user's demographics. For example,the target size may
`
`be based on the size of the user's household, recognizing that large families are more likely to
`
`buylarger sizes). Similarly, if the user is older, or has a smaller family, the preference may be
`biased towards smallersizes.
`.
`[0209]
`(5) In some embodiments, the system allows shoppersto save, re-use and
`delete lists, Track shopper’s purchase history at category and SKU level. Allows shopperto
`
`access frequently purchased itemslist (at a category to SKU Level) and add itemseasily to a
`
`current or new shoppinglist.
`[0210]
`(6) Allows family membersof a shopperto access final shoppinglist on their
`
`10
`
`smart phones. In some embodiments, the system allowsthe user to designate any other
`
`registered user of the system as having rights to access the user's final shopping list using their
`
`smart phone or other mobile device.
`
`15
`
`20
`
`(7) Allowsfor specific item-level decision rules or. broader decision rules
`[0211]
`(e.g., (1) Any brand in a category is acceptable, or (2) cheapest brandin a category is acceptable
`or (3) pick product A as the default and switch to product B if the price differenceis at least a
`threshold percentage (e.g., 30% or more). The shopper uses the decision rules to tell the system
`
`how the shopper wants to select between similar brand or products. Decision rules comprise
`relevant decision variables including but not limited to one or moreofprice, price differential,
`
`flavor, size, pack, color, price per unit of measure,relative price per unit of measure, brand
`name, company, organic, sodium levels, inclusion of peanutoil or other nutrition orallergy
`related metrics, combinations thereof, and the like.
`
`{0212]
`
`-
`
`(8) Allowsfor per unit/oz/item/gram/lbs cost comparisonsonall items in
`
`item and basket cost calculations (choice not driven by absolute price but comparable unit price
`
`25
`
`for substitutable items. If the user's preferences so indicate, the cost comparison's can be based
`
`on actual cost for the sameor similar size package or commercial unit, based on the most
`
`commonly sold size. For example, the cost comparison for spaghetti sauce may be based on the
`
`cost of a 16 oz jar across various brands and flavors, without considering the lower cost per oz or
`the 32 oz jars. In some embodiments, the cost comparison starts on costperunit at a given size
`and doesnot go to larger size even if doing so would reduce the average cost per ounce/gram of
`
`30
`
`product.
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`[0213] (9) Allows shoppersto viewalist of categorized and orprioritized (on
`
`potential savings) special incentives (coupons,retailer sales or promotions or promotions
`presented by the system) for items that mightnotbe onthe initial planned or entered shopping
`list developed in #1 but merit consideration based on internal program decision rules that can be
`based on prior purchasing habits, significance of discount, cost of money(interest rate), purchase
`cycle, use up rate, and other factors relating to identifying items the shopper would have
`considered worth adding to a Shoppinglist if they were doing following a completely manual
`process. Selected incentives and items are added to shoppers list.
`|
`[0214]
`(10) Calculates for shopper, and displays almost instantly, a lowest cost list of
`
`10
`
`recommended,specific, items (flavor, size, brand, pack) and atleast one retail store to purchase
`
`items at so as to minimize the total basket cost within shopper defined orinitial preset
`
`preference parameters taking into accountall available or “live” price discount mechanismsof
`
`any kind stored:in system database. This list initial recommendation is herein called the “Initial
`
`Lowest Cost”list (ILC). The processor executing the shopper Appofthe system displays basket
`
`15
`
`cost and amount saved versusthe nextretail store option or one oftheretail store options stored ,
`
`as favorite, alternative or substitute retail shopping locations if more than 2 stores are in the
`
`shopperspreference set. If only 1 store is included in shoppers preference set then the shopper
`
`App comparesprices for the product representing commercial substitutes at that one
`
`store.Allowsfor retailer Incentives (RIs) to alter the recommendedretail store outcome presented
`
`20
`
`whenthe ILC is calculated for the shopper by the inclusion of a post-ILC incentive that further
`
`reduces the basket cost and shift the cost minimization “win” from oneretailer to another as
`presented to the shopperin a final list. This list is herein called the Subsequent Lowest Cost List
`(SLC).
`If no retailer incentives are active the SLC and ILCare identical and the shopperis
`
`25
`
`presented with the ILC. Otherwise, if an RI changes the minimization outcome, the shopper sees
`the SLC list only. Some embodiments permit counter RI's (e.g., retailers submit bids).
`[0215]
`In one embodiment RI’s from oneor moreretailers are pre-programmedand are
`applied to the basket. totals to calculate SLC basket. In another embodiment, retailers “bid” in a
`real-time or preprogrammed fashion for the shopper’s basket such that the winning “bid”
`
`becomes the winningretail store that the algorithm will then present to the shopper. In one
`
`30
`
`embodimentthe RI’s are applied to the basket cost and then a second or more roundsof RIs are
`
`accepted until no more RIs are available to further reduce the basket cost. In one embodiment a
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`group of shoppers puts their baskets up for a bid wherein retailers can agree to sell the shopper or
`
`group ofshoppersthe item or itemsof the respective baskets at a specific price. Other
`embodiments do not permit counter RI's.
`
`(11) Displays a shoppinglist for the shopper that includes a “check box” icon
`[0216]
`or other similar graphic device oricon for items in the shopping cart and a visual icon indicating
`whether additional Incentives (MIs or RIs) are available for a particular item on the shoppinglist.
`[0217]
`_
`The icon whenpressed will display MIs that may be accepted by the shopper and
`result in: the replacementofthe item in shopping basket that was immediately adjacent to the
`icon, recalculation of basket cost and inclusion of additional incentives relevant to item and
`
`10
`
`basket costing. MIs can also increase the amountofthe items in the basket that the offeris
`
`associated with and can add anotheritem to the list, a manufacturer may decide that, anytime the
`consumeris a large household, the basket is greater than a threshold value, and the customer
`
`lives in a particular zip code, the manufacturer wants wantto offer $10 more off their basket. In
`various other embodiments, the offers may be own-brandtypes ofoffers to encourage the
`
`15
`
`shopper to buy more product, or competitive switching offers, to try to persuade the shopperto
`
`try a different brand instead of the brand on the user's list, These MIs and RIs are madeafter the
`ILC is calculated, and with a high degree of knowledge about what the consumeris aboutto do.
`[0218]
`FIG. 25A-25C showthe operation of the "Keyword to Category to SKU Funnel
`
`Logic" which enablesa userto enter a string or keyword, make a small number(e.g., 1 to 3) sub-
`
`20
`
`set descriptor selections, and receive from the system a display of commercially equivalent
`
`products. It the remaining items in the sub-set are sufficiently similar to be considered
`
`substitutes, an ADD button(or similar selection device) is displayed next to each remaining item.
`
`If the user clicks the displayed ADDbutton for selecting a sub-set descriptor to the user's basket
`
`(without ADDinga specific SKU),the user has specified enough information for the system to
`
`25
`
`make an automated minimumcost selection among equivalent products, and the system will
`
`recommendthe lowestprice productto satisfy the item onthelist.
`
`[0219]
`
`Proceeding from a list to a recommendation:
`
`[0220]
`
`©
`
`Consumers makelists of broad categories of items they wish to purchase when
`
`making a shoppinglist. In somecases, for the items on the list, the user may have in mind a
`
`30
`
`specific brand orgroup of brands that can meet that product need. Likewise, they havesizes,
`
`flavors, and types that can meettheir need.
`
`In other cases, the user may beindifferent between
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`products of comparable quality, size, flavor and type, and want the system to recommend the
`
`least cost alternative. Further, if the user has inputa list of stores containing more than onestore
`
`at which the useris willing to purchase the basket of products, the system can recommendthe
`
`store at which the total cost of the least expensive basket is minimum.
`
`[0221].
`
`- To maximize savings for shoppers, the system prefers that the shoppernot be
`
`overly specific. That is, the system allows the user to specify a narrow descriptor sub-set having
`
`a small numberof SKUs,or even to specify a single, specific SKU; but if the user doesso, the
`system issues a message suggesting to the user that the system can provide greater savings if the
`
`user selects a higherlevel, classification (broader sub-set descriptor), and allows the system to
`
`recommend a minimum cost basket based on a lowercost substitute product, To effectively
`
`minimize cost, the system requests that the shopper selects a classification level (corresponding
`
`to a sub-descriptor) specific enough in defining a product so the shopper Appcan initially
`
`recommend and display a reasonableset of items (e.g., 20 to 30) from which the system or the
`
`user can select the one of the recommendeditems having the lowest cost.
`
`[0222]
`
`[0223]
`
`Example:
`
`If the shopper types in “Bread”the user could mean “A loaf of bread,” “Bread
`
`Crumbs,”or “Bread dough.” Initially, the system does not know enoughabout what the shopper
`
`means or wants to make an effective recommendation or run the cost minimization algorithm.
`
`The system can display these sub-set descriptors and allow the userto select the next lowerlevel
`
`20
`
`classification. Assume the user selects "A loaf of bread." The system can then give the user a
`
`sub-set choice between fresh baked bread (from the in-store bakery) or pre-packaged bread from
`
`an outside supplier. Assumethe userselects "pre-packaged bread". The system can then display
`the next lowerset of sub-set descriptors, which may be "white", "whole-wheat,"
`"rye," multi-
`
`grain, and " brioche," In some embodiments,at each level, the sub-set descriptors are pre-
`
`25
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`determined so as to be mutually exclusive, and to be completely exhaustive of the available
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`products at the stores on the user's store list. That is, at a given level, each sub-set descriptor or
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`productthat is within the classification at that level is contained within exactly one sub-set at the
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`next lowerclassification level. By eliminating overlap between different sub-sets at a given
`classification level, the system is able to quickly reduce the number ofproducts from whichthe
`system will either seek to further reduce the number of recommended products from which to °
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`make a recommendation, or from which the system will actually make the recommendation.
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`40
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`

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`WO 2013/052081
`
`PCT/US2012/000426
`
`[0224]
`
`If the shopper selects “whole-wheat”, the user has identified. a sub-classification
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`containing commercial substitutes. There are now.a suitably small set (e.g., fewer than 20) of
`brands, sizes and types of pre-packaged whole wheat bread, from which the system can select the
`lowest price product. Oncealevel containing commercial substitutes is reached, the system
`displays an "ADD"button. Optionally the system allows the user to manually select their
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`favorite SKU by “whole wheat” display as brand choices. If the user makes a manualselection,
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`the user's preference for this type of whole wheat bread is recordedin the database. The system
`also remindsthe user that more moneycan be savedif the user selects a sub-set descriptor
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`instead of a specific SKU,and allows the system to makethe selection: If the user does not make
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`10
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`a manualselection of a specific SKU,or if the user returns to the "whole wheat bread"
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`classification subset, the system selects the lowest cost loaf from the set. Specifically, the lowest
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`cost pre-packaged whole-wheat bread.
`
`[0225]
`
`This Keyword-Category-SKU Funnelallows the shopper Appsto start with a
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`reasonable approachto selecting a specific item for consumers(as outlinedabove) and then
`quickly utilize “learning”to refine the approach overtime. |
`[0226]
`Logic Outline:
`
`[0227]
`
`The system has a database whichis organized into several (e.g., 10-11)
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`classification levels. The shopper begins by typing in a string or keyword. As a shoppertypes,
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`20
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`the shopper App of the system begins a keyword search of meta data, text, or of the available
`
`item database. In some embodiments, the database is organized so thatat level 6, each of the
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`classification sub-set descriptors defines a set of commercially equivalent, same or similar
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`products. The sub-set descriptors or SKUsator belowthis level are displayed to the user with
`
`an ADDbutton,indicating that the remaining items within that sub-set are considered
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`25
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`commercially equivalent substitutes. so that the system can make an automatic selection from that
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`sub-set without more lower-level selection by the user. The classifications above level 6 are
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`arranged so thatit is generally possible to traverse the index from a search term (e.g., bread) to
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`the level containing commercial substitutes within three or some other small number(e.g., 2)
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`sub-set descriptor selections. Although the system allowsthe user to continue to drill down to
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`30
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`level 0 or the lowest level, the greatest savings potential is provided when the userclicks the
`
`ADDbuttonto add to his or her cart the sub-set descriptor for a broader sub-set.
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`