Patent Description:
The present disclosure generally relates to systems for providing personalized product recommendation lists to retailers to enable retailers to target products to consumers in various digital and physical channels with targeted product promotions, product marketing messages, product discounts, product rankings in digital circulars, and a variety of other marketing related use-cases. More specifically, embodiments as disclosed herein relate to providing personalized product lists to a retailer that are derived from combining previous purchasing history of the consumer with a variety of other data sources that are fed into data science models and algorithms that output a personalized list of recommendations for each consumer based on their retailer loyalty card number.

Current retailer centric marketing programs rely on ad-hoc consumer databases owned by the retailers themselves, and therefore are not necessarily accurate, sufficiently updated, or simple to use. Also, retailers lack the skillset and tools to produce product level personalization targeted and tailored against each of their consumers that leads to wasted marketing dollars, inaccurate product level recommendations, fragmented consumer experiences, and the like. Accordingly, there is a desire to provide retailers with services and techniques to accurately recommend products to power their marketing and media targeting use-cases. <CIT> discloses a system for generating offers for a target user based on attributes and characteristics of the target user, whereby the target user is determined by providing identity information of the target to the system. The offers are provided by a presentation engine to the user and an offer tracker determines which offers are converted by the target.

The invention is defined as specfied in the independent claims.

In the figures, elements or steps having the same or similar labels are associated with features or processes having the same or similar description, unless otherwise stated.

In the following detailed description, numerous specific details are set forth to provide a full understanding of the present disclosure. It will be apparent, however, to one ordinarily skilled in the art, that the embodiments of the present disclosure may be practiced without some of these specific details. In other instances, well-known structures and techniques have not been shown in detail so as not to obscure the disclosure.

In some aspects, a server in embodiments as disclosed herein may host a vast personal identifiable information (PII) free consumer database containing loyalty card IDs linked with previous purchase transactions and other data sources and provide associated services to the consumers via one or more retailers. The server may include personalized information about the products and associated universal product codes (UPCs) carried at one or more retail stores. The server may include processors executing algorithms to return to a retailer a ranked list of the top UPCs for a given consumer, ranked according to a likelihood that a consumer associated with the consumer identification code will purchase the products. The retailer may then display the products, not limited to a digital advertising campaign to the consumer (e.g., via e-mail, instant messaging, or during multimedia downstream).

Solutions as disclosed herein are highly convenient for retailers to deliver personalized product recommendations to subscribers in their consumer loyalty network program or scheme. For example, the retailer may create digital advertising payloads highlighting the top products that consumers who are loyalty card holders of the retailer stores might find attractive and therefore lead to higher rates of purchase conversion for the retailer.

<FIG> illustrates an architecture <NUM> of devices and systems in a network <NUM> configured for providing personalized product recommendation lists to enable a retailer <NUM> to target products to a consumer <NUM>, according to some embodiments. A server 130A may be coupled with a database <NUM> storing a purchase history log for each of multiple consumers <NUM>. Database <NUM> may also include PII free consumer identification information or codes, such as frequent shopper cards (FSC) and the like. While the FSC identifies one consumer <NUM> from another, the data is de-personalized according to PII protocols, such as that the anonymity of consumer <NUM> and consumer households is preserved against any third party. Architecture <NUM> includes a first network server (server 130A) providing the product personalization-as-a-service through a private and secure API connection. In some embodiments, server 130A may include a supply side platform (SSP) server, configured to provide online media content to consumers (e.g., music, videos, movies, news and other broadcasts, podcasts, blogs, and the like). A second network server (server 130B), may be owned by a retailer <NUM> or a service provider for one or more retailers (e.g., grocery retailer, apparel, general store, and the like) to communicate with server 130A, e.g., an e-mail server provider (ESP), marketing and communications platform, and the like. For example, consumer <NUM> may use a client device <NUM>-<NUM>, <NUM>-<NUM>, or <NUM>-<NUM> to interface indirectly and receive personalized product recommendations on behalf of the retailer with the underlying marketing product personalization driven through communication from server 130B to server 130A (hereinafter, collectively referred to as "servers <NUM>").

Retailer <NUM> may include server 130B and client devices <NUM>-<NUM> (e.g., a printer) and <NUM>-<NUM> (e.g., a standing pad or touchscreen) including various digital and physical channels with targeted product promotions, product marketing messages, product discounts, product rankings in digital circulars, and a variety of other marketing related use-cases. Client devices <NUM>-<NUM> through <NUM>-<NUM> will be collectively referred to, hereinafter, as "client devices <NUM>.

Servers <NUM> may interact and communicate with other devices in network <NUM> via any one of multiple interfaces. The interfaces may include digital or physical channels by which the retailer communicates with the consumer. For example, some of the interfaces may include e-mail, mobile phone push notification services, text messaging, paper circular mailers, and the like. More specifically, servers <NUM> and client devices <NUM> may include an appropriate processor, memory, and communications capability, configured to interact with network <NUM> via a digital interface. Client devices <NUM> may include, for example, desktop computers, mobile computers, tablet computers (e.g., including e-book readers), a digital stand in a retailer store, mobile devices (e.g., a smartphone or PDA), wearable devices (e.g., smart watch and the like), or any other devices having appropriate processor, memory, and communications capabilities for accessing one or more of servers <NUM> through network <NUM>. In some embodiments, client devices <NUM> may include a Bluetooth radio or any other radio-frequency (RF) device for wireless access to network <NUM>. The memory in the client device from the retailer may include instructions from an application programming interface (API) hosted by server 130A (e.g., downloaded from, updated by, and in communication with server 130A). The API in client devices <NUM>-<NUM> or <NUM>-<NUM> may be configured to cause client devices <NUM>-<NUM> or <NUM>-<NUM> to execute steps consistent with methods disclosed herein.

Network <NUM> can include, for example, any one or more of a local area network (LAN), a wide area network (WAN), the Internet, and the like. Further, network <NUM> can include, but is not limited to, any one or more of the following network topologies, including a bus network, a star network, a ring network, a mesh network, a star-bus network, tree or hierarchical network, and the like.

In some embodiments, server 130A is configured to receive a request from server 130B, the request including a consumer identification code, to obtain a personalized list of universal product codes based on the consumer identification code and residing purchase history log in a database (e.g., with accompanying data sets), and to return the personalized list of universal product codes to server 130B.

<FIG> is a block diagram <NUM> illustrating an example server <NUM> and client device <NUM> in the architecture <NUM> of <FIG>, according to certain aspects of the disclosure. Client device <NUM> and server <NUM> are communicatively coupled over network <NUM> via respective communications modules <NUM>-<NUM> and <NUM>-<NUM> (hereinafter, collectively referred to as "communications modules <NUM>"). Communications modules <NUM> are configured to interface with network <NUM> to send and receive information, such as data, requests, responses, and commands to other devices on the network. Communications modules <NUM> can be, for example, modems or Ethernet cards. Client device <NUM> may be coupled with an input device <NUM> and with an output device <NUM>. Input device <NUM> may include a keyboard, a mouse, a pointer, or even a touch-screen display that a consumer may use to interact with client device <NUM>. Likewise, output device <NUM> may include a display and a speaker with which the consumer may retrieve results from client device <NUM>. Client device <NUM> may also include a processor <NUM>-<NUM>, configured to execute instructions stored in a memory <NUM>-<NUM>, and to cause client device <NUM> to perform at least some of the steps in methods consistent with the present disclosure. Memory <NUM>-<NUM> may further include an application <NUM>-<NUM>, including specific instructions which, when executed by processor <NUM>-<NUM>, cause a digital promotion payload <NUM> hosted by server <NUM> to be displayed for the consumer. Digital promotion payload <NUM> may include multiple digital promotions or coupons presented to the consumer by server <NUM>, and the consumer may store at least some of the digital promotions or coupons from digital promotion payload <NUM> in memory <NUM>-<NUM>.

In some embodiments, memory <NUM>-<NUM> may include a mobile wallet application <NUM>-<NUM>, configured to store a value offer (e.g., a coupon, a discount, and the like), which has been selected (e.g., "clipped") by the consumer from any one of the multiple digital promotions or coupons in digital promotion payload <NUM>. Further, in some embodiments, mobile wallet application <NUM>-<NUM> may associate the value offer selected by the consumer to an FSC ID for the retailer stored in memory <NUM>-<NUM>. Mobile wallet application <NUM>-<NUM> may be installed in memory <NUM>-<NUM> by the manufacturer, together with the installation of an operating system that controls all hardware operations of client device <NUM>. Moreover, in some embodiments, a consumer may download a retailer application <NUM>-<NUM> in client device <NUM> from the retailer (e.g., server <NUM>). The consumer may have an FSC ID associated with retailer application <NUM>-<NUM>. In some embodiments, in addition to one or more "brick and mortar" physical locations of stores for the retailer, the retailer may host an online shopping outlet hosted by a network server (e.g., server <NUM>). Application <NUM>-<NUM>, mobile wallet application <NUM>-<NUM>, and retailer application <NUM>-<NUM> will be collectively referred to, hereinafter, as "applications <NUM>.

Server <NUM> includes a memory <NUM>-<NUM>, a processor <NUM>-<NUM>, and communications module <NUM>-<NUM>. Processor <NUM>-<NUM> is configured to execute instructions, such as instructions physically coded into processor <NUM>-<NUM>, instructions received from software in memory <NUM>-<NUM>, or a combination of both. Memory <NUM>-<NUM> includes a personalization engine <NUM>. In some embodiments, personalization engine <NUM> includes a digital promotion tool <NUM> for integrating images, videos, and other multimedia files stored in a digital promotion database <NUM>-<NUM> into a digital promotion payload <NUM>. Digital promotion tool <NUM> may push digital promotions from digital promotion database <NUM>-<NUM> to a consumer of client device <NUM> that is a consumer of a retailer store or chain of stores through an application <NUM>-<NUM> or a web browser installed in client device <NUM>. Accordingly, applications <NUM> may be installed by server <NUM> and perform scripts and other routines provided by server <NUM>. In some embodiments, application <NUM>-<NUM> may be configured to display digital promotion payload <NUM> provided by digital promotion tool <NUM>.

Digital promotion tool <NUM> integrates digital promotion payload <NUM> based on information retrieved from a frequent shopper identification database <NUM>-<NUM> and a history log database <NUM>-<NUM>. History log database <NUM>-<NUM> includes the purchase history of multiple consumers listed in frequent shopper identification database <NUM>-<NUM>. To achieve this, in some embodiments, an algorithm <NUM> stores commands which, when executed by processor <NUM>-<NUM>, causes server <NUM> to integrate digital promotion payload <NUM>. Algorithm <NUM> may include a neural network (NN) trained over frequent shopper identification database <NUM>-<NUM> and history log database <NUM>-<NUM>, to select digital promotion payload <NUM> targeted to the specific preferences of a consumer when the consumer uses any one of applications <NUM> to access a network site hosted by server <NUM> (e.g., an SSP server). Accordingly, the SSP server hosting the network site accessed through application <NUM> may be different from a DSP server hosting digital promotion tool <NUM>.

In some embodiments, personalization engine <NUM> may be configured to personalize a list of UPCs from an FSC code associated with a consumer and provided through a network server using a product hierarchy from retailer <NUM>. Personalization engine <NUM> may execute a model that uses foundational personalization logic. Accordingly, personalization engine <NUM> may configure the API in a retailer client device <NUM> to return, upon request, a list of UPCs for a consumer selected by the retailer. In some embodiments, personalization engine <NUM> may configure the API in the retailer client device <NUM> to provide enhanced personalization and full product details, including additional shopper personalities to the model to enhance personalization of the UPC list. For example, in some embodiments, the API in retailer client device <NUM> may be integrated with other APIs from a retailer network to obtain the UPCs, pictures, and graphics of the products, product description, pricing, and the like, and to include one or more of the above in the API response.

In some embodiments, the API installed in the retailer client device is an exposed API that the retailer invokes using a preset and configured retailer Network ID and an FSC ID. The Network ID identifies the retailer as a client of the consumer network hosted by server <NUM>. The FSC ID identifies a specific consumer that the retailer desires to address with a target advertisement in an advertisement campaign. The API will return a list of personalized UPCs (e.g., one or more, up to eight, ten, or even more, depending on settings determined by the retailer). Each item in the UPC list may include a UPC number, a tracking pixel, and the FSC ID. In some embodiments, there may be a pre-selected number of items in the UPC list provided (e.g., eight or more). Accordingly, the personalization engine in server 130A may be configured to pad the UPC list with a default set of UPCs in addition to at least one personalized UPC, to complete the pre-selected number of items.

The API response (provided to the retailer by server <NUM>, upon request) includes a list of UPCs selected from a product hierarchy in the retailer. The product hierarchy may include a product classification according to a retailer preference (e.g., vegetables, packaged groceries, meats, grains, canned goods, bottled products, beverages, kitchen supplies, cleaning supplies, health products, pharmacy products, and the like). Each product in the retailer hierarchy or classification may have a UPC associated with it. The personalization engine in server <NUM> personalizes UPCs associated with the retailer, based on the FSC ID provided by the retailer through the API request. Accordingly, the personalization engine finds UPCs available in one or more of the retailer stores, that have a high likelihood of being purchased by the consumer associated with the FSC ID, based on a prior purchase history of the consumer. The API then returns the UPCs found by the personalization engine in server 130A to the retailer. The retailer could then use the UPC to look up and build creative pieces or use a third party to look up accompanying creative and product meta-data (e.g., graphics, price values, price reductions, offers, coupons, credits, redeemable values, and the like) in the advertisement payload to be delivered to the consumer via the consumer client device (e.g., through an e-mail, an instant message, an embedded message, or an advertisement embedded in a multimedia downstream). The advertisement may include pictures of the products in the UPC list, and a special offer to consumer <NUM>, including a price cut, a coupon, an extra item, or a combination of the above. In some embodiments, the offer may be tailored to the specific consumer. In that regard, the personalized list of UPCs returned by the API from server <NUM> may include a recommendation to the retailer (e.g., retailer <NUM>, cf. <FIG>) indicating a "high/medium/low" quality of the consumer vis-à-vis the retailer, in terms of likelihood of purchase, or total, overall value purchase, purchasing trends, and the like.

In some embodiments, the tracking pixel provides a measurement capability to the advertising or marketing campaign. In some embodiments, the pixel includes a script that executes a call to the API upon detection of a selected event in the interaction between the consumer and the advertisement payload. The call indicates a time stamp, the FSC ID, the UPC associated with the advertisement, and the channel through which the consumer interacted with the advertisement (e.g., e-mail, network browser, third party API, and the like).

In some embodiments, the measurement may include test and control actions provided by personalization engine <NUM>. In some embodiments, pixel data is concatenated with information provided by the API and accessible to server <NUM>. Server <NUM> may then execute analytics instructions to parse the pixel data and conduct a measurement. In some embodiments, the pixel data is stored in the database and the analytics instructions query the database for the pixel data.

In some embodiments, an API request as disclosed herein may include the following instructions and inputs provided to personalization engine <NUM>, to be executed by one or more processors in the server, as follows:
GET
http://XX. DDD:EEEE/ppaas/fsc/USA/<network_id>/<FSC_ID>
//ppaas = product-personalization-as-a-service.

Wherein X, Y, Z, D, and E are any digits between <NUM> and <NUM>.

In some embodiments, an API response as disclosed herein may include the following instructions and inputs provided from personalization engine <NUM> to be executed by one or more processors in the retailer client device, as follows:
[
{
"upc": "<NUM>",
"trackingpx1":"http. ",
},
{
"upc": "<NUM>",
"trackingpx1":" http.

In the response, a call to activate a tracking pixel is invoked by the end client to inform personalization engine <NUM> that a product exposure from the personalized product recommendation service has taken place. A sample tracking pixel structure may be as follows:
https://collect. com/event?account=cmc&profile=main&v=<NUM>&ua=UA-
[token] &did=
[insert_FSC_id_macro]&sid=
[utc_time_stamp_partner_to_{fill_in]&cm=ad&t=impression&upc=
[insert_offecid_macro]&coid=
[insert_client_offer_id_macro]&cs=email&ea=view& event=ge_ad2email
did = The FSCID. Extract this value out of the API request incoming call that hits
the system
sid = UTC timestamp is what the partner will fill in.

To create the API, personalization engine <NUM> may use information such as retailer name and network ID. For example, an ESP will send e-mails, messages, and other communications including the advertisement payload to consumers. In some embodiments, the media display in the advertisement payload is handled by the retailer, who may use their selected marketing platform that includes an ESP and a selected Demand Side Platform (DSP) to retrieve content and create the advertisement payload (e.g., via digital interface channels). As disclosed above, the retailer may use any of multiple interface channels to communicate with the consumer, such as a digital interface channel (e-mail, text messaging, and the like), or even physical interface channels (including regular mail, leaflets, bulletins and the like).

Further, the API may be subject to certain acceptance criteria by the retailer such as responding to requests under a pre-selected amount of time (e.g., X milliseconds). Another criterion for the API may be a pre-selected number of UPCs to include in the returned list (e.g., one or more, eight, ten, or even more), which may depend on the FSC ID. In some embodiments, at least one or more, or all of the JSON parameters in the API response reflect the associated metadata. In some embodiments, at least one or more, or all UPCs returned in the API response are personalized against the FSC ID (e.g., may be directly associated with the FSC ID). The tracking pixel fills in at least some, or all of the metadata filled in the API request.

In one or more implementations, digital promotion database <NUM>-<NUM> integrates digital promotion payloads including coupons and digital promotions for multiple products on sale by a retailer having one or more stores. A frequent shopper identification database <NUM>-<NUM> may include a list of frequent consumers of the retailer. The retailer may create, update, and maintain digital promotion database <NUM>-<NUM>, frequent shopper identification database <NUM>-<NUM>, and history log database <NUM>-<NUM>. In that regard, digital promotion database <NUM>-<NUM>, frequent shopper identification database <NUM>-<NUM>, and history log database <NUM>-<NUM> (hereinafter, collectively referred to as "databases <NUM>") may be hosted by the retailer, while the digital promotion tool <NUM> may be hosted by a DSP server or a dynamic creative rendering server. Accordingly, the DSP server may have access to one or more of databases <NUM> through business agreements with one or more retailers.

In certain aspects, processor <NUM>-<NUM> in server <NUM> hosted by a retailer is configured to determine data for history log database <NUM>-<NUM> by obtaining consumer purchasing data identifying the consumer via the frequent shopper identification used at multiple purchasing events in multiple locations, over a pre-selected span of time. In some embodiments, history log database <NUM>-<NUM> includes online purchasing history for the consumer through applications <NUM> or a network browser.

<FIG> illustrates a payload <NUM> provided to the consumer by a retailer server (e.g., server 130B), wherein the payload is created based on the personalized list of universal product codes provided to the retailer server by a personalization engine in a consumer network server (e.g., server 130A and personalization engine <NUM>). Payload <NUM> may include graphic images <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-<NUM> (hereinafter, collectively referred to as "images <NUM>") of the products, and price reductions for the consumer. Payload <NUM> may include special offers to the consumer, including a price cut, a coupon, an extra item, or a combination of the above. In some embodiments, the client device receiving payload <NUM> may have a mobile wallet application installed and hosted by a consumer network server, and a retailer application hosted by the retailer server (e.g., server 130A, mobile wallet application <NUM>-<NUM>, and retailer application <NUM>-<NUM>). Accordingly, as the consumer scrolls through the different images <NUM>, the consumer may activate an "add to cart" button <NUM>, or an "add to my list" button <NUM>. For example, button <NUM> may add the product in the selected image to a shopping cart, or a shopping list generated by the retailer application for the next time the consumer visits the retailer store. Likewise, button <NUM> may 'clip' an offer or coupon to the consumer wallet (e.g., at a specific retailer store), so that the consumer gets a discount, extra cash, or other offer, automatically during the purchase of the selected product or item.

<FIG> illustrates steps in a method <NUM> for providing personalized product recommendation lists to retailers for remotely accessing consumers. In method <NUM>, a retailer desires to make product recommendations to one or more customers (e.g., for an e-mail campaign, cf. retailer <NUM> and server 130B). The recommendation lists are provided by a consumer network service provider (e.g., server 130A), based on personalizing products using the UPC hierarchy and classification from the retailer. The advertisement payload from the retailer (e.g., through e-mail) may include promotions and sale items related to that shoppers store. The advertisement payload may also include a section titled 'Products Recommended for You,' which will be powered by a personalization engine in the consumer network service provider (e.g., personalization engine <NUM>).

In some embodiments, one or more of the steps in method <NUM> may be performed by a consumer network database server communicatively coupled, via a network, with a retailer client device and with a consumer client device (cf. servers <NUM>, client devices <NUM>, and network <NUM>). Further, one or more of the steps in method <NUM> may be performed by an ESP server, a DSP server, an SSP server (e.g., server 130A) coupled to the network, and the retailer client device (e.g., server 130B). Each one of the servers in method <NUM> may be coupled with a consumer network database to store in, and retrieve data from (cf. databases <NUM> or <NUM>). Moreover, in some embodiments, methods consistent with the present disclosure may include at least one of the steps in method <NUM> performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step <NUM> includes sorting a purchase history stored in the consumer network database for a consumer identified with an FSC ID and a UPC hierarchy, and classification of retailer products to form a first UPC list.

Step <NUM> includes removing from the UPC list, UPC values associated with products that are not carried by a selected number of retail stores (e.g., all retail stores). In some embodiments, step <NUM> includes using a retailer hierarchy or classification for UPCs based on the products carried at the retail stores. In some embodiments, step <NUM> includes limiting the scope of UPCs available for personalization to include UPCs carried in <NUM>% of the retailer stores.

Step <NUM> includes executing a model stored in the personalization engine, to sort the UPC list according to a likelihood of purchase by the consumer. In some embodiments, step <NUM> may include executing the personalization engine for the same consumer and the same retailer, at a selected frequency, incorporating new data in the consumer network database.

Step <NUM> includes determining whether the consumer has a sufficiently long purchase history in the consumer network database. When the consumer does not have a sufficiently long purchase history according to step <NUM>, step <NUM> includes getting a list of pre-determined default UPCs. Accordingly, in some embodiments, step <NUM> includes padding the UPC list up to a pre-selected quota (e.g., eight UPCs and the like). When the consumer has a sufficiently long purchase history according to step <NUM>, step <NUM> includes stacking the list of UPCs for the consumer, sorted (e.g., ranked) according to a likelihood of purchase by the consumer. In some embodiments, step <NUM> includes evaluating all of the retailer UPCs, and assigning each UPC a Consumer Relevancy Score (CRS), then putting the UPC in rank order for each consumer (#<NUM> to #X). In some embodiments, step <NUM> includes selecting (by the consumer network server, or by the retailer) the number of ranked products.

Step <NUM> includes removing, from the UPC list, the UPCs that have been purchased by the consumer within a selected, most recent period of time (e.g., within the last eight weeks and the like).

Step <NUM> includes determining whether after step <NUM> there are still enough UPCs in the UPC list to satisfy the pre-selected quota.

When there are more UPCs in the UPC list than the quota after step <NUM>, according to step <NUM>, step <NUM> includes returning the UPC list including at least as many UPCs as in the pre-selected quota. In some embodiments, step <NUM> includes building an advertisement payload, and transmitting the advertisement payload to the consumer during an advertisement campaign (e.g., approximately <NUM>,<NUM> - <NUM>,<NUM>,<NUM> e-mails go out per campaign weekly or monthly). In some embodiments, step <NUM> includes integrating a retailer API and an ESP API with the UPC list to retrieve an advertisement component, an offer title, and an offer save line and price data elements (by the consumer network server or by the retailer client device or another retail server). In some embodiments, step <NUM> includes querying retailer APIs to retrieve data elements associated with each UPC to build the content of the advertisement payload.

When there are fewer UPCs in the UPC list than the quota after step <NUM>, according to step <NUM>, the method is repeated from step <NUM>.

<FIG> illustrates a block diagram indicating devices and systems executing steps in a method <NUM> for providing personalized product recommendation lists to retailers for remotely accessing consumers, according to some embodiments. In some embodiments, one or more of the steps in method <NUM> may be performed by a consumer network database server communicatively coupled, via a network, with a retailer client device and with a consumer client device (cf. servers <NUM>, client devices <NUM>, and network <NUM>). Further, one or more of the steps in method <NUM> may be performed by an ESP server, a DSP server, an SSP server (e.g., server 130A) coupled to the network, and the retailer client device (e.g., server 130B). Each one of the servers in method <NUM> may be coupled with a consumer network database to store in, and retrieve data from (cf. databases <NUM> or <NUM>). Moreover, in some embodiments, methods consistent with the present disclosure may include at least one of the steps in method <NUM> performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step <NUM> includes calling, from the ESP server, an API hosted by server A. The call includes an FSC ID to request a list of personalized UPCs from server A. In some embodiments, the API is an exposed API that a retailer can call from a client device, using a Network ID and the FSC ID as input.

Step <NUM> includes requesting, from server A, a personalized UPC list from the personalization engine.

Step <NUM> includes looking up and creating a sorted UPC list based on the FSC ID.

Step <NUM> includes returning the personalized UPC list to server A when a quota is satisfied (e.g., eight UPCs and the like).

Step <NUM> includes returning an error message to server A when the list cannot be obtained, or the quota is not satisfied.

Step <NUM> includes sending a response to the ESP via the API. In some embodiments, step <NUM> includes inserting a tracking pixel in the response, upon explicit authorization by the retailer and/or the consumer.

Step <NUM> includes populating, at the ESP, an advertisement payload (e.g., an e-mail message).

Step <NUM> includes sending the advertisement payload to the consumer.

<FIG> illustrates steps in a method <NUM> for in-store printing of personalized, added value certificates, according to some embodiments. In some embodiments, one or more of the steps in method <NUM> may be performed by a consumer network database server communicatively coupled, via a network, with a retailer client device and with a consumer client device (cf. servers <NUM>, client devices <NUM>, and network <NUM>). Further, one or more of the steps in method <NUM> may be performed by an ESP server, a DSP server, an SSP server (e.g., server 130A) coupled to the network and the retailer client device (e.g., server 130B). Each one of the servers in method <NUM> may be coupled with a consumer network database to store in, and retrieve data from (cf. databases <NUM> or <NUM>). Moreover, in some embodiments, methods consistent with the present disclosure may include at least one of the steps in method <NUM> performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step <NUM> includes receiving, in a server, a request from the retailer or the retailer's service provider, the request including a consumer identification code. In some embodiments, step <NUM> includes receiving the request from an application programming interface hosted by the server, and returning the personalized list of universal product codes to the service provider includes returning the personalized list of universal product codes through the application programming interface within a pre-selected time period after receiving the request from the service provider.

Step <NUM> includes obtaining a personalized product list of UPCs based on the consumer identification code and a purchase history log in a database. In some embodiments, the list of personalized products not only involves the purchase log or history, but includes other data sources or data points. For example, other sources may include information provided by, or collected from, a different search engine that the consumer may subscribe or have access to, indicative of different needs or desires of the consumer in related areas, topics, or products. In some embodiments, step <NUM> includes sorting the personalized list of universal product codes based on a likelihood of product purchased by a consumer identified with the consumer identification code at a retail store serviced by the service provider. In some embodiments, step <NUM> includes selecting multiple products based on a time interval cutoff from the purchase history log in the database. In some embodiments, step <NUM> includes padding the personalized list of universal product codes with a default universal product code when the purchase history log is exhausted before completing a pre-selected quota. In some embodiments, step <NUM> includes selecting universal product codes associated with products that are for sale at a retail store serviced by the service provider. In some embodiments, step <NUM> includes training a non-linear algorithm for classifying the consumer identification code based on the purchase history log, and identifying a likelihood that a consumer associated with the consumer identification code, will purchase a product in the personalized list of universal product codes.

Step <NUM> includes returning the personalized list of UPCs to the retailer or to the service provider. In some embodiments, step <NUM> includes providing an ordered list of UPCs, ranked according to consumer preference, or purchase value (e.g., a factor including the price of the product and the likelihood of purchase by the consumer). In some embodiments, step <NUM> includes integrating the personalized list of universal product codes with an application programming interface hosted by the retailer or the retailer's service provider and returning a product picture, a product description, or a product pricing with the personalized list of universal product codes. In some embodiments, step <NUM> includes requesting, to the service provider, a data element associated with at least one product in the list of personalized universal product codes, and editing an advertisement for a consumer identified by the consumer identification code based on the data element. In some embodiments, step <NUM> includes returning to the service provider an instruction to be included in an advertisement for a consumer identified by the consumer identification code, the instruction configured to provide a measurement data to the service provider based on a consumer interaction with the advertisement.

Step <NUM> includes providing a product metadata for a consumer payload. In some embodiments, step <NUM> includes selecting the product metadata according to a channel for transmitting the consumer payload to the consumer. In some embodiments, the channel for transmitting the consumer payload to a client device with the consumer may include a web-based channel (e.g., via a web browser application), a mobile advertisement channel, a desktop advertisement channel, a home leaflet, and an e-mail. In some embodiments, step <NUM> includes providing the product metadata based on the exposure of a series of products handled by a consumer network server. In some embodiments, step <NUM> is powered by the retailer through a provider or publisher server (e.g., an ESP, DSP, or SSP server).

Step <NUM> includes receiving a tracking pixel indicative that the consumer has viewed or interacted with the consumer payload. The tracking pixel is embedded in the consumer payload and is configured to fire or trigger whenever the consumer is exposed to the payload (e.g., the consumer clicks an advertisement, or plays a video including the advertisement, and the like).

Step <NUM> includes notifying the retailer that the consumer has viewed the advertisement payload.

Step <NUM> includes correlating a purchase event of a product in the personalized list at the retailer with the trigger of a tracking pixel in the consumer payload.

<FIG> illustrates steps in a method <NUM> for providing a personalized product list to a remote server upon request, according to some embodiments. In some embodiments, one or more of the steps in method <NUM> may be performed by a consumer network database server communicatively coupled, via a network, with a retailer client device and with a consumer client device (cf. servers <NUM>, client devices <NUM>, and network <NUM>). Further, one or more of the steps in method <NUM> may be performed by an ESP server, a DSP server, an SSP server (e.g., server 130A) coupled to the network, and the retailer client device (e.g., server 130B). Each one of the servers in method <NUM> may be coupled with a consumer network database to store in, and retrieve data from (cf. databases <NUM> or <NUM>). Moreover, in some embodiments, methods consistent with the present disclosure may include at least one of the steps in method <NUM> performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step <NUM> includes parsing the purchase history of the consumer and verifying that there is sufficient depth in the purchase history of the consumer to provide a useful recommendation.

Step <NUM> includes evaluating the purchase history based on a UPC product hierarchy.

Step <NUM> includes filtering out UPCs below a selected threshold. In some embodiments, step <NUM> includes filtering out UPCs from the purchase history that appear less than the threshold number of times in the purchase history. In some embodiments, step <NUM> includes filtering out UPCs from the purchase history that are carried by less than the threshold number of stores in a given retailer.

Step <NUM> includes scoring a UPC in the list based on at least one of a purchase probability of the item by the consumer, the value of the item, and the stock availability of the item at the retailer. In some embodiments, step <NUM> includes scoring the UPC in the list based on a geographic location of the consumer and a geographic location of a "nearest" (or sufficiently near) retailer store carrying the UPC item. Accordingly, when there is a retailer store that has a given product from the purchase history of a consumer, the UPC for this product may be given a higher value score.

Step <NUM> includes providing a list including UPCs sorted according to a score indicative of a purchase probability of the item by the consumer.

Step <NUM> includes providing a standard list of default UPCs when the purchase history is not deep enough.

<FIG> illustrates steps in a method <NUM> for providing a personalized payload to a consumer via a remote channel, according to some embodiments. In some embodiments, one or more of the steps in method <NUM> may be performed by a consumer network database server communicatively coupled, via a network, with a retailer client device and with a consumer client device (cf. servers <NUM>, client devices <NUM>, and network <NUM>). Further, one or more of the steps in method <NUM> may be performed by an ESP server, a DSP server, an SSP server (e.g., server 130A) coupled to the network, and the retailer client device (e.g., server 130B). Each one of the servers in method <NUM> may be coupled with a consumer network database to store in, and retrieve data from (cf. databases <NUM> or <NUM>). Moreover, in some embodiments, methods consistent with the present disclosure may include at least one of the steps in method <NUM> performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step <NUM> includes requesting a personalized list of universal product codes to a consumer database based on a consumer identification code.

Step <NUM> includes populating parameters in the pixel with data linked to a consumer identifier in the consumer database server.

Step <NUM> includes assembling a consumer payload.

Step <NUM> includes providing the consumer payload to a consumer via a pre-selected channel.

<FIG> is a block diagram illustrating an exemplary computer system <NUM> with which the client device <NUM> and server <NUM> of <FIG> and <FIG>, and the methods of <FIG> can be implemented. In certain aspects, the computer system <NUM> may be implemented using hardware or a combination of software and hardware, either in a dedicated server, or integrated into another entity, or distributed across multiple entities.

Computer system <NUM> (e.g., client device <NUM> and server <NUM>) includes a bus <NUM> or other communication mechanism for communicating information, and a processor <NUM> (e.g., processors <NUM>) coupled with bus <NUM> for processing information. By way of example, the computer system <NUM> may be implemented with one or more processors <NUM>. Processor <NUM> may be a general-purpose microprocessor, a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable entity that can perform calculations or other manipulations of information.

Computer system <NUM> can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them stored in an included memory <NUM> (e.g., memories <NUM>), such as a Random Access Memory (RAM), a flash memory, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device, coupled with bus <NUM> for storing information and instructions to be executed by processor <NUM>. The processor <NUM> and the memory <NUM> can be supplemented by, or incorporated in, special purpose logic circuitry.

The instructions may be stored in the memory <NUM> and implemented in one or more computer program products, e.g., one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, the computer system <NUM>, and according to any method well known to those of skill in the art, including, but not limited to, computer languages such as data-oriented languages (e.g., SQL, dBase), system languages (e.g., C, Objective-C, C++, Assembly), architectural languages (e.g., Java,. NET), and application languages (e.g., PHP, Ruby, Perl, Python). Instructions may also be implemented in computer languages such as array languages, aspect-oriented languages, assembly languages, authoring languages, command line interface languages, compiled languages, concurrent languages, curly-bracket languages, dataflow languages, data-structured languages, declarative languages, esoteric languages, extension languages, fourth-generation languages, functional languages, interactive mode languages, interpreted languages, iterative languages, list-based languages, little languages, logic-based languages, machine languages, macro languages, metaprogramming languages, multiparadigm languages, numerical analysis, non-English-based languages, object-oriented class-based languages, object-oriented prototype-based languages, off-side rule languages, procedural languages, reflective languages, rule-based languages, scripting languages, stack-based languages, synchronous languages, syntax handling languages, visual languages, wirth languages, and xml-based languages. Memory <NUM> may also be used for storing temporary variable or other intermediate information during execution of instructions to be executed by processor <NUM>.

A computer program as discussed herein does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, subprograms, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and intercoupled by a communication network.

Computer system <NUM> further includes a data storage device <NUM> such as a magnetic disk or optical disk, coupled with bus <NUM> for storing information and instructions. Computer system <NUM> may be coupled via input/output module <NUM> to various devices. Input/output module <NUM> can be any input/output module. Exemplary input/output modules <NUM> include data ports such as USB ports. The input/output module <NUM> is configured to connect to a communications module <NUM>. Exemplary communications modules <NUM> (e.g., communications modules <NUM>) include networking interface cards, such as Ethernet cards and modems. In certain aspects, input/output module <NUM> is configured to connect to a plurality of devices, such as an input device <NUM> (e.g., input device <NUM>) and/or an output device <NUM> (e.g., output device <NUM>). Exemplary input devices <NUM> include a keyboard and a pointing device, e.g., a mouse or a trackball, by which a consumer can provide input to the computer system <NUM>. Other kinds of input devices <NUM> can be used to provide for interaction with a consumer as well, such as a tactile input device, visual input device, audio input device, or brain-computer interface device. For example, feedback provided to the consumer can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the consumer can be received in any form, including acoustic, speech, tactile, or brain wave input. Exemplary output devices <NUM> include display devices, such as an LCD (liquid crystal display) monitor, for displaying information to the consumer.

According to one aspect of the present disclosure, the client device <NUM> and server <NUM> can be implemented using a computer system <NUM> in response to processor <NUM> executing one or more sequences of one or more instructions contained in memory <NUM>. Such instructions may be read into memory <NUM> from another machine-readable medium, such as data storage device <NUM>. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in memory <NUM>. In alternative aspects, hard-wired circuitry may be used in place of or in combination with software instructions to implement various aspects of the present disclosure. Thus, aspects of the present disclosure are not limited to any specific combination of hardware circuitry and software.

The subject technology is illustrated, for example, according to various aspects described below. Various examples of aspects of the subject technology are described as numbered claims (claim <NUM>, <NUM>, etc.) for convenience. These are provided as examples, and do not limit the subject technology.

In one aspect, a method may be an operation, an instruction, or a function and vice versa. In one aspect, a clause may be amended to include some or all of the words (e.g., instructions, operations, functions, or components) recited in other one or more clauses, one or more words, one or more sentences, one or more phrases, one or more paragraphs, and/or one or more clauses.

To illustrate the interchangeability of hardware and software, items such as the various illustrative blocks, modules, components, methods, operations, instructions, and algorithms have been described generally in terms of their functionality. Whether such functionality is implemented as hardware, software, or a combination of hardware and software depends upon the particular application and design constraints imposed on the overall system.

As used herein, the phrase "at least one of" preceding a series of items, with the terms "and" or "or" to separate any of the items, modifies the list as a whole, rather than each member of the list (e.g., each item). The phrase "at least one of' does not require selection of at least one item; rather, the phrase allows a meaning that includes at least one of any one of the items, and/or at least one of any combination of the items, and/or at least one of each of the items.

A reference to an element in the singular is not intended to mean "one and only one" unless specifically stated, but rather "one or more. " Pronouns in the masculine (e.g., his) include the feminine and neuter gender (e.g., her and its) and vice versa. The term "some" refers to one or more. Underlined and/or italicized headings and subheadings are used for convenience only, do not limit the subject technology, and are not referred to in connection with the interpretation of the description of the subject technology.

While this specification contains many specifics, these should not be construed as limitations on the scope of what may be described, but rather as descriptions of particular implementations of the subject matter. Moreover, although features may be described above as acting in certain combinations and even initially described as such, one or more features from a described combination can in some cases be excised from the combination, and the described combination may be directed to a subcombination or variation of a subcombination.

The subject matter of this specification has been described in terms of particular aspects, but other aspects can be implemented and are within the scope of the following clauses. For example, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. The actions recited in the clauses can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. Moreover, the separation of various system components in the aspects described above should not be understood as requiring such separation in all aspects, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

The title, background, brief description of the drawings, abstract, and drawings are hereby incorporated into the disclosure and are provided as illustrative examples of the disclosure, not as restrictive descriptions. It is submitted with the understanding that they will not be used to limit the scope or meaning of the clauses. In addition, in the detailed description, it can be seen that the description provides illustrative examples and the various features are grouped together in various implementations for the purpose of streamlining the disclosure. The method of disclosure is not to be interpreted as reflecting an intention that the described subject matter requires more features than are expressly recited in each clause. Rather, as the clauses reflect, inventive subject matter lies in less than all features of a single disclosed configuration or operation. The clauses are hereby incorporated into the detailed description, with each clause standing on its own as a separately described subject matter.

The claims are not intended to be limited to the aspects described herein, but are to be accorded the full scope consistent with the language claims and to encompass all legal equivalents. Notwithstanding, none of the claims are intended to embrace subject matter that fails to satisfy the requirements of the applicable patent law, nor should they be interpreted in such a way.

Embodiments consistent with the present disclosure may include any one of Embodiments I, II and III, listed below.

In Embodiment I, a computer-implemented method includes receiving, in a server, a request from a service provider, the request including a consumer identification code associated with a consumer. The computer-implemented method also includes obtaining a personalized list of universal product codes based on the consumer identification code and a purchase history log in a database, providing the personalized list of universal product codes to the service provider, and receiving a tracking pixel indicative that the consumer has interacted with a consumer payload, wherein the consumer payload is associated with at least one product from the personalized list of universal product codes.

In Embodiment II, a system, includes a memory circuit storing instructions and one or more processors configured to execute the instructions to cause the system to receive, in a server, a request from a service provider, the request including a consumer identification code. The one or more processors also execute instructions to obtain a personalized list of universal product codes based on the consumer identification code and a purchase history log in a database, and to provide the personalized list of universal product codes to the service provider.

Claim 1:
A computer-implemented method, comprising:
receiving, in a server, a request from a service provider, the request including a consumer identification code associated with a consumer;
obtaining a personalized list of universal product codes based on the consumer identification code and a purchase history log in a database;
providing the personalized list of universal product codes to the service provider;
receiving a tracking pixel indicative that the consumer has interacted with a consumer payload, wherein the consumer payload is associated with at least one product from the personalized list of universal product codes; and
notifying a retailer that the consumer has interacted with the consumer payload based on the tracking pixel, wherein obtaining a personalized list of universal product codes comprises:
removing, from the personalized list of universal product codes, an item purchased by the consumer in a most recent selected period of time, and
padding the personalized list of universal product codes with a default universal product code when the purchase history log is exhausted before completing a pre-selected quota of universal product codes.