Patent Publication Number: US-10762141-B2

Title: Using on-line and off-line projections to control information delivery to mobile devices

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The present application claims the benefit of priority to U.S. Provisional Patent Application No. 62/470,119, filed Mar. 10, 2017, which is incorporated herein by reference in its entirety. 
    
    
     FIELD 
     The present application is related to information technologies, and more particularly to system and method of using on-line and off-line projections to control information delivery to mobile devices. 
     DESCRIPTION OF THE RELATED ART 
     Smart phones and other forms of mobile devices are becoming more and more widely used. Nowadays, people use their mobile devices to stay connected with other people and to obtain information and services provided by mobile service providers and application developers. To keep the information and services free or low-cost, mobile service providers and application developers fund their activities at least partially by delivering sponsored information to the mobile devices that are engaging with them. The sponsored information is provided by sponsors who are interested in delivering relevant information to mobile users&#39; mobile devices based on their locations. As a result, more and more mobile applications are designed to send location information of the mobile devices interacting with them (i.e., mobile supplies) to providers of location-based services (LBS). 
     To take advantage of the mobile nature of mobile phones, sophisticated computer technologies have been developed by information providers to estimate mobile device locations based on the signals they send and to select relevant and timely information to the mobile devices based on their estimated locations and other factors. Additionally, mechanisms are set up by hardware and software to track on-line activities using the mobile devices in response to the information they receive. These on-line activities have been used to derive performance measures for the delivered information and to control future information delivery. But, such performance measures are insufficient or inaccurate in many cases, especially when off-line conversions are the main responses to the delivered information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  is a schematic diagram illustrating an overview of an environment  101  in which some embodiments of the disclosed technology may operate. 
         FIG. 1B  is a block diagram of an information system according to certain embodiments. 
         FIG. 2A  is a diagrammatic representation a simple geo-fence in the shape of a circle. 
         FIG. 2B  is a diagrammatic representation of one or more polygon geo-fences defined in conformity with geographical configuration and surroundings of a store according to certain embodiments. 
         FIG. 2C  is a table illustrating examples of geo-fences stored in a geo-fence database according to certain embodiments. 
         FIG. 2D  is map image illustrating exemplary geo-blocks overlaid on a map of a geographical region created by the geo-block definition subsystem according to certain embodiments. 
         FIG. 2E  is a table illustrating exemplary data structures of meta data enriched geo-blocks according to certain embodiments. 
         FIGS. 3A-3C  are block diagrams illustrating some of the content of a request at different stages of processing by the request processing system according to certain embodiments. 
         FIG. 3D  is a table illustrating exemplary content in a location log database according to certain embodiments. 
         FIG. 4A  is a block diagram of an information server according to certain embodiments. 
         FIG. 4B  is a flowchart illustrating a method performed by the information server according to certain embodiments. 
         FIG. 4C  is a flowchart illustrating a method performed by a volume control unit in the information server according to certain embodiments. 
         FIG. 5A  is a block diagram of an evaluation module in the information system according to certain embodiments. 
         FIG. 5B  is a table illustrating exemplary content in the data stores of the evaluation module according to certain embodiments. 
         FIG. 6A  is a table illustrating location historical data of a few mobile users according to certain embodiments. 
         FIG. 6B-6E  are diagrams illustrating various time windows for an information campaign according to certain embodiments. 
         FIG. 7  is a flowchart illustrating a frequency modeling method to project an actual targeted response rate of mobile users exposed to an information campaign according to certain embodiments. 
         FIG. 8  is a plot illustrating targeted response rate data points calculated for respective frequency buckets being fitted to a model function. 
         FIG. 9A  is a diagram illustrating overlapping of qualified mobile devices (users) on a panel and qualified mobile devices (users) seen by the information system according to certain embodiments. 
         FIG. 9B  is a flowchart illustrating a panel-assisted method of estimating an actual targeted response rate according to certain embodiments. 
         FIG. 10  is a diagram illustrating time windows for deriving a model function for predicting targeted off-line responses according to certain embodiments. 
         FIG. 11A  is a block diagram illustrating certain functional units in the evaluation module in the information system according to further embodiments. 
         FIG. 11B  is a table illustrating exemplary content in the data stores of the evaluation module according to further embodiments. 
         FIG. 12  is a diagrammatic representation of a computer/server that performs one or more of the methodologies and/or to provide part or all of the information system according to embodiments. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
       FIG. 1A  is a schematic diagram illustrating an overview of an environment  101  in which some embodiments of the disclosed technology may operate. Environment  101  can include one or more computer systems  120  coupled to a packet-based network  100 . The packet-based network  100  in certain embodiments includes the Internet  110  and part or all of a cellular network  111  coupled to the Internet  110  via an Internet Gateway. The computers/servers  120  can be coupled to the Internet  110  using wired Ethernet and optionally Power over Ethernet (PoE), WiFi, and/or cellular connections via the cellular network  111  including a plurality of cellular towers  111   a.  The network may also include one or more network attached storage (NAS) systems  121 , which are computer data storage servers connected to a computer network to provide data access to a heterogeneous group of clients. As shown in  FIG. 1D , one or more mobile devices  130  such as smart phones or tablet computers are also coupled to the packet-based network via cellular connections to the cellular network  111 . When a WiFi hotspot (such as hotspot  135 ) is available, a mobile device  130  may connect to the Internet  110  via a WiFi hotspot  135  using its built-in WiFi connection. Thus, the mobile devices  130  may interact with other computers/servers coupled to the Internet  110 . A mobile device  130 , or its user, or anyone or anything associated with it, or any combination thereof, is sometimes referred to herein as a mobile entity. 
     The computer/servers  120  can include server computers, client computers, personal computers (PC), tablet PC, set-top boxes (STB), personal digital assistant devices (PDA), web appliances, network routers, switches or bridges, or any computing devices capable of executing instructions that specify actions to be taken by the computing devices. As shown in  FIG. 1A , some of the computers/servers  120  are coupled to each other via a local area network (LAN)  111 , which in turn is coupled to the Internet  110 . Also, each computer/server  120  referred herein can include any collection of computing devices that individually or jointly execute instructions to provide one or more of the systems, modules, methodologies, and functional units discussed herein. 
     As shown in  FIG. 1B , the computers/servers  120  coupled to the Internet may include mobile service provider (MSP) computers/servers  140  that interact with certain mobile devices  130  via software applications (apps) installed on the mobile devices  130 . The MSP computers/servers  140  (referred to individually and collectively as the MSP server  140 ) are coupled via the network  100  to an information delivery system  150  according to certain embodiments. The system  150  can be provided by one or more of the computers/servers  120 . As the MSP server  140  interacts with the mobile devices  130 , it generates mobile supplies in the form of requests for sponsored information. Each request is transmitted as one or more data packets and include request data such as: a request ID, an identifier that identifies the MSP (i.e., MSP ID), identification (UID) of the mobile device (and/or its user), maker/model of the mobile device (e.g., iPhone 6S), an operating system running on the mobile device (e.g., iOS 10.0.1), certain attributes about the user or mobile entity (e.g., age, gender, income level, education level, etc.), a time stamp, and location data (e.g., a latitude/longitude pair (lat/long, or LL), zip code (ZC), city-state (CS), IP address (IP), etc. Almost all of the request data, except the MSP ID, are derived by the MSP server  140  from the signals it receives from the associated mobile device. For example, the LL may be detected by the GPS function of the associated mobile device and packaged in the data packet it sends to the MSP server  140  if the mobile device is set up to allow its location be known by the MSP server  140 . The IP address may be the IP address of a WiFi router or an IP address assigned to the mobile device by a cellular network tower, via which the mobile device is interacting with the Internet. The MSP server  140  may post the mobile supplies on an exchanges for bidding by information providers or their agents, transmit the mobile supplies directly to information servers associated with information providers, or fulfill the supplies themselves. 
     According to certain embodiments, as shown in  FIG. 1B , the system  150  includes a request processor  152  that receives and processes the requests from the MSP server or Exchange  140 . In certain embodiments, the request processor  152  examines the location data in each request to determine whether they include a reliable LL pair, and if the request does not include a reliable LL pair, the request processor  152  would proceed to derive the location of the associated mobile device from other information in the location data, as described in more detail in commonly owned U.S. Pat. No. 9,886,703, issued on Feb. 6, 2018, which is incorporated herein by reference in its entirety. The detected mobile device location is input to a search engine  156 , which searches in a spatial index database  158  for one or more POI places that includes the detected location and returns the search results to the request processor  152 . 
     In certain embodiments, the system  150  further includes a geo-fencing system  160  that generates the spatial index defining geo-fences associated with the html/JavaScript files delivered by the information server  154 . In certain embodiments, the geo-fencing system  160  defines virtual perimeters of defined areas that mirror real-world geographical areas for mobile advertising. A defined area according to certain embodiments can be a static circle around a business location, e.g. a fence obtained using offline index databases such as InfoUSA (www.infousa.com), which provides a list of POIs and their locations, or areas specified by marketers using predefined boundaries, such as neighborhood boundaries, school attendance zones, or parcel boundaries, etc. 
     In certain embodiments, the defined areas include places computed by the geo-fencing system  160  using meta-information and/or geographical information associated with the POIs. As shown in  FIG. 3 , the geo-fencing system  160  has access to a (POI) data  151  (e.g., InfoUSA), which provides a list of POIs and their corresponding brand names, addresses, and geographical locations. The geo-fencing system  160  also has access to publicly available map data  152  (e.g., Open Street Map at www.openstreetmap.org/), which provides information about the surroundings of the POIs in the POI directory. The geo-fencing system  160  generates definitions of one or more places in the form of, for examples, a set of geographic points defining the perimeters of one or more places for each POI. 
     In certain embodiments, the geo-fencing system  160  generates or defines one or more places for each of a plurality of POIs in consideration of the map data around the POI. For example, as shown in  FIG. 2A , a simple geo-fence for the Costco Almaden store without consideration of the map data can be in the shape of a circle  202  around the store location  201 , based on the assumption that a user&#39;s intent to visit a given POI could be derived from his or her distance from the POI. However, as shown in  FIG. 2A , the circle fence encompasses a major highway, a residential area, and areas on the other side of the major highway. Information about the POI served to mobile devices in these areas would most likely be ignored because people living close to the POI, people traveling on the highway, and people on the other side of the highway are either already familiar with what the POI has to offer or are unlikely to bother to respond to information related to the POI. 
     Therefore, instead of or in addition to geo-fences based on a radius around a centroid of a business location, the geo-fencing system  160  according to certain embodiments uses the map data  151  to define places that are of more interests to information sponsors. As shown in  FIG. 2B , the geo-fencing system  160  defines one or more polygons in conformity with the geographical configuration and surroundings of the POI, such as a first polygon  210  around the building of the store, a second polygon  220  around the building and its parking lot, and/or a third polygon  430  around a shopping area or business region including the POI and other POIs. More details of such a geo-fencing system can be found in co-pending U.S. patent application Ser. No. 14/716,811, filed on May 19, 2015, which is incorporated herein by reference in its entirety. 
     In certain embodiments, different types of places may be defined for a POI so that information servers can provide information for delivering to mobile devices based on the type of places triggered by detected locations. For example, a request associated with a mobile device located inside the first polygon  210  around the building of the POI may be more valuable to an information sponsor and thus may be of higher value than a request associated with a mobile device that is in the shopping area (polygon  230 ) but not inside the store. Or, conversely, polygon  230  may be of higher value to another information sponsor who would like to attract mobile users in the business region than polygon  210 , which indicates that the mobile user is already in the store. In certain embodiments, these three types of places are defined by extracting building polygons, parking lot polygons and land-use polygons from local and national geographical information systems (GIS). In certain embodiments, some or all of the places can be defined manually with assistance of computer annotation tools and by consulting some external map and/or satellite data to make sure that the geo-fences are aligned with the real building and region boundary information surrounding the intended businesses. 
     In certain embodiments, the different types of places associated with a business that are offered to the information sponsors include, for example, (1) a business center (BC) represented by, for example, a polygon corresponding to the perimeter of the building of the business (e.g., the first polygon  210  in  FIG. 2B ); (2) a business premise (BP) represented by a polygon corresponding to the perimeter of the business building and the neighboring parking lots (e.g., the second polygon  220  in  FIG. 2B ); and (3) a business region (BR) or area represented by a polygon corresponding to the perimeter of a shopping center or business or commercial area in which this business is located (e.g., the third polygon  230  in  FIG. 2B ). If a business center is triggered by a mobile device location, it can be reliably inferred that the user of the mobile device is interested in the business by actually visiting it. Triggering of a business premise provides good indication of an intent to visit the business, but not as strong as triggering the business center. If a user triggers a business region, the intent may be regarded as valid but weaker than that from triggering a business premise. 
     The geo-fencing system  160  further generates spatial indices representing the areas defined by the geo-fencing system  160 , which are stored in the spatial index database  158  for searching by the search engine  156  with spatial queries, such as how far two points differ, or whether certain point falls within a spatial area of interest.  FIG. 2C  illustrates examples of spatial indices of geo-fences stored in the database  158 , according to certain embodiments. As shown, the site Costco in Almaden has three different types of places associated with it—place US/CA/Almaden/BC is a business center (BC) , which is a polygon around the store building and represented by spatial index a1, a2, . . . , ai; place US/CA/Almaden/BP is a polygon around the site&#39;s larger premise including its parking lot and represented by spatial index b1, b2, . . . , bj; and place US/CA/Almaden/BR is a polygon around the shopping center including the store and other POIs and represented by spatial index c1, c2, . . . , ck.  FIG. 2C  also shows that the site T.J. Maxx has three types of places associated with it, and the site Trader Joe&#39;s has at least a business center place associated with it. As shown in  FIG. 2C , each geo-fence entry in the database  158  includes the spatial indices associated with the respective place together with other information about the respective place, such as, for example, a name/brand associated with the place, a category of the place, a place identifier identifying a particular locale (e.g., city, district, etc.) for the place, the place type, and/or one or more doc IDs identifying one or more information documents (e.g., one or more html/JavaScript files) for the name/brand or the place. 
     In certain embodiments, in addition to the places associated with POIs, the geo-fencing system  160  further generates geo-blocks representing geographical regions with natural boundaries such as major roads, shorelines, mountain ranges, etc., as described in co-pending U.S. patent application Ser. No. 15/344,482, filed Nov. 4, 2016, entitled “Systems and Methods for Performance-Driven Dynamic Geo-Fence Based Targeting,” which is incorporated herein by reference in its entirety.  FIG. 2D  illustrates exemplary geo-blocks created by the subsystem  310  according to certain embodiments. In this example, for an area in the city of Santa Clara, California, the geo-blocks are shown as outlined in red boundaries overlaid on top of a map for the area, taken from, for example, Google Map, and the boundaries of the geo-blocks are mostly aligned with major roads, taking into account the road width so as to exclude mobile signals from travelers on the major roads. Each of the geo-blocks shown can be further partitioned into more granular blocks bordered by smaller roads. Real world entities present in these geo-blocks tend to serve common functional purposes (residential, retail etc.) and these blocks form the foundation for the construction of boundaries that are highly indicative of location dependent attributes such as intention and demographics. 
     To define the geo-blocks, the geo-fencing system  160  extracts geographical data of transportation route and natural boundary data from the map data  162 , and creates geo-blocks using the extracted geographical data. It also derives meta data associated with each of the geo-blocks such as city/state, functionality, major POIs in the geo-block, demographic of residents in the geo-block, etc., from the map data, and other information such as amount of requests received from mobile devices in the geo-block within a certain time period (inventory), demographic of users of the mobile devices (for non-residential geo-blocks), etc., from the logged requests data and events data in the databases  168  and  166 , and enriches the geo-blocks with relevant meta data. 
     Geometrically, transportation routes (highways, railways, waterways etc.), as well as natural boundaries (coastlines, lake boundaries etc.) are described as collections of line segments, together with meta data information such as their type, width and traffic speed. In certain embodiments, these line segments are collected and scored based on their significance, e.g., residential area roads in residential area score lower than highways do. Line segments scored above a threshold are collected to form a line set. The line set thus defined is then used to form polygons with boundaries aligned with the lines in the line set. The polygons thus formed, together with their associated meta data such as are initial geo-blocks. which are indexed and stored in the spatial index database  158 , as shown in  FIG. 2E . 
     In general, the definition of geographical regions is not limited to that described above. A different set of geographical regions with or without its own meta information can also be used for the subsequent processes. 
     In certain embodiment, the search engine  156  and some or all of the spatial index database  158 , the geo-fencing system, and the POI database  151  can be part of the request processor  152 . 
     In certain embodiments, as shown in  FIGS. 3A-3C , the request processor  152  receives request  301  from the MSP server  140  via network  100 . The request  301  includes mobile device location information including a plurality of location components, such as latitude and longitude coordinates (LL), IP addresses (IP), postal or zip codes (ZC), and/or city-state names (CS), etc, in addition to other information. In certain embodiments, the request processor  152  validates the location information by checking the validity and consistency of the location components and by weeding out any invalid location component(s). Generally, the LL is usually believed to be the most useful location component. However, when a mobile entity doesn&#39;t allow its location information to be known, mobile applications at the MSP server  140  typically provide only coarse location data in the form of, for example, an IP address, a ZC (e.g. entered by the user at the time of registration), or CS. Thus, mobile applications at the MSP server  140  frequently provide LLs obtained from geo-coding software, which translates ZC, CS, and other points of interests into one representative LL. In one embodiment, such representative LLs are categorized as “bad LLs”. A bad LL can be, for example:
         1. A centroid of a ZC/CS   2. Any fixed point on a map (e.g. (0,0) or an arbitrary location)       

     In certain embodiments, the request processor  152  is configured to weed out the bad LL&#39;s, so that location data with bad LL&#39;s are not provided to the next stage processing, by using the techniques disclosed in commonly owned U.S. patent application Ser. No. 14/716,816, entitled “System and Method for Estimating Mobile Device Locations,” filed on May 19, 2015, which is incorporated herein by reference in its entirety. 
     The request processor  152  estimates the location of the mobile device from the request  301  and generates location data to represent an estimated mobile device location, which may be a geographical point represented by a lat/long pair or one or more probably areas or regions the mobile device is estimated to be in, as shown in  FIG. 3B . The search engine  156  queries the spatial index database  158  with the lat/long pair to determine whether the location triggers one or more predefined places in the database  158 , and returns the triggered place(s) to the request processor  152 , which annotates the request  301  with the triggered place(s) to generate an annotated request  310 , and stores the annotated request  510  in the request log  168 . Additionally, the search engine  156  also queries the spatial index database  158  with the lat/long pair to determine whether the location is in one of the geo-blocks in the database  158 , and returns the triggered geo-block to the request processor  152 , which annotates the request  301  with the triggered geo-block. Thus, the annotated request  310  can also include the triggered geo-block, as shown in  FIG. 3C . 
     In certain embodiments, as shown in  FIG. 3A , the request  301  received from the Internet by the request processor includes other information as well as the location information, such as information about the mobile device and/or a mobile user associated with the mobile device, a time stamp indicating the time of the request (e.g., day, hour, minute, etc.), one or more keywords suggesting types of information for returning to the mobile device, and/or other information associated with the mobile user, the mobile device, and/or the MSP. In some cases, the location data can trigger multiple places. For example, as shown in  FIG. 2B , a request that triggers the BC place  410  of Costco Almaden also triggers the BR place  430  of any of the POIs in the same business region. Thus, the request may be annotated with the BC place of Costco Almaden and the BR place of one or more other POIs in the same business region. As shown in  FIG. 3C , each of the one or more places or geo-fences includes either or both of a place ID, a name and/or a category of the POI or its associated brand if any, and a place type (e.g., BC, BP, BR, or circle), some or all of which can be included in the annotated request  310 . 
     In certain embodiments, a panel of mobile devices  130  are signed up to provide periodic location updates to the request processor  152  by installing and running a software development kit (SDK). Each location update is transmitted from a mobile device  130  to the packet-based network  101  in the form of one or more data packets that include the mobile device information, a time stamp and a lat/long pair indicating the mobile device location. The request processor  152  processes each location update similarly as it processes an information request and logs the location updates in a designated field in the requests database  168 .  FIG. 3D  is a table illustrating examples of processed location updates. 
     In certain embodiments, the system  150  further includes an information server  154  that selects and transmits sponsored information to the MSP server (or Exchange) in response to each annotated request  310  output from the request processor  152 . In certain embodiments, the information server  154  is a computer server, e.g., a web server, backed by a database server  164  that information sponsors use to periodically the content thereof—e.g., information documents, which when loaded on a mobile device displays information in the form of, for examples, banners (static images/animations) or text.  FIG. 4B  illustrates a process  400  carried out by the information server  154  according to certain embodiments. As shown in  FIGS. 4A and 4B , the information server  154  includes a query builder  401  configured to build ( 410 ) a search query based on the data in the annotated request  310 , a search engine  403  configured to search ( 420 ) the documents database  164  for one or more matching documents, and a ranking module  405  configured to determine ( 430 ) a key performance index (KPI) for each of the one or more matching documents, and to rank ( 440 ) the one or more matching documents based on the KPI&#39;s. 
     In certain embodiments, the one or more matching documents may include at least one first matching document having an associated KPI based on related on-line activities of mobile devices (e.g., clicks, calls, secondary actions, etc.) and at least one second matching document having an associated KPI based on related off-line activities of mobile devices (e.g., detected site visits, etc.). The KPI may also be dependent on the location data in the request, as mobile devices at certain locations may indicate higher likelihood of clicks/calls or secondary actions or site visits, and therefore higher projected performance, than mobile devices at other locations. 
     In certain embodiments, as shown in  FIG. 4B , the ranking unit  405  is further configured to select ( 450 ) a matching document to fulfill the request based on the rankings, and the information server  154  further includes a network interface  407  configured to transmit information about the selected matching document to the requester via the packet-based network  110 . The information can be provided in the form of, for example, an html/JavaScript file, or a link to a universal resource location (URL), which can be used by the MSP or a mobile device to fetch the html/JavaScript file. The html/JavaScript file, once displayed or impressed on a mobile device, also include one or more links that an interested user can click to access a webpage or place a call using the mobile device. The webpage enables the user of the mobile device to take secondary actions such as downloading an app or make an on-line purchase. 
     In certain embodiments, the html/JavaScript file is designed such that when it is displayed or impressed on a mobile device, a signal is sent by the MSP server or the mobile device automatically so that the information server  154  can keep track of whether the file has really been impressed on the mobile device. In certain embodiments, mechanism are also put in place such that when any of the one or more links are clicked, or when the mobile user download an app or make a purchase from a linked webpage, a signal is also sent from the mobile device to the information server  154  in the background so that the information server  154  can keep track of the clicks/calls or secondary actions made in response to the impression. The network interface  407  receives and records ( 460 ) such events (i.e., impressions, clicks/calls, and secondary actions) in the events database or log  166 . 
     In certain embodiments, some or all of the documents in the documents database  164  may have limited budgets on the numbers of impressions/clicks/calls associated therewith. Thus, the ranking unit, before selecting a matching document to fulfill the request, may check to make sure that there is sufficient budget remaining for the matching document. Otherwise, the ranking unit may select the next ranked matching document. In certain embodiments, the information server  154  further includes a volume control unit  409  configured to adjust or update ( 470 ) the budget of a document in the documents database  154  in response to an impression/click/call event related to the document, or using a projection value of a possible site visit, as discussed further below. 
     Thus, logged data in the requests log  168  and the events log  166  collected over a period of time (e.g., six months) form a large collection of mobile device data (e.g., millions and millions of annotated requests and impression/click/call events). In certain embodiments, the system  150  further includes an evaluation module  170  having a set of specifically designed filters to query the big data in the requests log  168  and the events log  166  to obtain various data sets related to specific aspects of the performance of the delivered information documents. The evaluation module  180  further includes one or more electronic storages that stores the data sets for use by a set of computation engines designed to derive different types of performance measures or projections, which are used by the information server  154  to determine the KPI&#39;s of the matching documents. 
     Various methods have developed to fund mobile information campaigns geared at accommodating campaign budgets and campaign goals. Examples of such models include cost-per-mille (CPM), cost-per-install (CPI), and cost-per-click (CPC) models. These are just a few of the basic models for valuing mobile information delivery, which information providers can select to fund their information campaign on mobile devices. 
     CPM is the valuation model that is sometimes referred to as “pay-per-impressions.” CPM in contemporary English simply means “cost per thousand.” In a CPM campaign, an information provider pays the agreed bid rate for every 1,000 times a related document is displayed (i.e., impressed) on mobile devices. This model protects the mobile service providers, but does not provide any guarantee on results. Since CPM information providers pay for impressions and not for clicks and installs, they tend to use the delivered information mainly to raise awareness. 
     Moving one step closer to performance, information providers can also use the so called cost per click (CPC) model, (also known as PPC, i.e., pay per click), whether or not the clicks they pay result in actual conversions. With the CPC model, documents are chosen to be served to mobile device users based on a combination of the click-through rates (CTR) associated with the documents and the per-click bids that information providers make. 
     Cost per install (CPI), also known as cost-per-acquisition (CPA), charges information providers every time a delivered document results in a conversion, which can be, for example, people actually making a purchase, downloading an app, or performing another action recommended by the document. Thus, CPI campaigns help to give medium and small companies with limited marketing budgets a predictable return on their advertising investment. In addition, due to the growing fraud and viewability issues in online advertising, information providers commonly prefer pricing models such as CPI/CPA to get better value and protection for their money. The issues with this trend, however, is the increasing complexity of ad delivering systems in order to address issues such as conversion prediction, budget control, etc. 
     Furthermore, although the CPA model is appropriate for information campaigns with online conversion goals (such as in ecommerce), it is ineffective for tracking offline conversions. Therefore, some information sponsors may choose to pay for each physical site visit derived from an information campaign, so they do not need to be concerned about issues such as viewability, click fraud, etc. Also, in many industries, a site visit carries a known average purchase value. Thus, the value of each site visit can be clearly understood. Therefore, site visit based performance measure or projection allows the information providers to better understand their return of investment (ROI). 
     While a cost per visit (or CPV) model is desirable to many information providers, it requires novel techniques including: (a) a SVR (location visitation rate) estimation system, (b) a performance evaluation and budget control system bridging the CPM and CPV worlds, allowing a CPV model for some information sponsors even when the majority of the mobile service providers still charge on CPM; and (c) a adaptive attribution system capable of giving different location visitations different levels of credits. 
       FIG. 5A  is a block diagram of some of the components in the evaluation module  170  according to certain embodiments. As shown in  FIG. 5A , the evaluation module  170  includes a filter  510  configured to obtain data related to each of a plurality of documents in the documents database  164 , such as a document ID, a name/brand and/or category associated with the document, a type of performance measure (e.g., impression-based, click/call based, secondary action based, or site visit based, etc.) associated with the document, a price or cost for each impression, or click/call, or site visit, etc., to query the events database  166  for impression events related to the document, and to store data associated with the impression events in a data store  512 .  FIG. 5B  illustrates examples of the impression events in the data store  512  grouped by each document and listed by the UID&#39;s of the mobile devices impressed with the document. As shown, each of the impressed mobile devices can have one or more impressions of the document with different time stamps (Day/Hour). The evaluation module  170  may further includes a computation engine (CB)  524  configured to count the number of mobile devices listed in the data store  522  and the number of mobile devices each having had at least one click/call event, and to compute a performance measure (PM| CC ) that is based on click/call events (CC) as follows:
 
 PM|   CC   =CPC*CTRest* 1000,
 
where CPC is the cost per click/call, CTRest is an estimated click through rate, which can be the ratio of the number of mobile devices each having been impressed with the document and having had at least one click/call event over the number of mobile devices listed in the data store  522 . The multiplier of 1000 is to map the performance measure to the CPM valuation model.
 
     As shown in  FIG. 5A , the evaluation module  170  may further includes a filter  520  configured to obtain data related to each of the impressed mobile devices in the data store  510 , to query the events database  166  for click/call events related to the document, and to store data associated with the click/call events in a data store  522 .  FIG. 5B  illustrates examples of the click/call events in the data store  522  grouped by each document and listed by the UID&#39;s of the mobile devices impressed with the document. As shown each of the impressed mobile devices can have none or one or more click/call events related to the document with different time stamps (Day/Hour). 
     As shown in  FIG. 5A , the evaluation module  170  may further includes a filter  530  configured to obtain data related to each of the impressed mobile devices having had at least one click/call event according to the data in the data store  522 , to query the events database  166  for secondary action events related to the document, and to store data associated with the secondary action events in a data store  532 .  FIG. 5B  illustrates examples of the secondary action events in the data store  532  grouped by each document and listed by the UID&#39;s of the mobile devices impressed with the document. As shown each of the mobile devices having had at least one click/call event can have none or one or more secondary action events related to the document with different time stamps (Day/Hour). The evaluation module  170  may further includes a computation engine (CB)  534  configured to count the number of mobile devices listed in the data store  532  and the number of mobile devices each having had at least one secondary action event, and compute a performance measure (PM| SA ) that is based on secondary action events (SA) as follows:
 
 PM|   SA   =CPI*SARest* 1000,
 
where CPI is the cost per install (e.g., download, purchase, etc.), SARest is an estimated secondary action rate, which can be the ratio of the number of mobile devices each having been impressed with the document and having had at least one secondary action event over the number of mobile devices listed in the data store  532 . Again, the multiplier of 1000 is used to map the performance measure to the CPM valuation model used for some of the other documents.
 
     In certain embodiments, location visits are tracked based on request data packets associated with the group of users. The location module generates annotated requests from the request data packets, as described above. As shown in  FIG. 5A , the evaluation module  170  further includes a filter  540  configured to obtain data related to each of the impressed mobile devices in the data store  512 , to query the events database  166  for site visit events related to the document, and to store data associated with the site visit events in a data store  542 .  FIG. 5B  illustrates examples of the site visit events in the data store  542  grouped by each document and listed by the UID&#39;s of the mobile devices impressed with the document. As shown each of the mobile devices having had at least one impression event can have none or one or more site visit events related to the document with different time stamps (Day/Hour). The site visits are detected from the annotated requests, which are annotated with places having the same number/brand and/or category as the document. 
     Unlike the processes of obtaining the CC-based and SA-based PM values, where the clicks/calls and secondary actions can be tracked electronically as originating from the delivered information, this is not so with site visits because a detected site visit may or may not be the result of an impression event, especially when the visit happens before or long after the impression event. Therefore, the evaluation module  170  further includes a filter  546  configured to obtain information campaign data related to the document from a campaign database  547 , to filter the data in the data store  542  to obtain site visit data in different time windows, and to store the filtered site visits data in a data store  548 . 
     Referring to  FIG. 6B , the time windows include at least three time windows: a history window, a campaign window and an attribution window. The campaign window represents the timeframe when the campaign to deliver the document runs. The attribution window represents a limit on the time lag between the time of impression and the location visits where the location visits can still be attributed to the campaign. Note for impressions delivered at different times, the attribution windows can be sliding windows respectively defined for each impression time period, as discussed below and in co-pending U.S. patent application Ser. No. 15/289,104, filed Oct. 7, 2016, entitled “Method and Apparatus for Measuring Effect of Information Delivered to Mobile Devices,” which is incorporated herein by reference in its entirety. 
     For example, as shown in  FIG. 6A , an information campaign with a document identified with DocID D2750 runs from Feb. 21, 2017 to Feb. 28, 2017. A user with UID 56***845 was impressed on Feb. 24, 2017 with the ad, in response to a request identified by the request ID 0125785237. Another request associated with the user (request ID 0136819975) was later received on Mar. 1, 2017, which has location data indicating that the user was at the location identified as B123. Since the location B123 is associated with the campaign and the later received request is received during the attribution window, the later received request indicates a visit to the location and is recorded by the attribution module as a visit associated with the campaign. 
     In certain embodiments, a performance measure based on site visits (SA) can be determines as follows:
 
 PM|   SV   =CPV*SVRest* 1000,
 
or
 
 PM|   SV   =CPV*SVRest* ( freqv/freqi )*1000,
 
where CPV is the cost per visit, SVRest is an estimated site visit rate, freqv is the average visits per visitor within an attribution window of time after impressions are made for a campaign, and freqi is the average impression frequency. Typically (freqv/freqi) is considered as a per campaign level constant, and CPM is primarily determined by estimated SVRest. The multiplier 1000 is used to map the performance measure to the CPM valuation model used for some of the other documents. When equal weights are given to each site visit, SVRest can be estimated by selecting a first number of unique mobile devices each having had at least one impression event with the document in the campaign window, discovering a second number of unique mobile devices among the first number of unique mobile devices, each of the second number of unique mobile devices having had at least one site visit event in the attribution window, and computing the SVRest as the ratio of the second number to the first number. In certain embodiments, if there are multiple exposures followed by a visit, only one visit is considered in the above SVR estimation. In certain embodiments, if there are multiple visits following an exposure, only one visit is considered in the above SVR estimation.
 
     In certain embodiment, SVR is used as a measure of the likelihood that a user impressed with a document will visit one or more targeted locations during an observation window of time. Typically, the one or more locations are related to one or more brands associated with the information campaign. For example, for a McDonalds campaign, an SVR metric can be defined as the percentage of the impressed users who visited McDonalds restaurants within a predetermined attribution window. Note the targeted locations are defined by the scope of the information campaign, it could be nation-wide McDonalds restaurants, or just California McDonalds restaurants. 
     In certain embodiments, an information campaign flight (i.e., duration of an information campaign) is divided to include multiple windows, and location visit rate can be calculated for each window at first and then averaged over the multiple windows to arrive at the final SVR. For example, an information campaign flight may last several weeks, with an increasing number of mobile users becoming exposed to the information campaign as the number of impressions increase over the course of time, as illustrated by the curve  610  in  FIG. 6C . Note that a mobile user can be exposed to the information campaign multiple times during the campaign flight, so the number of impressions in  FIG. 9A  do not necessarily equal to the number of exposed mobile users. 
     As shown in  FIG. 6D , the flight of the information campaign is divided to include multiple exposure windows, e.g., EW1, EW2, . . . , and EW6, each is associated with a visit attribution window, e.g., AW1, AW2, . . . , and AW6, respectively. For each exposure window, a group of impressed users are determined based on information requests and document delivery during the exposure window, and an SVR is computed based on location visits during the associated visit attribution window. An overall SVR is computed by averaging over the multiple exposure windows. 
     In  FIG. 6D , each attribution window (e.g., AW1) is shown to overlap with its associated exposure window (e.g., EW1). In this case, location visits occurring during an exposure window (e.g., EW1) as well as afterwards are considered in the calculation of the location visit rate for the exposure window (e.g., SVL 1 ), even though the group of exposed users are determined at the end of the exposure window. In other embodiments, as shown in  FIG. 6E , each attribution window (e.g., AW1) does not overlap with its associated exposure window (e.g., EW1). Thus, location visits occurring during an exposure window (e.g., EW1) are not considered in the calculation of the location visit rate for that exposure window (e.g., SVL 1 ). 
     In certain embodiments, the effect of an information exposure is made to decay over time. Thus, as the lag between information exposure and location visitation increases, the effect of the information exposure contributing to that visit decreases. To avoid over statement in the location visit rate calculation, effect on the SVR calculation from an exposed user can be made to fade as the information campaign proceeds unless that user is exposed to the information campaign again. In certain embodiments, a decay function is defined which determines the contribution of a user to the SVR calculation based on how long ago the user has been exposure to an information campaign. 
     In certain embodiments, the history window is used to identify users who had already visited the targeted locations before the starting of an information campaign. These users can be excluded from accounting so information providers only pay for visits most likely to have been initiated by the campaign, i.e., visits by people who did not visit the targeted locations in the history window, or the people who visited the targeted locations with very low frequency. In other embodiments, different weight can be given for new visits by new visitors and repeated visits by repeating visitors. Different weight can also be applied to different situation. For example, some retailers may be willing to pay higher rate for Tuesday visits than weekend visits. 
     Furthermore, different weights can be given to visits to different sites, or to different types of places at the same site. For example, some retailers may be willing to pay higher rate for a visit to a business center (BC) place than a visit to a business premise (BP) place, or vice versa. Thus, in general, the SVRest can be estimated as follows: 
             SVRest   =       (     Weighted_Sum   ⁢   _Unique   ⁢   _MDs   ⁢   _with   ⁢   _SV   ⁢   _in   ⁢   _a   ⁢   _Group     )       (     Number_of   ⁢   _Unique   ⁢   _MDs   ⁢   _in   ⁢   _the   ⁢   _Group     )             
where MD stands for “Mobile Device” (thus, “MDs” stands for “Mobile Devices”), each unique MD counts as 1 in the weighted sum, and the weight for the MD in the weighted sum can be the weight associated with the highest weighted SV event for the unique MD. For example, if the MD had one SV event at a BP place associated with the document and another SV event at a BC place associated with the document, and the BC place is weighted higher than the BP place, the weight of the BC place is used as the weight for the MD in the weighted sum. The group of unique MDs can be the MDs impressed with the document during a campaign window.
 
     Note location visits determined via information requests is not usually an actual representation of the effect of an information campaign. In a typical mobile network setup, a user&#39;s location (e.g., latitude and longitude, or LL) is shared with the information servers only when an information request associated with the mobile user is sent to the information servers. If a user&#39;s mobile device is not running apps that send information requests to the information servers at the time of the user&#39;s location visitation, this visit is not visible to the request processor  152  and thus is not counted by the evaluation module  170 . 
     In certain embodiments, a frequency modeling method is used to project a more accurate count of mobile users who visited a targeted location after information exposure.  FIG. 7  is a flowchart of a frequency modeling method  700  according to certain embodiments. As shown in  FIGS. 5A and 7 , the evaluation module may further include one or more frequency filters  550 , which can be applied to the data in the data store  510  to divide the mobile users exposed to the document ( 710 ) into multiple frequency buckets each associated with a range of frequencies with which mobile user are seen by the system  150 . Data associated with the different frequency buckets are stored in a data store  552  and used by the computation engine  554  to compute ( 720 ) an SVR value for each of the frequency buckets. In certain embodiments, the frequency may be measured as the number of days requests related to a mobile user show up at the system  150  during a predetermined time window (30 days). Thus, the mobile users who showed up only in one of the 30 days are less likely to be captured during their visits to a targeted location than mobile users who showed up in 10 of the 30 days. Thus, the SVR calculated from the mobile users in the lower frequency bucket would be lower than the SVR calculated from the mobile users in the higher frequency bucket, as shown in  FIG. 8 . 
     Referring to  FIGS. 7 and 8 , the computation engine is further designed to fit ( 730 ) the computed SVR values against a model function. For example, the SVR data points in  FIG. 8  can be fitted to the following exponential model function:
 
 y=a /(1+exp(− b*x+ 1)).
 
By fitting this function to the data points in  FIG. 8 , with x corresponding to the bucket frequencies (Imp) and y corresponding to the SVR values for the respective buckets, the parameters a and b can be determined. The computation engine then determines ( 740 ) a convergence value for the model function when x approaches infinity, which in this case is equal to a. The actual SVR for the entire group of mobile users can be estimated ( 750 ) to be this convergence value, which correspond to the projected situation when the document delivery system can see the mobile users all the times during the predetermined time window. In other words, the plot shown in  FIG. 8  is extrapolated to find the SVR of a projected group of users who are seen an infinite number times on a information serving network.
 
     In certain embodiments, a panel-assisted method is used to estimate the actual SVR. Using this method, an initial panel of qualified mobile users is used to derive a multiplier value that is used in later SVR calculations by the system  150 . In certain embodiments, the panelists on the initial panel of users are qualified mobile users who have agreed to share their mobile device locations with the system  150  at a very high frequency (e.g., one data packet in every 20 minutes or 10 minutes or shorter) by installing and running a designated app (SDK) in the background on their mobile devices. The designated app on a mobile device is designed to provide the location (e.g., LL) of the mobile device at a predetermined frequency (e.g., every 10 minutes) in the form of, for example, data packets that also include identification of the respective mobile devices and other relevant information. Because of the high frequency of location sharing, most of the location visits by the panelists would be visible to the system  150 , which receives two types of incoming data packets, i.e., information requests from, for example, mobile service providers and/or exchanges, etc., and data packets from panel mobile devices running the designated app. 
       FIG. 9  illustrates three groups of mobile users, Group A being the qualified mobile users on the panel, Group B being qualified mobile users who have been “seen” by the system  150  because of associated information requests, and Group C being mobile users who are in both group A and group B. Thus, Group C are mobile users who have been using apps that cause information requests to be sent to the system  150  and who also belong to the panel with the designated app running in the background of their mobile devices. Group C will be used in the panel-assisted method to determine the multiplier value for actual SVR estimation. 
       FIG. 9B  illustrates a panel-assisted method  900  for estimating actual SVR according to certain embodiments. As shown in  FIG. 9 , using the method  900 , the request processor  152  receives and processes information requests from a first group of mobile users (e.g., Group A), and further receives and processes ( 910 ) panel data packets from a second group of mobile users (e.g., Group B). The processed information requests and panel data packets are stored in the requests database  168 , as discussed above. In certain embodiments, the evaluation module  170  may further include a calibration filter  560  that query the impression data in the data store  512  to look for a set of calibration mobile devices belong to a calibration user group (Group C) and then filter the data in the requests database  168  to obtain ( 920 ) request data and panel data associated with the calibration user group in which each user is among both the first set of mobile users and the second set of mobile users. The request data and panel data associated with the calibration user group are stored in the data store  562 . The evaluation module  170  may further include site visit filter  562  that query the panel data in the data store  562  to determine ( 930 ) a first number of mobile users who have visited at least one of a set of calibration POI&#39;s selected for calibration purposes. The site visit filter  564  is then applied to the requests data in the data store  562  to determine ( 940 ) a second number of mobile users who have visited the at least one of the set of calibration POI&#39;s. Now the first number should be more representative of the actual number of mobile users in the calibration group who have visited the at least one of the set of calibration POI&#39;s because their locations are much more frequently shared with the system  150 . The second number is the number of mobile users seen by the system  150  without the designated app. Thus the second number of mobile users are more representative of mobile users that can be tracked without the designated app. The first number and the second number associated with each of the set of calibration POI&#39;s are stored in the data store  566 . 
     In certain embodiments, the evaluation module  170  further includes a computation engine  570  configured to use the first number and the second number to compute ( 950 ) a calibration factor as an approximate representation, for any group of exposed mobile users, the ratio of the actual number of location visits to the count of location visits that can be detected by the system  150  using only information requests. In certain embodiments, this calibration factor (SVR_multiplier) can simply be the ratio of the first number over the second number. This SVR_multiplier is stored in the data store  566  and is used by the computation engine  570  in later SV based performance measure calculations. 
     In certain embodiments, any device id (in the form of IDFA, GIDFA) seen from regular information requests and panel data packets over a time window of, for example, 90 days, are stored in the requests log  168  as key-value locations. The key-value locations for information requests and panel data packets serve as the user location for regular users and panel users respectively. The users who are in both panel user location and regular user location are referred to above as forming the calibration user group. In certain embodiments, a time window (e.g., 1 week) is used as a calibration window, in which the first number of users and the second number of users are counted based on data packets from the designated app and regular information requests received by system  150 , respectively. 
     Thus, as the information delivery system  150  continues to receive and process ( 960 ) information requests, the computation engine  570  computes ( 970 ) a predicted SVR for future exposed mobile users as follows:
 
 SVR=SVR _observed* SVR _multiplier
 
where SVR_observed is observed SVR based on regular information request signals captured on the information servers, as defined in the above, i.e.,
 
     
       
         
           
             SVRest 
             = 
             
               
                 ( 
                 
                   Weighted_Sum 
                   ⁢ 
                   _Unique 
                   ⁢ 
                   _MDs 
                   ⁢ 
                   _with 
                   ⁢ 
                   _SV 
                   ⁢ 
                   _in 
                   ⁢ 
                   _a 
                   ⁢ 
                   _Group 
                 
                 ) 
               
               
                 ( 
                 
                   Number_of 
                   ⁢ 
                   _Unique 
                   ⁢ 
                   _MDs 
                   ⁢ 
                   _in 
                   ⁢ 
                   _the 
                   ⁢ 
                   _Group 
                 
                 ) 
               
             
           
         
       
     
     The SVR multiplier can be determined at different levels such as region-wise, verticals, brands, and campaigns, as discussed below. In certain embodiments, a different SVR_multiplier is estimated for different business vertical (i.e., a set of related brands). For that purpose, the calibration POI set (i.e., one or more target locations used to measure the SVR) is selected such that only the POIs belonging to one particular vertical or brand (e.g., McDonalds&#39;) is selected to determine that SVR multiplier for that particular vertical or brand. 
     To determine a region-wise multiplier, the calibration POI set is selected to include all major brands in a geographical region, which can be a country (e.g., United States), a state (e.g., California), a city (e.g., New York), or other municipalities or regions. With such large amount of data, the region-wise (e.g., country-level) multiplier can remain stable across an extended period of time. The region-wise multiplier, however, does not account for specific aspects of information campaigns that may directly influence the SVR, such as target audience and brand. 
     To determine a vertical-level multiplier, the calibration POI set is selected to include only POIs belonging to a vertical, e.g., a set (e.g., a category) of brands nationwide. The vertical-level multiplier improves upon the country-level multiplier by accounting for potential differences in location visitation among visitors at different types of locations, i.e. restaurants vs retailers. However, the brands within a vertical may exhibit different SVR patterns from each other. 
     To determine a brand-level multiplier, the calibration POI set is selected to include only POIs associated with one specific brand. As information campaigns are typically associated with brands, the brand-level multiplier allows for a direct multiplication. However, issues of sparse data begin to appear at this level, especially for international brands. Moreover, the brand-level multiplier is more subject to fluctuation than either the vertical-level or country-level multipliers, given the defined window of information exposure. 
     A campaign-level multiplier is equivalent to a brand-level multiplier, except that calculations are restricted to targeted user group defined by a specific information campaign. The campaign-level multiplier best captures the specific context of an individual campaign, but suffers sometimes from lack of scale. 
     Thus, each succeeding level captures missed visits more accurately, but may suffer from more fluctuation due to lack of scale. 
     Within each information campaign, there may be several document groups each associated with one or more brands, for which the corresponding multipliers can be applied. For example, for an information campaign for a brand, there may be a document group targeting mainly adult male mobile users, a document group targeting mainly adult female mobile users, a location-based document group (LBA) targeting mainly mobile users who are determined to be in one or more specified places, and on-premise document group targeting mainly mobile users who are determined to be on the premise (or business center) associated with the brand. In certain embodiments, a two step-process is used to derive the SVR for this information campaign. First, a SVR multiplier is determined for each of the document groups, except the location-based document groups (LBAs) and the on-premise document groups, which are excluded from the need for an SVR multiplier because these audiences have already been previously seen visiting the locations via information requests and panel data packets, thus are less likely to exhibit lost visits. Afterwards, a weighted average can be taken to derive the final SVR. 
     This method is applicable to information campaigns with both low and high observed SVRs. For the former type, the calculation can simplify be performed by applying the brand-level multipliers due to the lack of LBAs. For instance, consider an information campaign for Subway with an observed SVR of 0.39 percent. For this campaign, using the country-level multiplier of 3.9 results in a SVR of 1.54 percent, which is likely an underestimation given historical data. Indeed, panel-based analysis indicates that request-based tracking is underestimating count of visit to Subway by a factor of approximately 16. Because this campaign has no LBAs, a brand-level multiplier of 15 can simply be applied to the observed SVR to yield 5.86 percent, a result more in line with expectations. 
     In another example, consider an information campaign for four retailers—Target, Walgreens, CVS, and Rite Aid—with a relatively high observed SVR of 7 percent. Using the country-level multiplier SVR estimation, the reported SVR would be overestimated at 28 percent. Using the new method with brand-level multipliers and exclusion of LBAs, SVR is calculated to be a more reasonable 16 percent. Use of brand-level multipliers also yields more insight regarding location visitation patterns at these brands. 
     In certain embodiments, the evaluation module  170  includes both the components for panel-based SVR estimation and components for frequency-bracket based SVR estimation, and selects the result from one of the techniques based on a few factors, such as: (1) whether there are panel data available; whether there are sufficient request data to divide into a sufficient number of frequency buckets, etc. The evaluation module  170  could also take an average of the SAR estimates from both techniques. 
     In certain embodiments, the computation engine  570  is configured to model the SVR estimation as a typical Bernoulli process, where each user has a given probability of p to visit a location. The confidence interval for p estimation is therefore:
 
± z √{square root over ({circumflex over ( p )}(1−{circumflex over ( p )})/ n )}
 
where z is 1.96 for 95% confidence level, {circumflex over (p)} is the observed location visitation rate SVR. In the case of applying a multiplier to the observed SVR for projection purpose, the same multiplier is applied to the confidence interval.
 
     In further embodiments, the valuation module  170  is further configured to estimate SVR using user level behavior data. As illustrated in  FIG. 10 , for each user observed by system  150 , its location history is observed during a first period of time (observation window), which defines a set of features forming a feature vector X. In a second period of time (i.e., the adjacent training window), if the same user is observed to have visited one of the targeted locations, the targeted y=1, otherwise y=0. Multiple users&#39; behavior data are thus obtained and are used to fit an estimation model y=f(X), which predicts a visitation likelihood y given a known behavior feature vector X for a user. In one embodiment, the feature vector X is defined as (x1, x2, . . . , xn), where xi is the normalized visitation frequency by the user to locations associated with each of a set of n different documents, i.e.,
 
 xi=SVi/SVAi  
 
where SVi is the visitation frequency by the user related to the i th  document within the observation window, and SVAi is the average visitation frequency of a group of users related to the i th  document. Index i is in the range of (1, 2, . . . , n) where n denotes the number of documents (or brand) used to represent a user&#39;s location feature space. In this setup, the estimation model y=f(X) is to capture the user&#39;s behavior pattern&#39;s correlation with future location visitation. The model fitting could be realized using either linear models such as Logistic Regression or nonlinear models such as neural network models.
 
     In further embodiments, an SVR could be estimated for each of a plurality of geo-blocks in the vicinity of for any given group of branded locations associated with the document. As shown in  FIG. 11A , the evaluation module  170  may further include a geo-block filter  1110  configured to filter the geo-blocks in the spatial index database  158  for a plurality of geo-blocks related to the document. This can be done by building a query using the information associated with the document, such as name/brand/category, targeted demographic, targeted locations, etc., and search the spatial index database  158  for geo-blocks with meta data matching the query. The evaluation module  170  further includes a request filter  1120  that filters the annotated request in the requests database  168  for relevant requests that are annotated with the each geo-block among the matching geo-blocks, and stores the data associated with the relevant requests together with the each geo-block in a data store  1122 , as shown in  FIG. 11B . As discussed above, a request annotated with a geo-block indicates a detected location of an associated mobile device having been in the geo-block. 
     The evaluation module  170  further includes a site visit filter  1130  that filters the annotated request in the data store  1122  for relevant requests that are annotated with at least one place associated with the document indicating a site visit event, and stores the data associated with the site visit events in a data store  1132 , as shown in  FIG. 11B . The evaluation module  170  further includes a computation engine  1140  configured to compute a site visit rate (SVR) each of the matching geo-blocks and stores the SVR in the geo-block SVR store  1142 . In certain embodiments, the SVR for each geo-block is computed as the ratio of the number of unique mobile devices having visited both the geo-block and a site associated with the document and the number of unique mobile devices having visited the geo-block. Such an SVR indicates a likelihood of a visit to the site by a mobile user detected to be in the geo-block and is thus used to derive a performance measure of the document corresponding to the specific geo-block as follows:
 
 PM|   SV   =CPV*SVRest|   GB* 1000
 
where SVRest| GB  is the SVR estimate for the specific geo-block.
 
     Referring back to  FIGS. 4A and 4B , in certain embodiments, the ranking unit  405  in the information server  156  is configured to determine the KPI for each of the one or more matching document by first determining the type of performance measure or pricing model associated with the document. For example, if the performance measure is CPM-based, the performance measure can simply be the price a sponsor of the document has offered to pay for each impression and the KPI can be equal to the performance measure. If the performance measure is CPC-based or CPI-based, the ranking unit  405  can use the PM|CC value or the PM|SA value determined by the evaluation module  170 , as discussed above. 
     If the performance measure is CPV-based, the ranking unit  405  would at first determine whether the annotated request is annotated with a geo-block having a PM| SV  or SVRest| GB  value determined for that geo-block. If so, the ranking unit  405  may proceed to determine the KPI using the PM| SV  or SVRest| GB  value associated with the geo-block. On the other hand, if the annotated request is not annotated with such a geo-block, the ranking unit  405  would use the PM| SV  value computed using the panel multiplier or frequency bucket techniques, whichever one is produced by the evaluation module  170 . The ranking unit  405  may determine the KPI as the PM| SV  value multiplied by a weight that is based on the type of place related to the document in the annotated request and/or the time stamp of the annotated request. For example, different weights can be given to visits to different sites, or to different types of places at the same site. For example, some retailers may be willing to pay higher rate for a visit to a business center (BC) place than a visit to a business premise (BP) place, or vice versa. Also, different weight can also be applied to different time stamps. For example, some retailers may be willing to pay higher rate for Tuesday visits than weekend visits. 
     In certain embodiments, as shown in  FIGS. 4A through 4C , the information server  154  further includes a budget control function in the volume control unit  409 , which adjusts or updates ( 470 ) the associated campaign budget based on the corresponding pricing model, and removes documents running out of budget from the documents pool. Thus, the volume control unit  409  first determines ( 471 ) what pricing model is associated with the delivered document. For on-line result focused pricing models such as CPM, CPC, or CPI, this can be done after the information server  154  receives ( 460 ) signals indicating an impression/click/call/SA event, which typically happens within seconds or minutes after the delivery of the document. The related budget is updated ( 472 ) by deducting the cost for the impression/click/call/SA from the budget. For off-line result, it is tricky because a site visit may happen hours or days after the impression. By the time when it happens, the budget for the campaign may have already run out, or the campaign window is closed and not enough site visits are produced to make up for the cost of the campaign. 
     Thus, in certain embodiment, the volume control unit  409  is configured to generate ( 473 ) a site visit projection based on the SVR estimates upon receiving signals indicating the CPV-based document has been impressed, instead of waiting until a site visit actually occurs. The projection can be the cost per visit offered by the sponsor of the document multiplied by the estimated SVR used in the ranking module  405  to estimate the KPI. It may be further multiplied by the weight used by the ranking unit  405 . The volume control unit  409  then updates ( 475 ) the related budget by deducting the projection from the budget. 
     In certain embodiments, one or more computers/servers  120  are used to provide each, some or all of the request processor  152 , the information server, and the various filters and computation engines in the evaluation module. 
       FIG. 12  illustrates a diagrammatic representation of an exemplary computer/server  120  that can be used to provide any one or more components in the system  150  by executing proprietary software instructions. The computer/server  120  may operate as a standalone device or as a peer computing device in a peer-to-peer (or distributed) network computing environment. As shown in  FIG. 12 , the computer/server  120  includes one or more processors  1202  (e.g., a central processing unit (CPU), a graphic processing unit (GPU), and/or a digital signal processor (DSP)) and a system or main memory  1204  coupled to each other via a system bus  1200 . The computer/server  120  may further include static memory  1206 , a network interface device  1208 , a storage unit  1210 , one or more display devices  1230 , one or more input devices  1234 , and a signal generation device (e.g., a speaker)  1236 , with which the processor(s)  1202  can communicate via the system bus  1200 . 
     In certain embodiments, the display device(s)  1230  include one or more graphics display units (e.g., a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)). The input device(s)  1234  may include an alphanumeric input device (e.g., a keyboard), a cursor control device (e.g., a mouse, trackball, joystick, motion sensor, or other pointing instrument). The storage unit  1210  includes a machine-readable medium  1212  on which is stored instructions  1216  (e.g., software) that enable anyone or more of the systems, components, methodologies or functions described herein. The storage unit  1210  may also store data  1218  used and/or generated by the systems, components, methodologies or functions, including data in any, part, some, or all of the POI data  151 , the map data  152 , the spatial index database  158 , the request log  168 , the impression log  164 , click/call log  166 , the data filed  171 , the segment database  174 , the search index  925 , etc. The instructions  1216  (e.g., software) may be loaded, completely or partially, within the main memory  1204  or within the processor  1202  (e.g., within a processor&#39;s cache memory) during execution thereof by the computer/server  120 . Thus, the main memory  1204  and the processor  1102  also constituting machine-readable media. 
     While machine-readable medium  1212  is shown in an example implementation to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions (e.g., instructions  1124 ). The term “machine-readable medium” shall also be taken to include any medium that is capable of storing instructions (e.g., instructions  1216 ) for execution by the computer/server  120  and that cause the computing device  1100  to perform anyone or more of the methodologies disclosed herein. The term “machine-readable medium” includes, but not be limited to, data repositories in the form of solid-state memories, optical media, and magnetic media. In certain embodiments, the instructions  1216  and/or data  1218  can be stored in the network  100  and accessed by the computer/server  120  via its network interface device  1208 , which provides wired and/or wireless connections to a network, such as a local area network  111  and/or a wide area network (e.g., the Internet  110 ) via some type of network connectors  1280   a.  The instructions  1216  (e.g., software) and or data  1218  may be transmitted or received via the network interface device  208 .