DIGITAL CONSUMER IDENTIFICATION IN A SUPPLY CHAIN

A method is provided that includes collecting a first party data from a client device associated with a consumer and storing the first party data in a database. The method also includes comparing the first party data with a second party data in the database to identify a consumer profile associated with the first party data and with the second party data and providing, to the client device, a digital advertisement based on the consumer profile in the database and on the first party data. A system for performing the above method is also provided.

BACKGROUND

Field

The present disclosure generally relates to systems and procedures for identifying consumers using a variety of first and second party data sets comingled and joined with existing household and consumer identification graphs stored in a server database (e.g., for marketing purposes). More specifically, embodiments as disclosed herein relate to identifying a consumer using probabilistic techniques involving consumer interest data, geolocation data, demographic data, personality data, media consumption data, iAB supply categorization data, digital signals, and the like, that help create a robust profile of a consumer over time. The consumer profile can eventually be used to cluster and target consumers with digital advertising campaigns.

Description of the Related Art

Current digital advertising and marketing techniques use piecemeal information gathered from consumers who typically subscribe to specific network services. This leads to loss of marketing opportunities or even a wasteful use of advertising resources when the wrong target is addressed.

SUMMARY

In one embodiment, a computer-implemented method is provided that includes collecting a first party data from a client device associated with a consumer and storing the first party data in a database. The computer-implemented method also includes comparing the first party data with a second party data in the database to identify a consumer profile associated with the first party data and with the second party data and providing, to the client device, a digital advertisement based on the consumer profile in the database and on the first party data.

In a second embodiment, a computer-implemented method is disclosed that includes requesting, from a client device to a first server hosting a consumer network, a digital value added certificate, the client device being associated with a consumer that subscribes to the consumer network. The computer-implemented method also includes providing, to the first server, a first party data from the client device. The first party data is correlated to a second party data in a database accessible to the first server and at least one of the first party data or the second party data comprises a geolocation information indicative of a presence of the consumer within a radius from a retail store. The first server is configured to identify a consumer profile associated with the first party data and with the second party data and to generate the digital value added certificate for the consumer based on a likelihood that the geolocation information corresponds to the consumer profile. The computer-implemented method also includes receiving, in the client device, the digital value added certificate and validating the digital value added certificate at a point of sale in the retail store.

In yet another embodiment, a system is disclosed that includes a memory circuit storing instructions and one or more processors. The one or more processors are configured to execute at least one instruction to cause the system to collect a first party data from a client device associated with a consumer and to store the first party data in a database. The one or more processors also execute instructions to compare the first party data with a second party data in the database to identify a consumer profile associated with the first party data and with the second party data, and to provide, to the client device, a digital advertisement based on the consumer profile in the database and on the first party data.

In the figures, elements labeled with the same or similar reference numerals may have similar functionality and features, unless stated otherwise.

DETAILED DESCRIPTION

A system as provided herein is configured to collect a variety of first and second party datasets like consumer interest data, geolocation data, demographic data, personality data, media consumption data, iAB supply categorization data, digital signals, and the like, to store the information in a database, to run a variety of data science techniques and procedures to cluster and resolve unknown consumer identities from within the existing household and consumer identification graphs in a probabilistic manner, and to provide the capability to target the resolved consumer identity profiles in the digital space. In some embodiments, systems as disclosed herein may operate in the context of a consumer located inside a retail store, or at a point of sale (POS) for the retail store.

Embodiments as disclosed herein provide a solution to the problem arising in the realm of computer technology for developing advertising models directed to targeted payloads over large populations of consumers. Tools disclosed herein provide the technical ability to precisely identify a consumer or a group of consumers from digital information collected by multiple parties in a centralized database.

The subject systems and methods provide several advantages, including the display of data in correlated tables and graphs. The proposed solution further provides improvements to the functioning of the computer itself, because it can reduce processing time for finding correlations and identifying consumer patterns and networks. Additionally, the subject system decreases a burden on a computer processor, network hardware and resources, and/or device power for performing the correlations and updating models across one or more components of a system, resulting in improvements that may be realized in an observable manner

FIG. 1illustrates a system100configured for identifying a consumer101using geolocation data and other digital identification data collected in a network150. System100includes a server130A, a server130B (hereinafter, collectively referred to as “servers130”), a database152, and client devices110-1,110-2, and110-3(hereinafter, collectively referred to as “client devices110”). In some embodiments, client devices110include internet enabled devices such as a mobile device110-1, a tablet device110-2, a household computer110-3, and other appliances such as television sets, and any other sensors and devices, through network150. Client devices110, severs130, and database152may be communicatively coupled with one another wirelessly (e.g., cellular network, WiFi, BlueTooth, and the like) or through a wired channel (e.g., telephone line, Ethernet, and the like). Client devices110, servers130, and database152may each include at least a memory circuit and one or more processor circuits. The memory circuits may include instructions which, when executed by the processor circuits, cause the devices, servers A and B, and the database to perform at least some steps in methods as disclosed herein.

Client devices110may include one or more applications122such as application programming interfaces (APIs) or software development kits (SDKs) hosted by any one of servers130. Without limitation, multiple servers130may host multiple APIs or SDKs installed in the devices, providing different network services to consumer101. For example, server130B may be a network publishing host, providing multimedia content to consumer101, for downstream (e.g., news and entertainment, services, and the like). In some embodiments, server130B may host mapping and other geolocation services to the consumer (e.g., applications122-1and122-2, through a GPS device112in smart phone110-1). Likewise, client device110-3may be a desktop computer running application122-3(API) and/or an application122-4(SDK). More generally, servers130may provide any network service to consumer101including retail shopping, consumer brands, services, information, entertainment, travel, business, and the like.

Consumer101may have a loyalty card115including a loyalty card ID119-12, with membership to a network service hosted by server A. In some embodiments, client devices110or applications122may include personal identifiable information (PII) such as an advertising digital identifier119-1, a device identifier119-2, an IP address119-3, or any other ID naming scheme that is PII-safe, following the guidelines set forth by the iAB. Hereinafter, advertising digital identifier119-1, device identifier119-2, or IP address119-3will be collectively referred to as “digital identifiers119.” In some embodiments, consumer101may be located within a retail store (e.g., in a particular section of the store, aisle, or kiosk) or even at a point of sale (POS) of the retail store, and servers130may be able to retrieve digital identifiers119for use as in methods disclosed herein.

Database152may include a PII safe database and may store information125about consumer101. In some embodiments, information125may include digital identifiers119and other consumer information gathered by any one of applications122. Database152may include one or more databases accessible to at least one of servers130through network150. In some embodiments, servers130may include their own database, coupled to the network through a firewall configured to provide privacy and security to the information.

In some embodiments and without limitation, GPS device112may provide latitude and longitude coordinates of client device110-1to API122-1or SDK122-2running in memory. Either one of servers130, or both, may collect the geolocation of mobile device110-1, including other technographic information specific to the device (IP address, device operating system, “user-agent,” and the like), information about application122or website that consumer101is running, and other PII safe contextual information. Server130may also store digital identifiers119and other information125in database152upon consent and agreement by consumer101.

FIG. 2illustrates an example architecture200suitable for implementing the system inFIG. 1, according to some embodiments. A client device210is communicatively coupled with a server230via network150. Server230may also have access to a database252via network150. In some embodiments, database252may be part of server230. While architecture200only shows one server230and one client device210, in some embodiments, multiple client devices may couple to multiple servers through network150, of which client device210and server230are but one example. Client device210may include a personal computer, a portable or mobile computer (e.g., a laptop), a cell phone, a smartphone, a palm device, or any other device such as a printer, a smart printer, or a display (e.g., client devices110). Client device210may also include or be communicatively coupled with one or more peripheral devices such as an input device214and an output device216. In some embodiments, input device214includes any one of a touch screen, a stylus, a mouse, a keyboard, or a microphone. Likewise, output device216may include a display, a speaker, and the like.

Client device210and server230may include a processor212-1and a memory220-1(client device210), a processor212-2and a memory220-2(server230), hereinafter, collectively referred to as “processors212” and “memories220.” A communications module218-1in client device210and a communications module218-2in server230enable interfacing each of these devices with network150(hereinafter, collectively referred to as “communications modules218”). Communications modules218may include radio frequency circuitry and antennas configured to transmit and receive radio frequency signals such as Bluetooth, near field coupling, Wi-Fi, and the like. In that regard, communications modules218may enable one or more client devices as client device210and one or more servers as server230to communicate to one another directly, separately from network150.

Database252may include an identification table255. In some embodiments, identification table255may include a correlation table associating digital identifier219with a consumer purchasing history for multiple devices210and multiple consumers. In some embodiments, table355is a correlation table between multiple digital data sources. Database252may also include information from retail stores and brand manufacture products.

Memory220-1may include an application222(e.g., APIs, SDKs, and the like, cf. applications122) including instructions which, when executed by processor212-1, cause client device210to execute at least partially some of the methods disclosed herein. In some embodiments, application222may be installed and hosted by server230upon consumer authorization. Likewise, memory220-2may include a marketing engine240and a digital identification engine246having instructions which, when executed by processor212-2, cause server230to execute at least partially some of the methods consistent with the present disclosure. Client device210may also include a digital identifier219associated with the device and/or a digital identification number in application222(e.g., digital identifier119).

Marketing engine240performs marketing analysis and devises advertisement campaign strategies as well as promotional offers to consumers in view of business rules, advertisement rules, and consumer profiles. In some embodiments, marketing engine240includes a management tool242and an advertisement tool244. In some embodiments, management tool242includes analytics algorithms based on business rules. In some embodiments, advertisement tool244includes algorithms based on campaign rules, campaign management, campaign optimization, campaign analytics, and handling campaign errors. In some embodiments, marketing engine240includes identity resolution algorithms, and is configured to perform identity mapping and onboarding of new subscribers. In some embodiments, marketing engine240includes installing and updating digital platform adaptors and interfaces for client device210, including application222and devices related thereof (e.g., GPS device112).

Digital identification engine246may include a correlation tool248and a database management tool249. In some embodiments, correlation tool248includes techniques and procedures in the field of data science to probabilistically identify consumers for the purposes of targeting relevant and engaging advertising media, based on digital identifier219and identification table255. Database management tool249may create and update identification table255and digital identifier219from multiple client devices210compiled into a digital information225.

In some embodiments, correlation tool248and database management tool249may collect and aggregate data from one or more servers230(e.g., a first party data, a second party data, a third party data, and the like). In some embodiments, at least one of the first party data or the second party data includes digital identifier219, and correlation tool248compares the first party data with the second party data to determine a likelihood that client device210is used by a consumer associated with the consumer profile in database252. In some embodiments, correlation tool248correlates at least one of an internet protocol address, an operating system version in the client device, a user agent, an application, or a website running in the client device (e.g., in application222). In some embodiments, correlation tool248pairs the first party data and the second party data within a radius of a geolocation information associated with client device210. In some embodiments, correlation tool248associates a consumer profile with a group of consumers having a household identifier. In some embodiments, correlation tool248determines the second party data based on at least a portion of the first party data with a model trained with a data stored in the database. In some embodiments, the first party data includes a mobility data for the client device, and correlation tool248infers the second party data with a behavioral model based on the mobility data. In some embodiments, digital identification engine246receives a purchasing data associated with a digital advertisement and with the consumer, and updates a model for comparing the first party data with the second party data based on the purchasing data. The model may include a neural network model, a machine learning model, an artificial intelligence model, and any type of multilinear regression model, nonlinear model, or linear model. In some embodiments, correlation tool248updates the consumer profile based on the first party data and the second party data. In some embodiments, correlation tool248determines a correlation between the consumer and the consumer profile and determining a confidence level for the correlation. In some embodiments, correlation tool248receives a third party data associated with the consumer and to update the database with the third party data to improve a likelihood that the consumer profile is associated with the consumer.

FIG. 3illustrates a table355in a database for cross correlating multiple columns including a loyalty card ID with a second party sourced data (e.g., identification table255and database252), according to some embodiments. Table355includes a database for cross correlating a household ID (HHID) column311with a Loyalty Card ID (LCID) column312and a Digital ID (DID) column313using a variety of first and second party source data, according to some embodiments. Without limitations, a location information (e.g., provided by a GPS device, cf. GPS device112) may be included in a latitude column314, a longitude column315, and an IP address column316. Table355may further include consumer information that may be sourced from other data sources that contain PII safe consumer information that can be retrieved from a third party server (e.g., bought or licensed). Consumer information may be included in a “personality traits” column317, an “interests” column318, and an “iAB category” column319. In some embodiments, information in columns317through319may be provided by social networking servers that consumers may subscribe to, or by a machine learning algorithm, an artificial intelligence algorithm, operating on other digital data available for the consumer (e.g., any one of columns314-316). Table355may be stored in a database and used by a digital identification engine (e.g., digital identification engine246) to establish a correlation between the different columns in table355and a consumer identification.

In embodiments consistent with the present disclosure, table355is created or stored in a database coupled to a server (e.g., database252and server230). The database collects information from a variety of devices and first and second party data sources (e.g., information125and225) to correlate datasets and to enhance and create new consumer household, loyalty, and digital ID associations in no particular order. The correlation may include, for example, pairing together an entry in HHID column311with an entry in LCID column312and an entry in DID column313, based on the same (or similar) entries for latitude column314and longitude column315. For example, line5in table355indicates a consumer that logs into an account having LCID_5(LCID column312) in Server A, using a mobile device with DID_5(DID column313). The mobile device may further report a geolocation consistent with HHID_2(latitude: 37.301390, longitude: −122.043210). In such case, the correlation between the HHID_2value, the LCID_5value, and the DID_5value may be strong. That is, it is highly likely that the user of the mobile device with DID_5also has loyalty card LCID_5and lives within household HHID_2. Accordingly, lines1through5and7in table355may be associated with a deterministic match320.

Note that, as expected, more than one consumer may be associated with a given entry in HHID column311. For example, the household with HHID_2includes at least two individuals having LCID_4and LCID_5in LCID column312. Each individual uses a different mobile device, having DID_4and DID_5, respectively (cf. DID column313,11.4and5). The geolocation with latitude: 37.301390 and longitude: −122.043210 is presumably the home location for HHID_2. Further, the household with HHID_1includes at least three individuals having LCID_L LCID_2, and LCID_3. Each individual uses a different mobile device, having DID_L DID_2, and DID_3, respectively. The geolocation with latitude: 37.900921 and longitude: −108.525893 may be the home location for HHID_1, or a retail store location, or any other location associated with the mobile device having DID_3. Note that, in the particular case of HHID_1, the precise location of HHID_1may not be corroborated with DID_1and DID_2(e.g., no data in latitude column314or longitude column315for the first two lines in Table355). For example, server A may not have had access to the geolocation of the mobile devices DID_1and DID_2, but may still be able to correlate all to HHID-1, based on other information. The other information may include purchase patterns with the respective loyalty cards (based on the entries in LCID column312): the items, the retailer locations, and the time of purchase, over an extended sampling period.

Table355may also include entries having a weaker correlation wherein the LCID value may be missing or not yet established and probabilistic match340may be used to link, join, and increase the correlation from one ID set to another (e.g., amongst either one of columns311-319). This may be the case when a server (e.g., server130A) accesses data and information for consumers that are not loyalty card network subscribers. Accordingly, in household HHID_5, consumers having mobile devices DID_8, DID_9, and DID_10may be associated with geolocation (41.878113, −87.629799), (41.893353, −87.681200), and (41.875972, −87.669708), respectively (cf. lines8-10in Table355). These geolocations, although different, are very close to each other (cf. data block342A), which presumably indicates either the home address of HHID_5, or the address of a store or a mall where the different household members go shopping (e.g., at the same or overlapping times). However, in some embodiments, the difference between the geolocation of the household members in HHID_5(which in this particular instance may amount to a few square km) may reduce the degree of certitude that these DIDs should be associated with HHID_5. Another probabilistic match may link two consumers with mobile devices DID_11and DID_12with household HHID_6, having an IP address216.3.128.12(cf. lines11-12, block342B in Table355). For example, server A may have detected that both DID_11and DID_12log in to the network from IP address216.3.128.12late at night. Moreover, based on other information (gathered by server A from server B), both consumers in HHID_6may be listed as health enthusiasts interested in biking (e.g., through purchase history, usual location at parks and outdoors, typical speed of motion at those locations, and the like).

Additionally, Table355may indicate that household HHID_3may be linked with mobile device DID_6(cf. line6in Table355). However, whereas the geolocation (40.712776, −74.005974) and an IP address73.15.66.31have been established, the absence of a loyalty card to back up the data (e.g., no data in LCID column312), or the lack of more household members, soften the degree of certitude that the correlation indicated in Table355be correct. In some embodiments, the availability of a correlation between the HHID value, the LCID value, and the DID value indicates a strong, deterministic match320between HHID_4, LCID_6, and DID_7, even in the absence of a geolocation, or an IP address, or any further consumer data, other than the iAB category (iAB12), which can be determined from the loyalty card account associated with LCID_6.

FIG. 4illustrates a graph400correlating multiple household identifiers411-1,411-2,411-3,411-4, and411-5(hereinafter, collectively referred to as “household identifiers411”) and digital identifiers419-1,419-2,419-3,419-4,419-5,419-6,419-7,419-8,419-9,419-10,419-11, and419-12(hereinafter, collectively referred to as “digital identifiers419”) in a database, according to some embodiments. Graph400includes a mapping correlation between multiple household identifiers411and digital identifiers419in a database (e.g., databases152and252), according to some embodiments. In some embodiments, graph400may be obtained by a digital identification engine in a server from an identification table stored in a database (e.g., digital identification engine246, servers130, server230, database152, database252, identification table255, and table355). Accordingly, graph400associates DID_1, DID_2, and DID_3with HHID_1through a strong and deterministic link410-1. Likewise, DID_4and DID_5are associated with HHID_2through strong, deterministic link410-2, and DID_7with HHID_4through strong, deterministic link410-3. Graph400also illustrates DID_8, DID_9, and DID_10associated with HHID_5through a probabilistic link430-1(with a high degree of certainty, as the similar geolocation of the three mobile devices DID_8-10indicates, cf. Table355). Also, DID_11and DID_12are associated with HHID_2through a probabilistic link430-2(with a high degree of certainty, as the IP address associated with both devices is the same, and both consumers seem to have the same personality traits and share the same iAB category).

Graph400also illustrates a non-conclusive link420between DID419-6and HHID411-3, (cf. Table355), due to the missing information regarding the loyalty card. Note that in the case of HHID411-3, even though a geolocation is established, in addition to an IP address, the absence of a second mobile device or consumer correlated with the same household reduces somewhat the degree of certitude that the link between HHID411-3and DID419-6is accurate.

FIG. 5is a flowchart including steps in a method500for identifying consumers using first party owned and second party sourced datasets, according to some embodiments. In some embodiments, one or more of the steps in method500may be performed by one or more of the devices and components illustrated inFIGS. 1 and 2. For example, in some embodiments, one or more of the steps in method500may be performed by a client device or a server including processors executing instructions stored in memory circuits (e.g., client devices110and210, servers130and230, and processors212and memories220, cf.FIGS. 1, 2). In some embodiments, the memory may include a marketing engine having a management tool and an advertisement tool, and a digital identification engine having a correlation tool and a database management tool (e.g., marketing engine240, management tool242, advertisement tool244, digital identification engine246, correlation tool248, and database management tool249, cf.FIG. 2). In some embodiments, the server may also be communicatively coupled to a database storing digital information arranged in an identification table or any other tables and in a graph, via a network (e.g., databases152and252, digital information225, identification table255, table355, graph400, and network150, cf.FIGS. 1, 2, 3, and 4). In some embodiments, to communicate with each other and the network, the client devices and servers may execute steps consistent with method500using an input device, an output device, and a communications module (e.g., input device214, output device216, and communications module218, cf.FIG. 2). Moreover, in some embodiments, methods consistent with the present disclosure may include at least one of the steps in method500performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step502includes collecting information from devices where APIs and SDKs provide technographic and digital device data including second party sourced datasets that provide consumer interest data, geolocation data, demographic data, personality data, media consumption data, iAB supply categorization data, digital signals, and the like. In some embodiments, step502includes supplementing an existing consumer identity map database with the above collected data.

Step504includes storing the gathered location information in a database. In some embodiments, step504is performed by an application programming interface installed in the devices, and step504includes accessing the application programming interface with a second application programming interface installed in the devices referenced inFIG. 1. Without limitation, in some embodiments, step504includes storing an identifier of the device IDs in the database, and wherein comparing second party sourced information with a consumer profile in the database includes matching the identifier of the devices with a consumer household identifier in the database.

Step506includes running a set of analysis and procedures from the field of data science to probabilistically classify, link disjointed IDs, and identify households and devices within the identity map database. In some embodiments and without limitation, the consumer profile includes PII safe first party data like the purchasing history of a consumer at a retailer, technographic data, and second party sourced datasets that provide consumer interest data, geolocation data, demographic data, personality data, media consumption data, iAB supply categorization data, digital signals, and the like. In some embodiments, and without limitation, step506includes correlating transactions in a consumer packaged goods space with a variety of location data, consumer interest data, technographic data, demographic data, personality data, media consumption data, and the like.

Step508includes targeting digital ads, promotions, offers, and digital media to consumers within the marketing and advertising space based on the newly identified ID linkages. In some embodiments, first party acquired datasets like location information from devices includes location data within a retail store, the location associated with a class of consumer-packaged goods including consumer transaction data gathered in a PII safe manner. Accordingly, in some embodiments, step508includes correlating the consumer profile with the class of consumer-packaged goods. In some embodiments, step508includes providing a value added certificate for purchasing an item at a retail store. In some embodiments, step508includes feeding a media file including promotional information for display in the mobile device, and other devices. In some embodiments, step508includes updating the consumer profile in the database using information from first and second party datasets. In some embodiments, storing the location information in the database includes classifying a user of client devices in a consumer category (e.g., client devices110or210), and step508includes providing an advertisement associated with the consumer category for display across devices in mobile, desktop, connected TV, and the like.

FIG. 6is a flowchart including steps in a method600for providing a digital advertisement to a consumer upon identification of the consumer in a database, according to some embodiments. In some embodiments, one or more of the steps in method600may be performed by one or more of the devices and components illustrated inFIGS. 1 and 2. For example, in some embodiments, one or more of the steps in method600may be performed by a client device or a server including processors executing instructions stored in memory circuits (e.g., client devices110and210, servers130and230, and processors212and memories220, cf.FIGS. 1, 2). In some embodiments, the memory may include a marketing engine having a management tool and an advertisement tool, and a digital identification engine having a correlation tool and a database management tool (e.g., marketing engine240, management tool242, advertisement tool244, digital identification engine246, correlation tool248, and database management tool249, cf.FIG. 2). In some embodiments, the server may also be communicatively coupled to a database storing digital information arranged in an identification table or any other tables and in a graph, via a network (e.g., databases152and252, digital information225, identification table255, table355, graph400, and network150, cf.FIGS. 1, 2, 3, and 4). In some embodiments, to communicate with each other and the network, the client devices and servers may execute steps consistent with method600using an input device, an output device, and a communications module (e.g., input device214, output device216, and communications module218, cf.FIG. 2). Moreover, in some embodiments, methods consistent with the present disclosure may include at least one of the steps in method600performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step602includes collecting a first party data from a client device associated with a consumer. In some embodiments, at least one of the first party data or the second party data includes a geolocation information indicative of a presence of the consumer within a radius from a retail store, and step602includes determining a likelihood that the geolocation information corresponds to the consumer profile. In some embodiments, at least one of the first party data or the second party data includes a loyalty identification data for the consumer in a consumer retail service network, and step602includes determining a likelihood that the loyalty identification data, the consumer profile, and a device code associated with the client device correspond to a same consumer. In some embodiments, at least one of the first party data or the second party data includes a device code associated with the client device, and step602includes determining a likelihood that the client device is used by a consumer associated with the consumer profile. In some embodiments, step602includes correlating at least one of an internet protocol address, an operating system version in the client device, a user agent, an application, or a website running in the client device. In some embodiments, step602includes pairing the first party data and the second party data within a radius of a geolocation information associated with the client device. In some embodiments, step602includes associating the consumer profile with a group of consumers having a household identifier.

Step604includes storing the first party data in a database. In some embodiments, step604includes storing the first party data in a first database and retrieving the second party data from a second database.

Step606includes comparing the first party data with a second party data in the database to identify a consumer profile associated with the first party data and with the second party data. In some embodiments, step606includes determining the second party data based on at least a portion of the first party data with a model trained with a data stored in the database. In some embodiments, the first party data includes a mobility data for the client device, and step606includes inferring the second party data with a behavioral model based on the mobility data. In some embodiments, step606includes receiving a purchasing data associated with the digital advertisement and with the consumer, and updating a model for comparing the first party data with the second party data based on the purchasing data. In some embodiments, step606includes updating the consumer profile based on the first party data and the second party data. In some embodiments, step606includes determining a correlation between the consumer and the consumer profile and determining a confidence level for the correlation. In some embodiments, step606includes receiving a third party data associated with the consumer and updating the database with the third party data to improve a likelihood that the consumer profile is associated with the consumer. In some embodiments, the client device is associated with a group of consumers, and step606includes selecting a consumer from the group of consumers based on the first party data and the second party data to identify the consumer profile.

Step608includes providing, to the client device, a digital advertisement based on the consumer profile in the database and on the first party data. In some embodiments, step608includes providing a value added certificate to the consumer, the value added certificate associated with a product for sale in a retailer store identified according to the first party data. In some embodiments, step608includes receiving a request from the client device to access a consumer network and to receive a value added certificate with the digital advertisement. In some embodiments, step608includes modifying a correlation between the first party data and the second party data in the database based on a purchase at a retail store, the purchase based on the digital advertisement. In some embodiments, step608includes collecting the second party data from a second server providing a network service to the client device. In some embodiments, step608includes providing a printing instruction to a printer in a retail store identified according to the first party data, the printing instruction including a printing instruction of a value added certificate for the consumer.

FIG. 7is a flowchart including steps in a method700for requesting a digital value added certificate to a server hosting a consumer network, according to some embodiments. In some embodiments, one or more of the steps in method700may be performed by one or more of the devices and components illustrated inFIGS. 1 and 2. For example, in some embodiments, one or more of the steps in method700may be performed by a client device or a server including processors executing instructions stored in memory circuits (e.g., client devices110and210, servers130and230, and processors212and memories220, cf.FIGS. 1, 2). In some embodiments, the memory may include a marketing engine having a management tool and an advertisement tool, and a digital identification engine having a correlation tool and a database management tool (e.g., marketing engine240, management tool242, advertisement tool244, digital identification engine246, correlation tool248, and database management tool249, cf.FIG. 2). In some embodiments, the server may also be communicatively coupled to a database storing digital information arranged in an identification table or any other tables and in a graph, via a network (e.g., databases152and252, digital information225, identification table255, table355, graph400, and network150, cf.FIGS. 1, 2, 3, and 4). In some embodiments, to communicate with each other and the network, the client devices and servers may execute steps consistent with method700using an input device, an output device, and a communications module (e.g., input device214, output device216, and communications module218, cf.FIG. 2). Moreover, in some embodiments, methods consistent with the present disclosure may include at least one of the steps in method700performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

Step702includes requesting, from a client device to a first server hosting a consumer network, a digital value added certificate, the client device being associated with a consumer that subscribes to the consumer network.

Step704includes providing, to the first server, a first party data from the client device. In some embodiments, the first party data is correlated to a second party data in a database accessible to the first server. In some embodiments, at least one of the first party data or the second party data includes a geolocation information indicative of a presence of the consumer within a radius from a retail store. In some embodiments, the first server is configured to identify a consumer profile associated with the first party data and with the second party data and to generate the digital value added certificate for the consumer based on a likelihood that the geolocation information corresponds to the consumer profile. In some embodiments, step704includes providing at least one of an internet protocol address, an operating system version in the client device, a user agent, an application, or a website running in the client device. In some embodiments, step704further includes authorizing the first server to pair the first party data and the second party data within a radius of a geolocation information associated with the client device. In some embodiments, step704includes selecting the consumer profile from a group of consumers having a household identifier, the group of consumers provided by the first server based on a correlation of the first party data and the second party data. In some embodiments, step704includes authorizing the first server to determine the second party data based on at least a portion of the first party data with a model trained with a data stored in the database. In some embodiments, step704includes providing a mobility data for the client device to the first server, wherein the first server is configured to infer the second party data with a behavioral model based on the mobility data. In some embodiments, step704includes authorizing the first server to update the consumer profile based on the first party data and the second party data. In some embodiments, step704includes authorizing the first server to determine a correlation between the consumer and the consumer profile and determining a confidence level for the correlation. In some embodiments, step704includes authorizing a second server to provide the second party data to the first server. In some embodiments, step704includes authorizing the first server to update the database with the second party data to improve a likelihood that the consumer profile is associated with the consumer. In some embodiments, step704includes authorizing the first server to modify a correlation between the first party data and the second party data in the database based on a purchase at a retail store associated with the digital value added certificate. In some embodiments, the first party data includes an information retrieved by the first server from an application installed in the client device, and step704includes authorizing the first server to retrieve the information. In some embodiments, step704includes accessing a second server hosting a social network, and authorizing the second server to provide the second party data to the first server, wherein the second party data includes data associated to a social network activity of the consumer. In some embodiments, step704includes authorizing the first server to retrieve the second party data from a second database.

Step706includes receiving, in the client device, the digital value added certificate.

Step708includes validating the digital value added certificate at a point of sale in the retail store. In some embodiments, step708includes validating the digital value added certificate to the first server. In some embodiments, at least one of the first party data or the second party data includes a loyalty identification data for the consumer in a consumer retail service network, and step708includes validating the loyalty identification data to the first server with the client device. In some embodiments, at least one of the first party data or the second party data includes a device code associated with the client device, and step708includes validating to the first server that the client device is used by a consumer associated with the consumer profile. In some embodiments, step708includes providing, to the first server, a purchasing data associated with the digital value added certificate, wherein the first server is configured to update a model for comparing the first party data with the second party data based on the purchasing data. In some embodiments, the digital value added certificate is associated with a product for sale in the retailer store, and step708includes requesting a second digital value added certificate for a second product for sale in the retailer store. In some embodiments, step708includes providing a printing instruction to a printer in the retail store, the printing instruction including a printing instruction of the digital value added certificate.

Hardware Overview

FIG. 8is a block diagram illustrating an example computer system800with which the client and network device ofFIG. 1and the methods ofFIGS. 5-7can be implemented. In certain aspects, computer system800may be implemented using hardware or a combination of software and hardware, either in a dedicated network device, or integrated into another entity, or distributed across multiple entities.

Computer system800(e.g., client device110and server130) includes a bus808or other communication mechanism for communicating information, and a processor802(e.g., processors212) coupled with bus808for processing information. By way of example, the computer system800may be implemented with one or more processors802. Processor802may 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 system800can 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 memory804(e.g., memories220), 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 to bus808for storing information and instructions to be executed by processor802. The processor802and the memory804can be supplemented by, or incorporated in, special purpose logic circuitry.

Computer system800further includes a data storage device806such as a magnetic disk or optical disk, coupled to bus808for storing information and instructions. Computer system800may be coupled via input/output module810to various devices. Input/output module810can be any input/output module. Exemplary input/output modules810include data ports such as USB ports. The input/output module810is configured to connect to a communications module812. Exemplary communications modules812(e.g., communications modules218) include networking interface cards, such as Ethernet cards and modems. In certain aspects, input/output module810is configured to connect to a plurality of devices, such as an input device814(e.g., input device114) and/or an output device816(e.g., output device116). Exemplary input devices814include a keyboard and a pointing device, e.g., a mouse or a trackball, by which a consumer can provide input to the computer system800. Other kinds of input devices814can 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 devices816include 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 device110and server130can be implemented using a computer system800in response to processor802executing one or more sequences of one or more instructions contained in memory804. Such instructions may be read into memory804from another machine-readable medium, such as data storage device806. Execution of the sequences of instructions contained in main memory804causes processor802to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in memory804. 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.

Computer system800can include clients and network devices. A client and network device are generally remote from each other and typically interact through a communication network. The relationship of client and network device arises by virtue of computer programs running on the respective computers and having a client-network device relationship to each other. Computer system800can be, for example, and without limitation, a desktop computer, laptop computer, or tablet computer. Computer system800can also be embedded in another device, for example, and without limitation, a mobile telephone, a PDA, a mobile audio player, a Global Positioning System (GPS) receiver, a video game console, and/or a television set top box.

RECITATION OF EMBODIMENTS

I. A computer-implemented method that includes collecting a first party data from a client device associated with a consumer and storing the first party data in a database. The computer-implemented method also includes comparing the first party data with a second party data in the database to identify a consumer profile associated with the first party data and with the second party data and providing, to the client device, a digital advertisement based on the consumer profile in the database and on the first party data.

II. A computer-implemented method that includes requesting, from a client device to a first server hosting a consumer network, a digital value added certificate, the client device being associated with a consumer that subscribes to the consumer network. The computer-implemented method also includes providing, to the first server, a first party data from the client device. The first party data is correlated to a second party data in a database accessible to the first server and at least one of the first party data or the second party data comprises a geolocation information indicative of a presence of the consumer within a radius from a retail store. The first server is configured to identify a consumer profile associated with the first party data and with the second party data and to generate the digital value added certificate for the consumer based on a likelihood that the geolocation information corresponds to the consumer profile. The computer-implemented method also includes receiving, in the client device, the digital value added certificate and validating the digital value added certificate at a point of sale in the retail store.

III. A system that includes a memory storing instructions and a processor. The processor is configured to execute at least one instruction and cause the system to collect a first party data from a client device associated with a consumer and to store the first party data in a database. The one or more processors also execute instructions to compare the first party data with a second party data in the database to identify a consumer profile associated with the first party data and with the second party data, and to provide, to the client device, a digital advertisement based on the consumer profile in the database and on the first party data.

Additionally to embodiments I, II, and III, embodiments consistent with the present disclosure may include any one or more of the following elements, in any combination.

Element 1, wherein at least one of the first party data or the second party data comprises a geolocation information indicative of a presence of the consumer within a radius from a retail store, and comparing the first party data with the second party data comprises determining a likelihood that the geolocation information corresponds to the consumer profile. Element 2, wherein at least one of the first party data or the second party data comprises a loyalty identification data for the consumer in a consumer retail service network, and comparing the first party data with the second party data comprises determining a likelihood that the loyalty identification data, the consumer profile, and a device code associated with the client device correspond to a same consumer. Element 3, wherein at least one of the first party data or the second party data comprises a device code associated with the client device, and comparing the first party data with the second party data comprises determining a likelihood that the client device is used by a consumer associated with the consumer profile. Element 4, wherein comparing the first party data with the second party data comprises correlating at least one of an internet protocol address, an operating system version in the client device, a user agent, an application or a website running in the client device. Element 5, wherein comparing the first party data with the second party data comprises pairing the first party data and the second party data within a radius of a geolocation information associated with the client device. Element 6, wherein comparing the first party data with the second party data in the database comprises associating the consumer profile with a group of consumers having a household identifier. Element 7, further comprising determining the second party data based on at least a portion of the first party data with a model trained with a data stored in the database. Element 8, wherein the first party data comprises a mobility data for the client device, further comprising inferring the second party data with a behavioral model based on the mobility data. Element 9, further comprising receiving a purchasing data associated with the digital advertisement and with the consumer, and updating a model for comparing the first party data with the second party data based on the purchasing data. Element 10, further comprising updating the consumer profile based on the first party data and the second party data. Element 11, wherein comparing the first party data with the second party data comprises determining a correlation between the consumer and the consumer profile and determining a confidence level for the correlation. Element 12, further comprising receiving a third party data associated with the consumer and updating the database with the third party data to improve a likelihood that the consumer profile is associated with the consumer. Element 13, wherein providing the digital advertisement comprises providing a value added certificate to the consumer, the value added certificate associated with a product for sale in a retailer store identified according to the first party data. Element 14, further comprising receiving a request from the client device to access a consumer network and to receive a value added certificate with the digital advertisement. Element 15, further comprising modifying a correlation between the first party data and the second party data in the database based on a purchase at a retail store, the purchase based on the digital advertisement. Element 16, wherein the client device is associated with a group of consumers, further comprising selecting a consumer from the group of consumers based on the first party data and the second party data to identify the consumer profile. Element 17, further comprising collecting the second party data from a second server providing a network service to the client device. Element 18, wherein storing the first party data in the database comprises storing the first party data in a first database and retrieving the second party data from a second database. Element 19, wherein providing the digital advertisement based on the consumer profile and on the first party data comprises providing a printing instruction to a printer in a retail store identified according to the first party data, the printing instruction comprising a printing instruction of a value added certificate for the consumer.

Element 20, wherein at least one of the first party data or the second party data comprises a geolocation information indicative of a presence of the consumer within a radius from a retail store, and to compare the first party data with the second party data the one or more processors execute instructions to determine a likelihood that the geolocation information corresponds to the consumer profile. Element 21, wherein at least one of the first party data or the second party data comprises a loyalty identification data for the consumer in a consumer retail service network, and to compare the first party data with the second party data the one or more processors execute instructions to determine a likelihood that the loyalty identification data, the consumer profile, and a device code associated with the client device correspond to a same consumer. Element 22, wherein at least one of the first party data or the second party data comprises a device code associated with the client device, and to compare the first party data with the second party data the one or more processors execute instructions to determine a likelihood that the client device is used by a consumer associated with the consumer profile. Element 23, wherein to compare the first party data with the second party data the one or more processors execute instructions to correlate at least one of an internet protocol address, an operating system version in the client device, a user agent, an application or a website running in the client device. Element 24, wherein to compare the first party data with the second party data the one or more processors execute instructions to pair the first party data and the second party data within a radius of a geolocation information associated with the client device. Element 25, wherein to compare the first party data with the second party data in the database the one or more processors execute instructions to associate the consumer profile with a group of consumers having a household identifier. Element 26, wherein the one or more processors further execute instructions to determine the second party data based on at least a portion of the first party data with a model trained with a data stored in the database. Element 27, wherein the first party data comprises a mobility data for the client device, and wherein the one or more processors further execute instructions to infer the second party data with a behavioral model based on the mobility data. Element 28, wherein the one or more processors further execute instructions to receive a purchasing data associated with the digital advertisement and with the consumer, and to update a model for comparing the first party data with the second party data based on the purchasing data. Element 29, wherein the one or more processors further execute instructions to update the consumer profile based on the first party data and the second party data. Element 30, wherein to compare the first party data with the second party data the one or more processors execute instructions to determine a correlation between the consumer and the consumer profile and determining a confidence level for the correlation. Element 31, wherein the one or more processors further execute instructions to receive a third party data associated with the consumer and to update the database with the third party data to improve a likelihood that the consumer profile is associated with the consumer. Element 32, wherein to provide the digital advertisement the one or more processors execute instructions to provide a value added certificate to the consumer, the value added certificate associated with a product for sale in a retailer store identified according to the first party data. Element 33, wherein the one or more processors further execute instructions to receive a request from the client device to access a consumer network and to receive a value added certificate with the digital advertisement. Element 34, wherein the one or more processors further execute instructions to modify a correlation between the first party data and the second party data in the database based on a purchase at a retail store, the purchase based on the digital advertisement. Element 35, wherein the client device is associated with a group of consumers, and the one or more processors further execute instructions to select a consumer from the group of consumers based on the first party data and the second party data to identify the consumer profile. Element 36, wherein the one or more processors execute instructions to collect the second party data from a second server providing a network service to the client device. Element 37, wherein to store the first party data in the database the one or more processors execute instructions to store the first party data in a first database and to retrieve the second party data from a second database. Element 38, wherein to provide the digital advertisement based on the consumer profile and on the first party data the one or more processors execute instructions to provide a printing instruction to a printer in a retail store identified according to the first party data, the printing instruction comprising a printing instruction of a value added certificate for the consumer.

Element 39, further comprising validating the digital value added certificate to the first server. Element 40, wherein at least one of the first party data or the second party data comprises a loyalty identification data for the consumer in a consumer retail service network, further comprising validating the loyalty identification data to the first server with the client device. Element 41, wherein at least one of the first party data or the second party data comprises a device code associated with the client device, further comprising validating to the first server that the client device is used by a consumer associated with the consumer profile. Element 42, wherein providing the first party data comprises providing at least one of an internet protocol address, an operating system version in the client device, a user agent, an application or a website running in the client device. Element 43, further comprising authorizing the first server to pair the first party data and the second party data within a radius of a geolocation information associated with the client device. Element 44, further comprising selecting the consumer profile from a group of consumers having a household identifier, the group of consumers provided by the first server based on a correlation of the first party data and the second party data. Element 45, further comprising authorizing the first server to determine the second party data based on at least a portion of the first party data with a model trained with a data stored in the database. Element 46, wherein providing the first party data comprises providing a mobility data for the client device to the first server, wherein the first server is configured to infer the second party data with a behavioral model based on the mobility data. Element 47, further comprising providing, to the first server, a purchasing data associated with the digital value added certificate, wherein the first server is configured to update a model for comparing the first party data with the second party data based on the purchasing data. Element 48, further comprising authorizing the first server to update the consumer profile based on the first party data and the second party data. Element 49, further comprising authorizing the first server to determine a correlation between the consumer and the consumer profile and determining a confidence level for the correlation. Element 50, further comprising authorizing a second server to provide the second party data to the first server. Element 51, wherein the digital value added certificate is associated with a product for sale in the retailer store, further comprising requesting a second digital value added certificate for a second product for sale in the retailer store. Element 52, further comprising authorizing the first server to update the database with the second party data to improve a likelihood that the consumer profile is associated with the consumer. Element 53, further comprising authorizing the first server to modify a correlation between the first party data and the second party data in the database based on a purchase at a retail store associated with the digital value added certificate. Element 54, wherein the first party data comprises an information retrieved by the first server from an application installed in the client device, further comprising authorizing the first server to retrieve the information. Element 55, further comprising accessing a second server hosting a social network, and authorizing the second server to provide the second party data to the first server, wherein the second party data comprises data associated to a social network activity of the consumer. Element 56, further comprising authorizing the first server to retrieve the second party data from a second database. Element 57, further comprising providing a printing instruction to a printer in the retail store, the printing instruction comprising a printing instruction of the digital value added certificate.

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. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. 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 technology is illustrated, for example, according to various aspects in the below claims. Various examples of aspects of the subject technology are described in the claims. These are provided as examples, and do not limit the subject technology. 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.